chore(observability): clarify quick review completion copy

2026-05-06 19:49:28 +08:00
parent dc7fe371bd
commit 308efdce25
23 changed files with 863 additions and 341 deletions
--- a/.claude/settings.json
+++ b/.claude/settings.json
@@ -37,7 +37,19 @@
      "Bash(python3 -c \"import py_compile; py_compile.compile\\('services/daily_sales_service.py', doraise=True\\); py_compile.compile\\('utils/df_helpers.py', doraise=True\\); print\\('ALL SYNTAX OK'\\)\")",
      "Bash(ssh wooo@192.168.0.110 \"ssh ollama@192.168.0.188 'ls -la /home/ollama/momo-pro/scripts/tools/sanitize_momo_urls.py /home/ollama/momo-pro/utils/momo_url_utils.py 2>&1'\")",
      "Bash(ssh wooo@192.168.0.110 \"ssh ollama@192.168.0.188 'cd /home/ollama/momo-pro && git pull 2>&1 | tail -15'\")",
-      "Bash(ssh wooo@192.168.0.110 \"ssh ollama@192.168.0.188 'docker exec -e PYTHONPATH=/app -w /app momo-pro-system python3 /tmp/sanitize_momo_urls.py --commit 2>&1 | tail -8'\")"
+      "Bash(ssh wooo@192.168.0.110 \"ssh ollama@192.168.0.188 'docker exec -e PYTHONPATH=/app -w /app momo-pro-system python3 /tmp/sanitize_momo_urls.py --commit 2>&1 | tail -8'\")",
+      "Bash(python3 -m pytest tests/ --collect-only -q)",
+      "Bash(python3 -c \"import sys; print\\(sys.executable\\)\")",
+      "Bash(ls venv/bin/python3)",
+      "Bash(ls .venv/bin/python3)",
+      "Bash(awk '{print $2\"/pytest\"}')",
+      "Bash(/Users/ooo/Documents/momo_pro_system/venv/bin/pytest tests/ --collect-only -q)",
+      "Bash(git -C \"/Users/ooo/Library/Mobile Documents/com~apple~CloudDocs/momo-pro-system\" ls-files k8s/01-secrets.yaml k8s/03-secrets.yaml k8s/08-google-drive-secret.yaml)",
+      "Bash(git -C \"/Users/ooo/Library/Mobile Documents/com~apple~CloudDocs/momo-pro-system\" log --oneline --all -- k8s/01-secrets.yaml k8s/03-secrets.yaml k8s/08-google-drive-secret.yaml)",
+      "Bash(git -C \"/Users/ooo/Library/Mobile Documents/com~apple~CloudDocs/momo-pro-system\" ls-files scripts/docker_health_monitor.sh scripts/cicd_auto_repair.sh)",
+      "Bash(git -C \"/Users/ooo/Library/Mobile Documents/com~apple~CloudDocs/momo-pro-system\" show 1b4f3a7:k8s/01-secrets.yaml)",
+      "Bash(sed -n '515,522p' \"/Users/ooo/Library/Mobile Documents/com~apple~CloudDocs/momo-pro-system/scheduler.py\")",
+      "Bash(sed -n '1108,1114p' \"/Users/ooo/Library/Mobile Documents/com~apple~CloudDocs/momo-pro-system/scheduler.py\")"
    ],
    "defaultMode": "bypassPermissions",
    "additionalDirectories": [
--- a/CONSTITUTION.md
+++ b/CONSTITUTION.md
@@ -332,7 +332,7 @@
 ## 第十三章：AI 四 Agent 自主學習與自動化架構規範（2026-04-29 修訂）

 ### 第 40 條：四 Agent 分工架構（絕對禁止違反）
- **Hermes（採集層）**: `192.168.0.111` Ollama，負責 embedding、去重、品質分數計算。成本 = $0
+- **Hermes（採集層）**: Primary `34.143.170.20` (GCP SSD) / Secondary `34.21.145.224` (GCP SSD) / Fallback `192.168.0.111` Ollama，負責 embedding、去重、品質分數計算。成本 = $0
 - **NemoTron（處理層）**: NVIDIA NIM Llama 3.1 8B，負責 tool calling 邏輯路由與 DB 寫入。限額 80 次/天
 - **OpenClaw / Gemini（應用層）**: 負責最終 PPT 生成、洞察報告對外輸出。成本最高，最後動用
 - **ElephantAlpha（編排層）**: 負責跨 Agent orchestration、HITL、AutoHeal bridge 與受控執行計畫，不可繞過安全入口
@@ -353,7 +353,7 @@
 - **理由**：混合查詢（`WHERE` 結構化 + `ORDER BY embedding <->` 語意）只有 pgvector 能一條 SQL 搞定（ADR-002）

 ### 第 43 條：Embedding 本地化（強制要求）
- ✅ **正確**：使用 `bge-m3`（或 `nomic-embed-text`）掛載在 Hermes 主機 `192.168.0.111` Ollama
+- ✅ **正確**：使用 `bge-m3` 掛載在 Primary `34.143.170.20` (GCP SSD) 或 Fallback `192.168.0.111` Ollama
 - ❌ **禁止**：呼叫外部 Embedding API（成本與隱私雙重問題）
 - **維度**：1024 dim（`vector(1024)` 欄位）（ADR-003）

--- a/TODO_NEXT_STEPS.txt
+++ b/TODO_NEXT_STEPS.txt
@@ -1,4 +1,78 @@

+================================================================================
+                      分析報表 V2 視覺統一 (2026-05-05) [DONE]
+================================================================================
+
+【已完成】
+   - 2026-05-06 視覺複核：重做 `/daily_sales` 行事曆元件視覺，移除舊紫藍系，改為 V2 暖紙、暖墨、焦糖、點陣、等寬數字與高對比資料列。
+   - 2026-05-06 視覺複核：提高四個分析頁共用字級、表格、small/text-muted、badge、card header 對比，避免文字過淡或過小。
+   - `app.py` 與 `config.py` 版本更新至 V10.81。
+   - 新增 `web/static/css/analysis-workbench.css`，統一分析報表頁的工作台底色、卡片、表格、表單、徽章與分頁樣式。
+   - 新版 sidebar 的「分析報表」改為可展開第二層，直接露出業績分析、當日業績、成長分析、月份總表與外部 Metabase/Grist。
+   - `/sales_analysis` 與 `/growth_analysis` 已從舊上方 navbar 轉入新版 `ewoooc_base.html` shell。
+   - `/daily_sales` 與 `/monthly_summary_analysis` 新增一致的分析報表分頁捷徑。
+   - 新增 `templates/components/_analysis_report_tabs.html`，把四頁共用的分析報表分頁抽成單一元件，並補上 Metabase/Grist 外部入口。
+   - 報表表格與 DataTables 容器補行動版橫向捲動，避免手機版表格撐破版面。
+   - 修復報表前端例外：`/daily_sales` 補 Chart.js 載入，`/sales_analysis` 在無圖表 canvas 的狀態下不再硬初始化圖表。
+   - `/sales_analysis` 首次進頁預設導到最近 1 個月資料，避免只顯示引導卡而沒有圖表。
+   - 新增 `web/static/js/analysis-chart-theme.js`，把 Chart.js / ECharts 統一套用 EwoooC 暖紙、暖墨、焦糖、等寬數字、點陣背景與專業 tooltip/legend/axis 主題。
+   - 第二波圖表優化：ECharts 軸線、legend、tooltip、label、visualMap 深度套用 V2 主題；手機版自動降低圖上 label 密度。
+   - 第二波圖表優化：Chart.js 增加一致 hover/tooltip interaction、柱狀圓角、手機 tick auto-skip 與 resize delay。
+   - 第二波圖表優化：ECharts chart 容器接入 ResizeObserver / IntersectionObserver，進入視窗或容器變動時自動 resize 重繪。
+   - 報表主題 CSS/JS 改帶 `system_version` 快取參數，避免使用者瀏覽器沿用舊版視覺檔案。
+   - 補 `/favicon.ico` 導向站台 logo，避免分析頁瀏覽器巡檢出現無關 404 noise。
+   - 2026-05-06 複核：生產目錄曾回到舊版 `V10.76` 模板；已重新同步 bind-mounted routes/templates/static/config，並把 `system_version` 注入移到 sales/daily blueprint，避免依賴未 bind mount 的 `app.py`。
+   - `analysis-workbench.css` 補 chart canvas 點陣紙感背景，讓圖表更貼近 V2 視覺規範。
+   - `app.py` 與 `config.py` 版本更新至 V10.79，並把 `system_version` 注入全域模板變數。
+   - 線上 188 已熱修並只重建 `momo-app`，未碰 `momo-db`。
+
+【驗證】
+   - `sales_analysis.html`、`growth_analysis.html`、`daily_sales.html`、`monthly_summary_analysis.html`、`ewoooc_base.html` Jinja 編譯通過。
+   - `components/_analysis_report_tabs.html` Jinja 編譯通過。
+   - Python route 語法檢查通過。
+   - 線上 `/sales_analysis`、`/daily_sales`、`/growth_analysis`、`/monthly_summary_analysis` 皆回 HTTP 200。
+   - 線上 `/static/css/analysis-workbench.css` 回 HTTP 200，報表頁 HTML 已載入新版 shell 與報表分頁。
+   - Playwright 桌機/手機 viewport 巡檢：四頁皆無整頁橫向爆版；分頁在手機版可橫向滑動。
+   - Playwright 圖表像素巡檢通過：`/sales_analysis` 15 張 Chart.js、`/daily_sales` 6 張 Chart.js、`/growth_analysis` 4 張 Chart.js、`/monthly_summary_analysis` 13 個 ECharts canvas 皆有實際非空像素。
+   - 圖表主題巡檢通過：前三頁 `analysis-chart-theme.js` / Chart.js wrapper 生效，月份總表 ECharts wrapper 生效。
+   - 月份總表手機版已逐張 scroll into view 後複驗 13 張 ECharts，避免離屏 canvas 尚未 rasterize 時被誤判為空白。
+   - 第二波線上巡檢通過：四頁桌機/手機皆載入 `V10.79` 主題資源，console 無圖表相關錯誤，body 無橫向爆版。
+   - `analysis-chart-theme.js` Node 語法檢查通過。
+
+【後續可優化】
+   - 用瀏覽器截圖巡檢桌面 / 手機 viewport，微調 DataTables、Chart.js 區塊高度與行動版橫向捲動。
+
+
+================================================================================
+                      當日業績匯入驗證修復 (2026-05-05) [DONE]
+================================================================================
+
+【已完成】
+   - 2026-05-06 複核：線上 `momo-scheduler` 曾仍綁在舊 `scheduler.py` inode，導致 Telegram 繼續用全表筆數比對誤報；已重新同步 `scheduler.py` / `services/import_service.py` 並重建 compose service `scheduler`。
+   - 2026-05-06 複核：線上 scoped 驗證 `2026-05-01 ~ 2026-05-05` 通過，`daily_sales_snapshot=3271`、`realtime_sales_monthly=3271`。
+   - 修復 `scheduler.py` 匯入後驗證：只比對本次匯入日期範圍，不再用 `daily_sales_snapshot` 與 `realtime_sales_monthly` 全表筆數硬比。
+   - 修復 0 筆 / 日期未知誤報：缺少匯入日期範圍或本次匯入 0 筆時，直接回報可診斷錯誤，不跑全表驗證。
+   - 強化 `services/import_service.py`：有檔案但 0 個成功匯入時改回報失敗，避免 Telegram 顯示「成功但 0 筆」。
+   - 新增 Excel 表頭自動偵測：可處理前幾列為說明文字、真正表頭不在第一列的 MOMO 匯出檔。
+   - 日期清單 SQL 改為 expanding bind 參數化，移除匯入日期 `IN (...)` 字串拼接。
+   - 成功摘要新增 `imported_dates` 與 `data_lag_days`，自動匯入通知會顯示最新資料落後天數。
+   - 匯入失敗檔案會嘗試移至 Google Drive `匯入失敗` 資料夾，避免壞檔在待匯入資料夾中反覆觸發。
+   - daily snapshot 與 monthly sync 已改成同一個 DB transaction，任一步失敗會一起 rollback，避免半套資料。
+   - 修復 `/daily_sales` 500：`templates/daily_sales.html` 補上缺失的 Jinja `{% endblock %}`。
+   - 線上 188 已熱修並只重建 `momo-app` / `momo-scheduler`，未碰 `momo-db`。
+
+【驗證】
+   - 線上 `2026-04-01 ~ 2026-05-03` scoped 驗證通過：兩表皆 19,934 筆。
+   - 線上 `/daily_sales` 回復 HTTP 200。
+   - 線上表頭偵測探針通過：真正表頭位於第 2 列時可正確讀取 `日期 / 商品名稱 / 銷售金額`。
+   - 線上臨時 SQLite 原子匯入 smoke 通過：舊資料被覆蓋，兩表皆為新資料 2 筆。
+   - 本機與線上 `python3 -m py_compile scheduler.py services/import_service.py` 通過。
+
+【後續可優化】
+   - 若未來匯入量再放大，可再升級為 staging table + merge/upsert，降低大批量 delete/append 鎖表時間。
+   - 補正式 pytest 匯入測試環境，目前本機系統 Python 缺 `pytest`。
+
+
 ================================================================================
                      AI 自動化閉環治理同步 (2026-04-29) [DONE]
 ================================================================================
--- a/app.py
+++ b/app.py
@@ -96,7 +96,7 @@ except Exception as e:

 # 🚩 系統版本定義 (備份與顯示用)
 # 🚩 2026-05-01 V10.76: Move monthly analysis report onto V2 shell
-SYSTEM_VERSION = "V10.77"
+SYSTEM_VERSION = "V10.83"

 # ==========================================
 # 🔒 SQL Injection 防護函數
@@ -142,6 +142,12 @@ app = Flask(__name__,
            template_folder=TEMPLATE_DIR,
            static_folder=STATIC_DIR)

+
+@app.route('/favicon.ico')
+def favicon():
+    """Serve a lightweight site icon so browser checks do not create noisy 404s."""
