Интегрирован wesp в сайт

apps/api/app/modules/zootech/lab/__init__.py

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+"""Zootech lab module (Neoton tab integration)."""

apps/api/app/modules/zootech/lab/calc/__init__.py

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+from .engine import calculate_ration
+from .diff import compare_master_execution
+
+__all__ = ["calculate_ration", "compare_master_execution"]

apps/api/app/modules/zootech/lab/calc/compound.py

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+from __future__ import annotations
+
+from typing import Any
+
+from app.modules.zootech.lab.calc.derived import apply_content_derived
+from app.modules.zootech.lab.calc.nutrients import norm_diff, weighted_average
+from app.modules.zootech.lab.indicators import RATION_ALL_INDICATORS
+
+
+def _compound_active(lines: list[dict[str, Any]]) -> list[dict[str, Any]]:
+    return [
+        l
+        for l in lines
+        if l.get("in_compound") and (l.get("daily_kg") or 0) > 0
+    ]
+
+
+def _sum_kg(lines: list[dict[str, Any]]) -> float:
+    return sum(float(l.get("daily_kg") or 0) for l in lines)
+
+
+def _compound_indicators(
+    lines: list[dict[str, Any]],
+    total_kg: float,
+    norms: dict[str, dict[str, float | None]],
+) -> list[dict[str, Any]]:
+    content_by_key: dict[str, float | None] = {}
+    for defn in RATION_ALL_INDICATORS:
+        if defn.get("derived"):
+            continue
+        content_by_key[defn["key"]] = weighted_average(
+            lines,
+            total_kg,
+            defn["nutrient_keys"],
+            indicator_key=defn.get("key"),
+        )
+    for defn in RATION_ALL_INDICATORS:
+        if not defn.get("derived"):
+            continue
+        content_by_key[defn["key"]] = apply_content_derived(
+            content_by_key,
+            defn,
+            compound_mode=True,
+        )
+    rows = []
+    for defn in RATION_ALL_INDICATORS:
+        key = defn["key"]
+        content = content_by_key.get(key)
+        bounds = norms.get(key, {})
+        min_v = bounds.get("min")
+        max_v = bounds.get("max")
+        diff = norm_diff(content, min_v, max_v)
+        if content is None and min_v is None and max_v is None:
+            continue
+        rows.append(
+            {
+                "key": key,
+                "label": defn["label"],
+                "unit": defn["unit"],
+                "min": min_v,
+                "max": max_v,
+                "content": content,
+                "diff": diff,
+            }
+        )
+    return rows
+
+
+def calculate_compound_feed(
+    lines: list[dict[str, Any]],
+    norms: dict[str, dict[str, float | None]] | None = None,
+    *,
+    profile_mass_kg: float | None = None,
+    heads_per_trip: int = 1,
+) -> dict[str, Any] | None:
+    del profile_mass_kg, heads_per_trip
+    active = _compound_active(lines)
+    if not active:
+        return None
+    total_kg = _sum_kg(active)
+    cost = None
+    any_cost = False
+    for line in active:
+        kg = line.get("daily_kg")
+        price = line.get("price_per_kg")
+        if kg is None or price is None:
+            continue
+        cost = (cost or 0) + float(kg) * float(price)
+        any_cost = True
+    return {
+        "totals": [
+            {"key": "compound_kg", "label": "Масса комбикорма, кг", "value": total_kg},
+            {
+                "key": "compound_cost",
+                "label": "Стоимость комбикорма",
+                "value": cost if any_cost else None,
+            },
+        ],
+        "indicators": _compound_indicators(active, total_kg, norms or {}),
+        "lines": [
+            {
+                "ingredient_name": line.get("ingredient_name") or "—",
+                "daily_kg": line.get("daily_kg"),
+                "share_pct": (
+                    (float(line["daily_kg"]) / total_kg) * 100
+                    if total_kg > 0 and line.get("daily_kg") is not None
+                    else None
+                ),
+            }
+            for line in active
+        ],
+    }

apps/api/app/modules/zootech/lab/calc/derived.py

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+from __future__ import annotations
+
+from typing import Any
+
+from app.modules.zootech.lab.calc.nutrients import rnb
+
+
+def apply_content_derived(
+    content_by_key: dict[str, float | None],
+    defn: dict[str, Any],
+    *,
+    total_kg: float = 0,
+    heads_per_trip: int = 1,
+    profile_mass_kg: float | None = None,
+    compound_mode: bool = False,
+) -> float | None:
+    derived = defn.get("derived")
+    if derived == "alias":
+        return content_by_key.get(defn.get("alias_of"))
+    if derived == "pct_of_dm":
+        src = content_by_key.get(defn.get("from_key"))
+        dm = content_by_key.get("dry_matter")
+        if src is None or not dm or dm <= 0:
+            return None
+        return src / dm * 100.0
+    if derived == "g_per_kg_dm":
+        src = content_by_key.get(defn.get("from_key"))
+        dm = content_by_key.get("dry_matter")
+        if src is None or not dm or dm <= 0:
+            return None
+        if compound_mode:
+            return src
+        return src / (dm / 1000.0)
+    if derived == "nel_per_kg_dm":
+        dm = content_by_key.get("dry_matter")
+        nel = content_by_key.get("nel")
+        if not dm or dm <= 0 or nel is None:
+            return None
+        if compound_mode:
+            return nel
+        return nel / (dm / 1000.0)
+    if derived == "ratio":
+        num = content_by_key.get(defn.get("ratio_num"))
+        den = content_by_key.get(defn.get("ratio_den"))
+        if num is None or den is None or den == 0:
+            return None
+        return num / den
+    if derived == "dm_pct_bw":
+        dm = content_by_key.get("dry_matter")
+        if dm is None or not profile_mass_kg or profile_mass_kg <= 0:
+            return None
+        return (dm / 1000.0 / profile_mass_kg) * 100.0
+    if derived == "ration_pct_bw":
+        if not profile_mass_kg or profile_mass_kg <= 0 or total_kg <= 0:
+            return None
+        heads = max(int(heads_per_trip or 1), 1)
+        kg_per_head = total_kg / heads
+        return (kg_per_head / profile_mass_kg) * 100.0
+    if derived in ("rnb", "bra_rnb"):
+        return rnb(
+            content_by_key.get("crude_protein"),
+            content_by_key.get("usp"),
+        )
+    return None

apps/api/app/modules/zootech/lab/calc/diff.py

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+from __future__ import annotations
+
+from typing import Any
+
+from app.modules.zootech.lab.constants import DIFF_TOLERANCE_KG
+
+
+def compare_master_execution(
+    master_lines: list[dict[str, Any]],
+    execution_lines: list[dict[str, Any]],
+) -> dict[str, Any]:
+    master_map: dict[str, float] = {}
+    for line in master_lines:
+        if not line.get("in_ration"):
+            continue
+        cid = line.get("component_id")
+        if not cid:
+            continue
+        master_map[str(cid)] = float(line.get("daily_kg") or 0)
+
+    exec_map: dict[str, float] = {}
+    for line in execution_lines:
+        cid = line.get("component_id")
+        if not cid:
+            continue
+        exec_map[str(cid)] = float(line.get("daily_kg_total") or 0)
+
+    all_ids = set(master_map) | set(exec_map)
+    diff_lines = []
+    has_changes = False
+    for cid in sorted(all_ids):
+        m = master_map.get(cid)
+        e = exec_map.get(cid)
+        reasons = []
+        if m is None:
+            reasons.append("missing_in_master")
+            has_changes = True
+        if e is None:
+            reasons.append("missing_in_execution")
+            has_changes = True
+        if m is not None and e is not None and abs(m - e) > DIFF_TOLERANCE_KG:
+            reasons.append("kg_mismatch")
+            has_changes = True
+        if reasons:
+            diff_lines.append(
+                {
+                    "component_id": cid,
+                    "master_kg": m,
+                    "execution_kg": e,
+                    "reasons": reasons,
+                }
+            )
+    return {"has_changes": has_changes, "lines": diff_lines}

apps/api/app/modules/zootech/lab/calc/engine.py

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+from __future__ import annotations
+
+from datetime import datetime, timezone
+from typing import Any
+
+from app.modules.zootech.lab.calc.compound import calculate_compound_feed
+from app.modules.zootech.lab.calc.derived import apply_content_derived
+from app.modules.zootech.lab.calc.nutrients import daily_intake_total, get_nutrient_value, norm_diff, weighted_average
+from app.modules.zootech.lab.constants import RATION_TOTAL_KEYS
+from app.modules.zootech.lab.indicators import RATION_ALL_INDICATORS
+
+
+def _active_lines(lines: list[dict[str, Any]]) -> list[dict[str, Any]]:
+    return [
+        l
+        for l in lines
+        if l.get("in_ration") and (l.get("daily_kg") or 0) > 0
+    ]
+
+
+def _sum_kg(lines: list[dict[str, Any]]) -> float:
+    return sum(float(l.get("daily_kg") or 0) for l in lines)
+
+
+def _sum_cost(lines: list[dict[str, Any]]) -> float | None:
+    total = 0.0
+    any_cost = False
+    for line in lines:
+        kg = line.get("daily_kg")
+        price = line.get("price_per_kg")
+        if kg is None or price is None:
+            continue
+        total += float(kg) * float(price)
+        any_cost = True
+    return total if any_cost else None
+
+
+def _sum_ration_percent(lines: list[dict[str, Any]], total_kg: float) -> float | None:
+    if total_kg <= 0:
+        return None
+    return sum((float(l.get("daily_kg") or 0) / total_kg) * 100 for l in lines)
+
+
+def _compute_totals(
+    ration_type: str,
+    active: list[dict[str, Any]],
+    total_kg: float,
+) -> list[dict[str, Any]]:
+    defs = RATION_TOTAL_KEYS.get(ration_type, RATION_TOTAL_KEYS["BEEF"])
+    ration_kg = _sum_kg(active)
+    values = {
+        "total_kg": total_kg if total_kg > 0 else None,
+        "ration_kg": ration_kg if ration_kg > 0 else None,
+        "ration_pct_sum": _sum_ration_percent(active, total_kg),
+        "cost_total": _sum_cost(active),
+    }
+    return [{"key": d["key"], "label": d["label"], "value": values.get(d["key"])} for d in defs]
+
+
+def _indicator_content(
+    defn: dict[str, Any],
+    active: list[dict[str, Any]],
+    total_kg: float,
+    *,
+    heads_per_trip: int,
+) -> float | None:
+    key = defn.get("key")
+    if defn.get("aggregation") == "weighted_avg":
+        return weighted_average(
+            active,
+            total_kg,
+            defn["nutrient_keys"],
+            indicator_key=key,
+        )
+    return daily_intake_total(
+        active,
+        defn["nutrient_keys"],
+        heads_per_trip=heads_per_trip,
+        indicator_key=key,
+    )
+
+
+def _compute_indicators(
+    active: list[dict[str, Any]],
+    total_kg: float,
+    norms: dict[str, dict[str, float | None]],
+    *,
+    heads_per_trip: int = 1,
+    profile_mass_kg: float | None = None,
+) -> list[dict[str, Any]]:
+    content_by_key: dict[str, float | None] = {}
+    for defn in RATION_ALL_INDICATORS:
+        if defn.get("derived"):
+            continue
+        content_by_key[defn["key"]] = _indicator_content(
+            defn, active, total_kg, heads_per_trip=heads_per_trip
+        )
+
+    for defn in RATION_ALL_INDICATORS:
+        if not defn.get("derived"):
+            continue
+        content_by_key[defn["key"]] = apply_content_derived(
+            content_by_key,
+            defn,
+            total_kg=total_kg,
+            heads_per_trip=heads_per_trip,
+            profile_mass_kg=profile_mass_kg,
+        )
+
+    rows = []
+    for defn in RATION_ALL_INDICATORS:
+        key = defn["key"]
+        content = content_by_key.get(key)
+        bounds = norms.get(key, {})
+        min_v = bounds.get("min")
+        max_v = bounds.get("max")
+        diff = norm_diff(content, min_v, max_v)
+        if content is None and min_v is None and max_v is None:
+            continue
+        rows.append(
+            {
+                "key": key,
+                "label": defn["label"],
+                "unit": defn["unit"],
+                "min": min_v,
+                "max": max_v,
+                "content": content,
+                "diff": diff,
+            }
+        )
+    return rows
+
+
+def _missing_nutrient_warnings(active: list[dict[str, Any]]) -> list[str]:
+    warnings: list[str] = []
+    for line in active:
+        nutrients = line.get("nutrients") or {}
+        cp = get_nutrient_value(
+            line.get("dry_matter"),
+            nutrients,
+            ["Сыр. Протеин"],
+            indicator_key="crude_protein",
+        )
+        if cp is not None:
+            continue
+        name = line.get("ingredient_name") or line.get("component_id") or "?"
+        warnings.append(f"nutrients_missing:{name}")
+    return warnings
+
+
+def calculate_ration(
+    ration_type: str,
+    lines: list[dict[str, Any]],
+    norms: dict[str, dict[str, float | None]] | None = None,
+    *,
+    heads_per_trip: int = 1,
+    profile_mass_kg: float | None = None,
+) -> dict[str, Any]:
+    norms = norms or {}
+    errors: list[str] = []
+    active = _active_lines(lines)
+    total_kg = _sum_kg(active)
+    heads = max(int(heads_per_trip or 1), 1)
+    if not active:
+        errors.append("Нет строк сырья «в рационе» с дозировкой кг/день")
+    warnings = _missing_nutrient_warnings(active)
+    compound = calculate_compound_feed(
+        lines, norms, profile_mass_kg=profile_mass_kg, heads_per_trip=heads
+    )
+    return {
+        "calculated_at": datetime.now(timezone.utc).isoformat(),
+        "engine": "native",
+        "totals": _compute_totals(ration_type, active, total_kg),
+        "indicators": _compute_indicators(
+            active,
+            total_kg,
+            norms,
+            heads_per_trip=heads,
+            profile_mass_kg=profile_mass_kg,
+        ),
+        "compound": compound,
+        "errors": errors,
+        "warnings": warnings,
+    }

apps/api/app/modules/zootech/lab/calc/feed_groups.py

@@ -0,0 +1,211 @@
+"""Группы кормов для авторациона — канонический тип компонента + legacy-маппинг."""
+
+from __future__ import annotations
+
+from typing import Any
+
+from app.modules.zootech.wesp_bridge_models import Component
+
+FEED_GROUPS: tuple[dict[str, Any], ...] = (
+    {
+        "id": "rough",
+        "label": "База — грубые",
+        "shortLabel": "База",
+        "required": True,
+        "minPick": 1,
+        "hint": "Без каркаса партия не взлетит. Я не бизнесмен — я специалист.",
+        "step": 1,
+    },
+    {
+        "id": "succulent",
+        "label": "Влага — сочные",
+        "shortLabel": "Влага",
+        "required": False,
+        "minPick": 0,
+        "hint": "Сочное сырьё. Можно не мешать — но чистота пострадает.",
+        "step": 2,
+    },
+    {
+        "id": "concentrate",
+        "label": "Энергия — концентраты",
+        "shortLabel": "Энергия",
+        "required": False,
+        "minPick": 0,
+        "hint": "Концентрат дозируй как реагент — точно. Держись подальше от моей территории.",
+        "step": 3,
+    },
+    {
+        "id": "other",
+        "label": "Добавки",
+        "shortLabel": "Добавки",
+        "required": False,
+        "minPick": 0,
+        "hint": "Минералы и премикс. Необязательно. Но Хайзенберг бы не пропустил.",
+        "step": 4,
+    },
+)
+
+# Канонические значения component.type (выбор в /components)
+FEED_COMPONENT_TYPES: tuple[dict[str, str], ...] = (
+    {
+        "value": "Грубые корма",
+        "feedGroup": "rough",
+        "description": "Сено, солома.",
+    },
+    {
+        "value": "Сочные корма",
+        "feedGroup": "succulent",
+        "description": "Силос, корнеплоды.",
+    },
+    {
+        "value": "Концентрированные",
+        "feedGroup": "concentrate",
+        "description": "Зерно, комбикорм, жмых, шрот",
+    },
+    {
+        "value": "Добавки",
+        "feedGroup": "other",
+        "description": "Премикс, минералы, витамины, КЖП",
+    },
+)
+
+_GROUP_BY_ID = {g["id"]: g for g in FEED_GROUPS}
+
+_LEGACY_TYPE_TO_GROUP: dict[str, str] = {
+    "зерновые": "concentrate",
+    "энергетические": "concentrate",
+    "белковые": "concentrate",
+    "минеральные": "other",
+    "витаминные": "other",
+}
+
+# Старый тип «Объемные корма» — уточните до Грубые/Сочные; эвристика по имени
+_ROUGH_NAME_KEYS = ("солом", "сено", "hay", "straw")
+_SUCCULENT_NAME_KEYS = ("силос", "сенаж", "сочн", "корнеплод", "свекл", "морков", "тыкв", "зелен")
+_CONCENTRATE_NAME_KEYS = ("зерн", "концентр", "комбикорм", "комбик", "жмых", "шрот", "дробин", "пивн")
+
+
+def _norm(text: str | None) -> str:
+    return (text or "").strip().lower()
+
+
+def _canonical_type_map() -> dict[str, str]:
+    return {_norm(t["value"]): t["feedGroup"] for t in FEED_COMPONENT_TYPES}
+
+
+def _canonical_values() -> list[str]:
+    return [t["value"] for t in FEED_COMPONENT_TYPES]
+
+
+def list_component_feed_types() -> list[dict[str, str]]:
+    return [
+        {
+            "value": t["value"],
+            "feedGroup": t["feedGroup"],
+            "description": t["description"],
+            "groupLabel": _GROUP_BY_ID[t["feedGroup"]]["label"],
+        }
+        for t in FEED_COMPONENT_TYPES
+    ]
+
+
+def is_canonical_feed_type(component_type: str | None) -> bool:
+    return _norm(component_type) in _canonical_type_map()
+
+
+def feed_group_for_type(component_type: str | None) -> str | None:
+    """Группа авторациона по component.type (канон или legacy)."""
+    ctype = _norm(component_type)
+    if not ctype:
+        return None
+    canonical = _canonical_type_map().get(ctype)
+    if canonical:
+        return canonical
+    if ctype in ("объемные корма", "объёмные корма"):
+        return None
+    return _LEGACY_TYPE_TO_GROUP.get(ctype)
+
+
+def classify_feed_group(comp: Component) -> str:
+    """Группа для авторациона: сначала component.type, иначе эвристика по имени (legacy)."""
+    by_type = feed_group_for_type(comp.type)
+    if by_type:
+        return by_type
+
+    name = _norm(comp.name)
+
+    def has_any(keys: tuple[str, ...]) -> bool:
+        return any(k in name for k in keys)
+
+    if has_any(_ROUGH_NAME_KEYS):
+        return "rough"
+    if has_any(_SUCCULENT_NAME_KEYS):
+        return "succulent"
+    if has_any(_CONCENTRATE_NAME_KEYS):
+        return "concentrate"
+    if _norm(comp.type) in ("объемные корма", "объёмные корма"):
+        return "succulent"
+    return "other"
+
+
+def list_feed_groups_api() -> list[dict[str, Any]]:
+    return [
+        {
+            "id": g["id"],
+            "label": g["label"],
+            "shortLabel": g["shortLabel"],
+            "required": g["required"],
+            "minPick": g["minPick"],
+            "hint": g["hint"],
+            "step": g["step"],
+        }
+        for g in FEED_GROUPS
+    ]
+
+
+def parse_group_selections(raw: Any) -> dict[str, list[str]]:
+    if not isinstance(raw, dict):
+        return {}
+    out: dict[str, list[str]] = {}
+    for gid in _GROUP_BY_ID:
+        vals = raw.get(gid) or []
+        if isinstance(vals, list):
+            out[gid] = [str(x) for x in vals if x]
+    return out
+
+
+def flatten_group_selections(selections: dict[str, list[str]]) -> list[str]:
+    seen: list[str] = []
+    for gid in _GROUP_BY_ID:
+        for cid in selections.get(gid) or []:
+            if cid not in seen:
+                seen.append(cid)
+    return seen
+
+
+def validate_group_selections(selections: dict[str, list[str]]) -> list[str]:
+    errors: list[str] = []
+    for g in FEED_GROUPS:
+        gid = g["id"]
+        picked = selections.get(gid) or []
+        if g["required"] and len(picked) < int(g["minPick"]):
+            errors.append(f"{gid}:need_{g['minPick']}")
+    total = len(flatten_group_selections(selections))
+    if total < 3:
+        errors.append("pool:need_3")
+    return errors
+
+
+def triplet_meets_group_rules(
+    triplet_ids: set[str],
+    selections: dict[str, list[str]],
+) -> bool:
+    for g in FEED_GROUPS:
+        if not g["required"]:
+            continue
+        pool = set(selections.get(g["id"]) or [])
+        if not pool:
+            continue
+        if not triplet_ids & pool:
+            return False
+    return True

apps/api/app/modules/zootech/lab/calc/formulate.py

@@ -0,0 +1,417 @@
+from __future__ import annotations
+
+import itertools
+import time
+from dataclasses import dataclass, field
+from typing import Any
+
+from app.modules.zootech.lab.calc.engine import calculate_ration
+from app.modules.zootech.lab.calc.feed_groups import (
+    FEED_GROUPS,
+    flatten_group_selections,
+    triplet_meets_group_rules,
+    validate_group_selections,
+)
+from app.modules.zootech.lab.calc.feed_groups import classify_feed_group as _classify_feed_group
+from app.modules.zootech.lab.calc.formulate_optimize import optimize_shares_for_triplet
+from app.modules.zootech.lab.calc.formulate_score import (
+    build_calc_lines,
+    build_triplet_score_model,
+    score_model_shares,
+    violation_score,
+)
+from app.modules.zootech.lab.calc.formulate_validate import validate_component, validate_components
+from app.modules.zootech.lab.calc.norms_resolver import NormsParams, NormsResolveRequest, normalize_norms_method, resolve_norms
+from app.modules.zootech.lab.calc.nutrients import get_nutrient_value
+from app.modules.zootech.lab.indicators import RATION_ALL_INDICATORS
+from app.modules.zootech.lab.models import LabAnimalProfile
+from app.modules.zootech.lab.services.component_nutrients import nutrients_calc_dict_batch
+from app.modules.zootech.lab.services.profile_norms import load_norms_dict
+from app.modules.zootech.wesp_bridge_models import Component
+
+
+@dataclass
+class FormulateRequest:
+    profile_id: str
+    candidate_ids: list[str] = field(default_factory=list)
+    group_selections: dict[str, list[str]] = field(default_factory=dict)
+    main_feed_ids: list[str] = field(default_factory=list)  # legacy → rough
+    mass_kg: float | None = None
+    milk_yield_kg: float | None = None
+    heads_per_trip: int | None = None
+    total_kg_per_head: float = 7.3
+    optimize_keys: list[str] = field(default_factory=list)
+    objective: str = "min_cost"
+    cost_weight: float = 100.0
+    grid_step: float = 0.1
+    prefilter_k: int = 18
+    min_share: float = 0.05
+    norms_method: str = "wesp"
+    norms_params: dict[str, Any] = field(default_factory=dict)
+
+
+_DEFAULT_OPTIMIZE_KEYS = (
+    "dry_matter",
+    "usp",
+    "nel",
+    "crude_protein",
+    "rnb",
+    "nfc_pct_dm_uk",
+)
+
+
+def _indicator_def(key: str) -> dict[str, Any] | None:
+    for defn in RATION_ALL_INDICATORS:
+        if defn["key"] == key:
+            return defn
+    return None
+
+
+def _resolve_norms(profile: LabAnimalProfile, req: FormulateRequest) -> tuple[dict, dict]:
+    stored = load_norms_dict(profile.id)
+    mass = req.mass_kg if req.mass_kg is not None else profile.mass_kg
+    milk = req.milk_yield_kg if req.milk_yield_kg is not None else profile.milk_yield_kg
+    from app.modules.zootech.lab.services.norms_params import load_norms_params
+
+    method = normalize_norms_method(req.norms_method or profile.norms_method)
+    params = load_norms_params(profile)
+    if req.norms_params:
+        merged = {
+            "milkFatPct": params.milk_fat_pct,
+            "lactationNo": params.lactation_no,
+            "lactationStage": params.lactation_stage,
+            "bodyCondition": params.body_condition,
+            "housingSystem": params.housing_system,
+            "koncOeSv": params.konc_oe_sv,
+        }
+        merged.update(req.norms_params)
+        params = NormsParams.from_dict(merged)
+    resolved, meta = resolve_norms(
+        NormsResolveRequest(
+            method=method,
+            stored=stored,
+            mass_kg=mass,
+            milk_yield_kg=milk,
+            ration_type=profile.ration_type,
+            force_dynamic=method == "wesp",
+            params=params,
+        )
+    )
+    dynamic = meta.get("dynamicNorms") or meta.get("dynamic") or {}
+    return resolved, {"normsMethod": method, "dynamicNorms": dynamic, "normsMeta": meta.get("meta")}
+
+
+def _rough_component_value(
+    comp: Component,
+    optimize_keys: list[str],
+    norms: dict[str, dict[str, float | None]],
+    nutrient_cache: dict[str, dict[str, float]],
+) -> float | None:
+    nutrients = nutrient_cache.get(comp.id, {})
+    dm_pct = comp.dry_matter
+    total = 0.0
+    counted = 0
+    for key in optimize_keys:
+        defn = _indicator_def(key)
+        if defn is None or defn.get("derived"):
+            continue
+        bounds = norms.get(key) or {}
+        target_min = bounds.get("min")
+        target_max = bounds.get("max")
+        if target_min is None and target_max is None:
+            continue
+        target = None
+        if target_min is not None and target_max is not None:
+            target = (float(target_min) + float(target_max)) / 2.0
+        elif target_max is not None:
+            target = float(target_max) * 0.9
+        elif target_min is not None:
+            target = float(target_min) * 1.1
+        if target is None or target == 0:
+            continue
+        val = get_nutrient_value(
+            dm_pct,
+            nutrients,
+            defn.get("nutrient_keys") or [],
+            indicator_key=key,
+        )
+        if val is None:
+            continue
+        rel = (float(val) - target) / abs(target)
+        total += rel * rel
+        counted += 1
+    return total / counted if counted else None
+
+
+def _prefilter_candidates(
+    candidates: list[Component],
+    optimize_keys: list[str],
+    norms: dict[str, dict[str, float | None]],
+    nutrient_cache: dict[str, dict[str, float]],
+    *,
+    prefilter_k: int,
+    must_keep_ids: set[str] | None = None,
+) -> list[Component]:
+    must_keep_ids = must_keep_ids or set()
+    pinned = [c for c in candidates if c.id in must_keep_ids]
+    rest = [c for c in candidates if c.id not in must_keep_ids]
+    slots = max(prefilter_k - len(pinned), 0)
+    if len(candidates) <= prefilter_k:
+        return candidates
+    if slots <= 0:
+        return pinned[:prefilter_k]
+
+    prices = [float(c.price) for c in rest if c.price is not None]
+    median_price = sorted(prices)[len(prices) // 2] if prices else 1.0
+    if median_price <= 0:
+        median_price = 1.0
+
+    scored: list[tuple[float, Component]] = []
+    for comp in rest:
+        price = float(comp.price) if comp.price is not None else median_price
+        cost_part = price / median_price
+        nutrient_part = _rough_component_value(comp, optimize_keys, norms, nutrient_cache)
+        if nutrient_part is None:
+            score = cost_part
+        else:
+            score = 0.4 * cost_part + 0.6 * nutrient_part
+        scored.append((score, comp))
+    scored.sort(key=lambda x: x[0])
+    return pinned + [comp for _, comp in scored[:slots]]
+
+
+def _load_profile(profile_id: str) -> LabAnimalProfile:
+    profile = LabAnimalProfile.query.filter_by(id=profile_id, is_deleted=False).first()
+    if profile is None:
+        raise LookupError("Профиль не найден")
+    return profile
+
+
+def _load_eligible_components(candidate_ids: list[str]) -> tuple[list[Component], list[dict[str, Any]]]:
+    unique = list(dict.fromkeys(candidate_ids))
+    validations = validate_components(unique)
+    ineligible = [v for v in validations if not v["eligible"]]
+    if ineligible:
+        raise ValueError("ineligible_components", ineligible)
+    comps: list[Component] = []
+    for cid in unique:
+        comp = Component.query.filter_by(id=cid, is_deleted=False).first()
+        if comp is None:
+            raise ValueError("component_not_found", cid)
+        comps.append(comp)
+    return comps, validations
+
+
+def _resolve_group_selections(req: FormulateRequest) -> dict[str, list[str]]:
+    selections = dict(req.group_selections or {})
+    if req.main_feed_ids and not selections.get("rough"):
+        selections["rough"] = list(req.main_feed_ids)
+    return selections
+
+
+def formulate(req: FormulateRequest) -> dict[str, Any]:
+    group_selections = _resolve_group_selections(req)
+    candidate_ids = list(req.candidate_ids)
+    if group_selections:
+        pool_errors = validate_group_selections(group_selections)
+        if pool_errors:
+            raise ValueError("group_selection_invalid", pool_errors)
+        candidate_ids = flatten_group_selections(group_selections)
+
+    if len(candidate_ids) < 3:
+        raise ValueError("candidate_ids_min_3")
+    if len(set(candidate_ids)) != len(candidate_ids):
+        raise ValueError("candidate_ids_duplicate")
+
+    profile = _load_profile(req.profile_id)
+    candidates, _ = _load_eligible_components(candidate_ids)
+    nutrient_cache = nutrients_calc_dict_batch(candidate_ids)
+    norms, dynamic = _resolve_norms(profile, req)
+    optimize_keys = req.optimize_keys or list(_DEFAULT_OPTIMIZE_KEYS)
+    heads = max(int(req.heads_per_trip or 10), 1)
+    herd_scale = float(req.total_kg_per_head) * heads
+    profile_mass_kg = req.mass_kg if req.mass_kg is not None else profile.mass_kg
+
+    must_keep: set[str] = set()
+    for g in FEED_GROUPS:
+        if g["required"]:
+            must_keep.update(group_selections.get(g["id"]) or [])
+
+    prefilter_applied = len(candidates) > req.prefilter_k
+    shortlist = _prefilter_candidates(
+        candidates,
+        optimize_keys,
+        norms,
+        nutrient_cache,
+        prefilter_k=req.prefilter_k,
+        must_keep_ids=must_keep,
+    )
+
+    started = time.perf_counter()
+    evaluations = 0
+    triplets_evaluated = 0
+    triplet_winners: list[dict[str, Any]] = []
+
+    def _eval_triplet(
+        triplet: tuple[Component, Component, Component],
+    ) -> dict[str, Any] | None:
+        nonlocal evaluations
+        model = build_triplet_score_model(
+            triplet,
+            nutrient_cache=nutrient_cache,
+            norms=norms,
+            optimize_keys=optimize_keys,
+            herd_scale=herd_scale,
+            heads=heads,
+            cost_weight=req.cost_weight,
+            profile_mass_kg=profile_mass_kg,
+        )
+
+        def score_fn(shares: tuple[float, float, float]) -> float:
+            _violation, _cost_head, score = score_model_shares(model, shares)
+            return score
+
+        opt = optimize_shares_for_triplet(
+            triplet,
+            score_fn,
+            min_share=req.min_share,
+            grid_step=req.grid_step,
+        )
+        if opt is None:
+            return None
+        evaluations += opt.evaluations
+        violation, cost_head, score = score_model_shares(model, opt.shares)
+        lines = build_calc_lines(triplet, opt.shares, herd_scale, nutrient_cache)
+        daily = [opt.shares[i] * herd_scale for i in range(3)]
+        return {
+            "score": score,
+            "violation": violation,
+            "costHead": cost_head,
+            "costTotal": cost_head * heads,
+            "triplet": triplet,
+            "shares": opt.shares,
+            "daily": daily,
+            "lines": lines,
+        }
+
+    for triplet in itertools.combinations(shortlist, 3):
+        triplet_ids = {c.id for c in triplet}
+        if not triplet_meets_group_rules(triplet_ids, group_selections):
+            continue
+        triplets_evaluated += 1
+        best_triplet_result = _eval_triplet(triplet)
+        if best_triplet_result is None:
+            continue
+        triplet_winners.append(best_triplet_result)
+
+    if not triplet_winners:
+        if group_selections:
+            raise ValueError("no_feasible_solution_groups")
+        raise ValueError("no_feasible_solution")
+
+    triplet_winners.sort(key=lambda x: x["score"])
+    best = triplet_winners[0]
+
+    full_calc = calculate_ration(
+        profile.ration_type or "DAIRY",
+        best["lines"],
+        norms,
+        heads_per_trip=heads,
+        profile_mass_kg=profile_mass_kg,
+    )
+    best["calc"] = full_calc
+    best["violation"] = violation_score(
+        full_calc.get("indicators") or [],
+        optimize_keys,
+        norms,
+    )
+    best["score"] = best["violation"] * req.cost_weight + best["costHead"]
+
+    duration_ms = int((time.perf_counter() - started) * 1000)
+
+    alternatives = [
+        {
+            "componentIds": [c.id for c in item["triplet"]],
+            "names": [c.name for c in item["triplet"]],
+            "score": item["score"],
+            "violation": item["violation"],
+            "costPerHead": item["costHead"],
+        }
+        for item in triplet_winners[:3]
+    ]
+    alternatives.sort(key=lambda x: x["score"])
+    alternatives = alternatives[:3]
+
+    result_lines = []
+    for i, comp in enumerate(best["triplet"]):
+        s1, s2, s3 = best["shares"]
+        share = (s1, s2, s3)[i]
+        result_lines.append(
+            {
+                "componentId": comp.id,
+                "name": comp.name,
+                "dailyKg": round(best["daily"][i], 4),
+                "sharePct": round(share * 100, 2),
+                "pricePerKg": comp.price,
+                "dryMatterPct": comp.dry_matter,
+            }
+        )
+
+    violation = best["violation"]
+    return {
+        "lines": result_lines,
+        "candidatePoolSize": len(candidates),
+        "groupSelections": group_selections,
+        "shortlistedIds": [c.id for c in shortlist],
+        "costTotal": best["costTotal"],
+        "costPerHead": best["costHead"],
+        "score": best["score"],
+        "violation": violation,
+        "feasible": violation < 0.01,
+        "indicators": best["calc"].get("indicators") or [],
+        "totals": best["calc"].get("totals") or [],
+        "optimizeKeys": optimize_keys,
+        "dynamicNorms": dynamic.get("dynamicNorms") or None,
+        "normsMethod": dynamic.get("normsMethod"),
+        "normsMeta": dynamic.get("normsMeta"),
+        "alternatives": alternatives,
+        "searchStats": {
+            "evaluations": evaluations,
+            "tripletsEvaluated": triplets_evaluated,
+            "durationMs": duration_ms,
+            "prefilterApplied": prefilter_applied,
+            "prefilterK": req.prefilter_k,
+            "scoreEngine": "fast",
+            "optimizer": "slsqp",
+            "normsMethod": dynamic.get("normsMethod"),
+        },
+    }
+
+
+def list_formulate_components() -> list[dict[str, Any]]:
+    rows = (
+        Component.query.filter_by(is_active=True, is_deleted=False)
+        .order_by(Component.name)
+        .all()
+    )
+    out: list[dict[str, Any]] = []
+    for comp in rows:
+        v = validate_component(comp.id)
+        feed_group = _classify_feed_group(comp)
+        out.append(
+            {
+                "id": comp.id,
+                "name": comp.name,
+                "type": comp.type,
+                "feedGroup": feed_group,
+                "eligible": v["eligible"],
+                "missing": v["missing"],
+                "warnings": v["warnings"],
+                "dryMatterPct": v["dryMatterPct"],
+                "hasPrice": v["hasPrice"],
+                "price": v["price"],
+                "mainFeedDmGPerKg": v.get("mainFeedDmGPerKg"),
+                "isMainFeed": v.get("isMainFeed", False),
+            }
+        )
+    return out

apps/api/app/modules/zootech/lab/calc/formulate_optimize.py

@@ -0,0 +1,112 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any, Callable
+
+from scipy.optimize import minimize
+
+from app.modules.zootech.wesp_bridge_models import Component
+
+ShareTuple = tuple[float, float, float]
+ScoreFn = Callable[[ShareTuple], float]
+
+
+@dataclass
+class OptimizeSharesResult:
+    shares: ShareTuple
+    score: float
+    evaluations: int
+
+
+def _normalize_shares(s1: float, s2: float, min_share: float) -> ShareTuple | None:
+    s3 = 1.0 - s1 - s2
+    ms = max(min_share, 0.0)
+    if s1 < ms - 1e-9 or s2 < ms - 1e-9 or s3 < ms - 1e-9:
+        return None
+    if abs(s1 + s2 + s3 - 1.0) > 1e-6:
+        return None
+    return (round(s1, 8), round(s2, 8), round(s3, 8))
+
+
+def _start_points(min_share: float, grid_step: float) -> list[tuple[float, float]]:
+    """Multi-start seeds: simplex center, corners, and a few grid_step hints."""
+    ms = max(min_share, 0.0)
+    max_pair = max(1.0 - 2 * ms, ms)
+    center = round((1.0 - ms) / 3.0, 6)
+    points: list[tuple[float, float]] = [
+        (center, center),
+        (ms, ms),
+        (max_pair, ms),
+        (ms, max_pair),
+        (max_pair, max_pair),
+    ]
+    step = max(grid_step, 0.1)
+    if ms <= step <= max_pair:
+        points.append((step, ms))
+        points.append((ms, step))
+    deduped: list[tuple[float, float]] = []
+    seen: set[tuple[float, float]] = set()
+    for s1, s2 in points:
+        if s1 + s2 > 1.0 - ms + 1e-9:
+            continue
+        key = (round(s1, 6), round(s2, 6))
+        if key in seen:
+            continue
+        seen.add(key)
+        deduped.append(key)
+    return deduped
+
+
+def optimize_shares_for_triplet(
+    triplet: tuple[Component, Component, Component],
+    score_fn: ScoreFn,
+    *,
+    min_share: float,
+    grid_step: float = 0.1,
+) -> OptimizeSharesResult | None:
+    del triplet
+    ms = max(min_share, 0.0)
+    max_s1 = max(1.0 - 2 * ms, ms)
+    evaluations = 0
+
+    def objective(x: Any) -> float:
+        nonlocal evaluations
+        evaluations += 1
+        shares = _normalize_shares(float(x[0]), float(x[1]), ms)
+        if shares is None:
+            return 1e18
+        return score_fn(shares)
+
+    bounds = [(ms, max_s1), (ms, max_s1)]
+    constraints = [{"type": "ineq", "fun": lambda x: 1.0 - ms - float(x[0]) - float(x[1])}]
+
+    best_score: float | None = None
+    best_shares: ShareTuple | None = None
+
+    for s1, s2 in _start_points(ms, grid_step):
+        if s1 + s2 > 1.0 - ms + 1e-9:
+            continue
+        try:
+            res = minimize(
+                objective,
+                [s1, s2],
+                method="SLSQP",
+                bounds=bounds,
+                constraints=constraints,
+                options={"ftol": 1e-8, "maxiter": 40},
+            )
+        except Exception:
+            continue
+        if not res.success and res.fun >= 1e17:
+            continue
+        shares = _normalize_shares(float(res.x[0]), float(res.x[1]), ms)
+        if shares is None:
+            continue
+        score = float(res.fun)
+        if best_score is None or score < best_score:
+            best_score = score
+            best_shares = shares
+
+    if best_shares is None or best_score is None:
+        return None
+    return OptimizeSharesResult(shares=best_shares, score=best_score, evaluations=evaluations)

