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# -*- coding: utf-8 -*-
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import re
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import os
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import logging
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import pandas as pd
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from docx.oxml.ns import qn
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from fastapi import HTTPException
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from fastapi.responses import JSONResponse
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from docx import Document
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from pathlib import Path
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from datetime import datetime
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from docx.shared import Inches, Pt
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from app.tools.deal_excels import deal_excel, top5_dod_analysis, transform_data
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from app.tools.get_time import get_time
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from app.tools.replace_text import replace_text_in_docx
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from app.tools.replace_table import copy_table, copy_sta_table, copy_table_no_align
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from app.tools.style import table_style
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from app.tools.effective_date import get_next_day
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from app.tools.find_before_word import extract_overload_info_from_previous_day
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from app.tools.count_data import count_change_outage, count_outage_sentiment
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from app.tools.draw_picture import plot_electricity_comparison
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# 获取日志记录器
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logger = logging.getLogger(__name__)
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def deal_docx(folder_path, save_path=None, time_type=0):
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"""
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:param folder_path: 文件上传后保存的路径
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:param save_path: 最终生成的日报/简报的保存路径
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:param time_type: 判断时间的统计方式，0代表前一天17点之后到当天17点之前，1代表当天00:00:00到当天23:59:59
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:return: 返回生成日报的存储路径，保存到mysql
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"""
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# 拿到文件夹下所有文件名
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# folder_path = 'E:/work_data/work/三工单日报/20250308/源数据/源数据'
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# folder_path = 'E:/work_data/work/三工单日报/20250309/20250309'
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# folder_path = 'E:/work_data/work/三工单日报/20250310/20250310'
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try:
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logger.info("进入日报生成方法")
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files = os.listdir(folder_path)
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file_path_dict = {}
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# 拿到需要分析的三个文档
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for file in files:
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# 停电word
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if file.endswith(".docx") and "投诉服务" in file:
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file_path_dict["power_off_doc"] = folder_path + "/" + file
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continue
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# 舆情word
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if file.endswith(".docx") and "抢修投诉舆情" in file:
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file_path_dict["sentiment_doc"] = folder_path + "/" + file
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print(f"舆情文件路径{file_path_dict['sentiment_doc']}")
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continue
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# 投诉excel
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if file.endswith(".xlsx") and "投诉统计表" in file:
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file_path_dict["power_off_excel"] = folder_path + "/" + file
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continue
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# 如果传入的文件不对，抛出异常
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if len(file_path_dict) != 3:
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logger.exception("文件格式错误")
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raise HTTPException(
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status_code=400,
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detail="文档无法正确解析，请确认上传的生成日报的资料是否完整",
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)
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# ————————————————————————处理word文档—————————————————————————
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# 读取停电word文件信息
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doc_poweroff = Document(file_path_dict["power_off_doc"])
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# 读取舆情word文件
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doc_sentiment = Document(file_path_dict["sentiment_doc"])
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# 日报拼接数据字典
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doc_dict = {}
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if time_type == 0:
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# 旧版正则
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# update:2025-07-04 格式维06和7的匹配
