Merge branch 'main' of http://119.6.225.4:3000/lhx/project

.gitignore

@@ -11,4 +11,6 @@ temp/
 !README.md
 
 upload_app/data/
+upload_app/output/
+upload_app/*.json
 

app/api/export_excel.py

@@ -5,7 +5,7 @@ from typing import Optional, Dict, Any
 from ..core.database import get_db
 from ..core.response_code import ResponseCode, ResponseMessage
 from ..core.exceptions import BusinessException, DataNotFoundException, AccountNotFoundException
-from ..schemas.export_excel import ExportExcelRequest, ExportSettlementRequest
+from ..schemas.export_excel import ExportExcelRequest, ExportSettlementRequest, ExportLevelDataRequest
 from ..services.section_data import SectionDataService
 from ..services.export_excel import ExportExcelService
 import logging
@@ -175,3 +175,77 @@ def export_settlement_data(
             }
         )
 
+@router.post("/level_data")
+def export_level_data(
+    request: ExportLevelDataRequest,
+    db: Session = Depends(get_db)
+):
+    """导出水准数据Excel文件（以水准数据为主体，包含断面、观测点、沉降、原始数据）"""
+    try:
+        logger.info(f"导出水准数据，请求参数: project_name={request.project_name}")
+
+        # 生成文件名
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        filename = f"{request.project_name}_水准数据_{timestamp}.xlsx"
+
+        # 创建临时文件
+        temp_dir = tempfile.gettempdir()
+        file_path = os.path.join(temp_dir, filename)
+
+        # 调用服务层导出数据到文件
+        export_excel_service.export_level_data_to_file(
+            db,
+            project_name=request.project_name,
+            file_path=file_path
+        )
+
+        logger.info(f"成功生成水准数据Excel文件: {file_path}")
+
+        # 返回文件下载响应
+        return FileResponse(
+            path=file_path,
+            filename=filename,
+            media_type='application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
+        )
+
+    except AccountNotFoundException as e:
+        logger.warning(f"账号不存在: {str(e)}")
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail={
+                "code": e.code,
+                "message": e.message,
+                "data": None
+            }
+        )
+    except DataNotFoundException as e:
+        logger.warning(f"数据不存在: {str(e)}")
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail={
+                "code": e.code,
+                "message": e.message,
+                "data": None
+            }
+        )
+    except BusinessException as e:
+        logger.warning(f"业务异常: {str(e)}")
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail={
+                "code": e.code,
+                "message": e.message,
+                "data": None
+            }
+        )
+    except Exception as e:
+        logger.error(f"导出水准数据失败: {str(e)}", exc_info=True)
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail={
+                "code": ResponseCode.EXPORT_FAILED,
+                "message": f"{ResponseMessage.EXPORT_FAILED}: {str(e)}",
+                "data": None
+            }
+        )
+

app/api/level_data.py

@@ -0,0 +1,34 @@
+from fastapi import APIRouter, Depends, HTTPException, status
+from sqlalchemy.orm import Session
+from typing import List
+from ..core.database import get_db
+from ..core.response_code import ResponseCode, ResponseMessage
+from ..schemas.level_data import (
+    LevelDataRequest,
+    LevelDataListResponse,
+    LevelDataResponse
+)
+from ..services.level_data import LevelDataService
+
+router = APIRouter(prefix="/level_data", tags=["水准数据"])
+
+@router.post("/get_level_data_by_project", response_model=LevelDataListResponse)
+def get_level_data_by_project(request: LevelDataRequest, db: Session = Depends(get_db)):
+    """
+    通过标段名称获取全部水准线路
+    """
+    try:
+        level_service = LevelDataService()
+        level_data_list = level_service.get_level_data_by_project_name(db, request.project_name)
+
+        return LevelDataListResponse(
+            code=ResponseCode.SUCCESS,
+            message="查询成功",
+            total=len(level_data_list),
+            data=level_data_list
+        )
+    except Exception as e:
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail=f"查询失败: {str(e)}"
+        )

app/main.py

@@ -13,6 +13,7 @@ from .api.comprehensive_data import router as comprehensive_data_router
 from .api.export_excel import router as export_excel_router
 from .api.daily import router as daily_router
 from .api.section_data import router as section_data_router
+from .api.level_data import router as level_data_router
 from .utils.scheduler import task_scheduler
 
