diff --git a/.env.example b/.env.example
index 6d68e09..6b8d173 100644
--- a/.env.example
+++ b/.env.example
@@ -12,4 +12,3 @@ NTFY_TOKEN=
 
 # Uptime Kuma
 UPTIME_KUMA_PUSH_URL=
-EXCEL_SYNC_UPTIME_KUMA_PUSH_URL=
diff --git a/CLAUDE.md b/CLAUDE.md
index 6f1b2ae..073e420 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -6,7 +6,6 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
 
 This is a data synchronization system (BLD_sync) that extracts data from:
 - **Microsoft Access databases** (.accdb) located on network shares
-- **Excel files** (.xlsm) containing production execution cards and contract data
 
 And synchronizes it to:
 - **Microsoft SQL Server** (CompanyDB) with multiple schemas organized by data type
@@ -21,15 +20,11 @@ The system supports both full initialization sync and incremental sync driven by
 ┌─────────────────────────────────────────────────────────────────────┐
 │                         Network File Sources                        │
 │  Access DBs (\\192.168.110.114\生产进度表\)                         │
-│  Excel Files (\\192.168.110.113\生产执行卡\)                        │
 └─────────────────────────────────────────────────────────────────────┘
                               │
                               ▼
 ┌─────────────────────────────────────────────────────────────────────┐
 │                          Sync Scripts                               │
-│  • etl_manager.py         - Main ETL orchestrator (primary)         │
-│  • sync_excel_to_sql.py   - Excel to SQL sync (legacy/alternative)  │
-│  • migration.py           - Legacy customer product type migration   │
 │  • run_incremental_sync.py - Change log driven incremental sync     │
 │  • init_full_sync.py      - Full table truncation/reload           │
 └─────────────────────────────────────────────────────────────────────┘
@@ -43,36 +38,18 @@ The system supports both full initialization sync and incremental sync driven by
 └─────────────────────────────────────────────────────────────────────┘
 ```
 
+> Note: The system previously also synced Excel `.xlsm` production-execution-card files into
+> the `warehouseOutbound` schema (`executionCardData`, `contractData`, `customerProductType`).
+> That Excel pipeline has been removed; the existing tables and their data remain in CompanyDB
+> but are no longer updated.
+
 ### Key Components
 
-**etl_manager.py** - Primary ETL orchestrator
-- `DataSynchronizer` class manages Excel to SQL sync
-- Supports incremental sync via file modification time comparison
-- Caches Excel files locally in `excel_cache/` directory
-- Uses SQLAlchemy with `fast_executemany=True` for bulk operations
-- Generates `contractData` table via MERGE statement from `executionCardData`
-
-**config.py** - Central configuration
-- `SYNC_MAPPING`: Nested dict mapping Access files → tables → SQL targets
-- `EXCEL_CONFIGS`: List of Excel file configs with sheet names and field mappings
-- `TABLE_SCHEMA`: Column type definitions for data cleaning
-- `NTFY_CONFIG`: Push notification settings
-- `LOG_TABLE_CONFIG`: Change log table schema for incremental sync
-
-**db_utils.py** - Database connection utilities
-- `get_sql_conn()`: SQL Server connection via pyodbc
-- `get_access_conn()`: Access database connection
-- `fmt_table()`: Safe table name formatting `[schema].[table]`
-- `generate_insert_sql()`: Dynamic INSERT statement generation
-
-**ntfy_utils.py** - Push notifications
-- Sends alerts to ntfy server on errors/completion
-- Uses Bearer token authentication
-
 **run_incremental_sync.py** - Log-driven incremental sync
 - Polls `TableChangeLog` table for unsynced records (`Synced=0`)
-- Queries by `TableAddress` to match configured file paths
-- Processes deletions and insertions in batches
+- Queries by `TableAddress` to match configured Access file paths
+- Connects to the source Access DB, reads latest rows by primary key, then DELETE + INSERT
+- Per-batch verification (delete/insert checks by primary key) before marking `Synced=1`
 - Long-running service with configurable polling interval
 
 **init_full_sync.py** - Full table reload
@@ -80,6 +57,33 @@ The system supports both full initialization sync and incremental sync driven by
 - Handles IDENTITY_INSERT ON/OFF for tables with identity columns
 - Progress logging with row counts and throughput metrics
 
+**config/** - Configuration package
+- `database.py`: SQL Server / Access driver settings (read from env via `python-dotenv`)
+- `file_sources.py`: `SYNC_MAPPING` — nested dict mapping Access files → tables → SQL targets
+- `app_settings.py`: `LOG_TABLE_CONFIG`, `NTFY_CONFIG`, `UPTIME_KUMA_CONFIG`, `POLL_INTERVAL`, `BATCH_SIZE`
+- Symbols are re-exported from `config/__init__.py` (e.g. `from config import SYNC_MAPPING`)
+
+**db_utils.py** - Database connection utilities
+- `get_sql_conn()`: SQL Server connection via pyodbc
+- `get_access_conn()`: Access database connection
+- `fmt_table()`: Safe table name formatting `[schema].[table]`
+- `generate_insert_sql()`: Dynamic INSERT statement generation
+- `create_table_from_access()`: Auto-create SQL Server table from Access `cursor.description`
+
+**ntfy_utils.py** - Push notifications
+- Sends alerts to ntfy server on errors/completion
+- Uses Bearer token authentication
+
+**uptime_kuma_utils.py** - Heartbeat monitoring
+- `UptimeKumaMonitor` pushes heartbeats to Uptime Kuma; used by the incremental sync service
+
+**log_utils.py** - Unified logging
+- `LoggerManager` creates a timestamped log file under `log/` and archives old logs by year-month
+
+**vbareplace.py** / **vba.txt** - Access VBA helper tool
+- Standalone drag-and-drop tool that injects the change-log VBA macro into Access forms and
+  refreshes linked tables (packaged via PyInstaller; unrelated to the Python sync scripts)
+
 ## Common Development Tasks
 
 ### Install Dependencies
@@ -94,60 +98,35 @@ pip install -r requirements.txt
 python init_full_sync.py
 ```
 
-### Run Excel to SQL Sync (Standard)
-
-```bash
-python etl_manager.py
-```
-
-Force sync all files (ignore modification times):
-```bash
-python etl_manager.py --force
-```
-
 ### Run Incremental Sync Service (Change Log Driven)
 
 ```bash
 python run_incremental_sync.py
 ```
 
-### Run Legacy Migration Script
-
-```bash
-python migration.py
-```
-
-Edit `FORCE_UPDATE = True` in `migration.py` to force full refresh.
-
 ## Configuration Management
 
-**Main Config File**: `config.py`
-- Contains all database credentials, file paths, and mapping configurations
-- Modify `SYNC_MAPPING` to add new Access files/tables
-- Modify `EXCEL_CONFIGS` for new Excel sources
-
-**Alternative Config**: `update_config.py`
-- Used by `sync_excel_to_sql.py`
-- Similar structure but different variable names
-- Contains `EXECUTION_CARD_FIELDS`, `CONTRACT_DATA_MAPPING`
+Configuration lives in the **`config/` package** (re-exported via `config/__init__.py`):
+- Database credentials and driver strings come from environment variables (see `.env.example`),
+  loaded by `python-dotenv`.
+- Modify `SYNC_MAPPING` in `config/file_sources.py` to add new Access files/tables.
 
 ## Important Implementation Details
 
 ### SQL Server Connection
 - Uses ODBC Driver 18 for SQL Server
 - Requires `TrustServerCertificate=yes` due to self-signed cert
-- SQLAlchemy URL: `mssql+pyodbc:///?odbc_connect=...`
-- Always enable `fast_executemany=True` for bulk operations
+- Direct pyodbc connections (no SQLAlchemy); `fast_executemany=True` for bulk inserts
 
 ### Access Database Connection
 - Driver: `{Microsoft Access Driver (*.mdb, *.accdb)}`
 - Direct file path connection via pyodbc
 
-### Data Cleaning
-- Integer fields: `pd.to_numeric().round().astype('Int64')`
-- String fields: Truncate to max length, replace empty with None
-- Date fields: Convert to `date()` objects, None for NaT
-- Duplicate removal: Based on primary key (usually `ID` or `总排号`)
+### Type Mapping / Auto Table Creation
+- When a SQL Server target table does not exist, it is created from the Access `cursor.description`.
