wangyu
f5c59db92b
1. 按钢种分组 K_cal：cal_coeffs.json 升级为嵌套结构，
{kcal: {model: {_default, Q235, ...}}, phys: {...}}，
旧平铺格式首次加载时自动迁移。
2. 物理参数自适应：EA_R/K0/N_CONC 按钢种网格拟合
(7×5×3=105 组合)，每次校准追加样本到
production_samples.jsonl，≥10 条后自动触发拟合。
3. 数据飞轮：新增 POST /retrain 端点，后台子进程跑
train_models.py --use-real-data 混入实绩重训
(10× 权重)，完成后 ONNX 热重载，无需重启服务。
新增端点：
GET /calibration/samples 样本数统计
GET /calibration/phys-params 物理参数查询
POST /calibration/fit-phys/{key} 手动触发物理参数拟合
POST /retrain 启动重训
GET /retrain/status 重训进度
模型类签名变更：
TensionModel / QualityPredictionModel 新增 steel_grade 参数
AcidConsumptionModel 新增 fe_conc_avg 参数
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
427 lines
18 KiB
Python
427 lines
18 KiB
Python
import asyncio
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import subprocess
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import sys
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from datetime import datetime
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from pathlib import Path
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from typing import List, Optional
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from fastapi import APIRouter, BackgroundTasks, Depends, HTTPException
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from pydantic import BaseModel, Field
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from app.schemas.common import Response
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from app.services.auth_service import get_current_user
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from app.services.prediction import (
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AcidSpeedModel,
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TensionModel,
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QualityPredictionModel,
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AcidConsumptionModel,
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_load_cal,
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_save_cal,
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_get_phys,
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append_sample,
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get_sample_stats,
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fit_acid_phys_params,
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fit_quality_phys_params,
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reload_onnx,
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)
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router = APIRouter()
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TENSION_ZONES = ["inlet","s1_roller","acid_entry","acid1","acid2","acid3",
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"rinse","leveler","s2_roller","outlet"]
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_BACKEND_DIR = Path(__file__).parent.parent.parent
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# ─────────────────────────────────────────────────────────────────────────────
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# Prediction request schemas
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# ─────────────────────────────────────────────────────────────────────────────
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class AcidSpeedRequest(BaseModel):
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thickness: float = Field(..., gt=0)
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width: float = Field(..., gt=0)
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steel_grade: str
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acid_conc_list: List[float]
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acid_temp_list: List[float]
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scale_weight: Optional[float] = 8.5
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target_pi: Optional[float] = 95.0
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class TensionRequest(BaseModel):
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thickness: float = Field(..., gt=0)
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width: float = Field(..., gt=0)
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yield_strength: float = Field(..., gt=0)
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tension_coef: Optional[float] = 0.25
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steel_grade: Optional[str] = "_default"
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class QualityRequest(BaseModel):
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thickness: float = Field(..., gt=0)
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avg_speed: float = Field(..., gt=0)
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acid_conc_avg: float = Field(..., gt=0)
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acid_temp_avg: float = Field(..., gt=0)
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scale_weight: Optional[float] = 8.5
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fe_conc_avg: Optional[float] = 60.0
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steel_grade: Optional[str] = "_default"
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class ConsumptionRequest(BaseModel):
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thickness: float = Field(..., gt=0)
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width: float = Field(..., gt=0)
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coil_weight_kg: float = Field(..., gt=0)
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has_regen_station: Optional[bool] = True
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fe_conc_avg: Optional[float] = 60.0
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# ─────────────────────────────────────────────────────────────────────────────
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# Calibration request schemas
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# ─────────────────────────────────────────────────────────────────────────────
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class AcidCalibRequest(BaseModel):
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thickness: float = Field(..., gt=0)
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width: float = Field(..., gt=0)
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steel_grade: str
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acid_conc_list: List[float]
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acid_temp_list: List[float]
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scale_weight: Optional[float] = 8.5
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actual_max_speed: float = Field(..., gt=0, description="实测质量合格时的最高速度 m/min")
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actual_quality_ok: bool = Field(..., description="该速度下质量是否合格")
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note: Optional[str] = None
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class TensionCalibRequest(BaseModel):
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thickness: float = Field(..., gt=0)
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width: float = Field(..., gt=0)
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yield_strength: float = Field(..., gt=0)
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tension_coef: Optional[float] = 0.25
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steel_grade: Optional[str] = "_default"
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zone: str = Field(..., description="测量位置，如 s1_roller")
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measured_kn: float = Field(..., gt=0, description="实测张力 kN")
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note: Optional[str] = None
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class QualityCalibRequest(BaseModel):
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thickness: float = Field(..., gt=0)
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avg_speed: float = Field(..., gt=0)
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acid_conc_avg: float = Field(..., gt=0)
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acid_temp_avg: float = Field(..., gt=0)