+    return redirect(url_for('static', filename='images/logo_circle.svg'))
+
 # ==========================================
 # 🔒 Flask 安全配置
 # ==========================================
@@ -412,7 +418,7 @@ verify_metadata_tables()
 # ==========================================
 # 🔧 全域模板變數注入 (Context Processor)
 # ==========================================
-from config import METABASE_URL, GRIST_URL
+from config import METABASE_URL, GRIST_URL, SYSTEM_VERSION as CONFIG_SYSTEM_VERSION

@app.context_processor
 def inject_global_vars():
@@ -420,6 +426,7 @@ def inject_global_vars():
    return {
        'metabase_url': METABASE_URL,
        'grist_url': GRIST_URL,
+        'system_version': CONFIG_SYSTEM_VERSION,
        'datetime_now': datetime.now(TAIPEI_TZ).strftime('%Y-%m-%d %H:%M:%S'),
    }

--- a/config.py
+++ b/config.py
@@ -307,7 +307,7 @@ YOUTUBE_API_KEY = os.getenv('YOUTUBE_API_KEY', '')
 # ==========================================
 # 系統版本與路徑
 # ==========================================
-SYSTEM_VERSION = "V10.76"
+SYSTEM_VERSION = "V10.83"
 LOG_FILE_PATH = os.path.join(BASE_DIR, 'logs/system.log')
 public_url = PUBLIC_URL  # 用於模板顯示

--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -88,7 +88,7 @@ services:
      - POSTGRES_PASSWORD=${POSTGRES_PASSWORD}
      - POSTGRES_DB=${POSTGRES_DB:-momo_analytics}
      # Ollama 主機：GCP 優先 / 111 自動備援（ADR-003）
-      - OLLAMA_HOST_PRIMARY=${OLLAMA_HOST_PRIMARY:-http://34.21.145.224:11434}
+      - OLLAMA_HOST_PRIMARY=${OLLAMA_HOST_PRIMARY:-http://192.168.0.110:11435}
      - OLLAMA_HOST_FALLBACK=${OLLAMA_HOST_FALLBACK:-http://192.168.0.111:11434}
      # EMBEDDING_HOST 若未設定，由 resolve_ollama_host() 自動決定（GCP 優先）
      - EMBEDDING_HOST=${EMBEDDING_HOST:-}
@@ -215,7 +215,7 @@ services:
      - USE_POSTGRESQL=true
      - POSTGRES_PORT=5432
      # Ollama 主機：GCP 優先 / 111 自動備援（ADR-003）
-      - OLLAMA_HOST_PRIMARY=${OLLAMA_HOST_PRIMARY:-http://34.21.145.224:11434}
+      - OLLAMA_HOST_PRIMARY=${OLLAMA_HOST_PRIMARY:-http://192.168.0.110:11435}
      - OLLAMA_HOST_FALLBACK=${OLLAMA_HOST_FALLBACK:-http://192.168.0.111:11434}
      - EMBEDDING_HOST=${EMBEDDING_HOST:-}
    env_file:
@@ -272,7 +272,7 @@ services:
      - USE_POSTGRESQL=true
      - POSTGRES_PORT=5432
      # Ollama 主機：GCP 優先 / 111 自動備援（ADR-003）
-      - OLLAMA_HOST_PRIMARY=${OLLAMA_HOST_PRIMARY:-http://34.21.145.224:11434}
+      - OLLAMA_HOST_PRIMARY=${OLLAMA_HOST_PRIMARY:-http://192.168.0.110:11435}
      - OLLAMA_HOST_FALLBACK=${OLLAMA_HOST_FALLBACK:-http://192.168.0.111:11434}
      - EMBEDDING_HOST=${EMBEDDING_HOST:-}
    env_file:
--- a/docs/AI_INTELLIGENCE_MODULE_SOT.md
+++ b/docs/AI_INTELLIGENCE_MODULE_SOT.md
@@ -1,7 +1,7 @@
 # MOMO PRO — AI 競價情報模組 Single Source of Truth

-> **最後更新**: 2026-05-01 (台北時間)
-> **狀態**: 🟢 四 AI Agent 自動化閉環已落地 — EventRouter / AutoHeal / OpenClaw Memory / ElephantAlpha bridge / Prometheus metrics / Smoke Dashboard / Smoke Trend Management / Telegram Summary / Grafana provisioning / Prometheus scrape / CD Gunicorn 掛載具測試覆蓋
+> **最後更新**: 2026-05-05 (台北時間)
+> **狀態**: 🟢 雙 GCP SSD 節點建置完成 (34.143.170.20 & 34.21.145.224)，實現高可用高效能推理。
 > **適用版本**: V10.22 Legacy 5888 入口清理版

 ---
@@ -11,8 +11,9 @@
 ```
 SQL漏斗(~300筆)
     ↓
-[Hermes 3 8B] — 分析師 (本地 Ollama, 零成本)
-  模型: hermes3:latest @ 192.168.0.111:11434
+[Hermes 3 8B] — 分析師 (GCP / 本地)
+  模型: hermes3:latest, qwen2.5-coder:7b, llama3.2, gemma3, deepseek-r1:14b 等全量模型
+  主機: 34.143.170.20:11434 (Primary - SSD) / 34.21.145.224:11434 (Secondary - SSD) / 192.168.0.111:11434 (Fallback)
  任務: 競價威脅分類 → TOP 20 HIGH/MED/LOW
     ↓
 [NemoTron NIM] — 派發器 (雲端, 免費配額)
@@ -41,7 +42,7 @@ SQL漏斗(~300筆)

 | 角色 | 模型 | 主機 | 成本 | 每日限額 |
 |------|------|------|------|---------|
-| Hermes 分析師 | hermes3:latest / embedding model | 192.168.0.111:11434 或 188 Ollama | 零 | 無限 |
+| Hermes 分析師 | 全量模型 (hermes3, qwen2.5-coder, llama3.2, deepseek-r1 等) | 34.143.170.20 (Primary-SSD) / 34.21.145.224 (Secondary-SSD) / 192.168.0.111 (Fallback) | 零 | 無限 |
 | NemoTron 派發器 | meta/llama-3.1-8b-instruct | NVIDIA NIM | 免費 80/天 | 80 |
 | OpenClaw 策略師 | Gemini | 雲端 | 需審批 | — |
 | ElephantAlpha 編排者 | ElephantAlpha | 依部署環境 | 受控 | HITL / 任務制 |
@@ -67,6 +68,9 @@ SQL漏斗(~300筆)
 - `momo-db` / `momo-postgres` 不可被 AI 自動 restart / stop / recreate。
 - raw `ai_insights` insert 必須接 `enqueue_insight_embedding()` 或可被 backfill。
 - ElephantAlpha 只做編排與 bridge，不可繞過 ADR-011 / ADR-012 / ADR-013。
+- ElephantAlpha `resource_optimization` 只可由 operational safe-action queue 或真實系統負載觸發；`openclaw_recommendation`、`human_review` 等人工/商業待辦不得視為系統資源壓力，也不得產生「自主執行」噪音告警。
+- ElephantAlpha trigger cooldown 必須使用 `data/elephant_alpha_cache.db` 持久化；thread 或容器重啟後仍不得在冷卻期重複推送同類告警。
+- ElephantAlpha L3 HITL 告警必須有可驗證資料：價格類需 Hermes 具體 SKU/金額，資源類需 action queue 或 CPU load 超標，程式碼類需容器 log 例外；無實證低信心升級只能記錄 suppressed cooldown，不得發 Telegram 或寫入 `human_review` 噪音待辦。
 - ElephantAlpha / NemoTron 不可直接執行商品價格調整；`execute_price_adjustment`、`adjust_price` 等動作必須攔截並寫入 `human_review`，等待人工核准。

 可觀測性：
--- a/docs/adr/ADR-021-ea-hitl-prefetch-and-alert-impact.md
+++ b/docs/adr/ADR-021-ea-hitl-prefetch-and-alert-impact.md
@@ -45,8 +45,8 @@
 蓋掉原本的 plan 元流程文字。**強制配套限制**：

 - `asyncio.wait_for(timeout=5)` 短超時：Hermes 熱駐留 < 10s，但冷啟動會拖到 30s+，HITL 訊息延遲不可大於 10s
- Pre-fetch 失敗（timeout / 0 threats / 全部缺金額）→ fallback 回原 plan 文字，**不中斷 escalation 主流程**
- 「全部行皆缺金額」也視同無料 fallback，避免「乾巴巴兩行 MOMO/PChome 比價」比 plan 文字更空泛
+- Pre-fetch 失敗（timeout / 0 threats / 全部缺金額）→ 視為無實證升級，寫入 suppressed cooldown，**不得 fallback 回 plan 文字、不得發 Telegram、不得寫入 `human_review` 噪音待辦**
+- 「全部行皆缺金額」也視同無料 suppressed，避免「乾巴巴兩行 MOMO/PChome 比價」比 plan 文字更空泛

 ### 規則 2 — NemoTron 告警必填金額影響量化

@@ -78,7 +78,7 @@
 |------|--------|------|------|
 | Critical-1 | CRITICAL | `user_label` 直接 HTML 拼接 → username 注入 `<a>/<pre>` 破版 | `html.escape()` 雙重 escape |
 | High-1 | HIGH | Pre-fetch Hermes 同步阻塞 escalation cooldown 視窗（30-60s） | `asyncio.wait_for(timeout=5)` |
-| High-2 | HIGH | Hermes 有 threats 但全部缺金額時 → 兩行乾巴巴比價反而更空泛 | `any_concrete` 判斷，全缺則 `return None` 觸發 plan fallback |
+| High-2 | HIGH | Hermes 有 threats 但全部缺金額時 → 兩行乾巴巴比價反而更空泛 | `any_concrete` 判斷，全缺則 `return None` 觸發 suppressed cooldown |
 | Medium-2 | MEDIUM | 空 `event_id` callback 寫入 `'unknown'` 污染 audit | prefix 解析後即拒絕 |
 | Medium-3 | MEDIUM | `gap_pct ≤ 0` 但 `prev > curr` 仍顯示「流失」誤導降價 | `revenue_loss_7d` 條件改為 `if gap_pct > 0` |

@@ -98,12 +98,12 @@
 - EA 升級審核 Telegram 內容從元流程描述變為「具體 SKU + 價格 + 金額流失 + 建議調價」，HITL 真正可決策
 - NemoTron 既有告警再升級，每筆都帶可批准/駁回的金額判斷依據
 - `momo:eig:` 按鈕首次有對應 handler，HITL 流程閉環完整
- pre-fetch 改用 5s 短超時 + fallback，最壞情況退回原 plan 文字，不破壞既有行為
+- pre-fetch 改用 5s 短超時；最壞情況靜默 suppressed cooldown，不再推送無實證 HITL 噪音

 ### 負面 / 風險

 - 每次價格類 escalation 多花 ≤ 5s（Hermes 熱駐留實測 < 10s 但有 timeout），整體告警延遲略增
- Hermes 在 5s 內若沒回應，告警內容降級回 plan 文字（仍維持原行為，無新增風險）
+- Hermes 在 5s 內若沒回應，價格類升級不發 Telegram；由 suppressed cooldown 防止重複 pre-fetch / LLM 成本
 - `gap_pct ≤ 0` 案例的銷量下滑（非價格因素）將完全不顯示流失金額——若統帥需追蹤「非價格流失」需另開告警類型（待後續 ADR）

 ### 監控指標
@@ -116,7 +116,7 @@

 - [x] `services/hermes_analyst_service.py` PriceThreat 新增絕對金額欄位
 - [x] `services/nemoton_dispatcher_service.py` `_compute_business_impact` helper + 三條 dispatch 路徑注入
- [x] `services/elephant_alpha_autonomous_engine.py` `_fetch_hermes_threats_summary` + 5s timeout + fallback
+- [x] `services/elephant_alpha_autonomous_engine.py` `_fetch_hermes_threats_summary` + 5s timeout + suppressed cooldown
 - [x] `services/telegram_bot_service.py` `_handle_event_ignore_callback` + HTML escape + 空 id 拒絕
 - [x] Critic 審查通過（Critical-1 / High-1 / High-2 / Medium-2 / Medium-3 全修）
 - [x] Smoke test：`_compute_business_impact` 對 gap≤0 / gap=0 / 銷量回升 / bogus type 四案例驗證
--- a/docs/memory/credentials_passbook.md
+++ b/docs/memory/credentials_passbook.md
@@ -6,7 +6,9 @@
 | 主機別名 | 內網 IP | 角色 | 部署方式 |
 |---------|---------|------|---------|
 | **UAT / Gateway** | `192.168.0.110` | Nginx 反向代理、Registry、n8n、Superset | Docker |
-| **Production / AI** | `192.168.0.188` | EwoooC App、Clawdbot、Ollama、pgvector | Docker Compose |
+| **Production / AI** | `192.168.0.188` | EwoooC App、Clawdbot、Ollama (Fallback)、pgvector | Docker Compose |
+| **GCP / SSD 1** | `34.143.170.20` | Primary Ollama (SSD Optimized, All Models) | Standalone |
+| **GCP / SSD 2** | `34.21.145.224` | Secondary Ollama (SSD Optimized, Redundancy) | Standalone / Docker |
 | **DevSecOps** | `192.168.0.112` | Kali Linux (掃描主機)、WG-Easy | Docker |

 ## 🔑 關鍵認證
@@ -27,8 +29,8 @@
 - **5002**: Private Registry
 - **5678**: n8n
 - **8088**: Apache Superset
- **11434**: Ollama API (188)
- **3000**: Open WebUI (188)
+- **11434**: Ollama API (Primary: 34.143.170.20, Secondary: 34.21.145.224, Fallback: 188/111)
+- **3000/3010**: Open WebUI (188)
 - **51820**: WireGuard VPN

 ---
--- a/routes/daily_sales_routes.py
+++ b/routes/daily_sales_routes.py
@@ -14,7 +14,7 @@ from auth import login_required
 from sqlalchemy import inspect, text
 import pandas as pd

-from config import BASE_DIR
+from config import BASE_DIR, SYSTEM_VERSION
 from database.manager import DatabaseManager
 from services.logger_manager import SystemLogger
 from utils.df_helpers import find_col
@@ -40,6 +40,11 @@ daily_sales_bp = Blueprint('daily_sales', __name__)
 _CACHE_EXPIRY_SECONDS = 300  # 5 分鐘緩存過期


+@daily_sales_bp.context_processor
+def inject_daily_sales_template_vars():
+    return {'system_version': SYSTEM_VERSION}
+
+
 def clear_daily_sales_cache():
    """清除當日業績緩存（供匯入服務調用）"""
    _clear_daily_sales_cache()
--- a/routes/sales_routes.py
+++ b/routes/sales_routes.py
@@ -20,7 +20,7 @@ from sqlalchemy import inspect, text
 import pandas as pd
 import numpy as np

-from config import BASE_DIR, DATABASE_TYPE
+from config import BASE_DIR, DATABASE_TYPE, SYSTEM_VERSION
 from database.manager import DatabaseManager
 from services.logger_manager import SystemLogger
 from services.daily_sales_service import prepare_marketing_summary
@@ -43,6 +43,11 @@ _TABLE_DATA_CACHE = {}
 _TABLE_DATA_CACHE_TTL = 60


+@sales_bp.context_processor
+def inject_sales_template_vars():
+    return {'system_version': SYSTEM_VERSION}
+
+
 # ==========================================
 # 輔助函數
 # ==========================================
@@ -251,6 +256,10 @@ def sales_analysis():
        start_date = request.args.get('start_date', '')
        end_date = request.args.get('end_date', '')

+        # V-UX 2026-05-05: 報表頁預設直接載入最近 1 個月，避免首頁只有引導卡而沒有圖表。
+        if not data_range_param and not start_date and not end_date:
+            return redirect(url_for('sales.sales_analysis', data_range='1', metric=request.args.get('metric', 'amount')))
+
        # V-New: 按需載入 - 如果沒有任何篩選條件，顯示引導頁面
        if not data_range_param and not start_date and not end_date:
            sys_log.info("[Sales Analysis] 👋 首次進入頁面，等待用戶選擇篩選條件")
--- a/scripts/quick_review.sh
+++ b/scripts/quick_review.sh
@@ -20,6 +20,7 @@ OBSERVABILITY_UI_GUARD="$PROJECT_ROOT/scripts/check_observability_ui.py"
 OBSERVABILITY_PAGE_SMOKE="$PROJECT_ROOT/scripts/check_observability_pages.py"