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time_re = re.compile(r"^(\d+年)?\d+月\d+日\d+时至.{7,15}期间[,，]")
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elif time_type == 1:
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# 20250429过滤时间正则
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time_re = re.compile(
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r"^(\d+年)?\d+月\d+日[^,，。\.;；“”\']{0,10}至[^,，。\.;；“”\']{0,15}期间[,，]"
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)
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# 避免拿错段落，则进行遍历
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paragraphs_poweroff = doc_poweroff.paragraphs
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for para in paragraphs_poweroff:
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# 第一点内容
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if re.match(r".*全网累计停电.*", para.text):
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# print(para.text)
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doc_dict["first_point_para1"] = re.sub(time_re, "", para.text)
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continue
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if re.match(r".*全网故障抢修工单.*", para.text):
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# print(para.text)
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doc_dict["first_point_para2"] = re.sub(
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r"[，,]整体抢修工作态势正常。+$", "", para.text
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)
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continue
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# 第二点过载台数
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if re.search(r"过载\d+台", para.text):
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doc_dict["over_load"] = (
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re.search(r"过载\d+台", para.text).group().replace("过载", "")
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).replace("台", "")
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# print(doc_dict['over_load'])
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continue
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# 拿到舆情的段落
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paragraphs_sentiment = doc_sentiment.paragraphs
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for para in paragraphs_sentiment:
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if re.match(r".*舆情风险信息\d+条.*", para.text):
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text_temp = re.sub(time_re, "", para.text)
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doc_dict["sentiment_trend"] = re.search(
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r"[^,\\.。]*[,，]舆情态势[^,\\.。]*[\\.。]$", text_temp
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).group()
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doc_dict["sentiment_para"] = re.sub(
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r"[^,\\.。，]*[,，]舆情态势[^,\\.。]*[\\.。]$", "", text_temp
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)
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continue
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# 获取所有表格
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print("获取投诉服务的表格")
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tables = doc_poweroff.tables
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# 舆情直接取第一个表格
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print("舆情的表格")
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table_sentiment = doc_sentiment.tables[0]
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# 表1 “抢修、投诉、舆情”三工单监测汇总表
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table1 = tables[0]
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# 表2配变过载监测汇总表
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table2 = tables[1]
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# 表3停电用户前五供电局
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table3 = tables[2]
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# 新增表4 95598供电类投诉前五供电局统计表
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# table4 = doc_poweroff.add_table(6, 5)
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# ————————————————————————处理word文档—————————————————————————
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# -----------------------------------------------------------------------------------------
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# ————————————————————————表格环比统计——————————————————————————
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# 首先拿到分析时间，明确要分析哪天的数据
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(
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start_time,
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end_time,
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before_start_time,
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year,
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month,
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day,
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day_before,
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month_before,
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) = get_time(files, time_type)
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# 获取后一天的时间
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year_now, month_now, day_now = get_next_day(int(year), int(month), int(day))
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# 通过上述时间,统计停电excel的情况
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# 当天情况
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province_statistics, district_statistics = deal_excel(
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start_time, end_time, file_path_dict["power_off_excel"]
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)
