快取 PChome 競價摘要查詢

config.py

@@ -320,7 +320,7 @@ YOUTUBE_API_KEY = os.getenv('YOUTUBE_API_KEY', '')
 # ==========================================
 # 系統版本與路徑
 # ==========================================
-SYSTEM_VERSION = "V10.276"
+SYSTEM_VERSION = "V10.277"
 LOG_FILE_PATH = os.path.join(BASE_DIR, 'logs/system.log')
 public_url = PUBLIC_URL  # 用於模板顯示
 

docs/AI_INTELLIGENCE_MODULE_SOT.md

@@ -2,7 +2,7 @@
 
 > **最後更新**: 2026-05-19 (台北時間)
 > **狀態**: 🟢 四 AI Agent 自動化閉環已落地；LLM 路由紅線升級為 Ollama-first 三主機級聯，Gemini 僅備援 / 鎖定場景
-> **適用版本**: V10.276
+> **適用版本**: V10.277
 
 ---
 
@@ -52,6 +52,7 @@ SQL漏斗(~300筆)
 - 配對來源仍以 PChome crawler 真實搜尋結果為準；無競品資料時不生成挑品。
 - 比對覆蓋率補強入口：`POST /api/ai/pchome-match/backfill`，優先補抓仍無有效 PChome 配對的高價 ACTIVE 商品，完成後自動重算 AI 挑品清單。
 - 排程閉環：`run_pchome_match_backfill_task` 每日 10:30 執行，補抓 PChome 待比對商品、寫入歷史價格，再重算 `strategy='product_pick'` 清單。
+- PChome / MOMO 競價摘要出口 `services/competitor_intel_repository.py` 使用 5 分鐘共享快取（`COMPETITOR_INTEL_CACHE_TTL_SECONDS` 可調），避免 `/growth_analysis`、`/daily_sales`、PPT/AI 報表每次請求重跑昂貴覆蓋率與價差趨勢查詢；快取只包摘要輸出，不改 matcher 的高信心門檻與 identity_v2 準確性規則。
 - 商品看板第一屏：`/` 的 V2 看板直接以 `products`、`price_records`、`competitor_prices`、`ai_price_recommendations` 顯示比對覆蓋率、PChome 優勢、MOMO 威脅、AI 挑品與待比對優先清單；`filter=ai_picks` 可查看 50 品 AI 挑品列表，並在列表上方顯示平均信心、平均價差、最大價差與估算總價差空間，列表列內顯示 AI 排名與建議理由，且可透過 `/api/export/excel/ai-picks` 匯出 50 品 Excel 操作清單。商品看板深度快取同時寫入 `data/dashboard_full_cache.pkl`，供多個 Gunicorn worker 共用，避免部署後各 worker 重複重建 7,000+ 商品統計造成開頁變慢；所有資料異動與 AI 挑品重算都透過 `clear_dashboard_cache()` 同步清除記憶體與共享快取，手動重算 API 會立即預熱商品看板快取，避免第一位使用者承擔重建成本。
 
 | 角色 | 模型 | 主機 | 成本 | 每日限額 |

services/competitor_intel_repository.py

@@ -10,13 +10,23 @@
 
 from __future__ import annotations
 
+import os
+import pickle
+import time
 from datetime import date, datetime, timedelta
+from pathlib import Path
+from threading import Lock
 from typing import Any, Optional, Union
 
 from sqlalchemy import inspect, text
 
 
 PCHOME_MATCH_SCORE_FLOOR = 0.76
+COMPETITOR_INTEL_CACHE_TTL_SECONDS = int(os.getenv("COMPETITOR_INTEL_CACHE_TTL_SECONDS", "300"))
+_BASE_DIR = Path(__file__).resolve().parents[1]
+_CACHE_FILE = _BASE_DIR / "data" / "competitor_intel_cache.pkl"
+_CACHE_LOCK = Lock()
+_MEM_CACHE: dict[str, dict[str, Any]] = {}
 
 
 def _num(value: Any) -> float:
@@ -38,7 +48,81 @@ def _month_label(value: Any) -> str:
     return str(value or "")[:7]
 
