feat: 全量功能模块开发与集成测试修复
- 新增后端模块：Alert、APIAsset、Compliance、Lineage、Masking、Risk、SchemaChange、Unstructured、Watermark - 新增前端模块页面与API接口 - 新增Alembic迁移脚本(002-014)覆盖全量业务表 - 新增测试数据生成脚本与集成测试脚本 - 修复metadata模型JSON类型导入缺失导致启动失败的问题 - 修复前端Alert/APIAsset页面request模块路径错误 - 更新docker-compose与开发计划文档
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hiderfong
@@ -94,3 +94,155 @@ def generate_classification_report(db: Session, project_id: int) -> bytes:
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doc.save(buffer)
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buffer.seek(0)
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return buffer.read()
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def generate_excel_report(db: Session, project_id: int) -> bytes:
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"""Generate an Excel report for a classification project."""
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from openpyxl import Workbook
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from openpyxl.styles import Font, PatternFill, Alignment, Border, Side
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from openpyxl.chart import PieChart, Reference
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from sqlalchemy import func
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project = db.query(ClassificationProject).filter(ClassificationProject.id == project_id).first()
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if not project:
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raise ValueError("项目不存在")
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wb = Workbook()
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ws = wb.active
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ws.title = "报告概览"
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# Title
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ws.merge_cells('A1:D1')
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ws['A1'] = '数据分类分级项目报告'
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ws['A1'].font = Font(size=18, bold=True)
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ws['A1'].alignment = Alignment(horizontal='center')
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# Basic info
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ws['A3'] = '项目名称'
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ws['B3'] = project.name
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ws['A4'] = '报告生成时间'
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ws['B4'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
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ws['A5'] = '项目状态'
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ws['B5'] = project.status
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ws['A6'] = '模板版本'
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ws['B6'] = project.template.version if project.template else 'N/A'
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# Statistics
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results = db.query(ClassificationResult).filter(ClassificationResult.project_id == project_id).all()
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total = len(results)
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auto_count = sum(1 for r in results if r.source == 'auto')
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manual_count = sum(1 for r in results if r.source == 'manual')
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ws['A8'] = '总字段数'
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ws['B8'] = total
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ws['A9'] = '自动识别'
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ws['B9'] = auto_count
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ws['A10'] = '人工打标'
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ws['B10'] = manual_count
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# Level distribution
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ws['A12'] = '分级'
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ws['B12'] = '数量'
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ws['C12'] = '占比'
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ws['A12'].font = Font(bold=True)
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ws['B12'].font = Font(bold=True)
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ws['C12'].font = Font(bold=True)
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level_stats = {}
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for r in results:
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if r.level:
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level_stats[r.level.name] = level_stats.get(r.level.name, 0) + 1
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red_fill = PatternFill(start_color='FFCCCC', end_color='FFCCCC', fill_type='solid')
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row = 13
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for level_name, count in sorted(level_stats.items(), key=lambda x: -x[1]):
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ws.cell(row=row, column=1, value=level_name)
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ws.cell(row=row, column=2, value=count)
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pct = f'{count / total * 100:.1f}%' if total > 0 else '0%'
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ws.cell(row=row, column=3, value=pct)
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if 'L4' in level_name or 'L5' in level_name:
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for c in range(1, 4):
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ws.cell(row=row, column=c).fill = red_fill
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row += 1
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# High risk sheet
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ws2 = wb.create_sheet("高敏感数据清单")
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ws2.append(['字段名', '所属表', '分类', '分级', '来源', '置信度'])
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for cell in ws2[1]:
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cell.font = Font(bold=True)
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cell.fill = PatternFill(start_color='DDEBF7', end_color='DDEBF7', fill_type='solid')
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high_risk = [r for r in results if r.level and r.level.code in ('L4', 'L5')]
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for r in high_risk[:500]:
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ws2.append([
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r.column.name if r.column else 'N/A',
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r.column.table.name if r.column and r.column.table else 'N/A',
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r.category.name if r.category else 'N/A',
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r.level.name if r.level else 'N/A',
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'自动' if r.source == 'auto' else '人工',
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r.confidence,
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])
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# Auto-fit column widths roughly
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for ws_sheet in [ws, ws2]:
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for column in ws_sheet.columns:
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max_length = 0
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column_letter = column[0].column_letter
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for cell in column:
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try:
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if len(str(cell.value)) > max_length:
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max_length = len(str(cell.value))
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except:
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pass
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adjusted_width = min(max_length + 2, 50)
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ws_sheet.column_dimensions[column_letter].width = adjusted_width
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buffer = BytesIO()
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wb.save(buffer)
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buffer.seek(0)
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return buffer.read()
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def get_report_summary(db: Session, project_id: int) -> dict:
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"""Get aggregated report data for PDF generation (frontend)."""
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from sqlalchemy import func
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project = db.query(ClassificationProject).filter(ClassificationProject.id == project_id).first()
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if not project:
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raise ValueError("项目不存在")
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results = db.query(ClassificationResult).filter(ClassificationResult.project_id == project_id).all()
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total = len(results)
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auto_count = sum(1 for r in results if r.source == 'auto')
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manual_count = sum(1 for r in results if r.source == 'manual')
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level_stats = {}
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for r in results:
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if r.level:
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level_stats[r.level.name] = level_stats.get(r.level.name, 0) + 1
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high_risk = []
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for r in results:
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if r.level and r.level.code in ('L4', 'L5'):
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high_risk.append({
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"column_name": r.column.name if r.column else 'N/A',
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"table_name": r.column.table.name if r.column and r.column.table else 'N/A',
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"category_name": r.category.name if r.category else 'N/A',
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"level_name": r.level.name if r.level else 'N/A',
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"source": '自动' if r.source == 'auto' else '人工',
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"confidence": r.confidence,
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})
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return {
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"project_name": project.name,
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"status": project.status,
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"template_version": project.template.version if project.template else 'N/A',
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"generated_at": datetime.now().isoformat(),
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"total": total,
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"auto": auto_count,
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"manual": manual_count,
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"level_distribution": [
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{"name": name, "count": count}
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for name, count in sorted(level_stats.items(), key=lambda x: -x[1])
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],
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"high_risk": high_risk[:100],
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}
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