 # 初始化日志系统
@@ -74,6 +75,7 @@ app.include_router(comprehensive_data_router, prefix="/api")
 app.include_router(export_excel_router, prefix="/api")
 app.include_router(daily_router, prefix="/api")
 app.include_router(section_data_router, prefix="/api")
+app.include_router(level_data_router, prefix="/api")
 # app.include_router(test_router, prefix="/api")
 
 # 根路径

app/schemas/export_excel.py

@@ -14,6 +14,10 @@ class ExportExcelRequest(BaseModel):
 class ExportSettlementRequest(BaseModel):
     project_name: str  # 项目名称（标段）
 
+# 导出水准数据请求（以水准数据为主体）
+class ExportLevelDataRequest(BaseModel):
+    project_name: str  # 项目名称（标段）
+
 # 导出Excel响应
 class ExportExcelResponse(BaseModel):
     code: int

app/schemas/level_data.py

@@ -0,0 +1,31 @@
+from pydantic import BaseModel, Field, ConfigDict
+from typing import Optional, List, Any
+from datetime import datetime
+
+class LevelDataBase(BaseModel):
+    """水准数据基础模型"""
+    linecode: Optional[str] = None
+    benchmarkids: Optional[str] = None
+    wsphigh: Optional[str] = None
+    NYID: Optional[str] = None
+    mtype: Optional[str] = None
+
+class LevelDataResponse(LevelDataBase):
+    """水准数据响应模型"""
+    id: int
+    createDate: Optional[datetime] = None
+
+    model_config = ConfigDict(
+        from_attributes=True
+    )
+
+class LevelDataRequest(BaseModel):
+    """水准数据请求模型"""
+    project_name: str = Field(..., description="标段名称")
+
+class LevelDataListResponse(BaseModel):
+    """水准数据列表响应格式"""
+    code: int = 0
+    message: str
+    total: int
+    data: List[LevelDataResponse] = []

app/services/checkpoint.py

@@ -130,3 +130,7 @@ class CheckpointService(BaseService[Checkpoint]):
         if not section_ids:
             return []
         return db.query(Checkpoint).filter(Checkpoint.section_id.in_(section_ids)).all()
+
+    def get_by_section_ids(self, db: Session, section_ids: List[str]) -> List[Checkpoint]:
+        """根据多个section_id批量获取观测点数据"""
+        return db.query(Checkpoint).filter(Checkpoint.section_id.in_(section_ids)).all()

app/services/export_excel.py

@@ -5,6 +5,7 @@ from ..models.section_data import SectionData
 from ..models.checkpoint import Checkpoint
 from ..models.settlement_data import SettlementData
 from ..models.level_data import LevelData
+from ..models.original_data import get_original_data_model, get_table_name
 from ..services.section_data import SectionDataService
 from ..services.checkpoint import CheckpointService
 from ..services.settlement_data import SettlementDataService
@@ -255,3 +256,294 @@ class ExportExcelService:
                     worksheet.column_dimensions[column_letter].width = adjusted_width
 
