add physics model and residual to worker

.env.example

@@ -10,5 +10,7 @@ CORS_ORIGINS=
 RUL_WARNING=75
 RUL_CRITICAL=30
 RUL_CLIP=125
+WEATHER_LATITUDE=55.7558
+WEATHER_LONGITUDE=37.6173
 BOT_TOKEN=
 CHAT_ID=

.github/workflows/ci.yml

@@ -90,6 +90,10 @@ jobs:
         working-directory: ml
         run: python baseline_model_check.py ../data/test_FD001.txt --rul ../data/RUL_FD001.txt
 
+      - name: Run physics check
+        working-directory: ml
+        run: python physics_check.py ../data/train_FD001.txt
+
   pipeline:
     needs: [migrations, model]
     runs-on: ubuntu-latest
@@ -154,5 +158,7 @@ jobs:
           EQ=$(psql "$PSQL_URL" -tAc "SELECT count(*) FROM equipment")
           SR=$(psql "$PSQL_URL" -tAc "SELECT count(*) FROM sensor_readings")
           ML=$(psql "$PSQL_URL" -tAc "SELECT count(*) FROM ml_predictions")
-          echo "equipment=$EQ sensor_readings=$SR ml_predictions=$ML"
-          [ "$EQ" -eq 20 ] && [ "$SR" -gt 0 ] && [ "$ML" -eq "$SR" ]
+          PH=$(psql "$PSQL_URL" -tAc "SELECT count(*) FROM physics_predictions")
+          RS=$(psql "$PSQL_URL" -tAc "SELECT count(*) FROM ml_predictions WHERE residual <> 0")
+          echo "equipment=$EQ sensor_readings=$SR ml_predictions=$ML physics_predictions=$PH nonzero_residual=$RS"
+          [ "$EQ" -eq 20 ] && [ "$SR" -gt 0 ] && [ "$ML" -eq "$SR" ] && [ "$PH" -eq "$SR" ] && [ "$RS" -gt 0 ]

README.md

@@ -50,6 +50,8 @@ Frontend: React
 
 **Статусы оборудования по прогнозу RUL:** `normal` — более 75 циклов, `warning` — от 31 до 75, `critical` — 30 циклов и меньше.
 
+**Физическая модель** (`ml/physics.py`) считается воркером вместе с RUL. Фактическая деградация — проекция нормированных показаний 14 датчиков на первую главную компоненту, отмасштабированная так, что 0 — исправный двигатель, 1 — отказ (по двигателям 1–80), со сглаживанием EWMA. Ожидаемая деградация — экспоненциальный закон накопления повреждений D(τ) = a·(e^(b·τ) − 1), которым NASA моделировала износ в C-MAPSS (Saxena et al., 2008). Эквивалентная наработка τ за цикл растёт на коэффициент ускорения по Аррениусу от температуры воздуха из Open-Meteo: при 15 °C он равен 1, при 0 °C — 0,83, при 30 °C — 1,18. Координаты площадки — `WEATHER_LATITUDE` и `WEATHER_LONGITUDE` (по умолчанию Москва). Ожидаемая деградация пишется в `physics_predictions`, невязка «фактическая минус ожидаемая» — в `ml_predictions.residual`. Проверка модели: `python physics_check.py ../data/train_FD001.txt` из `ml/`.
+
 ## Масштаб системы
 
 Целевая аудитория системы - инженеры по эксплуатации и ответственные за производство на промышленных предприятиях. При оценке масштаба в 10 000 уникальных пользователей в сутки предполагается, что система обслуживает порядка N предприятий с M единицами оборудования на каждом, где каждый пользователь в среднем совершает K запросов в день (просмотр дашборда, проверка истории, получение алертов). Основная нагрузка - read-запросы (просмотр данных), запись происходит асинхронно от симулятора/датчиков оборудования, а не от действий пользователей напрямую.
@@ -125,6 +127,7 @@ api/v1 (эндпоинты, валидация) → services (бизнес-ло
 | GET   | `/equipment/{id}/status`   | Текущее состояние: статус, RUL, вероятность дефекта, невязка | < 150 мс |
 | GET   | `/equipment/{id}/history`  | История показаний датчиков (`from_cycle`, `to_cycle`, `limit`) | < 500 мс |
 | GET   | `/equipment/{id}/forecast` | Прогноз деградации: RUL по будущим циклам с доверительным интервалом | < 500 мс |
+| GET   | `/equipment/{id}/physics`  | Физическая модель против фактических показаний: ожидаемая и фактическая деградация, невязка (`limit`) | < 500 мс |
 