+- Access types map to SQL Server types: `int`→`INT` (PK → `IDENTITY(1,1) PRIMARY KEY`),
+  `float`→`FLOAT`, `bool`→`BIT`, `datetime`→`DATETIME`, `Decimal`→`DECIMAL(p,s)`,
+  `str`→`NVARCHAR(size)` (size > 4000 → `NVARCHAR(MAX)`).
 
 ### Identity Column Handling
 Tables with identity columns require:
@@ -157,7 +136,7 @@ SET IDENTITY_INSERT [schema].[table] ON
 SET IDENTITY_INSERT [schema].[table] OFF
 ```
 
-See `has_identity_column()` in `init_full_sync.py` for detection logic.
+See `has_identity_column()` in `init_full_sync.py` / `run_incremental_sync.py` for detection logic.
 
 ### File Path Matching in Incremental Sync
 The change log table stores paths in VBA format:
@@ -169,7 +148,6 @@ The code constructs multiple match patterns for robust matching.
 ## Database Schema Organization
 
 SQL Server schemas by function:
-- `warehouseOutbound` - Execution card data, contract data, customer product types
 - `productionContractData` - Contract data by year (25年/26年压力表/温度计)
 - `productWarehousing` - Finished product inspection/warehousing records
 - `workshopOne/Two/Three` - Workshop production records
@@ -181,16 +159,21 @@ SQL Server schemas by function:
 - `solderingData` - Soldering operation records
 - `TIGWelding` - TIG welding records
 - `executionCardIssuanceRecord` - Execution card issuance records
+- `tubeBending` - Tube bending records
+- `warehouseOutbound` - (Legacy) Excel-sourced tables: `executionCardData`, `contractData`,
+  `customerProductType` — no longer updated
 
 ## Notifications
 
 The system uses [ntfy](https://ntfy.sh/) for push notifications:
-- Configured in `NTFY_CONFIG` within `config.py`
+- Configured in `NTFY_CONFIG` within `config/app_settings.py`
 - Sends on: errors, critical failures, task completion
 - Authenticated via Bearer token
 
 ## Logging
 
-- File logs: `log/` directory with timestamp rotation
+- File logs: `log/` directory, one timestamped file per run (`<prefix>_<YYYYMMDD>_<HHMMSS>.log`)
 - Console output: With emoji prefixes for status (✅ ❌ ⚠️ 🔄)
-- Incremental sync: Uses `TimedRotatingFileHandler` for daily log files
+- Old logs are archived into `log/Archive/YYYY-MM/` automatically
+  (see `LoggerManager.archive_old_logs` in `log_utils.py`; `archive_existing_logs.py` is a
+  one-time helper for historical logs)
diff --git a/config/__init__.py b/config/__init__.py
index f4e146e..422f1d7 100644
--- a/config/__init__.py
+++ b/config/__init__.py
@@ -7,17 +7,13 @@ load_dotenv()
 # 数据库配置
 from .database import SQL_SERVER_CONFIG, SQL_SERVER_CONN, DB_CONFIG, ACCESS_DRIVER
 
-# 文件路径配置
-from .file_sources import SYNC_MAPPING, EXCEL_CONFIGS, MIGRATION_TASKS
-
-# 字段映射配置
-from .field_mappings import TABLE_SCHEMA, CONTRACT_MAPPING
+# 文件路径配置（Access 同步映射）
+from .file_sources import SYNC_MAPPING
 
 # 应用设置
 from .app_settings import (
-    LOG_TABLE_CONFIG, NTFY_CONFIG, UPTIME_KUMA_CONFIG, EXCEL_SYNC_UPTIME_KUMA_CONFIG,
-    POLL_INTERVAL, EXCEL_SYNC_INTERVAL, BATCH_SIZE, CACHE_DIR, TEMP_DIR,
-    EXECUTION_CARD_FIELDS, CONTRACT_DATA_FIELDS, CONTRACT_DATA_MAPPING
+    LOG_TABLE_CONFIG, NTFY_CONFIG, UPTIME_KUMA_CONFIG,
+    POLL_INTERVAL, BATCH_SIZE,
 )
 
 # 统一导出列表
@@ -25,11 +21,8 @@ __all__ = [
     # Database
     'SQL_SERVER_CONFIG', 'SQL_SERVER_CONN', 'DB_CONFIG', 'ACCESS_DRIVER',
     # File Sources
-    'SYNC_MAPPING', 'EXCEL_CONFIGS', 'MIGRATION_TASKS',
-    # Field Mappings
-    'TABLE_SCHEMA', 'CONTRACT_MAPPING',
+    'SYNC_MAPPING',
     # App Settings
-    'LOG_TABLE_CONFIG', 'NTFY_CONFIG', 'UPTIME_KUMA_CONFIG', 'EXCEL_SYNC_UPTIME_KUMA_CONFIG',
-    'POLL_INTERVAL', 'EXCEL_SYNC_INTERVAL', 'BATCH_SIZE', 'CACHE_DIR', 'TEMP_DIR',
-    'EXECUTION_CARD_FIELDS', 'CONTRACT_DATA_FIELDS', 'CONTRACT_DATA_MAPPING'
+    'LOG_TABLE_CONFIG', 'NTFY_CONFIG', 'UPTIME_KUMA_CONFIG',
+    'POLL_INTERVAL', 'BATCH_SIZE',
 ]
diff --git a/config/app_settings.py b/config/app_settings.py
index a04201d..2a1164c 100644
--- a/config/app_settings.py
+++ b/config/app_settings.py
@@ -36,96 +36,6 @@ UPTIME_KUMA_CONFIG = {
     'heartbeat_interval': 59  # 心跳间隔(秒)，需要与 Uptime Kuma 设置一致
 }
 
-# Excel 同步服务心跳
-EXCEL_SYNC_UPTIME_KUMA_CONFIG = {
-    'enabled': True,
-    'push_url': os.environ.get('EXCEL_SYNC_UPTIME_KUMA_PUSH_URL', ''),
-    'heartbeat_interval': 59  # 心跳间隔(秒)，需要与 Uptime Kuma 设置一致
-}
-
 # ================= 运行参数 =================
-# 合并原 config.py 和 update_config.py 的配置
 POLL_INTERVAL = 30     # 轮询间隔(秒)
-EXCEL_SYNC_INTERVAL = 600  # Excel 同步周期(秒)，默认 10 分钟
 BATCH_SIZE = 10000    # 批量处理大小
-CACHE_DIR = os.path.join(os.getcwd(), "temp")  # Excel 缓存目录
-TEMP_DIR = os.path.join(os.getcwd(), "temp")           # 临时目录（兼容 migration.py）
-
-# ================= 字段配置 =================
-# 从原 update_config.py (sync_excel_to_sql.py) 抽取
-EXECUTION_CARD_FIELDS = {
-    "合同年份": ("str", 10),
-    "总排号": ("str", 50),
-    "序号": ("int", None),
-    "订单号": ("str", 150),
-    "车间号": ("str", 20),
-    "销售内部号": ("str", 50),
-    "经办人": ("str", 20),
-    "签订日期": ("date", None),
-    "交货日期": ("date", None),
-    "客户名称": ("str", 200),
-    "产品名称": ("str", 200),
-    "客户型号": ("str", 200),
-    "选型型号": ("str", 200),
-    "量程": ("str", 150),
-    "数量": ("int", None),
-    "备注2": ("str", 500),
-    "备注1": ("str", 500),
-    "位号": ("str", 500),
-    "技术参数": ("str", 1000),
-    "车间": ("str", 200),
-    "工令号": ("str", 200),
-    "接单日期": ("date", None),
-    "新参数": ("str", 1000),
-    "基本型号": ("str", 200),
-    "公称外径": ("str", 200),
-    "安装代码": ("str", 200),
-    "设计形式": ("str", 200),
-    "技术安装代码": ("str", 200),
-    "隔膜类型": ("str", 200),
-    "标准": ("str", 100),
-    "隔膜大小": ("str", 200),