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print(f"省份统计{province_statistics}")
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print(f"地市统计{district_statistics}")
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province_statistics_list = list(province_statistics.values())
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# 当天省份总投诉
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province_statistics_total = sum(province_statistics.values())
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print(f"省份总投诉{province_statistics_total}")
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# 昨天情况
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province_stat_before, district_stat_before = deal_excel(
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before_start_time, start_time, file_path_dict["power_off_excel"]
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)
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print(f"省份昨日情况{province_stat_before}")
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# 昨天省份总投诉
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province_stat_be_total = sum(province_stat_before.values())
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print(f"省份昨日总投诉{province_stat_be_total}")
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# 省份环比
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province_dod = {
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k: province_statistics[k] - province_stat_before[k]
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for k in province_statistics.keys()
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}
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# 最终省份环比结果
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for key, value in province_dod.items():
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if int(value) > 0:
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province_dod[key] = "+" + str(value)
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elif int(value) == 0:
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province_dod[key] = "持平"
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print(f"省份环比{province_dod}")
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province_dod_list = list(province_dod.values())
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# 表1中剩余的省份统计数据及舆情的统计数据、环比情况
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table1_extra_data = transform_data(
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[province_statistics_list, province_dod_list]
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)
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logger.info(
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f"表1中剩余的省份统计数据及舆情的统计数据、环比情况：{table1_extra_data}"
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)
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# 将昨天的地市统计转成字典
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district_stat_before = dict(district_stat_before)
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# 查看今天的前五在昨天的情况
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"""
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情况1：今天的数据大于5，则可以直接用现有逻辑
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情况2：今天的数据小于5，值判断小于5的这几条，比如只有1条，就判断这一条的情况
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"""
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top_dod_dict = {}
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# 需要判断地市停电的有没有5个，分小于5或者大于等于5
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top5_name_list = []
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top5_poweroff_list = []
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# update:2025-07-04 修改供电类投诉前五供电局统计表的同排行
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need_district_statistics = (
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district_statistics[0:5]
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if len(district_statistics) > 5
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else district_statistics
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)
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other_district_statistic = (
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district_statistics[5:] if len(district_statistics) > 5 else []
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)
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other_count = 0
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if (
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len(other_district_statistic) > 0
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and district_statistics[4][1] == district_statistics[5][1]
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):
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for i in range(len(other_district_statistic)):
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if other_district_statistic[i][1] == district_statistics[4][1]:
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other_count += 1
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poweroff_value = need_district_statistics[len(need_district_statistics) - 1][1]
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count = 0
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for i in range(len(need_district_statistics)):
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current_poweroff_value = need_district_statistics[i][1]
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if current_poweroff_value == poweroff_value:
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count += 1
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else:
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top5_name_list.append(need_district_statistics[i][0])
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top5_poweroff_list.append(need_district_statistics[i][1])
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top_dod_dict[need_district_statistics[i][0]] = top5_dod_analysis(
