refactor: remove Excel migration pipeline; keep Access-only sync
Drop the Excel (.xlsm) production-execution-card pipeline now that the project only synchronizes Access databases to SQL Server. - Delete excel_sync_to_sql.py, migration.py, config/field_mappings.py - Remove Excel-only config symbols (EXCEL_CONFIGS, MIGRATION_TASKS, EXECUTION_CARD_FIELDS, CONTRACT_DATA_*, CACHE_DIR, TEMP_DIR, EXCEL_SYNC_* settings) from the config package - Drop now-unused deps from requirements.txt: pandas, sqlalchemy, openpyxl - Update .env.example, CLAUDE.md, and the uptime_kuma_utils docstring - Fix the tube-bending workshop Access table mapping in SYNC_MAPPING (source table renamed; old name no longer exists) The three Excel-sourced tables in warehouseOutbound (executionCardData, contractData, customerProductType) and their data are left untouched. Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Misaka_Company
@@ -12,4 +12,3 @@ NTFY_TOKEN=
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# Uptime Kuma
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UPTIME_KUMA_PUSH_URL=
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EXCEL_SYNC_UPTIME_KUMA_PUSH_URL=
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125
CLAUDE.md
125
CLAUDE.md
@@ -6,7 +6,6 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
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This is a data synchronization system (BLD_sync) that extracts data from:
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- **Microsoft Access databases** (.accdb) located on network shares
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- **Excel files** (.xlsm) containing production execution cards and contract data
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And synchronizes it to:
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- **Microsoft SQL Server** (CompanyDB) with multiple schemas organized by data type
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@@ -21,15 +20,11 @@ The system supports both full initialization sync and incremental sync driven by
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┌─────────────────────────────────────────────────────────────────────┐
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│ Network File Sources │
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│ Access DBs (\\192.168.110.114\生产进度表\) │
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│ Excel Files (\\192.168.110.113\生产执行卡\) │
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└─────────────────────────────────────────────────────────────────────┘
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│
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▼
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┌─────────────────────────────────────────────────────────────────────┐
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│ Sync Scripts │
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│ • etl_manager.py - Main ETL orchestrator (primary) │
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│ • sync_excel_to_sql.py - Excel to SQL sync (legacy/alternative) │
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│ • migration.py - Legacy customer product type migration │
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│ • run_incremental_sync.py - Change log driven incremental sync │
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│ • init_full_sync.py - Full table truncation/reload │
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└─────────────────────────────────────────────────────────────────────┘
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@@ -43,36 +38,18 @@ The system supports both full initialization sync and incremental sync driven by
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└─────────────────────────────────────────────────────────────────────┘
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```
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> Note: The system previously also synced Excel `.xlsm` production-execution-card files into
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> the `warehouseOutbound` schema (`executionCardData`, `contractData`, `customerProductType`).
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> That Excel pipeline has been removed; the existing tables and their data remain in CompanyDB
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> but are no longer updated.