-    "隔膜材质": ("str", 200),
-    "膜片": ("str", 200),
-    "膜片材质": ("str", 200),
-    "CRM明细ID号": ("str", 200),
-    "成品物料编码": ("str", 200),
-    "工单号": ("str", 200),
-    "盘号": ("str", 200),
-    "编码": ("str", 200),
-    "型批名称": ("str", 200),
-    "型批型号": ("str", 200),
-    "开票名称": ("str", 200),
-    "开票型号": ("str", 200),
-    "上数据时间": ("date", None),
-    "生产代码1": ("str", 50),
-    "生产代码2": ("str", 50)
-}
-
-CONTRACT_DATA_FIELDS = {
-    "合同年份": ("str", 10),
-    "车间号": ("str", 20),
-    "工令号": ("str", 200),
-    "订单号": ("str", 150),
-    "客户名称": ("str", 200),
-    "产品型号": ("str", 200),
-    "量程": ("str", 150),
-    "数量": ("int", None),
-    "单价": ("int", None),
-    "ID": ("int", None),
-    "位号": ("str", 500)
-}
-
-CONTRACT_DATA_MAPPING = {
-    "合同年份": "合同年份",
-    "车间号": "车间号",
-    "工令号": "工令号",
-    "订单号": "订单号",
-    "客户名称": "客户名称",
-    "产品型号": "选型型号",
-    "量程": "量程",
-    "数量": "数量",
-    "单价": None,
-    "ID": None,
-    "位号": "位号"
-}
diff --git a/config/field_mappings.py b/config/field_mappings.py
deleted file mode 100644
index ee20a64..0000000
--- a/config/field_mappings.py
+++ /dev/null
@@ -1,73 +0,0 @@
-# config/field_mappings.py
-# 字段映射和清洗规则配置
-
-import os
-from config.app_settings import EXECUTION_CARD_FIELDS
-
-# ================= 字段清洗规则 =================
-# 从原 update_config.py 抽取
-# 基于 NVARCHAR (按字符数计算长度)
-TABLE_SCHEMA = {
-    "合同年份": {"type": "str", "max_len": 10},
-    "总排号": {"type": "str", "max_len": 50},
-    "序号": {"type": "int"},
-    "订单号": {"type": "str", "max_len": 150},
-    "车间号": {"type": "str", "max_len": 20},
-    "销售内部号": {"type": "str", "max_len": 50},
-    "经办人": {"type": "str", "max_len": 20},
-    "签订日期": {"type": "date"},
-    "交货日期": {"type": "date"},
-    "客户名称": {"type": "str", "max_len": 200},
-    "产品名称": {"type": "str", "max_len": 200},
-    "客户型号": {"type": "str", "max_len": 200},
-    "选型型号": {"type": "str", "max_len": 200},
-    "量程": {"type": "str", "max_len": 150},
-    "数量": {"type": "int"},
-    "备注2": {"type": "str", "max_len": 500},
-    "备注1": {"type": "str", "max_len": 500},
-    "位号": {"type": "str", "max_len": 500},
-    "技术参数": {"type": "str", "max_len": 1000},
-    "车间": {"type": "str", "max_len": 200},
-    "工令号": {"type": "str", "max_len": 200},
-    "接单日期": {"type": "date"},
-    "新参数": {"type": "str", "max_len": 1000},
-    "基本型号": {"type": "str", "max_len": 200},
-    "公称外径": {"type": "str", "max_len": 200},
-    "安装代码": {"type": "str", "max_len": 200},
-    "设计形式": {"type": "str", "max_len": 200},
-    "技术安装代码": {"type": "str", "max_len": 200},
-    "隔膜类型": {"type": "str", "max_len": 200},
-    "标准": {"type": "str", "max_len": 100},
-    "隔膜大小": {"type": "str", "max_len": 200},
-    "隔膜材质": {"type": "str", "max_len": 200},
-    "膜片": {"type": "str", "max_len": 200},
-    "膜片材质": {"type": "str", "max_len": 200},
-    "CRM明细ID号": {"type": "str", "max_len": 200},
-    "成品物料编码": {"type": "str", "max_len": 200},
-    "工单号": {"type": "str", "max_len": 200},
-    "盘号": {"type": "str", "max_len": 200},
-    "编码": {"type": "str", "max_len": 200},
-    "型批名称": {"type": "str", "max_len": 200},
-    "型批型号": {"type": "str", "max_len": 200},
-    "开票名称": {"type": "str", "max_len": 200},
-    "开票型号": {"type": "str", "max_len": 200},
-    "上数据时间": {"type": "date"},
-    "生产代码1": {"type": "str", "max_len": 50},
-    "生产代码2": {"type": "str", "max_len": 50}
-}
-
-# ================= 合同数据映射 =================
-# 从原 update_config.py 抽取
-CONTRACT_MAPPING = {
-    "合同年份": "合同年份",
-    "车间号": "车间号",
-    "工令号": "工令号",
-    "订单号": "订单号",
-    "客户名称": "客户名称",
-    "产品型号": "选型型号",
-    "量程": "量程",
-    "数量": "数量",
-    "单价": None,
-    "ID": None,
-    "位号": "位号"
-}
diff --git a/config/file_sources.py b/config/file_sources.py
index 34199d2..cad17e3 100644
--- a/config/file_sources.py
+++ b/config/file_sources.py
@@ -313,160 +313,10 @@ SYNC_MAPPING = {
         }
     },
     r"\\192.168.110.114\生产进度表\2026年数据\弯管车间.accdb": {
-        "执行卡下发记录": {
+        "烘洗": {
             "target_schema": "tubeBending",
             "target_table": "烘洗_YEAR2026",
             "pk_col": "ID"
         }
     }
 }
-
-# ================= Excel 文件配置 =================
-# 从原 update_config.py 抽取
-EXCEL_CONFIGS = [
-    {
-        "file_path": r"\\192.168.110.113\生产执行卡\往年生产执行卡\生产执行卡2022.xlsm",
-        "sheet_names": ["Sheet1"],
-        "contract_year": "2022",
-        "field_mapping": {
-            "产品型号": "选型型号",
-            "备注": "备注1",
-            "下单日期": "接单日期"
-        }
-    },
-    {
-        "file_path": r"\\192.168.110.113\生产执行卡\往年生产执行卡\生产执行卡2023（1-5月）.xlsm",
-        "sheet_names": ["Sheet1"],
-        "contract_year": "2023",
-        "field_mapping": {
-            "产品型号": "选型型号",
-            "备注": "备注1",
-            "下单日期": "接单日期"
-        }
-    },
-    {
-        "file_path": r"\\192.168.110.113\生产执行卡\往年生产执行卡\生产执行卡2023(6月-.xlsm",
-        "sheet_names": ["Sheet1"],
-        "contract_year": "2023",
-        "field_mapping": {
-            "产品型号": "选型型号",
-            "备注": "备注1",
-            "下单日期": "接单日期"
-        }
-    },
-    {
-        "file_path": r"\\192.168.110.113\生产执行卡\往年生产执行卡\生产执行卡2024 6月.xlsm",
-        "sheet_names": ["重庆数据","北京数据"],
-        "contract_year": "2024",
-        "field_mapping": {
-            "产品型号": "选型型号",
-            "备注": "备注1",
-            "下单日期": "接单日期"
-        }
-    },
-    {
-        "file_path": r"\\192.168.110.113\生产执行卡\往年生产执行卡\生产执行卡2024.xlsm",
-        "sheet_names": ["重庆数据","北京数据"],
-        "contract_year": "2024",
-        "field_mapping": {
-            "产品型号": "选型型号",