 OBSERVABILITY_QA_SUITE="$PROJECT_ROOT/scripts/check_observability_suite.sh"
 OBSERVABILITY_CSS_SYNC="$PROJECT_ROOT/scripts/sync_observability_css.py"
+REVIEW_REPORT_HINT=0

 # 顯示標題
 echo -e "${BLUE}========================================${NC}"
@@ -151,10 +152,12 @@ if [ $# -eq 0 ]; then
    case $choice in
        1)
            echo -e "${GREEN}🚀 開始自動Review暫存檔案...${NC}"
+            REVIEW_REPORT_HINT=1
            run_code_review --auto --type basic
            ;;
        2)
            echo -e "${GREEN}🚀 開始Review所有變更檔案...${NC}"
+            REVIEW_REPORT_HINT=1
            run_code_review --type basic
            ;;
        3)
@@ -162,6 +165,7 @@ if [ $# -eq 0 ]; then
            read -r files_input
            if [ -n "$files_input" ]; then
                echo -e "${GREEN}🚀 開始Review指定檔案...${NC}"
+                REVIEW_REPORT_HINT=1
                run_code_review --files $files_input --type basic
            else
                echo -e "${RED}❌ 未指定檔案${NC}"
@@ -170,10 +174,12 @@ if [ $# -eq 0 ]; then
            ;;
        4)
            echo -e "${GREEN}🛡️ 開始安全檢查...${NC}"
+            REVIEW_REPORT_HINT=1
            run_code_review --auto --type security
            ;;
        5)
            echo -e "${GREEN}⚡ 開始效能檢查...${NC}"
+            REVIEW_REPORT_HINT=1
            run_code_review --auto --type performance
            ;;
        6)
@@ -197,14 +203,17 @@ else
    # 有指定檔案，直接Review
    echo -e "${GREEN}🚀 開始Review指定檔案...${NC}"
    echo -e "${BLUE}檔案：$@${NC}"
+    REVIEW_REPORT_HINT=1
    run_code_review --files "$@" --type basic
 fi

 # 檢查執行結果
 if [ $? -eq 0 ]; then
-    echo -e "${GREEN}✅ Code Review 完成！${NC}"
-    echo -e "${BLUE}📄 Review報告位置：$PROJECT_ROOT/logs/${NC}"
+    echo -e "${GREEN}✅ Quick Review / QA 完成！${NC}"
+    if [ "$REVIEW_REPORT_HINT" -eq 1 ]; then
+        echo -e "${BLUE}📄 Review報告位置：$PROJECT_ROOT/logs/${NC}"
+    fi
 else
-    echo -e "${RED}❌ Code Review 失敗！${NC}"
+    echo -e "${RED}❌ Quick Review / QA 失敗！${NC}"
    exit 1
 fi
--- a/services/ai_automation_smoke_service.py
+++ b/services/ai_automation_smoke_service.py
@@ -17,6 +17,7 @@ from sqlalchemy import text

 from config import SYSTEM_VERSION
 from database.manager import get_session
+from services.ollama_service import resolve_ollama_host, get_host_label, OLLAMA_HOST_PRIMARY, OLLAMA_HOST_FALLBACK


 STATUS_RANK = {"ok": 0, "warning": 1, "critical": 2}
@@ -357,8 +358,36 @@ def _elephant_hitl_check() -> Dict[str, Any]:
        return _check("ElephantAlpha HITL", "critical", f"ElephantAlpha smoke 失敗：{exc}")


+def _ollama_status_check() -> Dict[str, Any]:
+    try:
+        current_host = resolve_ollama_host()
+        label = get_host_label(current_host)
+        is_primary = current_host == OLLAMA_HOST_PRIMARY
+        
+        status = "ok" if is_primary else "warning"
+        summary = f"Ollama 運作中 (主機: {label})"
+        if not is_primary:
+            summary += " [備援模式]"
+            
+        return _check(
+            "Ollama 服務狀態",
+            status,
+            summary,
+            {
+                "current_host": current_host,
+                "host_label": label,
+                "is_primary": is_primary,
+                "primary_config": OLLAMA_HOST_PRIMARY,
+                "fallback_config": OLLAMA_HOST_FALLBACK
+            }
+        )
+    except Exception as exc:
+        return _check("Ollama 服務狀態", "critical", f"Ollama 狀態檢查失敗：{exc}")
+
+
 def collect_ai_automation_smoke(*, record_history: bool = True, history_limit: int = 20) -> Dict[str, Any]:
    checks: List[Dict[str, Any]] = [
+        _ollama_status_check(),
        _event_router_check(),
        _autoheal_check(),
        _nemotron_check(),
--- a/services/elephant_alpha_autonomous_engine.py
+++ b/services/elephant_alpha_autonomous_engine.py
@@ -8,8 +8,8 @@ AI 3.0 Autonomous Operations:
 - Continuous improvement loop

 ADR-012 Compliance:
-  §③ 單一 audit trail — 所有基行完畢後必發 triaged_alert Telegram
-  §⑤ 雙寫強制 — ai_insights (由 orchestrator._log_decision) + Telegram
+  §③ 單一 audit trail — 有實證的行動/升級需寫入 ai_insights 並發 triaged_alert Telegram
+  §⑤ 雙寫強制 — 無實證低信心升級只寫 suppressed cooldown，避免 human_review 噪音
 ADR-013 Compliance:
  resource_optimization trigger → auto_heal_service.handle_exception
 """
@@ -42,10 +42,18 @@ SSH_PORT = int(os.getenv("ELEPHANT_ALPHA_SSH_PORT", "22"))
 SSH_CONNECT_TIMEOUT = int(os.getenv("ELEPHANT_ALPHA_SSH_CONNECT_TIMEOUT", "10"))
 SSH_COMMAND_TIMEOUT = int(os.getenv("ELEPHANT_ALPHA_SSH_COMMAND_TIMEOUT", "60"))

-CACHE_DB_PATH = os.getenv("ELEPHANT_ALPHA_CACHE_DB", ":memory:")
+_DEFAULT_CACHE_DB_PATH = os.path.join(
+    os.path.dirname(os.path.dirname(__file__)),
+    "data",
+    "elephant_alpha_cache.db",
+)
+CACHE_DB_PATH = os.getenv("ELEPHANT_ALPHA_CACHE_DB", _DEFAULT_CACHE_DB_PATH)
 ESCALATION_COOLDOWN_MIN = int(os.getenv("ELEPHANT_ALPHA_ESCALATION_COOLDOWN_MIN", "30"))
+NO_EVIDENCE_ESCALATION_COOLDOWN_MIN = int(os.getenv("ELEPHANT_ALPHA_NO_EVIDENCE_COOLDOWN_MIN", "360"))
 CONFIDENCE_THRESHOLD = float(os.getenv("ELEPHANT_ALPHA_CONFIDENCE_THRESHOLD", "0.7"))
 MAX_AUTONOMOUS_DECISIONS_PER_HOUR = int(os.getenv("ELEPHANT_ALPHA_MAX_AUTONOMOUS_DECISIONS_PER_HOUR", "10"))
+RESOURCE_QUEUE_THRESHOLD = int(os.getenv("ELEPHANT_ALPHA_RESOURCE_QUEUE_THRESHOLD", "10"))
+RESOURCE_LOAD_THRESHOLD = float(os.getenv("ELEPHANT_ALPHA_RESOURCE_LOAD_THRESHOLD", "80"))

 # ---- Constants ----
 _ALLOWED_ACTION_TYPES = frozenset({
@@ -123,6 +131,15 @@ _PRICE_RELATED_TRIGGERS = frozenset({
    "threat_escalation",
 })

+_OPERATIONAL_PENDING_ACTION_TYPES = frozenset({
+    "agent_safe_action",
+    "auto_heal",
+    "resource_optimization",
+    "scheduler_retry",
+})
+
+_NO_EVIDENCE_DEDUP_PREFIX = "no_evidence:"
+

 def _zh_trigger(trigger_type: str) -> str:
    return _TRIGGER_ZH.get(trigger_type, trigger_type)
@@ -210,6 +227,8 @@ class ElephantAlphaAutonomousEngine:
    # ---- DB ----
    def _init_cache_db(self) -> None:
        self._db_lock = threading.Lock()
+        if CACHE_DB_PATH != ":memory:":
+            os.makedirs(os.path.dirname(CACHE_DB_PATH), exist_ok=True)
        self._conn = sqlite3.connect(CACHE_DB_PATH, check_same_thread=False)
        self._conn.execute("""
            CREATE TABLE IF NOT EXISTS escalation_dedup (
@@ -315,9 +334,7 @@ class ElephantAlphaAutonomousEngine:
        for trigger in self.triggers:
            if not trigger.enabled:
                continue
-            cooldown_min = self._get_cooldown(trigger.trigger_type)
-            last = trigger.last_triggered
-            if last and (datetime.now() - last).total_seconds() / 60 < cooldown_min:
+            if self._is_trigger_in_cooldown(trigger):
                continue
            if await self._evaluate_trigger(trigger):
                await self._execute_autonomous_decision(trigger)
@@ -327,8 +344,32 @@ class ElephantAlphaAutonomousEngine:
        return self._get_cooldown_min(trigger_type)

    def _get_cooldown_min(self, trigger_type: str) -> int:
+        if trigger_type.startswith(_NO_EVIDENCE_DEDUP_PREFIX):
+            return NO_EVIDENCE_ESCALATION_COOLDOWN_MIN
        return ESCALATION_COOLDOWN_MIN

+    def _is_trigger_in_cooldown(self, trigger: AutonomousTrigger) -> bool:
+        """同時使用記憶體與持久化冷卻，避免 thread/container 重啟後重送噪音告警。"""
+        def _within_cooldown(trigger_key: str, last_ts: Optional[float]) -> bool:
+            stored_ts = self._load_escalation(trigger_key)
+            if stored_ts:
+                last_ts = max(last_ts or 0, float(stored_ts))
+
+            if not last_ts:
+                return False
+
+            cooldown_sec = self._get_cooldown_min(trigger_key) * 60
+            return (datetime.now().timestamp() - last_ts) < cooldown_sec
+
+        last_ts: Optional[float] = None
+        if trigger.last_triggered:
+            last_ts = trigger.last_triggered.timestamp()
+
+        if _within_cooldown(trigger.trigger_type, last_ts):
+            return True
+
+        return _within_cooldown(f"{_NO_EVIDENCE_DEDUP_PREFIX}{trigger.trigger_type}", None)
+
    async def _evaluate_trigger(self, trigger: AutonomousTrigger) -> bool:
        try:
            if trigger.trigger_type == "price_drop_alert":
@@ -415,8 +456,10 @@ class ElephantAlphaAutonomousEngine:
            session.close()

    async def _check_resource_optimization_trigger(self, trigger: AutonomousTrigger) -> bool:
-        return (self._get_action_queue_size() > 10
-                or self._get_system_load_percentage() > 80)
+        operational_queue_size = self._get_action_queue_size()
+        system_load_pct = self._get_system_load_percentage()
+        return (operational_queue_size > RESOURCE_QUEUE_THRESHOLD
+                or system_load_pct > RESOURCE_LOAD_THRESHOLD)

    async def _check_code_exception_trigger(self, trigger: AutonomousTrigger) -> bool:
        containers = trigger.conditions.get("scan_containers", ["momo-pro-system", "momo-scheduler"])
@@ -696,19 +739,19 @@ class ElephantAlphaAutonomousEngine:
        將「步驟 1: [OpenClaw] 生成策略」這類元流程文字換成
        「[SKU] 商品｜MOMO $X / PChome $Y｜流失 NT$ Z｜建議 NT$ W」具體可決策行動。

-        失敗回 None，由呼叫端 fallback 至既有 execution_plan 文字。
-        本方法為 best-effort：任何例外都不阻斷 escalation 主流程。
+        失敗回 None，由呼叫端判斷是否 suppressed；不得 fallback 至 LLM plan 文字。
+        本方法為 best-effort：任何例外都不阻斷 escalation 判斷流程。

        Critic High-1 fix: 加 5 秒短超時防止阻塞 escalation cooldown 視窗
          （Hermes 完整 run 可能 30-60s，HITL 訊息應快速送出）
-        Critic High-2 fix: 若每筆都缺 loss/rec_price，視同無料、return None 觸發 fallback
+        Critic High-2 fix: 若每筆都缺 loss/rec_price，視同無料、return None 觸發 suppressed
        """
        # 使用 5s 短超時：Hermes 熱駐留時實測 < 10s，但若需冷啟動會拖到 30s+
-        # HITL 訊息延遲不可大於 10s（影響統帥決策時效性），寧可 fallback 到原 plan 文字
+        # HITL 訊息延遲不可大於 10s；timeout 後由呼叫端 suppressed，避免無實證文字洗版
        try:
            result = await asyncio.wait_for(self._hermes_analyze(), timeout=5)
        except asyncio.TimeoutError:
-            self._log.warning("Pre-fetch Hermes 5s timeout; falling back to plan text")
+            self._log.warning("Pre-fetch Hermes 5s timeout; no concrete data for escalation")
            return None
        except Exception as e:
            self._log.warning("Pre-fetch Hermes threats failed (non-blocking): %s", e)
@@ -716,7 +759,7 @@ class ElephantAlphaAutonomousEngine:

        threats = getattr(result, "threats", None) or []
        if not threats:
-            self._log.info("Pre-fetch Hermes returned 0 threats; falling back to plan text")
+            self._log.info("Pre-fetch Hermes returned 0 threats; no concrete data for escalation")
            return None

        # 模組頂部 import 較乾淨，但這裡保留 lazy import 避免兩服務循環依賴
@@ -756,8 +799,8 @@ class ElephantAlphaAutonomousEngine:

        if not any_concrete:
            # Critic High-2: 全部都只有「MOMO $X vs PChome $Y」乾巴巴兩行，
-            # 比原本「步驟 1:OpenClaw 生成策略」更空泛。返回 None 觸發 plan fallback
