Merge branch 'master' of ssh://114.215.109.124:29418/underway-vue

riku

2025-11-05 ba7065fdcf6fe48d002908a1a7666a480e805021

 src/views/historymode/component/MissionReport.vue

@@ -14,6 +14,21 @@
        ></OptionLocation2>
      </el-form-item>
      <OptionTime v-model="formObj.timeArray"></OptionTime>
      <el-form-item label="区县筛选" prop="removeOtherDistrict">
        <el-checkbox v-model="formObj.removeOtherDistrict"
          >移除其他区县</el-checkbox
        >
      </el-form-item>
      <el-form-item label="污染源筛选" prop="removeNoPollutedSource">
        <el-checkbox v-model="formObj.removeNoPollutedSource"
          >移除未发现污染源的线索</el-checkbox
        >
      </el-form-item>
      <el-form-item label="隐患区域" prop="showPollutedArea">
        <el-checkbox v-model="formObj.showPollutedArea"
          >将典型隐患区域表格作为附件展示</el-checkbox
        >
      </el-form-item>
      <el-form-item>
        <el-button
          type="primary"
@@ -23,18 +38,18 @@
        >
          下载报告
        </el-button>
        <el-button
        <!-- <el-button
          type="primary"
          class="el-button-custom"
          @click="handleGenerateImg"
          :loading="docLoading"
        >
          生成图片
        </el-button>
        </el-button> -->
      </el-form-item>
      <el-form-item>
      <!-- <el-form-item>
        <el-image :src="base64Url" fit="fill" :preview-src-list="[base64Url]" />
      </el-form-item>
      </el-form-item> -->
      <el-form-item>
        <el-button
          type="primary"
@@ -73,6 +88,7 @@
import { Legend } from '@/model/Legend';
import { getHexColor, getColorBetweenTwoColors } from '@/utils/color';
import { getGridDataDetailFactorValue } from '@/model/GridDataDetail';
import { factorName } from '@/constant/factor-name';

// 借用卫星遥测模块中的100米网格
const props = defineProps({
@@ -83,7 +99,7 @@
});

const formObj = ref({
  timeArray: [new Date('2025-07-01T00:00:00'), new Date('2025-08-31T23:59:59')],
  timeArray: [new Date('2025-07-01T00:00:00'), new Date('2025-09-30T23:59:59')],
  location: {}
});

@@ -108,6 +124,8 @@
      districtCode: formObj.value.location.dCode,
      districtName: formObj.value.location.dName
    },
    removeOtherDistrict: formObj.value.removeOtherDistrict,
    removeNoPollutedSource: formObj.value.removeNoPollutedSource,
    factorTypes: radioOptions(TYPE0).map((e) => e.name)
  };
});
@@ -133,16 +151,19 @@
      mainFactor: '',
      _abnormalFactors: '',
      sceneCount: 0,
      _kilometres: '1000'
      _kilometres: '1000',
      _keyScene: '1个国控点（静安监测站）和2个市控点（和田中学、市北高新）',
      exceptionCount: 0,
      _focusScene: ''
    }
  ],
  missionDetailList: [
    {
      _index: 1,
      missionCode: '',
      _startTime: '2025年07月29日',
      _time: '09:00至14:30',
      _kilometres: '1000',
      _keyScene: '1个国控点（静安监测站）和2个市控点（和田中学、市北高新）',
      _dataStatistics: [
        {
          factor: 'PM10',
@@ -176,6 +197,19 @@
                '1.上海依德汽车维修有限公司，汽修企业，位于上海市长宁区北虹路1079号，距仙霞站1887米。\r\n……'
            }
          ]
        }
      ],
      clueList: [
        {
          _factorNames: 'PM2.5',
          _time: '10:22:28 - 10:22:34',
          _riskRegion: '长宁区清溪路可乐东路',
          _exceptionType: '快速上升',
          _chart: '',
          _conclusion:
            '在10:22:28至10:22:34之间，出现快速上升，VOC最低值为135.95μg/m³，最高值为135.95μg/m³，均值为135.95μg/m³，发现3个风险源，包含2个加油站，1个汽修。',
          _scenes:
            '1.上海依德汽车维修有限公司，汽修企业，位于上海市长宁区北虹路1079号，距仙霞站1887米。\r\n……'
        }
      ]
    }
@@ -223,7 +257,9 @@
};