         logger.info("Excel文件生成完成")
+
+    # ------------------------------ 新导出业务：以水准数据为主体 ------------------------------
+
+    def parse_benchmarkids(self, benchmarkids_str: str) -> tuple:
+        """
+        解析benchmarkids，拆分为起始点和终止点
+
+        例如: "BTQX-1、BTQX-3" -> ("BTQX-1", "BTQX-3")
+
+        Args:
+            benchmarkids_str: benchmarkids字符串，格式为 "起始点、终止点"
+
+        Returns:
+            tuple: (起始点, 终止点)
+        """
+        if not benchmarkids_str:
+            return "", ""
+
+        # 按"、"拆分
+        parts = str(benchmarkids_str).split("、")
+        start_point = parts[0].strip() if len(parts) > 0 else ""
+        end_point = parts[1].strip() if len(parts) > 1 else ""
+
+        return start_point, end_point
+
+    def get_time_range_from_original_data(self, db: Session, account_id: int, nyids: List[str]) -> tuple:
+        """
+        从原始数据分表中查询给定NYID集合的mtime最早和最晚时间
+
+        Args:
+            db: 数据库会话
+            account_id: 账号ID
+            nyids: NYID列表
+
+        Returns:
+            tuple: (最早时间, 最晚时间)
+        """
+        if not nyids:
+            return "", ""
+
+        try:
+            # 获取动态原始数据表模型
+            OriginalDataModel = get_original_data_model(account_id)
+            table_name = get_table_name(account_id)
+
+            # 查询原始数据
+            original_data = db.query(OriginalDataModel).filter(
+                OriginalDataModel.NYID.in_(nyids)
+            ).all()
+
+            if not original_data:
+                return "", ""
+
+            # 找到mtime的最小值和最大值
+            mtimes = [data.mtime for data in original_data if data.mtime]
+            if not mtimes:
+                return "", ""
+
+            min_time = min(mtimes).strftime("%Y-%m-%d %H:%M:%S")
+            max_time = max(mtimes).strftime("%Y-%m-%d %H:%M:%S")
+
+            return min_time, max_time
+        except Exception as e:
+            logger.warning(f"查询原始数据表 {account_id} 失败: {str(e)}")
+            return "", ""
+
+    def export_level_data_to_file(self, db: Session, project_name: str, file_path: str):
+        """
+        根据项目名称导出水准数据Excel文件到指定路径（以水准数据为主体）
+
+        处理流程：
+        1. 根据project_name找到账号
+        2. 账号→断面→观测点→沉降→水准 数据链
+        3. 解析水准数据的benchmarkids为起始点和终止点
+        4. 收集同一水准线路的所有观测点名称
+        5. 查询原始数据计算时间范围
+        6. 按work_site分工作簿导出
+        """
+        logger.info(f"开始导出项目 '{project_name}' 的水准数据到文件: {file_path}")
+
+        # 1. 在账号表查询到账号id作为account_id
+        account_responses = self.account_service.search_accounts(db, project_name=project_name)
+        if not account_responses:
+            logger.warning(f"未找到项目名称为 '{project_name}' 的账号")
+            raise AccountNotFoundException(f"未找到项目名称为 '{project_name}' 的账号")
+
+        account_response = account_responses[0]
+        account_id = account_response.account_id
+        logger.info(f"找到账号 ID: {account_id}")
+
+        # 2. 通过 account_id 查询断面数据
+        sections = self.section_service.search_section_data(db, account_id=str(account_id), limit=10000)
+        if not sections:
+            logger.warning(f"账号 {account_id} 下未找到断面数据")
+            raise DataNotFoundException(f"账号 {account_id} 下未找到断面数据")
+
+        logger.info(f"找到 {len(sections)} 个断面")
+
+        # 3. 批量查询所有观测点数据（优化：使用in查询替代循环）
+        all_section_ids = [section.section_id for section in sections]
+        logger.info(f"开始批量查询 {len(all_section_ids)} 个断面的观测点数据")
+        all_checkpoints = self.checkpoint_service.get_by_section_ids(db, all_section_ids)
+
+        if not all_checkpoints:
+            logger.warning("未找到任何观测点数据")
+            raise DataNotFoundException("未找到任何观测点数据")
+
+        # 建立断面->观测点映射
+        section_checkpoint_map = {}  # section_id -> [checkpoints]
+        for checkpoint in all_checkpoints:
+            if checkpoint.section_id not in section_checkpoint_map:
+                section_checkpoint_map[checkpoint.section_id] = []
+            section_checkpoint_map[checkpoint.section_id].append(checkpoint)
+
+        logger.info(f"找到 {len(all_checkpoints)} 个观测点")
+
+        # 4. 批量查询沉降数据
+        point_ids = [cp.point_id for cp in all_checkpoints]
+        logger.info(f"开始批量查询 {len(point_ids)} 个观测点的沉降数据")
+        all_settlements = self.settlement_service.get_by_point_ids(db, point_ids)
+
+        if not all_settlements:
+            logger.warning("未找到任何沉降数据")
+            raise DataNotFoundException("未找到任何沉降数据")
+
+        logger.info(f"批量查询到 {len(all_settlements)} 条沉降数据")
+
+        # 5. 收集所有NYID，批量查询水准数据
+        nyid_set = {settlement.NYID for settlement in all_settlements if settlement.NYID}
+        nyid_list = list(nyid_set)
+        logger.info(f"开始批量查询 {len(nyid_list)} 个期数的水准数据")
+        all_level_data = self.level_service.get_by_nyids(db, nyid_list)
+
+        if not all_level_data:
+            logger.warning("未找到任何水准数据")
+            raise DataNotFoundException("未找到任何水准数据")
+
+        logger.info(f"批量查询到 {len(all_level_data)} 条水准数据")
+
+        # 6. 建立各种数据映射便于查询
+        checkpoint_dict = {cp.point_id: cp for cp in all_checkpoints}
+        section_dict = {section.section_id: section for section in sections}
+        settlement_by_nyid = {}  # NYID -> [settlements]
+        point_ids_by_nyid = set()  # 所有关联的point_id
+
+        for settlement in all_settlements:
+            if settlement.NYID not in settlement_by_nyid:
+                settlement_by_nyid[settlement.NYID] = []
+            settlement_by_nyid[settlement.NYID].append(settlement)
+            if settlement.point_id:
+                point_ids_by_nyid.add(settlement.point_id)
+
+        # 7. 按水准数据为主体处理数据（优化：减少重复查询）
+        work_site_records = {}  # work_site -> [records]
+        linecode_to_points = {}  # linecode -> 所有相关point_id（用于收集观测点名称）
+        linecode_to_nyids = {}  # linecode -> 所有NYID（用于计算时间范围）
+
+        # 先按linecode分组，收集所有需要的NYID
+        for level_data in all_level_data:
+            if level_data.linecode not in linecode_to_nyids:
+                linecode_to_nyids[level_data.linecode] = set()
+            linecode_to_nyids[level_data.linecode].add(level_data.NYID)
+
+        # 批量计算每个linecode的时间范围（只查询一次）
+        logger.info("开始批量计算每个水准线路的时间范围...")
+        linecode_to_time_range = {}  # linecode -> (min_mtime, max_mtime)
+        for linecode, nyids in linecode_to_nyids.items():
+            min_mtime, max_mtime = self.get_time_range_from_original_data(
+                db, account_id, list(nyids)
+            )
+            linecode_to_time_range[linecode] = (min_mtime, max_mtime)
+
+        # 再次遍历水准数据，生成结果记录
+        for level_data in all_level_data:
+            # 格式化createDate为日期
+            formatted_date = TimeUtils.datetime_to_date_string(level_data.createDate)
+
+            # 解析benchmarkids
+            start_point, end_point = self.parse_benchmarkids(level_data.benchmarkids)
+
+            # 找到该水准数据对应的沉降数据
+            related_settlements = settlement_by_nyid.get(level_data.NYID, [])
+
+            if not related_settlements:
+                logger.warning(f"NYID={level_data.NYID} 无对应沉降数据，跳过")
+                continue
+
+            # 获取所有相关的point_id
+            related_point_ids = [s.point_id for s in related_settlements if s.point_id]
+
+            # 通过point_id找到对应的section_id，然后找到work_site
+            work_site = ""
+            for point_id in related_point_ids:
+                checkpoint = checkpoint_dict.get(point_id)
+                if checkpoint:
+                    section = section_dict.get(checkpoint.section_id)
+                    if section and section.work_site:
+                        work_site = section.work_site
+                        break
+
