From 153e4c973417bef0f835ede98b1538d147979ad7 Mon Sep 17 00:00:00 2001 From: OoO Date: Wed, 6 May 2026 19:50:52 +0800 Subject: [PATCH] fix(observability): revert unrelated quick review commit files --- .claude/settings.json | 14 +- CONSTITUTION.md | 4 +- TODO_NEXT_STEPS.txt | 74 --- app.py | 11 +- config.py | 2 +- docker-compose.yml | 6 +- docs/AI_INTELLIGENCE_MODULE_SOT.md | 14 +- ...R-021-ea-hitl-prefetch-and-alert-impact.md | 12 +- docs/memory/credentials_passbook.md | 8 +- routes/daily_sales_routes.py | 7 +- routes/sales_routes.py | 11 +- services/ai_automation_smoke_service.py | 29 -- services/elephant_alpha_autonomous_engine.py | 242 +++------- services/elephant_alpha_orchestrator.py | 23 +- services/import_service.py | 455 +++++++----------- templates/components/_ewoooc_shell.html | 36 +- templates/daily_sales.html | 12 +- templates/ewoooc_base.html | 9 - templates/growth_analysis.html | 47 +- templates/monthly_summary_analysis.html | 4 - templates/sales_analysis.html | 46 +- tests/test_elephant_alpha_engine.py | 123 +---- 22 files changed, 338 insertions(+), 851 deletions(-) diff --git a/.claude/settings.json b/.claude/settings.json index 8eb928a..c59ece2 100644 --- a/.claude/settings.json +++ b/.claude/settings.json @@ -37,19 +37,7 @@ "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(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\")" + "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'\")" ], "defaultMode": "bypassPermissions", "additionalDirectories": [ diff --git a/CONSTITUTION.md b/CONSTITUTION.md index 1be21c7..3866b74 100644 --- 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）**: 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 +- **Hermes（採集層）**: `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` 掛載在 Primary `34.143.170.20` (GCP SSD) 或 Fallback `192.168.0.111` Ollama +- ✅ **正確**：使用 `bge-m3`（或 `nomic-embed-text`）掛載在 Hermes 主機 `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） diff --git a/TODO_NEXT_STEPS.txt b/TODO_NEXT_STEPS.txt index 133f502..115ee9c 100644 --- a/TODO_NEXT_STEPS.txt +++ b/TODO_NEXT_STEPS.txt @@ -1,78 +1,4 @@ -================================================================================ - 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] ================================================================================ diff --git a/app.py b/app.py index 9db298d..7f811ce 100644 --- 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 = "V10.83" +SYSTEM_VERSION = "V10.77" # ========================================== # 🔒 SQL Injection 防護函數 @@ -142,12 +142,6 @@ 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 # ========================================== @@ -418,7 +412,7 @@ verify_metadata_tables() # ========================================== # 🔧 全域模板變數注入 (Context Processor) # ========================================== -from config import METABASE_URL, GRIST_URL, SYSTEM_VERSION as CONFIG_SYSTEM_VERSION +from config import METABASE_URL, GRIST_URL @app.context_processor def inject_global_vars(): @@ -426,7 +420,6 @@ 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'), } diff --git a/config.py b/config.py index 8d309f8..1c0b1f7 100644 --- 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 = "V10.83" +SYSTEM_VERSION = "V10.76" LOG_FILE_PATH = os.path.join(BASE_DIR, 'logs/system.log') public_url = PUBLIC_URL # 用於模板顯示 diff --git a/docker-compose.yml b/docker-compose.yml index 8779696..b530041 100644 --- 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://192.168.0.110:11435} + - OLLAMA_HOST_PRIMARY=${OLLAMA_HOST_PRIMARY:-http://34.21.145.224:11434} - OLLAMA_HOST_FALLBACK=${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://192.168.0.110:11435} + - OLLAMA_HOST_PRIMARY=${OLLAMA_HOST_PRIMARY:-http://34.21.145.224:11434} - OLLAMA_HOST_FALLBACK=${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://192.168.0.110:11435} + - OLLAMA_HOST_PRIMARY=${OLLAMA_HOST_PRIMARY:-http://34.21.145.224:11434} - OLLAMA_HOST_FALLBACK=${OLLAMA_HOST_FALLBACK:-http://192.168.0.111:11434} - EMBEDDING_HOST=${EMBEDDING_HOST:-} env_file: diff --git a/docs/AI_INTELLIGENCE_MODULE_SOT.md b/docs/AI_INTELLIGENCE_MODULE_SOT.md index e015dd1..b863e8a 100644 --- 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**: 2026-05-05 (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。 +> **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 > **適用版本**: V10.22 Legacy 5888 入口清理版 --- @@ -11,9 +11,8 @@ ``` SQL漏斗(~300筆) ↓ -[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) +[Hermes 3 8B] — 分析師 (本地 Ollama, 零成本) + 模型: hermes3:latest @ 192.168.0.111:11434 任務: 競價威脅分類 → TOP 20 HIGH/MED/LOW ↓ [NemoTron NIM] — 派發器 (雲端, 免費配額) @@ -42,7 +41,7 @@ SQL漏斗(~300筆) | 角色 | 模型 | 主機 | 成本 | 每日限額 | |------|------|------|------|---------| -| 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 | +| Hermes 分析師 | hermes3:latest / embedding model | 192.168.0.111:11434 或 188 Ollama | 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 | @@ -68,9 +67,6 @@ 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： diff --git a/docs/adr/ADR-021-ea-hitl-prefetch-and-alert-impact.md b/docs/adr/ADR-021-ea-hitl-prefetch-and-alert-impact.md index d36ecc7..1d2f902 100644 --- 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）→ 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 +- 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 ### 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 `/
` 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 suppressed cooldown |
+| 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 |
 | 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；最壞情況靜默 suppressed cooldown，不再推送無實證 HITL 噪音
+- 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
 