const handleGenerateImg = () => {
  generateClueByRiskArea(params.value).then(() => {});
  generateClueByRiskArea(params.value).then(() => {
    generateDocx();
  });
};

function generateMissionSummary(param) {
@@ -232,6 +268,10 @@
      new Date(res.data.startTime),
      new Date(res.data.endTime)
    );
    templateParam.subTitle =
      templateParam.sryTime.indexOf('季度') !== -1
        ? templateParam.sryTime.replace(/（.*）/, '')
        : templateParam.sryTime;
    templateParam.sryArea = res.data.area.districtName;
    templateParam.sryCount = res.data.count;
    templateParam.sryKm = Math.round(res.data.kilometres / 1000);
@@ -257,9 +297,9 @@
  return dataAnalysisApi.fetchMissionList(param).then((res) => {
    templateParam.missionInfoList = res.data.map((item) => {
      item._time = formatDateTimeRange(item.startTime, item.endTime);
      item._airQulity = `AQI：${item.aqi}（${item.pollutionDegree}）`;
      item._airQulity = `${item.aqi}（${item.pollutionDegree}）`;
      item._abnormalFactors = item.abnormalFactors
        .map((factor) => factor)
        .map((factor) => factorName[factor])
        .join('、');
      item._kilometres = Math.round(item.kilometres / 1000);

@@ -294,7 +334,7 @@
      item._startTime = t[0];
      item._time = t[1];
      item._kilometres = Math.round(item.kilometres / 1000);
      item._airQulity = `AQI：${item.aqi}（${item.pollutionDegree}）`;
      item._airQulity = `${item.aqi}（${item.pollutionDegree}）`;

      const factorNames = radioOptions(TYPE0).map((e) => e.name);
      item._dataStatistics = item.dataStatistics.filter((e) => {
@@ -303,9 +343,9 @@

      radioOptions(TYPE0).forEach((f) => {
        const _factor = item.dataStatistics.find((e) => e.factor == f.name);
        item[`avgValue_${f.name}`] = _factor?.avgValue ?? '-';
        item[`maxValue_${f.name}`] = _factor?.maxValue ?? '-';
        item[`minValue_${f.name}`] = _factor?.minValue ?? '-';
        item[`avgValue_${f.name}`] = _factor?.avgValue.toFixed(0) ?? '-';
        item[`maxValue_${f.name}`] = _factor?.maxValue.toFixed(0) ?? '-';
        item[`minValue_${f.name}`] = _factor?.minValue.toFixed(0) ?? '-';
      });

      return item;
@@ -314,44 +354,264 @@
}

function generateClueByRiskArea(param) {
  return dataAnalysisApi.fetchClueByRiskArea(param).then((res) => {
    templateParam.clueByAreaList = res.data.map((item, index) => {
      return {
        _index: index + 1,
        _area: `${item.sceneInfo.type}${item.sceneInfo.name}周边`,
        clueByFactorList: item.clueByFactorList.map((cbf) => {
          return {
            factor: cbf.factor,
            clues: cbf.clues.map((clue) => {
  const indexArr = [
    'A',
    'B',
    'C',
    'D',
    'E',
    'F',
    'G',
    'H',
    'I',
    'J',
    'K',
    'L',
    'M',
    'N',
    'O',
    'P',
    'Q',
    'R',
    'S',
    'T',
    'U',
    'V',
    'W',
    'X',
    'Y',
    'Z'
  ];
  const _param = {
    area: param.area,
    startTime: param.startTime,
    endTime: param.endTime,
    removeOtherDistrict: param.removeOtherDistrict,
    removeNoPollutedSource: param.removeNoPollutedSource
  };
  return dataAnalysisApi.fetchClueByRiskArea(_param).then((res) => {
    templateParam.showPollutedArea = formObj.value.showPollutedArea;
    templateParam.clueByAreaList = res.data
      .groupBy((e) => e.township)
      .map((item, index) => {
        const { key: township, values: clues } = item;
        let typeCount = {};
        let lastSceneType;
        let sceneIndex = 0;

