feat(annotation): 添加导出格式与数据集类型的兼容性检查

- 实现 COCO 格式导出前的数据集类型验证
- COCO 格式仅适用于图像类和目标检测类数据集
- 文本类数据集尝试导出 COCO 格式时返回 HTTP 400 错误
- 添加清晰的错误提示信息，建议使用其他格式

新增功能：
- 数据集类型常量定义（TEXT、IMAGE、OBJECT_DETECTION）
- COCO 兼容类型集合
- 类型值标准化方法
- 数据集类型查询方法
- 模板标注类型解析方法
- 导出格式兼容性验证方法

相关文件：
- runtime/datamate-python/app/module/annotation/service/export.py (+94, -7)

Reviewed-by: Codex AI

runtime/datamate-python/app/module/annotation/service/export.py

@@ -28,6 +28,7 @@ from sqlalchemy.ext.asyncio import AsyncSession
 from app.core.logging import get_logger
 from app.db.models import (
     AnnotationResult,
+    AnnotationTemplate,
     Dataset,
     DatasetFiles,
     LabelingProject,
@@ -78,6 +79,15 @@ SEGMENTS_KEY = "segments"
 SEGMENT_RESULT_KEY = "result"
 SEGMENT_INDEX_KEY = "segmentIndex"
 SEGMENT_INDEX_FALLBACK_KEY = "segment_index"
+DATASET_TYPE_TEXT = "TEXT"
+DATASET_TYPE_IMAGE = "IMAGE"
+DATASET_TYPE_OBJECT_DETECTION = "OBJECT_DETECTION"
+LABELING_TYPE_CONFIG_KEY = "labeling_type"
+LABELING_TYPE_OBJECT_DETECTION = "OBJECT_DETECTION"
+COCO_COMPATIBLE_DATASET_TYPES = {
+    DATASET_TYPE_IMAGE,
+    DATASET_TYPE_OBJECT_DETECTION,
+}
 
 
 class AnnotationExportService:
@@ -137,6 +147,19 @@ class AnnotationExportService:
         """
         project = await self._get_project_or_404(project_id)
 
+        # 根据格式导出
+        format_type = (
+            ExportFormat(request.format)
+            if isinstance(request.format, str)
+            else request.format
+        )
+
+        # 兼容性检查
+        await self._validate_export_format_compatibility(
+            project=project,
+            format_type=format_type,
+        )
+
         # 获取标注数据
         items = await self._fetch_annotation_data(
             project_id=project_id,
@@ -145,13 +168,6 @@ class AnnotationExportService:
             include_data=request.include_data,
         )
 
-        # 根据格式导出
-        format_type = (
-            ExportFormat(request.format)
-            if isinstance(request.format, str)
-            else request.format
-        )
-
         if format_type == ExportFormat.JSON:
             return self._export_json(items, project.name)
         elif format_type == ExportFormat.JSONL:
@@ -180,6 +196,77 @@ class AnnotationExportService:
             raise HTTPException(status_code=404, detail=f"标注项目不存在: {project_id}")
         return project
 
+    @staticmethod
+    def _normalize_type_value(value: Optional[str]) -> str:
+        if not value:
+            return ""
+        return str(value).strip().upper().replace("-", "_")
+
+    async def _get_dataset_type(self, dataset_id: str) -> Optional[str]:
+        result = await self.db.execute(
+            select(Dataset.dataset_type).where(Dataset.id == dataset_id)
+        )
+        return result.scalar_one_or_none()
+
+    async def _get_template_labeling_type(
+        self, template_id: Optional[str]
+    ) -> Optional[str]:
+        if not template_id:
+            return None
+        result = await self.db.execute(
+            select(AnnotationTemplate.labeling_type).where(
+                AnnotationTemplate.id == template_id,
+                AnnotationTemplate.deleted_at.is_(None),
+            )
+        )
+        return result.scalar_one_or_none()
+
+    async def _resolve_project_labeling_type(
+        self, project: LabelingProject
+    ) -> Optional[str]:
+        configuration = getattr(project, "configuration", None)
+        if isinstance(configuration, dict):
+            labeling_type = configuration.get(LABELING_TYPE_CONFIG_KEY)
+            if isinstance(labeling_type, str) and labeling_type.strip():
+                return labeling_type
+        return await self._get_template_labeling_type(project.template_id)
+
+    async def _validate_export_format_compatibility(
+        self,
+        project: LabelingProject,
+        format_type: ExportFormat,
+    ) -> None:
+        if format_type != ExportFormat.COCO:
+            return
+
+        dataset_type = self._normalize_type_value(
+            await self._get_dataset_type(project.dataset_id)
+        )
+        labeling_type = self._normalize_type_value(
+            await self._resolve_project_labeling_type(project)
+        )
+
+        if dataset_type == DATASET_TYPE_TEXT:
+            raise HTTPException(
+                status_code=400,
+                detail="导出格式 COCO 不支持文本类数据集（TEXT），请改用 JSON/JSONL/CSV 格式",
+            )
+
+        if (
+            dataset_type in COCO_COMPATIBLE_DATASET_TYPES
+            or labeling_type == LABELING_TYPE_OBJECT_DETECTION
+        ):
+            return
+
+        raise HTTPException(
+            status_code=400,
+            detail=(
+                "导出格式 COCO 仅适用于图像类或目标检测类数据集，"
+                f"当前数据集类型: {dataset_type or 'UNKNOWN'}，"
+                f"标注类型: {labeling_type or 'UNKNOWN'}"
+            ),
+        )
+
     async def _fetch_annotation_data(
         self,
         project_id: str,