apps/api/app/modules/zootech/lab/calc/formulate_score.py

@@ -0,0 +1,371 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any
+
+from app.modules.zootech.lab.calc.derived import apply_content_derived
+from app.modules.zootech.lab.calc.nutrients import daily_intake_total, get_nutrient_value, norm_diff, weighted_average
+from app.modules.zootech.lab.indicators import RATION_ALL_INDICATORS, indicator_by_key
+from app.modules.zootech.wesp_bridge_models import Component
+
+
+def resolve_score_closure(optimize_keys: list[str]) -> frozenset[str]:
+    """Collect base + derived indicator keys needed to score optimize_keys."""
+    needed: set[str] = set(optimize_keys)
+    changed = True
+    while changed:
+        changed = False
+        for key in list(needed):
+            defn = indicator_by_key(key)
+            if defn is None:
+                continue
+            derived = defn.get("derived")
+            if derived == "alias":
+                dep = defn.get("alias_of")
+                if dep and dep not in needed:
+                    needed.add(dep)
+                    changed = True
+            elif derived in ("pct_of_dm", "g_per_kg_dm", "nel_per_kg_dm"):
+                dep = defn.get("from_key")
+                if dep and dep not in needed:
+                    needed.add(dep)
+                    changed = True
+            elif derived in ("rnb", "bra_rnb"):
+                for dep in ("crude_protein", "usp"):
+                    if dep not in needed:
+                        needed.add(dep)
+                        changed = True
+            elif derived == "ratio":
+                for dep in (defn.get("ratio_num"), defn.get("ratio_den")):
+                    if dep and dep not in needed:
+                        needed.add(dep)
+                        changed = True
+            elif derived == "dm_pct_bw":
+                if "dry_matter" not in needed:
+                    needed.add("dry_matter")
+                    changed = True
+    return frozenset(needed)
+
+
+def _indicator_content(
+    defn: dict[str, Any],
+    active: list[dict[str, Any]],
+    total_kg: float,
+    *,
+    heads_per_trip: int,
+) -> float | None:
+    key = defn.get("key")
+    if defn.get("aggregation") == "weighted_avg":
+        return weighted_average(
+            active,
+            total_kg,
+            defn["nutrient_keys"],
+            indicator_key=key,
+        )
+    return daily_intake_total(
+        active,
+        defn["nutrient_keys"],
+        heads_per_trip=heads_per_trip,
+        indicator_key=key,
+    )
+
+
+def compute_score_indicators(
+    active: list[dict[str, Any]],
+    total_kg: float,
+    norms: dict[str, dict[str, float | None]],
+    optimize_keys: list[str],
+    *,
+    heads_per_trip: int = 1,
+    profile_mass_kg: float | None = None,
+) -> list[dict[str, Any]]:
+    closure = resolve_score_closure(optimize_keys)
+    content_by_key: dict[str, float | None] = {}
+
+    for defn in RATION_ALL_INDICATORS:
+        key = defn["key"]
+        if key not in closure or defn.get("derived"):
+            continue
+        content_by_key[key] = _indicator_content(
+            defn, active, total_kg, heads_per_trip=heads_per_trip
+        )
+
+    for defn in RATION_ALL_INDICATORS:
+        key = defn["key"]
+        if key not in closure or not defn.get("derived"):
+            continue
+        content_by_key[key] = apply_content_derived(
+            content_by_key,
+            defn,
+            total_kg=total_kg,
+            heads_per_trip=heads_per_trip,
+            profile_mass_kg=profile_mass_kg,
+        )
+
+    rows: list[dict[str, Any]] = []
+    for key in optimize_keys:
+        if key not in closure:
+            continue
+        defn = indicator_by_key(key)
+        if defn is None:
+            continue
+        content = content_by_key.get(key)
+        bounds = norms.get(key, {})
+        min_v = bounds.get("min")
+        max_v = bounds.get("max")
+        diff = norm_diff(content, min_v, max_v)
+        if content is None and min_v is None and max_v is None:
+            continue
+        rows.append(
+            {
+                "key": key,
+                "label": defn["label"],
+                "unit": defn["unit"],
+                "min": min_v,
+                "max": max_v,
+                "content": content,
+                "diff": diff,
+            }
+        )
+    return rows
+
+
+def violation_score(
+    indicators: list[dict[str, Any]],
+    optimize_keys: list[str],
+    norms: dict[str, dict[str, float | None]],
+) -> float:
+    by_key = {row.get("key"): row for row in indicators if row.get("key")}
+    total = 0.0
+    counted = 0
+    for key in optimize_keys:
+        bounds = norms.get(key) or {}
+        if bounds.get("min") is None and bounds.get("max") is None:
+            continue
+        row = by_key.get(key)
+        if row is None:
+            continue
+        diff = row.get("diff")
+        if diff is None:
+            diff = norm_diff(row.get("content"), bounds.get("min"), bounds.get("max"))
+        if diff is None:
+            continue
+        total += float(diff) ** 2
+        counted += 1
+    return total if counted else 0.0
+
+
+def _violation_from_content(
+    content_by_key: dict[str, float | None],
+    optimize_keys: list[str],
+    norms: dict[str, dict[str, float | None]],
+) -> float:
+    total = 0.0
+    counted = 0
+    for key in optimize_keys:
+        bounds = norms.get(key) or {}
+        if bounds.get("min") is None and bounds.get("max") is None:
+            continue
+        content = content_by_key.get(key)
+        diff = norm_diff(content, bounds.get("min"), bounds.get("max"))
+        if diff is None:
+            continue
+        total += float(diff) ** 2
+        counted += 1
+    return total if counted else 0.0
+
+
+@dataclass
+class TripletScoreModel:
+    triplet: tuple[Component, Component, Component]
+    nutrient_cache: dict[str, dict[str, float]]
+    herd_scale: float
+    heads: int
+    cost_weight: float
+    profile_mass_kg: float | None
+    optimize_keys: list[str]
+    norms: dict[str, dict[str, float | None]]
+    closure: frozenset[str]
+    intake_unit: dict[str, tuple[float | None, float | None, float | None]]
+    weighted_vals: dict[str, tuple[float | None, float | None, float | None]]
+    prices: tuple[float, float, float]
+
+
+def build_triplet_score_model(
+    triplet: tuple[Component, Component, Component],
+    *,
+    nutrient_cache: dict[str, dict[str, float]],
+    norms: dict[str, dict[str, float | None]],
+    optimize_keys: list[str],
+    herd_scale: float,
+    heads: int,
+    cost_weight: float,
+    profile_mass_kg: float | None,
+) -> TripletScoreModel:
+    closure = resolve_score_closure(optimize_keys)
+    kg_per_share = herd_scale / max(heads, 1)
+    intake_unit: dict[str, tuple[float | None, float | None, float | None]] = {}
+    weighted_vals: dict[str, tuple[float | None, float | None, float | None]] = {}
+
+    for defn in RATION_ALL_INDICATORS:
+        key = defn["key"]
+        if key not in closure or defn.get("derived"):
+            continue
+        vals: list[float | None] = []
+        for comp in triplet:
+            nutrients = nutrient_cache.get(comp.id, {})
+            vals.append(
+                get_nutrient_value(
+                    comp.dry_matter,
+                    nutrients,
+                    defn["nutrient_keys"],
+                    indicator_key=key,
+                )
+            )
+        tup = (vals[0], vals[1], vals[2])
+        if defn.get("aggregation") == "weighted_avg":
+            weighted_vals[key] = tup
+        else:
+            intake_unit[key] = tuple(v * kg_per_share if v is not None else None for v in vals)
+
+    prices = tuple(
+        float(comp.price) if comp.price is not None else 0.0 for comp in triplet
+    )
+    return TripletScoreModel(
+        triplet=triplet,
+        nutrient_cache=nutrient_cache,
+        herd_scale=herd_scale,
+        heads=heads,
+        cost_weight=cost_weight,
+        profile_mass_kg=profile_mass_kg,
+        optimize_keys=optimize_keys,
+        norms=norms,
+        closure=closure,
+        intake_unit=intake_unit,
+        weighted_vals=weighted_vals,
+        prices=prices,
+    )
+
+
+def _content_from_shares(
+    model: TripletScoreModel,
+    shares: tuple[float, float, float],
+) -> dict[str, float | None]:
+    content_by_key: dict[str, float | None] = {}
+    total_kg = model.herd_scale
+
+    for key, coeffs in model.intake_unit.items():
+        parts = [
+            shares[i] * coeffs[i]
+            for i in range(3)
+            if coeffs[i] is not None
+        ]
+        content_by_key[key] = sum(parts) if parts else None
+
+    for key, vals in model.weighted_vals.items():
+        num = 0.0
+        den = 0.0
+        for i in range(3):
+            if vals[i] is None:
+                continue
+            num += shares[i] * vals[i]
+            den += shares[i]
+        content_by_key[key] = (num / den) if den > 0 else None
+
+    for defn in RATION_ALL_INDICATORS:
+        key = defn["key"]
+        if key not in model.closure or not defn.get("derived"):
+            continue
+        content_by_key[key] = apply_content_derived(
+            content_by_key,
+            defn,
+            total_kg=total_kg,
+            heads_per_trip=model.heads,
+            profile_mass_kg=model.profile_mass_kg,
+        )
+    return content_by_key
+
+
+def score_model_shares(
+    model: TripletScoreModel,
+    shares: tuple[float, float, float],
+) -> tuple[float, float, float]:
+    """Return (violation, cost_head, score) without building line dicts."""
+    content = _content_from_shares(model, shares)
+    violation = _violation_from_content(content, model.optimize_keys, model.norms)
+    cost_total = sum(shares[i] * model.prices[i] * model.herd_scale for i in range(3))
+    cost_head = cost_total / max(model.heads, 1)
+    score = violation * model.cost_weight + cost_head
+    return violation, cost_head, score
+
+
+def build_calc_lines(
+    triplet: tuple[Component, Component, Component],
+    shares: tuple[float, float, float],
+    herd_scale: float,
+    nutrient_cache: dict[str, dict[str, float]],
+) -> list[dict[str, Any]]:
+    lines: list[dict[str, Any]] = []
+    for i, comp in enumerate(triplet):
+        daily_kg = shares[i] * herd_scale
+        lines.append(
+            {
+                "component_id": comp.id,
+                "ingredient_name": comp.name,
+                "daily_kg": daily_kg,
+                "in_ration": True,
+                "in_compound": False,
+                "dry_matter": comp.dry_matter,
+                "price_per_kg": comp.price,
+                "nutrients": nutrient_cache.get(comp.id, {}),
+            }
+        )
+    return lines
+
+
+def cost_per_head(
+    triplet: tuple[Component, Component, Component],
+    shares: tuple[float, float, float],
+    herd_scale: float,
+    heads: int,
+) -> float:
+    total = 0.0
+    any_cost = False
+    for i, comp in enumerate(triplet):
+        kg = shares[i] * herd_scale
+        price = comp.price
+        if kg is None or price is None:
+            continue
+        total += float(kg) * float(price)
+        any_cost = True
+    if not any_cost:
+        return 0.0
+    return total / max(heads, 1)
+
+
+def score_triplet_shares(
+    triplet: tuple[Component, Component, Component],
+    shares: tuple[float, float, float],
+    *,
+    nutrient_cache: dict[str, dict[str, float]],
+    norms: dict[str, dict[str, float | None]],
+    optimize_keys: list[str],
+    herd_scale: float,
+    heads: int,
+    cost_weight: float,
+    profile_mass_kg: float | None,
+) -> tuple[float, float, float, list[dict[str, Any]]]:
+    """Return (violation, cost_head, score, lines)."""
+    model = build_triplet_score_model(
+        triplet,
+        nutrient_cache=nutrient_cache,
+        norms=norms,
+        optimize_keys=optimize_keys,
+        herd_scale=herd_scale,
+        heads=heads,
+        cost_weight=cost_weight,
+        profile_mass_kg=profile_mass_kg,
+    )
+    violation, cost_head, score = score_model_shares(model, shares)
+    lines = build_calc_lines(triplet, shares, herd_scale, nutrient_cache)
+    return violation, cost_head, score, lines

apps/api/app/modules/zootech/lab/calc/formulate_validate.py

@@ -0,0 +1,86 @@
+from __future__ import annotations
+
+from typing import Any
+
+from app.modules.zootech.lab.calc.feed_groups import classify_feed_group
+from app.modules.zootech.lab.calc.gfe_policies import default_om_digestibility_pct
+from app.modules.zootech.lab.nutrient_schema import read_from_mapping
+from app.modules.zootech.lab.services.component_nutrients import derive_context_for_component, nutrients_full_dict, nutrients_is_empty
+from app.modules.zootech.wesp_bridge_models import Component
+
+_REQUIRED_EAV_KEYS = ("Сыр. Протеин", "Сырая клетч", "Сырой жир")
+_MAIN_FEED_KEYS = ("Осн.Корм", "СВ Основной корм")
+_OMD_KEYS = ("ВРХ Орг Вещ", "КРС Орг Вещ")
+
+
+def main_feed_dm_g_per_kg(component_id: str | None) -> float | None:
+    if not component_id:
+        return None
+    full = nutrients_full_dict(component_id)
+    val = read_from_mapping(full, _MAIN_FEED_KEYS)
+    return float(val) if val is not None else None
+
+
+def validate_component(component_id: str) -> dict[str, Any]:
+    comp = Component.query.filter_by(id=component_id, is_deleted=False).first()
+    if comp is None:
+        return {
+            "id": component_id,
+            "name": None,
+            "eligible": False,
+            "missing": ["component_not_found"],
+            "warnings": [],
+            "dryMatterPct": None,
+            "hasPrice": False,
+            "price": None,
+        }
+
+    missing: list[str] = []
+    warnings: list[str] = []
+    dry_matter_pct = comp.dry_matter
+    if dry_matter_pct is None or float(dry_matter_pct) <= 0:
+        missing.append("dry_matter")
+
+    full = nutrients_full_dict(component_id)
+    if nutrients_is_empty(component_id):
+        missing.append("nutrients_empty")
+    else:
+        for key in _REQUIRED_EAV_KEYS:
+            if read_from_mapping(full, (key,)) is None:
+                missing.append(key)
+
+    has_price = comp.price is not None and float(comp.price) >= 0
+    if not has_price:
+        warnings.append("price_missing")
+
+    main_feed_dm = read_from_mapping(full, _MAIN_FEED_KEYS)
+    if main_feed_dm is None:
+        warnings.append("main_feed_unset")
+
+    feed_group = classify_feed_group(comp)
+    omd = read_from_mapping(full, _OMD_KEYS)
+    if feed_group in ("rough", "succulent") and omd is None:
+        warnings.append("omd_missing")
+    elif omd is None and comp.dry_matter and float(comp.dry_matter) > 0:
+        ctx = derive_context_for_component(component_id, full)
+        warnings.append(
+            f"omd_defaulted:{default_om_digestibility_pct(ctx):.0f}"
+        )
+
+    return {
+        "id": comp.id,
+        "name": comp.name,
+        "eligible": len(missing) == 0,
+        "missing": missing,
+        "warnings": warnings,
+        "dryMatterPct": dry_matter_pct,
+        "hasPrice": has_price,
+        "price": comp.price,
+        "mainFeedDmGPerKg": main_feed_dm,
+        "isMainFeed": main_feed_dm is not None and float(main_feed_dm) > 0,
+        "feedGroup": feed_group,
+    }
+
+
+def validate_components(component_ids: list[str]) -> list[dict[str, Any]]:
+    return [validate_component(cid) for cid in component_ids]

apps/api/app/modules/zootech/lab/calc/gfe_norms.py

@@ -0,0 +1,94 @@
+"""Динамические нормы по уравнениям GfE (Германия)."""
+
+from __future__ import annotations
+
+from typing import Any
+
+USP_DYNAMIC_KEY = "usp"
+NEL_DYNAMIC_KEY = "nel"
+
+# GfE 2001 Milchkühe — Erhaltung + Milch (Standardmilch / FCM)
+NEL_MAINTENANCE_COEFF = 0.293  # MJ NEL / (kg LM)^0,75 / Tag
+NEL_PER_KG_MILK_MJ = 3.3  # MJ NEL / kg Milch (FCM)
+
+
+def gfe_usp_min_g(
+    mass_kg: float | None,
+    milk_yield_kg: float | None = None,
+) -> float | None:
+    """
+    Минимальная суточная потребность в усвояемом протеине (уСП / nXP), г/сут.
+
+    GfE: 0,09 × (масса^0,75) × 6,25 + удой × 85 г.
+    """
+    if mass_kg is None or mass_kg <= 0:
+        return None
+    maintenance = 0.09 * (mass_kg**0.75) * 6.25
+    milk = max(float(milk_yield_kg or 0), 0.0) * 85.0
+    return maintenance + milk
+
+
+def gfe_nel_min_mj(
+    mass_kg: float | None,
+    milk_yield_kg: float | None = None,
+) -> float | None:
+    """
+    Минимальная суточная потребность в ЧЭЛ (NEL), МДж/сут.
+
+    GfE 2001: 0,293 × LM^0,75 + удой × 3,3 (MJ NEL на кг молока).
+    """
+    if mass_kg is None or mass_kg <= 0:
+        return None
+    maintenance = NEL_MAINTENANCE_COEFF * (mass_kg**0.75)
+    milk = max(float(milk_yield_kg or 0), 0.0) * NEL_PER_KG_MILK_MJ
+    return maintenance + milk
+
+
+def preview_dynamic_norms(
+    mass_kg: float | None,
+    milk_yield_kg: float | None = None,
+) -> dict[str, Any]:
+    """Расчётные min по GfE для UI (без учёта сохранённых норм)."""
+    _, dynamic = apply_dynamic_norms(
+        {},
+        mass_kg=mass_kg,
+        milk_yield_kg=milk_yield_kg,
+    )
+    return dynamic
+
+
+def apply_dynamic_norms(
+    stored: dict[str, dict[str, float | None]],
+    *,
+    mass_kg: float | None,
+    milk_yield_kg: float | None,
+    ration_type: str | None = None,
+    force_dynamic: bool = False,
+) -> tuple[dict[str, dict[str, float | None]], dict[str, Any]]:
+    """
+    Заполняет нормы по GfE, если в БД min не задан.
+    force_dynamic=True — пересчитать min уСП/ЧЭЛ по массе и удою даже при нормах в БД
+    (автоготовка с явными mass_kg / milk_yield_kg).
+    Возвращает (resolved_norms, dynamic_meta).
+    """
+    del ration_type
+    resolved: dict[str, dict[str, float | None]] = {
+        key: {"min": bounds.get("min"), "max": bounds.get("max")}
+        for key, bounds in stored.items()
+    }
+    dynamic: dict[str, Any] = {}
+
+    for key, compute, formula in (
+        (USP_DYNAMIC_KEY, gfe_usp_min_g, "0.09×масса^0.75×6.25 + удой×85"),
+        (NEL_DYNAMIC_KEY, gfe_nel_min_mj, "0.293×масса^0.75 + удой×3.3"),
+    ):
+        bounds = resolved.get(key, {"min": None, "max": None})
+        if force_dynamic or bounds.get("min") is None:
+            computed = compute(mass_kg, milk_yield_kg)
+            if computed is not None:
+                entry = dict(bounds)
+                entry["min"] = computed
+                resolved[key] = entry
+                dynamic[key] = {"min": computed, "formula": formula}
+
+    return resolved, dynamic

apps/api/app/modules/zootech/lab/calc/gfe_policies.py

@@ -0,0 +1,84 @@
+"""WESP-политики расчёта на базе GfE 2001 (отличия от zootech Excel — осознанные)."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Literal
+
+FeedGroup = Literal["rough", "succulent", "concentrate", "other", "unknown"]
+
+# --- GfE 2001 константы (формулы не меняем) ---
+NEL_Q_COEFF = 0.004
+NEL_Q_REF_PCT = 57.0
+NEL_BASE = 0.6
+
+GE_CP = 0.0239
+GE_FAT = 0.0398
+GE_FIBER = 0.0201
+GE_NFE = 0.0175
+
+ME_FAT = 0.0312
+ME_FIBER = 0.0136
+ME_OR_RESIDUE = 0.0147
+ME_CP = 0.00234
+
+USP_FAT_THRESHOLD_G_PER_KG_DM = 70.0  # 7% СЖ/кг СВ
+
+# DCAB (Na+K)-(Cl+S), мэкв при минералах в г/кг СВ
+DCAB_NA = 43.5
+DCAB_K = 25.6
+DCAB_CL = 28.2
+DCAB_S = 62.4
+
+# Дефолты переваримости при пустых коэфф. (Excel legacy, кроме ОВ)
+DEFAULT_CP_DIGEST_PCT = 86.0
+DEFAULT_FAT_DIGEST_PCT = 75.0
+DEFAULT_FIBER_DIGEST_PCT = 86.0
+DEFAULT_NFE_DIGEST_PCT = 94.0
+DEFAULT_INSOLUBLE_PROTEIN_PCT = 15.0
+DEFAULT_PROTEIN_FRACTION_PCT = 18.9
+
+# WESP: дефолт ВРХ орг. вещ. при пустом поле — по классу корма
+DEFAULT_OMD_ROUGH_PCT = 65.0
+DEFAULT_OMD_SUCCULENT_PCT = 72.0
+DEFAULT_OMD_CONCENTRATE_PCT = 91.0
+
+
+@dataclass(frozen=True)
+class DeriveContext:
+    feed_group: FeedGroup = "unknown"
+    is_main_feed: bool = False
+
+    @classmethod
+    def from_cells(cls, *, main_feed_g: float = 0.0, feed_group: FeedGroup = "unknown") -> DeriveContext:
+        return cls(
+            feed_group=feed_group,
+            is_main_feed=main_feed_g > 0,
+        )
+
+    @classmethod
+    def infer_from_cells(cls, cells: dict[str, float], feed_group: FeedGroup = "unknown") -> DeriveContext:
+        main_feed = cells.get("E", 0.0)
+        return cls.from_cells(main_feed_g=main_feed, feed_group=feed_group)
+
+
+def default_om_digestibility_pct(ctx: DeriveContext | None) -> float:
+    """Дефолт переваримости ОВ (%) при отсутствии ВРХ Орг Вещ."""
+    if ctx is None:
+        return DEFAULT_OMD_CONCENTRATE_PCT
+    if ctx.is_main_feed or ctx.feed_group == "rough":
+        return DEFAULT_OMD_ROUGH_PCT
+    if ctx.feed_group == "succulent":
+        return DEFAULT_OMD_SUCCULENT_PCT
+    return DEFAULT_OMD_CONCENTRATE_PCT
+
+
+def default_digestibility_coefficients() -> dict[str, float]:
+    return {
+        "cp": DEFAULT_CP_DIGEST_PCT,
+        "fat": DEFAULT_FAT_DIGEST_PCT,
+        "fiber": DEFAULT_FIBER_DIGEST_PCT,
+        "nfe": DEFAULT_NFE_DIGEST_PCT,
+        "insoluble_protein": DEFAULT_INSOLUBLE_PROTEIN_PCT,
+        "protein_fraction": DEFAULT_PROTEIN_FRACTION_PCT,
+    }

apps/api/app/modules/zootech/lab/calc/ingredient_catalog.py

@@ -0,0 +1,187 @@
+"""Каталог показателей zootech «База сырья» (109 колонок)."""
+
+from __future__ import annotations
+
+from openpyxl.utils import column_index_from_string, get_column_letter
+
+# Заголовки row 3 в xlsx_extracted/data_csv_cleaned/База сырья.csv
+INGREDIENT_HEADERS: tuple[str, ...] = (
+    "№",
+    "Наименование",
+    "Цена 1 кг",
+    "СВ",
+    "Осн.Корм",
+    "Сыр. Протеин",
+    "уСП",
+    "БРА",
+    "ЧЭЛ- КРС",
+    "ОЭ-КРС",
+    "Сырая клетч",
+    "Структур клетч",
+    "Сырой жир",
+    "НДК",
+    "КДК",
+    "NFC",
+    "Ca",
+    "P",
+    "Mg",
+    "Fe",
+    "Zn",
+    "Cu",
+    "Co",
+    "Mn",
+    "Se",
+    "J",
+    "Na",
+    "K",
+    "CL",
+    "S",
+    "DCAB Форм",
+    "Сахар и Крохм",
+    "Нераств  Крохм",
+    "Сахар",
+    "Крахмал",
+    "Нераств крахмал",
+    "Вит А",
+    "Вит D",
+    "Вит Е",
+    "Вит В1",
+    "Вит В2",
+    "Вит В6",
+    "Вит В12",
+    "Пант Кальц",
+    "Никот Ки-та",
+    "Фол ки-та",
+    "Холин",
+    "Биотин",
+    "Сырая зола",
+    "БЕР",
+    "Лизин",
+    "Метионин",
+    "Треонин",
+    "Триптофан",
+    "Изолейцин",
+    "Лейцин",
+    "Валин",
+    "Каротин",
+    "b -Каротин",
+    "Линолевая к-та",
+    "Линоленовая ки-та",
+    "Масляная ки-та",
+    "Арахидоновая ки-та",
+    "Полиэновая ки-та",
+    "Мочевина",
+    "СВ Основной корм",
+    "ВРХ Орг Вещ",
+    "Перевар Орг Вещ",
+    "КРС Протеин",
+    "Переварим Протеин",
+    "КРС Сырой жир",
+    "Переварим Сырой жир",
+    "КРС Сырая клетч",
+    "Переварим сырая клетч",
+    "КРС БЭВ",
+    "Переварим БЭВ",
+    "ВЕ",
+    "OЭ КРС форм",
+    "ЧЭЛ - КРС Форм",
+    "НДК Общ",
+    "НДК Осн. Корм",
+    "КДК общ",
+    "%  нераствор протеин",
+    "Нерастворим прот",
+    "СЖ/кг СВ",
+    "НСП/кг СВ",
+    "СП/кг СВ",
+    "пОВ/кг СВ",
+    "пСЖ/кг СВ",
+    "уСП<7%CЖ",
+    "уСП>7%CЖ",
+    "уСП/кг СВ формул",
+    "уСП в ОР",
+    "уСП формул",
+    "БРА",
+    "Нераств крохмал",
+    "доля крохмала",
+    "Доля белка",
+    "% перев в кишках",
+    "OEB",
+    "Синтез Мдж",
+    "Промеж рез 1",
+    "Промеж рез 2",
+    "Метаб ОЕТ",
+    "Метаб лизин",
+    "Метабол Треон",
+    "Метабол Лейцин",
+    "Метабол  Изолейц",
+    "Метабол Валин",
+)
+
+HEADER_TO_LETTER: dict[str, str] = {
+    header: get_column_letter(i + 1) for i, header in enumerate(INGREDIENT_HEADERS)
+}
+
+LETTER_TO_HEADER: dict[str, str] = {
+    get_column_letter(i + 1): header for i, header in enumerate(INGREDIENT_HEADERS)
+}
+
+# Колонки с формулами в шаблонной строке 6 (native port, не Excel runtime).
+DERIVED_LETTERS: frozenset[str] = frozenset(
+    {
+        "AE",
+        "AF",
+        "AG",
+        "AX",
+        "BN",
+        "BP",
+        "BR",
+        "BT",
+        "BV",
+        "BX",
+        "BY",
+        "BZ",
+        "CA",
+        "CF",
+        "CG",
+        "CH",
+        "CI",
+        "CJ",
+        "CK",
+        "CL",
+        "CM",
+        "CN",
+        "CP",
+        "CQ",
+        "CX",
+        "CY",
+        "CZ",
+        "DA",
+        "DB",
+        "DC",
+        "DD",
+        "DE",
+    }
+)
+
+# Отображаемые поля G–J синхронизируются с расчётными CP/CQ/CA/BZ.
+DISPLAY_SYNC: tuple[tuple[str, str], ...] = (
+    ("G", "CP"),  # уСП
+    ("H", "CQ"),  # RNB (legacy заголовок «БРА», дубль CQ)
+    ("I", "CA"),  # ЧЭЛ- КРС
+    ("J", "BZ"),  # ОЭ-КРС
+)
+
+DERIVED_HEADERS: frozenset[str] = frozenset(
+    LETTER_TO_HEADER[letter] for letter in DERIVED_LETTERS
+) | frozenset(LETTER_TO_HEADER[g] for g, _ in DISPLAY_SYNC)
+
+INPUT_HEADERS: frozenset[str] = frozenset(INGREDIENT_HEADERS) - DERIVED_HEADERS - frozenset(
+    ("№", "Наименование", "Цена 1 кг")
+)
+
+# Дублирующий заголовок «БРА»/RNB (H и CQ) — в derive используем CQ.
+DUPLICATE_HEADERS: frozenset[str] = frozenset({"БРА"})
+
+
+def letter_index(letter: str) -> int:
+    return column_index_from_string(letter) - 1

apps/api/app/modules/zootech/lab/calc/ingredient_derive.py

@@ -0,0 +1,216 @@
+"""Native derive формул zootech «База сырья» — WESP GfE 2001 engine."""
+
+from __future__ import annotations
+
+from typing import Any
+
+from app.modules.zootech.lab.calc.gfe_policies import (
+    DCAB_CL,
+    DCAB_K,
+    DCAB_NA,
+    DCAB_S,
+    DEFAULT_CP_DIGEST_PCT,
+    DEFAULT_FAT_DIGEST_PCT,
+    DEFAULT_FIBER_DIGEST_PCT,
+    DEFAULT_INSOLUBLE_PROTEIN_PCT,
+    DEFAULT_NFE_DIGEST_PCT,
+    DEFAULT_PROTEIN_FRACTION_PCT,
+    DeriveContext,
+    GE_CP,
+    GE_FAT,
+    GE_FIBER,
+    GE_NFE,
+    ME_CP,
+    ME_FAT,
+    ME_FIBER,
+    ME_OR_RESIDUE,
+    NEL_BASE,
+    NEL_Q_COEFF,
+    NEL_Q_REF_PCT,
+    USP_FAT_THRESHOLD_G_PER_KG_DM,
+    default_om_digestibility_pct,
+)
+from app.modules.zootech.lab.calc.ingredient_catalog import (
+    DISPLAY_SYNC,
+    HEADER_TO_LETTER,
+    INGREDIENT_HEADERS,
+    LETTER_TO_HEADER,
+)
+
+
+def _parse_num(value: Any) -> float | None:
+    if value is None or value == "":
+        return None
+    try:
+        n = float(value)
+    except (TypeError, ValueError):
+        return None
+    return None if n != n else n
+
+
+def _normalize_key(value: str) -> str:
+    return " ".join((value or "").split()).strip().lower()
+
+
+def _v(cells: dict[str, float], letter: str, default: float = 0.0) -> float:
+    return cells.get(letter, default)
+
+
+def _if_pos(test: float, when_true, when_false: float = 0.0) -> float:
+    """Excel IF(test>0, …) — ветка when_true не вычисляется при test<=0."""
+    if test > 0:
+        return when_true() if callable(when_true) else when_true
+    return when_false
+
+
+def dict_to_cells(data: dict[str, Any] | None) -> dict[str, float]:
+    """Словарь {заголовок: значение} → {буква колонки: значение}."""
+    cells: dict[str, float] = {}
+    if not data:
+        return cells
+    norm_index = {_normalize_key(h): h for h in INGREDIENT_HEADERS}
+    for raw_key, raw_val in data.items():
+        n = _parse_num(raw_val)
+        if n is None:
+            continue
+        nk = _normalize_key(str(raw_key))
+        header = norm_index.get(nk)
+        if header is None:
+            continue
+        letter = HEADER_TO_LETTER.get(header)
+        if letter:
+            cells[letter] = n
+    return cells
+
+
+def cells_to_dict(cells: dict[str, float]) -> dict[str, float]:
+    out: dict[str, float] = {}
+    for letter, value in cells.items():
+        header = LETTER_TO_HEADER.get(letter)
+        if header and header not in ("№", "Наименование", "Цена 1 кг"):
+            out[header] = value
+    return out
+
+
+def derive_cells(
+    cells: dict[str, float],
+    *,
+    context: DeriveContext | None = None,
+) -> dict[str, float]:
+    """Пересчёт derived-колонок по цепочке формул row 6 «База сырья»."""
+    c = dict(cells)
+    ctx = context or DeriveContext.infer_from_cells(c)
+    omd_default = default_om_digestibility_pct(ctx)
+
+    c["AE"] = DCAB_NA * _v(c, "AA") + DCAB_K * _v(c, "AB") - DCAB_CL * _v(c, "AC") - DCAB_S * _v(c, "AD")
+    c["AG"] = _v(c, "AJ") * _v(c, "AI") / 100.0
+    c["AF"] = _v(c, "AI") - c["AG"] + _v(c, "AH")
+    c["AX"] = _v(c, "D") - _v(c, "F") - _v(c, "K") - _v(c, "M") - _v(c, "AW")
+    c["BN"] = _if_pos(_v(c, "E"), lambda: _v(c, "D") / _v(c, "E") * 1000.0)
+    c["BP"] = _if_pos(
+        _v(c, "BO"),
+        lambda: (_v(c, "D") - _v(c, "AW")) * _v(c, "BO") / 100.0,
+        (_v(c, "D") - _v(c, "AW")) * omd_default / 100.0,
+    )
+    c["BR"] = _if_pos(
+        _v(c, "BQ"), lambda: _v(c, "F") * _v(c, "BQ") / 100.0, _v(c, "F") * DEFAULT_CP_DIGEST_PCT / 100.0
+    )
+    c["BT"] = _if_pos(
+        _v(c, "BS"), lambda: _v(c, "M") * _v(c, "BS") / 100.0, _v(c, "M") * DEFAULT_FAT_DIGEST_PCT / 100.0
+    )
+    c["BV"] = _if_pos(
+        _v(c, "BU"), lambda: _v(c, "K") * _v(c, "BU") / 100.0, _v(c, "K") * DEFAULT_FIBER_DIGEST_PCT / 100.0
+    )
+    c["BX"] = _if_pos(
+        _v(c, "BW"), lambda: c["AX"] * _v(c, "BW") / 100.0, c["AX"] * DEFAULT_NFE_DIGEST_PCT / 100.0
+    )
+    c["BY"] = GE_CP * _v(c, "F") + GE_FAT * _v(c, "M") + GE_FIBER * _v(c, "K") + GE_NFE * c["AX"]
+    c["BZ"] = (
+        ME_FAT * c["BT"]
+        + ME_FIBER * c["BV"]
+        + ME_OR_RESIDUE * (c["BP"] - c["BT"] - c["BV"])
+        + ME_CP * _v(c, "F")
+    )
+    c["CA"] = _if_pos(
+        c["BY"],
+        lambda: (NEL_BASE * (1.0 + NEL_Q_COEFF * (c["BZ"] / c["BY"] * 100.0 - NEL_Q_REF_PCT)) * c["BZ"]),
+    )
+    c["CF"] = _if_pos(
+        _v(c, "CE"),
+        lambda: _v(c, "F") * _v(c, "CE") / 100.0,
+        _v(c, "F") * DEFAULT_INSOLUBLE_PROTEIN_PCT / 100.0,
+    )
+    c["CG"] = _if_pos(_v(c, "D"), lambda: _v(c, "M") * 1000.0 / _v(c, "D"))
+    c["CH"] = _if_pos(_v(c, "D"), lambda: c["CF"] * 1000.0 / _v(c, "D"))
+    c["CI"] = _if_pos(_v(c, "D"), lambda: _v(c, "F") * 1000.0 / _v(c, "D"))
+    c["CJ"] = _if_pos(_v(c, "D"), lambda: c["BP"] / _v(c, "D"))
+    c["CK"] = _if_pos(_v(c, "D"), lambda: c["BT"] / _v(c, "D"))
+    c["CL"] = _if_pos(c["CI"], lambda: (187.7 - 115.4 * c["CH"] / c["CI"]) * c["CJ"] + 1.03 * c["CH"])
+    c["CM"] = _if_pos(c["CI"], lambda: (196.1 - 127.5 * c["CH"] / c["CI"]) * (c["CJ"] - c["CK"]) + 1.03 * c["CH"])
+    co = _v(c, "CO")
+    if c["CG"] < USP_FAT_THRESHOLD_G_PER_KG_DM + 0.01:
+        c["CN"] = c["CL"]
+    elif c["CG"] > USP_FAT_THRESHOLD_G_PER_KG_DM:
+        c["CN"] = c["CM"]
+    else:
+        c["CN"] = 0.0
+    if co < 1.01:
+        c["CP"] = c["CN"] * _v(c, "D") / 1000.0
+    elif co > 1.0:
+        c["CP"] = co
+    else:
+        c["CP"] = 0.0
+    c["CQ"] = (_v(c, "F") - c["CP"]) / 6.25
+    c["CX"] = _if_pos(
+        _v(c, "CT"),
+        lambda: _v(c, "F") * _v(c, "CT") / 100.0,
+        _v(c, "F") * DEFAULT_PROTEIN_FRACTION_PCT / 100.0,
+    )
+    nel = c["CA"]
+    c["CY"] = nel * _v(c, "CW")
+    f_val = _v(c, "F")
+    bo = _v(c, "BO")
+    if f_val == 0.0:
+        c["CZ"] = 0.0
+        c["DA"] = 0.0
+        c["DB"] = 0.0
+        c["DC"] = 0.0
+        c["DD"] = 0.0
+        c["DE"] = 0.0
+    else:
+        c["DA"] = c["CX"] * (_v(c, "AY") / 10.0) / (f_val / 10.0) * (bo / 100.0) + c["CY"] * 0.071 * 0.8
+        c["CZ"] = _if_pos(
+            bo,
+            lambda: c["CX"] * (_v(c, "AZ") / 10.0) / (f_val / 10.0) * (bo / 100.0) + c["CY"] * 0.018 * 0.8,
+        )
+        c["DB"] = _if_pos(
+            bo,
+            lambda: c["CX"] * (_v(c, "BA") / 10.0) / (f_val / 10.0) * (bo / 100.0) + c["CY"] * 0.044 * 0.8,
+        )
+        c["DC"] = _if_pos(
+            _v(c, "BD"),
+            lambda: c["CX"] * (_v(c, "BD") / 10.0) / (f_val / 10.0) * (bo / 100.0) + c["CY"] * 0.063 * 0.8,
+        )
+        c["DD"] = _if_pos(
+            _v(c, "BC"),
+            lambda: c["CX"] * (_v(c, "BC") / 10.0) / (f_val / 10.0) * (bo / 100.0) + c["CY"] * 0.049 * 0.8,
+        )
+        c["DE"] = _if_pos(
+            _v(c, "BE"),
+            lambda: c["CX"] * (_v(c, "BE") / 10.0) / (f_val / 10.0) * (bo / 100.0) + c["CY"] * 0.048 * 0.8,
+        )
+
+    for display, source in DISPLAY_SYNC:
+        c[display] = c[source]
+
+    return c
+
+
+def derive_ingredient_nutrients(
+    data: dict[str, Any] | None,
+    *,
+    context: DeriveContext | None = None,
+) -> dict[str, float]:
+    """Полный набор показателей: входные + пересчитанные derived."""
+    cells = dict_to_cells(data)
+    return cells_to_dict(derive_cells(cells, context=context))

apps/api/app/modules/zootech/lab/calc/norms.py

@@ -0,0 +1,38 @@
+from __future__ import annotations
+
+from typing import Any
+
+from app.modules.zootech.lab.calc.nutrients import parse_num
+from app.modules.zootech.lab.constants import NORM_COLUMN_ALIASES, RATION_QUALITY_INDICATORS
+
+
+def merge_norms_from_profile(profile_data: Any, ration_type: str) -> dict[str, dict[str, float | None]]:
+    del ration_type
+    if not profile_data or not isinstance(profile_data, dict):
+        return {}
+    indicators = profile_data.get("indicators")
+    if isinstance(indicators, dict):
+        out: dict[str, dict[str, float | None]] = {}
+        for key, bounds in indicators.items():
+            if not isinstance(bounds, dict):
+                continue
+            out[str(key)] = {
+                "min": parse_num(bounds.get("min")),
+                "max": parse_num(bounds.get("max")),
+            }
+        return out
+    out = {}
+    for defn in RATION_QUALITY_INDICATORS:
+        key = defn["key"]
+        aliases = NORM_COLUMN_ALIASES.get(key)
+        if not aliases:
+            continue
+        for alias in aliases:
+            entry = profile_data.get(alias)
+            if isinstance(entry, dict):
+                out[key] = {
+                    "min": parse_num(entry.get("min")),
+                    "max": parse_num(entry.get("max")),
+                }
+                break
+    return out

apps/api/app/modules/zootech/lab/calc/norms_derived.py

@@ -0,0 +1,86 @@
+"""Производные min/max норм из базовых показателей RACION."""
+
+from __future__ import annotations
+
+from typing import Any
+
+from app.modules.zootech.lab.indicators import RATION_ALL_INDICATORS, indicator_by_key
+
+
+def _norm_min(bounds: dict[str, float | None] | None) -> float | None:
+    if not bounds:
+        return None
+    v = bounds.get("min")
+    return float(v) if v is not None else None
+
+
+def _apply_derived_min(
+    norms: dict[str, dict[str, float | None]],
+    defn: dict[str, Any],
+    *,
+    mass_kg: float | None = None,
+) -> float | None:
+    derived = defn.get("derived")
+    key = defn["key"]
+    if derived == "alias":
+        src = norms.get(defn.get("alias_of") or "")
+        if src and src.get("min") is not None:
+            return src["min"]
+        return None
+    if derived == "pct_of_dm":
+        src = _norm_min(norms.get(defn.get("from_key") or ""))
+        dm = _norm_min(norms.get("dry_matter"))
+        if src is None or not dm or dm <= 0:
+            return None
+        return src / dm * 100.0
+    if derived == "g_per_kg_dm":
+        src = _norm_min(norms.get(defn.get("from_key") or ""))
+        dm = _norm_min(norms.get("dry_matter"))
+        if src is None or not dm or dm <= 0:
+            return None
+        return src / (dm / 1000.0)
+    if derived == "nel_per_kg_dm":
+        dm = _norm_min(norms.get("dry_matter"))
+        nel = _norm_min(norms.get("nel"))
+        if not dm or dm <= 0 or nel is None:
+            return None
+        return nel / (dm / 1000.0)
+    if derived == "ratio":
+        num = _norm_min(norms.get(defn.get("ratio_num") or ""))
+        den = _norm_min(norms.get(defn.get("ratio_den") or ""))
+        if num is None or den is None or den == 0:
+            return None
+        return num / den
+    if derived == "dm_pct_bw":
+        dm = _norm_min(norms.get("dry_matter"))
+        if dm is None or not mass_kg or mass_kg <= 0:
+            return None
+        return (dm / 1000.0 / mass_kg) * 100.0
+    if derived == "ration_pct_bw":
+        return None
+    if derived in ("rnb", "bra_rnb"):
+        return _norm_min(norms.get(key))
+    return None
+
+
+def apply_derived_norms(
+    norms: dict[str, dict[str, float | None]],
+    *,
+    mass_kg: float | None = None,
+) -> tuple[dict[str, dict[str, float | None]], dict[str, Any]]:
+    """Дополняет norms производными min; max не трогает."""
+    out = {k: dict(v) for k, v in norms.items()}
+    dynamic: dict[str, Any] = {}
+    for defn in RATION_ALL_INDICATORS:
+        if not defn.get("derived"):
+            continue
+        key = defn["key"]
+        if _norm_min(out.get(key)) is not None:
+            continue
+        val = _apply_derived_min(out, defn, mass_kg=mass_kg)
+        if val is None:
+            continue
+        rounded = round(val, 3)
+        out[key] = {"min": rounded, "max": out.get(key, {}).get("max")}
+        dynamic[key] = {"min": rounded, "derived": defn.get("derived")}
+    return out, dynamic

apps/api/app/modules/zootech/lab/calc/norms_resolver.py

@@ -0,0 +1,192 @@
+"""Роутер методик суточных норм: WESP / Москва / Петербург."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Any, Literal
+
+from app.modules.zootech.lab.calc.gfe_norms import apply_dynamic_norms
+from app.modules.zootech.lab.calc.racion.moscow import MoscowDairyParams, resolve_moscow_dairy_norms
+from app.modules.zootech.lab.calc.racion.piter import PiterDairyParams, PiterPrepError, resolve_piter_dairy_norms
+from app.modules.zootech.lab.indicators import RATION_ALL_INDICATORS
+
+NormsMethod = Literal["wesp", "racion_moscow", "racion_piter"]
+
+VALID_NORMS_METHODS: frozenset[str] = frozenset({"wesp", "racion_moscow", "racion_piter"})
+
+
+@dataclass
+class NormsParams:
+    milk_fat_pct: float | None = None
+    lactation_no: int | None = None
+    lactation_stage: int | None = None
+    body_condition: int | None = None
+    housing_system: int | None = None
+    konc_oe_sv: float | None = None
+
+    @classmethod
+    def from_dict(cls, raw: dict[str, Any] | None) -> NormsParams:
+        if not raw:
+            return cls()
+        return cls(
+            milk_fat_pct=_flt(raw.get("milkFatPct", raw.get("milk_fat_pct"))),
+            lactation_no=_int(raw.get("lactationNo", raw.get("lactation_no"))),
+            lactation_stage=_int(raw.get("lactationStage", raw.get("lactation_stage"))),
+            body_condition=_int(raw.get("bodyCondition", raw.get("body_condition"))),
+            housing_system=_int(raw.get("housingSystem", raw.get("housing_system"))),
+            konc_oe_sv=_flt(raw.get("koncOeSv", raw.get("konc_oe_sv"))),
+        )
+
+
+def _flt(v: Any) -> float | None:
+    if v is None or v == "":
+        return None
+    try:
+        return float(v)
+    except (TypeError, ValueError):
+        return None
+
+
+def _int(v: Any) -> int | None:
+    if v is None or v == "":
+        return None
+    try:
+        return int(v)
+    except (TypeError, ValueError):
+        return None
+
+
+def normalize_norms_method(method: str | None) -> NormsMethod:
+    m = (method or "wesp").strip().lower()
+    if m not in VALID_NORMS_METHODS:
+        return "wesp"
+    return m  # type: ignore[return-value]
+
+
+@dataclass
+class NormsResolveRequest:
+    method: NormsMethod = "wesp"
+    stored: dict[str, dict[str, float | None]] = field(default_factory=dict)
+    mass_kg: float | None = None
+    milk_yield_kg: float | None = None
+    ration_type: str | None = None
+    force_dynamic: bool = False
+    params: NormsParams = field(default_factory=NormsParams)
+
+
+def merge_hybrid_norms(
+    racion_resolved: dict[str, dict[str, float | None]],
+    stored: dict[str, dict[str, float | None]],
+    *,
+    dynamic_meta: dict[str, Any] | None = None,
+) -> tuple[dict[str, dict[str, float | None]], dict[str, Any]]:
+    """
+    RACION + derived имеют приоритет по min.
+    stored дополняет ключи без RACION-min; max всегда из stored, если задан.
+    """
+    out: dict[str, dict[str, float | None]] = {}
+    coverage: dict[str, list[str]] = {
+        "racion": [],
+        "derived": [],
+        "fallback": [],
+        "missing": [],
+    }
+    dynamic = dynamic_meta or {}
+    all_keys = {d["key"] for d in RATION_ALL_INDICATORS}
+    all_keys.update(racion_resolved.keys())
+    all_keys.update(stored.keys())
+
+    for key in sorted(all_keys):
+        rac = racion_resolved.get(key) or {}
+        st = stored.get(key) or {}
+        rac_min = rac.get("min")
+        st_min = st.get("min")
+        st_max = st.get("max")
+
+        source: str | None = None
+        min_v: float | None = None
+        if rac_min is not None:
+            min_v = rac_min
+            src = (dynamic.get(key) or {}).get("source")
+            source = "derived" if src == "derived" else "racion"
+        elif st_min is not None:
+            min_v = st_min
+            source = "fallback"
+
+        max_v = st_max if st_max is not None else rac.get("max")
+        if min_v is not None or max_v is not None:
+            out[key] = {"min": min_v, "max": max_v}
+        if source == "racion":
+            coverage["racion"].append(key)
+        elif source == "derived":
+            coverage["derived"].append(key)
+        elif source == "fallback":
+            coverage["fallback"].append(key)
+        elif key in {d["key"] for d in RATION_ALL_INDICATORS}:
+            coverage["missing"].append(key)
+
+    coverage["withMin"] = len([k for k, b in out.items() if b.get("min") is not None])
+    coverage["total"] = len(RATION_ALL_INDICATORS)
+    return out, coverage
+
+
+def _require_dairy_params(req: NormsResolveRequest) -> tuple[float, float]:
+    if req.mass_kg is None or req.mass_kg <= 0:
+        raise ValueError("Для методики Москва/Петербург укажите живую массу, кг")
+    if req.milk_yield_kg is None or req.milk_yield_kg <= 0:
+        raise ValueError("Для методики Москва/Петербург укажите суточный удой, кг")
+    return float(req.mass_kg), float(req.milk_yield_kg)
+
+
+def _moscow_params(req: NormsResolveRequest) -> MoscowDairyParams:
+    mass, milk = _require_dairy_params(req)
+    p = req.params
+    return MoscowDairyParams(
+        mass_kg=mass,
+        milk_yield_kg=milk,
+        milk_fat_pct=p.milk_fat_pct if p.milk_fat_pct is not None else 4.0,
+        lactation_no=p.lactation_no if p.lactation_no is not None else 2,
+        body_condition=p.body_condition if p.body_condition is not None else 1,
+        housing_system=p.housing_system if p.housing_system is not None else 1,
+    )
+
+
+def _piter_params(req: NormsResolveRequest) -> PiterDairyParams:
+    mass, milk = _require_dairy_params(req)
+    p = req.params
+    konc = p.konc_oe_sv
+    if konc is None or konc <= 0:
+        raise ValueError("Для методики Петербург укажите концентрацию ОЭ/СВ, МДж/кг СВ")
+    return PiterDairyParams(
+        mass_kg=mass,
+        milk_yield_kg=milk,
+        milk_fat_pct=p.milk_fat_pct if p.milk_fat_pct is not None else 4.0,
+        lactation_no=p.lactation_no if p.lactation_no is not None else 2,
+        body_condition=p.body_condition if p.body_condition is not None else 1,
+        housing_system=p.housing_system if p.housing_system is not None else 1,
+        konc_oe_sv=float(konc),
+    )
+
+
+def resolve_norms(req: NormsResolveRequest) -> tuple[dict[str, dict[str, float | None]], dict[str, Any]]:
+    method = normalize_norms_method(req.method)
+    if method == "wesp":
+        resolved, dynamic = apply_dynamic_norms(
+            req.stored,
+            mass_kg=req.mass_kg,
+            milk_yield_kg=req.milk_yield_kg,
+            ration_type=req.ration_type,
+            force_dynamic=req.force_dynamic,
+        )
+        return resolved, {"normsMethod": "wesp", "dynamicNorms": dynamic}
+
+    try:
+        if method == "racion_moscow":
+            racion, pack = resolve_moscow_dairy_norms(_moscow_params(req))
+        else:
+            racion, pack = resolve_piter_dairy_norms(_piter_params(req))
+    except PiterPrepError as exc:
+        raise ValueError(str(exc)) from exc
+
+    resolved, coverage = merge_hybrid_norms(racion, req.stored, dynamic_meta=pack.get("dynamic"))
+    return resolved, {"normsMethod": method, "coverage": coverage, **pack}