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### Key Components
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**etl_manager.py** - Primary ETL orchestrator
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- `DataSynchronizer` class manages Excel to SQL sync
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- Supports incremental sync via file modification time comparison
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- Caches Excel files locally in `excel_cache/` directory
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- Uses SQLAlchemy with `fast_executemany=True` for bulk operations
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- Generates `contractData` table via MERGE statement from `executionCardData`
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**config.py** - Central configuration
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- `SYNC_MAPPING`: Nested dict mapping Access files → tables → SQL targets
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- `EXCEL_CONFIGS`: List of Excel file configs with sheet names and field mappings
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- `TABLE_SCHEMA`: Column type definitions for data cleaning
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- `NTFY_CONFIG`: Push notification settings
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- `LOG_TABLE_CONFIG`: Change log table schema for incremental sync
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**db_utils.py** - Database connection utilities
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- `get_sql_conn()`: SQL Server connection via pyodbc
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- `get_access_conn()`: Access database connection
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- `fmt_table()`: Safe table name formatting `[schema].[table]`
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- `generate_insert_sql()`: Dynamic INSERT statement generation
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**ntfy_utils.py** - Push notifications
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- Sends alerts to ntfy server on errors/completion
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- Uses Bearer token authentication
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**run_incremental_sync.py** - Log-driven incremental sync
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- Polls `TableChangeLog` table for unsynced records (`Synced=0`)
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- Queries by `TableAddress` to match configured file paths
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- Processes deletions and insertions in batches
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- Queries by `TableAddress` to match configured Access file paths
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- Connects to the source Access DB, reads latest rows by primary key, then DELETE + INSERT
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- Per-batch verification (delete/insert checks by primary key) before marking `Synced=1`
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- Long-running service with configurable polling interval
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**init_full_sync.py** - Full table reload
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@@ -80,6 +57,33 @@ The system supports both full initialization sync and incremental sync driven by
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- Handles IDENTITY_INSERT ON/OFF for tables with identity columns
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- Progress logging with row counts and throughput metrics
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**config/** - Configuration package
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- `database.py`: SQL Server / Access driver settings (read from env via `python-dotenv`)
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- `file_sources.py`: `SYNC_MAPPING` — nested dict mapping Access files → tables → SQL targets
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- `app_settings.py`: `LOG_TABLE_CONFIG`, `NTFY_CONFIG`, `UPTIME_KUMA_CONFIG`, `POLL_INTERVAL`, `BATCH_SIZE`
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- Symbols are re-exported from `config/__init__.py` (e.g. `from config import SYNC_MAPPING`)
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**db_utils.py** - Database connection utilities
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- `get_sql_conn()`: SQL Server connection via pyodbc
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- `get_access_conn()`: Access database connection
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- `fmt_table()`: Safe table name formatting `[schema].[table]`
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- `generate_insert_sql()`: Dynamic INSERT statement generation
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- `create_table_from_access()`: Auto-create SQL Server table from Access `cursor.description`
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**ntfy_utils.py** - Push notifications
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- Sends alerts to ntfy server on errors/completion
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- Uses Bearer token authentication
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**uptime_kuma_utils.py** - Heartbeat monitoring
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- `UptimeKumaMonitor` pushes heartbeats to Uptime Kuma; used by the incremental sync service
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**log_utils.py** - Unified logging
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- `LoggerManager` creates a timestamped log file under `log/` and archives old logs by year-month
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**vbareplace.py** / **vba.txt** - Access VBA helper tool
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- Standalone drag-and-drop tool that injects the change-log VBA macro into Access forms and
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refreshes linked tables (packaged via PyInstaller; unrelated to the Python sync scripts)
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## Common Development Tasks
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### Install Dependencies
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@@ -94,60 +98,35 @@ pip install -r requirements.txt
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python init_full_sync.py
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```
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### Run Excel to SQL Sync (Standard)
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```bash
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python etl_manager.py
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```
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Force sync all files (ignore modification times):
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```bash
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python etl_manager.py --force
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```
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### Run Incremental Sync Service (Change Log Driven)
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```bash
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python run_incremental_sync.py
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```
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### Run Legacy Migration Script
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```bash
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python migration.py
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```
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Edit `FORCE_UPDATE = True` in `migration.py` to force full refresh.
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## Configuration Management
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**Main Config File**: `config.py`
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- Contains all database credentials, file paths, and mapping configurations
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- Modify `SYNC_MAPPING` to add new Access files/tables
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- Modify `EXCEL_CONFIGS` for new Excel sources
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**Alternative Config**: `update_config.py`
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- Used by `sync_excel_to_sql.py`
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- Similar structure but different variable names
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- Contains `EXECUTION_CARD_FIELDS`, `CONTRACT_DATA_MAPPING`
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Configuration lives in the **`config/` package** (re-exported via `config/__init__.py`):
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- Database credentials and driver strings come from environment variables (see `.env.example`),
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loaded by `python-dotenv`.