+            if not work_site:
+                work_site = "未知工点"
+
+            # 收集同一水准线路的所有point_id（用于获取观测点名称）
+            if level_data.linecode not in linecode_to_points:
+                linecode_to_points[level_data.linecode] = set()
+
+            # 将该水准数据对应的所有point_id加入到linecode集合中
+            linecode_to_points[level_data.linecode].update(related_point_ids)
+
+            # 获取时间范围（从预计算的linecode_to_time_range中获取）
+            min_mtime, max_mtime = linecode_to_time_range.get(level_data.linecode, ("", ""))
+
+            # 组合结果记录
+            result_row = {
+                "日期": formatted_date,
+                "水准线路": level_data.linecode,
+                "起始点": start_point,
+                "终止点": end_point,
+                "测点": "",  # 稍后填充
+                "起始时间": min_mtime,
+                "终止时间": max_mtime,
+                "类型": work_site
+            }
+
+            # 按work_site分组
+            if work_site not in work_site_records:
+                work_site_records[work_site] = []
+            work_site_records[work_site].append(result_row)
+
+        # 11. 填充测点列（优化：按水准线路计算测点，而不是按工点）
+        logger.info("开始收集观测点名称...")
+
+        # 先按linecode分组计算观测点名称（每个linecode对应一套观测点）
+        linecode_to_checkpoint_names = {}  # linecode -> 观测点名称字符串
+        for linecode, point_ids in linecode_to_points.items():
+            checkpoint_names = []
+            for point_id in point_ids:
+                checkpoint = checkpoint_dict.get(point_id)
+                if checkpoint and checkpoint.aname:
+                    checkpoint_names.append(checkpoint.aname)
+
+            # 去重并排序
+            unique_names = sorted(list(set(checkpoint_names)))
+            linecode_to_checkpoint_names[linecode] = ",".join(unique_names)
+
+        # 为每条记录设置对应的测点（根据其linecode）
+        for work_site, records in work_site_records.items():
+            for record in records:
+                linecode = record["水准线路"]
+                # 根据linecode获取对应的观测点名称
+                record["测点"] = linecode_to_checkpoint_names.get(linecode, "")
+
+        if not work_site_records:
+            logger.warning("未能合并任何数据记录")
+            raise DataNotFoundException("未能合并任何数据记录")
+
+        logger.info(f"共找到 {len(work_site_records)} 个工点，共 {sum(len(records) for records in work_site_records.values())} 条水准数据记录")
+
+        # 12. 导出到Excel文件（按 work_site 分工作簿）
+        logger.info("开始生成Excel文件...")
+        with pd.ExcelWriter(file_path, engine='openpyxl') as writer:
+            for work_site, records in work_site_records.items():
+                # 将工作表名称转换为有效字符
+                safe_work_site = work_site.replace('/', '_').replace('\\', '_').replace('?', '_').replace('*', '_').replace('[', '_').replace(']', '_')
+                if len(safe_work_site) > 31:
+                    safe_work_site = safe_work_site[:28] + "..."
+
+                logger.info(f"创建工作簿: {safe_work_site}，记录数: {len(records)}")
+
+                # 转换为DataFrame
+                df = pd.DataFrame(records)
+
+                # 写入工作簿
+                df.to_excel(writer, index=False, sheet_name=safe_work_site)
+
+                # 自动调整列宽
+                worksheet = writer.sheets[safe_work_site]
+                for column in worksheet.columns:
+                    max_length = 0
+                    column_letter = column[0].column_letter
+                    for cell in column:
+                        try:
+                            if cell.value and len(str(cell.value)) > max_length:
+                                max_length = len(str(cell.value))
+                        except:
+                            pass
+                    adjusted_width = min(max_length + 2, 50)
+                    worksheet.column_dimensions[column_letter].width = adjusted_width
+
+        logger.info("Excel文件生成完成")

