import moment from 'moment';
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import { exportDocx } from '@/utils/doc';
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import dataAnalysisApi from '@/api/dataAnalysisApi';
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import { FactorDatas } from '@/model/FactorDatas';
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import factorDataParser from '@/utils/chart/factor-data-parser';
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import chartToImg from '@/utils/chart/chart-to-img';
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const groupBy = (array, func) => {
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const groups = {};
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array.forEach((item) => {
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const key = func(item);
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if (!groups[key]) {
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groups[key] = [];
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}
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groups[key].push(item);
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});
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return Object.keys(groups).map((key) => ({
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key,
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values: groups[key]
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}));
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};
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const templateParam = () => {
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return {
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// 走航任务情况
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cityName: '上海市',
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districtName: '长宁区',
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createTime: '2024年9月9日',
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missionTime: '2024年9月6日',
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missionDate: '9月6日',
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startTime: '13:00',
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endTime: '17:00',
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missionPeriod: '下午',
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region: '仙霞站',
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radius: '2公里',
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// 走航当日天气状况
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pollutionDegree: '优',
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aqi: 46,
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mainFactor: '无',
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factorAvgDes:
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'PM2.5（46μg/m3）、PM10（66μg/m3）、O3-8H（138μg/m3）、SO2（15μg/m3）、NO2（41μg/m3）、CO（0.7mg/m3）',
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weather: '多云，18-28℃，东南风2级',
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// 走航路线图
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roadMapUrl: '',
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// 走航监测因子情况
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focusFactor: 'PM2.5、PM10、VOCs、O3',
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mainFactorStatus:
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'走航期间，PM2.5、PM10等监测因子浓度均值高于长宁空气质量背景，',
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// mainFactorStatus:'走航期间，PM2.5、PM10等监测因子浓度均值均处于优，与长宁空气质量背景基本一致，',
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factorDetailList: [
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{
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factor: 'PM2.5',
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avgValue: 22,
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minValue: 19,
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maxValue: 25
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}
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],
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clueByAreaList: [
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{
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_index: 1,
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_area: '某某区域周边',
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clueByFactorList: [
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{
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factor: 'PM₂.₅',
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clues: [
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{
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_factorNames: 'PM2.5',
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_time: '10:22:28 - 10:22:34',
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_riskRegion: '长宁区清溪路可乐东路',
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_exceptionType: '快速上升',
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_chart: '',
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_conclusion:
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'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。',
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_scenes:
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'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……'
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}
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]
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}
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]
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}
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]
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};
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};
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function genMission(param, mission) {
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param.cityName = mission.cityName;
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param.districtName = mission.districtName;
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param.createTime = moment().format('YYYY-MM-DD');
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param.missionTime = moment(mission.startTime).format('YYYY年MM月DD日');
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param.missionDate = moment(mission.startTime).format('MM月DD日');
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param.startTime = moment(mission.startTime).format('HH:mm');
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param.endTime = moment(mission.endTime).format('HH:mm');
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// 根据startTime计算所处时段
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const hour = moment(mission.startTime).hour();
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if (hour >= 0 && hour < 6) {
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param.missionPeriod = '凌晨';
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} else if (hour >= 6 && hour < 9) {
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param.missionPeriod = '早上';
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} else if (hour >= 9 && hour < 12) {
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param.missionPeriod = '上午';
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} else if (hour >= 12 && hour < 14) {
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param.missionPeriod = '中午';
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} else if (hour >= 14 && hour < 18) {
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param.missionPeriod = '下午';
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} else if (hour >= 18 && hour < 22) {
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param.missionPeriod = '晚上';
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} else {
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param.missionPeriod = '深夜';
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}
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param.region = mission.region;
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param.radius = mission.radius ? `${mission.radius}公里` : '';
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// 走航当日天气状况
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// param.pollutionDegree = mission.pollutionDegree;
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// param.aqi = mission.aqi;
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// param.mainFactor = mission.mainFactor;
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// param.factorAvgDes = mission.factorAvgDes;
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// param.weather = mission.weather;
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}
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function genMissionSummary(param, mission) {
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return dataAnalysisApi
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.fetchOneMissionSummary(mission.missionCode)
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.then((res) => {
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param.focusRegion = res.data.focusRegion.join('、');
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});