 ### Алерты
 

backend/app/api/equipment.py

@@ -2,7 +2,13 @@
 
 from app.core.db import DB
 from app.core.errors import NOT_FOUND, equipment_not_found
-from app.schemas.equipment import EquipmentOut, ForecastOut, ReadingOut, StatusOut
+from app.schemas.equipment import (
+    EquipmentOut,
+    ForecastOut,
+    PhysicsPointOut,
+    ReadingOut,
+    StatusOut,
+)
 from app.services import equipment_service
 
 router = APIRouter(prefix="/equipment", tags=["Оборудование"])
@@ -42,6 +48,13 @@ def get_history(equipment_id: int, db: DB,
     return _found(equipment_service.get_history(db, equipment_id, from_cycle, to_cycle, limit), equipment_id)
 
 
+@router.get("/{equipment_id}/physics", response_model=list[PhysicsPointOut], responses=NOT_FOUND,
+            summary="Физическая модель против фактических показаний: ожидаемая и фактическая деградация")
+def get_physics(equipment_id: int, db: DB,
+                limit: int = Query(500, ge=1, le=5000, description="Максимум записей (берутся последние)")):
+    return _found(equipment_service.get_physics(db, equipment_id, limit), equipment_id)
+
+
 @router.get("/{equipment_id}/forecast", response_model=ForecastOut, responses=NOT_FOUND,
             summary="Прогноз деградации: RUL по будущим циклам с доверительным интервалом")
 def get_forecast(equipment_id: int, db: DB):

backend/app/schemas/equipment.py

@@ -31,6 +31,13 @@ class ReadingOut(BaseModel):
     sensors: dict[str, float] = Field(description="Показания 14 рабочих датчиков: код → значение")
 
 
+class PhysicsPointOut(BaseModel):
+    cycle: int
+    expected: float = Field(description="Ожидаемая деградация по физической модели: 0 — исправный двигатель, 1 — отказ")
+    actual: float = Field(description="Фактическая деградация по показаниям датчиков")
+    residual: float = Field(description="Невязка: фактическая деградация минус ожидаемая")
+
+
 class ForecastPoint(BaseModel):
     cycle: int
     rul: float = Field(description="Ожидаемый остаточный ресурс на этом цикле")

backend/app/services/equipment_service.py

@@ -3,6 +3,7 @@
 from app.core.config import RUL_CLIP, RUL_CRITICAL, RUL_WARNING
 from app.models.equipment import Equipment
 from app.models.ml_predictions import MlPrediction
+from app.models.physics_predictions import PhysicsPrediction
 from app.models.sensor_readings import SensorReading
 
 SENSOR_CODES = ["s2", "s3", "s4", "s7", "s8", "s9", "s11", "s12", "s13", "s14", "s15", "s17", "s20", "s21"]
@@ -115,6 +116,26 @@ def get_history(db: Connection, equipment_id: int, from_cycle: int | None, to_cy
     return rows
 
 
+def get_physics(db: Connection, equipment_id: int, limit: int) -> list[dict] | None:
+    if not equipment_exists(db, equipment_id):
+        return None
+
+    query = (
+        select(PhysicsPrediction.cycle, PhysicsPrediction.predicted_degradation, MlPrediction.residual)
+        .join(MlPrediction, and_(MlPrediction.equipment_id == PhysicsPrediction.equipment_id,
+                                 MlPrediction.cycle == PhysicsPrediction.cycle))
+        .where(PhysicsPrediction.equipment_id == equipment_id)
+        .order_by(PhysicsPrediction.cycle.desc())
+        .limit(limit)
+    )
+
+    points = [{"cycle": row.cycle, "expected": row.predicted_degradation,
+               "actual": row.predicted_degradation + row.residual, "residual": row.residual}
+              for row in db.execute(query)]
+    points.reverse()
+    return points
+
+
 def get_forecast(db: Connection, equipment_id: int) -> dict | None:
     items = _latest_predictions(db, equipment_id)
     if not items:

backend/pytest.ini

@@ -0,0 +1,3 @@
+[pytest]
+pythonpath = .
+testpaths = tests

backend/tests/conftest.py

@@ -0,0 +1,76 @@
+import os
+from datetime import date
+
+import pytest
+from fastapi.testclient import TestClient
+from sqlalchemy import insert, text
+
+# Тесты очищают таблицы, поэтому БД для них задаётся отдельной переменной:
+# рабочий DATABASE_URL здесь намеренно не используется.
+os.environ["DATABASE_URL"] = os.environ["TEST_DATABASE_URL"]
+os.environ["RUL_WARNING"] = "75"
+os.environ["RUL_CRITICAL"] = "30"
+os.environ["RUL_CLIP"] = "125"
+
+TABLES = "alerts, ml_predictions, physics_predictions, sensor_readings, equipment"
+
+
+@pytest.fixture
+def db():
+    from app.core.db import engine
+
+    with engine.begin() as connection:
+        connection.execute(text(f"TRUNCATE {TABLES} RESTART IDENTITY CASCADE"))
+    with engine.connect() as connection:
+        yield connection
+
+
+@pytest.fixture
+def client(db):
+    from main import app
+
+    return TestClient(app)
+
+
+@pytest.fixture
+def add_equipment(db):
+    from app.models import Equipment
+
+    def add(unit_number: int = 81) -> int:
+        statement = insert(Equipment).values(
+            name=f"Turbofan №{unit_number}", type="turbofan", dataset="FD001",
+            unit_number=unit_number, install_date=date(2024, 1, 1),
+        )
+        equipment_id = db.execute(statement.returning(Equipment.id)).scalar_one()
+        db.commit()
+        return equipment_id
+
+    return add
+
+
+@pytest.fixture
+def add_prediction(db):
+    from app.models import MlPrediction
+
+    def add(equipment_id: int, cycle: int, rul: float) -> None:
+        db.execute(insert(MlPrediction).values(
+            equipment_id=equipment_id, cycle=cycle, predicted_rul=rul,
+            defect_probability=0, residual=0, model_version="test",
+        ))
+        db.commit()
+
+    return add
+
+
+@pytest.fixture
+def add_reading(db):
+    from app.models import SensorReading
+    from app.services.equipment_service import SENSOR_CODES
+
+    def add(equipment_id: int, cycle: int) -> dict[str, float]:
+        sensors = {code: float(i + cycle) for i, code in enumerate(SENSOR_CODES)}
+        db.execute(insert(SensorReading).values(equipment_id=equipment_id, cycle=cycle, **sensors))
+        db.commit()
+        return sensors
+
+    return add

backend/tests/test_physics.py

@@ -0,0 +1,61 @@
+import pytest
+from sqlalchemy import insert
+
+
+@pytest.fixture
+def add_physics(db):
+    from app.models import MlPrediction, PhysicsPrediction
+
+    def add(equipment_id: int, cycle: int, expected: float, residual: float) -> None:
+        db.execute(insert(PhysicsPrediction).values(
+            equipment_id=equipment_id, cycle=cycle, predicted_degradation=expected,
+        ))
+        db.execute(insert(MlPrediction).values(
+            equipment_id=equipment_id, cycle=cycle, predicted_rul=100,
+            defect_probability=0, residual=residual, model_version="test",
+        ))
+        db.commit()
+
+    return add
+
+
+def test_physics_returns_expected_actual_and_residual(client, add_equipment, add_physics):
+    equipment_id = add_equipment()
+    add_physics(equipment_id, 2, expected=0.2, residual=-0.05)
+    add_physics(equipment_id, 1, expected=0.1, residual=0.05)
+
+    response = client.get(f"/equipment/{equipment_id}/physics")
+
+    assert response.status_code == 200
+    points = response.json()
+    assert [p["cycle"] for p in points] == [1, 2]
+    assert points[0]["expected"] == pytest.approx(0.1)
+    assert points[0]["actual"] == pytest.approx(0.15)
+    assert points[0]["residual"] == pytest.approx(0.05)
+    assert points[1]["actual"] == pytest.approx(0.15)
+
+
+def test_physics_limit_keeps_latest_cycles(client, add_equipment, add_physics):
+    equipment_id = add_equipment()
+    for cycle in (1, 2, 3):
+        add_physics(equipment_id, cycle, expected=cycle / 10, residual=0)
+
+    response = client.get(f"/equipment/{equipment_id}/physics", params={"limit": 2})
+
+    assert [p["cycle"] for p in response.json()] == [2, 3]
+
+
+def test_physics_is_empty_before_worker(client, add_equipment, add_reading):
+    equipment_id = add_equipment()
+    add_reading(equipment_id, 1)
+
+    response = client.get(f"/equipment/{equipment_id}/physics")
+
+    assert response.status_code == 200
+    assert response.json() == []
+
+
+def test_physics_unknown_equipment(client, db):
+    response = client.get("/equipment/999/physics")
+
+    assert response.status_code == 404

frontend/src/api.ts

@@ -25,6 +25,13 @@ export interface Reading {
   sensors: Record<string, number>;
 }
 