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- Modify `SYNC_MAPPING` in `config/file_sources.py` to add new Access files/tables.
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## Important Implementation Details
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### SQL Server Connection
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- Uses ODBC Driver 18 for SQL Server
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- Requires `TrustServerCertificate=yes` due to self-signed cert
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- SQLAlchemy URL: `mssql+pyodbc:///?odbc_connect=...`
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- Always enable `fast_executemany=True` for bulk operations
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- Direct pyodbc connections (no SQLAlchemy); `fast_executemany=True` for bulk inserts
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### Access Database Connection
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- Driver: `{Microsoft Access Driver (*.mdb, *.accdb)}`
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- Direct file path connection via pyodbc
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### Data Cleaning
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- Integer fields: `pd.to_numeric().round().astype('Int64')`
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- String fields: Truncate to max length, replace empty with None
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- Date fields: Convert to `date()` objects, None for NaT
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- Duplicate removal: Based on primary key (usually `ID` or `总排号`)
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### Type Mapping / Auto Table Creation
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- When a SQL Server target table does not exist, it is created from the Access `cursor.description`.
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- Access types map to SQL Server types: `int`→`INT` (PK → `IDENTITY(1,1) PRIMARY KEY`),
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`float`→`FLOAT`, `bool`→`BIT`, `datetime`→`DATETIME`, `Decimal`→`DECIMAL(p,s)`,
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`str`→`NVARCHAR(size)` (size > 4000 → `NVARCHAR(MAX)`).
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### Identity Column Handling
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Tables with identity columns require:
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@@ -157,7 +136,7 @@ SET IDENTITY_INSERT [schema].[table] ON
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SET IDENTITY_INSERT [schema].[table] OFF
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```
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See `has_identity_column()` in `init_full_sync.py` for detection logic.
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See `has_identity_column()` in `init_full_sync.py` / `run_incremental_sync.py` for detection logic.
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### File Path Matching in Incremental Sync
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The change log table stores paths in VBA format:
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@@ -169,7 +148,6 @@ The code constructs multiple match patterns for robust matching.
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## Database Schema Organization
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SQL Server schemas by function:
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- `warehouseOutbound` - Execution card data, contract data, customer product types
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- `productionContractData` - Contract data by year (25年/26年压力表/温度计)
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- `productWarehousing` - Finished product inspection/warehousing records
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- `workshopOne/Two/Three` - Workshop production records
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@@ -181,16 +159,21 @@ SQL Server schemas by function:
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- `solderingData` - Soldering operation records
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- `TIGWelding` - TIG welding records
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- `executionCardIssuanceRecord` - Execution card issuance records
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- `tubeBending` - Tube bending records
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- `warehouseOutbound` - (Legacy) Excel-sourced tables: `executionCardData`, `contractData`,
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`customerProductType` — no longer updated
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## Notifications
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The system uses [ntfy](https://ntfy.sh/) for push notifications:
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- Configured in `NTFY_CONFIG` within `config.py`
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- Configured in `NTFY_CONFIG` within `config/app_settings.py`
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- Sends on: errors, critical failures, task completion
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- Authenticated via Bearer token
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## Logging
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- File logs: `log/` directory with timestamp rotation
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- File logs: `log/` directory, one timestamped file per run (`<prefix>_<YYYYMMDD>_<HHMMSS>.log`)
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- Console output: With emoji prefixes for status (✅ ❌ ⚠️ 🔄)
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- Incremental sync: Uses `TimedRotatingFileHandler` for daily log files
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- Old logs are archived into `log/Archive/YYYY-MM/` automatically
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(see `LoggerManager.archive_old_logs` in `log_utils.py`; `archive_existing_logs.py` is a