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need_district_statistics[i], district_stat_before
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)
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if count == 1 and other_count==0:
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top5_name_list.append(
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need_district_statistics[len(need_district_statistics) - 1][0]
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)
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top5_poweroff_list.append(
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need_district_statistics[len(need_district_statistics) - 1][1]
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)
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top_dod_dict[
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need_district_statistics[len(need_district_statistics) - 1][0]
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] = top5_dod_analysis(
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need_district_statistics[len(need_district_statistics) - 1],
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district_stat_before,
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)
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else:
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top5_name_list.append(f"其他{count + other_count}家单位")
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top5_poweroff_list.append(poweroff_value)
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top_dod_dict["其他单位"] = "—"
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# old_version
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"""
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if len(district_statistics) >= 5:
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# 地市前五统计
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# print(district_statistics)
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top1 = district_statistics[0]
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top2 = district_statistics[1]
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top3 = district_statistics[2]
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top4 = district_statistics[3]
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top5 = district_statistics[4]
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print(f'地市前五{top1}{top2}{top3}{top4}{top5}')
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top5_name_list = [top1[0], top2[0], top3[0], top4[0], top5[0]]
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top5_poweroff_list = [top1[1], top2[1], top3[1], top4[1], top5[1]]
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top_dod_dict[top1[0]] = top5_dod_analysis(top1, district_stat_before)
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top_dod_dict[top2[0]] = top5_dod_analysis(top2, district_stat_before)
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top_dod_dict[top3[0]] = top5_dod_analysis(top3, district_stat_before)
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top_dod_dict[top4[0]] = top5_dod_analysis(top4, district_stat_before)
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top_dod_dict[top5[0]] = top5_dod_analysis(top5, district_stat_before)
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elif 0 < len(district_statistics) < 5:
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for i in range(len(district_statistics)):
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top5_name_list.append(district_statistics[i][0])
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top5_poweroff_list.append(district_statistics[i][1])
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top_dod_dict[district_statistics[i][0]] = top5_dod_analysis(district_statistics[i], district_stat_before)
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print(f"地市前五名称{top5_name_list}")
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print(f"地市前五数据{top5_poweroff_list}")
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# top_dod_dict[top1[0]] = top5_dod_analysis(top1, district_stat_before)
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# top_dod_dict[top2[0]] = top5_dod_analysis(top2, district_stat_before)
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# top_dod_dict[top3[0]] = top5_dod_analysis(top3, district_stat_before)
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# top_dod_dict[top4[0]] = top5_dod_analysis(top4, district_stat_before)
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# top_dod_dict[top5[0]] = top5_dod_analysis(top5, district_stat_before)
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"""
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print(f"地市环比{top_dod_dict}")
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top5_stat_list = list(top_dod_dict.values())
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# 地市前5的名称、数据、环比放入列表并转至，方便写入表格4
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top5_list = transform_data([top5_name_list, top5_poweroff_list, top5_stat_list])
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# 省总的投诉情况及环比
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complain_dod = int(province_statistics_total) - int(province_stat_be_total)
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logger.info(f"省份总量环比{complain_dod}")
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# 计算省份总量环比
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if complain_dod > 0:
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# 使用 f-string 进行格式化
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complain_dod = f"增加{complain_dod / province_stat_be_total * 100:.2f}%"
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elif complain_dod < 0:
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# 使用 f-string 进行格式化
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complain_dod = f"减少{-complain_dod / province_stat_be_total * 100:.2f}%"
