記錄依賴盤點驗證結果

config.py

@@ -320,7 +320,7 @@ YOUTUBE_API_KEY = os.getenv('YOUTUBE_API_KEY', '')
 # ==========================================
 # 系統版本與路徑
 # ==========================================
-SYSTEM_VERSION = "V10.98"
+SYSTEM_VERSION = "V10.99"
 LOG_FILE_PATH = os.path.join(BASE_DIR, 'logs/system.log')
 public_url = PUBLIC_URL  # 用於模板顯示
 

docs/memory/claude_inventory_validation_20260513.md

@@ -30,6 +30,8 @@
 - 0-byte `database/momo*.db` 迷惑檔已不存在；真實 SQLite 僅在 `data/momo_database.db`。
 - `.gitignore` 已涵蓋 `.claude/worktrees/`、`.tmp_*`、`MOMO Pro/`、uploads/screenshots。
 - `cache_service.py` 已成為 `cache_manager.py` 的相容 shim，`_SALES_CACHE_TTL` 單一來源有測試鎖住。
+- `aiops-core/requirements.txt` 已不存在，`aiops-core/README.md` 已標記此目錄只保留歷史 stub，不應安裝或部署。
+- V2 提到的「死依賴」不可整批刪：`beautifulsoup4` 用於多個 crawler、`google-api-python-client` 用於 Google Drive、`google-generativeai` 用於 Gemini paths、`python-pptx` 用於 PPT generator、`matplotlib` 用於 Telegram/圖表/PPT。
 
 ## 不可盲動
 

routes/daily_sales_routes.py

@@ -61,10 +61,13 @@ def _get_data_fingerprint(engine, table_name='daily_sales_snapshot'):
     """DB 指紋 (max_snapshot_date, row_count)：資料一變動指紋就跳號，
     讓 4 worker 的 in-memory cache 在下一次 request 自然失效。"""
     try:
+        validate_table_name(table_name)
+        if engine.dialect.name == 'postgresql':
+            fingerprint_sql = f'SELECT MAX(snapshot_date)::text, COUNT(*) FROM "{table_name}"'
+        else:
+            fingerprint_sql = f'SELECT CAST(MAX(snapshot_date) AS TEXT), COUNT(*) FROM "{table_name}"'
         with engine.connect() as conn:
-            row = conn.execute(text(
-                f'SELECT MAX(snapshot_date)::text, COUNT(*) FROM "{table_name}"'
-            )).fetchone()
+            row = conn.execute(text(fingerprint_sql)).fetchone()
             return (row[0], row[1]) if row else (None, 0)
     except Exception as e:
         sys_log.warning(f"[Cache] fingerprint 查詢失敗（保守維持現有 cache）: {e}")
@@ -89,6 +92,45 @@ def _is_cache_valid(cache_key, engine=None, table_name='daily_sales_snapshot'):
     return True
 
 
+def _get_available_daily_dates(engine, table_name='daily_sales_snapshot'):
+    """取得可選日期清單，避免為了 date selector 載入整張業績表。"""
+    validate_table_name(table_name)
+    if engine.dialect.name == 'postgresql':
+        date_expr = 'snapshot_date::date'
+    else:
+        date_expr = 'date(snapshot_date)'
+
+    query = text(
+        f'SELECT DISTINCT {date_expr} AS snapshot_date '
+        f'FROM "{table_name}" '
+        'WHERE snapshot_date IS NOT NULL '
+        'ORDER BY snapshot_date DESC'
+    )
+    with engine.connect() as conn:
+        rows = conn.execute(query).fetchall()
+
+    dates = []
+    for row in rows:
+        try:
+            dates.append(pd.to_datetime(row[0]).normalize())
+        except Exception:
+            continue
+    return dates
+
+
+def _read_daily_sales_window(engine, table_name, start_date, end_date):
+    """只讀取畫面需要的日期窗口，降低冷 worker 首頁等待時間。"""
+    return safe_read_sql(
+        table_name,
+        engine=engine,
+        where_clause='"snapshot_date" >= :start_date AND "snapshot_date" <= :end_date',
+        params={
+            'start_date': start_date.strftime('%Y-%m-%d'),
+            'end_date': end_date.strftime('%Y-%m-%d'),
+        },
+    )
+
+
 # ==========================================
 # 輔助函數
 # ==========================================
@@ -373,35 +415,21 @@ def daily_sales():
                                    chart_data=None, categories=None, calendar_data=None, selected_month=None,
                                    datetime_now=datetime_now_str, active_page='daily_sales')
 