apps/api/app/modules/zootech/lab/calc/nutrients.py

@@ -0,0 +1,135 @@
+from __future__ import annotations
+
+from typing import Any, Mapping
+
+from app.modules.zootech.lab.nutrient_schema import resolve_sv_g_per_kg
+
+
+def normalize_key(value: str) -> str:
+    return " ".join((value or "").split()).strip().lower()
+
+
+def parse_num(value: Any) -> float | None:
+    if value is None or value == "":
+        return None
+    try:
+        n = float(value)
+    except (TypeError, ValueError):
+        return None
+    return None if n != n else n
+
+
+def get_nutrient_value(
+    dry_matter: float | None,
+    nutrients: Mapping[str, Any] | None,
+    nutrient_keys: list[str],
+    *,
+    indicator_key: str | None = None,
+) -> float | None:
+    """Только точное совпадение ключа (заголовок или indicator_key slug)."""
+    nutrients = nutrients or {}
+    if indicator_key:
+        for k, v in nutrients.items():
+            if normalize_key(str(k)) == normalize_key(indicator_key):
+                n = parse_num(v)
+                if n is not None:
+                    return n
+    for search in nutrient_keys:
+        target = normalize_key(search)
+        for k, v in nutrients.items():
+            if normalize_key(str(k)) != target:
+                continue
+            n = parse_num(v)
+            if n is not None:
+                return n
+    if any(normalize_key(k) == "св" for k in nutrient_keys):
+        return resolve_sv_g_per_kg(nutrients, dry_matter)
+    return None
+
+
+def weighted_average(
+    lines: list[dict[str, Any]],
+    total_kg: float,
+    nutrient_keys: list[str],
+    *,
+    indicator_key: str | None = None,
+) -> float | None:
+    if total_kg <= 0:
+        return None
+    total = 0.0
+    weight = 0.0
+    for line in lines:
+        kg = float(line.get("daily_kg") or 0)
+        if kg <= 0:
+            continue
+        v = get_nutrient_value(
+            line.get("dry_matter"),
+            line.get("nutrients"),
+            nutrient_keys,
+            indicator_key=indicator_key,
+        )
+        if v is None:
+            continue
+        total += kg * v
+        weight += kg
+    if weight <= 0:
+        return None
+    return total / weight
+
+
+def daily_intake_total(
+    lines: list[dict[str, Any]],
+    nutrient_keys: list[str],
+    *,
+    heads_per_trip: int = 1,
+    indicator_key: str | None = None,
+) -> float | None:
+    """Суточная доза на голову (г или МДж): Σ (кг/день/гол × г/кг). Как Excel Рацион КРС."""
+    heads = max(int(heads_per_trip or 1), 1)
+    total = 0.0
+    any_value = False
+    for line in lines:
+        herd_kg = float(line.get("daily_kg") or 0)
+        if herd_kg <= 0:
+            continue
+        kg = herd_kg / heads
+        v = get_nutrient_value(
+            line.get("dry_matter"),
+            line.get("nutrients"),
+            nutrient_keys,
+            indicator_key=indicator_key,
+        )
+        if v is None:
+            continue
+        total += kg * v
+        any_value = True
+    return total if any_value else None
+
+
+def rnb(
+    crude_protein: float | None,
+    usp: float | None,
+) -> float | None:
+    """RNB (ruminal nitrogen balance, GfE): (сырой протеин − уСП) / 6,25."""
+    if crude_protein is None or usp is None:
+        return None
+    return (crude_protein - usp) / 6.25
+
+
+def bra_rnb(crude_protein: float | None, usp: float | None) -> float | None:
+    """Deprecated alias for :func:`rnb`."""
+    return rnb(crude_protein, usp)
+
+
+def norm_diff(
+    content: float | None,
+    min_val: float | None,
+    max_val: float | None,
+) -> float | None:
+    if content is None:
+        return None
+    if min_val is not None and content < min_val:
+        return content - min_val
+    if max_val is not None and content > max_val:
+        return content - max_val
+    return 0.0

apps/api/app/modules/zootech/lab/calc/racion/__init__.py

@@ -0,0 +1 @@
+"""Российские методики суточных норм (Москва / Петербург)."""

apps/api/app/modules/zootech/lab/calc/racion/interp.py

@@ -0,0 +1,25 @@
+"""Линейная интерполяция (аналог FRAC в методичке)."""
+
+from __future__ import annotations
+
+
+def frac(numerator: float, denominator: float) -> float:
+    if denominator == 0:
+        return 0.0
+    return numerator / denominator
+
+
+def lerp(x: float, x1: float, n1: float, x2: float, n2: float) -> float:
+    """Norma = n1 + frac(n2 - n1, x2 - x1) * (x - x1)."""
+    if x2 == x1:
+        return n1
+    return n1 + frac(n2 - n1, x2 - x1) * (x - x1)
+
+
+def popr_index(udoy: float, boundaries: list[float]) -> int:
+    """1-based индекс столбца POPR_K по суточному удою."""
+    idx = 1
+    for i, bound in enumerate(boundaries, start=1):
+        if udoy >= bound:
+            idx = i + 1
+    return min(idx, len(boundaries) + 1)

apps/api/app/modules/zootech/lab/calc/racion/moscow.py

@@ -0,0 +1,107 @@
+"""Методика Москва — лактирующие коровы (NORM_1_1_CALC)."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any
+
+from app.modules.zootech.lab.calc.racion.interp import popr_index
+from app.modules.zootech.lab.calc.norms_derived import apply_derived_norms
+from app.modules.zootech.lab.calc.racion.npitv_map import DAIRY_LACTIR_NPITV, NPITV_TO_INDICATOR
+from app.modules.zootech.lab.calc.racion.tables import load_moskwa_lactir
+
+
+@dataclass(frozen=True)
+class MoscowDairyParams:
+    mass_kg: float
+    milk_yield_kg: float
+    milk_fat_pct: float = 4.0
+    lactation_no: int = 2
+    body_condition: int = 1
+    housing_system: int = 1
+
+
+def _row_lookup(rows: list[dict], npitv: int) -> dict | None:
+    for row in rows:
+        if row["npitv"] == npitv and row["pom"] == 1:
+            return row
+    return None
+
+
+def _popr_value(row: dict, udoy: float, boundaries: list[float]) -> tuple[float | None, float | None]:
+    popr = row.get("popr_k") or []
+    if not popr:
+        return None, None
+    idx = popr_index(udoy, boundaries) - 1
+    idx = max(0, min(idx, len(popr) - 1))
+    p_k = popr[idx]
+    koef = float(row.get("koef") or 0)
+    return p_k, koef
+
+
+def compute_moscow_norm(npitv: int, params: MoscowDairyParams) -> float | None:
+    data = load_moskwa_lactir()
+    boundaries = data.get("udoy_boundaries") or []
+    row = _row_lookup(data.get("rows") or [], npitv)
+    if row is None:
+        return None
+
+    mass = params.mass_kg
+    udoy = params.milk_yield_kg
+    jir = params.milk_fat_pct
+    wmassa = mass * 1.02 if params.body_condition > 1 else mass
+
+    p_k, koef = _popr_value(row, udoy, boundaries)
+    if p_k is None:
+        return None
+
+    norma: float | None = None
+    if npitv == 1:
+        temp = 0.005 if udoy <= 22 else 0.0025
+        norma = temp * (wmassa - 500) + p_k * udoy - ((4 - jir) * udoy) / 148
+    elif npitv == 2:
+        temp = 0.09 if udoy <= 22 else 0.065
+        norma = temp * (wmassa - 500) + p_k * udoy - ((4 - jir) * udoy) / 15
+    elif npitv == 3:
+        temp = 0.017 if udoy <= 22 else 0.015
+        norma = temp * (wmassa - 500) + p_k * udoy
+    elif 4 <= npitv <= 24:
+        norma = p_k * udoy + (wmassa - 500) * koef
+        if npitv == 10:
+            norma *= 0.393
+    else:
+        return None
+
+    if norma is None:
+        return None
+    if params.lactation_no == 1:
+        norma *= 0.95
+    elif params.lactation_no == 3:
+        norma *= 1.05
+    if params.housing_system == 2:
+        norma *= 1.1
+    return round(norma, 3)
+
+
+def resolve_moscow_dairy_norms(params: MoscowDairyParams) -> tuple[dict[str, dict[str, float | None]], dict[str, Any]]:
+    resolved: dict[str, dict[str, float | None]] = {}
+    dynamic: dict[str, Any] = {}
+    for npitv in DAIRY_LACTIR_NPITV:
+        value = compute_moscow_norm(npitv, params)
+        if value is None:
+            continue
+        key = NPITV_TO_INDICATOR.get(npitv)
+        if not key:
+            continue
+        resolved[key] = {"min": value, "max": None}
+        dynamic[key] = {"min": value, "npitv": npitv, "method": "racion_moscow", "source": "racion"}
+    resolved, derived_dyn = apply_derived_norms(resolved, mass_kg=params.mass_kg)
+    for k, v in derived_dyn.items():
+        dynamic[k] = {**v, "method": "racion_moscow", "source": "derived"}
+    meta = {
+        "method": "racion_moscow",
+        "massKg": params.mass_kg,
+        "milkYieldKg": params.milk_yield_kg,
+        "milkFatPct": params.milk_fat_pct,
+    }
+    return resolved, {"meta": meta, "dynamic": dynamic}

apps/api/app/modules/zootech/lab/calc/racion/npitv_map.py

@@ -0,0 +1,42 @@
+"""NPitV (справочник питательных веществ) → ключи показателей WESP."""
+
+from __future__ import annotations
+
+# NORMY_MOSKWA_LACTIR / NORM_1_1_CALC: NPitV 1–24 (pom=1)
+NPITV_TO_INDICATOR: dict[int, str] = {
+    1: "feed_units",
+    2: "oe",
+    3: "dry_matter",
+    4: "crude_protein",
+    5: "digestible_protein",
+    6: "crude_fat",
+    7: "nel",
+    8: "rnb",
+    9: "usp",
+    10: "sodium",
+    11: "magnesium",
+    12: "starch",
+    13: "potassium",
+    14: "calcium",
+    15: "phosphorus",
+    16: "iron",
+    17: "copper",
+    18: "zinc",
+    19: "manganese",
+    20: "cobalt",
+    21: "iodine",
+    22: "carotene",
+    23: "vitamin_d",
+    24: "vitamin_e",
+}
+
+DAIRY_LACTIR_NPITV: tuple[int, ...] = tuple(range(1, 25))
+
+# Обратная совместимость
+DAIRY_CORE_NPITV: tuple[int, ...] = (2, 3, 4, 5, 7, 8, 9, 10, 12, 14, 15)
+
+INDICATOR_TO_NPITV: dict[str, int] = {v: k for k, v in NPITV_TO_INDICATOR.items()}
+
+
+def indicator_for_npitv(npitv: int) -> str | None:
+    return NPITV_TO_INDICATOR.get(npitv)

apps/api/app/modules/zootech/lab/calc/racion/piter.py

@@ -0,0 +1,193 @@
+"""Методика Петербург — лактирующие коровы (NORM_1_2_CALC)."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any
+
+from app.modules.zootech.lab.calc.norms_derived import apply_derived_norms
+from app.modules.zootech.lab.calc.racion.interp import lerp
+from app.modules.zootech.lab.calc.racion.moscow import MoscowDairyParams
+from app.modules.zootech.lab.calc.racion.npitv_map import DAIRY_LACTIR_NPITV, NPITV_TO_INDICATOR
+from app.modules.zootech.lab.calc.racion.piter_prep import PiterPrepError, PiterPrepResult, prepare_piter_calc
+from app.modules.zootech.lab.calc.racion.tables import load_piter_lactir
+
+
+@dataclass(frozen=True)
+class PiterDairyParams(MoscowDairyParams):
+    konc_oe_sv: float = 10.3
+
+
+def _konc_bracket(konc: float, konc_values: list[float]) -> tuple[float, float]:
+    sorted_k = sorted(set(konc_values))
+    positive = [k for k in sorted_k if k > 0]
+    if not positive:
+        return konc, konc
+    if konc <= positive[0]:
+        return positive[0], positive[min(1, len(positive) - 1)]
+    for i in range(len(positive) - 1):
+        if positive[i] <= konc <= positive[i + 1]:
+            return positive[i], positive[i + 1]
+    return positive[-2], positive[-1]
+
+
+def _udoy_bracket(udoy_jir: float, udoys: list[float]) -> tuple[float, float]:
+    sorted_u = sorted(set(udoys))
+    if len(sorted_u) < 2:
+        return sorted_u[0], sorted_u[0]
+    if udoy_jir <= sorted_u[0]:
+        return sorted_u[0], sorted_u[1]
+    for i in range(len(sorted_u) - 1):
+        if sorted_u[i] <= udoy_jir < sorted_u[i + 1]:
+            return sorted_u[i], sorted_u[i + 1]
+    return sorted_u[-2], sorted_u[-1]
+
+
+def _norm_at_mass(
+    entry_normy: list[float],
+    mass_ind: int,
+    wmassa: float,
+    m_a: float,
+    m_b: float,
+) -> float | None:
+    i = mass_ind - 1
+    if i < 0 or i + 1 >= len(entry_normy):
+        return None
+    n_a, n_b = entry_normy[i], entry_normy[i + 1]
+    if n_a <= 0 or n_b <= 0:
+        return None
+    return lerp(wmassa, m_a, n_a, m_b, n_b)
+
+
+def _entries_for(data: dict, npitv: int) -> list[dict]:
+    npv = 4 if npitv == 5 else npitv
+    return [e for e in data.get("entries") or [] if e["npitv"] == npv]
+
+
+def _compute_with_konc(
+    entries: list[dict],
+    npitv: int,
+    params: PiterDairyParams,
+    prep: PiterPrepResult,
+    konc_pred: float,
+    konc_sled: float,
+) -> float | None:
+    by_konc = {k: [e for e in entries if abs(e["konc"] - k) < 1e-6] for k in (konc_pred, konc_sled)}
+
+    def _at_konc(konc: float) -> float | None:
+        rows = by_konc.get(konc) or []
+        if not rows:
+            return None
+        udoys = [e["udoy"] for e in rows]
+        ud_pred, ud_sled = _udoy_bracket(prep.udoy_jir, udoys)
+        if ud_pred == ud_sled:
+            return None
+        e_pred = next((e for e in rows if e["udoy"] == ud_pred), None)
+        e_sled = next((e for e in rows if e["udoy"] == ud_sled), None)
+        if not e_pred or not e_sled:
+            return None
+        n01 = _norm_at_mass(e_pred["normy"], prep.mass_ind, prep.wmassa, prep.m_a, prep.m_b)
+        n02 = _norm_at_mass(e_sled["normy"], prep.mass_ind, prep.wmassa, prep.m_a, prep.m_b)
+        if n01 is None or n02 is None:
+            return None
+        return lerp(prep.udoy_jir, ud_pred, n01, ud_sled, n02)
+
+    norma1 = _at_konc(konc_pred)
+    norma2 = _at_konc(konc_sled)
+    if norma1 is None or norma2 is None or konc_pred == konc_sled:
+        return None
+    norma = lerp(params.konc_oe_sv, konc_pred, norma1, konc_sled, norma2)
+    if npitv == 5:
+        norma *= 0.65
+    return round(norma, 3)
+
+
+def _compute_konc_independent(
+    entries: list[dict],
+    params: PiterDairyParams,
+    prep: PiterPrepResult,
+) -> float | None:
+    rows = [e for e in entries if e["konc"] == -10]
+    if not rows:
+        return None
+    udoys = [e["udoy"] for e in rows]
+    ud_pred, ud_sled = _udoy_bracket(prep.udoy_jir, udoys)
+    if ud_pred == ud_sled:
+        return None
+    e_pred = next((e for e in rows if e["udoy"] == ud_pred), None)
+    e_sled = next((e for e in rows if e["udoy"] == ud_sled), None)
+    if not e_pred or not e_sled:
+        return None
+    n01 = _norm_at_mass(e_pred["normy"], prep.mass_ind, prep.wmassa, prep.m_a, prep.m_b)
+    n02 = _norm_at_mass(e_sled["normy"], prep.mass_ind, prep.wmassa, prep.m_a, prep.m_b)
+    if n01 is None or n02 is None:
+        return None
+    norma = lerp(prep.udoy_jir, ud_pred, n01, ud_sled, n02)
+    if params.housing_system == 2:
+        norma *= 1.1
+    return round(norma, 3)
+
+
+def compute_piter_norm(npitv: int, params: PiterDairyParams, prep: PiterPrepResult | None = None) -> float | None:
+    data = load_piter_lactir()
+    entries = _entries_for(data, npitv)
+    if not entries:
+        return None
+
+    if prep is None:
+        prep = prepare_piter_calc(
+            mass_kg=params.mass_kg,
+            milk_yield_kg=params.milk_yield_kg,
+            milk_fat_pct=params.milk_fat_pct,
+            konc_oe_sv=params.konc_oe_sv,
+            body_condition=params.body_condition,
+        )
+
+    konc_vals = sorted({e["konc"] for e in entries if e["konc"] > 0})
+    if konc_vals:
+        min_k = min(konc_vals)
+        if min_k > 0:
+            konc_pred, konc_sled = _konc_bracket(params.konc_oe_sv, konc_vals)
+            return _compute_with_konc(entries, npitv, params, prep, konc_pred, konc_sled)
+    return _compute_konc_independent(entries, params, prep)
+
+
+def resolve_piter_dairy_norms(params: PiterDairyParams) -> tuple[dict[str, dict[str, float | None]], dict[str, Any]]:
+    prep = prepare_piter_calc(
+        mass_kg=params.mass_kg,
+        milk_yield_kg=params.milk_yield_kg,
+        milk_fat_pct=params.milk_fat_pct,
+        konc_oe_sv=params.konc_oe_sv,
+        body_condition=params.body_condition,
+    )
+    resolved: dict[str, dict[str, float | None]] = {}
+    dynamic: dict[str, Any] = {}
+    for npitv in DAIRY_LACTIR_NPITV:
+        value = compute_piter_norm(npitv, params, prep=prep)
+        if value is None:
+            continue
+        key = NPITV_TO_INDICATOR.get(npitv)
+        if not key:
+            continue
+        resolved[key] = {"min": value, "max": None}
+        dynamic[key] = {"min": value, "npitv": npitv, "method": "racion_piter", "source": "racion"}
+    resolved, derived_dyn = apply_derived_norms(resolved, mass_kg=params.mass_kg)
+    for k, v in derived_dyn.items():
+        dynamic[k] = {**v, "method": "racion_piter", "source": "derived"}
+    meta = {
+        "method": "racion_piter",
+        "massKg": params.mass_kg,
+        "milkYieldKg": params.milk_yield_kg,
+        "koncOeSv": params.konc_oe_sv,
+        "prep": {
+            "massInd": prep.mass_ind,
+            "mA": prep.m_a,
+            "mB": prep.m_b,
+            "koncPred": prep.konc_pred,
+            "koncSled": prep.konc_sled,
+        },
+    }
+    return resolved, {"meta": meta, "dynamic": dynamic}
+
+
+__all__ = ["PiterDairyParams", "PiterPrepError", "compute_piter_norm", "resolve_piter_dairy_norms"]

apps/api/app/modules/zootech/lab/calc/racion/piter_prep.py

@@ -0,0 +1,202 @@
+"""Подготовка параметров NORM_1_2_PREP (интервалы массы и концентрации)."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+from app.modules.zootech.lab.calc.racion.interp import lerp
+from app.modules.zootech.lab.calc.racion.tables import load_normy_info
+
+
+@dataclass(frozen=True)
+class PiterPrepResult:
+    mass_ind: int
+    konc_pred: float
+    konc_sled: float
+    m_a: float
+    m_b: float
+    udoy_jir: float
+    wmassa: float
+
+
+class PiterPrepError(ValueError):
+    def __init__(self, code: int, message: str, *, info: str | None = None) -> None:
+        super().__init__(message)
+        self.code = code
+        self.info = info
+
+
+def _info_values(nperem: int) -> list[float]:
+    data = load_normy_info()
+    for row in data.get("rows") or []:
+        if row.get("nperem") == nperem:
+            return list(row.get("znachenie") or [])
+    return []
+
+
+def _fl_from_str(values: list[float], index: int) -> float | None:
+    """1-based index как FlFromStr в RACION."""
+    i = index - 1
+    if i < 0 or i >= len(values):
+        return None
+    return float(values[i])
+
+
+def _wmassa(mass_kg: float, body_condition: int) -> float:
+    if body_condition > 1:
+        return mass_kg * 1.02
+    return mass_kg
+
+
+def prepare_piter_calc(
+    *,
+    mass_kg: float,
+    milk_yield_kg: float,
+    milk_fat_pct: float,
+    konc_oe_sv: float,
+    body_condition: int = 1,
+) -> PiterPrepResult:
+    """Порт NORM_1_2_PREP: интервалы для NORM_1_2_CALC."""
+    if milk_yield_kg <= 0:
+        raise PiterPrepError(-11, "Укажите суточный удой, кг")
+    if milk_fat_pct <= 0:
+        raise PiterPrepError(-12, "Укажите жирность молока, %")
+    if mass_kg <= 0:
+        raise PiterPrepError(-14, "Укажите живую массу, кг")
+    if konc_oe_sv <= 0:
+        raise PiterPrepError(-15, "Укажите концентрацию ОЭ/СВ, МДж/кг СВ")
+
+    wmassa = _wmassa(mass_kg, body_condition)
+    udoy_jir = milk_yield_kg * milk_fat_pct * 0.25
+
+    kol_konc_vals = _info_values(7)
+    kol_mass_vals = _info_values(7)
+    if len(kol_konc_vals) < 2 or len(kol_mass_vals) < 2:
+        raise PiterPrepError(-1, "Справочник NORMY_INFO (NPerem=7) не задан")
+    kol_konc = int(kol_konc_vals[0])
+    kol_mass = int(kol_mass_vals[1])
+    if kol_konc < 2 or kol_mass < 2:
+        raise PiterPrepError(-1, "Некорректные размеры таблицы концентраций/масс")
+
+    masses = _info_values(6) or _info_values(14)
+    koncss = _info_values(3)
+    if not masses or not koncss or len(masses) < 2 or len(koncss) < 2:
+        raise PiterPrepError(-1, "Справочник NORMY_INFO: массы или концентрации не заданы")
+
+    # Интервал концентрации
+    konc_ind = 1
+    for i in range(2, kol_konc):
+        v = _fl_from_str(koncss, i)
+        if v is not None and konc_oe_sv >= v:
+            konc_ind = i
+    konc_pred = _fl_from_str(koncss, konc_ind)
+    konc_sled = _fl_from_str(koncss, konc_ind + 1)
+    if konc_pred is None or konc_sled is None or konc_pred < 0 or konc_sled < 0 or konc_pred == konc_sled:
+        raise PiterPrepError(-1, "Не удалось определить интервал концентрации")
+
+    # Интервал массы
+    mass_ind = 1
+    for i in range(2, kol_mass):
+        v = _fl_from_str(masses, i)
+        if v is not None and wmassa >= v:
+            mass_ind = i
+    m_a = _fl_from_str(masses, mass_ind)
+    m_b = _fl_from_str(masses, mass_ind + 1)
+    if m_a is None or m_b is None or m_a < 0 or m_b < 0 or m_a == m_b:
+        raise PiterPrepError(-1, "Не удалось определить интервал массы")
+
+    # Проверка удоя (NPerem=4,5)
+    udoy_str_4 = _info_values(4)
+    udoy_str_5 = _info_values(5)
+    udoy_min = _fl_from_str(udoy_str_4, 1) if udoy_str_4 else None
+    udoy_max = _fl_from_str(udoy_str_5, kol_konc) if udoy_str_5 else None
+
+    if udoy_min is not None and udoy_max is not None:
+        if udoy_jir < udoy_min or udoy_jir > udoy_max:
+            jir_str = _info_values(2)
+            gr_udoy1 = max(udoy_min * 4 / milk_fat_pct, udoy_min)
+            gr_udoy2 = min(udoy_max * 4 / milk_fat_pct, udoy_max)
+            gr_jir1 = udoy_min * 4 / milk_yield_kg
+            gr_jir2 = udoy_max * 4 / milk_yield_kg
+            if jir_str:
+                if len(jir_str) >= 1:
+                    gr_jir1 = max(gr_jir1, jir_str[0])
+                if len(jir_str) >= 2:
+                    gr_jir2 = min(gr_jir2, jir_str[1])
+            code = -4 if udoy_jir < udoy_min else -5
+            info = f"{gr_udoy1:.4f};{gr_udoy2:.4f};{gr_jir1:.3f};{gr_jir2:.3f};"
+            raise PiterPrepError(code, "Удой вне допустимого диапазона для жирности", info=info)
+
+        # Допустимый диапазон концентрации для udoy_jir
+        i = 1
+        if udoy_jir >= (_fl_from_str(udoy_str_4, 1) or 0):
+            while i < kol_konc - 1:
+                nxt = _fl_from_str(udoy_str_4, i + 1)
+                if nxt is None or nxt > udoy_jir:
+                    break
+                i += 1
+        else:
+            while i < kol_konc - 1:
+                nxt = _fl_from_str(udoy_str_4, i + 1)
+                cur = _fl_from_str(udoy_str_4, i)
+                if nxt is None or cur is None or nxt != cur:
+                    break
+                i += 1
+        ud_a = _fl_from_str(udoy_str_4, i)
+        ud_b = _fl_from_str(udoy_str_4, i + 1)
+        konc_a = _fl_from_str(koncss, i)
+        konc_b = _fl_from_str(koncss, i + 1)
+        if ud_a is not None and ud_b is not None and konc_a is not None and konc_b is not None:
+            if ud_a == ud_b:
+                konc_max = konc_b
+            else:
+                konc_max = lerp(udoy_jir, ud_a, konc_a, ud_b, konc_b)
+
+            i = kol_konc
+            if udoy_jir <= (_fl_from_str(udoy_str_5, kol_konc) or udoy_jir):
+                while i > 2:
+                    prev = _fl_from_str(udoy_str_5, i - 1)
+                    if prev is None or prev < udoy_jir:
+                        break
+                    i -= 1
+            else:
+                while i > 2:
+                    prev = _fl_from_str(udoy_str_5, i - 1)
+                    last = _fl_from_str(udoy_str_5, kol_konc)
+                    if prev is None or last is None or prev != last:
+                        break
+                    i -= 1
+            ud_a2 = _fl_from_str(udoy_str_5, i - 1)
+            ud_b2 = _fl_from_str(udoy_str_5, i)
+            konc_a2 = _fl_from_str(koncss, i - 1)
+            konc_b2 = _fl_from_str(koncss, i)
+            if ud_a2 is not None and ud_b2 is not None and konc_a2 is not None and konc_b2 is not None:
+                if ud_a2 == ud_b2:
+                    konc_min = konc_b2
+                else:
+                    konc_min = lerp(udoy_jir, ud_a2, konc_a2, ud_b2, konc_b2)
+
+                konc_lo = _fl_from_str(koncss, 1) or konc_min
+                konc_hi = _fl_from_str(koncss, kol_konc) or konc_max
+                if konc_min < konc_lo:
+                    konc_min = konc_lo
+                if konc_max > konc_hi:
+                    konc_max = konc_hi
+                if round(konc_oe_sv, 1) < round(konc_min, 1) or round(konc_oe_sv, 1) > round(konc_max, 1):
+                    code = -2 if round(konc_oe_sv, 1) < round(konc_min, 1) else -3
+                    info = f"{round(konc_min, 1)};{round(konc_max, 1)};"
+                    raise PiterPrepError(
+                        code,
+                        f"Концентрация ОЭ/СВ вне допустимого диапазона ({round(konc_min, 1)}–{round(konc_max, 1)})",
+                        info=info,
+                    )
+
+    return PiterPrepResult(
+        mass_ind=mass_ind,
+        konc_pred=float(konc_pred),
+        konc_sled=float(konc_sled),
+        m_a=float(m_a),
+        m_b=float(m_b),
+        udoy_jir=udoy_jir,
+        wmassa=wmassa,
+    )

apps/api/app/modules/zootech/lab/calc/racion/tables.py

@@ -0,0 +1,75 @@
+"""Загрузка справочников норм RACION (БД → JSON fallback)."""
+
+from __future__ import annotations
+
+import json
+from functools import lru_cache
+from pathlib import Path
+
+_SEED_DIR = Path(__file__).resolve().parents[4] / "data" / "seed" / "racion"
+
+
+def _read_json(name: str) -> dict:
+    path = _SEED_DIR / name
+    if not path.exists():
+        raise FileNotFoundError(f"Справочник не найден: {path}")
+    return json.loads(path.read_text(encoding="utf-8"))
+
+
+def _load_from_db(loader_name: str) -> dict | None:
+    try:
+        from flask import has_app_context
+
+        if not has_app_context():
+            return None
+        from app.modules.zootech.lab.services.racion_reference import (
+            load_moskwa_lactir_from_db,
+            load_normy_info_from_db,
+            load_piter_lactir_from_db,
+        )
+
+        loaders = {
+            "moskwa": load_moskwa_lactir_from_db,
+            "piter": load_piter_lactir_from_db,
+            "info": load_normy_info_from_db,
+        }
+        fn = loaders.get(loader_name)
+        if fn is None:
+            return None
+        data = fn()
+        return data if data else None
+    except Exception:
+        return None
+
+
+@lru_cache(maxsize=1)
+def load_moskwa_lactir() -> dict:
+    data = _load_from_db("moskwa")
+    if data:
+        return data
+    return _read_json("moskwa_lactir.json")
+
+
+@lru_cache(maxsize=1)
+def load_piter_lactir() -> dict:
+    data = _load_from_db("piter")
+    if data:
+        return data
+    return _read_json("piter_lactir.json")
+
+
+@lru_cache(maxsize=1)
+def load_normy_info() -> dict:
+    data = _load_from_db("info")
+    if data:
+        return data
+    try:
+        return _read_json("normy_info.json")
+    except FileNotFoundError:
+        return {"rows": [], "mass_kg_values": [400, 450, 500, 550, 600, 650, 700, 750]}
+
+
+def clear_tables_cache() -> None:
+    load_moskwa_lactir.cache_clear()
+    load_piter_lactir.cache_clear()
+    load_normy_info.cache_clear()

apps/api/app/modules/zootech/lab/commands/__init__.py

@@ -0,0 +1,21 @@
+from .apply_from_master import apply_from_master
+from .ensure_empty_master import ensure_empty_master
+from .recalculate import recalculate_ration
+from .seed_demo_component_nutrients import seed_demo_component_nutrients_once
+from .seed_from_execution import seed_from_execution
+from .sync_from_execution import sync_from_execution
+from .delete_profile import delete_animal_profile
+from .upsert_profile import upsert_animal_profile
+from .upsert_ration import upsert_ration
+
+__all__ = [
+    "upsert_ration",
+    "recalculate_ration",
+    "apply_from_master",
+    "seed_demo_component_nutrients_once",
+    "seed_from_execution",
+    "ensure_empty_master",
+    "sync_from_execution",
+    "delete_animal_profile",
+    "upsert_animal_profile",
+]

apps/api/app/modules/zootech/lab/commands/apply_from_master.py

@@ -0,0 +1,76 @@
+from __future__ import annotations
+
+from app.modules.zootech.wesp_bridge_db import db
+from app.modules.zootech.lab.commands.write_audit import write_audit
+from app.modules.zootech.lab.db_guard import retry_locked
+from app.modules.zootech.lab.loaders.execution_loader import load_execution
+from app.modules.zootech.lab.loaders.ration_loader import load_ration
+from app.modules.zootech.wesp_bridge_models import Component, Ingredient, Recipe
+from app.modules.zootech.wesp_bridge_models import default_uuid
+from app.modules.zootech.wesp_bridge_models import _enqueue_recipe_children_sync
+
+
+@retry_locked
+def apply_from_master(recipe_id: str, user_id: str = "system") -> dict:
+    snapshot = load_ration(recipe_id)
+    if not snapshot.exists:
+        raise LookupError("Мастер рациона не найден")
+    recipe = Recipe.query.filter_by(id=recipe_id, is_deleted=False).first()
+    if recipe is None:
+        raise LookupError("Рецепт не найден")
+    heads = int(recipe.heads_per_trip or 0)
+    if heads <= 0:
+        raise ValueError("Количество голов должно быть > 0")
+
+    execution = load_execution(recipe_id)
+    by_component = {str(l.component_id): l for l in execution.lines if l.component_id}
+    master_by_component = {
+        str(l.component_id): l
+        for l in snapshot.lines
+        if l.component_id and l.in_ration
+    }
+
+    touched_ingredient_ids: list[str] = []
+    for comp_id, master_line in master_by_component.items():
+        daily_kg = float(master_line.daily_kg or 0)
+        weight_per_head = daily_kg / heads
+        ing = by_component.get(comp_id)
+        comp = Component.query.get(comp_id)
+        dm_pct = float(comp.dry_matter or 0) if comp else float(master_line.dry_matter or 0)
+        if ing:
+            row = Ingredient.query.get(ing.ingredient_id)
+            if row is None:
+                continue
+            row.weight_per_head = weight_per_head
+            row.amount = weight_per_head
+            row.dry_matter = dm_pct
+            row.updated_by = user_id
+            touched_ingredient_ids.append(row.id)
+        else:
+            comp_name = master_line.ingredient_name or comp_id
+            new_ing = Ingredient(
+                id=default_uuid(),
+                recipe_id=recipe_id,
+                component_id=comp_id,
+                name=comp_name[:100],
+                amount=weight_per_head,
+                weight_per_head=weight_per_head,
+                dry_matter=dm_pct,
+                order=len(touched_ingredient_ids),
+                created_by=user_id,
+                updated_by=user_id,
+            )
+            db.session.add(new_ing)
+            touched_ingredient_ids.append(new_ing.id)
+
+    master_ids = set(master_by_component)
+    for line in execution.lines:
+        if line.component_id and str(line.component_id) not in master_ids:
+            row = Ingredient.query.get(line.ingredient_id)
+            if row and not row.is_deleted:
+                row.soft_delete(user_id)
+
+    _enqueue_recipe_children_sync(recipe_id)
+    write_audit("APPLY_FROM_MASTER", "lab_recipe_ration", recipe_id, user_id)
+    db.session.commit()
+    return {"recipeId": recipe_id, "ingredientsUpdated": len(touched_ingredient_ids)}

apps/api/app/modules/zootech/lab/commands/audit_lab_data.py

@@ -0,0 +1,202 @@
+"""Аудит данных lab-модуля (профили, нормы, рационы) — без компонентов."""
+
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass, field
+
+from app.modules.zootech.wesp_bridge_db import db
+from app.modules.zootech.lab.constants import RATION_QUALITY_INDICATORS
+from app.modules.zootech.lab.models import (
+    LabAnimalProfile,
+    LabProfileNorm,
+    LabRationCalcIndicator,
+    LabRationLine,
+    LabRecipeRation,
+)
+from app.modules.zootech.lab.calc.feed_groups import classify_feed_group
+from app.modules.zootech.lab.nutrient_schema import read_from_mapping
+from app.modules.zootech.lab.services.component_nutrients import nutrients_full_dict
+from app.modules.zootech.wesp_bridge_models import Component, Recipe
+
+_OMD_KEYS = ("ВРХ Орг Вещ", "КРС Орг Вещ")
+
+_log = logging.getLogger(__name__)
+
+CALC_NORM_KEYS = tuple(d["key"] for d in RATION_QUALITY_INDICATORS)
+
+
+@dataclass
+class AuditIssue:
+    level: str  # error | warn | info
+    area: str
+    message: str
+
+
+@dataclass
+class LabDataAuditReport:
+    issues: list[AuditIssue] = field(default_factory=list)
+    profiles: int = 0
+    profiles_with_norms: int = 0
+    profile_norms_beef: int = 0
+    profile_norms_dairy: int = 0
+    recipe_rations: int = 0
+    ration_lines: int = 0
+    calc_indicators: int = 0
+    components: int = 0
+    components_omd_missing: int = 0
+
+    def add(self, level: str, area: str, message: str) -> None:
+        self.issues.append(AuditIssue(level, area, message))
+
+    def ok(self) -> bool:
+        return not any(i.level == "error" for i in self.issues)
+
+
+def _audit_components(report: LabDataAuditReport) -> None:
+    components = Component.query.filter_by(is_deleted=False).all()
+    report.components = len(components)
+    for comp in components:
+        full = nutrients_full_dict(comp.id)
+        if not full:
+            continue
+        feed_group = classify_feed_group(comp)
+        omd = read_from_mapping(full, _OMD_KEYS)
+        if feed_group in ("rough", "succulent") and omd is None:
+            report.components_omd_missing += 1
+            report.add(
+                "warn",
+                "component",
+                f"Компонент {comp.name!r} ({feed_group}): нет ВРХ Орг Вещ",
+            )
+        oe = read_from_mapping(full, ("ОЭ-КРС", "    ОЭ-КРС", "OЭ КРС форм"))
+        nel = read_from_mapping(full, ("ЧЭЛ- КРС", "    ЧЭЛ- КРС", "ЧЭЛ - КРС Форм"))
+        if (oe is not None and oe < 0) or (nel is not None and nel < 0):
+            report.add(
+                "warn",
+                "component",
+                f"Компонент {comp.name!r}: отрицательная энергия (ОЭ={oe}, ЧЭЛ={nel})",
+            )
+
+
+def audit_lab_data(*, include_components: bool = False) -> LabDataAuditReport:
+    report = LabDataAuditReport()
+
+    profiles = LabAnimalProfile.query.filter_by(is_deleted=False).all()
+    report.profiles = len(profiles)
+
+    for profile in profiles:
+        norms = LabProfileNorm.query.filter_by(profile_id=profile.id).all()
+        norm_keys = {n.indicator_key for n in norms}
+        if norms:
+            report.profiles_with_norms += 1
+        else:
+            report.add("warn", "profile", f"Профиль {profile.profile_key!r} без норм (lab_profile_norm пуст)")
+
+        if profile.mass_kg is None:
+            report.add("warn", "profile", f"Профиль {profile.profile_key!r}: mass_kg не задан")
+
+        missing = [k for k in CALC_NORM_KEYS if k not in norm_keys]
+        if norms and missing:
+            report.add(
+                "info",
+                "profile",
+                f"Профиль {profile.profile_key!r}: нет ключей {', '.join(missing[:5])}"
+                + ("…" if len(missing) > 5 else ""),
+            )
+
+        for norm in norms:
+            if norm.min_value is None and norm.max_value is None:
+                report.add(
+                    "warn",
+                    "profile",
+                    f"Профиль {profile.profile_key!r}: {norm.indicator_key} без min и max",
+                )
+
+    for ration_type in ("BEEF", "DAIRY"):
+        count = (
+            db.session.query(LabProfileNorm)
+            .join(LabAnimalProfile, LabAnimalProfile.id == LabProfileNorm.profile_id)
+            .filter(
+                LabAnimalProfile.ration_type == ration_type,
+                LabAnimalProfile.is_deleted.is_(False),
+            )
+            .count()
+        )
+        if ration_type == "BEEF":
+            report.profile_norms_beef = count
+        else:
+            report.profile_norms_dairy = count
+    if report.profile_norms_beef == 0 and report.profile_norms_dairy == 0:
+        report.add(
+            "warn",
+            "profile_norm",
+            "lab_profile_norm пуст — нормы не импортированы (scripts/import_seed.py --norms)",
+        )
+
+    headers = (
+        LabRecipeRation.query.filter_by(is_deleted=False)
+        .join(Recipe, Recipe.id == LabRecipeRation.recipe_id)
+        .filter(Recipe.is_deleted.is_(False))
+        .all()
+    )
+    report.recipe_rations = len(headers)
+
+    for header in headers:
+        recipe = Recipe.query.get(header.recipe_id)
+        name = recipe.name if recipe else header.recipe_id
+        if not header.animal_profile_id:
+            report.add("warn", "ration", f"Рецепт {name!r}: нет animal_profile_id")
+        elif LabAnimalProfile.query.get(header.animal_profile_id) is None:
+            report.add("error", "ration", f"Рецепт {name!r}: профиль {header.animal_profile_id} не найден")
+
+        lines = LabRationLine.query.filter_by(
+            recipe_id=header.recipe_id, is_deleted=False
+        ).all()
+        active = [ln for ln in lines if ln.in_ration and (ln.daily_kg or 0) > 0]
+        if not active:
+            report.add("info", "ration", f"Рецепт {name!r}: нет активных строк рациона")
+
+        for line in active:
+            if not line.component_id:
+                report.add("warn", "ration_line", f"Рецепт {name!r}, строка {line.row_index}: нет component_id")
+            if line.daily_kg is None:
+                report.add("warn", "ration_line", f"Рецепт {name!r}, строка {line.row_index}: daily_kg пуст")
+
+        if header.calculated_at and not LabRationCalcIndicator.query.filter_by(
+            recipe_id=header.recipe_id
+        ).first():
+            report.add("warn", "calc", f"Рецепт {name!r}: calculated_at есть, lab_ration_calc_indicator пуст")
+
+    report.ration_lines = LabRationLine.query.filter_by(is_deleted=False).count()
+    report.calc_indicators = LabRationCalcIndicator.query.count()
+
+    if report.profiles == 0:
+        report.add("warn", "profile", "Нет профилей стада (lab_animal_profile)")
+
+    if include_components:
+        _audit_components(report)
+
+    _log.info(
+        "lab audit: profiles=%s with_norms=%s rations=%s issues=%s",
+        report.profiles,
+        report.profiles_with_norms,
+        report.recipe_rations,
+        len(report.issues),
+    )
+    return report
+
+
+def format_audit_report(report: LabDataAuditReport) -> str:
+    lines = [
+        "=== Lab data audit ===",
+        f"Профили: {report.profiles} (с нормами: {report.profiles_with_norms})",
+        f"lab_profile_norm: BEEF={report.profile_norms_beef}, DAIRY={report.profile_norms_dairy}",
+        f"lab_recipe_ration: {report.recipe_rations}, строк: {report.ration_lines}",
+        f"lab_ration_calc_indicator: {report.calc_indicators}",
+        f"components: {report.components} (без ВРХ rough/succulent: {report.components_omd_missing})",
+        f"Замечаний: {len(report.issues)}",
+    ]
+    for issue in report.issues:
+        lines.append(f"  [{issue.level}] {issue.area}: {issue.message}")
+    return "\n".join(lines)

