Migrate ExcelConverter from Python to TypeScript

Replace pandas+openpyxl with exceljs for Excel file processing. This
migration enables seamless integration with the Electron main process
and maintains 1:1 functional parity with the Python implementation.

Key changes:
- Add exceljs dependency (v4.4.0)
- Implement ExcelConverter class in TypeScript with strict types
- Add comprehensive unit tests (13 test cases, all passing)
- Support field name mapping, order parsing, and material extraction
- Handle empty data tables and file conflicts gracefully

Verification:
- Successfully tested with sample Excel file (99 orders, 625 records)
- All unit tests passing
- Output format matches Python version

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

docs/excel-converter-migration.md

@@ -0,0 +1,106 @@
+# ExcelConverter Migration Summary
+
+## Task Completed: ExcelConverter Migration from Python to TypeScript
+
+Successfully migrated `playwrite/utils/excel_converter.py` to `src/main/utils/excelConverter.ts`.
+
+## Files Created/Modified
+
+### 1. ✅ `src/main/utils/excelConverter.ts` - Main Converter Class
+- Replaced `pandas` + `openpyxl` with `exceljs`
+- Preserved all parsing logic and data transformation
+- Implemented strict TypeScript types and interfaces
+- Followed project patterns matching `authService.ts`
+
+### 2. ✅ `tests/unit/excelConverter.test.ts` - Unit Tests
+- 13 comprehensive unit tests covering:
+  - Header row parsing logic
+  - Field name mapping
+  - Material extraction
+  - Empty data handling
+  - Multiple orders parsing
+  - Footer information handling
+  - Data conversion to records
+  - File output handling
+- All tests passing ✅
+
+### 3. ✅ `package.json` - Dependency Added
+- Added `exceljs`: ^4.4.0
+
+## Implementation Details
+
+### Core Methods Implemented
+
+| Python Method | TypeScript Method | Purpose |
+|--------------|-------------------|---------|
+| `convert(input_file, output_file)` | `async convert(inputPath: string, options?: ConverterOptions): Promise<ConverterResult>` | Main conversion entry |
+| `_parse_sheet(ws)` | `private parseSheet(worksheet: Worksheet): OrderData[]` | Parse worksheet into orders |
+| `_parse_header_row(row, info)` | `private parseHeaderRow(row: any[], info: Record<string, string>): void` | Extract field-value pairs |
+| `_convert_to_dataframe(orders)` | `private convertToRecords(orders: OrderData[]): MaterialRow[]` | Flatten to array |
+| `_handle_output_file(path)` | `private handleOutputFile(path: string): string` | Handle file conflicts |
+
+### Key Features
+
+1. **Field Name Mapping** - Resolves field name conflicts (计划数量 → 产品计划数量)
+2. **Order Parsing** - Extracts order info and materials from complex Excel layouts
+3. **Empty Data Handling** - Correctly handles orders with no material data
+4. **Footer Information** - Captures 制单人/打印人 information
+5. **File Conflict Handling** - Handles locked files by appending "_new" suffix
+
+## Verification
+
+### Unit Tests
+```bash
+npm test -- tests/unit/excelConverter.test.ts
+```
+**Result**: ✅ 13/13 tests passing
+
+### Manual Testing
+Tested with actual sample file `references/samples/离散备料计划数据样例.xlsx`:
+- **Orders processed**: 99
+- **Records extracted**: 625
+- **Output**: Successfully converted to Excel format
+
+## Success Criteria
+
+1. ✅ All tests pass (13/13 unit tests)
+2. ✅ Output matches Python version (verified with sample data)
+3. ✅ Code follows project conventions (matches `authService.ts` pattern)
+4. ✅ Proper TypeScript types with strict interfaces
+5. ✅ Proper error handling and logging (verbose mode)
+
+## Technical Notes
+
+### Excel Cell Reading Pattern
+```typescript
+// ExcelJS row.values[0] is undefined, actual data starts at index 1
+const cellValue = row.values[1]; // First column of actual data
+```
+
+### Row Iteration
+```typescript
+const allRows: any[][] = [];
+worksheet.eachRow((row, rowNumber) => {
+  allRows.push(row.values as any[]);
+});
+```
+
+### Field Parsing Logic
+The header rows contain field-value pairs separated by "：" (colon). The parser correctly handles:
+- Field names with colons
+- Empty cells between field names and values
+- Multiple field-value pairs per row
+
+## Integration Points
+
+The ExcelConverter can now be used in:
+- Main process services for Excel file processing
+- Batch conversion workflows
+- Data import/export functionality
+
+## Next Steps
+
+The ExcelConverter is ready for integration into the main application workflow. It can be called from:
+- IPC handlers for renderer process requests
+- Background data processing tasks
+- File system watchers for automatic conversion