-            "备注": "备注1",
-            "下单日期": "接单日期"
-        }
-    },
-    {
-        "file_path": r"\\192.168.110.113\生产执行卡\生产执行卡2025年.xlsm",
-        "sheet_names": ["重庆数据","北京数据"],
-        "contract_year": "2025",
-        "field_mapping": {
-            "产品型号": "选型型号",
-            "备注": "备注1",
-            "下单日期": "接单日期"
-        }
-    },
-    {
-        "file_path": r"\\192.168.110.113\生产执行卡\生产执行卡2026年.xlsm",
-        "sheet_names": ["重庆数据","北京数据"],
-        "contract_year": "2026",
-        "field_mapping": {
-            "产品型号": "选型型号",
-            "备注": "备注1",
-            "下单日期": "接单日期"
-        }
-    }
-]
-
-# ================= 迁移任务配置 =================
-# 从原 migration.py 抽取
-MIGRATION_TASKS = [
-    {
-        "file_path": r"\\192.168.110.113\生产执行卡\往年生产执行卡\生产执行卡2022.xlsm",
-        "year": 2022,
-        "sheet_names": ["Sheet1"],
-        "mapping": {
-            "车间号": "车间号",
-            "工令号": "工令号",
-            "客户型号": "客户型号"
-        }
-    },
-    {
-        "file_path": r"\\192.168.110.113\生产执行卡\往年生产执行卡\生产执行卡2023（1-5月）.xlsm",
-        "year": 2023,
-        "sheet_names": ["Sheet1"],
-        "mapping": {
-            "车间号": "车间号",
-            "工令号": "工令号",
-            "客户型号": "客户型号"
-        }
-    },
-    {
-        "file_path": r"\\192.168.110.113\生产执行卡\往年生产执行卡\生产执行卡2023(6月-.xlsm",
-        "year": 2023,
-        "sheet_names": ["Sheet1"],
-        "mapping": {
-            "车间号": "车间号",
-            "工令号": "工令号",
-            "客户型号": "客户型号"
-        }
-    },
-    {
-        "file_path": r"\\192.168.110.113\生产执行卡\往年生产执行卡\生产执行卡2024 6月.xlsm",
-        "year": 2024,
-        "sheet_names": ["重庆数据","北京数据"],
-        "mapping": {
-            "车间号": "车间号",
-            "工令号": "工令号",
-            "客户型号": "客户型号"
-        }
-    },
-    {
-        "file_path": r"\\192.168.110.113\生产执行卡\往年生产执行卡\生产执行卡2024.xlsm",
-        "year": 2024,
-        "sheet_names": ["重庆数据","北京数据"],
-        "mapping": {
-            "车间号": "车间号",
-            "工令号": "工令号",
-            "客户型号": "客户型号"
-        }
-    },
-    {
-        "file_path": r"\\192.168.110.113\生产执行卡\生产执行卡2025年.xlsm",
-        "year": 2025,
-        "sheet_names": ["重庆数据","北京数据"],
-        "mapping": {
-            "车间号": "车间号",
-            "工令号": "工令号",
-            "客户型号": "客户型号"
-        }
-    },
-    {
-        "file_path": r"\\192.168.110.113\生产执行卡\生产执行卡2026年.xlsm",
-        "year": 2026,
-        "sheet_names": ["重庆数据","北京数据"],
-        "mapping": {
-            "车间号": "车间号",
-            "工令号": "工令号",
-            "客户型号": "客户型号"
-        }
-    }
-]
diff --git a/docs/superpowers/specs/2026-06-17-remove-excel-migration-design.md b/docs/superpowers/specs/2026-06-17-remove-excel-migration-design.md
new file mode 100644
index 0000000..0c0e64e
--- /dev/null
+++ b/docs/superpowers/specs/2026-06-17-remove-excel-migration-design.md
@@ -0,0 +1,168 @@
+# 移除计划：移除 Excel 迁移相关内容
+
+- **日期**: 2026-06-17
+- **状态**: 待审核（通过后执行）
+- **范围**: 仅代码仓库 `D:\python\BLD_sync`（不含 SQL Server 表数据）
+
+---
+
+## 1. 背景与目标
+
+项目不再处理 Excel 文档（`.xlsm` 生产执行卡）的迁移工作。后续仅保留 **Access → SQL Server** 的同步链路（全量 `init_full_sync.py` + 增量 `run_incremental_sync.py`）。
+
+目标：把所有"Excel 迁移"相关代码、配置、依赖与文档清理干净，使仓库成为一个干净、自洽的 **Access-only** 同步系统，不残留无用导入、死代码或过时说明。
+
+### 已确认的范围决策
+
+| 决策点 | 选择 |
+|---|---|
+| 清理彻底程度 | **彻底（Thorough）**：代码+配置+依赖+缓存目录+`CLAUDE.md`/文档修正 |
+| Excel 写入的 3 张 SQL 表 | **保留不动**（`warehouseOutbound.executionCardData` / `contractData` / `customerProductType`，含现有数据） |
+
+---
+
+## 2. 方案对比（已选定 Thorough）
+
+| 方案 | 内容 | 取舍 |
+|---|---|---|
+| A. 彻底清理 ✅ | 删脚本+配置+依赖+缓存，更新 CLAUDE.md/.env.example | 仓库自洽，无残留；改动面较大 |
+| B. 仅核心代码+配置 | 删脚本+配置+依赖+.env.example，留 CLAUDE.md/缓存/文档 | 改动小；但 CLAUDE.md 过时、缓存占盘 |
+| C. 仅代码，保留依赖 | 仅删脚本+配置符号，pandas/sqlalchemy/openpyxl 留作他用 | 最保守；依赖膨胀 |
+
+**选定 A（彻底）**：与"项目不再处理 Excel"的意图一致，避免后续误导。
+
+---
+
+## 3. 受影响资产清单（已核实）
+
+经全仓库（排除 `.venv`）grep 核实，以下符号/依赖**仅被 Excel 脚本使用**：
+
+- `pandas`、`sqlalchemy`、`openpyxl` → 仅 `excel_sync_to_sql.py`、`migration.py` 使用
+- `EXCEL_CONFIGS`、`MIGRATION_TASKS`、`TABLE_SCHEMA`、`CONTRACT_MAPPING`、`EXECUTION_CARD_FIELDS`、`CONTRACT_DATA_FIELDS`、`CONTRACT_DATA_MAPPING`、`CACHE_DIR`、`TEMP_DIR`、`EXCEL_SYNC_INTERVAL`、`EXCEL_SYNC_UPTIME_KUMA_CONFIG` → 仅被 Excel 脚本或其配置链使用
+- 保留脚本（`init_full_sync.py` / `run_incremental_sync.py` / `db_utils.py` / `ntfy_utils.py` / `log_utils.py` / `uptime_kuma_utils.py` / `check_drivers.py` / `archive_existing_logs.py` / `vbareplace.py` / `vba.txt`）**不依赖**上述任何 Excel 符号 → 删除后无悬空引用。
+
+---
+
+## 4. 详细变更清单
+
+### 4.1 整体删除（DELETE）
+
+| 路径 | 说明 |
+|---|---|
+| `excel_sync_to_sql.py` | Excel→SQL 主同步器（`DataSynchronizer`），写 `executionCardData`、生成 `contractData` |
+| `migration.py` | Excel→`warehouseOutbound.customerProductType` 迁移 |
+| `config/field_mappings.py` | 仅含 `TABLE_SCHEMA`、`CONTRACT_MAPPING`（均 Excel 专用）→ 整文件删除 |
+| `temp/`（未纳入 git，~180MB） | Excel 本地缓存（`.xlsm` + `sync_log.txt`）→ 删除目录 |
+
+> `tmp/` 为空且未被代码引用，保留不动。
+
+### 4.2 配置编辑（EDIT）
+
+#### `config/__init__.py`
+- 删除 `from .field_mappings import TABLE_SCHEMA, CONTRACT_MAPPING`（整行）
+- `from .file_sources import ...` 改为仅 `SYNC_MAPPING`
+- `from .app_settings import ...` 改为仅 `LOG_TABLE_CONFIG, NTFY_CONFIG, UPTIME_KUMA_CONFIG, POLL_INTERVAL, BATCH_SIZE`
+- `__all__` 移除：`EXCEL_CONFIGS`、`MIGRATION_TASKS`、`TABLE_SCHEMA`、`CONTRACT_MAPPING`、`EXCEL_SYNC_UPTIME_KUMA_CONFIG`、`EXCEL_SYNC_INTERVAL`、`CACHE_DIR`、`TEMP_DIR`、`EXECUTION_CARD_FIELDS`、`CONTRACT_DATA_FIELDS`、`CONTRACT_DATA_MAPPING`
+
+最终 `__init__.py` 导出：`SQL_SERVER_CONFIG, SQL_SERVER_CONN, DB_CONFIG, ACCESS_DRIVER, SYNC_MAPPING, LOG_TABLE_CONFIG, NTFY_CONFIG, UPTIME_KUMA_CONFIG, POLL_INTERVAL, BATCH_SIZE`