        const _scenes = clues.flatMap((e) =>
          e.clue.pollutedSource.sceneList.map((s, index) => {
            const i = lastSceneType == s.type ? ++sceneIndex : 0;
            typeCount[s.type] = typeCount[s.type] ? typeCount[s.type] + 1 : 1;
            lastSceneType = s.type;
            return {
              des: `${s.type}${i + 1}：${s.name}，位于${s.location}，距${s.closestStation.name}${parseInt(s.length)}米；`
            };
          })
        );
        let _sceneDes = `走航过程中溯源到${_scenes.length}个风险源`;
        if (_scenes.length > 0) {
          _sceneDes += '，其类型是';
          _sceneDes += Object.keys(typeCount)
            .map((e) => `${e}（${typeCount[e]}个）`)
            .join('、');
          _sceneDes += '：';
        } else {
          _sceneDes = '走航过程中未溯源到风险源。';
        }
        return {
          _index: index + 1,
          // _area: `${item.sceneInfo.type}${item.sceneInfo.name}周边`,
          _area: `${township}`,
          _sceneDes,
          _scenes,
          clueList: clues.map((item3, index3) => {
            const clue = item3.clue;
            let _riskRegion = '';
            if (
              clue.pollutedArea.address.indexOf(
                clue.pollutedArea.streetNumber
              ) == -1
            ) {
              // _riskRegion +=
              //   (clue.pollutedArea.address ?? '') +
              //   '（' +
              //   (clue.pollutedArea.street ?? '') +
              //   (clue.pollutedArea.streetNumber ?? '') +
              //   (clue.pollutedArea.direction ?? '') +
              //   '）';
              _riskRegion = clue.pollutedArea.address;
            } else {
              _riskRegion = clue.pollutedArea.address;
            }
            return {
              index: indexArr[index3],
              showPollutedArea: formObj.value.showPollutedArea,
              _title: _riskRegion,
              _factorNames: Object.keys(clue.pollutedData.statisticMap)
                .map((e) => factorName[e])
<<<<<<< HEAD
                .join('、'),
              _date: moment(clue.pollutedData.startTime).format('YYYY-MM-DD'),
              _time:
                moment(clue.pollutedData.startTime).format('HH:mm:ss') +
                ' - ' +
                moment(clue.pollutedData.endTime).format('HH:mm:ss'),
              _riskRegion: _riskRegion,
              _exceptionType: clue.pollutedData.exception,
              _images: generateChartImg(clue.pollutedData),
              _conclusion: clue.pollutedSource.conclusion.replace(
                /，发现[0-9]*个风险源，包含[0-9]*个.*。/,
                '。'
              ),
              _hasScene: clue.pollutedSource.sceneList.length > 0
            };
          })
          // clueByFactorList: clues
          //   .groupBy((e) => e.factorTag)
          //   .map((item2, index2) => {
          //     const { key: factorTag, values: clues2 } = item2;
          //     const factorNames = [...new Set(clues2.flatMap((e) => e.factors))]
          //       .map((e) => factorName[e])
          //       .join('、');
          //     return {
          //       index: index2 + 1,
          //       factor: factorNames,
          //       clues: clues2.map((item3, index3) => {
          //         const clue = item3.clue;
          //         let _riskRegion = '';
          //         if (
          //           clue.pollutedArea.address.indexOf(
          //             clue.pollutedArea.streetNumber
          //           ) == -1
          //         ) {
          //           _riskRegion +=
          //             (clue.pollutedArea.address ?? '') +
          //             '（' +
          //             (clue.pollutedArea.street ?? '') +
          //             (clue.pollutedArea.streetNumber ?? '') +
          //             (clue.pollutedArea.direction ?? '') +
          //             '）';
          //         } else {
          //           _riskRegion = clue.pollutedArea.address;
          //         }
          //         return {
          //           index: index3 + 1 + '',
          //           showPollutedArea: formObj.value.showPollutedArea,
          //           _title:
          //             (clue.pollutedArea.street ?? '') +
          //             (clue.pollutedArea.streetNumber ?? '') +
          //             (clue.pollutedArea.direction ?? ''),
          //           _factorNames: Object.keys(clue.pollutedData.statisticMap)
          //             .map((e) => factorName[e])
          //             .join('、'),
          //           _time:
          //             moment(clue.pollutedData.startTime).format(
          //               'YYYY-MM-DD HH:mm:ss'
          //             ) +
          //             ' - ' +
          //             moment(clue.pollutedData.endTime).format('HH:mm:ss'),
          //           _riskRegion: _riskRegion,
          //           _exceptionType: clue.pollutedData.exception,
          //           _images: generateChartImg(clue.pollutedData),
          //           _conclusion: clue.pollutedSource.conclusion,
          //           _hasScene: clue.pollutedSource.sceneList.length > 0,
          //           // _scenes:
          //           //   clue.pollutedSource.sceneList.length > 0
          //           //     ? clue.pollutedSource.sceneList