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scale_weight: Optional[float] = 8.5
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fe_conc_avg: Optional[float] = 60.0
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steel_grade: Optional[str] = "_default"
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actual_grade: str = Field(..., description="实际质检等级 A1/A2/B1/B2/C")
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note: Optional[str] = None
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# ─────────────────────────────────────────────────────────────────────────────
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# Helper: append calibration history
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# ─────────────────────────────────────────────────────────────────────────────
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def _append_history(model_key: str, k_before, k_after,
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input_data: dict, note: str = ""):
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cal = _load_cal()
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history = cal.get("history", [])
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history.insert(0, {
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"ts": datetime.now().isoformat(timespec="seconds"),
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"model": model_key,
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"k_before": k_before,
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"k_after": k_after,
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"input": input_data,
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"note": note or "",
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})
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cal["history"] = history[:100]
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_save_cal(cal)
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# ─────────────────────────────────────────────────────────────────────────────
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# Prediction endpoints
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# ─────────────────────────────────────────────────────────────────────────────
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@router.post("/acid-speed", response_model=Response[dict])
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async def predict_acid_speed(body: AcidSpeedRequest, _=Depends(get_current_user)):
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try:
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model = AcidSpeedModel(
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thickness=body.thickness, width=body.width,
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steel_grade=body.steel_grade,
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acid_conc_list=body.acid_conc_list,
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acid_temp_list=body.acid_temp_list,
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scale_weight=body.scale_weight, target_pi=body.target_pi,
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)
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result = model.calculate()
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except ValueError as e:
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raise HTTPException(status_code=422, detail=str(e))
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return Response.ok(result)
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@router.post("/tension", response_model=Response[dict])
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async def predict_tension(body: TensionRequest, _=Depends(get_current_user)):
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model = TensionModel(
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thickness=body.thickness, width=body.width,
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yield_strength=body.yield_strength, tension_coef=body.tension_coef,
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steel_grade=body.steel_grade,
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)
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return Response.ok(model.calculate())
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@router.post("/quality", response_model=Response[dict])
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async def predict_quality(body: QualityRequest, _=Depends(get_current_user)):
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model = QualityPredictionModel(
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thickness=body.thickness, avg_speed=body.avg_speed,
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acid_conc_avg=body.acid_conc_avg, acid_temp_avg=body.acid_temp_avg,
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scale_weight=body.scale_weight, fe_conc_avg=body.fe_conc_avg,
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steel_grade=body.steel_grade,
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)
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return Response.ok(model.calculate())
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@router.post("/consumption", response_model=Response[dict])
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async def predict_consumption(body: ConsumptionRequest, _=Depends(get_current_user)):
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model = AcidConsumptionModel(
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thickness=body.thickness, width=body.width,
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coil_weight_kg=body.coil_weight_kg,
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has_regen_station=body.has_regen_station,
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fe_conc_avg=body.fe_conc_avg,
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)
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return Response.ok(model.calculate())
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# ─────────────────────────────────────────────────────────────────────────────
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# Calibration endpoints
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# ─────────────────────────────────────────────────────────────────────────────
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@router.get("/calibration", response_model=Response[dict])
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async def get_calibration(_=Depends(get_current_user)):
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"""返回各模型当前校准系数（按钢种）和历史记录。"""
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cal = _load_cal()
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return Response.ok({
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"kcal": cal.get("kcal", {}),
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"phys": cal.get("phys", {}),
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"history": cal.get("history", []),
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})
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@router.get("/calibration/samples", response_model=Response[dict])
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async def get_calibration_samples(_=Depends(get_current_user)):
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"""返回各模型 + 钢种的生产样本数量统计，以及重训所需的样本阈值。"""
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stats = get_sample_stats()
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return Response.ok({
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"stats": stats,
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"fit_threshold": 10,
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"retrain_tip": "样本累积足够后，POST /api/prediction/retrain 可触发 ONNX 重训",
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})
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@router.get("/calibration/phys-params", response_model=Response[dict])
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async def get_phys_params(steel_grade: Optional[str] = None, _=Depends(get_current_user)):
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"""查询物理参数（EA_R / K0 / N_CONC），可按钢种过滤。"""
