'commit'

Controller/YltAnalyticsController.py

@@ -4,11 +4,15 @@ import json
 from typing import List, Optional, Dict, Any
 
 from fastapi import APIRouter, HTTPException
-from fastapi.responses import StreamingResponse
+from fastapi.responses import StreamingResponse, FileResponse, JSONResponse
 
 from DbKit.Db import Db
 from Config.Config import DB_URL
 from Util.LlmUtil import get_llm_response
+from Tools.T6_Export import export_excel, DorisExcelExporter, extract_hourly_prices_from_schedule
+import tempfile
+import os
+import zipfile
 from Model.YltAnalyticsModel import (
     StationBase,
     CompetitorStation,
@@ -33,6 +37,89 @@ async def close_db():
     await db.close()
 
 
+@router.get("/api/operators/hourly-prices")
+async def get_operators_hourly_prices():
+    operators = ["新电途", "特来电", "驿来特", "艾特吉易充"]
+    exporter = DorisExcelExporter(db_url=DB_URL)
+    await exporter.init()
+    try:
+        result = []
+        for op in operators:
+            rows = await exporter.fetch_current_station_rows(op)
+            if not rows:
+                result.append({"operator": op, "series": [None] * 24})
+                continue
+            sums = [0.0] * 24
+            counts = [0] * 24
+            for row in rows:
+                schedule_json = row.get("schedule_json")
+                series = extract_hourly_prices_from_schedule(schedule_json)
+                for i in range(24):
+                    v = series[i]
+                    if v is None:
+                        continue
+                    sums[i] += float(v)
+                    counts[i] += 1
+            avg_series = []
+            for i in range(24):
+                c = counts[i]
+                if c > 0:
+                    avg_series.append(sums[i] / c)
+                else:
+                    avg_series.append(None)
+            result.append({"operator": op, "series": avg_series})
+    finally:
+        await exporter.close()
+    return {"operators": result}
+
+
+@router.get("/api/export/prices-zip")
+async def export_prices_zip():
+    operators = ["新电途", "特来电", "驿来特", "艾特吉易充"]
+    tmp_dir = tempfile.mkdtemp(prefix="price_export_")
+    excel_paths = []
+    for op in operators:
+        filename = f"{op}_{asyncio.get_event_loop().time():.0f}.xlsx"
+        output_path = os.path.join(tmp_dir, filename)
+        await export_excel(op, output_path)
+        excel_paths.append(output_path)
+    zip_path = os.path.join(tmp_dir, "prices_export.zip")
+    with zipfile.ZipFile(zip_path, "w", zipfile.ZIP_DEFLATED) as zf:
+        for p in excel_paths:
+            arcname = os.path.basename(p)
+            zf.write(p, arcname=arcname)
+    return FileResponse(
+        zip_path,
+        media_type="application/zip",
+        filename="多供应商电价导出.zip",
+    )
+
+
+@router.get("/api/ai/pricing/strategy-summary")
+async def ai_pricing_strategy_summary():
+    resp = await get_operators_hourly_prices()
+    data = resp.get("operators", [])
+    text_data = []
+    for item in data:
+        text_data.append({"operator": item.get("operator"), "series": item.get("series")})
+    text = (
+        "下面是四家供应商（新电途、特来电、驿来特、艾特吉易充）基于最新爬取数据计算出的平均24小时分时电价：\n"
+        f"{text_data}\n"
+        "请根据这些数据，综合分析各司的定价策略差异，重点对比我司（驿来特）与其他供应商的分时电价水平，"
+        "指出我司在不同时段可能存在的潜在问题和风险（例如明显偏贵、价格结构不合理等），并给出2-3条可执行的优化建议。"
+        "回答控制在600字以内。"
+    )
+    chunks: List[str] = []
+    async for chunk in get_llm_response(
+        text,
+        stream=False,
+        system_prompt="你是熟悉中国充电桩行业的电价策略分析顾问。",
+    ):
+        chunks.append(chunk)
+    summary_text = "".join(chunks)
+    return JSONResponse({"summary": summary_text})
+
+
 @router.get("/api/ylt/stations", response_model=List[StationBase])
 async def list_ylt_stations(q: Optional[str] = None):
     base_sql = """