apps/api/app/modules/zootech/lab/commands/cleanup_legacy_profiles.py

@@ -0,0 +1,84 @@
+"""Удаление legacy/fp_* профилей стада (одноразовая ops-команда)."""
+
+from __future__ import annotations
+
+import logging
+import re
+from dataclasses import dataclass, field
+
+from app.modules.zootech.wesp_bridge_db import db
+from app.modules.zootech.lab.models import LabAnimalProfile, LabProfileNorm, LabRecipeRation
+
+_log = logging.getLogger(__name__)
+
+_LEGACY_KEY = re.compile(r"^(dairy|beef)-\d+$", re.IGNORECASE)
+_FP_KEY = re.compile(r"^fp_", re.IGNORECASE)
+
+
+@dataclass
+class CleanupReport:
+    dry_run: bool = True
+    profiles_to_delete: list[str] = field(default_factory=list)
+    recipe_ids_cleared: list[str] = field(default_factory=list)
+    norms_deleted: int = 0
+    profiles_deleted: int = 0
+    errors: list[str] = field(default_factory=list)
+
+
+def _should_delete(profile_key: str | None) -> bool:
+    key = (profile_key or "").strip()
+    if not key:
+        return False
+    if key.startswith("lab_math_"):
+        return False
+    if _LEGACY_KEY.match(key):
+        return True
+    if _FP_KEY.match(key):
+        return True
+    return False
+
+
+def cleanup_legacy_profiles(*, dry_run: bool = True, user_id: str = "system") -> CleanupReport:
+    report = CleanupReport(dry_run=dry_run)
+    profiles = LabAnimalProfile.query.filter_by(is_deleted=False).all()
+    to_delete = [p for p in profiles if _should_delete(p.profile_key)]
+
+    for profile in to_delete:
+        report.profiles_to_delete.append(profile.profile_key or profile.id)
+
+        norms_count = LabProfileNorm.query.filter_by(profile_id=profile.id).count()
+        report.norms_deleted += norms_count
+
+        rations = LabRecipeRation.query.filter_by(animal_profile_id=profile.id, is_deleted=False).all()
+        for header in rations:
+            if header.recipe_id not in report.recipe_ids_cleared:
+                report.recipe_ids_cleared.append(header.recipe_id)
+
+        if dry_run:
+            continue
+
+        LabProfileNorm.query.filter_by(profile_id=profile.id).delete(synchronize_session=False)
+        for header in rations:
+            header.animal_profile_id = None
+            header.updated_by = user_id
+        if hasattr(profile, "soft_delete"):
+            profile.soft_delete(user_id)
+        else:
+            db.session.delete(profile)
+        report.profiles_deleted += 1
+
+    if not dry_run:
+        db.session.commit()
+        _log.info(
+            "cleanup_legacy_profiles user=%s deleted=%s rations_cleared=%s",
+            user_id,
+            report.profiles_deleted,
+            len(report.recipe_ids_cleared),
+        )
+    else:
+        _log.info(
+            "cleanup_legacy_profiles dry_run profiles=%s rations=%s",
+            len(report.profiles_to_delete),
+            len(report.recipe_ids_cleared),
+        )
+    return report

apps/api/app/modules/zootech/lab/commands/delete_profile.py

@@ -0,0 +1,30 @@
+from __future__ import annotations
+
+from app.modules.zootech.wesp_bridge_db import db
+from app.modules.zootech.lab.db_guard import retry_locked
+from app.modules.zootech.lab.models import (
+    LabAnimalProfile,
+    LabProfileNorm,
+    LabRecipeRation,
+)
+@retry_locked
+def delete_animal_profile(profile_id: str, user_id: str = "system") -> dict:
+    pid = (profile_id or "").strip()
+    if not pid:
+        raise LookupError("Профиль не найден")
+
+    profile = LabAnimalProfile.query.filter_by(id=pid, is_deleted=False).first()
+    if profile is None:
+        raise LookupError("Профиль не найден")
+
+    LabProfileNorm.query.filter_by(profile_id=profile.id).delete(synchronize_session=False)
+
+    rations = LabRecipeRation.query.filter_by(animal_profile_id=profile.id, is_deleted=False).all()
+    for header in rations:
+        header.animal_profile_id = None
+        header.updated_by = user_id
+
+    profile.updated_by = user_id
+    profile.soft_delete(user_id)
+    db.session.commit()
+    return {"id": profile.id, "deleted": True}

apps/api/app/modules/zootech/lab/commands/ensure_empty_master.py

@@ -0,0 +1,20 @@
+from __future__ import annotations
+
+from app.modules.zootech.wesp_bridge_db import db
+from app.modules.zootech.lab.db_guard import retry_locked
+from app.modules.zootech.lab.models import LabRecipeRation
+from app.modules.zootech.wesp_bridge_models import Recipe
+
+
+@retry_locked
+def ensure_empty_master(recipe_id: str, user_id: str = "system") -> dict:
+    recipe = Recipe.query.filter_by(id=recipe_id, is_deleted=False).first()
+    if recipe is None:
+        raise LookupError("Рецепт не найден")
+    existing = LabRecipeRation.query.filter_by(recipe_id=recipe_id, is_deleted=False).first()
+    if existing is not None:
+        return {"recipeId": recipe_id, "created": False}
+    header = LabRecipeRation(recipe_id=recipe_id, created_by=user_id, updated_by=user_id)
+    db.session.add(header)
+    db.session.commit()
+    return {"recipeId": recipe_id, "created": True}

apps/api/app/modules/zootech/lab/commands/import_seed.py

@@ -0,0 +1,299 @@
+"""Generic ETL: CSV из data/seed/ → lab profiles и component nutrients."""
+
+from __future__ import annotations
+
+import csv
+import logging
+from dataclasses import dataclass, field
+from pathlib import Path
+
+from app.modules.zootech.wesp_bridge_db import db
+from app.modules.zootech.lab.calc.ingredient_catalog import INGREDIENT_HEADERS
+from app.modules.zootech.lab.calc.ingredient_derive import derive_ingredient_nutrients
+from app.modules.zootech.lab.models import LabAnimalProfile
+from app.modules.zootech.lab.norm_catalog import norm_header, norm_title_to_key
+from app.modules.zootech.lab.seed_paths import norms_dir, nutrients_dir
+from app.modules.zootech.lab.services.component_nutrients import save_component_nutrients
+from app.modules.zootech.lab.services.profile_norms import save_norms_from_payload
+from app.modules.zootech.wesp_bridge_models import default_uuid
+from app.modules.zootech.wesp_bridge_models import Component
+
+_log = logging.getLogger(__name__)
+
+_SKIP_NUTRIENT_HEADERS = frozenset({"№", "Наименование", "Цена 1 кг"})
+
+
+@dataclass
+class NormsImportStats:
+    profiles_created: int = 0
+    profiles_updated: int = 0
+    norm_rows: int = 0
+    skipped: int = 0
+    unmapped_headers: list[str] = field(default_factory=list)
+    errors: list[str] = field(default_factory=list)
+
+
+@dataclass
+class NutrientsImportStats:
+    imported: int = 0
+    skipped: int = 0
+    unmatched: int = 0
+    errors: list[str] = field(default_factory=list)
+
+
+def _parse_min_max_groups(h1: list[str], h2: list[str]) -> list[tuple[str, int, int | None]]:
+    groups: list[tuple[str, int, int | None]] = []
+    current_name: str | None = None
+    limit = min(len(h1), len(h2))
+    for i in range(limit):
+        b = (h2[i] or "").strip().lower().replace("і", "и")
+        n = (h1[i] or "").strip()
+        if n and n not in ("№", "Наименование", "Масса", "КРС"):
+            current_name = norm_header(n)
+        if b.startswith("мин") and current_name:
+            max_i = i + 1 if i + 1 < limit and "мах" in (h2[i + 1] or "").lower() else None
+            groups.append((current_name, i, max_i))
+    return groups
+
+
+def norm_profile_key(ration_type: str, external_no: int) -> str:
+    return f"norm_{ration_type.lower()}_{external_no:04d}"
+
+
+def parse_norm_profile_row(
+    row: list[str],
+    groups: list[tuple[str, int, int | None]],
+    ration_type: str,
+    *,
+    unmapped: set[str],
+    profile_key_fn=norm_profile_key,
+) -> dict | None:
+    if len(row) < 2 or not (row[1] or "").strip():
+        return None
+    try:
+        external_no = int(float((row[0] or "").strip()))
+    except (ValueError, TypeError):
+        return None
+    if external_no <= 0:
+        return None
+
+    indicators: dict[str, dict[str, float | None]] = {}
+    for header, min_i, max_i in groups:
+        key = norm_title_to_key(header)
+        if not key:
+            unmapped.add(header)
+            continue
+
+        def _num(idx: int | None) -> float | None:
+            if idx is None or idx >= len(row):
+                return None
+            raw = (row[idx] or "").strip()
+            if not raw:
+                return None
+            try:
+                return float(raw)
+            except ValueError:
+                return None
+
+        min_v, max_v = _num(min_i), _num(max_i)
+        if min_v is None and max_v is None:
+            continue
+        indicators[key] = {"min": min_v, "max": max_v}
+
+    mass_kg = None
+    if len(row) > 2 and (row[2] or "").strip():
+        try:
+            mass_kg = float(row[2])
+        except ValueError:
+            pass
+
+    return {
+        "external_no": external_no,
+        "profile_key": profile_key_fn(ration_type, external_no),
+        "label": row[1].strip(),
+        "ration_type": ration_type,
+        "mass_kg": mass_kg,
+        "indicators": indicators,
+    }
+
+
+_IMPORT_BATCH_SIZE = 50
+
+
+def _lookup_reference_profile(profile_key: str) -> LabAnimalProfile | None:
+    """Поиск по profile_key включая soft-deleted (UNIQUE на всю таблицу)."""
+    return LabAnimalProfile.query.filter_by(profile_key=profile_key).first()
+
+
+def _restore_reference_profile(profile: LabAnimalProfile) -> None:
+    if not profile.is_deleted:
+        return
+    profile.is_deleted = False
+    profile.deleted_at = None
+    profile.deleted_by = None
+
+
+def _import_norm_sheet(
+    csv_path: Path,
+    ration_type: str,
+    stats: NormsImportStats,
+    unmapped: set[str],
+    *,
+    replace_reference: bool = True,
+) -> None:
+    if not csv_path.is_file():
+        stats.errors.append(f"Файл не найден: {csv_path}")
+        return
+
+    with csv_path.open(encoding="utf-8") as f:
+        rows = list(csv.reader(f))
+    if len(rows) < 8:
+        stats.errors.append(f"Слишком мало строк: {csv_path}")
+        return
+
+    groups = _parse_min_max_groups(rows[2], rows[3])
+    batch_count = 0
+    for row in rows[6:]:
+        parsed = parse_norm_profile_row(row, groups, ration_type, unmapped=unmapped)
+        if parsed is None:
+            stats.skipped += 1
+            continue
+
+        profile = _lookup_reference_profile(parsed["profile_key"])
+        if profile is not None:
+            if not replace_reference and not profile.is_deleted:
+                stats.skipped += 1
+                continue
+            if not replace_reference and profile.is_deleted:
+                stats.skipped += 1
+                continue
+            _restore_reference_profile(profile)
+            stats.profiles_updated += 1
+        else:
+            profile = LabAnimalProfile(
+                id=default_uuid(),
+                profile_key=parsed["profile_key"],
+                created_by="seed-import",
+            )
+            db.session.add(profile)
+            stats.profiles_created += 1
+
+        profile.label = parsed["label"]
+        profile.ration_type = ration_type
+        profile.external_no = parsed["external_no"]
+        save_norms_from_payload(
+            profile,
+            {
+                "massKg": parsed["mass_kg"],
+                "externalNo": parsed["external_no"],
+                "indicators": parsed["indicators"],
+            },
+        )
+        stats.norm_rows += len(parsed["indicators"])
+        batch_count += 1
+        if batch_count >= _IMPORT_BATCH_SIZE:
+            db.session.commit()
+            batch_count = 0
+
+
+def import_norm_profiles(
+    csv_dir: Path | None = None,
+    *,
+    replace_reference: bool = True,
+) -> NormsImportStats:
+    stats = NormsImportStats()
+    base = csv_dir or norms_dir()
+    unmapped: set[str] = set()
+
+    _import_norm_sheet(
+        base / "Нормы КРС.csv", "BEEF", stats, unmapped, replace_reference=replace_reference
+    )
+    _import_norm_sheet(
+        base / "Нормы Дойн.csv", "DAIRY", stats, unmapped, replace_reference=replace_reference
+    )
+    stats.unmapped_headers = sorted(unmapped)
+    db.session.commit()
+
+    _log.info(
+        "seed norms import: created=%s updated=%s norm_rows=%s",
+        stats.profiles_created,
+        stats.profiles_updated,
+        stats.norm_rows,
+    )
+    return stats
+
+
+def _parse_nutrient_row(row: list[str]) -> dict[str, float]:
+    nutrients: dict[str, float] = {}
+    for i, header in enumerate(INGREDIENT_HEADERS):
+        if header in _SKIP_NUTRIENT_HEADERS or i >= len(row):
+            continue
+        raw = (row[i] or "").strip()
+        if not raw:
+            continue
+        try:
+            nutrients[header] = float(raw)
+        except ValueError:
+            continue
+    return nutrients
+
+
+def _find_component(name: str, external_no: int | None) -> Component | None:
+    if external_no is not None:
+        hit = Component.query.filter_by(external_no=external_no, is_deleted=False).first()
+        if hit:
+            return hit
+    if name:
+        hit = Component.query.filter(Component.name == name, Component.is_deleted.is_(False)).first()
+        if hit:
+            return hit
+    return None
+
+
+def import_component_nutrients(
+    csv_path: Path | None = None,
+    *,
+    derive: bool = True,
+    dry_run: bool = False,
+) -> NutrientsImportStats:
+    path = csv_path or (nutrients_dir() / "База сырья.csv")
+    stats = NutrientsImportStats()
+    if not path.is_file():
+        stats.errors.append(f"CSV not found: {path}")
+        return stats
+
+    with path.open(encoding="utf-8") as f:
+        rows = list(csv.reader(f))
+
+    for row in rows[5:]:
+        name = row[1].strip() if len(row) > 1 else ""
+        if not name:
+            stats.skipped += 1
+            continue
+        try:
+            external_no = int(float(row[0])) if row[0].strip() else None
+        except (ValueError, IndexError):
+            external_no = None
+
+        comp = _find_component(name, external_no)
+        if comp is None:
+            stats.unmatched += 1
+            continue
+
+        nutrients = _parse_nutrient_row(row)
+        if derive:
+            nutrients = derive_ingredient_nutrients(nutrients)
+
+        if not dry_run:
+            save_component_nutrients(comp.id, nutrients, user_id="seed-import")
+        stats.imported += 1
+
+    if not dry_run:
+        db.session.commit()
+    _log.info(
+        "seed nutrients import: imported=%s skipped=%s unmatched=%s",
+        stats.imported,
+        stats.skipped,
+        stats.unmatched,
+    )
+    return stats

apps/api/app/modules/zootech/lab/commands/recalculate.py

@@ -0,0 +1,69 @@
+from __future__ import annotations
+
+import time
+
+from app.modules.zootech.wesp_bridge_db import db
+from app.modules.zootech.lab.calc.engine import calculate_ration
+from app.modules.zootech.lab.commands.write_audit import write_audit, write_calculation_run
+from app.modules.zootech.lab.db_guard import retry_locked
+from app.modules.zootech.lab.commands.seed_demo_component_nutrients import (
+    ensure_component_nutrients_if_empty,
+    supplement_component_nutrients_if_sparse,
+)
+from app.modules.zootech.lab.loaders.ration_loader import load_ration, ration_to_calc_lines
+from app.modules.zootech.lab.models import LabAnimalProfile, LabRecipeRation
+from app.modules.zootech.lab.services.ration_calc_store import save_calc_result
+
+
+@retry_locked
+def recalculate_ration(recipe_id: str, user_id: str = "system") -> dict:
+    snapshot = load_ration(recipe_id)
+    if not snapshot.lines:
+        raise ValueError("Пустой мастер — добавьте строки рациона")
+
+    seeded_any = False
+    for line in snapshot.lines:
+        if not line.component_id:
+            continue
+        if ensure_component_nutrients_if_empty(line.component_id, user_id=user_id):
+            seeded_any = True
+        elif supplement_component_nutrients_if_sparse(line.component_id, user_id=user_id):
+            seeded_any = True
+    if seeded_any:
+        db.session.commit()
+        snapshot = load_ration(recipe_id)
+
+    profile_mass_kg = None
+    if snapshot.animal_profile_id:
+        prof = LabAnimalProfile.query.get(snapshot.animal_profile_id)
+        if prof is not None:
+            profile_mass_kg = prof.mass_kg
+
+    started = time.perf_counter()
+    result = calculate_ration(
+        snapshot.ration_type,
+        ration_to_calc_lines(snapshot),
+        snapshot.norms,
+        heads_per_trip=snapshot.heads_per_trip,
+        profile_mass_kg=profile_mass_kg,
+    )
+    duration_ms = int((time.perf_counter() - started) * 1000)
+
+    header = LabRecipeRation.query.filter_by(recipe_id=recipe_id).first()
+    if header is None:
+        header = LabRecipeRation(recipe_id=recipe_id, created_by=user_id, updated_by=user_id)
+        db.session.add(header)
+    save_calc_result(recipe_id, result, header)
+    header.updated_by = user_id
+
+    status = "FAILED" if result.get("errors") else "COMPLETED"
+    write_calculation_run(
+        recipe_id,
+        status,
+        {"indicators": result.get("indicators", [])},
+        duration_ms=duration_ms,
+        error_message="; ".join(result.get("errors") or []) or None,
+    )
+    write_audit("RECALCULATE", "lab_recipe_ration", recipe_id, user_id)
+    db.session.commit()
+    return result

apps/api/app/modules/zootech/lab/commands/seed_demo_component_nutrients.py

@@ -0,0 +1,343 @@
+"""Стартовые nutrients для компонентов без EAV (демо-шаблоны, не лабораторный анализ).
+
+При старте и пересчёте рациона заполняет только пустые `lab_component_nutrient_value`.
+Маркер в data/ — журнал последнего прогона, не блокирует новые компоненты.
+"""
+
+from __future__ import annotations
+
+import logging
+from pathlib import Path
+from typing import Any
+
+from app.modules.zootech.wesp_bridge_db import db
+from app.modules.zootech.lab.nutrient_schema import read_from_mapping
+from app.modules.zootech.lab.services.component_nutrients import nutrients_full_dict, nutrients_is_empty, save_component_nutrients
+from app.modules.zootech.wesp_bridge_models import Component
+
+_log = logging.getLogger(__name__)
+
+_MARKER_NAME = ".lab_demo_nutrients_seeded"
+
+# Доп. поля для полной матрицы рациона (демо; заменить лабораторными при наличии).
+_MINERAL_SUPPLEMENT: dict[str, float] = {
+    "Ca": 8.0,
+    "P": 5.5,
+    "Mg": 3.2,
+    "Na": 1.2,
+    "K": 6.5,
+    "DCAB Форм": 45.0,
+    "Сырая зола": 55.0,
+    "Каротин": 5.0,
+}
+
+_CARB_SUPPLEMENT: dict[str, float] = {
+    "Сахар": 80.0,
+    "Крахмал": 280.0,
+    "Сахар и Крохм": 420.0,
+    "Нераств. Крохмал": 35.0,
+}
+
+_ROUGHAGE_TYPES = frozenset({"объемные корма", "грубые корма", "сочные корма"})
+
+_SUPPLEMENT_KEYS: tuple[str, ...] = tuple({**_MINERAL_SUPPLEMENT, **_CARB_SUPPLEMENT}.keys())
+
+_NAMED_SEEDS: dict[str, dict[str, Any]] = {
+    "комбикорм": {
+        "dry_matter": 88.0,
+        "nutrients": {
+            "Сыр. Протеин": 170,
+            "уСП": 142,
+            "БРА": 36,
+            "ОЭ-КРС": 12.5,
+            "ЧЭЛ- КРС": 7.8,
+            "Сырая клетчатка": 95,
+            "Структур. клетчатка": 28,
+            "Сырой жир": 38,
+        },
+    },
+    "жом свекловичный гранулы": {
+        "dry_matter": 90.0,
+        "nutrients": {
+            "Сыр. Протеин": 105,
+            "уСП": 88,
+            "ОЭ-КРС": 11.8,
+            "ЧЭЛ- КРС": 7.2,
+            "Сырая клетчатка": 220,
+            "Структур. клетчатка": 48,
+            "Сырой жир": 12,
+        },
+    },
+    "жом": {
+        "dry_matter": 90.0,
+        "nutrients": {
+            "Сыр. Протеин": 105,
+            "уСП": 88,
+            "ОЭ-КРС": 11.8,
+            "ЧЭЛ- КРС": 7.2,
+            "Сырая клетчатка": 220,
+            "Структур. клетчатка": 48,
+            "Сырой жир": 12,
+        },
+    },
+    "дробина": {
+        "dry_matter": 91.0,
+        "nutrients": {
+            "Сыр. Протеин": 100,
+            "уСП": 88,
+            "ОЭ-КРС": 11.5,
+            "ЧЭЛ- КРС": 7.0,
+            "Сырая клетчатка": 200,
+            "Структур. клетчатка": 40,
+            "Сырой жир": 12,
+        },
+    },
+    "добавки": {
+        "dry_matter": 99.0,
+        "nutrients": {
+            "Сыр. Протеин": 8,
+            "ОЭ-КРС": 2.0,
+            "ЧЭЛ- КРС": 1.2,
+            "Сырая клетчатка": 5,
+            "Сырой жир": 3,
+        },
+    },
+    "сено": {
+        "dry_matter": 85.0,
+        "nutrients": {
+            "Сыр. Протеин": 78,
+            "уСП": 62,
+            "ОЭ-КРС": 5.9,
+            "ЧЭЛ- КРС": 3.4,
+            "Сырая клетчатка": 328,
+            "Структур. клетчатка": 265,
+            "Сырой жир": 22,
+        },
+    },
+    "анионовые соли": {
+        "dry_matter": 95.0,
+        "nutrients": {
+            "Сыр. Протеин": 12,
+            "ОЭ-КРС": 1.2,
+            "ЧЭЛ- КРС": 0.8,
+            "Сырая клетчатка": 8,
+            "Сырой жир": 2,
+        },
+    },
+}
+
+_TYPE_DEFAULTS: dict[str, dict[str, Any]] = {
+    "зерновые": {
+        "dry_matter": 88.0,
+        "nutrients": {
+            "Сыр. Протеин": 120,
+            "уСП": 105,
+            "БРА": 28,
+            "ОЭ-КРС": 11.0,
+            "ЧЭЛ- КРС": 6.8,
+            "Сырая клетчатка": 85,
+            "Структур. клетчатка": 22,
+            "Сырой жир": 35,
+        },
+    },
+    "белковые": {
+        "dry_matter": 90.0,
+        "nutrients": {
+            "Сыр. Протеин": 380,
+            "уСП": 300,
+            "БРА": 80,
+            "ОЭ-КРС": 11.5,
+            "ЧЭЛ- КРС": 7.0,
+            "Сырая клетчатка": 120,
+            "Сырой жир": 45,
+        },
+    },
+    "минеральные": {
+        "dry_matter": 95.0,
+        "nutrients": {
+            "Сыр. Протеин": 12,
+            "ОЭ-КРС": 1.2,
+            "ЧЭЛ- КРС": 0.8,
+            "Сырая клетчатка": 8,
+            "Сырой жир": 2,
+        },
+    },
+    "витаминные": {
+        "dry_matter": 99.0,
+        "nutrients": {
+            "Сыр. Протеин": 8,
+            "ОЭ-КРС": 2.0,
+            "ЧЭЛ- КРС": 1.2,
+            "Сырая клетчатка": 5,
+            "Сырой жир": 3,
+        },
+    },
+    "энергетические": {
+        "dry_matter": 90.0,
+        "nutrients": {
+            "Сыр. Протеин": 100,
+            "уСП": 88,
+            "ОЭ-КРС": 11.5,
+            "ЧЭЛ- КРС": 7.0,
+            "Сырая клетчатка": 200,
+            "Структур. клетчатка": 40,
+            "Сырой жир": 12,
+        },
+    },
+    "объемные корма": {
+        "dry_matter": 85.0,
+        "nutrients": {
+            "Сыр. Протеин": 78,
+            "уСП": 62,
+            "ОЭ-КРС": 5.9,
+            "ЧЭЛ- КРС": 3.4,
+            "Сырая клетчатка": 328,
+            "Структур. клетчатка": 265,
+            "Сырой жир": 22,
+        },
+    },
+}
+
+_FALLBACK = {
+    "dry_matter": 88.0,
+    "nutrients": {
+        "Сыр. Протеин": 100,
+        "уСП": 85,
+        "ОЭ-КРС": 10.0,
+        "ЧЭЛ- КРС": 6.2,
+        "Сырая клетчатка": 150,
+        "Сырой жир": 25,
+    },
+}
+
+
+def _normalize_name(name: str | None) -> str:
+    return (name or "").strip().lower()
+
+
+def _merge_supplement(nutrients: dict[str, Any], component: Component) -> dict[str, float]:
+    merged = dict(_MINERAL_SUPPLEMENT)
+    type_key = _normalize_name(component.type)
+    if type_key not in _ROUGHAGE_TYPES:
+        merged.update(_CARB_SUPPLEMENT)
+    merged.update(nutrients)
+    return {k: float(v) for k, v in merged.items() if v is not None}
+
+
+def _payload_for_component(component: Component) -> dict[str, Any]:
+    name_key = _normalize_name(component.name)
+    if name_key in _NAMED_SEEDS:
+        payload = _NAMED_SEEDS[name_key]
+    else:
+        payload = None
+        for key in sorted(_NAMED_SEEDS, key=len, reverse=True):
+            if key in name_key:
+                payload = _NAMED_SEEDS[key]
+                break
+        if payload is None:
+            type_key = _normalize_name(component.type)
+            payload = _TYPE_DEFAULTS.get(type_key, _FALLBACK)
+    return {
+        "dry_matter": payload.get("dry_matter"),
+        "nutrients": _merge_supplement(payload.get("nutrients") or {}, component),
+    }
+
+
+def _apply_demo_payload(component: Component, payload: dict[str, Any], *, user_id: str) -> None:
+    if payload.get("dry_matter") is not None:
+        dm = float(payload["dry_matter"])
+        current_dm = float(component.dry_matter or 0)
+        if dm > 0 and (current_dm <= 0 or current_dm > 100):
+            component.dry_matter = dm
+    save_component_nutrients(
+        component.id,
+        payload["nutrients"],
+        user_id=user_id,
+    )
+    component.protein = 0.0
+    component.energy = 0.0
+    component.updated_by = "demo-nutrients-seed"
+
+
+def supplement_component_nutrients_if_sparse(
+    component_id: str | None,
+    *,
+    user_id: str = "system",
+) -> bool:
+    """Добавляет Ca/P/сахара и т.д., если EAV уже есть, но без минералов."""
+    if not component_id or nutrients_is_empty(component_id):
+        return False
+    component = Component.query.filter_by(id=component_id, is_deleted=False).first()
+    if component is None:
+        return False
+    full = nutrients_full_dict(component_id)
+    template = _payload_for_component(component)["nutrients"]
+    allowed = set(_MINERAL_SUPPLEMENT)
+    if _normalize_name(component.type) not in _ROUGHAGE_TYPES:
+        allowed.update(_CARB_SUPPLEMENT)
+    missing = {
+        key: float(template[key])
+        for key in allowed
+        if key in template and read_from_mapping(full, (key,)) is None
+    }
+    if not missing:
+        return False
+    save_component_nutrients(component_id, missing, user_id=user_id, pin=True)
+    return True
+
+
+def ensure_component_nutrients_if_empty(
+    component_id: str | None,
+    *,
+    user_id: str = "system",
+) -> bool:
+    """Заполняет EAV демо-шаблоном, если у компонента нет nutrients. Возвращает True при записи."""
+    if not component_id or not nutrients_is_empty(component_id):
+        return False
+    component = Component.query.filter_by(id=component_id, is_deleted=False).first()
+    if component is None:
+        return False
+    _apply_demo_payload(component, _payload_for_component(component), user_id=user_id)
+    return True
+
+
+def _marker_path() -> Path:
+    return Path(__file__).resolve().parents[4] / "data" / _MARKER_NAME
+
+
+def _already_seeded() -> bool:
+    return _marker_path().is_file()
+
+
+def _mark_seeded() -> None:
+    path = _marker_path()
+    path.parent.mkdir(parents=True, exist_ok=True)
+    path.write_text("1\n", encoding="utf-8")
+
+
+def seed_demo_component_nutrients_once(*, user_id: str = "system", force: bool = False) -> dict:
+    """Заполняет lab_component_nutrient_value у всех компонентов с пустой матрицей."""
+    updated = 0
+    skipped = 0
+    for component in Component.query.filter_by(is_deleted=False).all():
+        if not force and not nutrients_is_empty(component.id):
+            skipped += 1
+            continue
+        if force and not nutrients_is_empty(component.id):
+            skipped += 1
+            continue
+        _apply_demo_payload(component, _payload_for_component(component), user_id=user_id)
+        updated += 1
+
+    if updated:
+        db.session.commit()
+        _mark_seeded()
+        _log.info(
+            "demo component nutrients seed user=%s updated=%s skipped=%s",
+            user_id,
+            updated,
+            skipped,
+        )
+        return {"seeded": True, "updated": updated, "skipped": skipped}
+
+    return {"seeded": False, "reason": "nothing_to_update", "updated": 0, "skipped": skipped}

apps/api/app/modules/zootech/lab/commands/seed_from_execution.py

@@ -0,0 +1,40 @@
+from __future__ import annotations
+
+from app.modules.zootech.wesp_bridge_db import db
+from app.modules.zootech.lab.db_guard import retry_locked
+from app.modules.zootech.lab.loaders.execution_loader import load_execution
+from app.modules.zootech.lab.models import LabRationLine, LabRecipeRation
+from app.modules.zootech.wesp_bridge_models import Component
+from app.modules.zootech.wesp_bridge_models import default_uuid
+
+
+@retry_locked
+def seed_from_execution(recipe_id: str, user_id: str = "system") -> dict:
+    existing = LabRecipeRation.query.filter_by(recipe_id=recipe_id, is_deleted=False).first()
+    if existing is not None:
+        return {"recipeId": recipe_id, "seeded": False, "reason": "already_exists"}
+
+    execution = load_execution(recipe_id)
+    header = LabRecipeRation(recipe_id=recipe_id, created_by=user_id, updated_by=user_id)
+    db.session.add(header)
+
+    for idx, line in enumerate(execution.lines):
+        comp = Component.query.get(line.component_id) if line.component_id else None
+        daily_kg = line.daily_kg_total
+        db.session.add(
+            LabRationLine(
+                id=default_uuid(),
+                recipe_id=recipe_id,
+                component_id=line.component_id,
+                ingredient_name=line.name,
+                row_index=idx,
+                daily_kg=daily_kg,
+                in_ration=True,
+                in_compound=False,
+                created_by=user_id,
+                updated_by=user_id,
+            )
+        )
+    header.seed_source = "execution"
+    db.session.commit()
+    return {"recipeId": recipe_id, "seeded": True, "lines": len(execution.lines)}

apps/api/app/modules/zootech/lab/commands/seed_lab_math_ration.py

@@ -0,0 +1,358 @@
+"""Тестовые рецепты LAB: полная матрица показателей + рацион «в нормах»."""
+
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any
+
+from app.modules.zootech.wesp_bridge_db import db
+from app.modules.zootech.lab.commands.recalculate import recalculate_ration
+from app.modules.zootech.lab.models import LabAnimalProfile, LabRationLine, LabRecipeRation
+from app.modules.zootech.lab.services.component_nutrients import save_component_nutrients
+from app.modules.zootech.wesp_bridge_models import Component, Ingredient, Recipe
+from app.modules.zootech.wesp_bridge_models import default_uuid
+
+_log = logging.getLogger(__name__)
+
+PROFILE_KEY = "lab_math_dairy_01"
+HEADS = 10
+_MARKER = ".lab_math_ration_seeded"
+
+RECIPE_MATRIX = "LAB тест — полная матрица"
+RECIPE_IN_NORMS = "LAB тест — в нормах"
+
+# Реалистичные г/кг (и МДж для энергии) — покрывают расширенный каталог indicators.py
+_COMPONENTS: tuple[dict[str, Any], ...] = (
+    {
+        "name": "LAB тест — сено",
+        "type": "Грубые корма",
+        "dry_matter": 85.0,
+        "price": 12.0,
+        "nutrients": {
+            "СВ": 850,
+            "Осн.Корм": 850,
+            "Сыр. Протеин": 78,
+            "уСП": 62,
+            "ОЭ-КРС": 5.9,
+            "ЧЭЛ- КРС": 3.4,
+            "Сырая клетч": 328,
+            "НДК": 328,
+            "КДК": 210,
+            "Структур клетч": 265,
+            "Сырой жир": 22,
+            "Ca": 3.5,
+            "P": 2.1,
+            "Mg": 1.8,
+            "Na": 0.5,
+            "K": 18,
+            "DCAB Форм": 120,
+            "Сахар и Крохм": 120,
+            "Сахар": 40,
+            "Крахмал": 15,
+            "Нераств. Крохмал": 5,
+            "Каротин": 25,
+            "Сырая зола": 65,
+            "БЕР": 95,
+            "Переварим Протеин": 55,
+            "КРС Протеин": 68,
+            "Переварим сырая клетч": 195,
+            "Fe": 40,
+            "Zn": 22,
+            "Cu": 6,
+            "Mn": 35,
+            "Se": 0.04,
+            "J": 0.02,
+            "Вит А": 12000,
+            "Вит D": 800,
+            "Вит Е": 30,
+            "Вит В1": 2.5,
+            "Вит В2": 4.0,
+            "Вит В6": 3.0,
+            "Вит В12": 0.0,
+            "Лизин": 2.8,
+            "Метаб лизин": 1.2,
+            "% уСП/кг СВ": 7.3,
+            "Нер. СП / кг СВ": 11,
+        },
+    },
+    {
+        "name": "LAB тест — комбикорм",
+        "type": "Концентрированные",
+        "dry_matter": 88.0,
+        "price": 22.0,
+        "nutrients": {
+            "СВ": 880,
+            "Осн.Корм": 0,
+            "Сыр. Протеин": 170,
+            "уСП": 142,
+            "ОЭ-КРС": 12.5,
+            "ЧЭЛ- КРС": 7.8,
+            "Сырая клетч": 95,
+            "НДК": 95,
+            "КДК": 42,
+            "Структур клетч": 28,
+            "Сырой жир": 38,
+            "Ca": 8.0,
+            "P": 5.5,
+            "Mg": 3.2,
+            "Na": 1.2,
+            "K": 6.5,
+            "DCAB Форм": 45,
+            "Сахар и Крохм": 420,
+            "Сахар": 80,
+            "Крахмал": 280,
+            "Нераств. Крохмал": 35,
+            "Каротин": 5,
+            "Сырая зола": 55,
+            "БЕР": 520,
+            "Переварим Протеин": 125,
+            "КРС Протеин": 155,
+            "Fe": 85,
+            "Zn": 55,
+            "Cu": 12,
+            "Mn": 28,
+            "Se": 0.12,
+            "Вит А": 2500,
+            "Вит D": 400,
+            "Вит Е": 45,
+            "Вит В1": 5.5,
+            "Вит В2": 6.0,
+            "Лизин": 8.5,
+            "Метаб лизин": 4.2,
+            "% уСП/кг СВ": 16.1,
+            "Нер. СП / кг СВ": 28,
+        },
+    },
+    {
+        "name": "LAB тест — силос",
+        "type": "Сочные корма",
+        "dry_matter": 34.0,
+        "price": 4.0,
+        "nutrients": {
+            "СВ": 340,
+            "Осн.Корм": 0,
+            "Сыр. Протеин": 32,
+            "уСП": 28,
+            "ОЭ-КРС": 6.2,
+            "ЧЭЛ- КРС": 3.8,
+            "Сырая клетч": 220,
+            "НДК": 220,
+            "КДК": 125,
+            "Структур клетч": 45,
+            "Сырой жир": 28,
+            "Ca": 4.5,
+            "P": 2.8,
+            "Mg": 1.5,
+            "Na": 0.3,
+            "K": 12,
+            "DCAB Форм": 95,
+            "Сахар и Крохм": 180,
+            "Сахар": 60,
+            "Крахмал": 45,
+            "Нераств. Крохмал": 12,
+            "Каротин": 18,
+            "Сырая зола": 38,
+            "БЕР": 145,
+            "Переварим Протеин": 22,
+            "КРС Протеин": 28,
+            "Fe": 25,
+            "Zn": 18,
+            "Cu": 4,
+            "Mn": 15,
+            "Вит А": 3500,
+            "Вит Е": 18,
+            "Лизин": 1.1,
+            "% уСП/кг СВ": 8.2,
+        },
+    },
+)
+
+_RECIPE_SPECS: tuple[dict[str, Any], ...] = (
+    {
+        "name": RECIPE_MATRIX,
+        "profile_key": PROFILE_KEY,
+        "description": "перегруз для проверки красных отклонений",
+        "lines": (
+            ("LAB тест — сено", 5.0),
+            ("LAB тест — комбикорм", 12.0),
+            ("LAB тест — силос", 8.0),
+        ),
+    },
+    {
+        "name": RECIPE_IN_NORMS,
+        "profile_key": PROFILE_KEY,
+        "description": "поддержание 300 кг — большинство норм в зелёной зоне",
+        "lines": (
+            ("LAB тест — сено", 4.5),
+            ("LAB тест — комбикорм", 1.0),
+            ("LAB тест — силос", 1.8),
+        ),
+    },
+)
+
+
+@dataclass
+class LabMathRationStats:
+    recipe_ids: list[str] = field(default_factory=list)
+    created: int = 0
+    updated: int = 0
+    recalculated: int = 0
+    errors: list[str] = field(default_factory=list)
+
+    @property
+    def recipe_id(self) -> str | None:
+        return self.recipe_ids[0] if self.recipe_ids else None
+
+
+def _marker_path() -> Path:
+    return Path(__file__).resolve().parents[4] / "data" / _MARKER
+
+
+def _get_or_create_component(spec: dict[str, Any]) -> Component:
+    comp = Component.query.filter_by(name=spec["name"], is_deleted=False).first()
+    if comp is None:
+        comp = Component(
+            id=default_uuid(),
+            name=spec["name"],
+            type=spec["type"],
+            dry_matter=float(spec["dry_matter"]),
+            protein=0.0,
+            energy=0.0,
+            price=float(spec.get("price", 0)),
+            created_by="lab-math-ration",
+            updated_by="lab-math-ration",
+        )
+        db.session.add(comp)
+        db.session.flush()
+    else:
+        comp.dry_matter = float(spec["dry_matter"])
+        comp.price = float(spec.get("price", comp.price or 0))
+        comp.updated_by = "lab-math-ration"
+    nutrients = {k: float(v) for k, v in spec["nutrients"].items()}
+    save_component_nutrients(comp.id, nutrients, user_id="lab-math-ration", pin=True)
+    return comp
+
+
+def _seed_one_recipe(
+    spec: dict[str, Any],
+    *,
+    by_name: dict[str, Component],
+    profile: LabAnimalProfile,
+    stats: LabMathRationStats,
+) -> str:
+    recipe = Recipe.query.filter_by(name=spec["name"], is_deleted=False).first()
+    if recipe is None:
+        recipe = Recipe(
+            id=default_uuid(),
+            name=spec["name"],
+            heads_per_trip=HEADS,
+            ration_type="DAIRY",
+            mixing_time=0,
+            trip_percent=100.0,
+            created_by="lab-math-ration",
+            updated_by="lab-math-ration",
+        )
+        db.session.add(recipe)
+        stats.created += 1
+        db.session.flush()
+    else:
+        recipe.heads_per_trip = HEADS
+        recipe.ration_type = "DAIRY"
+        stats.updated += 1
+
+    for ing in Ingredient.query.filter_by(recipe_id=recipe.id, is_deleted=False).all():
+        ing.soft_delete("lab-math-ration")
+    for order, (cname, wph) in enumerate(spec["lines"]):
+        comp = by_name[cname]
+        db.session.add(
+            Ingredient(
+                id=default_uuid(),
+                recipe_id=recipe.id,
+                component_id=comp.id,
+                name=comp.name,
+                amount=wph * HEADS,
+                weight_per_head=wph,
+                dry_matter=comp.dry_matter,
+                order=order,
+                created_by="lab-math-ration",
+                updated_by="lab-math-ration",
+            )
+        )
+
+    header = LabRecipeRation.query.filter_by(recipe_id=recipe.id, is_deleted=False).first()
+    if header is None:
+        header = LabRecipeRation(
+            recipe_id=recipe.id,
+            created_by="lab-math-ration",
+            updated_by="lab-math-ration",
+        )
+        db.session.add(header)
+    header.animal_profile_id = profile.id
+    header.seed_source = "lab_math_test"
+
+    for line in LabRationLine.query.filter_by(recipe_id=recipe.id, is_deleted=False).all():
+        line.soft_delete("lab-math-ration")
+    for idx, (cname, wph) in enumerate(spec["lines"]):
+        comp = by_name[cname]
+        db.session.add(
+            LabRationLine(
+                id=default_uuid(),
+                recipe_id=recipe.id,
+                component_id=comp.id,
+                ingredient_name=comp.name,
+                row_index=idx,
+                daily_kg=wph * HEADS,
+                in_ration=True,
+                in_compound=False,
+                created_by="lab-math-ration",
+                updated_by="lab-math-ration",
+            )
+        )
+    return recipe.id
+
+
+def seed_lab_math_ration(*, force: bool = False, run_calc: bool = True) -> LabMathRationStats:
+    stats = LabMathRationStats()
+    if not force and _marker_path().is_file():
+        for spec in _RECIPE_SPECS:
+            existing = Recipe.query.filter_by(name=spec["name"], is_deleted=False).first()
+            if existing:
+                stats.recipe_ids.append(existing.id)
+        if stats.recipe_ids:
+            stats.updated = len(stats.recipe_ids)
+            return stats
+
+    profile = LabAnimalProfile.query.filter_by(profile_key=PROFILE_KEY, is_deleted=False).first()
+    if profile is None:
+        stats.errors.append(
+            f"Профиль {PROFILE_KEY} не найден — запустите scripts/seed_lab_math_profiles.py"
+        )
+        return stats
+
+    components = [_get_or_create_component(spec) for spec in _COMPONENTS]
+    by_name = {c.name: c for c in components}
+
+    for spec in _RECIPE_SPECS:
+        rid = _seed_one_recipe(spec, by_name=by_name, profile=profile, stats=stats)
+        stats.recipe_ids.append(rid)
+
+    db.session.commit()
+
+    if run_calc:
+        for rid in stats.recipe_ids:
+            try:
+                recalculate_ration(rid, "lab-math-ration")
+                stats.recalculated += 1
+            except Exception as exc:
+                stats.errors.append(f"пересчёт {rid}: {exc}")
+
+    _marker_path().parent.mkdir(parents=True, exist_ok=True)
+    _marker_path().write_text("\n".join(stats.recipe_ids) + "\n", encoding="utf-8")
+    _log.info(
+        "lab math rations: ids=%s matrix+in_norms heads=%s",
+        stats.recipe_ids,
+        HEADS,
+    )
+    return stats