 ### 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，價格類升級不發 Telegram；由 suppressed cooldown 防止重複 pre-fetch / LLM 成本
+- 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）
 - `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 + suppressed cooldown
+- [x] `services/elephant_alpha_autonomous_engine.py` `_fetch_hermes_threats_summary` + 5s timeout + fallback
 - [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
diff --git a/docs/memory/credentials_passbook.md b/docs/memory/credentials_passbook.md
index dbcffd8..7bf268f 100644
--- a/docs/memory/credentials_passbook.md
+++ b/docs/memory/credentials_passbook.md
@@ -6,9 +6,7 @@
 | 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 (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 |
+| **Production / AI** | `192.168.0.188` | EwoooC App、Clawdbot、Ollama、pgvector | Docker Compose |
 | **DevSecOps** | `192.168.0.112` | Kali Linux (掃描主機)、WG-Easy | Docker |
 
 ## 🔑 關鍵認證
@@ -29,8 +27,8 @@
 - **5002**: Private Registry
 - **5678**: n8n
 - **8088**: Apache Superset
-- **11434**: Ollama API (Primary: 34.143.170.20, Secondary: 34.21.145.224, Fallback: 188/111)
-- **3000/3010**: Open WebUI (188)
+- **11434**: Ollama API (188)
+- **3000**: Open WebUI (188)
 - **51820**: WireGuard VPN
 
 ---
diff --git a/routes/daily_sales_routes.py b/routes/daily_sales_routes.py
index 0aecb75..c78d130 100644
--- 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, SYSTEM_VERSION
+from config import BASE_DIR
 from database.manager import DatabaseManager
 from services.logger_manager import SystemLogger
 from utils.df_helpers import find_col
@@ -40,11 +40,6 @@ 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()
diff --git a/routes/sales_routes.py b/routes/sales_routes.py
index 32a84ce..aace760 100644
--- 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, SYSTEM_VERSION
+from config import BASE_DIR, DATABASE_TYPE
 from database.manager import DatabaseManager
 from services.logger_manager import SystemLogger
 from services.daily_sales_service import prepare_marketing_summary
@@ -43,11 +43,6 @@ _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
 # ==========================================
@@ -256,10 +251,6 @@ 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")
diff --git a/services/ai_automation_smoke_service.py b/services/ai_automation_smoke_service.py
index 5cb1bd8..44a51e9 100644
--- a/services/ai_automation_smoke_service.py
+++ b/services/ai_automation_smoke_service.py
@@ -17,7 +17,6 @@ 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}
@@ -358,36 +357,8 @@ 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(),
diff --git a/services/elephant_alpha_autonomous_engine.py b/services/elephant_alpha_autonomous_engine.py
index e116fba..f16c842 100644
--- 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 — 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
+  §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
 ADR-013 Compliance:
   resource_optimization trigger → auto_heal_service.handle_exception
 """
@@ -42,18 +42,10 @@ 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"))
 