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else:
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complain_dod = "持平"
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# 异常处置情况
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electricity_exception = "停电、投诉、舆情整体平稳，无异常情况。"
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standardize_date = None
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if time_type == 0:
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# ！！！旧版 前面已经过滤掉了时间信息，此处对时间进行单独赋值操作
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standardize_date = (
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f"{month_before}月{day_before}日17时至{month}月{day}日17时期间，"
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)
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elif time_type == 1:
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# -------------------------20250429更新，修改开始和结束时间---------------------------------
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standardize_date = f"{year}年{month}月{day}日0时至24时期间，"
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# standardize_date = ''
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# -------------------------20250429更新，修改开始和结束时间---------------------------------
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# ————————————————————————表格环比统计——————————————————————————
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# 获取当前文件夹路径
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current_path = Path(__file__).parent
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templates_path = str(os.path.join(current_path.parent, "templates")).replace(
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"\\", "/"
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)
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# 默认标题
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# 注意，标题会根据不同时期进行调整
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report_title = r"南方电网公司“停电抢修、投诉服务、舆情管控”三工单联动监测日报"
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# ————————————————————————组装完整简报——————————————————————————
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if time_type == 0:
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# 旧版正则
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sample_first_para = (
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f"{month_before}月{day_before}日17时至{month}月{day}日17时"
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)
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elif time_type == 1:
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# 20250429过滤时间正则
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sample_first_para = f"{year}年{month}月{day}日0时至24时"
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# 简报舆情信息
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doc_dict["sentiment_para_simple"] = doc_dict["sentiment_para"].replace(
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"全网监测到", ""
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)
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if re.search(r"重要用户停电[^0]户", doc_dict["first_point_para1"]):
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doc_dict["have_important"] = re.sub(
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"[,，]用户停电情况总体平稳",
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"",
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re.sub("其中[,，]", "", doc_dict["first_point_para1"]),
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)
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else:
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doc_dict["have_important"] = (
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re.sub(
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r"[,，]其中.{0,3}重要用户停电0户.{0,5}停电情况总体平稳[\\.。]",
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"",
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doc_dict["first_point_para1"],
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)
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+ "，无重要用户停电。"
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)
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# 获取停电数字信息
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(
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total_outage,
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short_term_outage,
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change_outage,
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percentage,
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short_precentage,
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important_stop_outage,
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type,
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) = count_change_outage(doc_dict["have_important"])
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# 获取舆情数字信息
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today_sentiment, type_sentiment, yesterday_sentiment, result_sentiment = (
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count_outage_sentiment(doc_dict["sentiment_para_simple"])
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)
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# 简报的舆情信息只要总数和环比
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complain_simple = (
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f"95598供电类投诉{province_statistics_total}条，环比{complain_dod}"
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)
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print(doc_dict["have_important"])
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print(doc_dict["sentiment_para_simple"])
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current_doc_name = f"南方电网公司停电抢修投诉服务舆情管控三工单联动监测日报{year}{int(month):02d}{int(day):02d}.docx"
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doc_dict_over_load = doc_dict["over_load"]
