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

backend/app/api/v1/classification.py

@@ -238,3 +238,43 @@ def auto_classify(
 ):
     result = classification_engine.run_auto_classification(db, project_id)
     return ResponseModel(data=result)
+
+
+@router.post("/ml-train")
+def ml_train(
+    background: bool = True,
+    model_name: Optional[str] = None,
+    algorithm: str = "logistic_regression",
+    db: Session = Depends(get_db),
+    current_user: User = Depends(require_admin),
+):
+    from app.tasks.ml_tasks import train_ml_model_task
+    from app.services.ml_service import train_model
+
+    if background:
+        task = train_ml_model_task.delay(model_name=model_name, algorithm=algorithm)
+        return ResponseModel(data={"task_id": task.id, "status": task.state})
+    else:
+        mv = train_model(db, model_name=model_name, algorithm=algorithm)
+        if mv:
+            return ResponseModel(data={"model_id": mv.id, "accuracy": mv.accuracy, "train_samples": mv.train_samples})
+        return ResponseModel(message="训练失败：样本不足或发生错误")
+
+
+@router.get("/ml-suggest/{project_id}")
+def ml_suggest(
+    project_id: int,
+    column_ids: Optional[str] = Query(None),
+    top_k: int = Query(3, ge=1, le=5),
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user),
+):
+    from app.services.ml_service import suggest_for_project_columns
+    ids = None
+    if column_ids:
+        ids = [int(x) for x in column_ids.split(",") if x.strip().isdigit()]
+    result = suggest_for_project_columns(db, project_id, column_ids=ids, top_k=top_k)
+    if not result.get("success"):
+        from fastapi import HTTPException, status
+        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail=result.get("message"))
+    return ResponseModel(data=result["suggestions"])