-_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)
+CACHE_DB_PATH = os.getenv("ELEPHANT_ALPHA_CACHE_DB", ":memory:")
 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({
@@ -131,15 +123,6 @@ _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)
@@ -227,8 +210,6 @@ 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 (
@@ -334,7 +315,9 @@ class ElephantAlphaAutonomousEngine:
         for trigger in self.triggers:
             if not trigger.enabled:
                 continue
-            if self._is_trigger_in_cooldown(trigger):
+            cooldown_min = self._get_cooldown(trigger.trigger_type)
+            last = trigger.last_triggered
+            if last and (datetime.now() - last).total_seconds() / 60 < cooldown_min:
                 continue
             if await self._evaluate_trigger(trigger):
                 await self._execute_autonomous_decision(trigger)
@@ -344,32 +327,8 @@ 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":
@@ -456,10 +415,8 @@ class ElephantAlphaAutonomousEngine:
             session.close()
 
     async def _check_resource_optimization_trigger(self, trigger: AutonomousTrigger) -> bool:
-        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)
+        return (self._get_action_queue_size() > 10
+                or self._get_system_load_percentage() > 80)
 
     async def _check_code_exception_trigger(self, trigger: AutonomousTrigger) -> bool:
         containers = trigger.conditions.get("scan_containers", ["momo-pro-system", "momo-scheduler"])
@@ -739,19 +696,19 @@ 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。
+        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。
 
         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 suppressed
+        Critic High-2 fix: 若每筆都缺 loss/rec_price，視同無料、return None 觸發 fallback
         """
         # 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；timeout 後由呼叫端 suppressed，避免無實證文字洗版
+        # 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
         try:
             result = await asyncio.wait_for(self._hermes_analyze(), timeout=5)
         except asyncio.TimeoutError:
-            self._log.warning("Pre-fetch Hermes 5s timeout; no concrete data for escalation")
+            self._log.warning("Pre-fetch Hermes 5s timeout; falling back to plan text")
             return None
         except Exception as e:
             self._log.warning("Pre-fetch Hermes threats failed (non-blocking): %s", e)
@@ -759,7 +716,7 @@ class ElephantAlphaAutonomousEngine:
 
         threats = getattr(result, "threats", None) or []
         if not threats:
-            self._log.info("Pre-fetch Hermes returned 0 threats; no concrete data for escalation")
+            self._log.info("Pre-fetch Hermes returned 0 threats; falling back to plan text")
             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
@@ -799,8 +756,8 @@ 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 suppressed
-            self._log.info("Pre-fetch threats lacked impact figures on all rows; no concrete data for escalation")
+            # 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")
             return None
 
         self._log.info("Pre-fetch Hermes threats produced %d concrete actions", len(lines))
@@ -924,106 +881,8 @@ 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(
@@ -1071,16 +930,68 @@ 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
 
-            ai_actions_payload = concrete_actions
+            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）"
+                )
 
             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",
+                        "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）")
+                        ),
                         "id": f"ea_review_{int(datetime.now().timestamp())}",
                     },
                     tier_label="🐘 Elephant Alpha · L3 HITL",
@@ -1148,19 +1059,10 @@ 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(f"""
-                    SELECT COUNT(*) AS count
-                    FROM action_plans
-                    WHERE status IN ('auto_pending', 'running')
-                       OR (
-                           status = 'pending'
-                           AND COALESCE(action_type, '') IN ('{operational_types}')
-                       )
-                """)
+                text("SELECT COUNT(*) AS count FROM action_plans WHERE status = 'pending'")
             ).fetchone()
-            return int(row[0] or 0) if row else 0
+            return row.count if row else 0
         finally:
             session.close()
 
