2025.10.17

riku

2025-10-17 ec763e1cb7dca873caf4afbc0dfde047b51753d3

 src/views/historymode/component/MissionReport.vue

@@ -14,6 +14,16 @@
        ></OptionLocation2>
      </el-form-item>
      <OptionTime v-model="formObj.timeArray"></OptionTime>
      <el-form-item label="移除其他区县" prop="removeOtherDistrict">
        <el-checkbox v-model="formObj.removeOtherDistrict"
          >移除其他区县</el-checkbox
        >
      </el-form-item>
      <el-form-item label="移除未污染源" 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>
        <el-button
          type="primary"
@@ -23,40 +33,77 @@
        >
          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-image :src="base64Url" fit="fill" :preview-src-list="[base64Url]" />
      </el-form-item> -->
      <!-- <el-form-item>
        <el-button
          type="primary"
          class="el-button-custom"
          @click="handleMixClick"
          :loading="docLoading"
        >
          生成网格图片
        </el-button>
        <el-form-item>
          <el-form-item>
            <el-image
              :src="gridBase64Url"
              fit="fill"
              :preview-src-list="[gridBase64Url]"
            />
          </el-form-item>
        </el-form-item>
      </el-form-item> -->
    </el-form>
    <el-form-item>
      <el-image :src="base64Url" fit="fill" :preview-src-list="[base64Url]" />
    </el-form-item>
  </CardDialog>
</template>
<script setup>
import { computed, ref } from 'vue';
import moment from 'moment';
import dataAnalysisApi from '@/api/dataAnalysisApi';
import gridApi from '@/api/gridApi';
import { exportDocx } from '@/utils/doc';
import { radioOptions } from '@/constant/radio-options';
import { TYPE0 } from '@/constant/device-type';
import { FactorDatas } from '@/model/FactorDatas';
import factorDataParser from '@/utils/chart/factor-data-parser';
import chartToImg from '@/utils/chart/chart-to-img';
import chartMap from '@/utils/chart/chart-map';
import chartMapAmap from '@/utils/chart/chart-map-amap';
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({
  groupId: {
    type: Number,
    default: import.meta.env.VITE_DATA_MODE == 'jingan' ? 2 : 3
  }
});

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: {}
});

const docLoading = ref(false);

const base64Url = ref(null);
const gridBase64Url = ref(null);

const gridCellList = ref([]);

const params = computed(() => {
  return {
@@ -71,7 +118,10 @@
      cityName: formObj.value.location.cName,
      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)
  };
});

@@ -86,6 +136,7 @@
  srySceneCount: 5,
  sryProbByFactor:
    '颗粒物（PM）相关X处，占比 %，主要涉及工地扬尘污染问题、道路扬尘污染问题等；VOC相关X处，占比 %，主要涉及加油站油气泄露、餐饮油烟污染等',
  sryFocusRegion: '聚焦区域',
  missionInfoList: [
    {
      missionCode: '',
@@ -94,16 +145,20 @@
      _airQulity: 'AQI：30（优）',
      mainFactor: '',
      _abnormalFactors: '',
      sceneCount: 0
      sceneCount: 0,
      _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',
@@ -112,6 +167,7 @@
          avgValue: 38
        }
      ],
      _airQulity: 'AQI：30（优）',
      aqi: 30,
      pollutionDegree: '优'
    }
@@ -139,6 +195,31 @@
        }
      ]
    }
  ],
  gridFusionByAQIList: [
    {
      pollutionDegree: '优',
      _areaDes: '走航区域大小',
      _gridDes: '100米正方形网格',
      _missionDes: '20250729、20250730两次',
      highRiskGridList: [
        {
          index: 1,
          factor: 'PM2.5',
          // 标准色网格图
          gridImgUrl1: '',
          // 对比色网格图
          gridImgUrl2: '',
          factorValue: 20,
          // 四至范围，顺序为最小经度，最大经度，最小纬度，最大纬度
          bounds: [121.4945, 121.4955, 31.2304, 31.2314],
          _boundsDes: '四至范围',
          // 涉及街镇
          town: '',
          _scenesDes: '涉及的污染场景'
        }
      ]
    }
  ]
};

@@ -148,7 +229,9 @@
    generateMissionList(params.value).then(() => {
      generateMissionDetail(params.value).then(() => {
        generateClueByRiskArea(params.value).then(() => {
          generateDocx();
          generateGridFusion(params.value).then(() => {
            generateDocx();
          });
        });
      });
    });
@@ -156,7 +239,9 @@
};

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

function generateMissionSummary(param) {
@@ -182,6 +267,7 @@
        return `${item.first}相关${item.second}处，占比 ${Math.round(item.third * 1000) / 10}%，主要涉及${getPollutingProblemTypes(item.first)}等`;
      })
      .join('；');
    templateParam.sryFocusRegion = res.data.focusRegion.join('、');
  });
}