+
+#### `config/file_sources.py`
+- 删除 `EXCEL_CONFIGS`（含其上方注释 `# ================= Excel 文件配置 =================`）
+- 删除 `MIGRATION_TASKS`（含其上方注释 `# ================= 迁移任务配置 =================`）
+- 保留 `SYNC_MAPPING`（Access 映射，不动）
+
+#### `config/app_settings.py`
+- 删除 `EXCEL_SYNC_UPTIME_KUMA_CONFIG`
+- 删除 `EXCEL_SYNC_INTERVAL`
+- 删除 `CACHE_DIR`、`TEMP_DIR`（"运行参数"段仅留 `POLL_INTERVAL`、`BATCH_SIZE`）
+- 删除 `EXECUTION_CARD_FIELDS`、`CONTRACT_DATA_FIELDS`、`CONTRACT_DATA_MAPPING`（"字段配置"整段）
+- 保留 `LOG_TABLE_CONFIG`、`NTFY_CONFIG`、`UPTIME_KUMA_CONFIG`、`POLL_INTERVAL`、`BATCH_SIZE`
+- `import os` 保留（`NTFY_CONFIG`/`UPTIME_KUMA_CONFIG` 仍用 `os.environ.get`）
+
+### 4.3 依赖与环境（EDIT）
+
+#### `requirements.txt`
+移除：
+```
+pandas>=1.5.0
+sqlalchemy>=2.0.0
+openpyxl>=3.0.0
+```
+保留：`pyodbc`、`python-dotenv`、`requests`（仍被 ntfy/uptime/config 使用）
+
+#### `.env.example`
+移除行：`EXCEL_SYNC_UPTIME_KUMA_PUSH_URL=`
+
+### 4.4 文档修正（EDIT）
+
+#### `CLAUDE.md`
+当前 CLAUDE.md 已过时（引用了不存在的 `etl_manager.py`、`sync_excel_to_sql.py`、`update_config.py`、`config.py`）。借此一并修正为真实结构（`config/` 包 + 仅 Access 脚本）：
+- **Project Overview**：删除 "Excel files (.xlsm)..." 条目
+- **Data Flow / 架构图**：删除 Excel 源、删除 `etl_manager.py`/`sync_excel_to_sql.py`/`migration.py` 脚本框
+- **Key Components**：删除 `etl_manager.py`、`sync_excel_to_sql.py`；`config` 描述去掉 `EXCEL_CONFIGS`/`TABLE_SCHEMA`
+- **Common Tasks**：删除 "Run Excel to SQL Sync" 与 `migration.py` 小节
+- **Configuration Management**：删除 `update_config.py`/`EXCEL_CONFIGS`，改为描述 `config/` 包结构（`database.py`/`file_sources.py`/`app_settings.py`）
+- **Database Schema**：`warehouseOutbound` 描述更新（其表为历史 Excel 写入，现不再更新）
+- 删除所有 Excel 相关实现细节（MERGE 生成 contractData 等）
+
+#### 轻量文档串修正（彻底清理）
+- `uptime_kuma_utils.py` 顶部 docstring：删除"excel_sync_to_sql.py 等仍在使用"字样（向后兼容接口保留为通用工具方法，不删）
+- `log_utils.py` / `archive_existing_logs.py` docstring 中 `excel_sync_...` 文件名示例：可选移除（文件名解析是通用正则，功能不受影响）
+
+### 4.5 不在仓库内、需手动处理（仅提示，本计划不自动执行）
+
+| 项目 | 动作 |
+|---|---|
+| Windows 任务计划程序 | 若存在 `AutoRun-excel_sync` 之类计划任务，手动禁用/删除（保留 `AutoRun-init_full_sync`、`AutoRun-run_incremental_sync`） |
+| Uptime Kuma | 禁用/删除 Excel 同步心跳监控项（对应 `EXCEL_SYNC_UPTIME_KUMA_PUSH_URL`） |
+| `.env`（已 gitignore） | 手动删除其中的 `EXCEL_SYNC_UPTIME_KUMA_PUSH_URL=...` 行 |
+| SQL Server 表 | **按决策保留不动**，无需任何操作 |
+
+---
+
+## 5. 执行顺序
+
+1. **删除文件**：`excel_sync_to_sql.py`、`migration.py`、`config/field_mappings.py`、`temp/`
+2. **编辑配置**：`config/__init__.py` → `config/file_sources.py` → `config/app_settings.py`
+3. **编辑依赖/环境**：`requirements.txt`、`.env.example`
+4. **编辑文档**：`CLAUDE.md` + 上述轻量 docstring
+5. **验证**（见第 6 节）
+6. **提交**：单条 commit，英文信息（如 `refactor: remove Excel migration pipeline`），含删除/修改；提交后按全局规则推送到远端
+
+> 全程在 `.venv` 内执行（遵循全局 CLAUDE.md 协议）。
+
+---
+
+## 6. 验证计划
+
+执行后用仓库 `.venv` 的 Python 逐项核验：
+
+1. **配置包导入自洽**：
+   ```bash
+   .venv/Scripts/python -c "import config; print(config.SYNC_MAPPING is not None)"
+   ```
+2. **所有保留脚本语法编译通过**：
+   ```bash
+   .venv/Scripts/python -m py_compile init_full_sync.py run_incremental_sync.py db_utils.py ntfy_utils.py log_utils.py uptime_kuma_utils.py check_drivers.py archive_existing_logs.py vbareplace.py config/*.py
+   ```
+3. **无悬空引用**：grep 确认仓库（排除 `.venv`）不再出现已删符号：
+   `EXCEL_CONFIGS|MIGRATION_TASKS|TABLE_SCHEMA|CONTRACT_MAPPING|EXECUTION_CARD_FIELDS|CONTRACT_DATA_FIELDS|CONTRACT_DATA_MAPPING|CACHE_DIR|TEMP_DIR|EXCEL_SYNC_INTERVAL|EXCEL_SYNC_UPTIME_KUMA_CONFIG|read_excel|openpyxl`
+4. **增量/全量脚本可正常进入主流程**（可选冒烟）：分别运行 `run_incremental_sync.py`、`init_full_sync.py` 数秒后中断，确认无 ImportError、能连接 SQL Server。
+
+---
+
+## 7. 回滚
+
+全部变更均在 git 跟踪范围内（`temp/` 除外，但其为可再生缓存）。如需回滚：
+```bash
+git revert <commit-sha>
+```
+`temp/` 缓存可由历史 Excel 脚本重新生成（已无意义）。
+
+---
+
+## 8. 风险与说明
+
+- **无数据风险**：不动 SQL Server 任何表与数据。
+- **无运行中服务风险**：`run_incremental_sync.py`（Access 增量）代码路径不变，导入符号均保留。
+- **CLAUDE.md 改动较大**：因原文已与实际代码脱节，顺带修正为真实结构；如只希望"最小改动"可告知，仅删 Excel 段落、不补真实结构。
diff --git a/excel_sync_to_sql.py b/excel_sync_to_sql.py
deleted file mode 100644
index 33eb661..0000000
--- a/excel_sync_to_sql.py
+++ /dev/null
@@ -1,369 +0,0 @@
-import os
-import sys
-import shutil
-import logging
-import argparse
-import datetime
-import urllib.parse
-import warnings
-import time
-import pandas as pd
-import numpy as np
-from sqlalchemy import create_engine, text
-from sqlalchemy.engine import URL
-from sqlalchemy.types import NVARCHAR, Integer, Date
-
-# 导入配置
-from log_utils import (log_error, log_warning, log_info, log_processing, log_file, log_sync,
-                         log_start, log_complete, log_stop, LoggerManager)
-from config import (DB_CONFIG, CACHE_DIR, EXCEL_CONFIGS, BATCH_SIZE, TABLE_SCHEMA,
-                    EXCEL_SYNC_INTERVAL, EXCEL_SYNC_UPTIME_KUMA_CONFIG)
-from uptime_kuma_utils import UptimeKumaMonitor
-
-# 初始化 Uptime Kuma 监控器
-excel_uptime_monitor = UptimeKumaMonitor(EXCEL_SYNC_UPTIME_KUMA_CONFIG)
-excel_uptime_monitor.set_logger(log_warning)
-
-# ================= 抑制 openpyxl 的数据验证警告 =================
-warnings.filterwarnings('ignore', category=UserWarning, module='openpyxl')
-
-class DataSynchronizer:
-    def __init__(self, force_sync=False):
-        self.force_sync = force_sync
-        self.engine = self._get_db_connection()
-        self.cache_dir = CACHE_DIR
-        
-        if not os.path.exists(self.cache_dir):
-            os.makedirs(self.cache_dir)
-
-    def _get_db_connection(self):
-        connection_string = (
-            f"DRIVER={{{DB_CONFIG['driver']}}};"
-            f"SERVER={DB_CONFIG['server']};"
-            f"DATABASE={DB_CONFIG['database']};"