 
+def clear_competitor_intel_cache() -> None:
+    """Clear cached PChome/MOMO intelligence after crawler/import updates."""
+    with _CACHE_LOCK:
+        _MEM_CACHE.clear()
+        try:
+            if _CACHE_FILE.exists():
+                _CACHE_FILE.unlink()
+        except OSError:
+            pass
+
+
+def _load_shared_cache() -> dict[str, dict[str, Any]]:
+    if not _CACHE_FILE.exists():
+        return {}
+    try:
+        with _CACHE_FILE.open("rb") as handle:
+            payload = pickle.load(handle)
+        return payload if isinstance(payload, dict) else {}
+    except Exception:
+        return {}
+
+
+def _write_shared_cache(payload: dict[str, dict[str, Any]]) -> None:
+    try:
+        _CACHE_FILE.parent.mkdir(parents=True, exist_ok=True)
+        tmp_file = _CACHE_FILE.with_suffix(f".{os.getpid()}.tmp")
+        with tmp_file.open("wb") as handle:
+            pickle.dump(payload, handle, protocol=pickle.HIGHEST_PROTOCOL)
+        os.replace(tmp_file, _CACHE_FILE)
+    except Exception:
+        try:
+            if "tmp_file" in locals() and tmp_file.exists():
+                tmp_file.unlink()
+        except OSError:
+            pass
+
+
+def _cached_payload(cache_key: str, producer, ttl_seconds: int = COMPETITOR_INTEL_CACHE_TTL_SECONDS):
+    if ttl_seconds <= 0:
+        return producer()
+    now = time.time()
+    with _CACHE_LOCK:
+        entry = _MEM_CACHE.get(cache_key)
+        if entry and now - float(entry.get("time", 0)) < ttl_seconds:
+            return entry.get("value")
+        shared = _load_shared_cache()
+        entry = shared.get(cache_key)
+        if entry and now - float(entry.get("time", 0)) < ttl_seconds:
+            _MEM_CACHE[cache_key] = entry
+            return entry.get("value")
+
+    value = producer()
+    entry = {"time": now, "value": value}
+    with _CACHE_LOCK:
+        _MEM_CACHE[cache_key] = entry
+        shared = _load_shared_cache()
+        shared[cache_key] = entry
+        stale_before = now - max(ttl_seconds * 4, 3600)
+        shared = {
+            key: item
+            for key, item in shared.items()
+            if isinstance(item, dict) and float(item.get("time", 0)) >= stale_before
+        }
+        _write_shared_cache(shared)
+    return value
+
+
 def fetch_competitor_coverage(engine) -> dict:
+    return _cached_payload(
+        f"coverage:v2:floor={PCHOME_MATCH_SCORE_FLOOR}",
+        lambda: _fetch_competitor_coverage_uncached(engine),
+    )
+
+
+def _fetch_competitor_coverage_uncached(engine) -> dict:
     """讀取目前 PChome 比價覆蓋率與待審分類。"""
     if not inspect(engine).has_table("competitor_prices"):
         return {
@@ -117,6 +201,14 @@ def fetch_competitor_coverage(engine) -> dict:
 
 
 def fetch_competitor_gap_trend(engine, days: int = 30) -> dict:
+    days = max(7, min(int(days or 30), 120))
+    return _cached_payload(
+        f"gap_trend:v2:days={days}:floor={PCHOME_MATCH_SCORE_FLOOR}",
+        lambda: _fetch_competitor_gap_trend_uncached(engine, days=days),
+    )
+
+
+def _fetch_competitor_gap_trend_uncached(engine, days: int = 30) -> dict:
     """近 N 天 PChome 價差壓力趨勢。"""
     if not inspect(engine).has_table("competitor_price_history"):
         return {"labels": [], "avg_gap_pct": [], "risk_count": [], "momo_advantage_count": [], "match_count": []}
@@ -167,6 +259,14 @@ def fetch_competitor_gap_trend(engine, days: int = 30) -> dict:
 
 
 def fetch_competitor_monthly_pressure(engine, months: int = 12) -> dict:
+    months = max(3, min(int(months or 12), 36))
+    return _cached_payload(
+        f"monthly_pressure:v2:months={months}:floor={PCHOME_MATCH_SCORE_FLOOR}",
+        lambda: _fetch_competitor_monthly_pressure_uncached(engine, months=months),
+    )
+
+
+def _fetch_competitor_monthly_pressure_uncached(engine, months: int = 12) -> dict:
     """月度競品價格壓力，用於 growth analysis。"""
     if not inspect(engine).has_table("competitor_price_history"):
         return {"labels": [], "avg_gap_pct": [], "risk_count": [], "match_count": []}
@@ -215,6 +315,14 @@ def fetch_competitor_monthly_pressure(engine, months: int = 12) -> dict:
 
 
 def fetch_top_competitor_risks(engine, limit: int = 10) -> list[dict]:
+    limit = max(1, min(int(limit or 10), 50))
+    return _cached_payload(
+        f"top_risks:v2:limit={limit}:floor={PCHOME_MATCH_SCORE_FLOOR}",
+        lambda: _fetch_top_competitor_risks_uncached(engine, limit=limit),
+    )
+
+
+def _fetch_top_competitor_risks_uncached(engine, limit: int = 10) -> list[dict]:
     """目前 MOMO 比 PChome 貴的高風險商品。"""
     if not inspect(engine).has_table("competitor_prices"):
         return []

tests/test_competitor_intel_cache.py

@@ -0,0 +1,36 @@
+from pathlib import Path
+
+
+def test_competitor_intel_cache_reuses_memory_and_shared_file(tmp_path, monkeypatch):
+    from services import competitor_intel_repository as repo
+
+    monkeypatch.setattr(repo, "_CACHE_FILE", Path(tmp_path) / "competitor_intel_cache.pkl")
+    repo._MEM_CACHE.clear()
+
+    calls = {"count": 0}
+
+    def producer():
+        calls["count"] += 1
+        return {"valid_matches": 7, "match_rate": 0.1}
+
+    first = repo._cached_payload("coverage:test", producer, ttl_seconds=60)
+    second = repo._cached_payload("coverage:test", producer, ttl_seconds=60)
+    repo._MEM_CACHE.clear()
+    third = repo._cached_payload("coverage:test", producer, ttl_seconds=60)
+
+    assert first == second == third == {"valid_matches": 7, "match_rate": 0.1}
+    assert calls["count"] == 1
+
+
+def test_clear_competitor_intel_cache_removes_shared_file(tmp_path, monkeypatch):
+    from services import competitor_intel_repository as repo
+
+    cache_file = Path(tmp_path) / "competitor_intel_cache.pkl"
+    monkeypatch.setattr(repo, "_CACHE_FILE", cache_file)
+    repo._MEM_CACHE["x"] = {"time": 1, "value": {"ok": True}}
+    cache_file.write_bytes(b"stale")
+
+    repo.clear_competitor_intel_cache()
+
+    assert repo._MEM_CACHE == {}
+    assert not cache_file.exists()