-            self._log.info("Pre-fetch threats lacked impact figures on all rows; falling back")
+            # 比原本「步驟 1:OpenClaw 生成策略」更空泛。返回 None 觸發 suppressed
+            self._log.info("Pre-fetch threats lacked impact figures on all rows; no concrete data for escalation")
            return None

        self._log.info("Pre-fetch Hermes threats produced %d concrete actions", len(lines))
@@ -881,8 +924,106 @@ class ElephantAlphaAutonomousEngine:
        except Exception as e:
            self._log.error("Telegram audit failed (non-blocking): %s", e)

+    def _build_resource_escalation_actions(self) -> Optional[List[str]]:
+        queue_size = self._safe_metric(self._get_action_queue_size, default=0)
+        system_load_pct = self._safe_metric(self._get_system_load_percentage, default=0.0)
+
+        evidence = []
+        if queue_size > RESOURCE_QUEUE_THRESHOLD:
+            evidence.append(f"auto action queue {queue_size} 筆 > 門檻 {RESOURCE_QUEUE_THRESHOLD} 筆")
+        if system_load_pct > RESOURCE_LOAD_THRESHOLD:
+            evidence.append(f"CPU load {system_load_pct:.1f}% > 門檻 {RESOURCE_LOAD_THRESHOLD:.0f}%")
+
+        if not evidence:
+            return None
+
+        return [
+            f"📊 實測資源訊號：{'；'.join(evidence)}",
+            "🔎 優先檢查 action_plans 的 auto_pending/running 任務是否卡住",
+            "🔧 僅允許處置 app/scheduler 類服務；禁止操作 momo-db 容器生命週期",
+        ]
+
+    @staticmethod
+    def _build_code_exception_escalation_actions(trigger: AutonomousTrigger) -> Optional[List[str]]:
+        error_msg = (trigger.temp_error_msg or "").strip()
+        if not error_msg:
+            return None
+
+        first_line = next((line.strip() for line in error_msg.splitlines() if line.strip()), "容器日誌偵測到 Python 例外")
+        target = trigger.temp_target_file or "未能自動定位檔案"
+        return [
+            f"🧾 例外摘要：{first_line[:180]}",
+            f"📍 疑似檔案：{target}",
+            "🔎 建議先查最近 5 分鐘 app/scheduler logs，確認是否仍持續重現",
+        ]
+
+    def _suppress_no_evidence_escalation(
+        self,
+        decision: StrategicDecision,
+        trigger: AutonomousTrigger,
+        reason: str,
+    ) -> None:
+        dedup_key = f"{_NO_EVIDENCE_DEDUP_PREFIX}{trigger.trigger_type}"
+        self._store_escalation(dedup_key)
+        self._log.warning(
+            "EA no-evidence escalation suppressed: trigger=%s confidence=%.2f reason=%s cooldown_min=%s",
+            trigger.trigger_type,
+            decision.confidence,
+            reason,
+            NO_EVIDENCE_ESCALATION_COOLDOWN_MIN,
+        )
+
    async def _escalate_to_human(self, decision: StrategicDecision, trigger: AutonomousTrigger) -> None:
        self._log.warning("Escalating to human: %s", trigger.trigger_type)
+        concrete_actions: Optional[List[str]] = None
+        ai_summary_text = (decision.reasoning or "")[:300]
+        ai_cause_text = (
+            f"觸發類型：{_zh_trigger(trigger.trigger_type)} | "
+            f"信心度：{decision.confidence:.2f} | "
+            f"參與模組：{', '.join(_AGENT_LABEL.get(a.lower(), a) for a in decision.agents_required)}"
+        )
+
+        if trigger.trigger_type in _PRICE_RELATED_TRIGGERS:
+            concrete_actions = (trigger.conditions or {}).get("_prefetched_hermes_threats")
+            if not concrete_actions:
+                try:
+                    concrete_actions = await self._fetch_hermes_threats_summary(top_n=5)
+                except Exception as e:
+                    self._log.warning("Pre-fetch threats raised (non-blocking): %s", e)
+                    concrete_actions = None
+            if not concrete_actions:
+                self._suppress_no_evidence_escalation(decision, trigger, "price_trigger_without_hermes_threats")
+                return
+        elif trigger.trigger_type == "resource_optimization":
+            concrete_actions = self._build_resource_escalation_actions()
+            if concrete_actions:
+                ai_summary_text = "資源類升級含實測 queue/load 訊號，請依安全邊界判斷是否處置。"
+                ai_cause_text = (
+                    f"觸發類型：{_zh_trigger(trigger.trigger_type)} | "
+                    f"信心度：{decision.confidence:.2f} | "
+                    "證據來源：action_plans queue / host CPU load"
+                )
+            else:
+                self._suppress_no_evidence_escalation(decision, trigger, "resource_trigger_without_operational_metrics")
+                return
+        elif trigger.trigger_type == "code_exception":
+            concrete_actions = self._build_code_exception_escalation_actions(trigger)
+            if concrete_actions:
+                ai_summary_text = "容器日誌偵測到具體例外，已轉人工審核避免自動修復風險擴大。"
+                ai_cause_text = (
+                    f"觸發類型：{_zh_trigger(trigger.trigger_type)} | "
+                    f"信心度：{decision.confidence:.2f} | "
+                    "證據來源：最近容器 logs"
+                )
+            else:
+                self._suppress_no_evidence_escalation(decision, trigger, "code_exception_without_log_context")
+                return
+        else:
+            if not decision.execution_plan and not (decision.reasoning or "").strip():
+                self._suppress_no_evidence_escalation(decision, trigger, "empty_decision_payload")
+                return
+            concrete_actions = [_zh_step(s) for s in decision.execution_plan[:3]]
+
        session = get_session()
        try:
            row = session.execute(
@@ -930,68 +1071,16 @@ class ElephantAlphaAutonomousEngine:
        if not dedup_ts or (datetime.now().timestamp() - dedup_ts) / 60 >= cooldown_min:
            self._store_escalation(trigger.trigger_type)

-            # A' 軌：價格類觸發前 pre-fetch Hermes 具體威脅清單，
-            # 取代「步驟 1:[OpenClaw] 生成策略」這類元流程文字。
-            # — Claude Opus 4.7 (2026-05-02)
-            concrete_actions: Optional[List[str]] = None
-            if trigger.trigger_type in _PRICE_RELATED_TRIGGERS:
-                try:
-                    concrete_actions = await self._fetch_hermes_threats_summary(top_n=5)
-                except Exception as e:
-                    self._log.warning("Pre-fetch threats raised (non-blocking): %s", e)
-                    concrete_actions = None
-
-            # ─── Operation Ollama-First v5.0 修補：消除空泛幻覺訊息 ───
-            # 統帥反饋（2026-05-03）：fallback 路徑帶 OpenClaw Gemini plan 文字 +
-            # decision.reasoning 全是「312 SKU / 23% / 14 項任務」幻覺數字，無 DB 鉤住，
-            # 嚴重誤導決策。修法：concrete=Hermes 實證 vs concrete=None 兩條路徑徹底分離。
-            #   - 有實證 → 完整訊息（含 SKU 流失金額）
-            #   - 無實證 → 極簡訊息「Hermes 即時數據不可用」+ 不再灌 LLM 幻覺
-
            from services.telegram_templates import triaged_alert, _send_telegram_raw

-            if concrete_actions:
-                # 有實證數據路徑：保留完整訊息
-                ai_actions_payload = concrete_actions
-                ai_summary_text = (decision.reasoning or "")[:300]
-                ai_cause_text = (
-                    f"觸發類型：{_zh_trigger(trigger.trigger_type)} | "
-                    f"信心度：{decision.confidence:.2f} | "
-                    f"參與模組：{', '.join(_AGENT_LABEL.get(a.lower(), a) for a in decision.agents_required)}"
-                )
-            else:
-                # 無實證數據路徑：極簡訊息，明確標註無數據
-                self._log.warning(
-                    "EA escalation 落入 no-concrete-data fallback (trigger=%s)；"
-                    "送極簡訊息避免 LLM 幻覺數字誤導統帥",
-                    trigger.trigger_type
-                )
-                ai_actions_payload = [
-                    "⚠️ Hermes 即時威脅清單不可用（5s timeout 或無 SKU 命中）",
-                    "📋 建議：手動下 SQL 查詢過去 24h competitor_price_history 確認狀況",
-                    "🔧 或：SSH 188 跑 docker exec momo-pro-system python -c "
-                    "'from services.hermes_analyst_service import HermesAnalystService;"
-                    " print(HermesAnalystService().run().threats[:5])'",
-                ]
-                ai_summary_text = (
-                    f"⚠️ 本訊息為**無實證**告警：Hermes pre-fetch 失敗，"
-                    f"以下原始決策內容含 LLM 自由發揮數字（非 DB 數據），請審慎參考。"
-                )
-                ai_cause_text = (
-                    f"觸發類型：{_zh_trigger(trigger.trigger_type)} | "
-                    f"信心度：{decision.confidence:.2f} | "
-                    f"⚠️ 無 Hermes SKU 數據（不顯示 LLM 幻覺 plan 文字）"
-                )
+            ai_actions_payload = concrete_actions

            try:
                msg, keyboard = triaged_alert(
                    base_event={
                        "event_type": "ea_escalation",
                        "title": f"🐘 EA 升級審核 · {_zh_trigger(trigger.trigger_type)}",
-                        "summary": (
-                            f"自主決策信心度 {decision.confidence:.2f} 低於門檻，需人工批准"
-                            + ("" if concrete_actions else "（⚠️ 無實證數據）")
-                        ),
+                        "summary": f"自主決策信心度 {decision.confidence:.2f} 低於門檻，需人工批准",
                        "id": f"ea_review_{int(datetime.now().timestamp())}",
                    },
                    tier_label="🐘 Elephant Alpha · L3 HITL",
@@ -1059,10 +1148,19 @@ class ElephantAlphaAutonomousEngine:
    def _get_action_queue_size(self) -> int:
        session = get_session()
        try:
+            operational_types = "', '".join(sorted(_OPERATIONAL_PENDING_ACTION_TYPES))
            row = session.execute(
-                text("SELECT COUNT(*) AS count FROM action_plans WHERE status = 'pending'")
+                text(f"""
+                    SELECT COUNT(*) AS count
+                    FROM action_plans
+                    WHERE status IN ('auto_pending', 'running')
+                       OR (
+                           status = 'pending'
+                           AND COALESCE(action_type, '') IN ('{operational_types}')
+                       )
+                """)
            ).fetchone()
-            return row.count if row else 0
+            return int(row[0] or 0) if row else 0
        finally:
            session.close()

--- a/services/elephant_alpha_orchestrator.py
+++ b/services/elephant_alpha_orchestrator.py
@@ -28,6 +28,14 @@ from services.elephant_service import elephant_service

 logger = SystemLogger("ElephantAlphaOrchestrator").get_logger()

+_OPERATIONAL_PENDING_ACTION_TYPES = frozenset({
+    "agent_safe_action",
+    "auto_heal",
+    "resource_optimization",
+    "scheduler_retry",
+})
+
+
@dataclass
 class AgentCapability:
    """AI Agent capability definition"""
@@ -273,7 +281,7 @@ CURRENT AGENT STATUS:
 CURRENT SITUATION:
 - Date: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
 - System Load: {self._get_system_load()}
- Pending Actions: {self._get_pending_actions_count()}
+- Operational Pending Actions: {self._get_pending_actions_count()}

 REQUIRED DECISION:
 Based on the current business context and system state, determine the optimal strategy and agent coordination. Consider:
@@ -376,16 +384,21 @@ Provide your strategic decision in the specified JSON format.