          //           //         .map(
          //           //           (s, index) =>
          //           //             `${index + 1}. ${s.name}，${s.type}，位于${s.location}，距${s.closestStation.name}${parseInt(s.length)}米；`
          //           //         )
          //           //         .join('\r\n')
          //           //     : '无',
          //           _scenes: clue.pollutedSource.sceneList.map((s, index) => {
          //             return {
          //               des: `${index + 1}. ${s.name}，${s.type}，位于${s.location}，距${s.closestStation.name}${parseInt(s.length)}米；`
          //             };
          //           })
          //         };
          //       })
          //     };
          //   })
=======
                .join('、');
              return {
                _factorNames: Object.keys(clue.pollutedData.statisticMap)
                  .map((e) => e)
                  .join('、'),
                _time:
                  moment(clue.pollutedData.startTime).format('HH:mm:ss') +
                  ' - ' +
                  moment(clue.pollutedData.endTime).format('HH:mm:ss'),
                _riskRegion: clue.pollutedArea.address
                  ? clue.pollutedArea.address
                  : '',
                _exceptionType: clue.pollutedData.exception,
                _images: generateChartImg(clue.pollutedData),
                _conclusion: clue.pollutedSource.conclusion,
                _scenes:
                  clue.pollutedSource.sceneList.length > 0
                    ? clue.pollutedSource.sceneList
                        .map(
                          (s, index) =>
                            `${index + 1}. ${s.name}，${s.type}，位于${s.location}，距${s.closestStation.name}${parseInt(s.length)}米；`
                        )
                        .join('\n')
                    : '无'
                index: index2 + 1,
                factor: factorNames,
                clues: clues2.map((item3, index3) => {
                  const clue = item3.clue;
                  let _riskRegion = '';
                  if (
                    parseInt(clue.pollutedArea.distance) <
                    parseInt(clue.pollutedArea.distance2)
                  ) {
                    if (
                      clue.pollutedArea.address.indexOf(
                        clue.pollutedArea.streetNumber
                      ) == -1
                    ) {
                      _riskRegion +=
                        (clue.pollutedArea.address ?? '') +
                        '（' +
                        (clue.pollutedArea.street ?? '') +
                        (clue.pollutedArea.streetNumber ?? '') +
                        (clue.pollutedArea.direction ?? '') +
                        '）';
                    } else {
                      _riskRegion = clue.pollutedArea.address;
                    }
                  } else {
                    _riskRegion +=
                      (clue.pollutedArea.address ?? '') +
                      '（' +
                      (clue.pollutedArea.street ?? '') +
                      (clue.pollutedArea.roadinter ?? '') +
                      '）';
                  }
                  return {
                    index: index3 + 1 + '',
                    showPollutedArea: formObj.value.showPollutedArea,
                    _title:
                      // (clue.pollutedArea.street ?? '') +
                      // (clue.pollutedArea.streetNumber ?? '') +
                      // (clue.pollutedArea.direction ?? ''),
                      clue.pollutedArea.address ?? '',
                    _factorNames: Object.keys(clue.pollutedData.statisticMap)
                      .map((e) => factorName[e])
                      .join('、'),
                    _time:
                      moment(clue.pollutedData.startTime).format(
                        'YYYY-MM-DD HH:mm:ss'
                      ) +
                      ' - ' +
                      moment(clue.pollutedData.endTime).format('HH:mm:ss'),
                    _riskRegion: _riskRegion,
                    _exceptionType: clue.pollutedData.exception,
                    _images: generateChartImg(clue.pollutedData),
                    _conclusion: clue.pollutedSource.conclusion,
                    _hasScene: clue.pollutedSource.sceneList.length > 0,
                    // _scenes:
                    //   clue.pollutedSource.sceneList.length > 0
                    //     ? clue.pollutedSource.sceneList
                    //         .map(
                    //           (s, index) =>
                    //             `${index + 1}. ${s.name}，${s.type}，位于${s.location}，距${s.closestStation.name}${parseInt(s.length)}米；`
                    //         )
                    //         .join('\r\n')
                    //     : '无',
                    _scenes: clue.pollutedSource.sceneList.map((s, index) => {
                      return {
                        des: `${index + 1}. ${s.name}，${s.type}，位于${s.location}，距${s.closestStation.name}${parseInt(s.length)}米；`
                      };
                    })
                  };
                })
              };
            })
          };
        })
      };
    });
>>>>>>> ce17d42203a17120736d796d0e83b3742c4ec441
        };
      });
  });
}