apps/api/app/modules/zootech/lab/commands/seed_math_test_profiles.py

@@ -0,0 +1,90 @@
+"""5 профилей норм для проверки математики рациона (строки 1–5 из data/seed/norms)."""
+
+from __future__ import annotations
+
+import csv
+import logging
+from dataclasses import dataclass, field
+from pathlib import Path
+
+from app.modules.zootech.wesp_bridge_db import db
+from app.modules.zootech.lab.commands.import_seed import _parse_min_max_groups, parse_norm_profile_row
+from app.modules.zootech.lab.seed_paths import norms_dir
+from app.modules.zootech.lab.models import LabAnimalProfile
+from app.modules.zootech.lab.services.profile_norms import save_norms_from_payload
+from app.modules.zootech.wesp_bridge_models import default_uuid
+
+_log = logging.getLogger(__name__)
+
+_MATH_SPECS: tuple[tuple[str, str, int, str], ...] = (
+    ("DAIRY", "Нормы Дойн.csv", 1, "lab_math_dairy_01"),
+    ("DAIRY", "Нормы Дойн.csv", 2, "lab_math_dairy_02"),
+    ("DAIRY", "Нормы Дойн.csv", 3, "lab_math_dairy_03"),
+    ("DAIRY", "Нормы Дойн.csv", 4, "lab_math_dairy_04"),
+    ("DAIRY", "Нормы Дойн.csv", 5, "lab_math_dairy_05"),
+)
+
+
+@dataclass
+class MathTestSeedStats:
+    created: int = 0
+    updated: int = 0
+    errors: list[str] = field(default_factory=list)
+
+
+def _load_csv_row(csv_path: Path, external_no: int, ration_type: str) -> dict | None:
+    with csv_path.open(encoding="utf-8") as f:
+        rows = list(csv.reader(f))
+    if len(rows) < 8:
+        return None
+    groups = _parse_min_max_groups(rows[2], rows[3])
+    data_row = rows[5 + external_no]
+
+    eno = external_no
+    return parse_norm_profile_row(
+        data_row,
+        groups,
+        ration_type,
+        unmapped=set(),
+        profile_key_fn=lambda rt, _ext: f"lab_math_{rt.lower()}_{eno:02d}",
+    )
+
+
+def seed_math_test_profiles(*, csv_dir: Path | None = None) -> MathTestSeedStats:
+    stats = MathTestSeedStats()
+    base = csv_dir or norms_dir()
+
+    for ration_type, filename, external_no, profile_key in _MATH_SPECS:
+        parsed = _load_csv_row(base / filename, external_no, ration_type)
+        if parsed is None:
+            stats.errors.append(f"Строка {external_no} не найдена в {filename}")
+            continue
+
+        profile = LabAnimalProfile.query.filter_by(profile_key=profile_key, is_deleted=False).first()
+        if profile is None:
+            profile = LabAnimalProfile(
+                id=default_uuid(),
+                profile_key=profile_key,
+                created_by="lab-math-test-seed",
+            )
+            db.session.add(profile)
+            stats.created += 1
+        else:
+            stats.updated += 1
+
+        profile.label = f"TEST математика #{external_no} — {parsed['label'][:48]}"
+        profile.ration_type = ration_type
+        profile.external_no = parsed["external_no"]
+        parsed["indicators"]["rnb"] = {"min": -10.0, "max": 20.0}
+        save_norms_from_payload(
+            profile,
+            {
+                "massKg": parsed["mass_kg"],
+                "externalNo": parsed["external_no"],
+                "indicators": parsed["indicators"],
+            },
+        )
+
+    db.session.commit()
+    _log.info("lab math test profiles: created=%s updated=%s", stats.created, stats.updated)
+    return stats

apps/api/app/modules/zootech/lab/commands/sync_from_execution.py

@@ -0,0 +1,49 @@
+from __future__ import annotations
+
+from app.modules.zootech.wesp_bridge_db import db
+from app.modules.zootech.lab.commands.write_audit import write_audit
+from app.modules.zootech.lab.db_guard import retry_locked
+from app.modules.zootech.lab.loaders.execution_loader import load_execution
+from app.modules.zootech.lab.models import LabRationLine, LabRecipeRation
+from app.modules.zootech.wesp_bridge_models import Component
+from app.modules.zootech.wesp_bridge_models import default_uuid
+
+
+@retry_locked
+def sync_from_execution(recipe_id: str, user_id: str = "system") -> dict:
+    """Обновить мастер из execution (обратный перенос /recipes → lab)."""
+    execution = load_execution(recipe_id)
+    header = LabRecipeRation.query.filter_by(recipe_id=recipe_id, is_deleted=False).first()
+    if header is None:
+        header = LabRecipeRation(recipe_id=recipe_id, created_by=user_id, updated_by=user_id)
+        db.session.add(header)
+    else:
+        header.updated_by = user_id
+
+    existing = LabRationLine.query.filter_by(recipe_id=recipe_id, is_deleted=False).all()
+    for line in existing:
+        line.soft_delete(user_id)
+
+    created = 0
+    for idx, line in enumerate(execution.lines):
+        comp = Component.query.get(line.component_id) if line.component_id else None
+        db.session.add(
+            LabRationLine(
+                id=default_uuid(),
+                recipe_id=recipe_id,
+                component_id=line.component_id,
+                ingredient_name=line.name or (comp.name if comp else None),
+                row_index=idx,
+                daily_kg=line.daily_kg_total,
+                in_ration=True,
+                in_compound=False,
+                created_by=user_id,
+                updated_by=user_id,
+            )
+        )
+        created += 1
+
+    header.seed_source = "synced_from"
+    write_audit("SYNC_FROM_EXECUTION", "lab_recipe_ration", recipe_id, user_id)
+    db.session.commit()
+    return {"recipeId": recipe_id, "lines": created}

apps/api/app/modules/zootech/lab/commands/upsert_profile.py

@@ -0,0 +1,62 @@
+from __future__ import annotations
+
+from typing import Any
+
+from app.modules.zootech.wesp_bridge_db import db
+from app.modules.zootech.lab.db_guard import retry_locked
+from app.modules.zootech.lab.models import LabAnimalProfile
+from app.modules.zootech.lab.calc.nutrients import parse_num
+from app.modules.zootech.lab.calc.norms_resolver import normalize_norms_method
+from app.modules.zootech.lab.services.norms_params import save_norms_params
+from app.modules.zootech.lab.services.profile_norms import save_norms_from_payload, sync_racion_norms_to_profile
+from app.modules.zootech.wesp_bridge_models import default_uuid
+
+
+@retry_locked
+def upsert_animal_profile(profile_id: str | None, payload: dict[str, Any], user_id: str = "system") -> dict:
+    pid = (profile_id or payload.get("id") or "").strip() or default_uuid()
+    profile = LabAnimalProfile.query.filter_by(id=pid, is_deleted=False).first()
+    is_new = profile is None
+    if is_new:
+        profile = LabAnimalProfile(id=pid, created_by=user_id)
+        db.session.add(profile)
+
+    key = (payload.get("profileKey") or payload.get("profile_key") or "").strip()
+    label = (payload.get("label") or "").strip()
+    ration_type = (payload.get("rationType") or payload.get("ration_type") or "BEEF").strip().upper()
+    if not key or not label:
+        raise ValueError("profileKey и label обязательны")
+
+    profile.profile_key = key
+    profile.label = label
+    profile.ration_type = ration_type
+    if "normsMethod" in payload or "norms_method" in payload:
+        profile.norms_method = normalize_norms_method(
+            payload.get("normsMethod", payload.get("norms_method"))
+        )
+    if "normsParams" in payload or "norms_params" in payload:
+        raw_params = payload.get("normsParams", payload.get("norms_params"))
+        save_norms_params(profile, raw_params if isinstance(raw_params, dict) else None)
+    if "milkYieldKg" in payload or "milk_yield_kg" in payload:
+        profile.milk_yield_kg = parse_num(payload.get("milkYieldKg", payload.get("milk_yield_kg")))
+
+    norms_payload: dict[str, Any] = {}
+    if "normsData" in payload or "norms_data" in payload:
+        raw = payload.get("normsData") if "normsData" in payload else payload.get("norms_data")
+        if isinstance(raw, dict):
+            norms_payload = dict(raw)
+    if payload.get("massKg") is not None or payload.get("mass_kg") is not None:
+        norms_payload["massKg"] = payload.get("massKg", payload.get("mass_kg"))
+    if "milkYieldKg" in payload or "milk_yield_kg" in payload:
+        norms_payload["milkYieldKg"] = payload.get("milkYieldKg", payload.get("milk_yield_kg"))
+    if payload.get("externalNo") is not None or payload.get("external_no") is not None:
+        norms_payload["externalNo"] = payload.get("externalNo", payload.get("external_no"))
+    if isinstance(payload.get("indicators"), dict):
+        norms_payload["indicators"] = payload["indicators"]
+    if norms_payload:
+        save_norms_from_payload(profile, norms_payload)
+
+    profile.updated_by = user_id
+    sync_racion_norms_to_profile(profile)
+    db.session.commit()
+    return {"id": profile.id, "profileKey": profile.profile_key, "label": profile.label}

apps/api/app/modules/zootech/lab/commands/upsert_ration.py

@@ -0,0 +1,65 @@
+from __future__ import annotations
+
+from typing import Any
+
+from app.modules.zootech.wesp_bridge_db import db
+from app.modules.zootech.lab.db_guard import retry_locked
+from app.modules.zootech.lab.models import LabRationLine, LabRecipeRation
+from app.modules.zootech.wesp_bridge_models import Recipe
+from app.modules.zootech.wesp_bridge_models import default_uuid
+
+
+@retry_locked
+def upsert_ration(recipe_id: str, payload: dict[str, Any], user_id: str = "system") -> dict[str, Any]:
+    recipe = Recipe.query.filter_by(id=recipe_id, is_deleted=False).first()
+    if recipe is None:
+        raise LookupError("Рецепт не найден")
+
+    header = LabRecipeRation.query.filter_by(recipe_id=recipe_id).first()
+    if header is None:
+        header = LabRecipeRation(recipe_id=recipe_id, created_by=user_id, updated_by=user_id)
+        db.session.add(header)
+    header.animal_profile_id = payload.get("animalProfileId") or payload.get("animal_profile_id")
+    if "params" in payload:
+        params = payload.get("params") or {}
+        if isinstance(params, dict):
+            seeded = params.get("seeded_from") or params.get("synced_from")
+            if seeded:
+                header.seed_source = str(seeded)
+    if "rationType" in payload or "ration_type" in payload:
+        recipe.ration_type = (payload.get("rationType") or payload.get("ration_type") or "").upper() or None
+    header.updated_by = user_id
+
+    incoming = payload.get("lines") or []
+    existing = {
+        str(l.id): l
+        for l in LabRationLine.query.filter_by(recipe_id=recipe_id, is_deleted=False).all()
+    }
+    seen_ids: set[str] = set()
+    for idx, row in enumerate(incoming):
+        line_id = str(row.get("id") or "")
+        line = existing.get(line_id) if line_id else None
+        if line is None:
+            line = LabRationLine(
+                id=default_uuid(),
+                recipe_id=recipe_id,
+                created_by=user_id,
+                updated_by=user_id,
+            )
+            db.session.add(line)
+        line.row_index = int(row.get("rowIndex", row.get("row_index", idx)))
+        line.component_id = row.get("componentId") or row.get("component_id")
+        line.ingredient_name = row.get("ingredientName") or row.get("ingredient_name")
+        line.daily_kg = row.get("dailyKg", row.get("daily_kg"))
+        line.in_ration = bool(row.get("inRation", row.get("in_ration", True)))
+        line.in_compound = bool(row.get("inCompound", row.get("in_compound", False)))
+        line.updated_by = user_id
+        if line.id:
+            seen_ids.add(str(line.id))
+
+    for lid, line in existing.items():
+        if lid not in seen_ids:
+            line.soft_delete(user_id)
+
+    db.session.commit()
+    return {"recipeId": recipe_id, "ok": True}

apps/api/app/modules/zootech/lab/commands/write_audit.py

@@ -0,0 +1,43 @@
+from __future__ import annotations
+
+import json
+import logging
+from typing import Any
+
+_log = logging.getLogger("app.lab.audit")
+
+
+def write_audit(
+    action: str,
+    entity_type: str,
+    entity_id: str | None,
+    user_id: str | None = None,
+    metadata: dict[str, Any] | None = None,
+) -> None:
+    _log.info(
+        "lab action=%s entity=%s/%s user=%s metadata=%s",
+        action,
+        entity_type,
+        entity_id,
+        user_id or "system",
+        json.dumps(metadata or {}, ensure_ascii=False),
+    )
+
+
+def write_calculation_run(
+    recipe_id: str,
+    status: str,
+    kpi_results: dict[str, Any],
+    *,
+    duration_ms: int | None = None,
+    error_message: str | None = None,
+) -> None:
+    _log.info(
+        "lab calculation recipe=%s status=%s duration_ms=%s engine=%s error=%s kpi=%s",
+        recipe_id,
+        status,
+        duration_ms,
+        "native-1",
+        error_message or "",
+        json.dumps(kpi_results, ensure_ascii=False),
+    )

apps/api/app/modules/zootech/lab/constants.py

@@ -0,0 +1,28 @@
+"""Lab ration calc constants (ported from tab @neoton/shared)."""
+
+from __future__ import annotations
+
+from app.modules.zootech.lab.indicators import RATION_ALL_INDICATORS, RATION_QUALITY_INDICATORS
+
+RATION_TOTAL_KEYS = {
+    "DAIRY": [
+        {"key": "total_kg", "label": "Итого кг/день"},
+        {"key": "ration_pct_sum", "label": "Сумма % в рационе"},
+    ],
+    "BEEF": [
+        {"key": "total_kg", "label": "Итого кг/день"},
+        {"key": "ration_kg", "label": "Сумма «из рациона», кг"},
+        {"key": "ration_pct_sum", "label": "Сумма % в рационе"},
+        {"key": "cost_total", "label": "Стоимость рациона (сумма)"},
+    ],
+}
+
+NORM_COLUMN_ALIASES = {
+    "dry_matter": ["Сухое Вещество", "Сухое вещество"],
+    "crude_protein": ["Сыр. Протеин"],
+    "usp": ["уСП"],
+    "oe": ["ОЭ-КРС", "    ОЭ-КРС"],
+    "nel": ["ЧЭЛ- КРС", "    ЧЭЛ- КРС"],
+}
+
+DIFF_TOLERANCE_KG = 0.05

apps/api/app/modules/zootech/lab/db_guard.py

@@ -0,0 +1,28 @@
+from __future__ import annotations
+
+import time
+from functools import wraps
+from typing import Any, Callable, TypeVar
+
+from sqlalchemy.exc import OperationalError
+
+F = TypeVar("F", bound=Callable[..., Any])
+
+
+def retry_locked(fn: F, *, attempts: int = 3, delay: float = 0.15) -> F:
+    @wraps(fn)
+    def wrapper(*args: Any, **kwargs: Any) -> Any:
+        last_exc: Exception | None = None
+        for i in range(attempts):
+            try:
+                return fn(*args, **kwargs)
+            except OperationalError as exc:
+                if "locked" not in str(exc).lower():
+                    raise
+                last_exc = exc
+                time.sleep(delay * (i + 1))
+        if last_exc:
+            raise last_exc
+        return None
+
+    return wrapper  # type: ignore[return-value]

apps/api/app/modules/zootech/lab/dto/__init__.py

@@ -0,0 +1,3 @@
+from .ration import ExecutionSnapshot, RationSnapshot
+
+__all__ = ["RationSnapshot", "ExecutionSnapshot"]

apps/api/app/modules/zootech/lab/dto/ration.py

@@ -0,0 +1,52 @@
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Any
+
+
+@dataclass(frozen=True)
+class RationLineSnapshot:
+    id: str | None
+    component_id: str | None
+    ingredient_name: str | None
+    row_index: int
+    daily_kg: float | None
+    in_ration: bool
+    in_compound: bool
+    dry_matter: float | None
+    price_per_kg: float | None
+    nutrients: dict[str, Any]
+
+
+@dataclass(frozen=True)
+class RationSnapshot:
+    recipe_id: str
+    recipe_name: str
+    ration_type: str
+    heads_per_trip: int
+    animal_profile_id: str | None
+    params: dict[str, Any]
+    lines: tuple[RationLineSnapshot, ...] = field(default_factory=tuple)
+    norms: dict[str, dict[str, float | None]] = field(default_factory=dict)
+    ration_results: dict[str, Any] = field(default_factory=dict)
+    compound_results: dict[str, Any] = field(default_factory=dict)
+    calculated_at: str | None = None
+    exists: bool = True
+    norms_profile_key: str | None = None
+    legacy_norms_remapped: bool = False
+
+
+@dataclass(frozen=True)
+class ExecutionLineSnapshot:
+    ingredient_id: str
+    component_id: str | None
+    name: str
+    weight_per_head: float
+    daily_kg_total: float
+
+
+@dataclass(frozen=True)
+class ExecutionSnapshot:
+    recipe_id: str
+    heads_per_trip: int
+    lines: tuple[ExecutionLineSnapshot, ...] = field(default_factory=tuple)

apps/api/app/modules/zootech/lab/etl/__init__.py

@@ -0,0 +1 @@
+"""Offline ETL from tab reference PostgreSQL."""

apps/api/app/modules/zootech/lab/etl/agrostar_pdf_import.py

@@ -0,0 +1,88 @@
+"""AgroStar PDF (печатная форма анализа) → AgrostarParseResult."""
+
+from __future__ import annotations
+
+import re
+from typing import Any
+
+from app.modules.zootech.lab.etl.agrostar_tabular import (
+    parse_tabular_triplet_lines,
+    rows_to_label_values,
+    sample_from_agro_fields,
+    tabular_rows_to_agro_fields,
+)
+from app.modules.zootech.lab.etl.agrostar_xml_import import AgrostarParseResult
+
+
+def _pdf_text(data: bytes) -> str:
+    try:
+        import fitz  # pymupdf
+    except ImportError as exc:
+        raise RuntimeError("Для PDF нужен pymupdf (pip install pymupdf)") from exc
+    doc = fitz.open(stream=data, filetype="pdf")
+    parts = [doc[i].get_text() for i in range(doc.page_count)]
+    doc.close()
+    return "\n".join(parts)
+
+
+def _line_after(lines: list[str], marker: str) -> str:
+    for i, line in enumerate(lines):
+        if line == marker and i + 1 < len(lines):
+            return lines[i + 1]
+    return ""
+
+
+def _is_agrostar_pdf(text: str) -> bool:
+    t = text or ""
+    return "ИНФОРМАЦИЯ ОБ ОБРАЗЦЕ" in t or "РЕЗУЛЬТАТЫ АНАЛИЗА" in t
+
+
+def parse_agrostar_pdf(data: bytes) -> AgrostarParseResult:
+    result = AgrostarParseResult(lab_name="АгроСтар")
+    try:
+        text = _pdf_text(data)
+    except RuntimeError as exc:
+        result.errors.append(str(exc))
+        return result
+
+    if not _is_agrostar_pdf(text):
+        result.errors.append("Не похоже на отчёт AgroStar (PDF)")
+        return result
+
+    lines = [ln.strip() for ln in text.splitlines() if ln.strip()]
+    sample_no = _line_after(lines, "Образец №")
+    farm_name = _line_after(lines, "Клиент")
+    date_printed = _line_after(lines, "Дата анализа")
+    desc = _line_after(lines, "Описание")
+
+    label_parts: list[str] = []
+    if "Образец" in lines:
+        idx = lines.index("Образец")
+        if idx + 1 < len(lines) and lines[idx + 1] != "Описание":
+            label_parts.append(lines[idx + 1])
+        if idx + 2 < len(lines) and lines[idx + 2] not in ("Описание", "Клиент"):
+            label_parts.append(lines[idx + 2])
+
+    triplets = parse_tabular_triplet_lines(lines)
+    agro_fields = tabular_rows_to_agro_fields(rows_to_label_values(triplets))
+    if not agro_fields:
+        result.errors.append("В PDF не найдены показатели анализа")
+        return result
+
+    sample = sample_from_agro_fields(
+        sample_no=sample_no,
+        farm_name=farm_name,
+        date_printed=date_printed,
+        desc_1=desc or " ".join(label_parts),
+        agro_fields=agro_fields,
+    )
+    result.samples.append(sample)
+    return result
+
+
+def looks_like_agrostar_pdf(data: bytes) -> bool:
+    try:
+        text = _pdf_text(data)
+    except RuntimeError:
+        return False
+    return _is_agrostar_pdf(text)

apps/api/app/modules/zootech/lab/etl/agrostar_tabular.py

@@ -0,0 +1,129 @@
+"""AgroStar табличные форматы (PDF, xlsx) → те же AgrostarSample, что и XML."""
+
+from __future__ import annotations
+
+import re
+from typing import Any
+
+from app.modules.zootech.lab.etl.agrostar_xml_import import (
+    AgrostarParseResult,
+    AgrostarSample,
+    _parse_float,
+    _pct_dm_to_g_per_kg_sv,
+    finalize_agrostar_sample,
+)
+
+# Подпись в отчёте → ключ AgroStar XML (дальше — общий _AGROSTAR_MAP)
+_TABULAR_LABEL_TO_AGRO: dict[str, str] = {
+    "Сухое вещество (DM)": "DM",
+    "Сырой протеин (Crude Protein)": "CP",
+    "КДК (ADF)": "ADF",
+    "НДК (aNDF)": "NDF",
+    "НДК по орг. веществу (aNDFom)": "aNDFom",
+    "Сырой жир (Fat EE)": "Fat_EE",
+    "Сырая зола (Ash)": "Ash",
+    "Кальций (Ca)": "Ca",
+    "Фосфор (P)": "P",
+    "Магний (Mg)": "Mg",
+    "Калий (K)": "K",
+    "Сера (S)": "S",
+    "Хлор (Cl)": "Cl",
+    "Водорастворимый сахар (WSC)": "Sugar_WSC",
+    "Cпирторастворимый сахар (ESC)": "Sugar_ESC",
+    "Крахмал (Starch)": "Starch",
+    "Безволокнистые углеводы (NFC)": "NFC",
+    "Переваримые питательные вещества (TDN)": "TDN",
+    "НДК протеин (ND-ICP)": "NDICP_CP",
+    "Лизин": "Lys",
+    "Метионин": "Met",
+    "Лейцин": "Leu",
+    "Изолейцин": "Ile",
+    "Валин": "Val",
+    "Усваиваемость НДК 30 ч (NDFD30)": "NDFDom_IV_30hr",
+}
+
+_SECTION_HEADERS = frozenset(
+    {
+        "Протеин:",
+        "Клетчатка:",
+        "Сахара и крахмал:",
+        "Жир и жирные кислоты:",
+        "Минералы:",
+        "Энергия:",
+    }
+)
+
+
+def map_tabular_label(label: str) -> str | None:
+    text = (label or "").strip()
+    if not text or text in _SECTION_HEADERS:
+        return None
+    if text in _TABULAR_LABEL_TO_AGRO:
+        return _TABULAR_LABEL_TO_AGRO[text]
+    lowered = text.lower()
+    for key, ag_key in sorted(_TABULAR_LABEL_TO_AGRO.items(), key=lambda item: len(item[0]), reverse=True):
+        if key.lower() in lowered:
+            return ag_key
+    return None
+
+
+def tabular_rows_to_agro_fields(rows: list[tuple[str, str]]) -> dict[str, float]:
+    """[(label, raw_value), ...] → raw_fields AgroStar."""
+    out: dict[str, float] = {}
+    for label, raw in rows:
+        ag_key = map_tabular_label(label)
+        if not ag_key:
+            continue
+        val = _parse_float(raw)
+        if val is not None:
+            out[ag_key] = val
+    return out
+
+
+def sample_from_agro_fields(
+    *,
+    sample_no: str,
+    farm_name: str,
+    farm_id: str = "",
+    date_printed: str = "",
+    feed_type: str = "Mixed haylage",
+    desc_1: str = "",
+    desc_2: str = "",
+    desc_3: str = "",
+    agro_fields: dict[str, float],
+) -> AgrostarSample:
+    sample = AgrostarSample(
+        sample_no=sample_no,
+        farm_name=farm_name,
+        farm_id=farm_id,
+        date_printed=date_printed,
+        feed_type=feed_type,
+        desc_1=desc_1,
+        desc_2=desc_2,
+        desc_3=desc_3,
+        dry_matter_pct=agro_fields.get("DM"),
+    )
+    sample.raw_fields = {k: v for k, v in agro_fields.items() if k != "DM"}
+    return finalize_agrostar_sample(sample)
+
+
+def parse_tabular_triplet_lines(lines: list[str]) -> list[tuple[str, str, str]]:
+    """Строки PDF: name, unit (%DM|%CP|%), value."""
+    rows: list[tuple[str, str, str]] = []
+    i = 0
+    units = frozenset({"%DM", "%CP", "%"})
+    while i < len(lines):
+        line = lines[i]
+        if line in units and i >= 1:
+            name = lines[i - 1]
+            val = lines[i + 1] if i + 1 < len(lines) else ""
+            if val and (val[0].isdigit() or val.startswith("<")):
+                rows.append((name, line, val))
+                i += 2
+                continue
+        i += 1
+    return rows
+
+
+def rows_to_label_values(triplets: list[tuple[str, str, str]]) -> list[tuple[str, str]]:
+    return [(name, val) for name, _unit, val in triplets]

apps/api/app/modules/zootech/lab/etl/agrostar_xlsx_import.py

@@ -0,0 +1,72 @@
+"""AgroStar xlsx (сводка проб) → AgrostarParseResult."""
+
+from __future__ import annotations
+
+import io
+from typing import Any
+
+from openpyxl import load_workbook
+
+from app.modules.zootech.lab.etl.agrostar_tabular import map_tabular_label, sample_from_agro_fields, tabular_rows_to_agro_fields
+from app.modules.zootech.lab.etl.agrostar_xml_import import AgrostarParseResult, _parse_float
+
+
+def parse_agrostar_xlsx(data: bytes) -> AgrostarParseResult:
+    result = AgrostarParseResult(lab_name="АгроСтар")
+    try:
+        wb = load_workbook(io.BytesIO(data), data_only=True, read_only=False)
+    except Exception as exc:  # noqa: BLE001
+        result.errors.append(f"Не удалось открыть xlsx: {exc}")
+        return result
+
+    ws = wb.active
+    if ws.max_row < 4 or ws.max_column < 2:
+        result.errors.append("Пустой или неузнаваемый xlsx")
+        return result
+
+    sample_nos: list[str] = []
+    farms: list[str] = []
+    labels: list[str] = []
+    for col in range(2, ws.max_column + 1):
+        no = ws.cell(1, col).value
+        if no is None:
+            continue
+        sample_nos.append(str(no).strip())
+        farms.append(str(ws.cell(2, col).value or "").strip())
+        labels.append(str(ws.cell(3, col).value or "").strip())
+
+    if not sample_nos:
+        result.errors.append("В xlsx нет колонок с пробами")
+        return result
+
+    row_values: list[tuple[str, list[str]]] = []
+    for row in range(4, ws.max_row + 1):
+        label = ws.cell(row, 1).value
+        if label is None:
+            continue
+        label_str = str(label).strip()
+        if not label_str or not map_tabular_label(label_str):
+            continue
+        vals = []
+        for col in range(2, 2 + len(sample_nos)):
+            raw = ws.cell(row, col).value
+            vals.append("" if raw is None else str(raw))
+        row_values.append((label_str, vals))
+
+    for idx, sample_no in enumerate(sample_nos):
+        pairs = [(label, vals[idx]) for label, vals in row_values if idx < len(vals)]
+        agro_fields = tabular_rows_to_agro_fields(pairs)
+        if "DM" not in agro_fields:
+            result.errors.append(f"Проба {sample_no}: нет СВ")
+            continue
+        sample = sample_from_agro_fields(
+            sample_no=sample_no,
+            farm_name=farms[idx] if idx < len(farms) else "",
+            desc_1=labels[idx] if idx < len(labels) else sample_no,
+            agro_fields=agro_fields,
+        )
+        result.samples.append(sample)
+
+    if not result.samples and not result.errors:
+        result.errors.append("Не удалось разобрать пробы из xlsx")
+    return result