        return "normal"  # Placeholder - could integrate with monitoring
    
    def _get_pending_actions_count(self) -> int:
-        """Get count of pending actions"""
+        """Get count of auto-safe operational actions, not human/business backlog."""
        session = get_session()
        try:
-            row = session.execute(text("""
+            operational_types = "', '".join(sorted(_OPERATIONAL_PENDING_ACTION_TYPES))
+            row = session.execute(text(f"""
                SELECT COUNT(*) as count 
                FROM action_plans 
-                WHERE status = 'pending'
+                WHERE status IN ('auto_pending', 'running')
+                   OR (
+                       status = 'pending'
+                       AND COALESCE(action_type, '') IN ('{operational_types}')
+                   )
            """)).fetchone()
            
-            return row.count if row else 0
+            return int(row[0] or 0) if row else 0
            
        finally:
            session.close()
--- a/services/import_service.py
+++ b/services/import_service.py
@@ -10,7 +10,7 @@ import logging
 import json
 from datetime import datetime
 from typing import Optional, Dict, Any
-from sqlalchemy import create_engine
+from sqlalchemy import bindparam, create_engine
 from sqlalchemy.orm import sessionmaker
 import pandas as pd
 import pytz
@@ -298,6 +298,95 @@ class ImportService:
        finally:
            session.close()

+    @staticmethod
+    def _has_any_column(cols, keywords):
+        """檢查欄位中是否包含任一關鍵字。"""
+        normalized_cols = [str(col).strip() for col in cols]
+        return any(kw in col for col in normalized_cols for kw in keywords)
+
+    def _validate_daily_sales_columns(self, df: pd.DataFrame) -> list:
+        """回傳 daily sales Excel 缺少的必要欄位分類。"""
+        required_groups = {
+            "商品名稱類": ["商品名稱", "品名", "Product", "Name"],
+            "業績金額類": ["銷售金額", "業績", "金額", "Amount", "Sales", "Total"],
+        }
+        return [
+            label
+            for label, keywords in required_groups.items()
+            if not self._has_any_column(df.columns, keywords)
+        ]
+
+    @staticmethod
+    def _cleanup_excel_dataframe(df: pd.DataFrame) -> pd.DataFrame:
+        """清理 Excel 讀取後的全空列欄與欄位名稱。"""
+        df = df.dropna(axis=0, how='all').dropna(axis=1, how='all')
+        df.columns = [str(col).strip() for col in df.columns]
+        return df
+
+    def _read_daily_sales_excel(self, file_path: str) -> pd.DataFrame:
+        """
+        讀取當日業績 Excel，若預設第一列不是表頭，會掃描前 20 列尋找真正表頭。
+        """
+        df = self._cleanup_excel_dataframe(pd.read_excel(file_path, engine='openpyxl', dtype=str))
+        if not df.empty and not self._validate_daily_sales_columns(df):
+            return df
+
+        excel = pd.ExcelFile(file_path, engine='openpyxl')
+        for sheet_name in excel.sheet_names:
+            preview = pd.read_excel(
+                file_path,
+                sheet_name=sheet_name,
+                header=None,
+                nrows=20,
+                engine='openpyxl',
+                dtype=str,
+            )
+            for header_row in range(len(preview.index)):
+                candidate_columns = preview.iloc[header_row].dropna().astype(str).str.strip().tolist()
+                if not candidate_columns:
+                    continue
+                candidate_df = pd.DataFrame(columns=candidate_columns)
+                if self._validate_daily_sales_columns(candidate_df):
+                    continue
+
+                detected_df = pd.read_excel(
+                    file_path,
+                    sheet_name=sheet_name,
+                    header=header_row,
+                    engine='openpyxl',
+                    dtype=str,
+                )
+                detected_df = self._cleanup_excel_dataframe(detected_df)
+                logger.info(
+                    f"Excel 表頭自動偵測成功: sheet={sheet_name}, header_row={header_row + 1}"
+                )
+                return detected_df
+
+        return df
+
+    @staticmethod
+    def _normalize_dates_for_sql(date_values) -> list:
+        """將日期值正規化成 YYYY-MM-DD 字串，供 SQL expanding bind 使用。"""
+        normalized_dates = []
+        for value in date_values:
+            if value is None or pd.isna(value):
+                continue
+            parsed = pd.to_datetime(value, errors='coerce')
+            if pd.notna(parsed):
+                normalized_dates.append(str(parsed.date()))
+        return sorted(set(normalized_dates))
+
+    @staticmethod
+    def _calculate_data_lag_days(date_max: str) -> Optional[int]:
+        """計算匯入資料最大日期距今天數。"""
+        if not date_max:
+            return None
+        parsed = pd.to_datetime(date_max, errors='coerce')
+        if pd.isna(parsed):
+            return None
+        today = datetime.now(TAIPEI_TZ).date()
+        return max((today - parsed.date()).days, 0)
+
    def process_daily_sales_import(self, job_id: int, file_path: str) -> bool:
        """
        處理當日業績匯入
@@ -314,7 +403,7 @@ class ImportService:

            # 讀取 Excel 檔案
            logger.info(f"開始讀取 Excel 檔案: {file_path}")
-            df = pd.read_excel(file_path, engine='openpyxl', dtype=str)
+            df = self._read_daily_sales_excel(file_path)

            if df.empty:
                error_msg = "Excel 檔案為空"
@@ -326,15 +415,7 @@ class ImportService:
            # 原因：若 Excel 欄位名靜默變更，匯入會成功但 Hermes SQL JOIN 會找不到數據 → 告警管線失真
            # 規則：至少需偵測到「商品名稱」與「銷售金額」類欄位 (容忍多種別名)
            # ─────────────────────────────────────────────
-            def _has_any(cols, keywords):
-                return any(kw in c for c in cols for kw in keywords)
-
-            required_groups = {
-                "商品名稱類": ["商品名稱", "品名", "Product", "Name"],
-                "業績金額類": ["銷售金額", "業績", "金額", "Amount", "Sales", "Total"],
-            }
-            missing = [label for label, kws in required_groups.items()
-                       if not _has_any(df.columns, kws)]
+            missing = self._validate_daily_sales_columns(df)
            if missing:
                error_msg = (
                    f"Excel 欄位防禦失敗：缺少必要欄位分類 {missing}。"
@@ -372,211 +453,178 @@ class ImportService:
            self.update_job_progress(job_id, total_rows=total_rows, processed_rows=0)

            # 取得此次匯入的日期範圍
-            import_dates = df['snapshot_date'].unique()
+            import_dates = self._normalize_dates_for_sql(df['snapshot_date'].unique())
            logger.info(f"本次匯入包含 {len(import_dates)} 個日期的資料")

-            # 刪除資料庫中相同日期的舊資料（覆蓋邏輯）
-            if len(import_dates) > 0:
-                # 過濾掉 None 值
-                valid_dates = [d for d in import_dates if d is not None]
-
-                if valid_dates:
-                    # 將日期轉換為字串格式用於 SQL 查詢
-                    date_list = ', '.join([f"'{d}'" for d in valid_dates])
-
-                    with engine.connect() as conn:
-                        # 刪除相同日期的舊資料
-                        delete_query = text(f"DELETE FROM {table_name} WHERE snapshot_date IN ({date_list})")
-                        result = conn.execute(delete_query)
-                        deleted_count = result.rowcount
-                        conn.commit()
-
-                        if deleted_count > 0:
-                            logger.info(f"已刪除 {deleted_count} 筆舊資料（覆蓋模式）")
-
-            # 寫入資料庫（帶驗證和重試機制）
-            max_retries = 2
-            retry_count = 0
-            write_success = False
-            
-            while retry_count <= max_retries and not write_success:
-                try:
-                    if retry_count > 0:
-                        logger.warning(f"任務 {job_id} 第 {retry_count} 次重試寫入...")
-                        self.update_job_status(job_id, 'importing', 60, f'重試寫入中 ({retry_count}/{max_retries})...')
-                    
-                    df.to_sql(
-                        table_name,
-                        engine,
-                        if_exists='append',
-                        index=False,
-                        method='multi',
-                        chunksize=1000
-                    )
-                    
-                    # V-Fix: 匯入後驗證 - 確認資料已正確寫入資料庫
-                    self.update_job_status(job_id, 'importing', 85, '驗證資料寫入...')
-                    
-                    # 取得本次匯入的日期
-                    import_dates = df['snapshot_date'].dropna().unique()
-                    if len(import_dates) > 0:
-                        # 查詢資料庫中這些日期的資料筆數
-                        from sqlalchemy import text
-                        valid_dates = [str(d) for d in import_dates if d is not None]
-                        date_list = ', '.join([f"'{d}'" for d in valid_dates])
-                        
-                        with engine.connect() as conn:
-                            verify_query = text(f"SELECT COUNT(*) FROM {table_name} WHERE snapshot_date IN ({date_list})")
-                            result = conn.execute(verify_query)
-                            db_count = result.scalar()
-                        
-                        # 驗證：資料庫筆數應該 >= 本次匯入筆數（可能有其他日期的舊資料）
-                        expected_count = len(df[df['snapshot_date'].isin(valid_dates)])
-                        
-                        if db_count >= expected_count:
-                            logger.info(f"任務 {job_id} 驗證成功: 預期 {expected_count} 筆, 資料庫有 {db_count} 筆")
-                            write_success = True
-                        else:
-                            logger.warning(f"任務 {job_id} 驗證失敗: 預期 {expected_count} 筆, 資料庫只有 {db_count} 筆")
-                            retry_count += 1
-                    else:
-                        # 沒有有效日期，跳過驗證
-                        logger.warning(f"任務 {job_id} 無法驗證: 沒有有效的 snapshot_date")
-                        write_success = True
-                        
-                except Exception as write_error:
-                    logger.error(f"任務 {job_id} 寫入失敗 (嘗試 {retry_count + 1}): {str(write_error)}")
-                    retry_count += 1
-                    if retry_count > max_retries:
-                        raise write_error
-            
-            if not write_success:
-                error_msg = f"資料寫入驗證失敗，已重試 {max_retries} 次"
-                self.update_job_status(job_id, 'failed', 85, '驗證失敗', error_msg)
+            if not import_dates:
+                error_msg = "匯入資料缺少有效日期，拒絕寫入以避免日期未知資料污染"
+                self.update_job_status(job_id, 'failed', 55, '日期驗證失敗', error_msg)
                logger.error(f"任務 {job_id} {error_msg}")
                return False

            # === V-New 2026-01-15: 同步寫入 realtime_sales_monthly ===
            # 目的：讓當日業績 raw data 同時呈現在「業績分析儀表板」
-            # 2026-01-30 修復：加強欄位驗證、同步狀態追蹤、失敗告警
-            self.update_job_status(job_id, 'importing', 90, '同步至業績分析儀表板...')