@@ -367,13 +627,17 @@
  factorDatas.setData(pollutedData.historyDataList, 0, () => {
    for (const key in pollutedData.statisticMap) {
      const value = pollutedData.statisticMap[key];
      const _chartOptions = factorDataParser.parseData(factorDatas, [
        {
          label: value.factorName,
          name: value.factorName,
          value: value.factorId + ''
        }
      ]);
      const _chartOptions = factorDataParser.parseData(
        factorDatas,
        [
          {
            label: value.factorName,
            name: value.factorName,
            value: value.factorId + ''
          }
        ],
        false
      );
      _chartOptions.forEach((o) => {
        images.push({
          url: chartToImg.generateEchartsImage(o, exceptionIndexArr, 20)
@@ -409,7 +673,37 @@
        _missionDes: `${item.missionList.map((m) => m.missionCode).join('、')}共${item.missionList.length}次`
      };
      const _highRiskGridList = [];
      item.highRiskGridList.forEach((g, i) => {
      Object.keys(item.highRiskGridMap).forEach((key, i) => {
        const g = item.highRiskGridMap[key][0];

        const infoDes = item.highRiskGridMap[key].map((e) => {
          return {
            factorValue: e.factorValue,
            // 四至范围，顺序为最小经度，最大经度，最小纬度，最大纬度
            _boundsDes: `经度${e.bounds[0]}至${e.bounds[1]}，纬度${e.bounds[2]}至${e.bounds[3]}`,
            // 涉及街镇
            town: e.town,
            _scenesDes:
              e.highRiskScenes.length > 0
                ? `涉及的污染场景包括${e.highRiskScenes.map((s) => s.name).join('、')}`
                : '网格内可能存在隐藏风险源'
          };
        });
        const infoDes2 = item.highRiskGridMap2[key].map((e) => {
          return {
            factorValue: e.factorValue,
            // 四至范围，顺序为最小经度，最大经度，最小纬度，最大纬度
            _boundsDes: `经度${e.bounds[0]}至${e.bounds[1]}，纬度${e.bounds[2]}至${e.bounds[3]}`,
            // 涉及街镇
            town: e.town,
            _scenesDes:
              e.highRiskScenes.length > 0
                ? `涉及的污染场景包括${e.highRiskScenes.map((s) => s.name).join('、')}`
                : '网格内可能存在隐藏风险源'
          };
        });
        // })
        // item.highRiskGridList.forEach((g, i) => {
        // const g = item.highRiskGridList[0];
        // const i = 0;
        const p = generateGridFusionImg(g.factorType, item.gridFusionList).then(
@@ -417,20 +711,13 @@
            const { url1, url2 } = url;
            _highRiskGridList.push({
              index: i + 1,
              factor: g.factorType,
              factor: factorName[g.factorType],
              // 标准色网格图
              gridImgUrl1: url1,
              // 对比色网格图
              gridImgUrl2: url2,
              factorValue: g.factorValue,
              // 四至范围，顺序为最小经度，最大经度，最小纬度，最大纬度
              _boundsDes: `经度${g.bounds[0]}至${g.bounds[1]}，纬度${g.bounds[2]}至${g.bounds[3]}`,
              // 涉及街镇
              town: g.town,
              _scenesDes:
                g.highRiskScenes.length > 0
                  ? `涉及的污染场景包括${g.highRiskScenes.map((s) => s.name).join('、')}`
                  : '网格内可能存在隐藏风险源'
              infoDes: infoDes,
              infoDes2: infoDes2
            });
          }
        );
@@ -558,70 +845,6 @@

function handleMixClick({ tags = [10, 11], factorName = 'PM25' }) {
  generateGridFusion(params.value).then(() => {});
  // const fetchGridData = () => {
  //   gridApi.mixUnderwayGridData(props.groupId, tags).then((res) => {
  //     var min = 1000000;
  //     var max = 0;
  //     res.data.forEach((v) => {
  //       min = Math.min(min, getGridDataDetailFactorValue(v, factorName));
  //       max = Math.max(max, getGridDataDetailFactorValue(v, factorName));
  //     });

