550 lines
27 KiB
Python
550 lines
27 KiB
Python
from sqlalchemy.orm import Session
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from typing import List, Dict, Any, Optional
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from ..models.account import Account
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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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from ..services.level_data import LevelDataService
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from ..services.account import AccountService
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from ..core.exceptions import DataNotFoundException, AccountNotFoundException
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import pandas as pd
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import logging
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from ..utils.time_utils import TimeUtils
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from datetime import datetime
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logger = logging.getLogger(__name__)
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class ExportExcelService:
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def __init__(self):
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self.account_service = AccountService()
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self.section_service = SectionDataService()
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self.checkpoint_service = CheckpointService()
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self.settlement_service = SettlementDataService()
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self.level_service = LevelDataService()
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def merge_settlement_with_related_data(self,
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db: Session,
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settlement_data: SettlementData,
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section_data: SectionData,
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checkpoint_data: Checkpoint,
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level_data: Optional[LevelData]) -> Dict[str, Any]:
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"""
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合并沉降数据与关联数据，去除重复和id字段
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"""
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result = {}
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# 导出数据列格式
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desired_column_config = [
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{"display_name": "观测点名称", "model_class": Checkpoint, "field_name_in_model": "aname"},
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{"display_name": "断面里程", "model_class": SectionData, "field_name_in_model": "mileage"},
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{"display_name": "工点名称", "model_class": SectionData, "field_name_in_model": "work_site"},
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{"display_name": "水准线路编码", "model_class": LevelData, "field_name_in_model": "linecode"},
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{"display_name": "修正量（mm）", "model_class": SettlementData, "field_name_in_model": "CVALUE"},
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{"display_name": "成果值（m）", "model_class": SettlementData, "field_name_in_model": "MAVALUE"},
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{"display_name": "埋设日期", "model_class": Checkpoint, "field_name_in_model": "burial_date"},
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{"display_name": "观测时间", "model_class": SettlementData, "field_name_in_model": "MTIME_W"},
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{"display_name": "观测阶段", "model_class": SettlementData, "field_name_in_model": "workinfoname"},
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{"display_name": "累计天数", "model_class": SettlementData, "field_name_in_model": "day"},
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{"display_name": "两次观测时间间隔", "model_class": SettlementData, "field_name_in_model": "day_jg"},
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{"display_name": "本次沉降（mm）", "model_class": SettlementData, "field_name_in_model": "mavalue_bc"},
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{"display_name": "累计沉降(mm)", "model_class": SettlementData, "field_name_in_model": "mavalue_lj"},
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{"display_name": "上传时间", "model_class": SettlementData, "field_name_in_model": "createdate"},
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{"display_name": "司镜人员", "model_class": SettlementData, "field_name_in_model": "sjName"},
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{"display_name": "基础类型", "model_class": SectionData, "field_name_in_model": "basic_types"},
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{"display_name": "桥墩台高度", "model_class": SectionData, "field_name_in_model": "height"},
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{"display_name": "断面状态", "model_class": SectionData, "field_name_in_model": "status"},
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{"display_name": "桥梁墩(台)编号", "model_class": SectionData, "field_name_in_model": "number"},
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{"display_name": "过渡段", "model_class": SectionData, "field_name_in_model": "transition_paragraph"},
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{"display_name": "设计填土高度", "model_class": SectionData, "field_name_in_model": "design_fill_height"},
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{"display_name": "压实层厚度", "model_class": SectionData, "field_name_in_model": "compression_layer_thickness"},
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{"display_name": "处理深度", "model_class": SectionData, "field_name_in_model": "treatment_depth"},
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{"display_name": "地基处理方法", "model_class": SectionData, "field_name_in_model": "foundation_treatment_method"},
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{"display_name": "围岩级别", "model_class": SectionData, "field_name_in_model": "rock_mass_classification"},
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{"display_name": "工作基点名称序列", "model_class": LevelData, "field_name_in_model": "benchmarkids"},
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{"display_name": "工作基点高程序列(m)", "model_class": LevelData, "field_name_in_model": "wsphigh"},
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{"display_name": "水准_上传时间", "model_class": LevelData, "field_name_in_model": "createDate"},
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{"display_name": "备注", "model_class": SettlementData, "field_name_in_model": "upd_remark"}
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]
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result = {item["display_name"]: None for item in desired_column_config}
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data_map_by_class = {
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SettlementData: settlement_data.to_dict(),
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SectionData: section_data.to_dict(),
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Checkpoint: checkpoint_data.to_dict(),
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}
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# 对于可选的 level_data，只有当它存在时才添加到映射中
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if level_data is not None:
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data_map_by_class[LevelData] = level_data.to_dict()
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for config_item in desired_column_config:
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display_name = config_item["display_name"]
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model_class = config_item["model_class"]
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field_name_in_model = config_item["field_name_in_model"]
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# 检查这个模型类的数据是否存在于映射中
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if model_class in data_map_by_class:
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source_dict = data_map_by_class[model_class]
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# 检查模型数据中是否包含这个字段
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if field_name_in_model in source_dict:
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# 将从源数据中取出的值赋给结果字典中对应的 display_name 键
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result[display_name] = source_dict[field_name_in_model]
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return result
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def export_settlement_data_to_file(self, db: Session, project_name: str, file_path: str):
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"""
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根据项目名称导出沉降数据Excel文件到指定路径（批量查询优化版本）
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"""
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logger.info(f"开始导出项目 '{project_name}' 的沉降数据到文件: {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 = str(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=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. 收集所有观测点数据，建立断面->观测点映射
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section_dict = {section.section_id: section for section in sections}
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section_checkpoint_map = {} # section_id -> [checkpoints]
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all_checkpoints = []
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for section in sections:
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checkpoints = self.checkpoint_service.get_by_section_id(db, section.section_id)
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if checkpoints:
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section_checkpoint_map[section.section_id] = checkpoints
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all_checkpoints.extend(checkpoints)
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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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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（关键优化点）
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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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# logger.info(f"观测点id集合{point_ids}")
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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. 建立观测点->沉降数据映射
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checkpoint_dict = {cp.point_id: cp for cp in all_checkpoints}
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point_settlement_map = {} # point_id -> [settlements]
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nyid_set = set()
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for settlement in all_settlements:
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if settlement.point_id not in point_settlement_map:
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point_settlement_map[settlement.point_id] = []
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point_settlement_map[settlement.point_id].append(settlement)
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if settlement.NYID:
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nyid_set.add(settlement.NYID)
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# 6. 批量查询水准数据（关键优化点）
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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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logger.info(f"批量查询到 {len(all_level_data)} 条水准数据")
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# 建立NYID->水准数据映射
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nyid_level_map = {}
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for level_data in all_level_data:
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level_data.createDate = TimeUtils.datetime_to_date_string(level_data.createDate)
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if level_data.NYID not in nyid_level_map:
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nyid_level_map[level_data.NYID] = level_data
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# 7. 合并数据并按 work_site 分组
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work_site_records = {} # work_site -> [records]
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for section in sections:
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# 获取工点名称
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work_site = section.work_site or "未知工点"
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checkpoints = section_checkpoint_map.get(section.section_id, [])
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for checkpoint in checkpoints:
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checkpoint.burial_date = TimeUtils.string_to_date_string(checkpoint.burial_date)
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settlements = point_settlement_map.get(checkpoint.point_id, [])
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for settlement in settlements:
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settlement.MTIME_W = TimeUtils.datetime_to_date_string(settlement.MTIME_W)
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settlement.createdate = TimeUtils.datetime_to_date_string(settlement.createdate)
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import decimal
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d = decimal.Decimal(settlement.CVALUE)
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bc = decimal.Decimal(settlement.mavalue_bc)
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lj = decimal.Decimal(settlement.mavalue_lj)
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d = d * 1000
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bc = bc * 1000
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lj = lj * 1000
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if d == d.to_integral_value():
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settlement.CVALUE = str(int(d))
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else:
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settlement.CVALUE = str(d)
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if bc == bc.to_integral_value():
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settlement.mavalue_bc = str(int(bc))
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else:
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settlement.mavalue_bc = str(bc)
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if lj == lj.to_integral_value():
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settlement.mavalue_lj = str(int(lj))
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else:
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settlement.mavalue_lj = str(lj)
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# 从映射中获取水准数据
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level_data = nyid_level_map.get(settlement.NYID)
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# 合并数据
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merged_record = self.merge_settlement_with_related_data(
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db, settlement, section, checkpoint, level_data
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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(merged_record)
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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())} 条沉降数据记录")
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# 导出到Excel文件（按 work_site 分工作簿）
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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（Excel工作表名称不能包含：/、\、?、*、[、]等）
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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: # Excel工作表名称最大长度限制
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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 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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# ------------------------------ 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")
|
||
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")
|