@@ -193,18 +279,6 @@
      item._abnormalFactors = item.abnormalFactors
        .map((factor) => factor)
        .join('、');
      return item;
    });
  });
}

function generateMissionDetail(param) {
  return dataAnalysisApi.fetchMissionDetail(param).then((res) => {
    templateParam.missionDetailList = res.data.map((item, index) => {
      const t = formatDateTimeRange(item.startTime, item.endTime).split(' ');
      item._index = index + 1;
      item._startTime = t[0];
      item._time = t[1];
      item._kilometres = Math.round(item.kilometres / 1000);

      const keySceneMap = new Map();
@@ -221,16 +295,35 @@
            `${info.count}个${type}（${info.scenes.map((s) => s.name).join('、')}）`
        )
        .join('、');
      item._dataStat = item.dataStatistics
        .map(
          (e) =>
            `${e.factor.des}（范围${e.minValue}–${e.maxValue}μg/m³，均值${e.avgValue}μg/m³）`
        )
        .join('、');
      item._focusScene =
        item.scenes.length > 0
          ? item.scenes.map((s) => s.name).join('、')
          : '道路交通密集区和部分施工周边';
      return item;
    });
  });
}

function generateMissionDetail(param) {
  return dataAnalysisApi.fetchMissionDetail(param).then((res) => {
    templateParam.missionDetailList = res.data.map((item, index) => {
      const t = formatDateTimeRange(item.startTime, item.endTime).split(' ');
      item._index = index + 1;
      item._startTime = t[0];
      item._time = t[1];
      item._kilometres = Math.round(item.kilometres / 1000);
      item._airQulity = `AQI：${item.aqi}（${item.pollutionDegree}）`;

      const factorNames = radioOptions(TYPE0).map((e) => e.name);
      item._dataStatistics = item.dataStatistics.filter((e) => {
        return factorNames.indexOf(e.factor) != -1;
      });

      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 ?? '-';
      });

      return item;
@@ -239,44 +332,80 @@
}

function generateClueByRiskArea(param) {
  return dataAnalysisApi.fetchClueByRiskArea(param).then((res) => {
    templateParam.clueByAreaList = res.data.map((item, index) => {
      return {
        _index: index + 1,
        _area: item.sceneInfo.name + '周边',
        clueByFactorList: item.clueByFactorList.map((cbf) => {
          return {
            factor: cbf.factor,
            clues: cbf.clues.map((clue) => {
  const _param = {
    area: param.area,
    startTime: param.startTime,
    endTime: param.endTime,
    removeOtherDistrict: param.removeOtherDistrict,
    removeNoPollutedSource: param.removeNoPollutedSource
  };
  return dataAnalysisApi.fetchClueByRiskArea(_param).then((res) => {
    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: `${township}`,
          clueByFactorList: clues
            .groupBy((e) => e.factorTag)
            .map((item2) => {
              const { key: factorTag, values: clues2 } = item2;
              const factorNames = [...new Set(clues2.flatMap((e) => e.factors))]
                .map((e) => factorName(e))
                .join('、');
              return {
                _factorNames: Object.keys(clue.pollutedData.statisticMap)
                  .map((e) => e)
                  .join('、'),
                _time:
                  moment(clue.pollutedData.startTime).format('HH:mm:ss') +
                  ' - ' +
                  moment(clue.pollutedData.endTime).format('HH:mm:ss'),
                _riskRegion: clue.pollutedArea.address
                  ? clue.pollutedArea.address
                  : '',
                _exceptionType: clue.pollutedData.exception,
                _images: generateChartImg(clue.pollutedData),
                _conclusion: clue.pollutedSource.conclusion,
                _scenes:
                  clue.pollutedSource.sceneList.length > 0
                    ? clue.pollutedSource.sceneList
                        .map(
                          (s, index) =>
                            `${index + 1}. ${s.name}，${s.type}，位于${s.location}，距${s.closestStation.name}${parseInt(s.length)}米；`
                        )
                        .join('\n')
                    : '无'
                factor: factorNames,
                clues: clues2.map((clue) => {
                  // 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);
                  // }
                  return {
                    _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'
                  };
                })
              };
            })
          };
        })
      };
    });
        };
      });
  });
}

