258 lines
14 KiB
Python
258 lines
14 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 ..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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