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over_load_before = extract_overload_info_from_previous_day(
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current_word=current_doc_name
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)
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if over_load_before:
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# 将字符串转换为浮点数
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over_load_before = float(over_load_before)
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doc_dict_over_load = float(doc_dict_over_load)
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if over_load_before > doc_dict_over_load:
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over_load_percent = (
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(over_load_before - doc_dict_over_load) / over_load_before * 100
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)
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over_load_percent = f"{over_load_percent:.2f}%"
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over_load_type = "减少"
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elif over_load_before < doc_dict_over_load:
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over_load_percent = (
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(doc_dict_over_load - over_load_before) / over_load_before * 100
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)
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over_load_percent = f"{over_load_percent:.2f}%"
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over_load_type = "增加"
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else:
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over_load_percent = ""
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over_load_type = "持平"
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else:
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over_load_before = ""
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over_load_percent = "，缺少上一天数据"
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over_load_type = "无法估计"
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# 组装替换的文本
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replacements_simple = {
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"{{standardize_date}}": standardize_date,
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"{{total_outage}}": str(total_outage),
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"{{short_term_outage}}": str(short_term_outage),
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"{{change_outage}}": str(change_outage),
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"{{percentage}}": str(percentage),
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"{{short_precentage}}": str(short_precentage),
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"{{important_stop_outage}}": str(important_stop_outage),
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"{{type}}": type,
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|
"{{have_important}}": doc_dict["have_important"],
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"{{over_load}}": doc_dict["over_load"],
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|
"{{over_load_percent}}": str(over_load_percent),
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|
"{{over_load_type}}": over_load_type,
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"{{complain}}": complain_simple,
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"{{sample_first_para}}": sample_first_para,
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"{{today_sentiment}}": str(today_sentiment),
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"{{type_sentiment}}": type_sentiment,
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"{{yesterday_sentiment}}": str(yesterday_sentiment),
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"{{result_sentiment}}": str(result_sentiment) if result_sentiment!="" else "",
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"{{year}}": year,
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"{{month}}": month,
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"{{day}}": day,
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}
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# 组装简报
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electricity_daily_simple = Document(f"{templates_path}/简报模板.docx")
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# 替换模板字符串
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replace_text_in_docx(electricity_daily_simple, replacements_simple)
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datas = {
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"停电用户\n（万户）": {
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"昨天": total_outage + change_outage,
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"今天": total_outage,
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|
},
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"过载配变\n（台）": {"昨天": over_load_before, "今天": doc_dict_over_load},
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"95598供电类\n投诉（条）": {
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|
"昨天": province_stat_be_total,
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"今天": province_statistics_total,
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},
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"涉电力供应类舆情\n风险信息（条）": {
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|
"昨天": yesterday_sentiment,
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"今天": today_sentiment,
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},
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}
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|
# 将数据转换为DataFrame
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|
df = pd.DataFrame(datas)
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# 遍历 datas 中的每个值，将 None 或 空字符串替换为 0