app/services/level_data.py

@@ -3,6 +3,12 @@ from typing import List, Optional, Dict, Any
 from ..models.level_data import LevelData
 from .base import BaseService
 from ..models.settlement_data import SettlementData
+from ..models.checkpoint import Checkpoint
+from ..models.section_data import SectionData
+from ..models.account import Account
+import logging
+
+logger = logging.getLogger(__name__)
 
 class LevelDataService(BaseService[LevelData]):
     def __init__(self):
@@ -141,3 +147,83 @@ class LevelDataService(BaseService[LevelData]):
             'failed_count': failed_count,
             'failed_items': failed_items
         }
+
+    def get_level_data_by_project_name(self, db: Session, project_name: str) -> List[Dict[str, Any]]:
+        """
+        通过project_name获取全部水准线路
+        业务逻辑:
+        1. 查询账号表获取账号数据 (通过project_name)
+        2. 查询断面表获取断面数据 (通过account_id)
+        3. 查询观测点表获取观测点数据 (通过section_id)
+        4. 查询沉降数据表获取沉降数据 (通过point_id)
+        5. 查询水准数据表获取水准数据 (通过NYID)
+        6. 将水准数据依照linecode去重（同linecode只需保留一个）
+        """
+        try:
+            logger.info(f"开始查询project_name={project_name}对应的水准线路数据")
+
+            # 1. 查询账号表获取账号数据
+            accounts = db.query(Account).filter(Account.project_name.like(f"%{project_name}%")).all()
+            if not accounts:
+                logger.warning(f"未查询到project_name={project_name}对应的账号")
+                return []
+
+            account_ids = [str(account.id) for account in accounts]
+            logger.info(f"查询到{len(account_ids)}个账号: {account_ids}")
+
+            # 2. 查询断面表获取断面数据 (通过account_id)
+            sections = db.query(SectionData).filter(SectionData.account_id.in_(account_ids)).all()
+            if not sections:
+                logger.warning(f"未查询到对应的断面数据")
+                return []
+
+            section_ids = [section.section_id for section in sections]
+            logger.info(f"查询到{len(section_ids)}个断面: {section_ids}")
+
+            # 3. 查询观测点表获取观测点数据 (通过section_id)
+            checkpoints = db.query(Checkpoint).filter(Checkpoint.section_id.in_(section_ids)).all()
+            if not checkpoints:
+                logger.warning(f"未查询到对应的观测点数据")
+                return []
+
+            point_ids = [checkpoint.point_id for checkpoint in checkpoints]
+            logger.info(f"查询到{len(point_ids)}个观测点")
+
+            # 4. 查询沉降数据表获取沉降数据 (通过point_id)
+            settlements = db.query(SettlementData).filter(SettlementData.point_id.in_(point_ids)).all()
+            if not settlements:
+                logger.warning(f"未查询到对应的沉降数据")
+                return []
+
+            nyid_list = list(set([settlement.NYID for settlement in settlements if settlement.NYID]))
+            logger.info(f"查询到{len(nyid_list)}个期数ID")
+
+            # 5. 查询水准数据表获取水准数据 (通过NYID)
+            level_data_list = db.query(LevelData).filter(LevelData.NYID.in_(nyid_list)).all()
+            if not level_data_list:
+                logger.warning(f"未查询到对应的水准数据")
+                return []
+
+            # 6. 将水准数据依照linecode去重（同linecode只需保留一个）
+            linecode_seen = set()
+            unique_level_data = []
+            for level in level_data_list:
+                if level.linecode not in linecode_seen:
+                    linecode_seen.add(level.linecode)
+                    level_dict = {
+                        "id": level.id,
+                        "linecode": level.linecode,
+                        "benchmarkids": level.benchmarkids,
+                        "wsphigh": level.wsphigh,
+                        "NYID": level.NYID,
+                        "mtype": level.mtype,
+                        "createDate": level.createDate.strftime("%Y-%m-%d %H:%M:%S") if level.createDate else None
+                    }
+                    unique_level_data.append(level_dict)
+
+            logger.info(f"查询完成，共{len(unique_level_data)}条去重后的水准数据")
+            return unique_level_data
+
+        except Exception as e:
+            logger.error(f"查询project_name={project_name}的水准数据失败: {str(e)}", exc_info=True)
+            raise e

app/services/original_data.py

@@ -245,8 +245,8 @@ class OriginalDataService(BaseService[OriginalData]):
 
     def batch_import_original_data(self, db: Session, data: List) -> Dict[str, Any]:
         """
-        批量导入原始数据到指定账号的分表,直接新增,无需检查重复
-        支持事务回滚,失败时重试一次
+        批量导入原始数据到指定账号的分表 - 性能优化版
+        使用批量插入替代逐条插入，大幅提升导入速度
 