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cal = _load_cal()
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phys = cal.get("phys", {})
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if steel_grade:
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result = {}
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for m in ("acid_speed", "quality"):
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result[m] = _get_phys(m, steel_grade)
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return Response.ok({"steel_grade": steel_grade, "phys_params": result})
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return Response.ok(phys)
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@router.post("/calibration/acid-speed", response_model=Response[dict])
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async def calibrate_acid_speed(body: AcidCalibRequest, _=Depends(get_current_user)):
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"""录入实测速度，更新对应钢种 K_cal；样本 ≥10 后自动触发物理参数拟合。"""
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try:
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model = AcidSpeedModel(
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thickness=body.thickness, width=body.width,
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steel_grade=body.steel_grade,
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acid_conc_list=body.acid_conc_list,
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acid_temp_list=body.acid_temp_list,
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scale_weight=body.scale_weight,
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)
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except ValueError as e:
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raise HTTPException(status_code=422, detail=str(e))
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k_before = model.K_cal
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predicted_speed = model.calculate()["max_speed"]
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k_after = model.calibrate(
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actual_max_speed=body.actual_max_speed,
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actual_quality_ok=body.actual_quality_ok,
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)
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_append_history(
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f"acid_speed[{body.steel_grade}]", k_before, k_after,
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{"actual_speed": body.actual_max_speed,
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"quality_ok": body.actual_quality_ok,
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"predicted_speed": predicted_speed},
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body.note or "",
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)
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return Response.ok({
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"steel_grade": body.steel_grade,
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"k_before": k_before,
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"k_after": k_after,
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"predicted_speed": predicted_speed,
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"adjustment": round((k_after / k_before - 1) * 100, 2),
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})
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@router.post("/calibration/tension", response_model=Response[dict])
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async def calibrate_tension(body: TensionCalibRequest, _=Depends(get_current_user)):
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"""录入实测张力，更新对应钢种 + 区段的 K_cal。"""
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model = TensionModel(
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thickness=body.thickness, width=body.width,
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yield_strength=body.yield_strength, tension_coef=body.tension_coef,
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steel_grade=body.steel_grade,
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)
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calc = model.calculate()
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predicted_kn= calc["zones"].get(body.zone, {}).get("tension_kN", 0)
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k_before = model.zone_kcal.get(body.zone, 1.0)
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new_zone_kcal = model.calibrate(zone=body.zone, measured_kn=body.measured_kn)
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k_after = new_zone_kcal.get(body.zone, 1.0)
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_append_history(
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f"tension[{body.steel_grade}][{body.zone}]", k_before, k_after,
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{"zone": body.zone, "measured_kn": body.measured_kn, "predicted_kn": predicted_kn},
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body.note or "",
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)
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return Response.ok({
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"steel_grade": body.steel_grade,
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"zone": body.zone,
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"k_before": k_before,
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"k_after": k_after,
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"predicted_kn": predicted_kn,
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"measured_kn": body.measured_kn,
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"adjustment": round((k_after / k_before - 1) * 100, 2),
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"zone_kcal": new_zone_kcal,
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})
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@router.post("/calibration/quality", response_model=Response[dict])
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async def calibrate_quality(body: QualityCalibRequest, _=Depends(get_current_user)):
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"""录入实际质检等级，更新对应钢种 K_cal；样本 ≥10 后自动触发物理参数拟合。"""
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model = QualityPredictionModel(
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thickness=body.thickness, avg_speed=body.avg_speed,
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acid_conc_avg=body.acid_conc_avg, acid_temp_avg=body.acid_temp_avg,
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scale_weight=body.scale_weight, fe_conc_avg=body.fe_conc_avg,
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steel_grade=body.steel_grade,
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)
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k_before = model.K_cal
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calc = model.calculate()
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predicted_grade = calc["overall_grade"]
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k_after = model.calibrate(actual_grade=body.actual_grade)
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_append_history(
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f"quality[{body.steel_grade}]", k_before, k_after,
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{"actual_grade": body.actual_grade, "predicted_grade": predicted_grade},
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body.note or "",
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)
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return Response.ok({
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"steel_grade": body.steel_grade,
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"k_before": k_before,
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"k_after": k_after,
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"predicted_grade": predicted_grade,
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"actual_grade": body.actual_grade,
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"adjustment": round((k_after / k_before - 1) * 100, 2),