diff --git a/services/elephant_alpha_orchestrator.py b/services/elephant_alpha_orchestrator.py
index a7b55e5..8b6d80f 100644
--- a/services/elephant_alpha_orchestrator.py
+++ b/services/elephant_alpha_orchestrator.py
@@ -28,14 +28,6 @@ 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"""
@@ -281,7 +273,7 @@ CURRENT AGENT STATUS:
 CURRENT SITUATION:
 - Date: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
 - System Load: {self._get_system_load()}
-- Operational Pending Actions: {self._get_pending_actions_count()}
+- 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:
@@ -384,21 +376,16 @@ 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 auto-safe operational actions, not human/business backlog."""
+        """Get count of pending actions"""
         session = get_session()
         try:
-            operational_types = "', '".join(sorted(_OPERATIONAL_PENDING_ACTION_TYPES))
-            row = session.execute(text(f"""
+            row = session.execute(text("""
                 SELECT COUNT(*) as count 
                 FROM action_plans 
-                WHERE status IN ('auto_pending', 'running')
-                   OR (
-                       status = 'pending'
-                       AND COALESCE(action_type, '') IN ('{operational_types}')
-                   )
+                WHERE status = 'pending'
             """)).fetchone()
             
-            return int(row[0] or 0) if row else 0
+            return row.count if row else 0
             
         finally:
             session.close()
diff --git a/services/import_service.py b/services/import_service.py
index 5b4a954..e789725 100644
--- 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 bindparam, create_engine
+from sqlalchemy import create_engine
 from sqlalchemy.orm import sessionmaker
 import pandas as pd
 import pytz
@@ -298,95 +298,6 @@ 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
@@ -403,7 +314,7 @@ class ImportService:
 
             # 讀取 Excel 檔案
             logger.info(f"開始讀取 Excel 檔案: {file_path}")
-            df = self._read_daily_sales_excel(file_path)
+            df = pd.read_excel(file_path, engine='openpyxl', dtype=str)
 
             if df.empty:
                 error_msg = "Excel 檔案為空"
@@ -415,7 +326,15 @@ 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)
             # ─────────────────────────────────────────────
-            missing = self._validate_daily_sales_columns(df)
+            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)]
             if missing:
                 error_msg = (
                     f"Excel 欄位防禦失敗：缺少必要欄位分類 {missing}。"
@@ -453,178 +372,211 @@ 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 = self._normalize_dates_for_sql(df['snapshot_date'].unique())
+            import_dates = df['snapshot_date'].unique()
             logger.info(f"本次匯入包含 {len(import_dates)} 個日期的資料")
 
-            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)
+            # 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)
                 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」
-            # 2026-05-05 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...')
+            # 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...')
 
             sync_success = False
             sync_error_msg = None
             monthly_table = 'realtime_sales_monthly'
 
-            # 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')
+            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')
 
-            # 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)
 
-            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")
+                # 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")
 
-            # 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("""
+                # 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"""
                         SELECT column_name FROM information_schema.columns
-                        WHERE table_name = :table_name AND column_name != 'id'
+                        WHERE table_name = '{monthly_table}' AND column_name != 'id'
                         ORDER BY ordinal_position
                     """)
-                    target_columns = {
-                        row[0] for row in conn.execute(col_query, {'table_name': monthly_table})
-                    }
+                    result = conn.execute(col_query)
+                    target_columns = set([row[0] for row in result])
 
-                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}")
 
-            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
+                # 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")
 
-            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)
+                    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])
 
-            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})...')
+                        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()
 
-                    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）")
+                            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")
 
-                        df.to_sql(
-                            table_name,
-                            conn,
-                            if_exists='append',
-                            index=False,
-                            method='multi',
-                            chunksize=1000
-                        )
+                # 寫入 realtime_sales_monthly
+                df_monthly.to_sql(
+                    monthly_table,
+                    engine,
+                    if_exists='append',
+                    index=False,
+                    method='multi',
+                    chunksize=1000
+                )
 
-                        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"
-                            )
+                logger.info(f"任務 {job_id} 已同步 {len(df_monthly)} 筆資料至 {monthly_table}")
 
-                        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")
+                # 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()
 
-                        df_monthly.to_sql(
-                            monthly_table,
-                            conn,
-                            if_exists='append',
-                            index=False,
-                            method='multi',
-                            chunksize=1000
-                        )
+                        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
 
-                        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"
-                            )
+            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()}")
 
