2025.10.18 修改嫉妒报告生成逻辑

feiyu02

2025-10-20 eb4111e0fd7110e5aa6a00608da2da9af21a3035

 src/views/historymode/component/MissionReport.vue

@@ -45,7 +45,7 @@
      <!-- <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"
          class="el-button-custom"
@@ -63,7 +63,7 @@
            />
          </el-form-item>
        </el-form-item>
      </el-form-item>
      </el-form-item> -->
    </el-form>
  </CardDialog>
</template>
@@ -83,6 +83,7 @@
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({
@@ -145,16 +146,19 @@
      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',
@@ -235,7 +239,9 @@
};

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

function generateMissionSummary(param) {
@@ -271,7 +277,7 @@
      item._time = formatDateTimeRange(item.startTime, item.endTime);
      item._airQulity = `AQI：${item.aqi}（${item.pollutionDegree}）`;
      item._abnormalFactors = item.abnormalFactors
        .map((factor) => factor)
        .map((factor) => factorName[factor])
        .join('、');
      item._kilometres = Math.round(item.kilometres / 1000);

@@ -315,9 +321,9 @@

      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;
@@ -334,46 +340,75 @@
    removeNoPollutedSource: param.removeNoPollutedSource
  };
  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`,
        _area: `${item.address}`,
        clueByFactorList: item.clueByFactorList.map((cbf) => {
          return {
            factor: cbf.factor,
            clues: cbf.clues.map((clue) => {
    templateParam.clueByAreaList = res.data
      .groupBy((e) => e.township)
      .map((item, index) => {
        const { key: township, values: clues } = item;
        return {
          _index: index + 1,
          // _area: `${item.sceneInfo.type}${item.sceneInfo.name}周边`,
          _area: `${township}`,
          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 {
                _factorNames: Object.keys(clue.pollutedData.statisticMap)
                  .map((e) => e)
                  .join('、'),
                _time:
                  moment(clue.pollutedData.startTime).format(
                    'YYYY-MM-DD 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 _riskRegion = [];
                  // if (clue.pollutedArea.address) {
                  //   _riskRegion.push(clue.pollutedArea.address);
                  // }
                  // if (clue.pollutedArea.streetNumber) {
                  //   _riskRegion.push(clue.pollutedArea.streetNumber);
                  // }
                  // if (clue.pollutedArea.roadinter) {
                  //   _riskRegion.push(clue.pollutedArea.roadinter);
                  // }
                  const clue = item3.clue
                  return {
                    index: index3+1,
                    _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:
                      (clue.pollutedArea.address ?? '') +
                      (clue.pollutedArea.street ?? '') +
                      (clue.pollutedArea.streetNumber ?? '') +
                      (clue.pollutedArea.direction ?? ''),
                    _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('\n')
                        : '无'
                  };
                })
              };
            })
          };
        })
      };
    });
        };
      });
  });
}

@@ -431,7 +466,24 @@
        _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${e.highRiskScenes.map((s) => s.name).join('、')}`
                : '网格内可能存在隐藏风险源'
          };
        });
        // })
        // item.highRiskGridList.forEach((g, i) => {
        // const g = item.highRiskGridList[0];
        // const i = 0;
        const p = generateGridFusionImg(g.factorType, item.gridFusionList).then(
@@ -439,20 +491,12 @@
            const { url1, url2 } = url;
            _highRiskGridList.push({
              index: i + 1,
              factor: g.factorType,
              factor: factorName[g.factorType],
              // 标准色网格图
              gridImgUrl1: url1,
              // 对比色网格图
              gridImgUrl2: url2,
              factorValue: g.factorValue,
              // 四至范围，顺序为最小经度，最大经度，最小纬度，最大纬度
              _boundsDes: `经度${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
            });
          }
        );