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})
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@router.post("/calibration/fit-phys/{model_key}", response_model=Response[dict])
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async def fit_phys_params_api(model_key: str, steel_grade: str, _=Depends(get_current_user)):
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"""
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手动触发指定模型 + 钢种的物理参数拟合（自动触发也会调用此逻辑）。
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model_key: acid_speed | quality
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"""
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if model_key == "acid_speed":
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result = fit_acid_phys_params(steel_grade)
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elif model_key == "quality":
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result = fit_quality_phys_params(steel_grade)
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else:
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raise HTTPException(status_code=404, detail="model_key 仅支持 acid_speed / quality")
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if result is None:
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from app.services.prediction import _FIT_MIN_SAMPLES
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return Response.ok({
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"fitted": False,
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"reason": f"样本不足，需 ≥{_FIT_MIN_SAMPLES} 条，请继续录入校准数据",
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})
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return Response.ok({"fitted": True, "steel_grade": steel_grade, "phys_params": result})
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@router.post("/calibration/reset/{model_key}", response_model=Response[dict])
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async def reset_calibration(model_key: str, steel_grade: Optional[str] = None,
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_=Depends(get_current_user)):
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"""
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重置校准系数为 1.0。
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steel_grade 为空时重置该模型所有钢种；否则只重置指定钢种。
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"""
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cal = _load_cal()
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if model_key == "tension":
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for z in TENSION_ZONES:
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key = f"tension_{z}"
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if steel_grade:
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cal.setdefault("kcal", {}).setdefault(key, {}).pop(steel_grade, None)
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else:
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cal.setdefault("kcal", {})[key] = {"_default": 1.0}
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_append_history("tension", None, 1.0, {"action": "reset", "steel_grade": steel_grade})
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elif model_key in ("acid_speed", "quality"):
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if steel_grade:
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cal.setdefault("kcal", {}).setdefault(model_key, {}).pop(steel_grade, None)
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cal.setdefault("phys", {}).setdefault(model_key, {}).pop(steel_grade, None)
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else:
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cal.setdefault("kcal", {})[model_key] = {"_default": 1.0}
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from app.services.prediction import _DEFAULT_PHYS
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cal.setdefault("phys", {})[model_key] = {"_default": _DEFAULT_PHYS.copy()}
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_append_history(model_key, None, 1.0,
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{"action": "reset", "steel_grade": steel_grade or "all"})
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else:
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raise HTTPException(status_code=404, detail="未知模型")
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_save_cal(cal)
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return Response.ok({"model": model_key, "steel_grade": steel_grade or "all", "reset": True})
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# ─────────────────────────────────────────────────────────────────────────────
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# 数据飞轮：ONNX 重训端点
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# ─────────────────────────────────────────────────────────────────────────────
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_retrain_lock = asyncio.Lock()
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_retrain_status: dict = {"running": False, "last_ts": None, "last_result": None}
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def _run_retrain():
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"""在子进程中运行 train_models.py --use-real-data，完成后热重载 ONNX。"""
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global _retrain_status
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_retrain_status["running"] = True
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_retrain_status["last_ts"] = datetime.now().isoformat(timespec="seconds")
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try:
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result = subprocess.run(
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[sys.executable, str(_BACKEND_DIR / "train_models.py"), "--use-real-data"],
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capture_output=True, text=True, timeout=600,
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)
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ok = result.returncode == 0
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_retrain_status["last_result"] = {
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"success": ok,
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"stdout": result.stdout[-2000:] if result.stdout else "",
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"stderr": result.stderr[-1000:] if result.stderr else "",
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}
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if ok:
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reload_onnx()
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except subprocess.TimeoutExpired:
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_retrain_status["last_result"] = {"success": False, "stderr": "训练超时（>600s）"}
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except Exception as e:
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_retrain_status["last_result"] = {"success": False, "stderr": str(e)}
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finally:
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_retrain_status["running"] = False
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@router.post("/retrain", response_model=Response[dict])
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async def trigger_retrain(background_tasks: BackgroundTasks, _=Depends(get_current_user)):
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"""
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触发 ONNX 重训（使用 production_samples.jsonl 中的生产实绩）。
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训练在后台子进程运行，完成后自动热重载模型。
|
||
"""
|
||
if _retrain_status["running"]:
|
||
raise HTTPException(status_code=409, detail="重训任务正在进行中，请稍后再试")
|
||
stats = get_sample_stats()
|
||
background_tasks.add_task(_run_retrain)
|
||
return Response.ok({
|
||
"message": "重训任务已启动，完成后 ONNX 将自动热重载",
|
||
"sample_stats": stats,
|
||
})
|
||
|
||
|
||
@router.get("/retrain/status", response_model=Response[dict])
|
||
async def retrain_status(_=Depends(get_current_user)):
|
||
"""查询重训任务状态。"""
|
||
return Response.ok(_retrain_status)
|