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|
for key, value in datas.items():
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for sub_key, sub_value in value.items():
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if sub_value is None or sub_value == "":
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datas[key][sub_key] = None # 将不存在或为空的值设置为 0
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else:
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datas[key][sub_key] = int(sub_value) # 确保值是整数
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# 生成柱状图
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|
img_path = plot_electricity_comparison(year, month, day, datas)
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# 查找插入图片的位置（假设模板中有"{{IMG_PLACEHOLDER}}"作为占位符）
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|
img_placeholder = "{{IMG_PLACEHOLDER}}"
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|
img_inserted = False
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|
for paragraph in electricity_daily_simple.paragraphs:
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|
if img_placeholder in paragraph.text:
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# 删除占位符文本
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|
paragraph.text = paragraph.text.replace(img_placeholder, "")
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# 插入图片
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|
run = paragraph.add_run()
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|
run.add_picture(img_path, width=Inches(6.0))
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img_inserted = True
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|
break
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|
if not img_inserted:
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|
# 如果未找到占位符，则在文档末尾添加图片
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|
|
p = electricity_daily_simple.add_paragraph()
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|
run = p.add_run()
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run.add_picture(img_path, width=Inches(6.0))
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|
# 将表格写入简报
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|
# 设置全局样式
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|
|
style = electricity_daily_simple.styles["Normal"]
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|
|
style.font.name = "Times New Roman"
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|
|
# 按照月份分门别类的文件夹
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|
|
save_folder = f"{year}{str(month).zfill(2)}"
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|
# 创建子文件夹
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|
|
if not os.path.exists(f"{save_path}/{save_folder}"):
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|
|
os.makedirs(f"{save_path}/{save_folder}", exist_ok=True)
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|
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|
|
|
final_file = None
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|
|
final_sim_file = None
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|
|
# 最终保存文件的路径情况
|
|
|
if time_type == 0:
|
|
|
final_file = f"{save_path}/{save_folder}/{report_title}-{year}{str(month).zfill(2)}{str(day).zfill(2)}.docx"
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|
|
final_sim_file = f"{save_path}/{save_folder}/【简版】{report_title}-{year}{str(month).zfill(2)}{str(day).zfill(2)}.docx"
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|
|
|
|
elif time_type == 1:
|
|
|
final_file = f"{save_path}/{save_folder}/{report_title}-{year_now}{str(month_now).zfill(2)}{str(day_now).zfill(2)}.docx"
|
|
|
final_sim_file = f"{save_path}/{save_folder}/【简版】{report_title}-{year_now}{str(month_now).zfill(2)}{str(day_now).zfill(2)}.docx"
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|
|
|
|
|
# 删除旧文件，方便文件更新
|
|
|
delete_old_file(final_file)
|
|
|
delete_old_file(final_sim_file)
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|
|
|
|
|
# 生成简报
|
|
|
# 接口保存路径地址
|
|
|
# 保存为Excel文件
|
|
|
path = f"{save_path}/{save_folder}/{year}{str(month).zfill(2)}{str(day).zfill(2)}电力统计数据.xlsx"
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|
|
df.to_excel(path, index=True)
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|
|
electricity_daily_simple.save(final_sim_file)
|
|
|
# 测试保存路径
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|
|
# electricity_daily_simple.save(f'【简版】公司全国“两会”保供电期间配网设备运行及三工单监测日报-{year}{str(month).zfill(2)}{str(day).zfill(2)}.docx')
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|
|
|
|
|
# # ————————————————————————组装完整简报——————————————————————————
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|
|
|
|
|
# -----------------------------------------------------------------------------------------
|
|
|
|
|
|
# ————————————————————————组装完整日报——————————————————————————
|
|
|
|
|
|
# 将数据组装相关的时间内容
|
|
|
doc_dict["first_point_para1"] = standardize_date + doc_dict["first_point_para1"]
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|
|
doc_dict["sentiment_para"] = standardize_date + doc_dict["sentiment_para"]
|
|
|
|
|
|
# {{standardize_date}}全网收到{{complain_num}}条供电类投诉，环比{{complain_dod}}条；
|
|
|
complain_text = (
|
|
|
standardize_date
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|
|
+ f"全网收到{str(province_statistics_total)}条供电类投诉，环比{complain_dod}"
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|
|
)
|
|
|
# update:2025-07-04 备注,增加过载环比
|
|
|
replacements = {}
|
|
|
if time_type == 0:
|
|
|
# 组装替换的文本
|
|
|
replacements = {
|
|
|
"{{year}}": year,
|
|
|
"{{month}}": month,
|
|
|
"{{day}}": day,
|
|
|
"{{power_off_one}}": doc_dict["first_point_para1"],
|
|
|
"{{power_off_two}}": doc_dict["first_point_para2"],
|
|
|
"{{over_load}}": doc_dict["over_load"],
|
|
|
"{{over_load_percent}}": str(over_load_percent),
|
|
|
"{{over_load_type}}": over_load_type,
|
|
|
"{{complain}}": complain_simple,
