Temp_Diag/api_tempdiag.py

# main.py

import os, glob
import pandas as pd
from fastapi import FastAPI
from fastapi.responses import JSONResponse
from pydantic import BaseModel, model_validator
from typing import List

from Temp_Diag import MSET_Temp

app = FastAPI(title="Temperature Diagnosis API")

# 全局模型缓存：{ windCode: { channel_name: MSET_Temp, … }, … }
MODEL_STORE: dict[str, dict[str, MSET_Temp]] = {}

# 英文→中文映射
cn_map = {
    'main_bearing_temperature': '主轴承温度',
    'gearbox_oil_temperature': '齿轮箱油温',
    'generatordrive_end_bearing_temperature': '发电机驱动端轴承温度',
    'generatornon_drive_end_bearing_temperature': '发电机非驱动端轴承温度'
}

class TemperatureInput(BaseModel):
    windCode: str
    windTurbineNumberList: List[str]
    startTime: str
    endTime: str

    @model_validator(mode='before')
    def ensure_list(cls, v):
        raw = v.get('windTurbineNumberList')
        if isinstance(raw, str):
            v['windTurbineNumberList'] = [raw]
        return v

class TemperatureThresholdInput(TemperatureInput):
    pageNo:   int
    pageSize: int

@app.on_event("startup")
def load_all_models():
    for f in glob.glob("models/*/*.pkl"):
        wc = os.path.basename(os.path.dirname(f))  # 取目录名 → windCode
        ch = os.path.splitext(os.path.basename(f))[0]  # 取文件名（去 .pkl）→ channel
        MODEL_STORE.setdefault(wc, {})[ch] = MSET_Temp.load_model(f)
    print("模型加载完成:", {k: list(v.keys()) for k,v in MODEL_STORE.items()})

@app.post("/temperature/threshold")
async def route_threshold(inp: TemperatureThresholdInput):
    try:
        analyzer = MSET_Temp(inp.windCode, inp.windTurbineNumberList, inp.startTime, inp.endTime)
        df = analyzer._get_data_by_filter()
        if df.empty:
            return {"data":{"type":"temperature_threshold","records":[],"totalSize":0},"code":200,"message":"success"}

        df['time_stamp'] = pd.to_datetime(df['time_stamp'])
        records = []
        for eng, cn in cn_map.items():
            if eng not in df.columns: continue
            sub = df[['time_stamp', eng]].dropna()
            arr = sub[eng].values.reshape(-1,1)
            ts  = sub['time_stamp'].dt.strftime("%Y-%m-%d %H:%M:%S").tolist()
            model = MODEL_STORE.get(inp.windCode,{}).get(eng)
            if not model: continue
            flags = model.predict_SPRT(arr, decisionGroup=1)
            for i, sc in enumerate(flags):
                records.append({
                    "time_stamp": ts[i],
                    "temp_channel": cn,
                    "SPRT_score": sc,
                    "status": "危险" if sc>0.99 else "正常"
                })

        total = len(records)
        start = (inp.pageNo-1)*inp.pageSize
        end   = start+inp.pageSize
        return {"data":{"type":"temperature_threshold",
                        "records":records[start:end],
                        "totalSize":total},"code":200,"message":"success"}
    except Exception as e:
        return JSONResponse(status_code=500, content={"code":500,"message":"analysis failed","detail":str(e)})

@app.post("/SPRT/trend")
async def route_trend(inp: TemperatureInput):
    try:
        analyzer = MSET_Temp(inp.windCode, inp.windTurbineNumberList, inp.startTime, inp.endTime)
        df = analyzer._get_data_by_filter()
        df['time_stamp'] = pd.to_datetime(df['time_stamp'])
        result = {}
        for eng, key in {
            'main_bearing_temperature':'main_bearing',
            'gearbox_oil_temperature':'gearbox_oil',
            'generatordrive_end_bearing_temperature':'generator_drive_end',
            'generatornon_drive_end_bearing_temperature':'generator_nondrive_end'
        }.items():
            if eng not in df.columns:
                result[key] = {"timestamps":[],"values":[]}
                continue
            sub = df[['time_stamp',eng]].dropna()
            arr = sub[eng].values.reshape(-1,1)
            ts  = sub['time_stamp'].dt.strftime("%Y-%m-%d %H:%M:%S").tolist()
            model = MODEL_STORE.get(inp.windCode,{}).get(eng)
            vals  = model.predict_SPRT(arr, decisionGroup=1) if model else []
            result[key] = {"timestamps": ts, "values": vals}

        return {"data":{"type":"SPRT_trend",**result},"code":200,"message":"success"}
    except Exception as e:
        return JSONResponse(status_code=500, content={"code":500,"message":"analysis failed","detail":str(e)})

if __name__ == "__main__":
    import uvicorn
    uvicorn.run("main:app", host="0.0.0.0", port=8000, reload=True)