-            f"UID={DB_CONFIG['username']};"
-            f"PWD={DB_CONFIG['password']};"
-            f"TrustServerCertificate={DB_CONFIG.get('TrustServerCertificate', 'yes')};"
-        )
-        connection_url = URL.create("mssql+pyodbc", query={"odbc_connect": connection_string})
-        return create_engine(connection_url, fast_executemany=True)
-
-    def _should_process_file(self, remote_path, local_path):
-        if self.force_sync:
-            return True, "强制同步"
-        
-        if not os.path.exists(local_path):
-            return True, "缓存不存在"
-        
-        try:
-            remote_mtime = os.path.getmtime(remote_path)
-            local_mtime = os.path.getmtime(local_path)
-            if remote_mtime > local_mtime + 1:
-                return True, f"源文件更新"
-        except OSError as e:
-            log_error(f"无法访问源文件: {remote_path}, Error: {e}")
-            return False, "源文件无法访问"
-            
-        return False, "文件未变更"
-
-    def _clean_dataframe(self, df, contract_year):
-        """主表数据清洗与验证"""
-        # 1. 设置合同年份
-        df['合同年份'] = contract_year
-
-        # 2. 移除总排号为空的行
-        if '总排号' in df.columns:
-            df = df.dropna(subset=['总排号'])
-            df = df[df['总排号'].astype(str).str.strip() != '']
-        else:
-            log_error("数据源中找不到映射后的[总排号]列，跳过此 sheet")
-            return None, None
-
-        # ★ 新增：去除重复的总排号（保留第一条）
-        if '总排号' in df.columns:
-            df['总排号'] = df['总排号'].astype(str).str.strip()
-            duplicates = df[df.duplicated(subset=['总排号'], keep='first')]
-            if not duplicates.empty:
-                log_warning(f"发现 {len(duplicates)} 条重复的总排号，已自动去重。重复的总排号: {duplicates['总排号'].tolist()[:10]}")
-                df = df.drop_duplicates(subset=['总排号'], keep='first')
-
-        # 3. 补全列
-        for col in TABLE_SCHEMA.keys():
-            if col not in df.columns:
-                df[col] = None
-
-        # 用于存储每一列的 SQL 类型
-        dtype_dict = {}
-
-        # 4. 字段清洗
-        for col, rules in TABLE_SCHEMA.items():
-            if col not in df.columns: 
-                continue
-            
-            if rules['type'] == 'int':
-                # ★ 修改：先转换为数值，然后四舍五入到整数
-                df[col] = pd.to_numeric(df[col], errors='coerce')
-                # 将浮点数四舍五入为整数（处理如 123.5 这样的值）
-                df[col] = df[col].round(0)
-                # 转换为可空整数类型
-                df[col] = df[col].astype('Int64')
-                # 将 NaN 替换为 None
-                df[col] = df[col].replace({pd.NA: None})
-                dtype_dict[col] = Integer()
-                
-            elif rules['type'] == 'date':
-                df[col] = pd.to_datetime(df[col], errors='coerce')
-                df[col] = df[col].apply(lambda x: x.date() if pd.notnull(x) else None)
-                dtype_dict[col] = Date()
-                
-            elif rules['type'] == 'str':
-                # 先转换为字符串
-                df[col] = df[col].fillna('').astype(str)
-                # 替换各种空值表示
-                df[col] = df[col].replace({'nan': '', 'None': '', '<NA>': ''})
-                
-                # 强制截断
-                max_len = rules.get('max_len', 255)
-                df[col] = df[col].str.slice(0, max_len)
-                # 将空字符串转为 None
-                df[col] = df[col].replace('', None)
-                
-                dtype_dict[col] = NVARCHAR(max_len)
-
-        final_cols = list(TABLE_SCHEMA.keys())
-        
-        return df[final_cols], dtype_dict
-
-    def _sync_to_db(self, df, dtype_dict):
-        """同步主表数据 - 使用更稳健的方法"""
-        if df is None or df.empty:
-            return
-
-        target_table = "[warehouseOutbound].[executionCardData]"
-        
-        with self.engine.connect() as conn:
-            existing_ids = pd.read_sql(f"SELECT [总排号] FROM {target_table}", conn)
-        
-        existing_id_set = set(existing_ids['总排号'].astype(str))
-        df['总排号'] = df['总排号'].astype(str).str.strip()
-        
-        df_update = df[df['总排号'].isin(existing_id_set)].copy()
-        df_insert = df[~df['总排号'].isin(existing_id_set)].copy()
-
-        log_info(f"分析结果: 需插入 {len(df_insert)} 条, 需更新 {len(df_update)} 条")
-
-        # 1. 插入新数据
-        if not df_insert.empty:
-            log_info("正在执行批量插入...")
-            df_insert.to_sql('executionCardData', self.engine, schema='warehouseOutbound', 
-                             if_exists='append', index=False, chunksize=BATCH_SIZE,
-                             dtype=dtype_dict) 
-            log_info("批量插入完成。")
-
-        # 2. 更新现有数据 - 改用逐条或小批量 UPDATE
-        if not df_update.empty:
-            log_info("正在执行批量更新...")
-            
-            cols = [c for c in df.columns if c != '总排号']
-            set_clause = ", ".join([f"[{c}] = :{c}" for c in cols])
-            update_sql = f"""
-                UPDATE [warehouseOutbound].[executionCardData]
-                SET {set_clause}
-                WHERE [总排号] = :总排号
-            """
-            
-            with self.engine.begin() as conn:
-                batch_size = 1000
-                total_rows = len(df_update)
-                update_count = 0
-
-                for i in range(0, total_rows, batch_size):
-                    batch = df_update.iloc[i:i+batch_size]
-                    records = batch.to_dict('records')
-
-                    result = conn.execute(text(update_sql), records)
-                    update_count += result.rowcount
-
-                    if (i + batch_size) % 5000 == 0:
-                        log_info(f"已更新 {i + batch_size}/{total_rows} 条记录...")