         Args:
             db: 数据库会话
@@ -318,7 +318,7 @@ class OriginalDataService(BaseService[OriginalData]):
                 failed_count = 0
                 failed_items = []
 
-                nyid = data[0].get('NYID')
+                nyid = str(data[0].get('NYID'))  # 统一转换为字符串
                 # 检查该期数数据是否已存在
                 check_query = text(f"SELECT COUNT(*) as cnt FROM `{table_name}` WHERE NYID = :nyid")
                 is_exists = db.execute(check_query, {"nyid": nyid}).fetchone()[0]
@@ -334,42 +334,61 @@ class OriginalDataService(BaseService[OriginalData]):
                         'failed_items': failed_items
                     }
 
-                for item_data in data:
-                    try:
-                        # 判断期数id是否存在
-                        settlement = self._check_settlement_exists(db, item_data.get('NYID'))
-                        if not settlement:
-                            logger.error(f"Settlement {item_data.get('NYID')} not found")
-                            raise Exception(f"Settlement {item_data.get('NYID')} not found")
+                # ===== 性能优化：批量查询沉降数据 =====
+                # 统一转换为字符串处理（数据库NYID字段是VARCHAR类型）
+                nyid_list = list(set(str(item.get('NYID')) for item in data if item.get('NYID')))
+                logger.info(f"Querying settlement data for nyid list: {nyid_list}")
+                settlements = db.query(SettlementData).filter(SettlementData.NYID.in_(nyid_list)).all()
+                logger.info(f"Found {len(settlements)} settlement records")
+                settlement_map = {s.NYID: s for s in settlements}
+                missing_nyids = set(nyid_list) - set(settlement_map.keys())
 
-                        # 构建插入SQL
-                        insert_sql = text(f"""
-                            INSERT INTO `{table_name}`
-                            (account_id, bfpcode, mtime, bffb, bfpl, bfpvalue, NYID, sort)
-                            VALUES
-                            (:account_id, :bfpcode, :mtime, :bffb, :bfpl, :bfpvalue, :NYID, :sort)
-                        """)
+                if missing_nyids:
+                    db.rollback()
+                    return {
+                        'success': False,
+                        'message': f'以下期数在沉降表中不存在: {list(missing_nyids)}',
+                        'total_count': total_count,
+                        'success_count': 0,
+                        'failed_count': total_count,
+                        'failed_items': []
+                    }
 
-                        db.execute(insert_sql, {
-                            "account_id": account_id,
-                            "bfpcode": item_data.get('bfpcode'),
-                            "mtime": item_data.get('mtime'),
-                            "bffb": item_data.get('bffb'),
-                            "bfpl": item_data.get('bfpl'),
-                            "bfpvalue": item_data.get('bfpvalue'),
-                            "NYID": item_data.get('NYID'),
-                            "sort": item_data.get('sort')
+                # ===== 性能优化：使用批量插入 =====
+                # 将数据分组，每组1000条（MySQL默认支持）
+                batch_size = 1000
+                for i in range(0, len(data), batch_size):
+                    batch_data = data[i:i + batch_size]
+
+                    # 构建批量参数
+                    values_list = []
+                    params = {}
+                    for idx, item_data in enumerate(batch_data):
+                        values_list.append(
+                            f"(:account_id_{idx}, :bfpcode_{idx}, :mtime_{idx}, :bffb_{idx}, "
+                            f":bfpl_{idx}, :bfpvalue_{idx}, :NYID_{idx}, :sort_{idx})"
+                        )
+                        params.update({
+                            f"account_id_{idx}": account_id,
+                            f"bfpcode_{idx}": item_data.get('bfpcode'),
+                            f"mtime_{idx}": item_data.get('mtime'),
+                            f"bffb_{idx}": item_data.get('bffb'),
+                            f"bfpl_{idx}": item_data.get('bfpl'),
+                            f"bfpvalue_{idx}": item_data.get('bfpvalue'),
+                            f"NYID_{idx}": item_data.get('NYID'),
+                            f"sort_{idx}": item_data.get('sort')
                         })
-                        logger.info(f"Created original data: {item_data.get('bfpcode')}-{item_data.get('NYID')} in table {table_name}")
-                        success_count += 1
-                    except Exception as e:
-                        failed_count += 1
-                        failed_items.append({
-                            'data': item_data,
-                            'error': str(e)
-                        })
-                        logger.error(f"Failed to process original data {item_data.get('bfpcode')}-{item_data.get('NYID')}: {str(e)}")
-                        raise e
+
+                    # 批量插入SQL - 使用字符串拼接（修复TextClause拼接问题）
+                    insert_sql = f"""
+                        INSERT INTO `{table_name}`
+                        (account_id, bfpcode, mtime, bffb, bfpl, bfpvalue, NYID, sort)
+                        VALUES {", ".join(values_list)}
+                    """
+                    final_sql = text(insert_sql)
+                    db.execute(final_sql, params)
+                    success_count += len(batch_data)
+                    logger.info(f"Inserted batch {i//batch_size + 1}: {len(batch_data)} records")
 