-        cache_key = f'{table_name}_daily'
-        # TTL + DB fingerprint 雙閘檢查（資料變動即自動失效，跨 worker 強一致）
-        if _is_cache_valid(cache_key, engine, table_name):
-            df = _SALES_PROCESSED_CACHE[cache_key]['df']
-            sys_log.debug(f"使用緩存數據，剩餘有效時間: {_CACHE_EXPIRY_SECONDS - (datetime.now() - _SALES_PROCESSED_CACHE[cache_key]['timestamp']).total_seconds():.0f}秒")
-        else:
-            df = safe_read_sql(table_name, engine=engine)
-            if df.empty:
-                return render_template('daily_sales.html',
-                                       error="資料表為空，請先匯入當日業績資料。",
-                                       selected_date=None, available_dates=[], current=None, dod=None, wow=None,
-                                       chart_data=None, categories=None, calendar_data=None, selected_month=None,
-                                       datetime_now=datetime_now_str, active_page='daily_sales')
+        available_dates = _get_available_daily_dates(engine, table_name)
+        if not available_dates:
+            return render_template('daily_sales.html',
+                                   error="資料表為空，請先匯入當日業績資料。",
+                                   selected_date=None, available_dates=[], current=None, dod=None, wow=None,
+                                   chart_data=None, categories=None, calendar_data=None, selected_month=None,
+                                   datetime_now=datetime_now_str, active_page='daily_sales')
 
-            df = preprocess_daily_sales_data(df)
-            _SALES_PROCESSED_CACHE[cache_key] = {
-                'df': df, 'timestamp': datetime.now(),
-                'fingerprint': _get_data_fingerprint(engine, table_name),
-            }
-            sys_log.info(f"已重新載入數據並更新緩存，共 {len(df)} 筆記錄")
-
-        available_dates = sorted(df['snapshot_date'].unique(), reverse=True)
-        available_dates_str = [d.strftime('%Y-%m-%d') if isinstance(d, pd.Timestamp) else str(d) for d in available_dates]
+        available_dates_str = [d.strftime('%Y-%m-%d') for d in available_dates]
 
         selected_date_param = request.args.get('date')
         if selected_date_param:
             selected_date = pd.to_datetime(selected_date_param)
         else:
-            selected_date = df['snapshot_date'].max()
+            selected_date = available_dates[0]
 
         selected_month_param = request.args.get('month')
         if selected_month_param:
@@ -413,12 +441,42 @@ def daily_sales():
         if selected_month_param and not selected_date_param:
             is_month_view = True
 
+        month_start = selected_month.replace(day=1)
+        month_end = (month_start + pd.DateOffset(months=1)) - pd.Timedelta(days=1)
+        data_start = min(
+            selected_date - pd.Timedelta(days=30),
+            selected_date - pd.Timedelta(days=7),
+            month_start - pd.Timedelta(days=1),
+        )
+        data_end = max(selected_date, month_end)
+
+        cache_key = f"{table_name}_daily_{data_start.strftime('%Y%m%d')}_{data_end.strftime('%Y%m%d')}"
+        # TTL + DB fingerprint 雙閘檢查（資料變動即自動失效，跨 worker 強一致）
+        if _is_cache_valid(cache_key, engine, table_name):
+            df = _SALES_PROCESSED_CACHE[cache_key]['df']
+            sys_log.debug(f"使用緩存數據，剩餘有效時間: {_CACHE_EXPIRY_SECONDS - (datetime.now() - _SALES_PROCESSED_CACHE[cache_key]['timestamp']).total_seconds():.0f}秒")
+        else:
+            df = _read_daily_sales_window(engine, table_name, data_start, data_end)
+            if df.empty:
+                return render_template('daily_sales.html',
+                                       error="所選日期區間沒有業績資料。",
+                                       selected_date=None, available_dates=available_dates_str, current=None, dod=None, wow=None,
+                                       chart_data=None, categories=None, calendar_data=None, selected_month=None,
+                                       datetime_now=datetime_now_str, active_page='daily_sales')
+
+            df = preprocess_daily_sales_data(df)
+            _SALES_PROCESSED_CACHE[cache_key] = {
+                'df': df, 'timestamp': datetime.now(),
+                'fingerprint': _get_data_fingerprint(engine, table_name),
+            }
+            sys_log.info(
+                f"已載入當日業績窗口 {data_start.strftime('%Y-%m-%d')}~{data_end.strftime('%Y-%m-%d')}，共 {len(df)} 筆記錄"
+            )
+
         current_kpi = calculate_daily_kpis(df, selected_date)
         dod_kpi = calculate_dod(df, selected_date)
         wow_kpi = calculate_wow(df, selected_date)
 
-        month_start = selected_month.replace(day=1)
-        month_end = (month_start + pd.DateOffset(months=1)) - pd.Timedelta(days=1)
         month_df = df[(df['snapshot_date'] >= month_start) & (df['snapshot_date'] <= month_end)]
 
         cols = month_df.columns.tolist()