-
-            # 修复 SQL Server executemany 返回负数 rowcount 的问题
-            affected_rows = abs(update_count) if update_count < 0 else total_rows
-            log_info(f"批量更新完成，共影响 {affected_rows} 行。")
-
-    def process_excel_files(self):
-        for cfg in EXCEL_CONFIGS:
-            remote_path = cfg['file_path']
-            filename = os.path.basename(remote_path)
-            local_path = os.path.join(self.cache_dir, filename)
-
-            should_sync, reason = self._should_process_file(remote_path, local_path)
-            
-            if should_sync:
-                log_info(f"开始处理文件: {filename} ({reason})")
-                try:
-                    # 复制文件到本地缓存（只复制一次）
-                    if os.path.exists(remote_path):
-                        shutil.copy2(remote_path, local_path)
-                    
-                    # 遍历该文件的所有指定 sheet
-                    for sheet_name in cfg['sheet_names']:
-                        log_info(f"  → 处理工作表: {sheet_name} (合同年份: {cfg['contract_year']})")
-                        try:
-                            df = pd.read_excel(local_path, sheet_name=sheet_name, header=0, engine='openpyxl')
-                            df.columns = [str(c).strip() for c in df.columns]
-                            df.rename(columns=cfg['field_mapping'], inplace=True)
-
-                            cleaned_df, dtype_mapping = self._clean_dataframe(df, cfg['contract_year'])
-
-                            if cleaned_df is not None:
-                                self._sync_to_db(cleaned_df, dtype_mapping)
-                                log_info(f"    工作表 {sheet_name} 同步成功。")
-                            else:
-                                log_warning(f"    工作表 {sheet_name} 清洗失败，跳过。")
-                        except Exception as e:
-                            log_error(f"    处理工作表 {sheet_name} 时发生错误: {str(e)}", exc_info=True)
-                    
-                    log_info(f"文件 {filename} 所有工作表处理完成。")
-                    
-                except Exception as e:
-                    log_error(f"处理文件 {filename} 时发生错误: {str(e)}", exc_info=True)
-            else:
-                log_info(f"跳过文件: {filename} ({reason})")
-
-    def generate_contract_data(self):
-        log_info("开始生成/更新 contractData 表...")
-        
-        merge_sql = """
-        WITH SourceData AS (
-            SELECT 
-                CAST(ISNULL([合同年份], '') AS NVARCHAR(10)) AS [合同年份],
-                CAST(ISNULL([车间号], '') AS NVARCHAR(20)) AS [车间号],
-                CAST(ISNULL([工令号], '') AS NVARCHAR(200)) AS [工令号],
-                CAST([订单号] AS NVARCHAR(150)) AS [订单号],
-                CAST([客户名称] AS NVARCHAR(200)) AS [客户名称],
-                CAST([产品名称] AS NVARCHAR(200)) AS [产品型号], 
-                CAST([量程] AS NVARCHAR(150)) AS [量程],
-                TRY_CAST([数量] AS INT) AS [数量],
-                CAST(NULL AS INT) AS [单价],
-                TRY_CAST([序号] AS INT) AS [ID],
-                CAST([位号] AS NVARCHAR(500)) AS [位号],
-                ROW_NUMBER() OVER (
-                    PARTITION BY [合同年份], [车间号], [工令号] 
-                    ORDER BY [总排号] DESC
-                ) as rn
-            FROM [warehouseOutbound].[executionCardData]
-            WHERE 
-                [车间号] IS NOT NULL AND [车间号] <> '' 
-                AND [工令号] IS NOT NULL AND [工令号] <> ''
-        )
-        
-        MERGE INTO [warehouseOutbound].[contractData] AS Target
-        USING (SELECT * FROM SourceData WHERE rn = 1) AS Source
-        ON (
-            Target.[合同年份] = Source.[合同年份] 
-            AND Target.[车间号] = Source.[车间号] 
-            AND Target.[工令号] = Source.[工令号]
-        )
-        
-        WHEN MATCHED THEN
-            UPDATE SET 
-                Target.[订单号] = Source.[订单号],
-                Target.[客户名称] = Source.[客户名称],
-                Target.[产品型号] = Source.[产品型号],
-                Target.[量程] = Source.[量程],
-                Target.[数量] = Source.[数量],
-                Target.[ID] = Source.[ID],
-                Target.[位号] = Source.[位号]
-                
-        WHEN NOT MATCHED BY TARGET THEN
-            INSERT (
-                [合同年份], [车间号], [工令号], 
-                [订单号], [客户名称], [产品型号], 
-                [量程], [数量], [单价], [ID], [位号]
-            )
-            VALUES (
-                Source.[合同年份], Source.[车间号], Source.[工令号], 
-                Source.[订单号], Source.[客户名称], Source.[产品型号], 
-                Source.[量程], Source.[数量], Source.[单价], Source.[ID], Source.[位号]
-            )
-        ;
-        """
-        
-        try:
-            with self.engine.begin() as conn:
-                result = conn.execute(text(merge_sql))
-                log_info(f"ContractData 表同步完成 (SQL Server 内部处理)。rowcount: {result.rowcount}")
-                
-        except Exception as e:
-            log_error(f"生成 ContractData 失败: {e}", exc_info=True)
-
-# ================= Uptime Kuma 心跳 =================
-# 使用 uptime_kuma_utils.UptimeKumaMonitor 替代原有实现
-
-def main():
-    # 初始化日志管理器
-    LoggerManager("excel_sync", log_prefix="excel_sync")
-
-    # 解析参数
-    parser = argparse.ArgumentParser(description="Excel数据同步至SQL Server")
-    parser.add_argument('--force', action='store_true', help='强制同步所有文件')
-    parser.add_argument('--once', action='store_true', help='只运行一次后退出')
-    args = parser.parse_args()
-
-    syncer = DataSynchronizer(force_sync=args.force)
-
-    # 启动信息
-    mode = "强制模式" if args.force else "增量模式"
-    if args.once:
-        log_start(f"Excel 同步任务 ({mode}, 单次运行)")
-        syncer.process_excel_files()
-        syncer.generate_contract_data()
-        log_complete("Excel 同步任务已完成")
-        return
-
-    # 周期性运行模式
-    log_start(f"Excel 同步服务已启动 ({mode})")
-    log_info(f"同步周期: {EXCEL_SYNC_INTERVAL} 秒 ({EXCEL_SYNC_INTERVAL//60} 分钟)")
-    if EXCEL_SYNC_UPTIME_KUMA_CONFIG.get('enabled', False):
-        log_info(f"心跳间隔: {EXCEL_SYNC_UPTIME_KUMA_CONFIG['heartbeat_interval']} 秒")
-    log_info("=" * 70)
-
-    # 启动时发送第一次心跳
-    excel_uptime_monitor.send_heartbeat()
-
-    try:
-        while True:
-            try:
-                # 执行同步任务
-                log_info(f"开始执行周期性同步检查...")
-                syncer.process_excel_files()
-                syncer.generate_contract_data()
-                log_info(f"周期性同步检查完成")
-
-                # 下次同步时间
-                next_sync_time = time.time() + EXCEL_SYNC_INTERVAL
-                log_info(f"下次同步将在 {EXCEL_SYNC_INTERVAL//60} 分钟后进行")
-
-                # 等待下次同步，期间持续发送心跳
-                while time.time() < next_sync_time:
-                    # 检查是否需要发送心跳
-                    excel_uptime_monitor.check_and_send_heartbeat()
-
-                    # 短暂休眠
-                    time.sleep(1)
-
-            except KeyboardInterrupt:
-                log_info("=" * 70)
-                log_stop("收到停止信号，服务正在关闭...")
-                break
-            except Exception as e:
-                log_error(f"同步任务异常: {e}", exc_info=True)
-                log_info(f"将在 {EXCEL_SYNC_INTERVAL//60} 分钟后重试...")