|
|
|
"{{sentiment}}": doc_dict["sentiment_para"],
|
|
|
"{{sentiment_trend}}": doc_dict["sentiment_trend"],
|
|
|
"{{exception}}": electricity_exception,
|
|
|
}
|
|
|
|
|
|
elif time_type == 1:
|
|
|
# 组装替换的文本
|
|
|
replacements = {
|
|
|
"{{year}}": str(year_now),
|
|
|
"{{month}}": str(month_now),
|
|
|
"{{day}}": str(day_now),
|
|
|
"{{power_off_one}}": doc_dict["first_point_para1"],
|
|
|
"{{power_off_two}}": doc_dict["first_point_para2"],
|
|
|
"{{over_load}}": doc_dict["over_load"],
|
|
|
"{{over_load_percent}}": str(over_load_percent),
|
|
|
"{{over_load_type}}": over_load_type,
|
|
|
"{{complain}}": complain_simple,
|
|
|
"{{sentiment}}": doc_dict["sentiment_para"],
|
|
|
"{{sentiment_trend}}": doc_dict["sentiment_trend"],
|
|
|
"{{exception}}": electricity_exception,
|
|
|
}
|
|
|
|
|
|
# 组装日报
|
|
|
electricity_daily = Document(f"{templates_path}/日报模板.docx")
|
|
|
|
|
|
#
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|
|
replace_text_in_docx(electricity_daily, replacements)
|
|
|
|
|
|
# 将表格添加到新的文档里
|
|
|
# 组装表1的数据
|
|
|
# 此处缺少省份统计数据和舆情数据
|
|
|
logger.info("将投诉服务的表格1写入日报的表格1，从2-8行，2-9列写入")
|
|
|
# 定义要查看的区域范围
|
|
|
start_row1 = 2 # 起始行索引（从0开始）
|
|
|
end_row1 = 8 # 结束行索引（不包括）
|
|
|
start_col1 = 2 # 起始列索引（从0开始）
|
|
|
end_col1 = 9 # 结束列索引（不包括）
|
|
|
copy_table(
|
|
|
table1,
|
|
|
electricity_daily.tables[0],
|
|
|
start_row1,
|
|
|
end_row1,
|
|
|
start_col1,
|
|
|
end_col1,
|
|
|
0,
|
|
|
)
|
|
|
|
|
|
|
|
|
# 插入各个省份的投诉数据及环比
|
|
|
logger.info("将投诉数据写入到表1，第二行开始，第11列开始")
|
|
|
# 省份统计的表格数据在表格中的起始位置
|
|
|
start_row_pro_sta = 2
|
|
|
start_col_pro_sta = 11
|
|
|
copy_sta_table(
|
|
|
electricity_daily.tables[0],
|
|
|
table1_extra_data,
|
|
|
start_row_pro_sta,
|
|
|
start_col_pro_sta,
|
|
|
)
|
|
|
|
|
|
# 放入舆情的数据
|
|
|
logger.info("将舆情数据写入到表1，第二行开始，第11列开始")
|
|
|
# 定义要查看的区域范围
|
|
|
start_row1_1 = 2 # 起始行索引（从0开始）
|
|
|
end_row1_1 = 8 # 结束行索引（不包括）
|
|
|
start_col1_1 = 10 # 起始列索引（从0开始）
|
|
|
end_col1_1 = 13 # 结束列索引（不包括）
|
|
|
copy_table(
|
|
|
table_sentiment,
|
|
|
electricity_daily.tables[0],
|
|
|
start_row1_1,
|
|
|
end_row1_1,
|
|
|
start_col1_1,
|
|
|
end_col1_1,
|
|
|
3,
|
|
|
)
|
|
|
|
|
|
# 复制表1的数据
|
|
|
logger.info("将配变过载写入表0，第二行开始，第9列开始")
|
|
|
# 定义要查看的区域范围
|
|
|
start_row2 = 1 # 起始行索引（从0开始）
|
|
|
end_row2 = 7 # 结束行索引（不包括）
|
|
|
start_col2 = 2 # 起始列索引（从0开始）
|
|
|
end_col2 = 4 # 结束列索引（不包括）
|
|
|
target_row=1
|
|
|
target_col=7
|
|
|
copy_table_no_align(
|
|
|
table2,
|
|
|
electricity_daily.tables[0],
|
|
|
start_row2,
|
|
|
end_row2,
|
|
|
start_col2,
|
|
|
end_col2,
|
|
|
target_row,
|
|
|
target_col
|
|
|
)
|
|
|
"""
|
|
|
copy_table(
|
|
|
table2,
|
|
|
electricity_daily.tables[1],
|
|
|
start_row2,
|
|
|
end_row2,
|
|
|
start_col2,
|
|
|
end_col2,
|
|
|
0,
|
|
|
)
|
|
|
"""
|
|
|
|
|
|
# 复制表3的数据
|
|
|
logger.info("将停电前五插入表格2")
|
|
|
# 定义要查看的区域范围
|
|
|
start_row3 = 2 # 起始行索引（从0开始）
|
|
|
end_row3 = 7 # 结束行索引（不包括）
|
|
|
start_col3 = 1 # 起始列索引（从0开始）
|
|
|
end_col3 = 5 # 结束列索引（不包括）
|
|
|
copy_table(
|
|
|
table3,
|
|
|
electricity_daily.tables[1],
|
|
|
start_row3,
|
|
|
end_row3,
|
|
|
start_col3,
|
|
|
end_col3,
|
|
|
0,
|
|
|
)
|
|
|
"""
|
|
|
copy_table(
|
|
|
table3,
|
|
|
electricity_daily.tables[2],
|
|
|
start_row3,
|
|
|
end_row3,
|
|
|
start_col3,
|
|
|
end_col3,
|
|
|
0,
|
|
|
)
|
|
|
"""
|
|
|
# 填充表格4
|
|
|
# 需要判断是否前五数据不存在
|
|
|
logger.info("将自行统计的数据插入表格5")
|
|
|
# 表4中的插入位置
|
|
|
start_tb4_row = 2
|
|
|
start_tb4_col = 5
|
|
|
if top5_list:
|
|
|
copy_sta_table(
|
|
|
electricity_daily.tables[1],
|
|
|
top5_list,
|
|
|
start_tb4_row,
|
|
|
start_tb4_col,
|
|
|
is_dynamics=False
|
|
|
)
|
|
|
|
|
|
# copy_sta_table(electricity_daily.tables[3], top5_list, start_tb4_row, start_tb4_col)
|
|
|
|
|
|
# 将表格中的字体中文设置成仿宋，英文数字设置成新罗马，均为11号大小
|
|
|
for table in electricity_daily.tables:
|
|
|
table_style(table)
|
|
|
|
|
|
# 设置英文数字样式
|
|
|
# 设置全局样式
|
|
|
# 显式设置每个段落的字体
|
|
|
for paragraph in electricity_daily.paragraphs:
|
|
|
for run in paragraph.runs:
|
|
|
run.font.name = "Times New Roman"
|
|
|
#run.font.name = "仿宋"
|
|
|
#run._element.rPr.rFonts.set(qn('w:eastAsia'), '仿宋')
|
|
|
#run.font.size = Pt(16)
|
|
|
|
|
|
# 接口保存路径
|
|
|
electricity_daily.save(final_file)
|
|
|
|
|
|
# 返回doc、年月日，然后在接口代码里进行分析后，提取表1的数据保存到数据库
|
|
|
# return electricity_daily, year, month, day
|
|
|
# 日报本身的时间
|
|
|
statistics_time = None
|
|
|
|
|
|
if time_type == 0:
|
|
|
statistics_time = datetime(int(year), int(month), int(day))
|
|
|
|
|
|
elif time_type == 1:
|
|
|
statistics_time = datetime(int(year_now), int(month_now), int(day_now))
|
|
|
|
|
|
# 返回值保存到数据库，以二进制保存
|
|
|
if time_type == 0:
|
|
|
return {
|
|
|
"report_title": f"{report_title}-{year}{str(month).zfill(2)}{str(day).zfill(2)}.docx",
|
|
|
"daily_report": final_file,
|
|
|
"daily_repo_simple": final_sim_file,
|
|
|
"statistics_time": statistics_time,
|
|
|
"save_folder": save_folder,
|
|
|
# 'excel':path,
|
|
|
# 'img':img_path
|
|
|
}
|
|
|
elif time_type == 1:
|
|
|
return {
|
|
|
"report_title": f"{report_title}-{year_now}{str(month_now).zfill(2)}{str(day_now).zfill(2)}.docx",
|
|
|
"daily_report": final_file,
|
|
|
"daily_repo_simple": final_sim_file,
|
|
|
"statistics_time": statistics_time,
|
|
|
"save_folder": save_folder,
|
|
|
# 'excel':path,
|
|
|
# 'img':img_path
|
|
|
}
|
|
|
# 测试保存路径
|
|
|
# electricity_daily.save(f'公司全国“两会”保供电期间配网设备运行及三工单监测日报-{year}{str(month).zfill(2)}{str(day).zfill(2)}.docx')
|
|
|
# # ————————————————————————组装完整日报——————————————————————————
|
|
|
|
|
|
except Exception as e:
|
|
|
logger.exception("最终渲染阶段失败")
|
|
|
return JSONResponse(
|
|
|
content={"status_code": 500, "detail": f"word解析异常：{e}"}
|
|
|
)
|
|
|
|
|
|
|
|
|
# 从磁盘删除旧文件方法
|
|
|
def delete_old_file(file):
|
|
|
try:
|
|
|
if os.path.exists(file):
|
|
|
os.remove(file)
|
|
|
logger.info("磁盘里的旧文件删除成功")
|
|
|
|
|
|
except Exception as e:
|
|
|
logger.info(f"删除旧文件失败：{e}")
|
|
|
|
|
|
|
|
|
# if __name__ == '__main__':
|
|
|
# folder_path = 'E:/work_data/work/三工单日报/20250310/20250310'
|
|
|
#
|
|
|
# deal_docx(folder_path)
|