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one-time helper for historical logs)
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@@ -7,17 +7,13 @@ load_dotenv()
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# 数据库配置
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from .database import SQL_SERVER_CONFIG, SQL_SERVER_CONN, DB_CONFIG, ACCESS_DRIVER
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# 文件路径配置
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from .file_sources import SYNC_MAPPING, EXCEL_CONFIGS, MIGRATION_TASKS
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# 字段映射配置
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from .field_mappings import TABLE_SCHEMA, CONTRACT_MAPPING
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# 文件路径配置（Access 同步映射）
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from .file_sources import SYNC_MAPPING
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# 应用设置
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from .app_settings import (
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LOG_TABLE_CONFIG, NTFY_CONFIG, UPTIME_KUMA_CONFIG, EXCEL_SYNC_UPTIME_KUMA_CONFIG,
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POLL_INTERVAL, EXCEL_SYNC_INTERVAL, BATCH_SIZE, CACHE_DIR, TEMP_DIR,
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EXECUTION_CARD_FIELDS, CONTRACT_DATA_FIELDS, CONTRACT_DATA_MAPPING
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LOG_TABLE_CONFIG, NTFY_CONFIG, UPTIME_KUMA_CONFIG,
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POLL_INTERVAL, BATCH_SIZE,
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)
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# 统一导出列表
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@@ -25,11 +21,8 @@ __all__ = [
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# Database
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'SQL_SERVER_CONFIG', 'SQL_SERVER_CONN', 'DB_CONFIG', 'ACCESS_DRIVER',
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# File Sources
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'SYNC_MAPPING', 'EXCEL_CONFIGS', 'MIGRATION_TASKS',
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# Field Mappings
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'TABLE_SCHEMA', 'CONTRACT_MAPPING',
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'SYNC_MAPPING',
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# App Settings
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'LOG_TABLE_CONFIG', 'NTFY_CONFIG', 'UPTIME_KUMA_CONFIG', 'EXCEL_SYNC_UPTIME_KUMA_CONFIG',
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'POLL_INTERVAL', 'EXCEL_SYNC_INTERVAL', 'BATCH_SIZE', 'CACHE_DIR', 'TEMP_DIR',
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'EXECUTION_CARD_FIELDS', 'CONTRACT_DATA_FIELDS', 'CONTRACT_DATA_MAPPING'
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'LOG_TABLE_CONFIG', 'NTFY_CONFIG', 'UPTIME_KUMA_CONFIG',
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'POLL_INTERVAL', 'BATCH_SIZE',
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]
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@@ -36,96 +36,6 @@ UPTIME_KUMA_CONFIG = {
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'heartbeat_interval': 59 # 心跳间隔(秒)，需要与 Uptime Kuma 设置一致
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}
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# Excel 同步服务心跳
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EXCEL_SYNC_UPTIME_KUMA_CONFIG = {
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'enabled': True,
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'push_url': os.environ.get('EXCEL_SYNC_UPTIME_KUMA_PUSH_URL', ''),
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'heartbeat_interval': 59 # 心跳间隔(秒)，需要与 Uptime Kuma 设置一致
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}
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# ================= 运行参数 =================
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# 合并原 config.py 和 update_config.py 的配置
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POLL_INTERVAL = 30 # 轮询间隔(秒)
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EXCEL_SYNC_INTERVAL = 600 # Excel 同步周期(秒)，默认 10 分钟
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BATCH_SIZE = 10000 # 批量处理大小
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CACHE_DIR = os.path.join(os.getcwd(), "temp") # Excel 缓存目录
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TEMP_DIR = os.path.join(os.getcwd(), "temp") # 临时目录（兼容 migration.py）
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# ================= 字段配置 =================
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# 从原 update_config.py (sync_excel_to_sql.py) 抽取
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EXECUTION_CARD_FIELDS = {
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"合同年份": ("str", 10),
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"总排号": ("str", 50),
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"序号": ("int", None),
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"订单号": ("str", 150),
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"车间号": ("str", 20),
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"销售内部号": ("str", 50),
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"经办人": ("str", 20),
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"签订日期": ("date", None),
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"交货日期": ("date", None),
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"客户名称": ("str", 200),
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"产品名称": ("str", 200),
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"客户型号": ("str", 200),
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"选型型号": ("str", 200),
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"量程": ("str", 150),
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"数量": ("int", None),
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"备注2": ("str", 500),
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"备注1": ("str", 500),
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"位号": ("str", 500),