+            # 2026-05-05 修復：daily 與 monthly 改成同一個 transaction，避免半成功。
+            self.update_job_status(job_id, 'importing', 80, '準備同步至業績分析儀表板...')

            sync_success = False
            sync_error_msg = None
            monthly_table = 'realtime_sales_monthly'

-            try:
-                # 準備資料：移除 snapshot_date 欄位（realtime_sales_monthly 不需要此欄位）
-                df_monthly = df.drop(columns=['snapshot_date'], errors='ignore')
+            # 準備資料：移除 snapshot_date 欄位（realtime_sales_monthly 不需要此欄位）
+            df_monthly = df.drop(columns=['snapshot_date'], errors='ignore')

-                # 2026-01-30 修正：強化欄位名稱轉換
-                # 將特殊字符轉換為 PostgreSQL 安全格式
-                column_mapping = {}
-                for col in df_monthly.columns:
-                    new_col = col.replace('%', '_pct').replace('(', '_').replace(')', '_')
-                    column_mapping[col] = new_col
-                df_monthly = df_monthly.rename(columns=column_mapping)
+            # 2026-01-30 修正：強化欄位名稱轉換
+            # 將特殊字符轉換為 PostgreSQL 安全格式
+            column_mapping = {}
+            for col in df_monthly.columns:
+                new_col = col.replace('%', '_pct').replace('(', '_').replace(')', '_')
+                column_mapping[col] = new_col
+            df_monthly = df_monthly.rename(columns=column_mapping)

-                # 記錄轉換的欄位
-                converted_cols = [f"'{k}' -> '{v}'" for k, v in column_mapping.items() if k != v]
-                if converted_cols:
-                    logger.info(f"任務 {job_id} 欄位名稱轉換: {', '.join(converted_cols)}")
-                logger.info(f"任務 {job_id} 欄位轉換完成，共 {len(df_monthly.columns)} 個欄位")
+            converted_cols = [f"'{k}' -> '{v}'" for k, v in column_mapping.items() if k != v]
+            if converted_cols:
+                logger.info(f"任務 {job_id} 欄位名稱轉換: {', '.join(converted_cols)}")
+            logger.info(f"任務 {job_id} 欄位轉換完成，共 {len(df_monthly.columns)} 個欄位")

-                # 2026-01-30 新增：驗證 DataFrame 欄位和目標表欄位是否一致
-                with engine.connect() as conn:
-                    col_query = text(f"""
+            # 2026-01-30 新增：驗證 DataFrame 欄位和目標表欄位是否一致
+            with engine.connect() as conn:
+                if engine.dialect.name == 'sqlite':
+                    col_query = text(f'PRAGMA table_info("{monthly_table}")')
+                    target_columns = {row[1] for row in conn.execute(col_query) if row[1] != 'id'}
+                else:
+                    col_query = text("""
                        SELECT column_name FROM information_schema.columns
-                        WHERE table_name = '{monthly_table}' AND column_name != 'id'
+                        WHERE table_name = :table_name AND column_name != 'id'
                        ORDER BY ordinal_position
                    """)
-                    result = conn.execute(col_query)
-                    target_columns = set([row[0] for row in result])
+                    target_columns = {
+                        row[0] for row in conn.execute(col_query, {'table_name': monthly_table})
+                    }

-                    df_columns = set(df_monthly.columns)
-                    missing_in_table = df_columns - target_columns
-                    missing_in_df = target_columns - df_columns
+                df_columns = set(df_monthly.columns)
+                missing_in_table = df_columns - target_columns
+                missing_in_df = target_columns - df_columns

-                    if missing_in_table:
-                        logger.warning(f"任務 {job_id} 欄位警告: DataFrame 有但表中沒有: {missing_in_table}")
-                        # 移除表中沒有的欄位，避免 INSERT 失敗
-                        df_monthly = df_monthly.drop(columns=list(missing_in_table), errors='ignore')
-                        logger.info(f"任務 {job_id} 已移除多餘欄位，剩餘 {len(df_monthly.columns)} 個欄位")
+                if missing_in_table:
+                    logger.warning(f"任務 {job_id} 欄位警告: DataFrame 有但表中沒有: {missing_in_table}")
+                    # 移除表中沒有的欄位，避免 INSERT 失敗
+                    df_monthly = df_monthly.drop(columns=list(missing_in_table), errors='ignore')
+                    logger.info(f"任務 {job_id} 已移除多餘欄位，剩餘 {len(df_monthly.columns)} 個欄位")

-                    if missing_in_df:
-                        logger.warning(f"任務 {job_id} 欄位警告: 表中有但 DataFrame 沒有: {missing_in_df}")
+                if missing_in_df:
+                    logger.warning(f"任務 {job_id} 欄位警告: 表中有但 DataFrame 沒有: {missing_in_df}")

-                # 取得本次匯入的日期列表（使用原始「日期」欄位）
-                unique_dates = []
-                if '日期' in df.columns:
-                    unique_dates = df['日期'].dropna().unique().tolist()
-                    logger.info(f"任務 {job_id} 準備同步 {len(unique_dates)} 個日期的資料")
+            unique_dates = self._normalize_dates_for_sql(
+                df[date_col].dropna().unique() if date_col and date_col in df.columns else df['snapshot_date'].dropna().unique()
+            )
+            logger.info(f"任務 {job_id} 準備同步 {len(unique_dates)} 個日期的資料")
+            if not unique_dates:
+                error_msg = "realtime_sales_monthly 同步缺少有效日期，拒絕寫入"
+                self.update_job_status(job_id, 'failed', 85, '同步日期驗證失敗', error_msg)
+                logger.error(f"任務 {job_id} {error_msg}")
+                return False

-                    if len(unique_dates) > 0:
-                        # 刪除 realtime_sales_monthly 中相同日期的舊資料（去重）
-                        date_list_monthly = ', '.join([f"'{d}'" for d in unique_dates])
+            snapshot_date_expr = 'date(snapshot_date)' if engine.dialect.name == 'sqlite' else 'snapshot_date::date'
+            monthly_date_expr = 'date("日期")' if engine.dialect.name == 'sqlite' else '"日期"::date'
+            expected_daily_count = len(df[df['snapshot_date'].astype(str).isin(import_dates)])
+            expected_monthly_count = len(df_monthly)

-                        with engine.connect() as conn:
-                            delete_monthly_query = text(f'DELETE FROM {monthly_table} WHERE "日期" IN ({date_list_monthly})')
-                            result = conn.execute(delete_monthly_query)
-                            deleted_monthly = result.rowcount
-                            conn.commit()
+            max_retries = 2
+            retry_count = 0
+            while retry_count <= max_retries and not sync_success:
+                try:
+                    if retry_count > 0:
+                        logger.warning(f"任務 {job_id} 第 {retry_count} 次重試原子匯入...")
+                        self.update_job_status(job_id, 'importing', 82, f'重試原子匯入中 ({retry_count}/{max_retries})...')

-                            if deleted_monthly > 0:
-                                logger.info(f"任務 {job_id} 已從 {monthly_table} 刪除 {deleted_monthly} 筆同日期舊資料")
+                    self.update_job_status(job_id, 'importing', 85, '原子寫入兩張業績表...')
+                    with engine.begin() as conn:
+                        delete_snapshot_query = text(
+                            f"DELETE FROM {table_name} WHERE {snapshot_date_expr} IN :dates"
+                        ).bindparams(bindparam('dates', expanding=True))
+                        deleted_snapshot = conn.execute(delete_snapshot_query, {'dates': import_dates}).rowcount
+                        if deleted_snapshot > 0:
+                            logger.info(f"已刪除 {deleted_snapshot} 筆 daily_sales_snapshot 舊資料（覆蓋模式）")

-                # 寫入 realtime_sales_monthly
-                df_monthly.to_sql(
-                    monthly_table,
-                    engine,
-                    if_exists='append',
-                    index=False,
-                    method='multi',
-                    chunksize=1000
-                )
+                        df.to_sql(
+                            table_name,
+                            conn,
+                            if_exists='append',
+                            index=False,
+                            method='multi',
+                            chunksize=1000
+                        )

-                logger.info(f"任務 {job_id} 已同步 {len(df_monthly)} 筆資料至 {monthly_table}")
+                        verify_snapshot_query = text(
+                            f"SELECT COUNT(*) FROM {table_name} WHERE {snapshot_date_expr} IN :dates"
+                        ).bindparams(bindparam('dates', expanding=True))
+                        daily_count = conn.execute(verify_snapshot_query, {'dates': import_dates}).scalar()
+                        if daily_count < expected_daily_count:
+                            raise RuntimeError(
+                                f"daily_sales_snapshot 寫入驗證失敗: 預期 {expected_daily_count} 筆, 實際 {daily_count} 筆"
+                            )

-                # 驗證同步結果
-                if len(unique_dates) > 0:
-                    with engine.connect() as conn:
-                        date_list_verify = ', '.join([f"'{d}'" for d in unique_dates])
-                        verify_query = text(f'SELECT COUNT(*) FROM {monthly_table} WHERE "日期" IN ({date_list_verify})')
-                        verify_count = conn.execute(verify_query).scalar()
+                        delete_monthly_query = text(
+                            f'DELETE FROM {monthly_table} WHERE {monthly_date_expr} IN :dates'
+                        ).bindparams(bindparam('dates', expanding=True))
+                        deleted_monthly = conn.execute(delete_monthly_query, {'dates': unique_dates}).rowcount
+                        if deleted_monthly > 0:
+                            logger.info(f"任務 {job_id} 已從 {monthly_table} 刪除 {deleted_monthly} 筆同日期舊資料")

-                        if verify_count >= len(df_monthly):
-                            logger.info(f"任務 {job_id} 同步驗證成功: {monthly_table} 現有 {verify_count} 筆資料")
-                            sync_success = True
-                        else:
-                            sync_error_msg = f"同步驗證失敗: 預期 {len(df_monthly)} 筆, 實際 {verify_count} 筆"
-                            logger.error(f"任務 {job_id} {sync_error_msg}")
-                else:
-                    sync_success = True  # 沒有日期資料時視為成功
+                        df_monthly.to_sql(
+                            monthly_table,
+                            conn,
+                            if_exists='append',
+                            index=False,
+                            method='multi',
+                            chunksize=1000
+                        )

-            except Exception as sync_error:
-                # 同步失敗，記錄完整錯誤
-                import traceback
-                sync_error_msg = str(sync_error)
-                logger.error(f"任務 {job_id} 同步至 {monthly_table} 失敗: {sync_error_msg}")
-                logger.error(f"任務 {job_id} 同步錯誤堆疊:\n{traceback.format_exc()}")
+                        verify_monthly_query = text(
+                            f'SELECT COUNT(*) FROM {monthly_table} WHERE {monthly_date_expr} IN :dates'
+                        ).bindparams(bindparam('dates', expanding=True))
+                        monthly_count = conn.execute(verify_monthly_query, {'dates': unique_dates}).scalar()
+                        if monthly_count < expected_monthly_count:
+                            raise RuntimeError(
+                                f"{monthly_table} 寫入驗證失敗: 預期 {expected_monthly_count} 筆, 實際 {monthly_count} 筆"
+                            )

-                # 2026-01-30 新增：發送同步失敗告警
+                    sync_success = True
+                    logger.info(
+                        f"任務 {job_id} 原子匯入成功: daily={expected_daily_count} 筆, "
+                        f"monthly={expected_monthly_count} 筆"
+                    )
+                except Exception as transaction_error:
+                    retry_count += 1
+                    sync_error_msg = str(transaction_error)
+                    logger.error(
+                        f"任務 {job_id} 原子匯入失敗 (嘗試 {retry_count}/{max_retries + 1}): {sync_error_msg}",
+                        exc_info=True,
+                    )
+                    if retry_count > max_retries:
+                        break
+
+            if not sync_success:
+                error_msg = f"原子匯入失敗，兩張表已回滾: {sync_error_msg}"
+                self.update_job_status(job_id, 'failed', 90, '原子匯入失敗', error_msg)
+                logger.error(f"任務 {job_id} {error_msg}")
                try:
                    from services.notification_manager import NotificationManager
                    notifier = NotificationManager()
                    alert_msg = (
-                        f"⚠️ 業績資料同步失敗告警\n"
+                        f"⚠️ 業績資料原子匯入失敗告警\n"
                        f"{'='*30}\n"
                        f"任務 ID: {job_id}\n"
-                        f"目標表: {monthly_table}\n"
                        f"錯誤: {sync_error_msg[:200]}\n"
                        f"{'='*30}\n"
-                        f"daily_sales_snapshot 已匯入成功，但業績分析儀表板需要手動同步"
+                        f"本次 daily_sales_snapshot / realtime_sales_monthly 已一起 rollback，請檢查匯入檔案"