+export interface PhysicsPoint {
+  cycle: number;
+  expected: number;
+  actual: number;
+  residual: number;
+}
+
 export interface ForecastPoint {
   cycle: number;
   rul: number;
@@ -69,6 +76,7 @@ export const api = {
   getEquipment: (id: number) => request<Equipment>(`/equipment/${id}`),
   getHistory: (id: number, limit = 500) => request<Reading[]>(`/equipment/${id}/history?limit=${limit}`),
   getForecast: (id: number) => request<Forecast>(`/equipment/${id}/forecast`),
+  getPhysics: (id: number, limit = 500) => request<PhysicsPoint[]>(`/equipment/${id}/physics?limit=${limit}`),
   listAlerts: (limit = 100) => request<Alert[]>(`/alerts?limit=${limit}`),
   alertsByEquipment: (id: number, limit = 50) => request<Alert[]>(`/alerts/${id}?limit=${limit}`),
   predict: (id: number) =>

frontend/src/format.ts

@@ -41,3 +41,12 @@ export function formatDateTime(value: string): string {
 export function formatRul(value: number): string {
   return Math.round(value).toString();
 }
+
+export function formatPercent(value: number): string {
+  return `${Math.round(value * 100)}%`;
+}
+
+export function formatResidual(value: number): string {
+  const points = Math.round(value * 100);
+  return `${points > 0 ? "+" : ""}${points} п.п.`;
+}

frontend/src/index.css

@@ -205,17 +205,6 @@ h2 {
 .grid > .card:last-child {
   grid-column: 1 / -1;
 }
-.placeholder {
-  height: 260px;
-  display: flex;
-  align-items: center;
-  justify-content: center;
-  text-align: center;
-  padding: 24px;
-  color: #6b7280;
-  border: 2px dashed #e5e7eb;
-  border-radius: 6px;
-}
 
 .muted {
   color: #6b7280;

frontend/src/pages/EquipmentCard.tsx

@@ -4,6 +4,7 @@ import {
   Area,
   CartesianGrid,
   ComposedChart,
+  Legend,
   Line,
   LineChart,
   ReferenceLine,
@@ -18,7 +19,7 @@ import { AlertsTable } from "../components/AlertsTable";
 import { LiveIndicator } from "../components/LiveIndicator";
 import { Empty, ErrorMessage, Loading } from "../components/State";
 import { StatusBadge } from "../components/StatusBadge";
-import { SENSORS, formatDate, formatRul } from "../format";
+import { SENSORS, formatDate, formatPercent, formatResidual, formatRul } from "../format";
 import { useLiveFetch } from "../useLiveFetch";
 
 const RUL_WARNING = 75;
@@ -84,7 +85,7 @@ export function EquipmentCard() {
       <div className="grid">
         <ForecastChart id={id} />
         <HistoryChart id={id} />
-        <PhysicsChart />
+        <PhysicsChart id={id} />
         <EquipmentAlerts id={id} />
       </div>
     </section>
@@ -158,15 +159,32 @@ function ForecastChart({ id }: { id: number }) {
   );
 }
 
-function PhysicsChart() {
+function PhysicsChart({ id }: { id: number }) {
+  const { data, error } = useLiveFetch(() => api.getPhysics(id), [id]);
+  const last = data && data.length > 0 ? data[data.length - 1] : null;
+
   return (
     <div className="card">
       <div className="card-title">
         <h2>Физическая модель против фактических показаний</h2>
+        {last && <span className="muted">невязка {formatResidual(last.residual)}</span>}
       </div>
-      <div className="placeholder">
-        Заглушка
-      </div>
+      {error && <ErrorMessage message={error} />}
+      {!data && !error && <Loading />}
+      {data && data.length === 0 && <Empty text="Физическая модель ещё не посчитана." />}
+      {data && data.length > 0 && (
+        <ResponsiveContainer width="100%" height={260}>
+          <LineChart data={data}>
+            <CartesianGrid strokeDasharray="3 3" stroke="#e5e7eb" />
+            <XAxis dataKey="cycle" label={{ value: "цикл", position: "insideBottomRight", offset: -4 }} />
+            <YAxis width={50} tickFormatter={formatPercent} />
+            <Tooltip formatter={(v: number, name: string) => [formatPercent(v), name]} labelFormatter={(c) => `Цикл ${c}`} />
+            <Legend />
+            <Line dataKey="expected" name="ожидаемая деградация" stroke="#d97706" strokeDasharray="5 4" dot={false} isAnimationActive={false} />
+            <Line dataKey="actual" name="фактическая по датчикам" stroke="#2563eb" dot={false} isAnimationActive={false} />
+          </LineChart>
+        </ResponsiveContainer>
+      )}
     </div>
   );
 }