  //     const gridData = res.data.map((v) => {
  //       const data = getGridDataDetailFactorValue(v, factorName);
  //       const grid = gridCellList.value.find((g) => {
  //         return g.cellIndex == v.cellId;
  //       });
  //       // const { color, nextColor, range, nextRange } =
  //       //   Legend.getStandardColorAndNext('PM25', data);
  //       // const ratio = (data - range) / (nextRange - range);
  //       // const _color = getColorBetweenTwoColors(
  //       //   color.map((v) => v * 255),
  //       //   nextColor.map((v) => v * 255),
  //       //   ratio
  //       // );

  //       // 根据遥测数据计算网格颜色
  //       const { color, nextColor, range, nextRange } =
  //         Legend.getCustomColorAndNext(data, min, max);
  //       const ratio = (data - range) / (nextRange - range);

  //       const _color = getColorBetweenTwoColors(
  //         color.map((v) => v * 255),
  //         nextColor.map((v) => v * 255),
  //         ratio
  //       );
  //       return {
  //         centerLng: grid.longitude,
  //         centerLat: grid.latitude,
  //         value: _color,
  //         coordinates: [
  //           [grid.point1Lon, grid.point1Lat],
  //           [grid.point2Lon, grid.point2Lat],
  //           [grid.point3Lon, grid.point3Lat],
  //           [grid.point4Lon, grid.point4Lat]
  //         ]
  //       };
  //     });
  //     // chartMapAmap.generateGridMap(gridData).then((url) => {
  //     //   gridBase64Url.value = url;
  //     // });
  //     chartMap.generateGridMap(gridData).then((url) => {
  //       gridBase64Url.value = url;
  //     });
  //   });
  // };

  // if (gridCellList.value.length == 0) {
  //   gridApi
  //     .fetchGridCell(props.groupId)
  //     .then((res) => {
  //       gridCellList.value = res.data;
  //     })
  //     .then(() => fetchGridData());
  // } else {
  //   fetchGridData();
  // }
}

function generateDocx() {
@@ -675,19 +898,28 @@

  // 不是季度第一天则返回具体日期范围
  if (!quarter) {
    return `${startYear}年${startMonth + 1}月${startDate}日-${endYear}年${endMonth + 1}月${endDate}日`;
    // return `${startYear}年${startMonth + 1}月${startDate}日-${endYear}年${endMonth + 1}月${endDate}日`;
    return startYear == endYear
      ? `${startYear}年${startMonth + 1}月-${endMonth + 1}月`
      : `${startYear}年${startMonth + 1}月-${endYear}年${endMonth + 1}月`;
  }

  // 验证是否为对应季度最后一个月
  const expectedEndMonth = quarter * 3 - 1; // Q1:2(3月), Q2:5(6月), Q3:8(9月), Q4:11(12月)
  if (endMonth !== expectedEndMonth) {
    return `${startYear}年${startMonth + 1}月${startDate}日-${endYear}年${endMonth + 1}月${endDate}日`;
    // return `${startYear}年${startMonth + 1}月${startDate}日-${endYear}年${endMonth + 1}月${endDate}日`;
    return startYear == endYear
      ? `${startYear}年${startMonth + 1}月-${endMonth + 1}月`
      : `${startYear}年${startMonth + 1}月-${endYear}年${endMonth + 1}月`;
  }

  // 验证是否为季度最后一天
  const lastDayOfEndMonth = new Date(endYear, endMonth + 1, 0).getDate();
  if (endDate !== lastDayOfEndMonth) {
    return `${startYear}年${startMonth + 1}月${startDate}日-${endYear}年${endMonth + 1}月${endDate}日`;
    // return `${startYear}年${startMonth + 1}月${startDate}日-${endYear}年${endMonth + 1}月${endDate}日`;
    return startYear == endYear
      ? `${startYear}年${startMonth + 1}月-${endMonth + 1}月`
      : `${startYear}年${startMonth + 1}月-${endYear}年${endMonth + 1}月`;
  }

  const quarterNames = ['', '第一季度', '第二季度', '第三季度', '第四季度'];
@@ -754,3 +986,8 @@
  return `${datePart} ${startTimePart}至${endTimePart}`;
}
</script>
<style scoped>
.el-checkbox {
  --el-checkbox-text-color: white;
}
</style>