                    )
                    notifier._send_telegram_messages([alert_msg])
-                    logger.info(f"任務 {job_id} 已發送同步失敗告警")
+                    logger.info(f"任務 {job_id} 已發送原子匯入失敗告警")
                except Exception as notify_error:
                    logger.error(f"任務 {job_id} 發送告警失敗: {notify_error}")
+                return False


            # 更新成功資訊
@@ -602,12 +650,15 @@ class ImportService:
            # 計算日期範圍
            date_min = None
            date_max = None
-            valid_dates = df['snapshot_date'].dropna().unique()
+            imported_dates = self._normalize_dates_for_sql(df['snapshot_date'].dropna().unique())
+            data_lag_days = None
+            valid_dates = imported_dates
            if len(valid_dates) > 0:
-                sorted_dates = sorted([d for d in valid_dates if d is not None])
+                sorted_dates = sorted(valid_dates)
                if sorted_dates:
-                    date_min = str(sorted_dates[0])
-                    date_max = str(sorted_dates[-1])
+                    date_min = sorted_dates[0]
+                    date_max = sorted_dates[-1]
+                    data_lag_days = self._calculate_data_lag_days(date_max)
                    logger.info(f"任務 {job_id} 日期範圍: {date_min} ~ {date_max}")

            # 更新匯入摘要 (2026-01-30 修正：加入同步狀態)
@@ -625,6 +676,8 @@ class ImportService:
                'verified': True,  # daily_sales_snapshot 驗證
                'date_min': date_min,
                'date_max': date_max,
+                'imported_dates': imported_dates,
+                'data_lag_days': data_lag_days,
                'message': sync_message
            }

@@ -714,6 +767,8 @@ class ImportService:
            imported_count = 0
            total_rows = 0
            all_dates = []  # 收集所有匯入的日期
+            failed_files = []
+            data_lag_days = None

            for file in files:
                file_id = file['id']
@@ -725,6 +780,8 @@ class ImportService:
                # 建立匯入任務
                job_id = self.create_import_job('daily_sales', file_id, file_name, file_size)
                if not job_id:
+                    failed_files.append(file_name)
+                    logger.error(f"建立匯入任務失敗，跳過檔案: {file_name}")
                    continue

                # 下載檔案
@@ -736,6 +793,8 @@ class ImportService:

                if not drive_service.download_file(file_id, local_path):
                    self.update_job_status(job_id, 'failed', 10, '下載失敗', '無法從 Google Drive 下載檔案')
+                    failed_files.append(file_name)
+                    logger.error(f"Google Drive 檔案下載失敗: {file_name}")
                    continue

                # 更新本地路徑
@@ -777,6 +836,19 @@ class ImportService:
                                all_dates.append(summary['date_min'])
                            if summary.get('date_max'):
                                all_dates.append(summary['date_max'])
+                            all_dates.extend(summary.get('imported_dates') or [])
+                            if summary.get('data_lag_days') is not None:
+                                lag_value = summary.get('data_lag_days')
+                                data_lag_days = lag_value if data_lag_days is None else max(data_lag_days, lag_value)
+                        elif job:
+                            # V-Fix: 防止摘要缺失時通知顯示 0 筆、日期未知。
+                            # summary 是驗證與通知的主要來源；若缺失，至少回退到進度欄位並留下告警。
+                            fallback_rows = job.success_rows or job.total_rows or 0
+                            total_rows += fallback_rows
+                            logger.warning(
+                                f"任務 {job_id} 匯入成功但缺少 import_summary，"
+                                f"已使用 job 進度欄位回補筆數: {fallback_rows}"
+                            )
                    finally:
                        session.close()

@@ -786,6 +858,14 @@ class ImportService:
                        logger.info(f"已清理本地檔案: {local_path}")
                    except Exception as e:
                        logger.warning(f"清理本地檔案失敗: {str(e)}")
+                else:
+                    failed_files.append(file_name)
+                    logger.error(f"檔案匯入失敗，準備移至失敗資料夾: {file_name}")
+                    failed_folder = self.get_config('gdrive_failed_folder', '匯入失敗')
+                    if drive_service.move_file(file_id, failed_folder):
+                        logger.info(f"已移動失敗檔案到「{failed_folder}」: {file_name}")
+                    else:
+                        logger.warning(f"無法移動失敗檔案，保留於原資料夾待人工檢查: {file_name}")

            # 計算日期範圍
            date_range = None
@@ -797,13 +877,32 @@ class ImportService:
                        'max': sorted_dates[-1]
                    }

+            if failed_files or imported_count == 0:
+                failed_count = len(files) - imported_count
+                failed_label = '、'.join(failed_files[:5]) if failed_files else '無成功匯入檔案'
+                return {
+                    'success': False,
+                    'message': (
+                        f'找到 {len(files)} 個檔案，但成功匯入 {imported_count} 個、'
+                        f'失敗 {failed_count} 個：{failed_label}'
+                    ),
+                    'file_count': len(files),
+                    'imported_count': imported_count,
+                    'failed_count': failed_count,
+                    'failed_files': failed_files,
+                    'total_rows': total_rows,
+                    'date_range': date_range,
+                    'data_lag_days': data_lag_days
+                }
+
            return {
                'success': True,
                'message': f'成功匯入 {imported_count} 個檔案',
                'file_count': len(files),
                'imported_count': imported_count,
                'total_rows': total_rows,
-                'date_range': date_range
+                'date_range': date_range,
+                'data_lag_days': data_lag_days
            }

        except Exception as e:
--- a/templates/components/_ewoooc_shell.html
+++ b/templates/components/_ewoooc_shell.html
@@ -194,11 +194,37 @@
                <span class="momo-nav-label">活動看板</span>
                <span class="momo-nav-code momo-mono">02</span>
            </a>
-            <a class="momo-nav-link {% if _active_page in ['sales', 'daily_sales', 'monthly', 'growth'] %}is-active{% endif %}" href="/sales_analysis">
-                <span class="momo-nav-icon"><i class="fas fa-chart-line"></i></span>
-                <span class="momo-nav-label">分析報表</span>
-                <span class="momo-nav-code momo-mono">03</span>
-            </a>
+            <details class="momo-nav-tree" {% if _active_page in ['sales', 'daily_sales', 'monthly', 'growth'] %}open{% endif %}>
+                <summary class="momo-nav-link momo-nav-tree-summary {% if _active_page in ['sales', 'daily_sales', 'monthly', 'growth'] %}is-active{% endif %}">
+                    <span class="momo-nav-icon"><i class="fas fa-chart-line"></i></span>
+                    <span class="momo-nav-label">分析報表</span>
+                    <span class="momo-nav-code momo-mono">03</span>
+                </summary>
+                <div class="momo-nav-subtree">
+                    <a class="momo-nav-sublink {% if _active_page == 'sales' %}is-active{% endif %}" href="/sales_analysis">
+                        <i class="fas fa-chart-bar"></i><span>業績分析</span><span class="momo-nav-code momo-mono">01</span>
+                    </a>
+                    <a class="momo-nav-sublink {% if _active_page == 'daily_sales' %}is-active{% endif %}" href="/daily_sales">
+                        <i class="fas fa-calendar-day"></i><span>當日業績</span><span class="momo-nav-code momo-mono">02</span>
+                    </a>
+                    <a class="momo-nav-sublink {% if _active_page == 'growth' %}is-active{% endif %}" href="/growth_analysis">
+                        <i class="fas fa-chart-line"></i><span>成長分析</span><span class="momo-nav-code momo-mono">03</span>
+                    </a>
+                    <a class="momo-nav-sublink {% if _active_page == 'monthly' %}is-active{% endif %}" href="/monthly_summary_analysis">
+                        <i class="fas fa-table"></i><span>月份總表</span><span class="momo-nav-code momo-mono">04</span>
+                    </a>
+                    {% if metabase_url %}
+                    <a class="momo-nav-sublink" href="{{ metabase_url }}" target="_blank" rel="noopener">
+                        <i class="fas fa-chart-pie"></i><span>Metabase</span><span class="momo-nav-code momo-mono"><i class="fas fa-up-right-from-square"></i></span>
+                    </a>
+                    {% endif %}
+                    {% if grist_url %}
+                    <a class="momo-nav-sublink" href="{{ grist_url }}" target="_blank" rel="noopener">
+                        <i class="fas fa-table"></i><span>Grist</span><span class="momo-nav-code momo-mono"><i class="fas fa-up-right-from-square"></i></span>
+                    </a>
+                    {% endif %}
+                </div>
+            </details>
        </div>

        <div class="momo-nav-group">
--- a/templates/daily_sales.html
+++ b/templates/daily_sales.html
@@ -846,6 +846,7 @@
            }
        }
    </style>
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/analysis-workbench.css') }}?v={{ system_version|default('v2') }}">
 {% endblock %}

 {% block ewooo_content %}
@@ -875,6 +876,8 @@
            </div>
        </section>

+        {% include 'components/_analysis_report_tabs.html' %}
+
        <!-- Calendar View -->
        {% if calendar_data %}
        <div class="calendar-container">
@@ -1303,8 +1306,11 @@
        </div>
        {% endif %}
    </div>
+{% endblock %}

-    <script src="https://cdn.jsdelivr.net/npm/bootstrap@5.3.3/dist/js/bootstrap.bundle.min.js"></script>
+{% block extra_js %}
+    <script src="https://cdn.jsdelivr.net/npm/chart.js@3.9.1/dist/chart.min.js"></script>
+    <script src="{{ url_for('static', filename='js/analysis-chart-theme.js') }}?v={{ system_version|default('v2') }}"></script>
    <script src="https://code.jquery.com/jquery-3.6.0.min.js"></script>
    <script src="https://cdn.datatables.net/1.11.5/js/jquery.dataTables.min.js"></script>
    <script src="https://cdn.datatables.net/1.11.5/js/dataTables.bootstrap5.min.js"></script>
@@ -1872,6 +1878,4 @@
        }
        {% endif %}
    </script>
-</body>
-
-</html>
+{% endblock %}
--- a/templates/ewoooc_base.html
+++ b/templates/ewoooc_base.html
@@ -1,3 +1,11 @@
+{% set _path_active_page = (
+    'sales' if request.path == '/sales_analysis' else
+    'daily_sales' if request.path == '/daily_sales' else
+    'growth' if request.path == '/growth_analysis' else
+    'monthly' if request.path == '/monthly_summary_analysis' else
+    ''
+) if request is defined else '' %}
+{% set active_page = active_page|default(_path_active_page, true) %}
 <!DOCTYPE html>
 <html lang="zh-TW">
 <head>
@@ -5,6 +13,7 @@
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <meta name="csrf-token" content="{{ csrf_token() }}">
    <title>{% block title %}EwoooC{% endblock %}</title>
+    <link rel="icon" type="image/svg+xml" href="{{ url_for('static', filename='images/logo_circle.svg') }}">

    <link rel="preconnect" href="https://fonts.googleapis.com">
    <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
--- a/templates/growth_analysis.html
+++ b/templates/growth_analysis.html
@@ -1,13 +1,7 @@
-<!-- cspell:ignore MOMO -->
-<!DOCTYPE html>
-<html lang="zh-TW">
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>營運成長報表 - MOMO 監控系統</title>
-    <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.3.3/dist/css/bootstrap.min.css" rel="stylesheet">
-    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.0.0/css/all.min.css">
-    <script src="https://cdn.jsdelivr.net/npm/chart.js@3.9.1/dist/chart.min.js"></script>
+{% extends 'ewoooc_base.html' %}
+{% block title %}營運成長報表 - WOOO TECH{% endblock %}
+
+{% block extra_css %}
    <style>
        body { font-family: 'Inter', -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif; background-color: #f4f6f9; }
        .navbar { box-shadow: 0 2px 4px rgba(0,0,0,0.05); }
@@ -23,15 +17,22 @@
        .trend-up { color: #2ecc71; }
        .trend-down { color: #e74c3c; }
    </style>
-</head>
-<body class="bg-body-tertiary">
-    {% include 'components/_navbar.html' %}
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/analysis-workbench.css') }}?v={{ system_version|default('v2') }}">
+{% endblock %}

-    <div class="container-fluid px-4">
-        <div class="d-flex justify-content-between align-items-center mb-4 mt-4">
-            <h4 class="mb-0 fw-bold text-dark"><i class="fas fa-rocket me-2 text-success"></i>營運成長策略報表</h4>
-            <span class="text-muted small">數據更新至: {{ chart_data.labels[-1] if chart_data.labels else '-' }}</span>
-        </div>
+{% block ewooo_content %}