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"技术参数": ("str", 1000),
|
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"车间": ("str", 200),
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"工令号": ("str", 200),
|
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"接单日期": ("date", None),
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"新参数": ("str", 1000),
|
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"基本型号": ("str", 200),
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"公称外径": ("str", 200),
|
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"安装代码": ("str", 200),
|
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"设计形式": ("str", 200),
|
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"技术安装代码": ("str", 200),
|
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"隔膜类型": ("str", 200),
|
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"标准": ("str", 100),
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"隔膜大小": ("str", 200),
|
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"隔膜材质": ("str", 200),
|
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"膜片": ("str", 200),
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"膜片材质": ("str", 200),
|
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"CRM明细ID号": ("str", 200),
|
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"成品物料编码": ("str", 200),
|
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"工单号": ("str", 200),
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"盘号": ("str", 200),
|
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"编码": ("str", 200),
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"型批名称": ("str", 200),
|
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"型批型号": ("str", 200),
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"开票名称": ("str", 200),
|
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"开票型号": ("str", 200),
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"上数据时间": ("date", None),
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"生产代码1": ("str", 50),
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"生产代码2": ("str", 50)
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}
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|
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CONTRACT_DATA_FIELDS = {
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"合同年份": ("str", 10),
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"车间号": ("str", 20),
|
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"工令号": ("str", 200),
|
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"订单号": ("str", 150),
|
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"客户名称": ("str", 200),
|
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"产品型号": ("str", 200),
|
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"量程": ("str", 150),
|
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"数量": ("int", None),
|
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"单价": ("int", None),
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"ID": ("int", None),
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"位号": ("str", 500)
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}
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CONTRACT_DATA_MAPPING = {
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"合同年份": "合同年份",
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"车间号": "车间号",
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"工令号": "工令号",
|
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"订单号": "订单号",
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"客户名称": "客户名称",
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"产品型号": "选型型号",
|
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"量程": "量程",
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"数量": "数量",
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"单价": None,
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"ID": None,
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"位号": "位号"
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}
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@@ -1,73 +0,0 @@
|
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# config/field_mappings.py
|
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# 字段映射和清洗规则配置
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|
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import os
|
||||
from config.app_settings import EXECUTION_CARD_FIELDS
|
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|
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# ================= 字段清洗规则 =================
|
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# 从原 update_config.py 抽取
|
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# 基于 NVARCHAR (按字符数计算长度)
|
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TABLE_SCHEMA = {
|
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"合同年份": {"type": "str", "max_len": 10},
|
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"总排号": {"type": "str", "max_len": 50},
|
||||
"序号": {"type": "int"},
|
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"订单号": {"type": "str", "max_len": 150},
|
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"车间号": {"type": "str", "max_len": 20},
|
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"销售内部号": {"type": "str", "max_len": 50},
|
||||
"经办人": {"type": "str", "max_len": 20},
|
||||
"签订日期": {"type": "date"},
|
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"交货日期": {"type": "date"},
|
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"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"
|
||||
}
|
||||
@@ -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"
|
||||
}
|
||||
}
|
||||
]
|
||||
|
||||
@@ -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。
|
||||
@@ -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()
|
||||
171
migration.py
171
migration.py
@@ -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()
|
||||
@@ -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
|
||||
@@ -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
|
||||
|
||||
Reference in New Issue
Block a user