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