+    <div class="analysis-workbench growth-analysis-page">
+        <section class="analysis-page-hero">
+            <div>
+                <h1 class="analysis-page-title"><i class="fas fa-rocket"></i>營運成長策略報表</h1>
+                <p class="text-muted mb-0 mt-2">觀察年累計業績、客單價、訂單數與毛利率趨勢。</p>
+            </div>
+            <div class="analysis-page-meta">
+                <span class="badge bg-secondary"><i class="fas fa-clock me-1"></i>數據更新至 {{ chart_data.labels[-1] if chart_data.labels else '-' }}</span>
+            </div>
+        </section>
+
+        {% include 'components/_analysis_report_tabs.html' %}

        <!-- KPI Cards -->
        <div class="row mb-4">
@@ -134,9 +135,12 @@
            </div>
        </div>
    </div>
+{% endblock %}
+
+{% block extra_js %}
+    <script src="https://cdn.jsdelivr.net/npm/chart.js@3.9.1/dist/chart.min.js"></script>
+    <script src="{{ url_for('static', filename='js/analysis-chart-theme.js') }}?v={{ system_version|default('v2') }}"></script>

-    <script src="https://cdn.jsdelivr.net/npm/bootstrap@5.3.3/dist/js/bootstrap.bundle.min.js"></script>
-    
    <!-- Data Injection -->
    <script id="chart-data" type="application/json">
        {{ chart_data | tojson }}
@@ -246,5 +250,4 @@
            }
        });
    </script>
-</body>
-</html>
+{% endblock %}
--- a/templates/monthly_summary_analysis.html
+++ b/templates/monthly_summary_analysis.html
@@ -159,6 +159,7 @@
            }
        }
    </style>
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/analysis-workbench.css') }}?v={{ system_version|default('v2') }}">
 {% endblock %}

 {% block ewooo_content %}
@@ -178,6 +179,8 @@
            </div>
        </section>

+        {% include 'components/_analysis_report_tabs.html' %}
+
        <!-- ═══════ 進階篩選器 (對標業績分析) ═══════ -->
        <div class="filter-section">
            <div class="row g-3">
@@ -574,6 +577,7 @@
    <script src="https://cdn.datatables.net/1.11.5/js/jquery.dataTables.min.js"></script>
    <script src="https://cdn.datatables.net/1.11.5/js/dataTables.bootstrap5.min.js"></script>
    <script src="https://cdn.jsdelivr.net/npm/echarts@5.4.3/dist/echarts.min.js"></script>
+    <script src="{{ url_for('static', filename='js/analysis-chart-theme.js') }}?v={{ system_version|default('v2') }}"></script>
    <script>
        let table;
        let compareChart = echarts.init(document.getElementById('compareChart'));
--- a/templates/sales_analysis.html
+++ b/templates/sales_analysis.html
@@ -1,16 +1,10 @@
-<!-- cspell:ignore MOMO datatables Treemap -->
-<!DOCTYPE html>
-<html lang="zh-TW">
+{% extends 'ewoooc_base.html' %}
+{% block title %}業績分析 - WOOO TECH{% endblock %}

-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+{% block extra_css %}
    <meta http-equiv="Cache-Control" content="no-cache, no-store, must-revalidate">
    <meta http-equiv="Pragma" content="no-cache">
    <meta http-equiv="Expires" content="0">
-    <title>業績分析 - WOOO TECH</title>
-    <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.3.3/dist/css/bootstrap.min.css" rel="stylesheet">
-    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.0.0/css/all.min.css">
    <!-- DataTables CSS -->
    <link rel="stylesheet" href="https://cdn.datatables.net/1.11.5/css/dataTables.bootstrap5.min.css">
    <!-- V-New: Flatpickr 日期選擇器 CSS -->
@@ -18,6 +12,7 @@
    <!-- V-Fix: 使用 Chart.js v3.9.1 以確保與 Treemap v2.0.2 相容 -->
    <script src="https://cdn.jsdelivr.net/npm/chart.js@3.9.1/dist/chart.min.js"></script>
    <script src="https://cdn.jsdelivr.net/npm/chartjs-chart-treemap@2.0.2/dist/chartjs-chart-treemap.min.js"></script>
+    <script src="{{ url_for('static', filename='js/analysis-chart-theme.js') }}?v={{ system_version|default('v2') }}"></script>
    <style>
        body {
            font-family: 'Inter', -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif;
@@ -563,10 +558,10 @@
            color: rgba(255, 255, 255, 0.75);
        }
    </style>
-</head>
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/analysis-workbench.css') }}?v={{ system_version|default('v2') }}">
+{% endblock %}

-<body class="bg-body-tertiary">
-    {% include 'components/_navbar.html' %}
+{% block ewooo_content %}

    <!-- Loading Overlay -->
    <div id="loadingOverlay">
@@ -607,17 +602,18 @@
        </div>
    </div>

-    <div class="container-fluid px-4">
-        <div class="d-flex justify-content-between align-items-center mb-4 mt-4">
-            <div class="d-flex align-items-center gap-3">
-                <h4 class="mb-0 fw-bold text-dark"><i class="fas fa-chart-pie me-2 text-primary"></i>業績分析儀表板</h4>
+    <div class="analysis-workbench sales-analysis-page">
+        <section class="analysis-page-hero">
+            <div>
+                <h1 class="analysis-page-title"><i class="fas fa-chart-pie"></i>業績分析儀表板</h1>
+                <p class="text-muted mb-0 mt-2">檢視即時業績、分類、品牌、廠商與行銷活動貢獻。</p>
                {% if db_data_range %}
-                <span class="badge bg-secondary" style="font-size: 0.85rem; font-weight: normal;">
+                <span class="badge bg-secondary mt-2" style="font-size: 0.85rem; font-weight: normal;">
                    <i class="fas fa-calendar-alt me-1"></i>資料期間: {{ db_data_range }}
                </span>
                {% endif %}
            </div>
-            <div class="text-end">
+            <div class="analysis-page-meta">
                {% if not no_filter %}
                <span class="badge bg-success">
                    <i class="fas fa-database me-1"></i>
@@ -636,7 +632,9 @@
                </span>
                {% endif %}
            </div>
-        </div>
+        </section>
+
+        {% include 'components/_analysis_report_tabs.html' %}

        {% if error %}
        <div class="alert alert-warning">
@@ -1180,8 +1178,7 @@
                </div>
            </div>
            <div class="col-md-2">
-                <div class="card kpi-card bg-success text-white h-100 shadow-sm"
-                    style="background-color: #20c997 !important;">
+                <div class="card kpi-card bg-success text-white h-100 shadow-sm">
                    <div class="card-body p-3">
                        <div class="kpi-label text-white-50">毛利率 (%)</div>
                        <div class="kpi-value">{{ "{:.1f}%".format(kpi.gross_margin_rate) }}</div>
@@ -1960,6 +1957,7 @@
        const cols = salesData.cols;
        const selectedMetric = salesData.selectedMetric;

+        if (document.getElementById('barChart')) {
        // 1. 橫向長條圖 (Horizontal Bar Chart) - 更易讀
        const ctxBar = document.getElementById('barChart').getContext('2d');
        new Chart(ctxBar, {
@@ -2620,6 +2618,7 @@
                }
            }
        });
+        }

        // 3. 初始化 DataTables (分頁、搜尋、排序)
        $(document).ready(function () {
@@ -3160,7 +3159,4 @@
        });
    </script>

-    <script src="https://cdn.jsdelivr.net/npm/bootstrap@5.3.3/dist/js/bootstrap.bundle.min.js"></script>
-</body>
-
-</html>
+{% endblock %}
--- a/tests/test_elephant_alpha_engine.py
+++ b/tests/test_elephant_alpha_engine.py
@@ -1,7 +1,8 @@
 import asyncio


-def test_run_with_timeout_supports_sync_function():
+def test_run_with_timeout_supports_sync_function(monkeypatch):
+    monkeypatch.setenv("ELEPHANT_ALPHA_CACHE_DB", ":memory:")
    from services.elephant_alpha_autonomous_engine import ElephantAlphaAutonomousEngine

    result = asyncio.run(ElephantAlphaAutonomousEngine._run_with_timeout(lambda value: value + 1, 41))
@@ -9,7 +10,8 @@ def test_run_with_timeout_supports_sync_function():
    assert result == 42


-def test_execute_step_rejects_unknown_action():
+def test_execute_step_rejects_unknown_action(monkeypatch):
+    monkeypatch.setenv("ELEPHANT_ALPHA_CACHE_DB", ":memory:")
    from services.elephant_alpha_autonomous_engine import ElephantAlphaAutonomousEngine

    engine = ElephantAlphaAutonomousEngine()
@@ -23,6 +25,7 @@ def test_execute_step_rejects_unknown_action():


 def test_execute_step_routes_code_fix_to_autoheal(monkeypatch):
+    monkeypatch.setenv("ELEPHANT_ALPHA_CACHE_DB", ":memory:")
    from services.elephant_alpha_autonomous_engine import ElephantAlphaAutonomousEngine

    calls = []
@@ -43,6 +46,7 @@ def test_execute_step_routes_code_fix_to_autoheal(monkeypatch):


 def test_execute_step_routes_price_adjustment_to_human_review(monkeypatch):
+    monkeypatch.setenv("ELEPHANT_ALPHA_CACHE_DB", ":memory:")
    from services.elephant_alpha_autonomous_engine import ElephantAlphaAutonomousEngine

    calls = []
@@ -82,3 +86,118 @@ def test_autoheal_derives_python_exception_from_traceback():
    svc = AutoHealService()

    assert svc._derive_error_type({"traceback_str": "Traceback (most recent call last):\nNameError"}) == "python_exception"
+
+
+def test_action_queue_counts_only_operational_actions(monkeypatch):
+    from sqlalchemy import create_engine, text
+    import services.elephant_alpha_autonomous_engine as engine_mod
+
+    monkeypatch.setattr(engine_mod, "CACHE_DB_PATH", ":memory:")
+    db = create_engine("sqlite:///:memory:")
+    conn = db.connect()
+    conn.execute(text("CREATE TABLE action_plans (status TEXT, action_type TEXT)"))
+    conn.execute(text("""
+        INSERT INTO action_plans (status, action_type) VALUES
+        ('pending', 'openclaw_recommendation'),
+        ('pending', 'human_review'),
+        ('pending', 'auto_heal'),
+        ('auto_pending', 'code_review_fix'),
+        ('auto_disabled', 'code_review_fix')
+    """))
+    conn.commit()
+
+    class Session:
+        def execute(self, stmt):
+            return conn.execute(stmt)
+
+        def close(self):
+            pass
+
+    monkeypatch.setattr(engine_mod, "get_session", lambda: Session())
+
+    try:
+        engine = engine_mod.ElephantAlphaAutonomousEngine()
+
+        assert engine._get_action_queue_size() == 2
+    finally:
+        conn.close()
+        db.dispose()
+
+
+def test_trigger_check_respects_persistent_cooldown(monkeypatch, tmp_path):
+    import services.elephant_alpha_autonomous_engine as engine_mod
+
+    monkeypatch.setattr(engine_mod, "CACHE_DB_PATH", str(tmp_path / "ea_cache.db"))
+    engine = engine_mod.ElephantAlphaAutonomousEngine()
+    trigger = engine_mod.AutonomousTrigger("resource_optimization", {}, 0.6, True)
+    engine._store_escalation("resource_optimization")
+
+    next_engine = engine_mod.ElephantAlphaAutonomousEngine()
+    next_engine.triggers = [trigger]
+    called = {"evaluated": False}
+
+    async def evaluate(_trigger):
+        called["evaluated"] = True
+        return True
+
+    async def execute(_trigger):
+        raise AssertionError("cooldown should skip execution")
+
+    monkeypatch.setattr(next_engine, "_evaluate_trigger", evaluate)
+    monkeypatch.setattr(next_engine, "_execute_autonomous_decision", execute)
+
+    asyncio.run(next_engine._check_triggers())
+
+    assert called["evaluated"] is False
+
+
+def test_no_evidence_resource_escalation_is_suppressed(monkeypatch, tmp_path):
+    import services.elephant_alpha_autonomous_engine as engine_mod
+    from services.elephant_alpha_orchestrator import StrategicDecision
+
+    monkeypatch.setattr(engine_mod, "CACHE_DB_PATH", str(tmp_path / "ea_cache.db"))
+    engine = engine_mod.ElephantAlphaAutonomousEngine()
+    trigger = engine_mod.AutonomousTrigger("resource_optimization", {}, 0.6, True)
+    decision = StrategicDecision(
+        priority="medium",
+        agents_required=["elephant_alpha"],
+        reasoning="Hermes pre-fetch 失敗，沒有可驗證資料",
+        expected_outcome="",
+        confidence=0.60,
+        execution_plan=[],
+        resource_requirements={},
+    )
+
+    monkeypatch.setattr(engine, "_get_action_queue_size", lambda: 0)
+    monkeypatch.setattr(engine, "_get_system_load_percentage", lambda: 0.0)
+    monkeypatch.setattr(
+        engine_mod,
+        "get_session",
+        lambda: (_ for _ in ()).throw(AssertionError("suppressed escalation should not write DB")),
+    )
+
+    async def fail_send(*_args, **_kwargs):
+        raise AssertionError("suppressed escalation should not send Telegram")
+
+    monkeypatch.setattr(engine, "_run_with_timeout", fail_send)
+
+    asyncio.run(engine._escalate_to_human(decision, trigger))
+
+    assert engine._load_escalation("no_evidence:resource_optimization") is not None
+    assert engine._is_trigger_in_cooldown(trigger) is True
+
+
+def test_resource_escalation_uses_operational_evidence(monkeypatch, tmp_path):
+    import services.elephant_alpha_autonomous_engine as engine_mod
+
+    monkeypatch.setattr(engine_mod, "CACHE_DB_PATH", str(tmp_path / "ea_cache.db"))
+    engine = engine_mod.ElephantAlphaAutonomousEngine()
+
+    monkeypatch.setattr(engine, "_get_action_queue_size", lambda: 17)
+    monkeypatch.setattr(engine, "_get_system_load_percentage", lambda: 42.0)
+
+    actions = engine._build_resource_escalation_actions()
+
+    assert actions is not None
+    assert "auto action queue 17 筆" in actions[0]
+    assert "Hermes" not in "\n".join(actions)