apps/api/app/modules/zootech/lab/etl/agrostar_xml_import.py

@@ -0,0 +1,787 @@
+"""Импорт лабораторных анализов AgroStar (Standard_XML_Data) → zootech nutrients WESP."""
+
+from __future__ import annotations
+
+import re
+import xml.etree.ElementTree as ET
+from dataclasses import dataclass, field
+from difflib import SequenceMatcher
+from typing import Any
+
+from app.modules.zootech.wesp_bridge_db import db
+from app.modules.zootech.lab.calc.feed_groups import classify_feed_group
+from app.modules.zootech.lab.services.component_nutrients import save_component_nutrients
+from app.modules.zootech.wesp_bridge_models import Component
+
+# AgroStar Name → (WESP заголовок «База сырья», kind)
+# pct_dm: %DM → г/кг СВ (×10); pct: процент как есть; extra: дублировать в доп. ключи
+_AGROSTAR_MAP: tuple[tuple[str, str, str], ...] = (
+    ("CP", "Сыр. Протеин", "pct_dm"),
+    ("NDF", "Сырая клетч", "pct_dm"),
+    ("ADF", "КДК", "pct_dm"),
+    ("Fat_EE", "Сырой жир", "pct_dm"),
+    ("Ash", "Сырая зола", "pct_dm"),
+    ("Ca", "Ca", "pct_dm"),
+    ("P", "P", "pct_dm"),
+    ("Mg", "Mg", "pct_dm"),
+    ("K", "K", "pct_dm"),
+    ("S", "S", "pct_dm"),
+    ("Cl", "CL", "pct_dm"),
+    ("Starch", "Крахмал", "pct_dm"),
+    ("Sugar_WSC", "Сахар", "pct_dm"),
+    ("NFC", "NFC", "pct_dm"),
+    ("Lys", "Лизин", "pct_dm"),
+    ("Met", "Метионин", "pct_dm"),
+    ("Leu", "Лейцин", "pct_dm"),
+    ("Ile", "Изолейцин", "pct_dm"),
+    ("Val", "Валин", "pct_dm"),
+    ("NDICP_CP", "%  нераствор протеин", "pct"),
+    ("TDN", "ВРХ Орг Вещ", "pct"),
+)
+
+_AGROSTAR_MAPPED_FIELDS = frozenset(key for key, _, _ in _AGROSTAR_MAP) | frozenset({"DM", "Sugar_ESC", "aNDFom"})
+
+_UNSUPPORTED_AGROSTAR: dict[str, str] = {
+    "NDFDom_IV_30hr": "нет поля для переваримости NDF",
+    "NDFDom_IV_12hr": "нет поля для переваримости NDF",
+    "NDFDom_IV_120hr": "нет поля для переваримости NDF",
+    "NDFDom_IV_240hr": "нет поля для переваримости NDF",
+}
+
+# Подписи AgroStar-полей, которые не пишем в WESP (для блока «Не попадёт»)
+_AGROSTAR_FIELD_RU: dict[str, str] = {
+    "Moisture": "Влажность",
+    "pH": "pH",
+    "SP_CP": "Растворимый протеин",
+    "SP": "Растворимый протеин (абс.)",
+    "ADICP": "КДК-протеин (AD-ICP)",
+    "ADICP_CP": "КДК-протеин, %CP",
+    "NDICP": "НДК-протеин (абс.)",
+    "NH3CPE": "Небелковый азот (NH₃-CP)",
+    "Lignin": "Лигнин",
+    "Lignin_NDF": "Лигнин, %NDF",
+    "TFA": "Жирные кислоты, всего",
+    "RFV": "RFV (относит. корм. ценность)",
+    "RFQ": "RFQ (относит. качество)",
+    "uNDFom_IV_12hr": "Нерасщепляемая NDF 12 ч",
+    "uNDFom_IV_30hr": "Нерасщепляемая NDF 30 ч",
+    "uNDFom_IV_120hr": "Нерасщепляемая NDF 120 ч",
+    "uNDFom_IV_240hr": "Нерасщепляемая NDF 240 ч",
+    "NDFDom_IV_12hr": "Переваримость NDF 12 ч",
+    "NDFDom_IV_30hr": "Переваримость NDF 30 ч",
+    "NDFDom_IV_120hr": "Переваримость NDF 120 ч",
+    "NDFDom_IV_240hr": "Переваримость NDF 240 ч",
+    "NELMcallb": "NEL (Mcalf/lb)",
+    "NELMcalkg": "NEL (Mcal/kg)",
+    "NELMJkg": "NEL (MJ/kg)",
+    "NEMMcallb": "NEM (Mcal/lb)",
+    "NEMMcalkg": "NEM (Mcal/kg)",
+    "NEMMJkg": "NEM (MJ/kg)",
+    "NEGMcallb": "NEG (Mcal/lb)",
+    "NEGMcalkg": "NEG (Mcal/kg)",
+    "NEGMJkg": "NEG (MJ/kg)",
+    "MilktonH": "Молоко/тонна (Holstein)",
+    "Horse_DE_Mcallb": "DE лошади (Mcal/lb)",
+    "Acetic": "Уксусная кислота",
+    "Propionic": "Пропионовая кислота",
+    "Butyric": "Масляная кислота",
+    "Lactic": "Молочная кислота",
+    "Total_acid": "Органические кислоты, всего",
+    "C160": "C16:0",
+    "C180": "C18:0",
+    "C181": "C18:1",
+    "C182": "C18:2",
+    "C183": "C18:3",
+    "C160_TFA": "C16:0, %TFA",
+    "C180_TFA": "C18:0, %TFA",
+    "C181_TFA": "C18:1, %TFA",
+    "C182_TFA": "C18:2, %TFA",
+    "C183_TFA": "C18:3, %TFA",
+    "His": "Гистидин",
+    "TAA": "Сумма аминокислот",
+    "Lys_CP": "Лизин, %CP",
+    "Met_CP": "Метионин, %CP",
+    "Leu_CP": "Лейцин, %CP",
+    "Ile_CP": "Изолейцин, %CP",
+    "Val_CP": "Валин, %CP",
+    "His_CP": "Гистидин, %CP",
+    "TAA_CP": "Сумма аминокислот, %CP",
+}
+
+_UNSUPPORTED_GROUP_ORDER: tuple[str, ...] = (
+    "переваримость NDF",
+    "энергия",
+    "кислоты силоса",
+    "жирные кислоты",
+    "дробный протеин",
+    "аминокислоты (%CP)",
+    "клетчатка и RF",
+    "качество силоса",
+    "прочее",
+)
+
+
+def _unsupported_group_key(ag_key: str) -> str:
+    if ag_key in {"Moisture", "pH"}:
+        return "качество силоса"
+    if ag_key.startswith(("NDFDom", "uNDFom")):
+        return "переваримость NDF"
+    if ag_key.startswith(("NEL", "NEM", "NEG", "Milk", "Horse")):
+        return "энергия"
+    if ag_key in {"Acetic", "Propionic", "Butyric", "Lactic", "Total_acid"}:
+        return "кислоты силоса"
+    if ag_key.startswith("C1") or ag_key == "TFA":
+        return "жирные кислоты"
+    if ag_key in {"SP_CP", "SP", "ADICP", "ADICP_CP", "NDICP", "NH3CPE"}:
+        return "дробный протеин"
+    if ag_key.endswith("_CP") or ag_key in {"His", "TAA"}:
+        return "аминокислоты (%CP)"
+    if ag_key.startswith(("Lignin", "RF")):
+        return "клетчатка и RF"
+    return "прочее"
+
+
+def _summarize_unsupported(
+    unsupported: list[dict[str, str]],
+    *,
+    nutrient_count: int,
+    total_in_report: int,
+) -> tuple[list[dict[str, int | str]], str]:
+    counts: dict[str, int] = {}
+    for row in unsupported:
+        group = _unsupported_group_key(row["agroKey"])
+        counts[group] = counts.get(group, 0) + 1
+
+    groups: list[dict[str, int | str]] = []
+    for label in _UNSUPPORTED_GROUP_ORDER:
+        count = counts.pop(label, 0)
+        if count:
+            groups.append({"label": label, "count": count})
+    for label, count in sorted(counts.items()):
+        groups.append({"label": label, "count": count})
+
+    skip = len(unsupported)
+    if skip == 0:
+        return groups, ""
+
+    parts = [f"{g['label']} ({g['count']})" for g in groups]
+    group_text = ", ".join(parts)
+    stored = nutrient_count + 0  # nutrients without DM line in count; DM stored separately
+    return (
+        groups,
+        f"В отчёте {total_in_report} показателей — запишем СВ и {stored} в карточку реагента. "
+        f"Ещё {skip} останутся только в AgroStar: {group_text}.",
+    )
+
+# WESP nutrient key → AgroStar raw field(s), первый найденный — для колонки «В документе»
+_PREVIEW_AGRO_SOURCE: dict[str, tuple[str, ...]] = {
+    "Сыр. Протеин": ("CP",),
+    "%  нераствор протеин": ("NDICP_CP",),
+    "Сырая клетч": ("NDF",),
+    "КДК": ("ADF",),
+    "Структур. клетч": ("aNDFom", "ADF"),
+    "Сырой жир": ("Fat_EE",),
+    "Сырая зола": ("Ash",),
+    "NFC": ("NFC",),
+    "Сахар": ("Sugar_WSC", "Sugar_ESC"),
+    "Крахмал": ("Starch",),
+    "Ca": ("Ca",),
+    "P": ("P",),
+    "Mg": ("Mg",),
+    "K": ("K",),
+    "S": ("S",),
+    "CL": ("Cl",),
+    "ВРХ Орг Вещ": ("TDN",),
+    "Лизин": ("Lys",),
+    "Метионин": ("Met",),
+    "Лейцин": ("Leu",),
+    "Изолейцин": ("Ile",),
+    "Валин": ("Val",),
+}
+
+_AGRO_SOURCE_UNIT: dict[str, str] = {
+    "CP": "%DM",
+    "NDF": "%DM",
+    "ADF": "%DM",
+    "aNDFom": "%DM",
+    "Fat_EE": "%DM",
+    "Ash": "%DM",
+    "Ca": "%DM",
+    "P": "%DM",
+    "Mg": "%DM",
+    "K": "%DM",
+    "S": "%DM",
+    "Cl": "%DM",
+    "Starch": "%DM",
+    "Sugar_WSC": "%DM",
+    "Sugar_ESC": "%DM",
+    "NFC": "%DM",
+    "TDN": "%DM",
+    "Lys": "%DM",
+    "Met": "%DM",
+    "Leu": "%DM",
+    "Ile": "%DM",
+    "Val": "%DM",
+    "NDICP_CP": "%CP",
+}
+
+_META_FIELDS = frozenset({"Sample_No", "Name", "Farm_ID", "Lot_name", "Date_Printed", "Type", "Desc_1", "Desc_2", "Desc_3", "Product_code"})
+
+_FEED_TYPE_RU: dict[str, str] = {
+    "Mixed haylage": "Смешанный сенаж",
+    "Haylage": "Сенаж",
+    "Corn silage": "Кукурузный силос",
+    "Grass silage": "Травяной силос",
+}
+
+_AGROSTAR_FEED_TO_COMPONENT_TYPE: dict[str, str] = {
+    "Mixed haylage": "Сочные корма",
+    "Haylage": "Сочные корма",
+    "Corn silage": "Сочные корма",
+    "Grass silage": "Сочные корма",
+}
+
+_FEED_GROUP_TO_COMPONENT_TYPE: dict[str, str] = {
+    "rough": "Грубые корма",
+    "succulent": "Сочные корма",
+    "concentrate": "Концентрированные",
+    "other": "Добавки",
+}
+
+_WARNING_RU: dict[str, str] = {
+    "dm_missing": "В файле нет сухого вещества (DM)",
+    "omd_missing": "Нет данных для ВРХ — при расчёте подставится дефолт WESP",
+    "omd_from_tdn": "ВРХ орг. вещ. оценили по TDN, не прямой OMD из лаборатории",
+}
+
+# Порядок показа в «понюхали»: (ключ WESP, подпись, единица)
+_PREVIEW_WRITE_ORDER: tuple[tuple[str, str, str], ...] = (
+    ("__dry_matter__", "Сухое вещество (в карточку компонента)", "%"),
+    ("Сыр. Протеин", "Сырой протеин", "г/кг СВ"),
+    ("%  нераствор протеин", "Нерастворимый протеин, %", "%"),
+    ("Сырая клетч", "Сырая клетчатка (NDF)", "г/кг СВ"),
+    ("КДК", "Кислая детергентная клетчатка (ADF)", "г/кг СВ"),
+    ("Структур. клетч", "Структурная клетчатка", "г/кг СВ"),
+    ("Сырой жир", "Сырой жир", "г/кг СВ"),
+    ("Сырая зола", "Сырая зола", "г/кг СВ"),
+    ("NFC", "Безволокнистые углеводы (NFC)", "г/кг СВ"),
+    ("Сахар", "Сахар (WSC/ESC)", "г/кг СВ"),
+    ("Крахмал", "Крахмал", "г/кг СВ"),
+    ("Ca", "Кальций", "г/кг СВ"),
+    ("P", "Фосфор", "г/кг СВ"),
+    ("Mg", "Магний", "г/кг СВ"),
+    ("K", "Калий", "г/кг СВ"),
+    ("S", "Сера", "г/кг СВ"),
+    ("CL", "Хлор", "г/кг СВ"),
+    ("ВРХ Орг Вещ", "Переваримость ОВ (из TDN)", "%"),
+    ("Лизин", "Лизин", "г/кг СВ"),
+    ("Метионин", "Метионин", "г/кг СВ"),
+    ("Лейцин", "Лейцин", "г/кг СВ"),
+    ("Изолейцин", "Изолейцин", "г/кг СВ"),
+    ("Валин", "Валин", "г/кг СВ"),
+)
+
+
+def _parse_float(value: str | None) -> float | None:
+    if value is None:
+        return None
+    text = str(value).strip().replace(",", ".")
+    if not text or text.startswith("<"):
+        return None
+    try:
+        n = float(text)
+    except ValueError:
+        return None
+    return None if n != n else n
+
+
+def _normalize_label(value: str) -> str:
+    return re.sub(r"\s+", " ", (value or "").strip().lower())
+
+
+def _pct_dm_to_g_per_kg_sv(pct_dm: float) -> float:
+    return round(pct_dm * 10.0, 4)
+
+
+@dataclass
+class AgrostarSample:
+    sample_no: str
+    farm_name: str
+    farm_id: str
+    date_printed: str
+    feed_type: str
+    desc_1: str
+    desc_2: str
+    desc_3: str
+    dry_matter_pct: float | None
+    raw_fields: dict[str, float] = field(default_factory=dict)
+    nutrients: dict[str, float] = field(default_factory=dict)
+    warnings: list[str] = field(default_factory=list)
+
+    @property
+    def label(self) -> str:
+        parts = [p for p in (self.desc_1, self.desc_2, self.desc_3) if p]
+        return parts[0] if parts else self.sample_no
+
+    def to_api_dict(self) -> dict[str, Any]:
+        return {
+            "sampleNo": self.sample_no,
+            "farmName": self.farm_name,
+            "farmId": self.farm_id,
+            "datePrinted": self.date_printed,
+            "feedType": self.feed_type,
+            "desc1": self.desc_1,
+            "desc2": self.desc_2,
+            "desc3": self.desc_3,
+            "label": self.label,
+            "dryMatterPct": self.dry_matter_pct,
+            "nutrients": self.nutrients,
+            "warnings": self.warnings,
+            "rawFieldCount": len(self.raw_fields),
+        }
+
+
+@dataclass
+class AgrostarParseResult:
+    lab_name: str
+    samples: list[AgrostarSample] = field(default_factory=list)
+    errors: list[str] = field(default_factory=list)
+
+    def to_api_dict(self) -> dict[str, Any]:
+        return {
+            "labName": self.lab_name,
+            "samples": [s.to_api_dict() for s in self.samples],
+            "errors": self.errors,
+            "sampleCount": len(self.samples),
+        }
+
+
+@dataclass
+class ComponentMatch:
+    component_id: str
+    name: str
+    score: float
+
+    def to_api_dict(self) -> dict[str, Any]:
+        return {"componentId": self.component_id, "name": self.name, "score": round(self.score, 3)}
+
+
+@dataclass
+class ApplyAssignmentResult:
+    sample_no: str
+    component_id: str | None
+    component_name: str | None
+    dry_matter_pct: float | None
+    nutrient_count: int
+    warnings: list[str]
+    skipped: bool = False
+    reason: str | None = None
+
+    def to_api_dict(self) -> dict[str, Any]:
+        return {
+            "sampleNo": self.sample_no,
+            "componentId": self.component_id,
+            "componentName": self.component_name,
+            "dryMatterPct": self.dry_matter_pct,
+            "nutrientCount": self.nutrient_count,
+            "warnings": self.warnings,
+            "skipped": self.skipped,
+            "reason": self.reason,
+        }
+
+
+@dataclass
+class ApplyResult:
+    applied: int = 0
+    skipped: int = 0
+    results: list[ApplyAssignmentResult] = field(default_factory=list)
+    dry_run: bool = False
+
+    def to_api_dict(self) -> dict[str, Any]:
+        return {
+            "applied": self.applied,
+            "skipped": self.skipped,
+            "dryRun": self.dry_run,
+            "results": [r.to_api_dict() for r in self.results],
+        }
+
+
+def _apply_nutrients_from_raw(sample: AgrostarSample) -> AgrostarSample:
+    nutrients: dict[str, float] = {}
+    for ag_key, wesp_key, kind in _AGROSTAR_MAP:
+        val = sample.raw_fields.get(ag_key)
+        if val is None:
+            continue
+        if kind == "pct_dm":
+            nutrients[wesp_key] = _pct_dm_to_g_per_kg_sv(val)
+        elif kind == "pct":
+            nutrients[wesp_key] = round(val, 4)
+
+    if "Sugar_ESC" in sample.raw_fields and "Sugar_WSC" not in sample.raw_fields:
+        esc = sample.raw_fields["Sugar_ESC"]
+        nutrients["Сахар"] = _pct_dm_to_g_per_kg_sv(esc)
+
+    struct_raw = sample.raw_fields.get("aNDFom")
+    if struct_raw is None:
+        struct_raw = sample.raw_fields.get("ADF")
+    if struct_raw is not None:
+        nutrients["Структур. клетч"] = _pct_dm_to_g_per_kg_sv(struct_raw)
+
+    if "Структур. клетч" in nutrients:
+        nutrients["Структур клетч"] = nutrients["Структур. клетч"]
+
+    sample.nutrients = nutrients
+
+    if sample.dry_matter_pct is None:
+        sample.warnings.append("dm_missing")
+    if "ВРХ Орг Вещ" not in nutrients and "TDN" not in sample.raw_fields:
+        sample.warnings.append("omd_missing")
+    elif "TDN" in sample.raw_fields:
+        sample.warnings.append("omd_from_tdn")
+
+    ndfd30 = sample.raw_fields.get("NDFDom_IV_30hr")
+    if ndfd30 is not None:
+        sample.warnings.append(f"ndfd30={ndfd30}")
+
+    return sample
+
+
+def finalize_agrostar_sample(sample: AgrostarSample) -> AgrostarSample:
+    """Общий финал: raw_fields → nutrients + warnings (XML/PDF/xlsx)."""
+    return _apply_nutrients_from_raw(sample)
+
+
+def _read_sample_block(block: ET.Element) -> AgrostarSample:
+    fields: dict[str, str] = {}
+    for child in block:
+        name = child.get("Name") or child.tag
+        fields[name] = child.get("Value") or ""
+
+    sample = AgrostarSample(
+        sample_no=fields.get("Sample_No", ""),
+        farm_name=fields.get("Name", ""),
+        farm_id=fields.get("Farm_ID", ""),
+        date_printed=fields.get("Date_Printed", ""),
+        feed_type=fields.get("Type", ""),
+        desc_1=fields.get("Desc_1", ""),
+        desc_2=fields.get("Desc_2", ""),
+        desc_3=fields.get("Desc_3", ""),
+        dry_matter_pct=_parse_float(fields.get("DM")),
+    )
+
+    for key, raw in fields.items():
+        if key in _META_FIELDS or key == "DM":
+            continue
+        val = _parse_float(raw)
+        if val is not None:
+            sample.raw_fields[key] = val
+
+    return finalize_agrostar_sample(sample)
+
+
+def parse_agrostar_xml(text: str) -> AgrostarParseResult:
+    result = AgrostarParseResult(lab_name="")
+    if not (text or "").strip():
+        result.errors.append("Пустой файл")
+        return result
+    try:
+        root = ET.fromstring(text)
+    except ET.ParseError as exc:
+        result.errors.append(f"Некорректный XML: {exc}")
+        return result
+
+    if root.tag != "Standard_XML_Data":
+        result.errors.append(f"Ожидался Standard_XML_Data, получен {root.tag}")
+
+    for child in root:
+        tag = child.tag
+        if tag == "Lab_Name":
+            result.lab_name = child.get("Value") or ""
+        elif tag == "Sample_Data":
+            try:
+                result.samples.append(_read_sample_block(child))
+            except Exception as exc:  # noqa: BLE001 — собрать все пробы
+                result.errors.append(f"Ошибка пробы: {exc}")
+
+    if not result.samples and not result.errors:
+        result.errors.append("В файле нет блоков Sample_Data")
+
+    return result
+
+
+def _match_score(label: str, component_name: str) -> float:
+    a = _normalize_label(label)
+    b = _normalize_label(component_name)
+    if not a or not b:
+        return 0.0
+    if a in b or b in a:
+        return 0.95
+    # ключевые токены: яма, номер, силос
+    tokens_a = set(re.findall(r"[a-zа-яё0-9]+", a, re.I))
+    tokens_b = set(re.findall(r"[a-zа-яё0-9]+", b, re.I))
+    if tokens_a and tokens_b:
+        overlap = len(tokens_a & tokens_b) / max(len(tokens_a), len(tokens_b))
+        if overlap >= 0.4:
+            return 0.5 + overlap * 0.4
+    return SequenceMatcher(None, a, b).ratio()
+
+
+def suggest_component_matches(
+    label: str,
+    *,
+    limit: int = 8,
+    min_score: float = 0.25,
+) -> list[ComponentMatch]:
+    components = (
+        Component.query.filter(Component.is_deleted.is_(False), Component.is_active.is_(True))
+        .order_by(Component.name)
+        .all()
+    )
+    scored: list[ComponentMatch] = []
+    for comp in components:
+        score = _match_score(label, comp.name or "")
+        if score >= min_score:
+            scored.append(ComponentMatch(comp.id, comp.name or "", score))
+    scored.sort(key=lambda m: (-m.score, m.name.lower()))
+    return scored[:limit]
+
+
+def _fmt_preview_num(value: float) -> str:
+    rounded = round(value, 2)
+    if abs(rounded - round(rounded)) < 0.01:
+        return f"{round(rounded):g}"
+    text = f"{rounded:.2f}".rstrip("0").rstrip(".")
+    return text or "0"
+
+
+def _preview_source_for_key(sample: AgrostarSample, wesp_key: str) -> tuple[float | None, str]:
+    for ag_key in _PREVIEW_AGRO_SOURCE.get(wesp_key, ()):
+        val = sample.raw_fields.get(ag_key)
+        if val is not None:
+            return val, _AGRO_SOURCE_UNIT.get(ag_key, "%DM")
+    return None, ""
+
+
+def suggest_canonical_feed_type(sample: AgrostarSample) -> str:
+    """Канонический component.type для POST /api/components."""
+    mapped = _AGROSTAR_FEED_TO_COMPONENT_TYPE.get(sample.feed_type)
+    if mapped:
+        return mapped
+    stub = Component(name=sample.label, type="")
+    group = classify_feed_group(stub)
+    return _FEED_GROUP_TO_COMPONENT_TYPE.get(group, "Сочные корма")
+
+
+def _warning_to_text(code: str) -> str:
+    if code in _WARNING_RU:
+        return _WARNING_RU[code]
+    if code.startswith("ndfd30="):
+        val = code.split("=", 1)[1]
+        return _UNSUPPORTED_AGROSTAR.get("NDFDom_IV_30hr", f"ndfd30={val}")
+    return code
+
+
+def build_storage_report(sample: AgrostarSample) -> dict[str, Any]:
+    """Сводка: что сохранится в WESP и что из AgroStar не мапится."""
+    unsupported: list[dict[str, str]] = []
+    for ag_key, val in sample.raw_fields.items():
+        if ag_key in _AGROSTAR_MAPPED_FIELDS:
+            continue
+        label = _AGROSTAR_FIELD_RU.get(ag_key, ag_key)
+        reason = _UNSUPPORTED_AGROSTAR.get(ag_key, "нет поля в WESP")
+        unsupported.append(
+            {
+                "agroKey": ag_key,
+                "label": label,
+                "value": _fmt_preview_num(val),
+                "reason": reason,
+            }
+        )
+    unsupported.sort(key=lambda row: row["label"].lower())
+
+    nutrient_keys = set(sample.nutrients)
+    stored_count = len(nutrient_keys) + (1 if sample.dry_matter_pct is not None else 0)
+    total_in_report = len(sample.raw_fields) + (1 if sample.dry_matter_pct is not None else 0)
+    groups, unsupported_message = _summarize_unsupported(
+        unsupported,
+        nutrient_count=len(nutrient_keys),
+        total_in_report=total_in_report,
+    )
+    return {
+        "storedCount": stored_count,
+        "nutrientCount": len(nutrient_keys),
+        "unsupportedCount": len(unsupported),
+        "unsupportedGroups": groups,
+        "unsupportedMessage": unsupported_message,
+        "unsupported": unsupported,
+    }
+
+
+def build_sample_preview(sample: AgrostarSample) -> dict[str, Any]:
+    """Человекочитаемый разбор пробы для UI «Сначала понюхаем»."""
+    feed_ru = _FEED_TYPE_RU.get(sample.feed_type, sample.feed_type or "—")
+    desc_parts = [p for p in (sample.desc_1, sample.desc_2, sample.desc_3) if p]
+    description = " · ".join(desc_parts) if desc_parts else sample.label
+
+    meta: list[dict[str, str]] = [
+        {"label": "№ пробы", "value": sample.sample_no or "—"},
+        {"label": "Хозяйство", "value": sample.farm_name or "—"},
+        {"label": "Код хозяйства", "value": sample.farm_id or "—"},
+        {"label": "Дата отчёта", "value": sample.date_printed or "—"},
+        {"label": "Тип корма", "value": feed_ru},
+        {"label": "Описание", "value": description},
+    ]
+
+    will_write: list[dict[str, str]] = []
+    if sample.dry_matter_pct is not None:
+        will_write.append(
+            {
+                "label": "Сухое вещество (поле компонента)",
+                "value": _fmt_preview_num(sample.dry_matter_pct),
+                "unit": "%",
+                "sourceValue": _fmt_preview_num(sample.dry_matter_pct),
+                "sourceUnit": "%",
+            }
+        )
+    for key, label, unit in _PREVIEW_WRITE_ORDER:
+        if key == "__dry_matter__":
+            continue
+        val = sample.nutrients.get(key)
+        if val is None:
+            continue
+        row: dict[str, str] = {"label": label, "value": _fmt_preview_num(val), "unit": unit}
+        source_val, source_unit = _preview_source_for_key(sample, key)
+        if source_val is not None:
+            row["sourceValue"] = _fmt_preview_num(source_val)
+            row["sourceUnit"] = source_unit
+        will_write.append(row)
+
+    notes = [_warning_to_text(w) for w in sample.warnings]
+    storage = build_storage_report(sample)
+
+    return {
+        "title": sample.label,
+        "feedTypeRu": feed_ru,
+        "suggestedName": sample.label,
+        "suggestedType": suggest_canonical_feed_type(sample),
+        "meta": meta,
+        "willWrite": will_write,
+        "notes": notes,
+        "recognizedCount": len(will_write),
+        "storage": storage,
+    }
+
+
+def enrich_parse_with_matches(parse: AgrostarParseResult) -> dict[str, Any]:
+    body = parse.to_api_dict()
+    for i, sample in enumerate(parse.samples):
+        matches = suggest_component_matches(sample.label)
+        body["samples"][i]["suggestedComponents"] = [m.to_api_dict() for m in matches]
+        body["samples"][i]["preview"] = build_sample_preview(sample)
+    return body
+
+
+def apply_agrostar_import(
+    assignments: list[dict[str, Any]],
+    *,
+    user_id: str = "agrostar-import",
+    dry_run: bool = False,
+) -> ApplyResult:
+    """assignments: [{sampleNo, componentId}] — данные проб из предыдущего parse или повторный parse."""
+    result = ApplyResult(dry_run=dry_run)
+    by_sample: dict[str, dict[str, Any]] = {
+        str(a.get("sampleNo") or a.get("sample_no") or ""): a for a in assignments
+    }
+
+    for sample_no, row in by_sample.items():
+        if not sample_no:
+            result.skipped += 1
+            result.results.append(
+                ApplyAssignmentResult(
+                    sample_no="",
+                    component_id=None,
+                    component_name=None,
+                    dry_matter_pct=None,
+                    nutrient_count=0,
+                    warnings=[],
+                    skipped=True,
+                    reason="missing_sample_no",
+                )
+            )
+            continue
+
+        component_id = row.get("componentId") or row.get("component_id")
+        nutrients = row.get("nutrients") or {}
+        dry_matter = row.get("dryMatterPct") or row.get("dry_matter_pct")
+
+        if not component_id:
+            result.skipped += 1
+            result.results.append(
+                ApplyAssignmentResult(
+                    sample_no=sample_no,
+                    component_id=None,
+                    component_name=None,
+                    dry_matter_pct=dry_matter,
+                    nutrient_count=len(nutrients),
+                    warnings=list(row.get("warnings") or []),
+                    skipped=True,
+                    reason="no_component",
+                )
+            )
+            continue
+
+        comp = Component.query.filter_by(id=component_id, is_deleted=False).first()
+        if comp is None:
+            result.skipped += 1
+            result.results.append(
+                ApplyAssignmentResult(
+                    sample_no=sample_no,
+                    component_id=component_id,
+                    component_name=None,
+                    dry_matter_pct=dry_matter,
+                    nutrient_count=len(nutrients),
+                    warnings=[],
+                    skipped=True,
+                    reason="component_not_found",
+                )
+            )
+            continue
+
+        warnings = list(row.get("warnings") or [])
+        if dry_run:
+            result.applied += 1
+            result.results.append(
+                ApplyAssignmentResult(
+                    sample_no=sample_no,
+                    component_id=comp.id,
+                    component_name=comp.name,
+                    dry_matter_pct=dry_matter,
+                    nutrient_count=len(nutrients),
+                    warnings=warnings,
+                )
+            )
+            continue
+
+        if dry_matter is not None:
+            try:
+                comp.dry_matter = float(dry_matter)
+            except (TypeError, ValueError):
+                warnings.append("invalid_dry_matter")
+
+        save_component_nutrients(comp.id, nutrients, user_id=user_id)
+        comp.updated_by = user_id
+        db.session.commit()
+
+        result.applied += 1
+        result.results.append(
+            ApplyAssignmentResult(
+                sample_no=sample_no,
+                component_id=comp.id,
+                component_name=comp.name,
+                dry_matter_pct=comp.dry_matter,
+                nutrient_count=len(nutrients),
+                warnings=warnings,
+            )
+        )
+
+    return result

apps/api/app/modules/zootech/lab/etl/lab_import_router.py

@@ -0,0 +1,243 @@
+"""Единый роутер импорта lab: XML / PDF / xlsx → preview API."""
+
+from __future__ import annotations
+
+import re
+from typing import Any
+
+from app.modules.zootech.lab.etl.agrostar_pdf_import import looks_like_agrostar_pdf, parse_agrostar_pdf
+from app.modules.zootech.lab.etl.agrostar_xlsx_import import parse_agrostar_xlsx
+from app.modules.zootech.lab.etl.agrostar_xml_import import AgrostarParseResult, enrich_parse_with_matches, parse_agrostar_xml
+from app.modules.zootech.lab.etl.plinor_pdf_import import (
+    enrich_plinor_composition,
+    is_plinor_sos,
+    is_plinor_zoo,
+    parse_plinor_sos,
+    parse_plinor_zoo,
+)
+
+SOURCE_LABELS: dict[str, str] = {
+    "agrostar_xml": "AgroStar XML",
+    "agrostar_pdf": "AgroStar PDF",
+    "agrostar_xlsx": "AgroStar Excel",
+    "plinor_sos": "ПЛИНОР — состав рациона",
+    "plinor_zoo": "ПЛИНОР — показатели рациона",
+}
+
+_PDF_MAGIC = b"%PDF"
+_XLSX_MAGIC = b"PK\x03\x04"
+_XML_MARKERS = (b"Standard_XML_Data", b"<?xml", b"<Standard_XML_Data")
+
+
+def _filename_hint_plinor_zoo(name: str) -> bool:
+    n = (name or "").lower()
+    return "зоо" in n or re.search(r"(^|[^a-z])zoo([^a-z]|$)", n) is not None
+
+
+def _filename_hint_plinor_sos(name: str) -> bool:
+    n = (name or "").lower()
+    return "сос" in n or re.search(r"(^|[^a-z])sos([^a-z]|$)", n) is not None
+
+
+def _looks_like_xml_agrostar(data: bytes) -> bool:
+    head = data[:16384]
+    return any(marker in head for marker in _XML_MARKERS)
+
+
+def _looks_like_xlsx(data: bytes) -> bool:
+    return data.startswith(_XLSX_MAGIC)
+
+
+def _looks_like_pdf(data: bytes) -> bool:
+    return data.startswith(_PDF_MAGIC)
+
+
+def _pdf_import_available() -> bool:
+    try:
+        import fitz  # noqa: F401
+    except ImportError:
+        return False
+    return True
+
+
+def detect_source_format(filename: str, data: bytes) -> str:
+    """Определение формата по содержимому и имени (расширение — запасной вариант)."""
+    name = (filename or "").lower()
+
+    if _looks_like_pdf(data):
+        return _detect_pdf_format(data, name)
+    if _looks_like_xlsx(data):
+        return "agrostar_xlsx"
+    if _looks_like_xml_agrostar(data):
+        return "agrostar_xml"
+
+    if name.endswith(".xml"):
+        return "agrostar_xml"
+    if name.endswith(".xlsx") or name.endswith(".xls"):
+        return "agrostar_xlsx"
+    if name.endswith(".pdf"):
+        return _detect_pdf_format(data, name)
+    return "unknown"
+
+
+def _detect_pdf_format(data: bytes, name: str) -> str:
+    head = data[:8192]
+    if b"Standard_XML_Data" in head:
+        return "agrostar_xml"
+    if looks_like_agrostar_pdf(data):
+        return "agrostar_pdf"
+
+    pdf_text = ""
+    if _pdf_import_available():
+        try:
+            pdf_text = _peek_pdf_text(data)
+        except Exception:  # noqa: BLE001
+            pdf_text = ""
+
+    if pdf_text:
+        if is_plinor_zoo(pdf_text):
+            return "plinor_zoo"
+        if is_plinor_sos(pdf_text):
+            return "plinor_sos"
+        if looks_like_agrostar_pdf(data):
+            return "agrostar_pdf"
+
+    if _filename_hint_plinor_zoo(name):
+        return "plinor_zoo"
+    if _filename_hint_plinor_sos(name):
+        return "plinor_sos"
+    return "unknown_pdf"
+
+
+def _peek_pdf_text(data: bytes) -> str:
+    try:
+        import fitz
+    except ImportError as exc:
+        raise RuntimeError("Для PDF нужен pymupdf (pip install pymupdf)") from exc
+    doc = fitz.open(stream=data, filetype="pdf")
+    text = doc[0].get_text() if doc.page_count else ""
+    doc.close()
+    return text
+
+
+def parse_lab_import(filename: str, data: bytes) -> dict[str, Any]:
+    """Разбор файла → единый JSON для /lab комбайна."""
+    if not data:
+        return {"error": True, "message": "Пустой файл", "kind": "error"}
+
+    fmt = detect_source_format(filename, data)
+    source_label = SOURCE_LABELS.get(fmt, "Неизвестный формат")
+
+    if fmt == "agrostar_xml":
+        text = _decode_text(data)
+        parsed = parse_agrostar_xml(text)
+        if parsed.errors and not parsed.samples:
+            return {
+                "error": True,
+                "message": "; ".join(parsed.errors),
+                "kind": "error",
+                "sourceFormat": fmt,
+                **parsed.to_api_dict(),
+            }
+        body = enrich_parse_with_matches(parsed)
+        body["kind"] = "lab_samples"
+        body["sourceFormat"] = fmt
+        body["sourceLabel"] = source_label
+        body["fileName"] = filename
+        if parsed.errors:
+            body["warnings"] = parsed.errors
+        return body
+
+    if fmt == "agrostar_pdf":
+        parsed = parse_agrostar_pdf(data)
+        return _lab_samples_response(parsed, fmt, source_label, filename)
+
+    if fmt == "agrostar_xlsx":
+        parsed = parse_agrostar_xlsx(data)
+        return _lab_samples_response(parsed, fmt, source_label, filename)
+
+    if fmt == "plinor_sos":
+        parsed = parse_plinor_sos(data)
+        if parsed.errors and not parsed.feed_lines:
+            return {
+                "error": True,
+                "message": "; ".join(parsed.errors),
+                "kind": "error",
+                "sourceFormat": fmt,
+            }
+        body = enrich_plinor_composition(parsed.to_api_dict())
+        body["kind"] = "ration_composition"
+        body["sourceFormat"] = fmt
+        body["sourceLabel"] = source_label
+        body["fileName"] = filename
+        if parsed.errors:
+            body["warnings"] = parsed.errors
+        return body
+
+    if fmt == "plinor_zoo":
+        parsed = parse_plinor_zoo(data)
+        if parsed.errors and not parsed.indicators:
+            return {
+                "error": True,
+                "message": "; ".join(parsed.errors),
+                "kind": "error",
+                "sourceFormat": fmt,
+            }
+        body = parsed.to_api_dict()
+        body["kind"] = "ration_indicators"
+        body["sourceFormat"] = fmt
+        body["sourceLabel"] = source_label
+        body["fileName"] = filename
+        if parsed.errors:
+            body["warnings"] = parsed.errors
+        return body
+
+    if fmt == "unknown_pdf" and _looks_like_pdf(data) and not _pdf_import_available():
+        return {
+            "error": True,
+            "message": "Для PDF нужен pymupdf — выполните: pip install pymupdf",
+            "kind": "error",
+            "sourceFormat": fmt,
+            "fileName": filename,
+        }
+
+    return {
+        "error": True,
+        "message": "Формат не распознан. Поддерживаются: AgroStar xml/pdf/xlsx, ПЛИНОР сос/зоо pdf",
+        "kind": "error",
+        "sourceFormat": fmt,
+        "fileName": filename,
+    }
+
+
+def _lab_samples_response(
+    parsed: AgrostarParseResult,
+    fmt: str,
+    source_label: str,
+    filename: str,
+) -> dict[str, Any]:
+    if parsed.errors and not parsed.samples:
+        return {
+            "error": True,
+            "message": "; ".join(parsed.errors),
+            "kind": "error",
+            "sourceFormat": fmt,
+            **parsed.to_api_dict(),
+        }
+    body = enrich_parse_with_matches(parsed)
+    body["kind"] = "lab_samples"
+    body["sourceFormat"] = fmt
+    body["sourceLabel"] = source_label
+    body["fileName"] = filename
+    if parsed.errors:
+        body["warnings"] = parsed.errors
+    return body
+
+
+def _decode_text(data: bytes) -> str:
+    for encoding in ("utf-8", "utf-8-sig", "cp1251"):
+        try:
+            return data.decode(encoding)
+        except UnicodeDecodeError:
+            continue
+    return data.decode("utf-8", errors="replace")

apps/api/app/modules/zootech/lab/etl/plinor_pdf_import.py

@@ -0,0 +1,215 @@
+"""ПЛИНОР PDF (сос / зоо) → состав рациона или показатели."""
+
+from __future__ import annotations
+
+import re
+from dataclasses import dataclass, field
+from typing import Any
+
+from app.modules.zootech.lab.etl.agrostar_xml_import import _parse_float, suggest_component_matches
+
+
+def _pdf_text(data: bytes) -> str:
+    try:
+        import fitz
+    except ImportError as exc:
+        raise RuntimeError("Для PDF нужен pymupdf (pip install pymupdf)") from exc
+    doc = fitz.open(stream=data, filetype="pdf")
+    parts = [doc[i].get_text() for i in range(doc.page_count)]
+    doc.close()
+    return "\n".join(parts)
+
+
+def _meta_from_lines(lines: list[str]) -> dict[str, str]:
+    meta: dict[str, str] = {}
+    for line in lines:
+        for key, prefix in (
+            ("group", "Группа:"),
+            ("farm", "Хозяйство:"),
+            ("region", "Район:"),
+            ("rationDate", "Дата рациона:"),
+            ("calcDate", "Дата расчетов:"),
+        ):
+            if line.startswith(prefix):
+                meta[key] = line.split(":", 1)[1].strip()
+        if line.startswith("Суточный удой"):
+            m = re.search(r"(\d+(?:[,\.]\d+)?)", line)
+            if m:
+                meta["milkYieldKg"] = m.group(1).replace(",", ".")
+    for i, line in enumerate(lines):
+        if line.startswith("Цена (руб.)") and i + 1 < len(lines):
+            meta["totalCostRub"] = lines[i + 1].replace(",", ".")
+        if line == "Масса (кг)" and i + 1 < len(lines):
+            meta["totalMassKg"] = lines[i + 1].replace(",", ".")
+    return meta
+
+
+@dataclass
+class PlinorFeedLine:
+    feed_name: str
+    daily_kg: float
+    cost_rub: float
+
+    def to_api_dict(self) -> dict[str, Any]:
+        return {"feedName": self.feed_name, "dailyKg": self.daily_kg, "costRub": self.cost_rub}
+
+
+@dataclass
+class PlinorIndicatorRow:
+    name: str
+    norm: float | None
+    current: float | None
+
+    def to_api_dict(self) -> dict[str, Any]:
+        return {"name": self.name, "norm": self.norm, "current": self.current}
+
+
+@dataclass
+class PlinorCompositionResult:
+    meta: dict[str, str] = field(default_factory=dict)
+    feed_lines: list[PlinorFeedLine] = field(default_factory=list)
+    errors: list[str] = field(default_factory=list)
+
+    def to_api_dict(self) -> dict[str, Any]:
+        return {
+            "meta": self.meta,
+            "feedLines": [line.to_api_dict() for line in self.feed_lines],
+            "feedCount": len(self.feed_lines),
+            "errors": self.errors,
+        }
+
+
+@dataclass
+class PlinorIndicatorsResult:
+    meta: dict[str, str] = field(default_factory=dict)
+    indicators: list[PlinorIndicatorRow] = field(default_factory=list)
+    errors: list[str] = field(default_factory=list)
+
+    def to_api_dict(self) -> dict[str, Any]:
+        return {
+            "meta": self.meta,
+            "indicators": [row.to_api_dict() for row in self.indicators],
+            "indicatorCount": len(self.indicators),
+            "errors": self.errors,
+        }
+
+
+def _is_plinor(text: str) -> bool:
+    return "ПЛИНОР" in (text or "") or 'ИАС "РАЦИОНЫ"' in (text or "")
+
+
+def is_plinor_sos(text: str) -> bool:
+    if not _is_plinor(text):
+        return False
+    if "Состав рациона" in text:
+        return True
+    return bool(re.search(r"Таблица 1\.1(?!0)", text))
+
+
+def is_plinor_zoo(text: str) -> bool:
+    if not _is_plinor(text):
+        return False
+    if "Зоотехнические показатели" in text:
+        return True
+    return "Таблица 1.10" in text
+
+
+def parse_plinor_sos(data: bytes) -> PlinorCompositionResult:
+    result = PlinorCompositionResult()
+    try:
+        text = _pdf_text(data)
+    except RuntimeError as exc:
+        result.errors.append(str(exc))
+        return result
+    if not is_plinor_sos(text):
+        result.errors.append("Не похоже на ПЛИНОР «Состав рациона» (табл. 1.1)")
+        return result
+
+    lines = [ln.strip() for ln in text.splitlines() if ln.strip()]
+    result.meta = _meta_from_lines(lines)
+
+    feeds: list[PlinorFeedLine] = []
+    i = 0
+    while i < len(lines):
+        if lines[i] == "кг" and i >= 1 and i + 2 < len(lines):
+            name = lines[i - 1]
+            amt = _parse_float(lines[i + 1])
+            cost = _parse_float(lines[i + 2])
+            if (
+                amt is not None
+                and cost is not None
+                and name not in ("Дача", "изм.", "Корма")
+                and "кг" not in name.lower()
+            ):
+                feeds.append(PlinorFeedLine(name, amt, cost))
+            i += 3
+            continue
+        i += 1
+
+    if not feeds:
+        result.errors.append("Не найдены строки кормов")
+    result.feed_lines = feeds
+    return result
+
+
+def parse_plinor_zoo(data: bytes) -> PlinorIndicatorsResult:
+    result = PlinorIndicatorsResult()
+    try:
+        text = _pdf_text(data)
+    except RuntimeError as exc:
+        result.errors.append(str(exc))
+        return result
+    if not is_plinor_zoo(text):
+        result.errors.append("Не похоже на ПЛИНОР «Зоотехнические показатели» (табл. 1.10)")
+        return result
+
+    lines = [ln.strip() for ln in text.splitlines() if ln.strip()]
+    result.meta = _meta_from_lines(lines)
+
+    skip_prefixes = (
+        "Район:",
+        "Хозяйство:",
+        "Ферма:",
+        "Двор:",
+        "Подразделение:",
+        "Группа:",
+        "Суточный",
+        "Стадия",
+        "Живая",
+        "Система",
+        "Конц.",
+        "Кр.опт.",
+        "Дата",
+    )
+    skip_exact = frozenset(
+        {"1", "2", "3", "По польз.", "норме", "Текущий рацион", "Наименование", "Значение"}
+    )
+
+    rows: list[PlinorIndicatorRow] = []
+    i = 0
+    while i < len(lines):
+        line = lines[i]
+        if line in skip_exact or any(line.startswith(p) for p in skip_prefixes):
+            i += 1
+            continue
+        if i + 2 < len(lines) and len(line) > 8:
+            norm = _parse_float(lines[i + 1])
+            cur = _parse_float(lines[i + 2])
+            if norm is not None and cur is not None:
+                rows.append(PlinorIndicatorRow(line, norm, cur))
+                i += 3
+                continue
+        i += 1
+
+    if not rows:
+        result.errors.append("Не найдены показатели рациона")
+    result.indicators = rows
+    return result
+
+
+def enrich_plinor_composition(body: dict[str, Any]) -> dict[str, Any]:
+    for i, line in enumerate(body.get("feedLines") or []):
+        name = line.get("feedName") or ""
+        matches = suggest_component_matches(name, limit=5)
+        body["feedLines"][i]["suggestedComponents"] = [m.to_api_dict() for m in matches]
+    return body

apps/api/app/modules/zootech/lab/etl/reference_db_import.py

@@ -0,0 +1,265 @@
+"""Import tab reference PostgreSQL → WESP SQLite (offline admin ETL)."""
+
+from __future__ import annotations
+
+import json
+import os
+from dataclasses import dataclass, field
+from datetime import datetime
+from typing import Any
+
+import psycopg2
+from psycopg2.extras import RealDictCursor
+
+from app.modules.zootech.wesp_bridge_db import db
+from app.modules.zootech.lab.models import LabAnimalProfile
+from app.modules.zootech.wesp_bridge_models import Component, Recipe, WESP_SUPPRESS_SYNC_ENQUEUE
+from app.modules.zootech.lab.services.profile_norms import import_norms_from_legacy_text, save_norms_from_payload
+
+
+DEFAULT_PG_URL = "postgresql://neoton:neoton_secret@localhost:5432/neoton"
+
+
+@dataclass
+class ImportStats:
+    components_enriched: int = 0
+    components_unmatched: int = 0
+    tab_components_purged: int = 0
+    animal_profiles: int = 0
+    recipe_links: int = 0
+    errors: list[str] = field(default_factory=list)
+
+
+def _pg_url() -> str:
+    return os.environ.get("TAB_REFERENCE_DATABASE_URL", DEFAULT_PG_URL).strip()
+
+
+def _json_text(value: Any) -> str:
+    if value is None:
+        return "{}"
+    if isinstance(value, str):
+        return value if value else "{}"
+    return json.dumps(value, ensure_ascii=False)
+
+
+def _dt(value: Any) -> datetime | None:
+    if value is None:
+        return None
+    if isinstance(value, datetime):
+        return value
+    return None
+
+
+def _fetch_all(conn, sql: str) -> list[dict[str, Any]]:
+    with conn.cursor(cursor_factory=RealDictCursor) as cur:
+        cur.execute(sql)
+        return list(cur.fetchall())
+
+
+def _normalize_name(name: str) -> str:
+    return " ".join((name or "").lower().split())
+
+
+def _find_wesp_component(row: dict[str, Any]) -> Component | None:
+    """Match tab feed_ingredient → existing WESP component (never create)."""
+    external_no = row.get("external_no")
+    name = (row.get("name") or "").strip()
+    norm = _normalize_name(name)
+
+    if external_no is not None:
+        hit = Component.query.filter_by(external_no=external_no, is_deleted=False).first()
+        if hit is not None:
+            return hit
+
+    if name:
+        hit = Component.query.filter(
+            Component.name == name, Component.is_deleted.is_(False)
+        ).first()
+        if hit is not None:
+            return hit
+
+    if norm:
+        for comp in Component.query.filter(Component.is_deleted.is_(False)).all():
+            if _normalize_name(comp.name) == norm:
+                return comp
+        if len(name) >= 12:
+            prefix = name[:20].lower()
+            for comp in Component.query.filter(Component.is_deleted.is_(False)).all():
+                if prefix in (comp.name or "").lower():
+                    return comp
+    return None
+
+
+def _enrich_wesp_component(component: Component, row: dict[str, Any]) -> None:
+    """Copy zootech nutrients from tab; WESP id/name/dry_matter/price stay canonical."""
+    from app.modules.zootech.lab.services.component_nutrients import save_component_nutrients
+
+    raw = row.get("nutrients")
+    if isinstance(raw, str):
+        try:
+            raw = json.loads(raw)
+        except json.JSONDecodeError:
+            raw = {}
+    if not isinstance(raw, dict):
+        raw = {}
+    save_component_nutrients(component.id, raw, user_id="reference-db-import")
+    if row.get("external_no") is not None:
+        component.external_no = row.get("external_no")
+    component.updated_by = "tab-enrich"
+
+
+def _purge_tab_imported_components(stats: ImportStats) -> None:
+    """Remove components created from tab feed_ingredients (not used in WESP calc)."""
+    tab_ids = [
+        c.id
+        for c in Component.query.filter(
+            Component.created_by == "tab-import", Component.is_deleted.is_(False)
+        ).all()
+    ]
+    if not tab_ids:
+        return
+    for comp in Component.query.filter(Component.id.in_(tab_ids)).all():
+        comp.soft_delete("tab-import-cleanup")
+        stats.tab_components_purged += 1
+
+
+def _import_feed_ingredients(conn, stats: ImportStats) -> None:
+    """Map tab feed_ingredient → WESP component; enrich nutrients only."""
+    rows = _fetch_all(
+        conn,
+        """
+        SELECT id, external_no, name, price_per_kg, dry_matter, nutrients, row_index
+        FROM feed_ingredients
+        ORDER BY row_index NULLS LAST, external_no NULLS LAST
+        """,
+    )
+    for row in rows:
+        component = _find_wesp_component(row)
+        if component is None:
+            stats.components_unmatched += 1
+            continue
+        _enrich_wesp_component(component, row)
+        stats.components_enriched += 1
+
+
+def build_feed_ingredient_mapping(rows: list[dict[str, Any]], stats: ImportStats | None = None) -> dict[str, str | None]:
+    st = stats or ImportStats()
+    mapping: dict[str, str | None] = {}
+    for row in rows:
+        tab_id = str(row.get("id") or "")
+        component = _find_wesp_component(row)
+        if component is None:
+            st.components_unmatched += 1
+            mapping[tab_id] = None
+            continue
+        _enrich_wesp_component(component, row)
+        mapping[tab_id] = component.id
+        st.components_enriched += 1
+    return mapping
+
+
+def apply_animal_profile_rows(rows: list[dict[str, Any]], stats: ImportStats | None = None) -> ImportStats:
+    """Импорт профилей из списка строк (PG-формат или fixtures JSON)."""
+    st = stats or ImportStats()
+    for row in rows:
+        _upsert_animal_profile_row(row, st)
+    return st
+
+
+def _upsert_animal_profile_row(row: dict[str, Any], stats: ImportStats) -> None:
+    profile = LabAnimalProfile.query.get(row["id"])
+    if profile is None:
+        profile = LabAnimalProfile(id=row["id"])
+        db.session.add(profile)
+    profile.profile_key = row["key"]
+    profile.label = row["label"]
+    profile.ration_type = str(row["type"])
+    norms_raw = row.get("norms_data")
+    if isinstance(norms_raw, dict):
+        save_norms_from_payload(profile, norms_raw)
+    else:
+        import_norms_from_legacy_text(profile, norms_raw)
+    profile.created_at = _dt(row.get("created_at")) or profile.created_at
+    profile.updated_at = _dt(row.get("updated_at")) or profile.updated_at
+    profile.created_by = "tab-import"
+    profile.updated_by = "tab-import"
+    stats.animal_profiles += 1
+
+
+def apply_feed_ingredient_rows(rows: list[dict[str, Any]], stats: ImportStats | None = None) -> ImportStats:
+    """Обогащение WESP component из строк feed_ingredients (без PG)."""
+    st = stats or ImportStats()
+    for row in rows:
+        component = _find_wesp_component(row)
+        if component is None:
+            st.components_unmatched += 1
+            continue
+        _enrich_wesp_component(component, row)
+        st.components_enriched += 1
+    return st
+
+
+def _import_animal_profiles(conn, stats: ImportStats) -> None:
+    rows = _fetch_all(
+        conn,
+        """
+        SELECT id, key, label, type, norms_data, created_at, updated_at
+        FROM animal_profiles
+        ORDER BY key
+        """,
+    )
+    apply_animal_profile_rows(rows, stats)
+
+
+def _recipe_by_name(name: str) -> Recipe | None:
+    if not name:
+        return None
+    return Recipe.query.filter(Recipe.name == name, Recipe.is_deleted.is_(False)).first()
+
+
+def _link_recipes_from_tab_projects(conn, stats: ImportStats) -> None:
+    """Только ration_type на recipe по имени — без staging-таблиц."""
+    rows = _fetch_all(
+        conn,
+        """
+        SELECT name, type
+        FROM ration_projects
+        ORDER BY updated_at DESC NULLS LAST
+        """,
+    )
+    seen: set[str] = set()
+    for row in rows:
+        name = (row.get("name") or "").strip()
+        if not name or name in seen:
+            continue
+        linked = _recipe_by_name(name)
+        if linked is None:
+            continue
+        if row.get("type"):
+            linked.ration_type = str(row["type"])
+        seen.add(name)
+        stats.recipe_links += 1
+
+
+def import_from_reference_db(pg_url: str | None = None) -> ImportStats:
+    """Full dump from tab PostgreSQL into WESP SQLite."""
+    stats = ImportStats()
+    url = pg_url or _pg_url()
+    conn = psycopg2.connect(url)
+    db.session.info[WESP_SUPPRESS_SYNC_ENQUEUE] = True
+    try:
+        _purge_tab_imported_components(stats)
+        db.session.flush()
+        _import_feed_ingredients(conn, stats)
+        db.session.flush()
+        _import_animal_profiles(conn, stats)
+        _link_recipes_from_tab_projects(conn, stats)
+        db.session.commit()
+    except Exception as exc:
+        db.session.rollback()
+        stats.errors.append(str(exc))
+        raise
+    finally:
+        db.session.info.pop(WESP_SUPPRESS_SYNC_ENQUEUE, None)
+        conn.close()
+    return stats