-                    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}")
+                # 2026-01-30 新增：發送同步失敗告警
                 try:
                     from services.notification_manager import NotificationManager
                     notifier = NotificationManager()
                     alert_msg = (
-                        f"⚠️ 業績資料原子匯入失敗告警\n"
+                        f"⚠️ 業績資料同步失敗告警\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"本次 daily_sales_snapshot / realtime_sales_monthly 已一起 rollback，請檢查匯入檔案"
+                        f"daily_sales_snapshot 已匯入成功，但業績分析儀表板需要手動同步"
                     )
                     notifier._send_telegram_messages([alert_msg])
-                    logger.info(f"任務 {job_id} 已發送原子匯入失敗告警")
+                    logger.info(f"任務 {job_id} 已發送同步失敗告警")
                 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
@@ -650,15 +602,12 @@ class ImportService:
             # 計算日期範圍
             date_min = None
             date_max = None
-            imported_dates = self._normalize_dates_for_sql(df['snapshot_date'].dropna().unique())
-            data_lag_days = None
-            valid_dates = imported_dates
+            valid_dates = df['snapshot_date'].dropna().unique()
             if len(valid_dates) > 0:
-                sorted_dates = sorted(valid_dates)
+                sorted_dates = sorted([d for d in valid_dates if d is not None])
                 if sorted_dates:
-                    date_min = sorted_dates[0]
-                    date_max = sorted_dates[-1]
-                    data_lag_days = self._calculate_data_lag_days(date_max)
+                    date_min = str(sorted_dates[0])
+                    date_max = str(sorted_dates[-1])
                     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)
@@ -676,8 +625,6 @@ 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
             }
 
@@ -767,8 +714,6 @@ 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']
@@ -780,8 +725,6 @@ 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
@@ -793,8 +736,6 @@ 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
@@ -836,19 +777,6 @@ 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()
 
@@ -858,14 +786,6 @@ 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
@@ -877,32 +797,13 @@ 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,
-                'data_lag_days': data_lag_days
+                'date_range': date_range
             }
 
         except Exception as e:
diff --git a/templates/components/_ewoooc_shell.html b/templates/components/_ewoooc_shell.html
index 3d90399..f62d016 100644
--- a/templates/components/_ewoooc_shell.html
+++ b/templates/components/_ewoooc_shell.html
@@ -194,37 +194,11 @@
                 活動看板
                 02
             
-            

-                
-                    
-                    分析報表
-                    03
-                
-                

-                    
-                        業績分析01
-                    
-                    
-                        當日業績02
-                    
-                    
-                        成長分析03
-                    
-                    
-                        月份總表04
-                    
-                    {% if metabase_url %}
-                    
-                        Metabase
-                    
-                    {% endif %}
-                    {% if grist_url %}
-                    
-                        Grist
-                    
-                    {% endif %}
-                

-            

+            
+                
+                分析報表
+                03
+            
         
 
         

diff --git a/templates/daily_sales.html b/templates/daily_sales.html
index 44e33d6..589ac55 100644
--- a/templates/daily_sales.html
+++ b/templates/daily_sales.html
@@ -846,7 +846,6 @@
             }
         }
     
-    
 {% endblock %}
 
 {% block ewooo_content %}
@@ -876,8 +875,6 @@
             

         
 
-        {% include 'components/_analysis_report_tabs.html' %}
-
         
         {% if calendar_data %}
         

@@ -1306,11 +1303,8 @@
         

         {% endif %}
     
-{% endblock %}
 
-{% block extra_js %}
-    
-    
+    
     
     
     
@@ -1878,4 +1872,6 @@
         }
         {% endif %}
     
-{% endblock %}
+
+
+
diff --git a/templates/ewoooc_base.html b/templates/ewoooc_base.html
index a2c0315..fff38db 100644
--- a/templates/ewoooc_base.html
+++ b/templates/ewoooc_base.html
@@ -1,11 +1,3 @@
-{% 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) %}
 
 
 
@@ -13,7 +5,6 @@
     
     
     {% block title %}EwoooC{% endblock %}
-    
 
     
     
diff --git a/templates/growth_analysis.html b/templates/growth_analysis.html
index 53932be..d88b2cd 100644
--- a/templates/growth_analysis.html
+++ b/templates/growth_analysis.html
@@ -1,7 +1,13 @@
-{% extends 'ewoooc_base.html' %}
-{% block title %}營運成長報表 - WOOO TECH{% endblock %}
-
-{% block extra_css %}
+
+
+
+
+    
+    
+    營運成長報表 - MOMO 監控系統
+    
+    
+    
     