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}
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function genRoadMap(param, mission) {}
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function genFactorDetail(param, mission, focusFactor) {
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return dataAnalysisApi
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.fetchOneMissionDetail(mission.missionCode)
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.then((res) => {
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param.focusFactor = focusFactor.join('、');
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param.mainFactorStatus =
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'走航期间，PM2.5、PM10等监测因子浓度均值高于长宁空气质量背景，';
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param.factorDetailList = res.data.dataStatistics
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.filter((item) => {
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return focusFactor.includes(item.factor);
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})
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.forEach((item) => {
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item.avgValue = item.avgValue.toFixed(2);
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item.minValue = item.minValue.toFixed(2);
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item.maxValue = item.maxValue.toFixed(2);
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});
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});
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}
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function genClueByRiskArea(param, mission) {
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return dataAnalysisApi
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.fetchOneClueByRiskArea(mission.missionCode)
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.then((res) => {
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const clues = res.data.flatMap((item) =>
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item.clueByFactorList.flatMap((cbf) => cbf.clues)
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);
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param.clueByAreaList = groupBy(
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clues,
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(clue) => clue.pollutedArea.address
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).map((item) => {
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const { key: area, values: clues } = item;
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return {
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_area: area,
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clues: clues.map((clue) => {
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return {
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_factorNames: Object.keys(clue.pollutedData.statisticMap)
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.map((e) => e)
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.join('、'),
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_time:
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moment(clue.pollutedData.startTime).format('HH:mm:ss') +
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' - ' +
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moment(clue.pollutedData.endTime).format('HH:mm:ss'),
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_riskRegion: clue.pollutedArea.address
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? clue.pollutedArea.address
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: '',
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_exceptionType: clue.pollutedData.exception,
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_images: generateChartImg(clue.pollutedData),
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_conclusion: clue.pollutedSource.conclusion,
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_scenes:
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clue.pollutedSource.sceneList.length > 0
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? clue.pollutedSource.sceneList
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.map(
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(s, index) =>
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`${index + 1}. ${s.name}，${s.type}，位于${s.location}，距${s.closestStation.name}${parseInt(s.length)}米；`
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)
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.join('\n')
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: '无'
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};
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})
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};
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});
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});
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}
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function generateChartImg(pollutedData) {
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const exceptionIndexArr = [];
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pollutedData.dataVoList.forEach((e) => {
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const i = pollutedData.historyDataList.findIndex((v) => v.time == e.time);
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exceptionIndexArr.push([i - 1 < 0 ? 0 : i - 1, i]);
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});
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const factorDatas = new FactorDatas();
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const images = [];
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factorDatas.setData(pollutedData.historyDataList, 0, () => {
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for (const key in pollutedData.statisticMap) {
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const value = pollutedData.statisticMap[key];
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const _chartOptions = factorDataParser.parseData(factorDatas, [
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{
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label: value.factorName,
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name: value.factorName,
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value: value.factorId + ''
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}
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]);
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_chartOptions.forEach((o) => {
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images.push({
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url: chartToImg.generateEchartsImage(o, exceptionIndexArr, 20)
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});
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});
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}
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});
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return images;
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}
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async function genTemplateParams(mission, focusFactor) {
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const param = templateParam();
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await genMission(param, mission);
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await genMissionSummary(param, mission);
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await genRoadMap(param, mission);
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await genFactorDetail(param, mission, focusFactor);
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await genClueByRiskArea(param, mission);
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Object.keys(param).forEach((key) => {
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if (typeof param[key] === 'string') {
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param[key] = param[key]?.trim() || '';
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}
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});
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console.log('genTemplateParams', param);
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return param;
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}
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function downloadReport(mission, focusFactor = ['PM25', 'PM10']) {
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return genTemplateParams(mission, focusFactor).then((param) => {
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exportDocx('/underway_mission_report.docx', param, `走航任务报告.docx`, {
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horizontalHeight: 250,
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verticalWidth: 568,
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scale: 2
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});
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});
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}
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export { downloadReport };
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