                 db.commit()
                 logger.info(f"Batch import original data completed. Success: {success_count}, Failed: {failed_count}")

upload_app/process_parquet_to_excel.py

@@ -11,7 +11,7 @@ Parquet数据处理与Excel导出脚本
    - 数据关联链：断面→观测点→沉降→水准→原始
 3. 以水准数据为主体整理数据
    - 拆分的benchmarkids（起始点/终止点）
-   - 收集测点（同一水准线路的所有观测点）
+   - 收集测点（同一水准线路的所有观测点名称aname）
    - 计算时间范围（原始数据mtime范围）
    - 格式化日期（YYYY-MM-DD）
 4. 导出为Excel文件
@@ -28,7 +28,14 @@ pip install pandas numpy openpyxl
 
 作者：Claude Code
 日期：2025-11-08
-版本：1.0
+版本：1.2.1
+
+更新日志：
+- v1.2.1: 测点列优化：从point_id改为aname（观测点名称）
+- v1.2.0: 彻底修复numpy array布尔值判断错误
+- v1.2: 新增NYID期数ID重复检查功能
+- v1.1: 新增数据质量检验机制
+- v1.0: 初始版本
 """
 
 import os
@@ -338,7 +345,7 @@ def process_folder_data(folder_name, folder_path, files):
                             else:
                                 work_site = ""
 
-            # 6. 收集同一水准线路编码的所有水准数据对应的沉降数据，进而获取观测点
+            # 6. 收集同一水准线路编码的所有水准数据对应的沉降数据，进而获取观测点名称
             # 找到所有具有相同linecode的水准数据
             same_line_levels = level_df[level_df["linecode"] == linecode]
             same_line_nyids = same_line_levels["NYID"].unique()
@@ -348,7 +355,18 @@ def process_folder_data(folder_name, folder_path, files):
 
             # 获取这些沉降数据对应的观测点point_id
             all_point_ids = all_settlements_same_line["point_id"].unique()
-            point_ids_str = ",".join(map(str, sorted(all_point_ids)))
+
+            # 从观测点数据中获取对应的aname（观测点名称）
+            if not checkpoint_df.empty:
+                related_checkpoints_for_names = checkpoint_df[checkpoint_df["point_id"].isin(all_point_ids)]
+                if not related_checkpoints_for_names.empty:
+                    # 获取所有的aname
+                    all_anames = related_checkpoints_for_names["aname"].dropna().unique()
+                    anames_str = ",".join(sorted(map(str, all_anames)))
+                else:
+                    anames_str = ""
+            else:
+                anames_str = ""
 
             # 7. 计算时间范围（通过同一水准线路编码的所有NYID）
             if has_original:
@@ -364,7 +382,7 @@ def process_folder_data(folder_name, folder_path, files):
                 "水准线路": linecode,
                 "起始点": start_point,
                 "终止点": end_point,
-                "测点": point_ids_str,
+                "测点": anames_str,  # 修改为观测点名称
                 "起始时间": min_mtime,
                 "终止时间": max_mtime,
                 "类型": work_site