-    
-{% endblock %}
+
+
+    {% include 'components/_navbar.html' %}
 
-{% block ewooo_content %}
-    

-        

-            

-                
營運成長策略報表

-                
觀察年累計業績、客單價、訂單數與毛利率趨勢。

-            

-            

-                數據更新至 {{ chart_data.labels[-1] if chart_data.labels else '-' }}
-            

-        

-
-        {% include 'components/_analysis_report_tabs.html' %}
+    

+        

+            
營運成長策略報表

+            數據更新至: {{ chart_data.labels[-1] if chart_data.labels else '-' }}
+        

 
         
         

@@ -135,12 +134,9 @@
             

         

     

-{% endblock %}
-
-{% block extra_js %}
-    
-    
 
+    
+    
     
     
-{% endblock %}
+
+
\ No newline at end of file
diff --git a/templates/monthly_summary_analysis.html b/templates/monthly_summary_analysis.html
index 93a2479..18fa57d 100644
--- a/templates/monthly_summary_analysis.html
+++ b/templates/monthly_summary_analysis.html
@@ -159,7 +159,6 @@
             }
         }
     
-    
 {% endblock %}
 
 {% block ewooo_content %}
@@ -179,8 +178,6 @@
             
         
 
-        {% include 'components/_analysis_report_tabs.html' %}
-
         
         

             

@@ -577,7 +574,6 @@
     
     
     
-    
     
     
-    
     
-    
-{% endblock %}
+
 
-{% block ewooo_content %}
+
+    {% include 'components/_navbar.html' %}
 
     
     

@@ -602,18 +607,17 @@
         

     

 
-    

-        

-            

-                
業績分析儀表板

-                
檢視即時業績、分類、品牌、廠商與行銷活動貢獻。

+    

+        

+            

+                
業績分析儀表板

                 {% if db_data_range %}
-                
+                
                     資料期間: {{ db_data_range }}
                 
                 {% endif %}
             

-            

+            

                 {% if not no_filter %}
                 
                     
@@ -632,9 +636,7 @@
                 
                 {% endif %}
             

-        

-
-        {% include 'components/_analysis_report_tabs.html' %}
+        

 
         {% if error %}
         

@@ -1178,7 +1180,8 @@
                 

             

             

-                

+                

                     

                         
毛利率 (%)

                         
{{ "{:.1f}%".format(kpi.gross_margin_rate) }}

@@ -1957,7 +1960,6 @@
         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, {
@@ -2618,7 +2620,6 @@
                 }
             }
         });
-        }
 
         // 3. 初始化 DataTables (分頁、搜尋、排序)
         $(document).ready(function () {
@@ -3159,4 +3160,7 @@
         });
     
 
-{% endblock %}
+    
+
+
+
\ No newline at end of file
diff --git a/tests/test_elephant_alpha_engine.py b/tests/test_elephant_alpha_engine.py
index 661bdf2..e719a3b 100644
--- a/tests/test_elephant_alpha_engine.py
+++ b/tests/test_elephant_alpha_engine.py
@@ -1,8 +1,7 @@
 import asyncio
 
 
-def test_run_with_timeout_supports_sync_function(monkeypatch):
-    monkeypatch.setenv("ELEPHANT_ALPHA_CACHE_DB", ":memory:")
+def test_run_with_timeout_supports_sync_function():
     from services.elephant_alpha_autonomous_engine import ElephantAlphaAutonomousEngine
 
     result = asyncio.run(ElephantAlphaAutonomousEngine._run_with_timeout(lambda value: value + 1, 41))
@@ -10,8 +9,7 @@ def test_run_with_timeout_supports_sync_function(monkeypatch):
     assert result == 42
 
 
-def test_execute_step_rejects_unknown_action(monkeypatch):
-    monkeypatch.setenv("ELEPHANT_ALPHA_CACHE_DB", ":memory:")
+def test_execute_step_rejects_unknown_action():
     from services.elephant_alpha_autonomous_engine import ElephantAlphaAutonomousEngine
 
     engine = ElephantAlphaAutonomousEngine()
@@ -25,7 +23,6 @@ def test_execute_step_rejects_unknown_action(monkeypatch):
 
 
 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 = []
@@ -46,7 +43,6 @@ 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 = []
@@ -86,118 +82,3 @@ 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)