apps/api/app/modules/zootech/lab/indicators.py

@@ -0,0 +1,169 @@
+"""Все показатели рациона для расчёта и сравнения с нормами профиля."""
+
+from __future__ import annotations
+
+from typing import Any
+
+
+def _ind(
+    key: str,
+    label: str,
+    unit: str,
+    nutrient_keys: list[str] | None = None,
+    *,
+    aggregation: str = "daily_total",
+    derived: str | None = None,
+    **extra: Any,
+) -> dict[str, Any]:
+    row: dict[str, Any] = {
+        "key": key,
+        "label": label,
+        "unit": unit,
+        "nutrient_keys": nutrient_keys or [],
+        "aggregation": aggregation,
+    }
+    if derived:
+        row["derived"] = derived
+    row.update(extra)
+    return row
+
+
+# Порядок: базовые → расширенные zootech → производные
+RATION_ALL_INDICATORS: list[dict[str, Any]] = [
+    _ind("dry_matter", "Сухое вещество", "г", ["СВ"]),
+    _ind("dm_main", "СВ — основной корм", "г", ["Осн.Корм", "СВ Основной корм"]),
+    _ind("oe", "ОЭ — КРС / Дойн", "MJ", ["ОЭ-КРС", "    ОЭ-КРС", "OЭ КРС форм"]),
+    _ind("nel", "ЧЭЛ — КРС / Дойн", "MJ", ["ЧЭЛ- КРС", "    ЧЭЛ- КРС", "ЧЭЛ - КРС Форм"]),
+    _ind("nel_per_kg_dm", "ЧЭЛ/кг СВ", "MJ", derived="nel_per_kg_dm"),
+    _ind("crude_protein", "Сырой протеин", "г", ["Сыр. Протеин"]),
+    _ind("rup_per_kg_dm", "Нер. СП / кг СВ", "г", ["Нер. СП / кг СВ"], aggregation="weighted_avg"),
+    _ind("insoluble_protein", "Нерастворимый протеин", "г", ["Нераствор протеин ", "Нерастворим прот ", "Нерастворим прот"]),
+    _ind("insoluble_protein_pct", "% нераств. протеин", "%", ["% нераств. протеин", "%  нераствор протеин"], aggregation="weighted_avg"),
+    _ind("usp", "уСП", "г", ["уСП", "уСП формул"]),
+    _ind("usp_pct_dm", "% уСП/кг СВ", "%", ["% уСП/кг СВ", "% уСП/кг СР"], aggregation="weighted_avg"),
+    _ind("usp_per_kg_dm", "уСП/кг СВ", "г", derived="g_per_kg_dm", from_key="usp"),
+    _ind("usp_in_om", "уСП в ОВ", "г", ["уСП в ОР", "уСП в ОВ"]),
+    _ind("usp_low_fat", "уСП <7% СЖ", "г", ["уСП<7%CЖ"], aggregation="weighted_avg"),
+    _ind("usp_high_fat", "уСП >7% СЖ", "г", ["уСП>7%CЖ"], aggregation="weighted_avg"),
+    _ind("ndf", "Сырая клетчатка", "г", ["Сырая клетч", "Сырая клетчатка", "НДК"]),
+    _ind("ndf_total", "НДК общ", "г", ["НДК Общ", "НДК"]),
+    _ind("ndf_main_feed", "НДК осн. корм", "г", ["НДК Осн. Корм"]),
+    _ind("adf_total", "КДК общ", "г", ["КДК общ", "КДК"]),
+    _ind("structural_fiber", "Структур. клетчатка", "г", ["Структур клетч", "Структур. клетчатка"]),
+    _ind("structural_fiber_pct", "% стр. клетчатки", "%", ["% стр Сыр. Клетчатк.", "% стр Сыр. Клетч."], aggregation="weighted_avg"),
+    _ind("crude_fat", "Сырой жир", "г", ["Сырой жир"]),
+    _ind("rnb", "RNB", "г", ["RNB", "БРА", "    БРА "], derived="rnb"),
+    _ind("calcium", "Ca", "г", ["Ca"]),
+    _ind("phosphorus", "P", "г", ["P"]),
+    _ind("magnesium", "Mg", "г", ["Mg"]),
+    _ind("sodium", "Na", "г", ["Na"]),
+    _ind("potassium", "K", "г", ["K"]),
+    _ind("dcab", "DCAB", "мэкв", ["DCAB Форм", "DCAB"]),
+    _ind("sugar_starch", "Сахар и крахмал", "г", ["Сахар и Крохм"]),
+    _ind("sugar_digestible_starch", "Сахар и усв. крахмал", "г", ["Сахар и усв. крох"]),
+    _ind("insoluble_starch", "Нераств. крахмал", "г", ["Нераств. Крохмал", "Нераств  Крохм", "Нераств крахмал"]),
+    _ind("insoluble_starch_alt", "Нераств. крахмал (2)", "г", ["Нераств крохмал"]),
+    _ind("insoluble_starch_pct", "% нераств. крахмала", "%", ["% Нераств крохм"], aggregation="weighted_avg"),
+    _ind("sugar", "Сахар", "г", ["Сахар"]),
+    _ind("starch", "Крахмал", "г", ["Крахмал"]),
+    _ind("starch_pct_dm", "% крахмала СВ", "%", ["% Крохмал. СВ", "доля крохмала"], aggregation="weighted_avg"),
+    _ind("carotene", "Каротин", "мг", ["Каротин"]),
+    _ind("beta_carotene", "β-каротин", "мг", ["b -Каротин"]),
+    _ind("linoleic_acid", "Линолевая к-та", "г", ["Линолевая к-та"]),
+    _ind("linolenic_acid", "Линоленовая к-та", "г", ["Линоленовая ки-та"]),
+    _ind("butyric_acid", "Масляная к-та", "г", ["Масляная ки-та"]),
+    _ind("arachidonic_acid", "Арахидоновая к-та", "г", ["Арахидоновая ки-та"]),
+    _ind("polyenoic_acid", "Полиэновая к-та", "г", ["Полиэновая ки-та"]),
+    _ind("urea", "Мочевина", "г", ["Мочевина"]),
+    _ind("crude_ash", "Сырая зола", "г", ["Сырая зола"]),
+    _ind("nfe", "БЭВ", "г", ["БЕР", "БЭВ"]),
+    _ind("tdn_cattle", "КРС орг. вещество", "г", ["КРС Орг Вещ", "ВРХ Орг Вещ"]),
+    _ind("digestible_organic_matter", "Перевар. орг. вещество", "г", ["Перевар Орг Вещ", "Переварим Орг Вещ"]),
+    _ind("cp_cattle", "КРС протеин", "г", ["КРС Протеин"]),
+    _ind("digestible_protein", "Перевар. протеин", "г", ["Перевар Протеин", "Переварим Протеин"]),
+    _ind("fat_cattle", "КРС сырой жир", "г", ["КРС Сырой жир", "КРС Сирий жир", "КРС Сирой жир"]),
+    _ind("digestible_fat", "Перевар. сырой жир", "г", ["Перевар Сырой жир", "Перевар Сирой жир", "Переварим Сырой жир"]),
+    _ind("fiber_cattle", "КРС сырая клетчатка", "г", ["КРС Сырая клетч"]),
+    _ind("digestible_fiber", "Перевар. сырая клетчатка", "г", ["Перевар сыр клетч", "Переварим сырая клетч"]),
+    _ind("nfe_cattle", "КРС БЭВ", "г", ["КРС БЭВ"]),
+    _ind("digestible_nfe", "Перевар. БЭВ", "г", ["Перевар БЭВ", "Переварим БЭВ"]),
+    _ind("feed_value", "ВЕ", "", ["ВЕ"], aggregation="weighted_avg"),
+    _ind("fat_per_kg_dm", "СЖ/кг СВ", "г", derived="g_per_kg_dm", from_key="crude_fat"),
+    _ind("nsp_per_kg_dm", "НСП/кг СВ", "г", ["НСП/кг СВ"], aggregation="weighted_avg"),
+    _ind("cp_per_kg_dm", "СП/кг СВ", "г", derived="g_per_kg_dm", from_key="crude_protein"),
+    _ind("dom_per_kg_dm", "пОВ/кг СВ", "г", derived="g_per_kg_dm", from_key="digestible_organic_matter"),
+    _ind(
+        "digestible_fat_per_kg_dm",
+        "пСЖ/кг СВ",
+        "г",
+        derived="g_per_kg_dm",
+        from_key="digestible_fat",
+    ),
+    _ind("fiber_pct_per_kg_dm", "%СК/кг СВ", "%", derived="pct_of_dm", from_key="ndf"),
+    _ind("fat_pct_per_kg_dm", "%-СЖ/кг СВ", "%", derived="pct_of_dm", from_key="crude_fat"),
+    _ind("usp_pct_per_kg_dm", "%-уСП / кг СВ", "%", derived="pct_of_dm", from_key="usp"),
+    _ind("ca_pct_per_kg_dm", "%-Ca / кг СВ", "%", derived="pct_of_dm", from_key="calcium"),
+    _ind("p_pct_per_kg_dm", "%-P / кг СВ", "%", derived="pct_of_dm", from_key="phosphorus"),
+    _ind("k_pct_per_kg_dm", "%-К / кг СВ", "%", derived="pct_of_dm", from_key="potassium"),
+    _ind("na_pct_per_kg_dm", "%-Na / кг СВ", "%", derived="pct_of_dm", from_key="sodium"),
+    _ind("mg_pct_per_kg_dm", "%-Mg / кг СВ", "%", derived="pct_of_dm", from_key="magnesium"),
+    _ind("ndf_pct_dm", "НДК % от СВ", "%", derived="pct_of_dm", from_key="ndf"),
+    _ind("ndf_main_pct_dm", "НДК-ОК % от СВ", "%", derived="pct_of_dm", from_key="ndf_main_feed"),
+    _ind("adf_pct_dm", "КДК % от СВ", "%", derived="pct_of_dm", from_key="adf_total"),
+    _ind("nfc_pct_dm", "НКВ % от СВ", "%", derived="pct_of_dm", from_key="nfe"),
+    _ind("sw_per_kg_dm", "SW / кг СВ", "", ["SW / кг СВ ()"], aggregation="weighted_avg"),
+    _ind("metab_oet", "Метаб. ОЕТ", "г", ["Метаб ОЕТ", "OEB"]),
+    _ind("metab_lysine", "Метаб. лизин", "г", ["Метаб лизин", "Лизин"]),
+    _ind("metab_threonine", "Метаб. треонин", "г", ["Метабол Треон", "Треонин"]),
+    _ind("metab_leucine", "Метаб. лейцин", "г", ["Метаб Лейц", "Метабол Лейцин", "Лейцин"]),
+    _ind("metab_isoleucine", "Метаб. изолейцин", "г", ["Метаб Изол", "Метабол  Изолейц", "Изолейцин"]),
+    _ind("metab_valine", "Метаб. валин", "г", ["Метаб Вал", "Метабол Валин", "Валин"]),
+    _ind("vitamin_a", "Витамин А", "МЕ", ["Вит А"]),
+    _ind("vitamin_d", "Витамин D", "МЕ", ["Вит D"]),
+    _ind("vitamin_e", "Витамин Е", "мг", ["Вит Е"]),
+    _ind("vitamin_b1", "Витамин В1", "мг", ["Вит В1"]),
+    _ind("vitamin_b2", "Витамин В2", "мг", ["Вит В2"]),
+    _ind("vitamin_b6", "Витамин В6", "мг", ["Вит В6"]),
+    _ind("vitamin_b12", "Витамин В12", "мкг", ["Вит В12"]),
+    _ind("calcium_pantothenate", "Пантотенат кальция", "мг", ["Пант Кальц", "Пантеонат Кальция"]),
+    _ind("niacin", "Никотиновая к-та", "мг", ["Никот Ки-та", "Никотиновая кислота"]),
+    _ind("folic_acid", "Фолиевая к-та", "мг", ["Фол ки-та", "Фолиева кислота"]),
+    _ind("choline", "Холин", "мг", ["Холин"]),
+    _ind("biotin", "Биотин", "мкг", ["Биотин"]),
+    _ind("iron", "Fe", "мг", ["Fe"]),
+    _ind("zinc", "Zn", "мг", ["Zn"]),
+    _ind("copper", "Cu", "мг", ["Cu"]),
+    _ind("cobalt", "Co", "мг", ["Co"]),
+    _ind("manganese", "Mn", "мг", ["Mn"]),
+    _ind("selenium", "Se", "мг", ["Se"]),
+    _ind("iodine", "J", "мг", ["J"]),
+    _ind("oet", "OET", "", ["OEB", "OET"]),
+    _ind("synthesis_mj", "Синтез", "МДж", ["Синтез Мдж"]),
+    _ind("ca_p_ratio", "Са:P", "", derived="ratio", ratio_num="calcium", ratio_den="phosphorus"),
+    _ind("k_na_ratio", "K:Na", "", derived="ratio", ratio_num="potassium", ratio_den="sodium"),
+    _ind("ration_dm_pct_bw", "Рацион % СВ от веса", "%", derived="dm_pct_bw"),
+    _ind("ration_pct_bw", "Рацион % от веса", "%", derived="ration_pct_bw"),
+    # Дубликаты ключей норм (укр. заголовки) — тот же расчёт
+    _ind("ndf_pct_dm_uk", "НДК % від СВ", "%", derived="alias", alias_of="ndf_pct_dm"),
+    _ind("ndf_main_pct_dm_uk", "НДК-ОК % від СВ", "%", derived="alias", alias_of="ndf_main_pct_dm"),
+    _ind("adf_pct_dm_uk", "КДК % від СВ", "%", derived="alias", alias_of="adf_pct_dm"),
+    _ind("nfc_pct_dm_uk", "НКВ % від СВ", "%", derived="alias", alias_of="nfc_pct_dm"),
+]
+
+# Обратная совместимость
+RATION_QUALITY_INDICATORS = RATION_ALL_INDICATORS
+
+_INDICATOR_BY_KEY = {d["key"]: d for d in RATION_ALL_INDICATORS}
+_LABEL_TO_KEY = {d["label"]: d["key"] for d in RATION_ALL_INDICATORS}
+
+
+def indicator_by_key(key: str) -> dict[str, Any] | None:
+    return _INDICATOR_BY_KEY.get(key)
+
+
+def label_to_indicator_key(label: str) -> str | None:
+    return _LABEL_TO_KEY.get(label)
+
+
+def calc_indicator_keys() -> frozenset[str]:
+    return frozenset(_INDICATOR_BY_KEY)

apps/api/app/modules/zootech/lab/loaders/__init__.py

@@ -0,0 +1,5 @@
+from .execution_loader import load_execution
+from .profile_loader import list_animal_profiles, load_animal_profile
+from .ration_loader import load_ration
+
+__all__ = ["load_ration", "load_execution", "list_animal_profiles", "load_animal_profile"]

apps/api/app/modules/zootech/lab/loaders/_json.py

@@ -0,0 +1,17 @@
+from __future__ import annotations
+
+import json
+from typing import Any
+
+
+def parse_json_text(value: Any, default: Any = None) -> Any:
+    if default is None:
+        default = {}
+    if value is None or value == "":
+        return default
+    if isinstance(value, dict):
+        return value
+    try:
+        return json.loads(value)
+    except (TypeError, json.JSONDecodeError):
+        return default

apps/api/app/modules/zootech/lab/loaders/execution_loader.py

@@ -0,0 +1,29 @@
+from __future__ import annotations
+
+from app.modules.zootech.lab.dto.ration import ExecutionLineSnapshot, ExecutionSnapshot
+from app.modules.zootech.wesp_bridge_models import Ingredient, Recipe
+
+
+def load_execution(recipe_id: str) -> ExecutionSnapshot:
+    recipe = Recipe.query.filter_by(id=recipe_id, is_deleted=False).first()
+    if recipe is None:
+        raise LookupError("Рецепт не найден")
+    heads = int(recipe.heads_per_trip or 1)
+    ingredients = (
+        Ingredient.query.filter_by(recipe_id=recipe_id, is_deleted=False)
+        .order_by(Ingredient.order)
+        .all()
+    )
+    lines = []
+    for ing in ingredients:
+        wph = float(ing.weight_per_head or 0)
+        lines.append(
+            ExecutionLineSnapshot(
+                ingredient_id=ing.id,
+                component_id=ing.component_id,
+                name=ing.name,
+                weight_per_head=wph,
+                daily_kg_total=wph * heads,
+            )
+        )
+    return ExecutionSnapshot(recipe_id=recipe_id, heads_per_trip=heads, lines=tuple(lines))

apps/api/app/modules/zootech/lab/loaders/profile_loader.py

@@ -0,0 +1,58 @@
+from __future__ import annotations
+
+from app.modules.zootech.lab.models import LabAnimalProfile
+from app.modules.zootech.lab.services.profile_norms import load_norms_api
+
+
+def list_animal_profiles(ration_type: str | None = None) -> list[dict]:
+    q = LabAnimalProfile.query.filter_by(is_deleted=False)
+    if ration_type:
+        q = q.filter_by(ration_type=ration_type.upper())
+    rows = q.order_by(LabAnimalProfile.profile_key).all()
+    return [_profile_dict(p, include_resolved=False) for p in rows]
+
+
+def load_animal_profile(profile_id: str) -> dict:
+    profile = LabAnimalProfile.query.filter_by(id=profile_id, is_deleted=False).first()
+    if profile is None:
+        raise LookupError("Профиль не найден")
+    return _profile_dict(profile, resolve_norms=True, include_resolved=True)
+
+
+def _profile_dict(
+    profile: LabAnimalProfile,
+    *,
+    resolve_norms: bool = False,
+    include_resolved: bool = False,
+) -> dict:
+    norms_api = load_norms_api(profile, resolve_dynamic=include_resolved) if resolve_norms else {}
+    if not resolve_norms:
+        from app.modules.zootech.lab.services.profile_norms import load_norms_dict
+
+        indicators = load_norms_dict(profile.id)
+        norms_api = {"indicators": indicators}
+    from app.modules.zootech.lab.services.norms_params import norms_params_api
+
+    out: dict = {
+        "id": profile.id,
+        "profileKey": profile.profile_key,
+        "label": profile.label,
+        "rationType": profile.ration_type,
+        "massKg": profile.mass_kg,
+        "milkYieldKg": profile.milk_yield_kg,
+        "externalNo": profile.external_no,
+        "normsMethod": profile.norms_method or "wesp",
+        "normsParams": norms_params_api(profile),
+        "normsData": norms_api,
+        "norms": norms_api.get("indicators") or {},
+        "normsProfileKey": profile.profile_key,
+        "legacyNormsRemapped": False,
+    }
+    if include_resolved:
+        out["resolvedNorms"] = norms_api.get("resolvedIndicators") or {}
+        out["dynamicNorms"] = norms_api.get("dynamicNorms") or {}
+        if norms_api.get("coverage"):
+            out["coverage"] = norms_api["coverage"]
+        if norms_api.get("normsMeta"):
+            out["normsMeta"] = norms_api["normsMeta"]
+    return out

apps/api/app/modules/zootech/lab/loaders/ration_loader.py

@@ -0,0 +1,125 @@
+from __future__ import annotations
+
+from app.modules.zootech.lab.calc.nutrients import norm_diff
+from app.modules.zootech.lab.indicators import label_to_indicator_key
+from app.modules.zootech.lab.dto.ration import RationLineSnapshot, RationSnapshot
+from app.modules.zootech.lab.models import LabAnimalProfile, LabRationLine, LabRecipeRation
+from app.modules.zootech.lab.services.component_nutrients import nutrients_calc_dict
+from app.modules.zootech.lab.services.profile_norms import resolve_norms_for_profile
+from app.modules.zootech.lab.services.ration_calc_store import (
+    load_compound_results,
+    load_params,
+    load_ration_results,
+)
+from app.modules.zootech.wesp_bridge_models import Component, Recipe
+
+
+def _refresh_indicator_norms(
+    indicators: list[dict],
+    norms: dict[str, dict[str, float | None]],
+) -> list[dict]:
+    """min/max/diff в сохранённом расчёте — по текущему профилю норм."""
+    if not indicators or not norms:
+        return indicators
+    out: list[dict] = []
+    for row in indicators:
+        item = dict(row)
+        key = item.get("key") or label_to_indicator_key(str(item.get("label") or ""))
+        if key and key in norms:
+            bounds = norms[key]
+            min_v = bounds.get("min")
+            max_v = bounds.get("max")
+            item["min"] = min_v
+            item["max"] = max_v
+            item["diff"] = norm_diff(item.get("content"), min_v, max_v)
+        out.append(item)
+    return out
+
+
+def load_ration(recipe_id: str) -> RationSnapshot:
+    recipe = Recipe.query.filter_by(id=recipe_id, is_deleted=False).first()
+    if recipe is None:
+        raise LookupError("Рецепт не найден")
+    header = LabRecipeRation.query.filter_by(recipe_id=recipe_id, is_deleted=False).first()
+    ration_type = recipe.ration_type or "BEEF"
+    norms: dict = {}
+    params: dict = {}
+    ration_results: dict = {}
+    compound_results: dict = {}
+    calculated_at = None
+    animal_profile_id = None
+    norms_profile_key = None
+    if header:
+        animal_profile_id = header.animal_profile_id
+        params = load_params(recipe_id, header)
+        ration_results = load_ration_results(recipe_id, header)
+        compound_results = load_compound_results(recipe_id)
+        calculated_at = header.calculated_at.isoformat() if header.calculated_at else None
+        if header.animal_profile_id:
+            profile = LabAnimalProfile.query.filter_by(
+                id=header.animal_profile_id, is_deleted=False
+            ).first()
+            if profile:
+                norms_profile_key = profile.profile_key
+                norms = resolve_norms_for_profile(profile)
+    if ration_results.get("indicators") and norms:
+        ration_results = {
+            **ration_results,
+            "indicators": _refresh_indicator_norms(ration_results["indicators"], norms),
+        }
+    lines_q = (
+        LabRationLine.query.filter_by(recipe_id=recipe_id, is_deleted=False)
+        .order_by(LabRationLine.row_index)
+        .all()
+    )
+    line_snaps = []
+    for line in lines_q:
+        comp = Component.query.get(line.component_id) if line.component_id else None
+        nutrients = nutrients_calc_dict(line.component_id) if line.component_id else {}
+        dry_matter_pct = comp.dry_matter if comp else None
+        line_snaps.append(
+            RationLineSnapshot(
+                id=line.id,
+                component_id=line.component_id,
+                ingredient_name=line.ingredient_name or (comp.name if comp else None),
+                row_index=line.row_index,
+                daily_kg=line.daily_kg,
+                in_ration=bool(line.in_ration),
+                in_compound=bool(line.in_compound),
+                dry_matter=dry_matter_pct,
+                price_per_kg=comp.price if comp else None,
+                nutrients=nutrients,
+            )
+        )
+    return RationSnapshot(
+        recipe_id=recipe_id,
+        recipe_name=recipe.name,
+        ration_type=ration_type,
+        heads_per_trip=int(recipe.heads_per_trip or 1),
+        animal_profile_id=animal_profile_id,
+        params=params,
+        lines=tuple(line_snaps),
+        norms=norms,
+        ration_results=ration_results,
+        compound_results=compound_results,
+        calculated_at=calculated_at,
+        exists=header is not None,
+        norms_profile_key=norms_profile_key,
+        legacy_norms_remapped=False,
+    )
+
+
+def ration_to_calc_lines(snapshot: RationSnapshot) -> list[dict]:
+    return [
+        {
+            "daily_kg": line.daily_kg,
+            "in_ration": line.in_ration,
+            "in_compound": line.in_compound,
+            "ingredient_name": line.ingredient_name,
+            "price_per_kg": line.price_per_kg,
+            "dry_matter": line.dry_matter,
+            "nutrients": line.nutrients,
+            "component_id": line.component_id,
+        }
+        for line in snapshot.lines
+    ]

apps/api/app/modules/zootech/lab/models/__init__.py

@@ -0,0 +1,17 @@
+"""Re-export WESP lab model aliases for ported lab package."""
+
+from app.modules.zootech.wesp_bridge_models import (  # noqa: F401
+    LabAnimalProfile,
+    LabComponentNutrientValue,
+    LabProfileNorm,
+    LabRacionNormyInfo,
+    LabRacionNormyMoskwa,
+    LabRacionNormyMoskwaMeta,
+    LabRacionNormyPiter,
+    LabRacionNormyPiterMeta,
+    LabRationCalcIndicator,
+    LabRationCalcTotal,
+    LabRationCompoundLine,
+    LabRationLine,
+    LabRecipeRation,
+)

apps/api/app/modules/zootech/lab/norm_catalog.py

@@ -0,0 +1,290 @@
+"""Каталог показателей норм профиля стада."""
+
+from __future__ import annotations
+
+import re
+import unicodedata
+
+from app.modules.zootech.lab.indicators import RATION_ALL_INDICATORS, calc_indicator_keys
+
+_CALC_META = {d["key"]: d for d in RATION_ALL_INDICATORS}
+
+# Заголовок колонки CSV «Нормы Дойн/КРС» (после нормализации) → indicator_key в lab_profile_norm
+NORM_HEADER_TO_KEY: dict[str, str] = {
+    "Сухое Вещество": "dry_matter",
+    "Сыр. Протеин": "crude_protein",
+    "уСП": "usp",
+    "RNB": "rnb",
+    "БРА": "rnb",
+    "ЧЭЛ- КРС": "nel",
+    "ОЭ-КРС": "oe",
+    "Сырая клетч": "ndf",
+    "Сырая клетчат": "ndf",
+    "Сырая клетчатка": "ndf",
+    "Структур клетчатк": "structural_fiber",
+    "Сырой жир": "crude_fat",
+    "Сирой жир": "crude_fat",
+    "Ca": "calcium",
+    "P": "phosphorus",
+    "Mg": "magnesium",
+    "Na": "sodium",
+    "DCAB": "dcab",
+    "Сахар и Крохм": "sugar_starch",
+    "Нераств. Крохмал": "insoluble_starch",
+    "Нерозч. Крохмал": "insoluble_starch",
+    "Сахар": "sugar",
+    "Крохмал": "starch",
+    "Каротин": "carotene",
+    "b -Каротин": "beta_carotene",
+    "Линолевая к-та": "linoleic_acid",
+    "Линоленовая ки-та": "linolenic_acid",
+    "Масляная ки-та": "butyric_acid",
+    "Мочевина": "urea",
+    "СВ Основной корм": "dm_main",
+    "КРС Орг Вещ": "tdn_cattle",
+    "Перевар Орг Вещ": "digestible_organic_matter",
+    "КРС Протеин": "cp_cattle",
+    "Перевар Протеин": "digestible_protein",
+    "КРС Сирий жир": "fat_cattle",
+    "КРС Сирой жир": "fat_cattle",
+    "Перевар Сырой жир": "digestible_fat",
+    "Перевар Сирой жир": "digestible_fat",
+    "КРС Сырая клетч": "fiber_cattle",
+    "Перевар сыр клетч": "digestible_fiber",
+    "КРС БЭВ": "nfe_cattle",
+    "Перевар БЭВ": "digestible_nfe",
+    "ВЕ": "feed_value",
+    "НДК Общ": "ndf_total",
+    "НДК Осн. Корм": "ndf_main_feed",
+    "КДК общ": "adf_total",
+    "% нераств. протеин": "insoluble_protein_pct",
+    "Нераствор протеин": "insoluble_protein",
+    "СЖ/кг СВ": "fat_per_kg_dm",
+    "НСП/кг СВ": "nsp_per_kg_dm",
+    "СП/кг СВ": "cp_per_kg_dm",
+    "пОВ/кг СВ": "dom_per_kg_dm",
+    "пСЖ/кг СВ": "digestible_fat_per_kg_dm",
+    "уСП<7%CЖ": "usp_low_fat",
+    "уСП>7%CЖ": "usp_high_fat",
+    "уСП/кг СВ": "usp_per_kg_dm",
+    "уСП в ОВ": "usp_in_om",
+    "Нераств крохмал": "insoluble_starch_alt",
+    "Метаб ОЕТ": "metab_oet",
+    "Метаб лизин": "metab_lysine",
+    "Метабол Треон": "metab_threonine",
+    "Метаб Лейц": "metab_leucine",
+    "Метаб Изол": "metab_isoleucine",
+    "Метаб Вал": "metab_valine",
+    "НДК % від СВ ()": "ndf_pct_dm_uk",
+    "НДК % от СВ ()": "ndf_pct_dm",
+    "НДК-ОК % від СВ ()": "ndf_main_pct_dm_uk",
+    "НДК-ОК % от СВ ()": "ndf_main_pct_dm",
+    "КДК % від СВ ()": "adf_pct_dm_uk",
+    "КДК % от СВ ()": "adf_pct_dm",
+    "НКВ % від СВ ()": "nfc_pct_dm_uk",
+    "НКВ % от СВ ()": "nfc_pct_dm",
+    "SW / кг СВ ()": "sw_per_kg_dm",
+    "ЧЕЛ/кг СВ (МДж)": "nel_per_kg_dm",
+    "Нер. СП / кг СВ": "rup_per_kg_dm",
+    "%СК/кг СВ ()": "fiber_pct_per_kg_dm",
+    "%-СЖ/кг СВ ()": "fat_pct_per_kg_dm",
+    "%-уСП / кг СВ ()": "usp_pct_per_kg_dm",
+    "% уСП/кг СВ": "usp_pct_dm",
+    "%-Ca / кг СВ ()": "ca_pct_per_kg_dm",
+    "%-P / кг СВ ()": "p_pct_per_kg_dm",
+    "%-К / кг СВ ()": "k_pct_per_kg_dm",
+    "%-Na / кг СВ ()": "na_pct_per_kg_dm",
+    "%-Mg / кг СВ ()": "mg_pct_per_kg_dm",
+    "% стр Сыр. Клетчатк.": "structural_fiber_pct",
+    "% стр Сыр. Клетч.": "structural_fiber_pct",
+    "% Крохмал. СВ": "starch_pct_dm",
+    "% Нераств крохм": "insoluble_starch_pct",
+    "Са:P": "ca_p_ratio",
+    "K:Na": "k_na_ratio",
+    "Рацион % СВ от Веса КРС": "ration_dm_pct_bw",
+    "Рацион % от Веса КРС": "ration_pct_bw",
+    "Витамин А": "vitamin_a",
+    "Витамин D": "vitamin_d",
+    "Витамин Е": "vitamin_e",
+    "Витамин В1": "vitamin_b1",
+    "Витамин В2": "vitamin_b2",
+    "Витамин В6": "vitamin_b6",
+    "Витамин В12": "vitamin_b12",
+    "Пантеонат Кальция": "calcium_pantothenate",
+    "Пантеонат Кальцию": "calcium_pantothenate",
+    "Никотиновая кислота": "niacin",
+    "Фолиева кислота": "folic_acid",
+    "Фолиевач кислота": "folic_acid",
+    "Холин": "choline",
+    "Биотин": "biotin",
+    "Fe": "iron",
+    "Zn": "zinc",
+    "Cu": "copper",
+    "Co": "cobalt",
+    "Mn": "manganese",
+    "Se": "selenium",
+    "J": "iodine",
+    "K": "potassium",
+    "Сахар и усв. крох": "sugar_digestible_starch",
+    "Сырая зола": "crude_ash",
+    "Сірая зола": "crude_ash",
+    "БЭВ": "nfe",
+    "БЄВ": "nfe",
+    "Арахидоновая ки-та": "arachidonic_acid",
+    "Полиэновая ки-та": "polyenoic_acid",
+    "OET": "oet",
+    "Синтез Мдж": "synthesis_mj",
+}
+
+# Метаданные для UI (label/unit) — показатели вне расчёта рациона
+_EXTRA_NORM_META: dict[str, dict[str, str]] = {
+    "beta_carotene": {"label": "β-каротин", "unit": "мг"},
+    "linoleic_acid": {"label": "Линолевая к-та", "unit": "г"},
+    "linolenic_acid": {"label": "Линоленовая к-та", "unit": "г"},
+    "butyric_acid": {"label": "Масляная к-та", "unit": "г"},
+    "urea": {"label": "Мочевина", "unit": "г"},
+    "tdn_cattle": {"label": "КРС орг. вещество", "unit": "г"},
+    "digestible_organic_matter": {"label": "Перевар. орг. вещество", "unit": "г"},
+    "cp_cattle": {"label": "КРС протеин", "unit": "г"},
+    "digestible_protein": {"label": "Перевар. протеин", "unit": "г"},
+    "fat_cattle": {"label": "КРС сырой жир", "unit": "г"},
+    "digestible_fat": {"label": "Перевар. сырой жир", "unit": "г"},
+    "fiber_cattle": {"label": "КРС сырая клетчатка", "unit": "г"},
+    "digestible_fiber": {"label": "Перевар. сырая клетчатка", "unit": "г"},
+    "nfe_cattle": {"label": "КРС БЭВ", "unit": "г"},
+    "digestible_nfe": {"label": "Перевар. БЭВ", "unit": "г"},
+    "feed_value": {"label": "ВЕ", "unit": ""},
+    "ndf_total": {"label": "НДК общ", "unit": "г"},
+    "ndf_main_feed": {"label": "НДК осн. корм", "unit": "г"},
+    "adf_total": {"label": "КДК общ", "unit": "г"},
+    "insoluble_protein_pct": {"label": "% нераств. протеин", "unit": "%"},
+    "fat_per_kg_dm": {"label": "СЖ/кг СВ", "unit": "г"},
+    "nsp_per_kg_dm": {"label": "НСП/кг СВ", "unit": "г"},
+    "cp_per_kg_dm": {"label": "СП/кг СВ", "unit": "г"},
+    "dom_per_kg_dm": {"label": "пОВ/кг СВ", "unit": "г"},
+    "digestible_fat_per_kg_dm": {"label": "пСЖ/кг СВ", "unit": "г"},
+    "usp_low_fat": {"label": "уСП <7% СЖ", "unit": "г"},
+    "usp_high_fat": {"label": "уСП >7% СЖ", "unit": "г"},
+    "usp_per_kg_dm": {"label": "уСП/кг СВ", "unit": "г"},
+    "usp_in_om": {"label": "уСП в ОВ", "unit": "г"},
+    "insoluble_starch_alt": {"label": "Нераств. крахмал (2)", "unit": "г"},
+    "metab_oet": {"label": "Метаб. ОЕТ", "unit": "г"},
+    "metab_lysine": {"label": "Метаб. лизин", "unit": "г"},
+    "metab_threonine": {"label": "Метаб. треонин", "unit": "г"},
+    "metab_leucine": {"label": "Метаб. лейцин", "unit": "г"},
+    "metab_isoleucine": {"label": "Метаб. изолейцин", "unit": "г"},
+    "metab_valine": {"label": "Метаб. валин", "unit": "г"},
+    "ndf_pct_dm": {"label": "НДК % от СВ", "unit": "%"},
+    "ndf_pct_dm_uk": {"label": "НДК % від СВ", "unit": "%"},
+    "ndf_main_pct_dm": {"label": "НДК-ОК % от СВ", "unit": "%"},
+    "ndf_main_pct_dm_uk": {"label": "НДК-ОК % від СВ", "unit": "%"},
+    "adf_pct_dm": {"label": "КДК % от СВ", "unit": "%"},
+    "adf_pct_dm_uk": {"label": "КДК % від СВ", "unit": "%"},
+    "nfc_pct_dm": {"label": "НКВ % от СВ", "unit": "%"},
+    "nfc_pct_dm_uk": {"label": "НКВ % від СВ", "unit": "%"},
+    "sw_per_kg_dm": {"label": "SW / кг СВ", "unit": ""},
+    "fiber_pct_per_kg_dm": {"label": "%СК/кг СВ", "unit": "%"},
+    "fat_pct_per_kg_dm": {"label": "%-СЖ/кг СВ", "unit": "%"},
+    "usp_pct_per_kg_dm": {"label": "%-уСП / кг СВ", "unit": "%"},
+    "ca_pct_per_kg_dm": {"label": "%-Ca / кг СВ", "unit": "%"},
+    "p_pct_per_kg_dm": {"label": "%-P / кг СВ", "unit": "%"},
+    "k_pct_per_kg_dm": {"label": "%-К / кг СВ", "unit": "%"},
+    "na_pct_per_kg_dm": {"label": "%-Na / кг СВ", "unit": "%"},
+    "mg_pct_per_kg_dm": {"label": "%-Mg / кг СВ", "unit": "%"},
+    "structural_fiber_pct": {"label": "% стр. клетчатки", "unit": "%"},
+    "starch_pct_dm": {"label": "% крахмала СВ", "unit": "%"},
+    "insoluble_starch_pct": {"label": "% нераств. крахмала", "unit": "%"},
+    "ca_p_ratio": {"label": "Са:P", "unit": ""},
+    "k_na_ratio": {"label": "K:Na", "unit": ""},
+    "ration_dm_pct_bw": {"label": "Рацион % СВ от веса", "unit": "%"},
+    "ration_pct_bw": {"label": "Рацион % от веса", "unit": "%"},
+    "vitamin_a": {"label": "Витамин А", "unit": "МЕ"},
+    "vitamin_d": {"label": "Витамин D", "unit": "МЕ"},
+    "vitamin_e": {"label": "Витамин Е", "unit": "мг"},
+    "vitamin_b1": {"label": "Витамин В1", "unit": "мг"},
+    "vitamin_b2": {"label": "Витамин В2", "unit": "мг"},
+    "vitamin_b6": {"label": "Витамин В6", "unit": "мг"},
+    "vitamin_b12": {"label": "Витамин В12", "unit": "мкг"},
+    "calcium_pantothenate": {"label": "Пантотенат кальция", "unit": "мг"},
+    "niacin": {"label": "Никотиновая к-та", "unit": "мг"},
+    "folic_acid": {"label": "Фолиевая к-та", "unit": "мг"},
+    "choline": {"label": "Холин", "unit": "мг"},
+    "biotin": {"label": "Биотин", "unit": "мкг"},
+    "iron": {"label": "Fe", "unit": "мг"},
+    "zinc": {"label": "Zn", "unit": "мг"},
+    "copper": {"label": "Cu", "unit": "мг"},
+    "cobalt": {"label": "Co", "unit": "мг"},
+    "manganese": {"label": "Mn", "unit": "мг"},
+    "selenium": {"label": "Se", "unit": "мг"},
+    "iodine": {"label": "J", "unit": "мг"},
+    "potassium": {"label": "K", "unit": "г"},
+    "sugar_digestible_starch": {"label": "Сахар и усв. крахмал", "unit": "г"},
+    "crude_ash": {"label": "Сырая зола", "unit": "г"},
+    "nfe": {"label": "БЭВ", "unit": "г"},
+    "arachidonic_acid": {"label": "Арахидоновая к-та", "unit": "г"},
+    "polyenoic_acid": {"label": "Полиэновая к-та", "unit": "г"},
+    "oet": {"label": "OET", "unit": ""},
+    "synthesis_mj": {"label": "Синтез", "unit": "МДж"},
+}
+
+_CALC_KEYS = calc_indicator_keys()
+
+
+def norm_header(value: str) -> str:
+    return " ".join((value or "").split()).strip()
+
+
+def norm_title_to_key(title: str) -> str | None:
+    """Заголовок колонки CSV → indicator_key (или None)."""
+    key = NORM_HEADER_TO_KEY.get(norm_header(title))
+    if key:
+        return key
+    return _fallback_key(title)
+
+
+def _fallback_key(title: str) -> str | None:
+    n = norm_header(title)
+    if not n:
+        return None
+    low = n.lower().replace("і", "и").replace("є", "е")
+    for header, key in NORM_HEADER_TO_KEY.items():
+        if header.lower() == low:
+            return key
+    slug = re.sub(r"[^a-z0-9]+", "_", unicodedata.normalize("NFKD", low).encode("ascii", "ignore").decode())
+    slug = slug.strip("_")[:48]
+    return f"norm_{slug}" if slug else None
+
+
+def norm_indicator_meta(key: str) -> dict[str, str | bool]:
+    calc = _CALC_META.get(key)
+    if calc:
+        return {
+            "key": key,
+            "label": str(calc["label"]),
+            "unit": str(calc.get("unit") or ""),
+            "inCalc": True,
+        }
+    extra = _EXTRA_NORM_META.get(key, {})
+    return {
+        "key": key,
+        "label": extra.get("label", key),
+        "unit": extra.get("unit", ""),
+        "inCalc": key in _CALC_KEYS,
+    }
+
+
+def list_norm_indicators() -> list[dict[str, str | bool]]:
+    keys: list[str] = []
+    seen: set[str] = set()
+    for key in NORM_HEADER_TO_KEY.values():
+        if key not in seen:
+            seen.add(key)
+            keys.append(key)
+    for key in sorted(_EXTRA_NORM_META):
+        if key not in seen:
+            keys.append(key)
+    return [norm_indicator_meta(k) for k in keys]
+
+
+def calc_norm_keys() -> frozenset[str]:
+    return _CALC_KEYS

apps/api/app/modules/zootech/lab/nutrient_keys.py

@@ -0,0 +1,28 @@
+"""Канонические indicator_key для нутриентов компонента (без fuzzy-match)."""
+
+from __future__ import annotations
+
+from typing import Any
+
+from app.modules.zootech.lab.norm_catalog import NORM_HEADER_TO_KEY
+
+
+def augment_with_indicator_keys(data: dict[str, Any] | None) -> dict[str, float]:
+    """Дублирует значения под slug indicator_key."""
+    if not data:
+        return {}
+    out: dict[str, float] = {}
+    for raw_key, raw_val in data.items():
+        try:
+            out[str(raw_key)] = float(raw_val)
+        except (TypeError, ValueError):
+            continue
+    for header, indicator_key in NORM_HEADER_TO_KEY.items():
+        if header in out and indicator_key not in out:
+            out[indicator_key] = out[header]
+    return out
+
+
+def canonicalize_for_storage(data: dict[str, Any] | None) -> dict[str, float]:
+    """При записи в EAV: slug indicator_key + исходный заголовок."""
+    return augment_with_indicator_keys(data)

apps/api/app/modules/zootech/lab/nutrient_schema.py

@@ -0,0 +1,163 @@
+"""Канонические поля zootech-показателей компонента (parity tab INGREDIENT_NUTRIENT_EDIT_ROWS)."""
+
+from __future__ import annotations
+
+from typing import Any
+
+# column_name -> ключи в API/calc (первый — канон для UI)
+NUTRIENT_FIELD_SPECS: tuple[tuple[str, tuple[str, ...]], ...] = (
+    ("crude_protein", ("Сыр. Протеин",)),
+    ("usp", ("уСП",)),
+    ("rnb", ("RNB", "БРА", "    БРА ")),
+    ("nel_cattle", ("ЧЭЛ- КРС", "    ЧЭЛ- КРС", "ЧЭЛ - КРС Форм")),
+    ("oe_cattle", ("ОЭ-КРС", "    ОЭ-КРС", "OЭ КРС форм")),
+    ("ndf", ("Сырая клетчатка", "Сырая клетч")),
+    ("structural_fiber", ("Структур. клетчатка", "Структур клетч")),
+    ("crude_fat", ("Сырой жир",)),
+)
+
+
+def _normalize_key(value: str) -> str:
+    return " ".join((value or "").split()).strip().lower()
+
+
+def _parse_num(value: Any) -> float | None:
+    if value is None or value == "":
+        return None
+    try:
+        n = float(value)
+    except (TypeError, ValueError):
+        return None
+    return None if n != n else n
+
+
+def dry_matter_g_per_kg(dry_matter_pct: float | None) -> float | None:
+    """СВ г/кг из канонического component.dry_matter (%): 88.7% → 887 г/кг."""
+    dm = _parse_num(dry_matter_pct)
+    if dm is None:
+        return None
+    if 0 < dm <= 100:
+        return dm * 10.0
+    if dm > 100:
+        return dm
+    return None
+
+
+def resolve_sv_g_per_kg(
+    nutrients: dict[str, Any] | None,
+    dry_matter_pct: float | None,
+) -> float | None:
+    """Deprecated alias: СВ только из component.dry_matter (%). nutrients игнорируется."""
+    return dry_matter_g_per_kg(dry_matter_pct)
+
+
+def read_from_mapping(data: dict[str, Any] | None, keys: tuple[str, ...]) -> float | None:
+    if not data:
+        return None
+    for search in keys:
+        target = _normalize_key(search)
+        for k, v in data.items():
+            if _normalize_key(str(k)) != target:
+                continue
+            n = _parse_num(v)
+            if n is not None:
+                return n
+    return None
+
+
+def api_dict_to_column_values(data: dict[str, Any] | None) -> dict[str, float | None]:
+    payload = data or {}
+    out: dict[str, float | None] = {}
+    for col, keys in NUTRIENT_FIELD_SPECS:
+        out[col] = read_from_mapping(payload, keys)
+    return out
+
+
+def derived_to_column_values(data: dict[str, Any] | None) -> dict[str, float | None]:
+    return api_dict_to_column_values(data)
+
+
+def column_values_to_mapping(values: dict[str, float | None]) -> dict[str, float]:
+    out: dict[str, float] = {}
+    for col, keys in NUTRIENT_FIELD_SPECS:
+        val = values.get(col)
+        n = _parse_num(val)
+        if n is not None:
+            out[keys[0]] = n
+    return out
+
+
+def mapping_to_calc_dict(data: dict[str, Any] | None) -> dict[str, float]:
+    if not data:
+        return {}
+    from app.modules.zootech.lab.indicators import RATION_ALL_INDICATORS
+
+    result: dict[str, float] = {}
+    for _col, keys in NUTRIENT_FIELD_SPECS:
+        val = read_from_mapping(data, keys)
+        if val is None:
+            continue
+        for key in keys:
+            result[key] = val
+    seen: set[str] = set()
+    for defn in RATION_ALL_INDICATORS:
+        for key in defn.get("nutrient_keys") or []:
+            if key in seen:
+                continue
+            seen.add(key)
+            val = read_from_mapping(data, (key,))
+            if val is not None:
+                result[key] = val
+    return result
+
+
+def row_to_calc_dict(row: Any | None) -> dict[str, float]:
+    if row is None:
+        return {}
+    result: dict[str, float] = {}
+    for col, keys in NUTRIENT_FIELD_SPECS:
+        val = getattr(row, col, None)
+        if val is None:
+            continue
+        n = _parse_num(val)
+        if n is None:
+            continue
+        for key in keys:
+            result[key] = n
+    return result
+
+
+def row_to_api_dict(row: Any | None) -> dict[str, float]:
+    """API nutrients — канонические ключи tab."""
+    if row is None:
+        return {}
+    out: dict[str, float] = {}
+    for col, keys in NUTRIENT_FIELD_SPECS:
+        val = getattr(row, col, None)
+        n = _parse_num(val)
+        if n is not None:
+            out[keys[0]] = n
+    return out
+
+
+def row_is_empty(row: Any | None) -> bool:
+    if row is None:
+        return True
+    for col, _keys in NUTRIENT_FIELD_SPECS:
+        if _parse_num(getattr(row, col, None)) is not None:
+            return False
+    return True
+
+
+def legacy_json_to_column_values(raw: str | None) -> dict[str, float | None]:
+    if not raw or not str(raw).strip() or str(raw).strip() == "{}":
+        return {col: None for col, _ in NUTRIENT_FIELD_SPECS}
+    import json
+
+    try:
+        data = json.loads(raw)
+    except (json.JSONDecodeError, TypeError):
+        data = {}
+    if not isinstance(data, dict):
+        data = {}
+    return api_dict_to_column_values(data)