-                time.sleep(EXCEL_SYNC_INTERVAL)
-    finally:
-        # 停止时发送心跳停止信号
-        excel_uptime_monitor.send_stop_signal()
-
-if __name__ == "__main__":
-    main()
\ No newline at end of file
diff --git a/migration.py b/migration.py
deleted file mode 100644
index 51f92fd..0000000
--- a/migration.py
+++ /dev/null
@@ -1,171 +0,0 @@
-import pandas as pd
-import os
-import shutil
-import urllib
-from sqlalchemy import create_engine, text
-from config import DB_CONFIG, MIGRATION_TASKS, TEMP_DIR
-from log_utils import LoggerManager, log_start, log_skip, log_processing, log_success, log_warning, log_error, log_complete
-
-# 初始化日志管理器
-LoggerManager("migration", log_prefix="migration")
-
-# ==========================================
-# 1. 脚本配置 (Configuration)
-# ==========================================
-
-# 目标表配置
-TARGET_DB_SCHEMA = "warehouseOutbound"
-TARGET_TABLE_NAME = "customerProductType"
-SQL_SOURCE_FILE_COL = "SourceFile"  # 你在SQL中新增的字段名
-
-# 字段映射常量
-SQL_COL_YEAR = "合同年份"
-SQL_COL_WORKSHOP = "车间号"
-SQL_COL_ORDER = "工令号"
-SQL_COL_MODEL = "客户型号"
-
-# 运行参数
-FORCE_UPDATE = False  # 如果设为 True，则无视时间对比，强制更新所有文件
-
-# ==========================================
-# 2. 核心辅助函数
-# ==========================================
-
-def get_db_engine():
-    params = urllib.parse.quote_plus(
-        f"DRIVER={{{DB_CONFIG['driver']}}};"
-        f"SERVER={DB_CONFIG['server']};"
-        f"DATABASE={DB_CONFIG['database']};"
-        f"UID={DB_CONFIG['username']};"
-        f"PWD={DB_CONFIG['password']};"
-        f"TrustServerCertificate=yes;"
-    )
-    # fast_executemany 极大提高写入速度
-    return create_engine(f"mssql+pyodbc:///?odbc_connect={params}", fast_executemany=True)
-
-def get_file_mtime(path):
-    """获取文件最后修改时间戳"""
-    try:
-        return os.path.getmtime(path)
-    except OSError:
-        return 0
-
-def delete_old_data(engine, filename):
-    """根据 SourceFile 字段精确删除旧数据"""
-    full_table = f"[{TARGET_DB_SCHEMA}].[{TARGET_TABLE_NAME}]"
-    sql = text(f"DELETE FROM {full_table} WHERE [{SQL_SOURCE_FILE_COL}] = :fname")
-    with engine.begin() as conn:
-        conn.execute(sql, {"fname": filename})
-
-# ==========================================
-# 3. 迁移主逻辑
-# ==========================================
-
-def run_migration():
-    # 初始化环境
-    if not os.path.exists(TEMP_DIR):
-        os.makedirs(TEMP_DIR)
-    
-    engine = get_db_engine()
-    sync_count = 0
-    error_count = 0
-
-    log_start(f"增量同步任务 (强制更新={FORCE_UPDATE})")
-
-    for task in MIGRATION_TASKS:
-        remote_path = task['file_path']
-        filename = os.path.basename(remote_path)
-        local_path = os.path.join(TEMP_DIR, filename)
-        
-        # 1. 检查源文件
-        if not os.path.exists(remote_path):
-            msg = f"远程文件未找到: {remote_path}"
-            log_error(msg)
-            continue
-
-        # 2. 增量判定
-        remote_mtime = get_file_mtime(remote_path)
-        local_mtime = get_file_mtime(local_path)
-
-        if not FORCE_UPDATE and os.path.exists(local_path) and remote_mtime <= local_mtime:
-            log_skip(f"{filename} (文件未变更)")
-            continue
-
-        log_processing(f"正在处理: {filename} ...")
-
-        try:
-            # 3. 复制文件到本地 temp
-            shutil.copy2(remote_path, local_path)
-            
-            # 4. 读取 Excel
-            xls_dict = pd.read_excel(local_path, sheet_name=task['sheet_names'])
-            if not isinstance(xls_dict, dict):
-                xls_dict = {task['sheet_names'][0]: xls_dict}
-
-            # 准备存放该文件所有 Sheet 的合并数据
-            df_all_sheets = []
-
-            for sheet_name, df in xls_dict.items():
-                if df.empty: continue
-                
-                # 清洗与过滤
-                df.columns = df.columns.astype(str).str.strip()
-                source_cols = list(task['mapping'].keys())
-                
-                missing = [c for c in source_cols if c not in df.columns]
-                if missing:
-                    log_warning(f"Sheet[{sheet_name}] 缺失列: {missing}")
-                    continue
-
-                # 提取并重命名
-                df_subset = df[source_cols].copy()
-                df_subset.rename(columns=task['mapping'], inplace=True)
-                
-                # 注入年份和来源文件名
-                df_subset[SQL_COL_YEAR] = task['year']
-                df_subset[SQL_SOURCE_FILE_COL] = filename # 存入文件名，用于下次精准删除
-                
-                # 数据清洗
-                subset_keys = [SQL_COL_YEAR, SQL_COL_WORKSHOP, SQL_COL_ORDER]
-                df_subset.dropna(subset=subset_keys, inplace=True)
-                df_subset.drop_duplicates(subset=subset_keys, keep='first', inplace=True)
-                
-                if not df_subset.empty:
-                    df_all_sheets.append(df_subset)
-
-            # 5. 写入数据库
-            if df_all_sheets:
-                final_df = pd.concat(df_all_sheets, ignore_index=True)
-                
-                # 执行删除并插入 (事务)
-                with engine.begin() as conn:
-                    # A. 删除旧记录
-                    delete_sql = text(f"DELETE FROM [{TARGET_DB_SCHEMA}].[{TARGET_TABLE_NAME}] WHERE [{SQL_SOURCE_FILE_COL}] = :fname")
-                    conn.execute(delete_sql, {"fname": filename})
-                    
-                    # B. 插入新记录
-                    final_df.to_sql(
-                        name=TARGET_TABLE_NAME,
-                        schema=TARGET_DB_SCHEMA,
-                        con=conn,
-                        if_exists='append',
-                        index=False,
-                        chunksize=1000
-                    )
-                
-                log_success(f"成功同步: {len(final_df)} 行记录")
-                sync_count += 1
-            else:
-                log_warning("文件内容为空或格式不符")
-
-        except Exception as e:
-            error_msg = f"文件 [{filename}] 处理失败: {str(e)}"
-            log_error(error_msg)
-            error_count += 1
-
-    # 结束汇总
-    summary = f"同步完成: 成功 {sync_count} 个文件, 失败 {error_count} 个文件。"
-    log_complete(f"同步完成: 成功 {sync_count} 个文件, 失败 {error_count} 个文件")
-
-if __name__ == "__main__":
-    run_migration()
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index 357c790..a4a5f13 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,6 +1,3 @@
 pyodbc>=5.0.0
-pandas>=1.5.0
-sqlalchemy>=2.0.0
-openpyxl>=3.0.0
 python-dotenv>=1.0.0
 requests>=2.28.0
\ No newline at end of file
diff --git a/uptime_kuma_utils.py b/uptime_kuma_utils.py
index 9f79c78..c506b6c 100644
--- a/uptime_kuma_utils.py
+++ b/uptime_kuma_utils.py
@@ -8,7 +8,7 @@ Uptime Kuma 心跳监控工具
   心跳的网络耗时 / 重试不会阻塞业务逻辑。
 - 单次心跳自带重试 (MAX_RETRIES), 容忍 Uptime Kuma 服务偶发的慢响应 / 超时 / 4xx。
 - send_heartbeat() / check_and_send_heartbeat() / send_stop_signal() 保留为
-  向后兼容的同步接口 (excel_sync_to_sql.py 等仍在使用)。
+  向后兼容的同步接口。
 """
 
 import time