@@ -311,6 +440,187 @@
    }
  });
  return images;
}

function generateGridFusion(param) {
  return dataAnalysisApi.fetchGridFusion(param).then((res) => {
    const promiseList = [];
    templateParam.gridFusionByAQIList = [];

    res.data.forEach((item) => {
      const scenes = [];
      item.missionList.forEach((m) => {
        m.keyScene.map((s) => {
          if (scenes.indexOf(s.name) == -1) {
            scenes.push(s.name);
          }
        });
      });
      const gfbAQI = {
        pollutionDegree: item.pollutionDegree,
        _areaDes: `走航区域经过${scenes.join('、')}`,
        _gridDes: `${item.gridLen}米正方形网格`,
        _missionDes: `${item.missionList.map((m) => m.missionCode).join('、')}共${item.missionList.length}次`
      };
      const _highRiskGridList = [];
      Object.keys(item.highRiskGridMap).forEach((key, i) => {
        const g = item.highRiskGridMap[key][0];

        const infoDes = item.highRiskGridMap[key].map((e) => {
          return {
            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${g.highRiskScenes.map((s) => s.name).join('、')}`
                : '网格内可能存在隐藏风险源'
          };
        });
        // })
        // item.highRiskGridList.forEach((g, i) => {
        // const g = item.highRiskGridList[0];
        // const i = 0;
        const p = generateGridFusionImg(g.factorType, item.gridFusionList).then(
          (url) => {
            const { url1, url2 } = url;
            _highRiskGridList.push({
              index: i + 1,
              factor: factorName(g.factorType),
              // 标准色网格图
              gridImgUrl1: url1,
              // 对比色网格图
              gridImgUrl2: url2,
              infoDes: infoDes
            });
          }
        );
        promiseList.push(p);
      });
      gfbAQI.highRiskGridList = _highRiskGridList;
      templateParam.gridFusionByAQIList.push(gfbAQI);
    });
    return Promise.all(promiseList).then(() => {
      return templateParam.gridFusionByAQIList;
    });
    // templateParam.gridFusionByAQIList = res.data.map((item) => {
    //   const scenes = [];
    //   item.missionList.forEach((m) => {
    //     m.keyScene.map((s) => {
    //       if (scenes.indexOf(s.name) == -1) {
    //         scenes.push(s.name);
    //       }
    //     });
    //   });
    //   return {
    //     pollutionDegree: item.pollutionDegree,
    //     _areaDes: `走航区域经过${scenes.join('、')}`,
    //     _gridDes: `${item.gridLen}米正方形网格`,
    //     _missionDes: `${item.missionList.map((m) => m.missioncode).join('、')}${item.missionList.length}次`,
    //     highRiskGridList: item.highRiskGridList.map(async (g, i) => {
    //       const { url1, url2 } = await generateGridFusionImg(
    //         g.factorType,
    //         item.gridFusionList
    //       );
    //       return {
    //         index: i + 1,
    //         factor: g.factorType,
    //         // 标准色网格图
    //         gridImgUrl1: url1,
    //         // 对比色网格图
    //         gridImgUrl2: url2,
    //         factorValue: g.factorValue,
    //         // 四至范围，顺序为最小经度，最大经度，最小纬度，最大纬度
    //         _boundsDes: `经度${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.map((s) => s.name).join('、')
    //       };
    //     })
    //   };
    // });
  });
}

async function generateGridFusionImg(factorName, dataList) {
  let min = 1000000;
  let max = 0;
  dataList.forEach((v) => {
    min = Math.min(min, getGridDataDetailFactorValue(v.data, factorName));
    max = Math.max(max, getGridDataDetailFactorValue(v.data, factorName));
  });

  const gridDataStand = [];
  const gridDataCustom = [];
  dataList.forEach((v) => {
    const data = getGridDataDetailFactorValue(v.data, factorName);
    const grid = v.cell;

    // 标准色
    const {
      color: color1,
      nextColor: nextColor1,
      range: range1,
      nextRange: nextRange1
    } = Legend.getStandardColorAndNext(factorName, data);
    const ratio1 = (data - range1) / (nextRange1 - range1);
    const _color1 = getColorBetweenTwoColors(
      color1.map((v) => v * 255),
      nextColor1.map((v) => v * 255),
      ratio1
    );

    // 对比色
    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
    );

    gridDataStand.push({
      centerLng: grid.longitude,
      centerLat: grid.latitude,
      value: _color1,
      coordinates: [
        [grid.point1Lon, grid.point1Lat],
        [grid.point2Lon, grid.point2Lat],
        [grid.point3Lon, grid.point3Lat],
        [grid.point4Lon, grid.point4Lat]
      ]
    });
    gridDataCustom.push({
      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]
      ]
    });
  });
  const url1 = await chartMap.generateGridMap(gridDataStand);
  const url2 = await chartMap.generateGridMap(gridDataCustom);
  if (gridBase64Url.value == null) {
    gridBase64Url.value = url1;
  }
  return {
    url1,
    url2
  };
}

function handleMixClick({ tags = [10, 11], factorName = 'PM25' }) {
  generateGridFusion(params.value).then(() => {});
}

function generateDocx() {
@@ -443,3 +753,8 @@
  return `${datePart} ${startTimePart}至${endTimePart}`;
}
</script>
<style scoped>
.el-checkbox {
  --el-checkbox-text-color: white;
}
</style>