apps/api/app/modules/zootech/lab/reference_profiles.py

@@ -0,0 +1,9 @@
+"""Справочные zootech-профили (norm_*) vs пользовательские стандарты."""
+
+from __future__ import annotations
+
+_REFERENCE_PREFIX = "norm_"
+
+
+def is_reference_profile(profile_key: str | None) -> bool:
+    return bool(profile_key) and profile_key.startswith(_REFERENCE_PREFIX)

apps/api/app/modules/zootech/lab/seed_paths.py

@@ -0,0 +1,23 @@
+"""Пути к seed-данным WESP (data/seed/)."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+_API_ROOT = Path(__file__).resolve().parents[4]
+_REPO_ROOT = _API_ROOT.parent.parent
+
+
+def seed_dir() -> Path:
+    local = _API_ROOT / "data" / "seed"
+    if local.exists():
+        return local
+    return _REPO_ROOT / "WESP_REL" / "data" / "seed"
+
+
+def norms_dir() -> Path:
+    return seed_dir() / "norms"
+
+
+def nutrients_dir() -> Path:
+    return seed_dir() / "nutrients"

apps/api/app/modules/zootech/lab/serde/__init__.py

@@ -0,0 +1,3 @@
+from .api import ration_to_api
+
+__all__ = ["ration_to_api"]

apps/api/app/modules/zootech/lab/serde/api.py

@@ -0,0 +1,52 @@
+from __future__ import annotations
+
+from typing import Any
+
+from app.modules.zootech.lab.dto.ration import RationSnapshot
+
+
+def ration_to_api(snapshot: RationSnapshot) -> dict[str, Any]:
+    return {
+        "recipeId": snapshot.recipe_id,
+        "recipeName": snapshot.recipe_name,
+        "rationType": snapshot.ration_type,
+        "headsPerTrip": snapshot.heads_per_trip,
+        "exists": snapshot.exists,
+        "animalProfileId": snapshot.animal_profile_id,
+        "params": snapshot.params,
+        "norms": snapshot.norms,
+        "normsProfileKey": snapshot.norms_profile_key,
+        "legacyNormsRemapped": snapshot.legacy_norms_remapped,
+        "rationResults": snapshot.ration_results,
+        "compoundResults": snapshot.compound_results,
+        "calculatedAt": snapshot.calculated_at,
+        "lines": [
+            {
+                "id": line.id,
+                "componentId": line.component_id,
+                "ingredientName": line.ingredient_name,
+                "rowIndex": line.row_index,
+                "dailyKg": line.daily_kg,
+                "inRation": line.in_ration,
+                "inCompound": line.in_compound,
+                "dryMatter": line.dry_matter,
+                "pricePerKg": line.price_per_kg,
+            }
+            for line in snapshot.lines
+        ],
+    }
+
+
+def diff_to_api(diff: dict[str, Any]) -> dict[str, Any]:
+    return {
+        "hasChanges": diff.get("has_changes", False),
+        "lines": [
+            {
+                "componentId": row.get("component_id"),
+                "masterKg": row.get("master_kg"),
+                "executionKg": row.get("execution_kg"),
+                "reasons": row.get("reasons", []),
+            }
+            for row in diff.get("lines", [])
+        ],
+    }

apps/api/app/modules/zootech/lab/services/component_nutrients.py

@@ -0,0 +1,249 @@
+from __future__ import annotations
+
+import uuid
+from typing import Any
+
+from app.modules.zootech.wesp_bridge_db import db
+from app.modules.zootech.lab.calc.feed_groups import classify_feed_group
+from app.modules.zootech.lab.calc.gfe_policies import DeriveContext
+from app.modules.zootech.lab.calc.ingredient_catalog import DERIVED_HEADERS
+from app.modules.zootech.lab.calc.ingredient_derive import derive_ingredient_nutrients
+from app.modules.zootech.lab.models import LabComponentNutrientValue
+from app.modules.zootech.lab.nutrient_keys import augment_with_indicator_keys, canonicalize_for_storage
+from app.modules.zootech.lab.nutrient_schema import (
+    dry_matter_g_per_kg,
+    mapping_to_calc_dict,
+    read_from_mapping,
+)
+from app.modules.zootech.wesp_bridge_models import Component
+
+_DERIVE_INPUT_KEYS = ("СВ", "Сыр. Протеин", "Сырая клетч", "Сырой жир")
+_EAV_SKIP_KEYS = frozenset({"СВ"})
+_MAIN_FEED_KEYS = ("Осн.Корм", "СВ Основной корм")
+
+
+def derive_context_for_component(
+    component_id: str | None,
+    merged: dict[str, Any] | None = None,
+) -> DeriveContext:
+    """Контекст derive: тип корма + признак основного корма."""
+    main_feed = read_from_mapping(merged or {}, _MAIN_FEED_KEYS)
+    is_main = main_feed is not None and float(main_feed) > 0
+    feed_group: str = "unknown"
+    if component_id:
+        comp = Component.query.filter_by(id=component_id, is_deleted=False).first()
+        if comp is not None:
+            feed_group = classify_feed_group(comp)
+    return DeriveContext(feed_group=feed_group, is_main_feed=is_main)  # type: ignore[arg-type]
+
+
+def _component_dry_matter_pct(component_id: str | None) -> float | None:
+    if not component_id:
+        return None
+    comp = Component.query.get(component_id)
+    return comp.dry_matter if comp else None
+
+
+def _inject_sv_for_derive(merged: dict[str, Any], dry_matter_pct: float | None) -> dict[str, Any]:
+    out = dict(merged)
+    sv = dry_matter_g_per_kg(dry_matter_pct)
+    if sv is not None:
+        out["СВ"] = sv
+    return out
+
+
+def _should_derive(merged: dict[str, Any]) -> bool:
+    """Derive при полном базовом вводе (СВ из component.dry_matter + 3 показателя)."""
+    return all(read_from_mapping(merged, (k,)) is not None for k in _DERIVE_INPUT_KEYS)
+
+
+def nutrients_full_dict(component_id: str | None) -> dict[str, float]:
+    if not component_id:
+        return {}
+    rows = LabComponentNutrientValue.query.filter_by(component_id=component_id).all()
+    return {
+        row.nutrient_key: float(row.value)
+        for row in rows
+        if row.value is not None and row.nutrient_key not in _EAV_SKIP_KEYS
+    }
+
+
+def nutrients_api_dict(component_id: str | None) -> dict[str, float]:
+    return nutrients_full_dict(component_id)
+
+
+def _fill_missing_derived(
+    merged: dict[str, float],
+    *,
+    component_id: str | None,
+) -> dict[str, float]:
+    """Добавляет derived-поля (пОВ, переваримые фракции), не перезаписывая введённые лаб. значения."""
+    if not _should_derive(merged):
+        return merged
+    ctx = derive_context_for_component(component_id, merged)
+    derived = derive_ingredient_nutrients(merged, context=ctx)
+    out = dict(merged)
+    for header in DERIVED_HEADERS:
+        if read_from_mapping(out, (header,)) is not None:
+            continue
+        val = read_from_mapping(derived, (header,))
+        if val is not None:
+            out[header] = float(val)
+    return out
+
+
+def _repair_for_ration_calc(
+    full: dict[str, float],
+    dry_matter_pct: float | None,
+    component_id: str | None = None,
+) -> dict[str, float]:
+    merged = _inject_sv_for_derive(full, dry_matter_pct)
+    oe = read_from_mapping(merged, ("ОЭ-КРС", "    ОЭ-КРС"))
+    nel = read_from_mapping(merged, ("ЧЭЛ- КРС", "    ЧЭЛ- КРС"))
+    energy_bad = (oe is not None and oe < 0) or (nel is not None and nel < 0)
+    if energy_bad:
+        minimal = {key: read_from_mapping(merged, (key,)) for key in _DERIVE_INPUT_KEYS}
+        if all(v is not None for v in minimal.values()):
+            ctx = derive_context_for_component(component_id, merged)
+            merged.update(derive_ingredient_nutrients(minimal, context=ctx))
+    else:
+        merged = _fill_missing_derived(merged, component_id=component_id)
+    return merged
+
+
+def _calc_dict_from_full(
+    full: dict[str, float],
+    dry_matter_pct: float | None,
+    component_id: str | None,
+) -> dict[str, float]:
+    repaired = _repair_for_ration_calc(full, dry_matter_pct, component_id)
+    return mapping_to_calc_dict(augment_with_indicator_keys(repaired))
+
+
+def nutrients_calc_dict(component_id: str | None) -> dict[str, float]:
+    full = nutrients_full_dict(component_id)
+    dry_matter_pct = _component_dry_matter_pct(component_id)
+    return _calc_dict_from_full(full, dry_matter_pct, component_id)
+
+
+def nutrients_calc_dict_batch(component_ids: list[str]) -> dict[str, dict[str, float]]:
+    """Batch-load EAV + dry_matter for formulate (one SQL round-trip per table)."""
+    unique = list(dict.fromkeys(cid for cid in component_ids if cid))
+    if not unique:
+        return {}
+
+    eav_by_id: dict[str, dict[str, float]] = {cid: {} for cid in unique}
+    rows = LabComponentNutrientValue.query.filter(
+        LabComponentNutrientValue.component_id.in_(unique),
+    ).all()
+    for row in rows:
+        if row.value is None or row.nutrient_key in _EAV_SKIP_KEYS:
+            continue
+        eav_by_id.setdefault(row.component_id, {})[row.nutrient_key] = float(row.value)
+
+    dm_by_id: dict[str, float | None] = {cid: None for cid in unique}
+    for comp in Component.query.filter(
+        Component.id.in_(unique),
+        Component.is_deleted.is_(False),
+    ).all():
+        dm_by_id[comp.id] = comp.dry_matter
+
+    return {
+        cid: _calc_dict_from_full(eav_by_id.get(cid, {}), dm_by_id.get(cid), cid)
+        for cid in unique
+    }
+
+
+def nutrients_is_empty(component_id: str | None) -> bool:
+    if not component_id:
+        return True
+    return (
+        LabComponentNutrientValue.query.filter(
+            LabComponentNutrientValue.component_id == component_id,
+            LabComponentNutrientValue.nutrient_key.notin_(_EAV_SKIP_KEYS),
+        ).first()
+        is None
+    )
+
+
+def _upsert_eav(component_id: str, nutrients: dict[str, float]) -> None:
+    existing = {
+        row.nutrient_key: row
+        for row in LabComponentNutrientValue.query.filter_by(component_id=component_id).all()
+    }
+    for key in list(existing):
+        if key in _EAV_SKIP_KEYS:
+            db.session.delete(existing[key])
+    existing = {k: v for k, v in existing.items() if k not in _EAV_SKIP_KEYS}
+    for key, value in nutrients.items():
+        if value is None or key in _EAV_SKIP_KEYS:
+            continue
+        row = existing.get(key)
+        if row is None:
+            row = LabComponentNutrientValue(
+                id=str(uuid.uuid4()),
+                component_id=component_id,
+                nutrient_key=key,
+                value=float(value),
+            )
+            db.session.add(row)
+            existing[key] = row
+        else:
+            row.value = float(value)
+
+
+def _prepare_payload(
+    component_id: str,
+    nutrients: dict[str, Any] | None,
+    *,
+    pin: bool = False,
+) -> dict[str, float]:
+    dry_matter_pct = _component_dry_matter_pct(component_id)
+    merged = _inject_sv_for_derive(
+        {**nutrients_full_dict(component_id), **(nutrients or {})},
+        dry_matter_pct,
+    )
+    if pin:
+        payload = merged
+    elif _should_derive(merged):
+        ctx = derive_context_for_component(component_id, merged)
+        derived = derive_ingredient_nutrients(merged, context=ctx)
+        payload = {**merged, **derived}
+    else:
+        payload = merged
+    stored = canonicalize_for_storage(payload)
+    return {k: v for k, v in stored.items() if k not in _EAV_SKIP_KEYS}
+
+
+def save_component_nutrients(
+    component_id: str,
+    nutrients: dict[str, Any] | None,
+    *,
+    user_id: str = "system",
+    pin: bool = False,
+) -> dict[str, Any]:
+    """Единственная точка записи EAV + derive. Возвращает stored dict и affectedRecipeIds."""
+    stored = _prepare_payload(component_id, nutrients, pin=pin)
+    _upsert_eav(component_id, stored)
+    from app.modules.zootech.lab.services.ration_recalc import find_rations_by_component
+
+    affected = find_rations_by_component(component_id)
+    return {"stored": stored, "affectedRecipeIds": affected, "userId": user_id}
+
+
+# Backward-compatible aliases for tests and gradual migration
+def upsert_from_api_dict(component_id: str, nutrients: dict[str, Any] | None) -> dict[str, float]:
+    result = save_component_nutrients(component_id, nutrients)
+    return result["stored"]
+
+
+def pin_nutrient_values(component_id: str, values: dict[str, float]) -> None:
+    save_component_nutrients(component_id, values, pin=True)
+
+
+def upsert_from_column_values(component_id: str, values: dict[str, float | None]) -> dict[str, float]:
+    from app.modules.zootech.lab.nutrient_schema import column_values_to_mapping
+
+    merged = nutrients_full_dict(component_id)
+    merged.update(column_values_to_mapping(values))
+    return upsert_from_api_dict(component_id, merged)

apps/api/app/modules/zootech/lab/services/norms_params.py

@@ -0,0 +1,52 @@
+"""Сериализация параметров методики норм на профиле."""
+
+from __future__ import annotations
+
+import json
+from typing import Any
+
+from app.modules.zootech.lab.calc.norms_resolver import NormsParams
+from app.modules.zootech.lab.models import LabAnimalProfile
+
+
+def load_norms_params(profile: LabAnimalProfile) -> NormsParams:
+    raw = profile.norms_params_json
+    if not raw:
+        return NormsParams()
+    try:
+        data = json.loads(raw)
+    except (json.JSONDecodeError, TypeError):
+        return NormsParams()
+    return NormsParams.from_dict(data if isinstance(data, dict) else {})
+
+
+def save_norms_params(profile: LabAnimalProfile, params: NormsParams | dict[str, Any] | None) -> None:
+    if params is None:
+        profile.norms_params_json = None
+        return
+    if isinstance(params, NormsParams):
+        payload = {
+            k: v
+            for k, v in (
+                ("milkFatPct", params.milk_fat_pct),
+                ("lactationNo", params.lactation_no),
+                ("lactationStage", params.lactation_stage),
+                ("bodyCondition", params.body_condition),
+                ("housingSystem", params.housing_system),
+                ("koncOeSv", params.konc_oe_sv),
+            )
+            if v is not None
+        }
+    else:
+        payload = dict(params)
+    profile.norms_params_json = json.dumps(payload, ensure_ascii=False) if payload else None
+
+
+def norms_params_api(profile: LabAnimalProfile) -> dict[str, Any]:
+    if not profile.norms_params_json:
+        return {}
+    try:
+        data = json.loads(profile.norms_params_json)
+        return data if isinstance(data, dict) else {}
+    except (json.JSONDecodeError, TypeError):
+        return {}

apps/api/app/modules/zootech/lab/services/profile_norms.py

@@ -0,0 +1,226 @@
+from __future__ import annotations
+
+import json
+from typing import Any
+
+from app.modules.zootech.wesp_bridge_db import db
+from app.modules.zootech.lab.calc.norms_resolver import NormsResolveRequest, normalize_norms_method, resolve_norms
+from app.modules.zootech.lab.services.norms_params import load_norms_params
+from app.modules.zootech.lab.calc.norms import merge_norms_from_profile
+from app.modules.zootech.lab.calc.nutrients import parse_num
+from app.modules.zootech.lab.models import LabAnimalProfile, LabProfileNorm
+from app.modules.zootech.wesp_bridge_models import default_uuid
+
+
+def _parse_profile_payload(data: Any) -> dict[str, Any]:
+    if data is None:
+        return {}
+    if isinstance(data, str):
+        try:
+            data = json.loads(data or "{}")
+        except (json.JSONDecodeError, TypeError):
+            return {}
+    return data if isinstance(data, dict) else {}
+
+
+def load_norms_dict(profile_id: str | None) -> dict[str, dict[str, float | None]]:
+    if not profile_id:
+        return {}
+    rows = (
+        LabProfileNorm.query.filter_by(profile_id=profile_id)
+        .order_by(LabProfileNorm.indicator_key)
+        .all()
+    )
+    return {
+        row.indicator_key: {
+            "min": parse_num(row.min_value),
+            "max": parse_num(row.max_value),
+        }
+        for row in rows
+    }
+
+
+def resolve_norms_for_profile(
+    profile: LabAnimalProfile,
+    *,
+    method: str | None = None,
+    params_override: dict | None = None,
+    force_dynamic: bool = False,
+) -> dict[str, dict[str, float | None]]:
+    """Нормы для расчёта рациона по выбранной методике."""
+    stored = load_norms_dict(profile.id)
+    norms_method = normalize_norms_method(method or profile.norms_method)
+    params = load_norms_params(profile)
+    if params_override:
+        from app.modules.zootech.lab.calc.norms_resolver import NormsParams
+
+        base = {
+            "milkFatPct": params.milk_fat_pct,
+            "lactationNo": params.lactation_no,
+            "lactationStage": params.lactation_stage,
+            "bodyCondition": params.body_condition,
+            "housingSystem": params.housing_system,
+            "koncOeSv": params.konc_oe_sv,
+        }
+        base.update(params_override)
+        params = NormsParams.from_dict(base)
+    resolved, _ = resolve_norms(
+        NormsResolveRequest(
+            method=norms_method,
+            stored=stored,
+            mass_kg=profile.mass_kg,
+            milk_yield_kg=profile.milk_yield_kg,
+            ration_type=profile.ration_type,
+            force_dynamic=force_dynamic,
+            params=params,
+        )
+    )
+    return resolved
+
+
+def load_norms_api(profile: LabAnimalProfile, *, resolve_dynamic: bool = True) -> dict[str, Any]:
+    stored = load_norms_dict(profile.id)
+    payload: dict[str, Any] = {}
+    if profile.mass_kg is not None:
+        payload["massKg"] = profile.mass_kg
+    if profile.milk_yield_kg is not None:
+        payload["milkYieldKg"] = profile.milk_yield_kg
+    if profile.external_no is not None:
+        payload["externalNo"] = profile.external_no
+    if stored:
+        payload["indicators"] = stored
+    if resolve_dynamic:
+        resolved, meta = resolve_norms(
+            NormsResolveRequest(
+                method=normalize_norms_method(profile.norms_method),
+                stored=stored,
+                mass_kg=profile.mass_kg,
+                milk_yield_kg=profile.milk_yield_kg,
+                ration_type=profile.ration_type,
+                params=load_norms_params(profile),
+            )
+        )
+        payload["resolvedIndicators"] = resolved
+        payload["normsMethod"] = meta.get("normsMethod", profile.norms_method or "wesp")
+        dynamic = meta.get("dynamicNorms") or meta.get("dynamic") or {}
+        if dynamic:
+            payload["dynamicNorms"] = dynamic
+        if meta.get("meta"):
+            payload["normsMeta"] = meta["meta"]
+        if meta.get("coverage"):
+            payload["coverage"] = meta["coverage"]
+    return payload
+
+
+def clear_profile_norms(profile_id: str) -> None:
+    LabProfileNorm.query.filter_by(profile_id=profile_id).delete(synchronize_session=False)
+
+
+def save_norms_from_payload(profile: LabAnimalProfile, data: Any) -> None:
+    payload = _parse_profile_payload(data)
+    profile.mass_kg = parse_num(payload.get("massKg", payload.get("mass_kg")))
+    if "milkYieldKg" in payload or "milk_yield_kg" in payload:
+        profile.milk_yield_kg = parse_num(payload.get("milkYieldKg", payload.get("milk_yield_kg")))
+    ext = payload.get("externalNo", payload.get("external_no"))
+    profile.external_no = int(ext) if ext is not None and str(ext).strip() != "" else None
+
+    merged = merge_norms_from_profile(payload, profile.ration_type)
+    skip_keys = {"massKg", "mass_kg", "externalNo", "external_no", "indicators"}
+    for key, bounds in payload.items():
+        if key in skip_keys or not isinstance(bounds, dict):
+            continue
+        if "min" in bounds or "max" in bounds:
+            merged[str(key)] = {
+                "min": parse_num(bounds.get("min")),
+                "max": parse_num(bounds.get("max")),
+            }
+    indicators = payload.get("indicators")
+    if isinstance(indicators, dict):
+        for key, bounds in indicators.items():
+            if not isinstance(bounds, dict):
+                continue
+            merged[str(key)] = {
+                "min": parse_num(bounds.get("min")),
+                "max": parse_num(bounds.get("max")),
+            }
+
+    clear_profile_norms(profile.id)
+    for indicator_key, bounds in merged.items():
+        min_v = bounds.get("min")
+        max_v = bounds.get("max")
+        if min_v is None and max_v is None:
+            continue
+        db.session.add(
+            LabProfileNorm(
+                id=default_uuid(),
+                profile_id=profile.id,
+                indicator_key=indicator_key,
+                min_value=min_v,
+                max_value=max_v,
+            )
+        )
+
+
+def import_norms_from_legacy_text(profile: LabAnimalProfile, raw: str | None) -> None:
+    save_norms_from_payload(profile, raw)
+
+
+def _can_sync_racion(profile: LabAnimalProfile) -> bool:
+    method = normalize_norms_method(profile.norms_method)
+    if method not in ("racion_moscow", "racion_piter"):
+        return False
+    if not profile.mass_kg or profile.mass_kg <= 0:
+        return False
+    if not profile.milk_yield_kg or profile.milk_yield_kg <= 0:
+        return False
+    if method == "racion_piter":
+        from app.modules.zootech.lab.services.norms_params import load_norms_params
+
+        params = load_norms_params(profile)
+        if params.konc_oe_sv is None or params.konc_oe_sv <= 0:
+            return False
+    return True
+
+
+def sync_racion_norms_to_profile(profile: LabAnimalProfile) -> int:
+    """Вычислить RACION-нормы и upsert min в lab_profile_norm (max сохраняется)."""
+    if not _can_sync_racion(profile):
+        return 0
+    stored = load_norms_dict(profile.id)
+    resolved, _ = resolve_norms(
+        NormsResolveRequest(
+            method=normalize_norms_method(profile.norms_method),
+            stored=stored,
+            mass_kg=profile.mass_kg,
+            milk_yield_kg=profile.milk_yield_kg,
+            ration_type=profile.ration_type,
+            params=load_norms_params(profile),
+        )
+    )
+    existing = {
+        row.indicator_key: row
+        for row in LabProfileNorm.query.filter_by(profile_id=profile.id).all()
+    }
+    updated = 0
+    for key, bounds in resolved.items():
+        min_v = bounds.get("min")
+        if min_v is None:
+            continue
+        row = existing.get(key)
+        if row is None:
+            db.session.add(
+                LabProfileNorm(
+                    id=default_uuid(),
+                    profile_id=profile.id,
+                    indicator_key=key,
+                    min_value=min_v,
+                    max_value=bounds.get("max"),
+                )
+            )
+            updated += 1
+        elif row.min_value != min_v:
+            row.min_value = min_v
+            if bounds.get("max") is not None:
+                row.max_value = bounds.get("max")
+            updated += 1
+    return updated

apps/api/app/modules/zootech/lab/services/racion_reference.py

@@ -0,0 +1,156 @@
+"""Импорт и загрузка справочников RACION из БД."""
+
+from __future__ import annotations
+
+import json
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any
+
+from app.modules.zootech.wesp_bridge_db import db
+from app.modules.zootech.lab.calc.racion.tables import clear_tables_cache
+from app.modules.zootech.lab.models import (
+    LabRacionNormyInfo,
+    LabRacionNormyMoskwa,
+    LabRacionNormyMoskwaMeta,
+    LabRacionNormyPiter,
+    LabRacionNormyPiterMeta,
+)
+from app.modules.zootech.lab.seed_paths import seed_dir
+from app.modules.zootech.wesp_bridge_models import default_uuid
+
+_SEED_DIR = seed_dir() / "racion"
+
+
+@dataclass
+class RacionReferenceImportStats:
+    moskwa_rows: int = 0
+    piter_rows: int = 0
+    info_rows: int = 0
+    errors: list[str] = field(default_factory=list)
+
+
+def _read_seed(name: str) -> dict:
+    path = _SEED_DIR / name
+    return json.loads(path.read_text(encoding="utf-8"))
+
+
+def import_racion_reference(*, replace: bool = True) -> RacionReferenceImportStats:
+    stats = RacionReferenceImportStats()
+    try:
+        moskwa = _read_seed("moskwa_lactir.json")
+        piter = _read_seed("piter_lactir.json")
+        info = _read_seed("normy_info.json")
+    except (OSError, json.JSONDecodeError) as exc:
+        stats.errors.append(str(exc))
+        return stats
+
+    if replace:
+        LabRacionNormyMoskwa.query.delete(synchronize_session=False)
+        LabRacionNormyMoskwaMeta.query.delete(synchronize_session=False)
+        LabRacionNormyPiter.query.delete(synchronize_session=False)
+        LabRacionNormyPiterMeta.query.delete(synchronize_session=False)
+        LabRacionNormyInfo.query.delete(synchronize_session=False)
+
+    for row in moskwa.get("rows") or []:
+        db.session.add(
+            LabRacionNormyMoskwa(
+                id=default_uuid(),
+                npitv=int(row["npitv"]),
+                pom=int(row.get("pom") or 1),
+                koef=row.get("koef"),
+                popr_k_json=json.dumps(row.get("popr_k") or [], ensure_ascii=False),
+            )
+        )
+        stats.moskwa_rows += 1
+
+    db.session.add(
+        LabRacionNormyMoskwaMeta(
+            id=default_uuid(),
+            meta_key="udoy_boundaries",
+            meta_json=json.dumps(moskwa.get("udoy_boundaries") or [], ensure_ascii=False),
+        )
+    )
+
+    for entry in piter.get("entries") or []:
+        db.session.add(
+            LabRacionNormyPiter(
+                id=default_uuid(),
+                npitv=int(entry["npitv"]),
+                konc=float(entry["konc"]),
+                udoy=float(entry["udoy"]),
+                normy_json=json.dumps(entry.get("normy") or [], ensure_ascii=False),
+            )
+        )
+        stats.piter_rows += 1
+
+    db.session.add(
+        LabRacionNormyPiterMeta(
+            id=default_uuid(),
+            meta_key="mass_kg_values",
+            meta_json=json.dumps(piter.get("mass_kg_values") or [], ensure_ascii=False),
+        )
+    )
+
+    for row in info.get("rows") or []:
+        db.session.add(
+            LabRacionNormyInfo(
+                id=default_uuid(),
+                nperem=int(row["nperem"]),
+                znachenie_json=json.dumps(row.get("znachenie") or [], ensure_ascii=False),
+            )
+        )
+        stats.info_rows += 1
+
+    db.session.commit()
+    clear_tables_cache()
+    return stats
+
+
+def load_moskwa_lactir_from_db() -> dict | None:
+    if LabRacionNormyMoskwa.query.count() == 0:
+        return None
+    rows = []
+    for r in LabRacionNormyMoskwa.query.order_by(LabRacionNormyMoskwa.npitv, LabRacionNormyMoskwa.pom).all():
+        rows.append(
+            {
+                "npitv": r.npitv,
+                "pom": r.pom,
+                "koef": r.koef,
+                "popr_k": json.loads(r.popr_k_json or "[]"),
+            }
+        )
+    meta = LabRacionNormyMoskwaMeta.query.filter_by(meta_key="udoy_boundaries").first()
+    boundaries = json.loads(meta.meta_json or "[]") if meta else []
+    return {"udoy_boundaries": boundaries, "rows": rows}
+
+
+def load_piter_lactir_from_db() -> dict | None:
+    if LabRacionNormyPiter.query.count() == 0:
+        return None
+    entries = []
+    for r in LabRacionNormyPiter.query.order_by(
+        LabRacionNormyPiter.npitv, LabRacionNormyPiter.konc, LabRacionNormyPiter.udoy
+    ).all():
+        entries.append(
+            {
+                "npitv": r.npitv,
+                "konc": r.konc,
+                "udoy": r.udoy,
+                "normy": json.loads(r.normy_json or "[]"),
+            }
+        )
+    meta = LabRacionNormyPiterMeta.query.filter_by(meta_key="mass_kg_values").first()
+    masses = json.loads(meta.meta_json or "[]") if meta else [400, 450, 500, 550, 600, 650, 700, 750]
+    return {"mass_kg_values": masses, "entries": entries}
+
+
+def load_normy_info_from_db() -> dict | None:
+    if LabRacionNormyInfo.query.count() == 0:
+        return None
+    rows = []
+    for r in LabRacionNormyInfo.query.order_by(LabRacionNormyInfo.nperem).all():
+        rows.append({"nperem": r.nperem, "znachenie": json.loads(r.znachenie_json or "[]")})
+    by_nperem = {row["nperem"]: row["znachenie"] for row in rows}
+    mass_kg_values = by_nperem.get(14) or by_nperem.get(13) or [400, 450, 500, 550, 600, 650, 700, 750]
+    return {"rows": rows, "mass_kg_values": mass_kg_values}

apps/api/app/modules/zootech/lab/services/ration_calc_store.py

@@ -0,0 +1,204 @@
+from __future__ import annotations
+
+import logging
+from datetime import datetime
+from typing import Any
+
+from app.modules.zootech.wesp_bridge_db import db
+from app.modules.zootech.lab.calc.nutrients import parse_num
+from app.modules.zootech.lab.indicators import label_to_indicator_key
+from app.modules.zootech.lab.models import (
+    LabRationCalcIndicator,
+    LabRationCalcTotal,
+    LabRationCompoundLine,
+    LabRecipeRation,
+)
+from app.modules.zootech.wesp_bridge_models import default_uuid
+
+_log = logging.getLogger("app.lab.calc")
+
+
+def _indicator_key(row: dict[str, Any]) -> str | None:
+    key = row.get("key")
+    if key:
+        return str(key)
+    label = row.get("label")
+    if label:
+        return label_to_indicator_key(str(label))
+    return None
+
+
+def _log_calc_errors(recipe_id: str, errors: list[str] | None) -> None:
+    for message in errors or []:
+        text = str(message or "").strip()
+        if text:
+            _log.warning("ration calc recipe=%s: %s", recipe_id, text)
+
+
+def clear_calc(recipe_id: str) -> None:
+    for model in (
+        LabRationCalcTotal,
+        LabRationCalcIndicator,
+        LabRationCompoundLine,
+    ):
+        model.query.filter_by(recipe_id=recipe_id).delete(synchronize_session=False)
+
+
+def _save_totals(recipe_id: str, scope: str, totals: list[dict[str, Any]] | None) -> None:
+    for idx, row in enumerate(totals or []):
+        db.session.add(
+            LabRationCalcTotal(
+                id=default_uuid(),
+                recipe_id=recipe_id,
+                scope=scope,
+                metric_key=str(row.get("key") or f"metric_{idx}"),
+                label=str(row.get("label") or ""),
+                value=parse_num(row.get("value")),
+                sort_order=idx,
+            )
+        )
+
+
+def _save_indicators(
+    recipe_id: str,
+    scope: str,
+    indicators: list[dict[str, Any]] | None,
+) -> None:
+    for idx, row in enumerate(indicators or []):
+        db.session.add(
+            LabRationCalcIndicator(
+                id=default_uuid(),
+                recipe_id=recipe_id,
+                scope=scope,
+                indicator_key=_indicator_key(row),
+                label=str(row.get("label") or ""),
+                unit=str(row.get("unit") or ""),
+                min_value=parse_num(row.get("min")),
+                max_value=parse_num(row.get("max")),
+                content=parse_num(row.get("content")),
+                diff=parse_num(row.get("diff")),
+                sort_order=idx,
+            )
+        )
+
+
+def _save_compound_lines(recipe_id: str, lines: list[dict[str, Any]] | None) -> None:
+    for idx, row in enumerate(lines or []):
+        db.session.add(
+            LabRationCompoundLine(
+                id=default_uuid(),
+                recipe_id=recipe_id,
+                row_index=idx,
+                ingredient_name=row.get("ingredient_name"),
+                daily_kg=parse_num(row.get("daily_kg")),
+                share_pct=parse_num(row.get("share_pct")),
+            )
+        )
+
+
+def save_calc_result(recipe_id: str, result: dict[str, Any], header: LabRecipeRation) -> None:
+    clear_calc(recipe_id)
+    _log_calc_errors(recipe_id, result.get("errors"))
+    header.calc_engine = str(result.get("engine") or "native")
+    calculated_at = result.get("calculated_at")
+    if calculated_at:
+        try:
+            header.calculated_at = datetime.fromisoformat(str(calculated_at).replace("Z", "+00:00"))
+        except ValueError:
+            header.calculated_at = datetime.utcnow()
+    else:
+        header.calculated_at = datetime.utcnow()
+
+    _save_totals(recipe_id, "ration", result.get("totals"))
+    _save_indicators(recipe_id, "ration", result.get("indicators"))
+
+    compound = result.get("compound")
+    if compound:
+        _save_totals(recipe_id, "compound", compound.get("totals"))
+        _save_indicators(recipe_id, "compound", compound.get("indicators"))
+        _save_compound_lines(recipe_id, compound.get("lines"))
+
+
+def _load_totals(recipe_id: str, scope: str) -> list[dict[str, Any]]:
+    rows = (
+        LabRationCalcTotal.query.filter_by(recipe_id=recipe_id, scope=scope)
+        .order_by(LabRationCalcTotal.sort_order)
+        .all()
+    )
+    return [
+        {"key": row.metric_key, "label": row.label, "value": row.value}
+        for row in rows
+    ]
+
+
+def _load_indicators(recipe_id: str, scope: str) -> list[dict[str, Any]]:
+    rows = (
+        LabRationCalcIndicator.query.filter_by(recipe_id=recipe_id, scope=scope)
+        .order_by(LabRationCalcIndicator.sort_order)
+        .all()
+    )
+    return [
+        {
+            "key": row.indicator_key,
+            "label": row.label,
+            "unit": row.unit,
+            "min": row.min_value,
+            "max": row.max_value,
+            "content": row.content,
+            "diff": row.diff,
+        }
+        for row in rows
+    ]
+
+
+def load_compound_results(recipe_id: str) -> dict[str, Any]:
+    totals = _load_totals(recipe_id, "compound")
+    indicators = _load_indicators(recipe_id, "compound")
+    lines = (
+        LabRationCompoundLine.query.filter_by(recipe_id=recipe_id)
+        .order_by(LabRationCompoundLine.row_index)
+        .all()
+    )
+    if not totals and not indicators and not lines:
+        return {}
+    return {
+        "totals": totals,
+        "indicators": indicators,
+        "lines": [
+            {
+                "ingredient_name": row.ingredient_name,
+                "daily_kg": row.daily_kg,
+                "share_pct": row.share_pct,
+            }
+            for row in lines
+        ],
+    }
+
+
+def load_ration_results(recipe_id: str, header: LabRecipeRation | None) -> dict[str, Any]:
+    if header is None or header.calculated_at is None:
+        return {}
+    totals = _load_totals(recipe_id, "ration")
+    indicators = _load_indicators(recipe_id, "ration")
+    if not totals and not indicators:
+        return {}
+    compound = load_compound_results(recipe_id)
+    payload: dict[str, Any] = {
+        "calculated_at": header.calculated_at.isoformat(),
+        "engine": header.calc_engine or "native",
+        "totals": totals,
+        "indicators": indicators,
+    }
+    if compound:
+        payload["compound"] = compound
+    return payload
+
+
+def load_params(recipe_id: str, header: LabRecipeRation | None) -> dict[str, Any]:
+    if header is None or not header.seed_source:
+        return {}
+    if header.seed_source == "execution":
+        return {"seeded_from": "execution"}
+    if header.seed_source == "synced_from":
+        return {"synced_from": "execution"}
+    return {"source": header.seed_source}

apps/api/app/modules/zootech/lab/services/ration_recalc.py

@@ -0,0 +1,40 @@
+from __future__ import annotations
+
+from app.modules.zootech.lab.models import LabRationLine
+
+
+def find_rations_by_component(component_id: str) -> list[str]:
+    """Recipe IDs с не удалёнными строками рациона, использующими component."""
+    if not component_id:
+        return []
+    rows = (
+        LabRationLine.query.filter_by(component_id=component_id, is_deleted=False)
+        .with_entities(LabRationLine.recipe_id)
+        .distinct()
+        .all()
+    )
+    return sorted({str(r[0]) for r in rows if r[0]})
+
+
+def recalculate_rations(recipe_ids: list[str], user_id: str = "system") -> dict:
+    from app.modules.zootech.lab.commands.recalculate import recalculate_ration
+
+    ok: list[str] = []
+    failed: dict[str, str] = {}
+    results: dict[str, dict] = {}
+    for recipe_id in recipe_ids:
+        try:
+            results[recipe_id] = recalculate_ration(recipe_id, user_id)
+            ok.append(recipe_id)
+        except Exception as exc:
+            failed[recipe_id] = str(exc)
+    return {"ok": ok, "failed": failed, "results": results}
+
+
+def on_component_nutrients_changed(component_id: str, user_id: str = "system") -> dict:
+    """Найти и пересчитать все рационы с данным компонентом (для будущего UI)."""
+    recipe_ids = find_rations_by_component(component_id)
+    if not recipe_ids:
+        return {"recipeIds": [], "ok": [], "failed": {}}
+    report = recalculate_rations(recipe_ids, user_id)
+    return {"recipeIds": recipe_ids, **report}

apps/api/app/modules/zootech/lab/services/seed_norms_catalog.py

@@ -0,0 +1,83 @@
+"""Справочник zootech-норм из БД (lab_animal_profile norm_* + lab_profile_norm)."""
+
+from __future__ import annotations
+
+from app.modules.zootech.lab.commands.import_seed import norm_profile_key
+from app.modules.zootech.lab.models import LabAnimalProfile
+from app.modules.zootech.lab.reference_profiles import is_reference_profile
+from app.modules.zootech.lab.services.profile_norms import load_norms_dict
+
+_VALID_RATIONS = frozenset({"DAIRY", "BEEF"})
+
+
+class ReferenceNormsCatalogEmptyError(LookupError):
+    """Справочник norm_* в БД пуст — нужен import_seed.py --norms."""
+
+
+def _normalize_ration(ration_type: str) -> str:
+    ration = (ration_type or "").strip().upper()
+    if ration not in _VALID_RATIONS:
+        raise ValueError(f"Неизвестная линейка: {ration_type}")
+    return ration
+
+
+def _reference_profiles_query(ration_type: str):
+    ration = _normalize_ration(ration_type)
+    return (
+        LabAnimalProfile.query.filter(
+            LabAnimalProfile.is_deleted.is_(False),
+            LabAnimalProfile.ration_type == ration,
+            LabAnimalProfile.profile_key.like("norm_%"),
+        )
+        .order_by(LabAnimalProfile.external_no, LabAnimalProfile.profile_key)
+    )
+
+
+def list_seed_norm_catalog(ration_type: str) -> list[dict]:
+    """Краткий список строк справочника для выпадающего списка."""
+    rows = _reference_profiles_query(ration_type).all()
+    if not rows:
+        raise ReferenceNormsCatalogEmptyError(
+            "Справочник пуст — выполните: python3 scripts/import_seed.py --norms"
+        )
+    out: list[dict] = []
+    for profile in rows:
+        if profile.external_no is None:
+            continue
+        indicators = load_norms_dict(profile.id)
+        out.append(
+            {
+                "profileId": profile.id,
+                "profileKey": profile.profile_key,
+                "externalNo": profile.external_no,
+                "label": profile.label,
+                "rationType": profile.ration_type,
+                "massKg": profile.mass_kg,
+                "indicatorCount": len(indicators),
+            }
+        )
+    out.sort(key=lambda row: row["externalNo"])
+    return out
+
+
+def get_seed_norm_entry(ration_type: str, external_no: int) -> dict | None:
+    """Полная строка справочника: метаданные + indicators min/max."""
+    ration = _normalize_ration(ration_type)
+    profile = _reference_profiles_query(ration).filter_by(external_no=external_no).first()
+    if profile is None:
+        profile = LabAnimalProfile.query.filter_by(
+            profile_key=norm_profile_key(ration, external_no),
+            is_deleted=False,
+        ).first()
+    if profile is None or not is_reference_profile(profile.profile_key):
+        return None
+    indicators = load_norms_dict(profile.id)
+    return {
+        "profileId": profile.id,
+        "externalNo": profile.external_no if profile.external_no is not None else external_no,
+        "profileKey": profile.profile_key,
+        "label": profile.label,
+        "rationType": profile.ration_type,
+        "massKg": profile.mass_kg,
+        "indicators": indicators,
+    }

apps/api/app/modules/zootech/lab/templates/print_ration.html

@@ -0,0 +1,51 @@
+<!DOCTYPE html>
+<html lang="ru">
+<head>
+  <meta charset="utf-8">
+  <title>{{ title }}</title>
+  <style>
+    body { font-family: system-ui, sans-serif; margin: 24px; color: #111; }
+    h1 { font-size: 1.25rem; margin: 0 0 8px; }
+    .meta { color: #555; margin-bottom: 16px; font-size: 0.9rem; }
+    table { width: 100%; border-collapse: collapse; margin-bottom: 20px; }
+    th, td { border: 1px solid #ccc; padding: 6px 8px; text-align: left; }
+    th { background: #f5f5f5; }
+    .num { text-align: right; }
+    .indicators { display: flex; flex-wrap: wrap; gap: 8px; }
+    .badge { border: 1px solid #ddd; border-radius: 4px; padding: 4px 8px; font-size: 0.85rem; }
+    @media print { body { margin: 12px; } }
+  </style>
+</head>
+<body>
+  <h1>{{ title }}</h1>
+  <div class="meta">
+    Рецепт: {{ recipe_name }} · Тип: {{ ration_type }} · Голов/рейс: {{ heads }}
+  </div>
+  <table>
+    <thead>
+      <tr>
+        <th>Ингредиент</th>
+        <th class="num">кг/день</th>
+        {% if mode == "compound" %}<th>В комб.</th>{% else %}<th>В рац.</th>{% endif %}
+      </tr>
+    </thead>
+    <tbody>
+      {% for line in lines %}
+      <tr>
+        <td>{{ line.name }}</td>
+        <td class="num">{{ line.daily_kg }}</td>
+        <td>{{ "да" if line.included else "нет" }}</td>
+      </tr>
+      {% endfor %}
+    </tbody>
+  </table>
+  {% if indicators %}
+  <div class="indicators">
+    {% for ind in indicators %}
+    <span class="badge">{{ ind.label }}: {{ ind.content }} {{ ind.unit }}</span>
+    {% endfor %}
+  </div>
+  {% endif %}
+  <script>window.addEventListener("load", () => window.print());</script>
+</body>
+</html>