package com.ycl.platform.service.impl;
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import com.baomidou.mybatisplus.core.metadata.IPage;
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import com.baomidou.mybatisplus.extension.conditions.query.LambdaQueryChainWrapper;
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import com.github.pagehelper.Page;
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import com.github.pagehelper.PageHelper;
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import com.mongodb.client.AggregateIterable;
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import com.mongodb.client.MongoCollection;
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import com.mongodb.client.MongoDatabase;
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import com.ycl.platform.domain.entity.CheckIndexVideo;
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import com.ycl.platform.domain.entity.ImageResourceSecurityDetail;
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import com.ycl.platform.domain.entity.YwPoint;
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import com.ycl.platform.domain.query.DataCenterQuery;
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import com.ycl.platform.domain.result.HK.*;
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import com.ycl.platform.domain.result.UY.*;
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import com.ycl.platform.mapper.ImageResourceSecurityDetailMapper;
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import com.ycl.platform.mapper.YwPointMapper;
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import com.ycl.platform.service.DataCenterService;
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import com.ycl.platform.service.ICheckIndexCarService;
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import com.ycl.platform.service.ICheckIndexVideoService;
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import com.ycl.platform.service.YwPointService;
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import com.ycl.system.Result;
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import com.ycl.system.page.PageUtil;
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import com.ycl.utils.DateUtils;
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import com.ycl.utils.MongoUtil;
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import lombok.RequiredArgsConstructor;
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import org.apache.commons.collections.CollectionUtils;
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import org.bson.Document;
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import org.springframework.data.domain.Sort;
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import org.springframework.data.mongodb.core.MongoTemplate;
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import org.springframework.data.mongodb.core.query.Criteria;
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import org.springframework.data.mongodb.core.query.Query;
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import org.springframework.data.mongodb.core.query.TextCriteria;
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import org.springframework.stereotype.Service;
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import java.math.BigDecimal;
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import java.math.RoundingMode;
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import java.util.*;
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import java.util.stream.Collectors;
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/**
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* 数据中心接口
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*
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* @author：xp
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* @date：2024/8/2 11:07
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*/
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@Service
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@RequiredArgsConstructor
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public class DataCenterServiceImpl implements DataCenterService {
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private final MongoTemplate mongoTemplate;
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private final ImageResourceSecurityDetailMapper securityDetailMapper;
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private final YwPointMapper pointMapper;
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private final ICheckIndexVideoService checkIndexVideoService;
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private final static String TIME_FIELD = "mongoCreateTime";
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/**
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* 视频：点位在线率
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*
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* @param params
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* @return
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*/
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@Override
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public Result videoPointOnlineRate(DataCenterQuery params) {
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// 生成查询
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List<String> likeFileds = Arrays.asList("arealayerName", "ipAddr", "deviceName", "deviceId");
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Query query = MongoUtil.getQuery(params, "deviceId", TIME_FIELD, likeFileds, null);
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// 先查总数再分页
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long total = mongoTemplate.count(query, VideoOnlineResult.class);
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MongoUtil.setPage(query, params, TIME_FIELD);
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List<VideoOnlineResult> resultList = mongoTemplate.find(query, VideoOnlineResult.class);
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// 统计数量
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MongoDatabase database = mongoTemplate.getDb();
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MongoCollection<Document> collection = database.getCollection("uy_online");
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long distinctCount = collection.distinct("deviceId", String.class).into(new ArrayList<>()).size();
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Date now = new Date();
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List<CheckIndexVideo> videoList = new LambdaQueryChainWrapper<>(checkIndexVideoService.getBaseMapper())
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.select(CheckIndexVideo::getSiteOnline)
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.between(CheckIndexVideo::getCreateTime, DateUtils.getDayStart(now), DateUtils.getDayEnd(now))
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.list();
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BigDecimal onlineRate = BigDecimal.ZERO;
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if (CollectionUtils.isNotEmpty(videoList)) {
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BigDecimal sum = videoList.stream().map(CheckIndexVideo::getSiteOnline).reduce(BigDecimal.ZERO, BigDecimal::add);
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BigDecimal count = BigDecimal.valueOf(videoList.size());
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onlineRate = sum.divide(count, 4, RoundingMode.HALF_UP).multiply(BigDecimal.valueOf(100));
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}
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HashMap<String, Object> map = new HashMap<>();
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map.put("count", Arrays.asList(distinctCount + "",onlineRate + "%"));
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map.put("list", resultList);
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return Result.ok().data(map).total(total);
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}
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/**
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* 视频：部级点位在线率
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*
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* @param params
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* @return
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*/
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@Override
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public Result deptVideoPointOnlineRate(DataCenterQuery params) {
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// 先查出部级点位的国标
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List<String> deptGBList = pointMapper.getDeptPointGB(0);
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List<String> likeFileds = Arrays.asList("arealayerName", "ipAddr", "deviceName", "deviceId");
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// 生成查询
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Query query = MongoUtil.getQuery(params, "deviceId", TIME_FIELD, likeFileds, deptGBList);
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// 先查总数再分页
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long total = mongoTemplate.count(query, VideoOnlineResult.class);
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MongoUtil.setPage(query, params, TIME_FIELD);
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List<VideoOnlineResult> resultList = mongoTemplate.find(query, VideoOnlineResult.class);
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// 统计数量
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MongoDatabase database = mongoTemplate.getDb();
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MongoCollection<Document> collection = database.getCollection("uy_online");
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Document filter = new Document("deviceId", new Document("$in", deptGBList));
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// 构建聚合管道
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List<Document> pipeline = Arrays.asList(
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// 只要部级的
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new Document("$match", filter),
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// $group 去重
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new Document("$group", new Document("_id", "$deviceId")),
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new Document("$count", "uniqueDeviceIds")
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);
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// 执行聚合查询并获取结果
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AggregateIterable<Document> result = collection.aggregate(pipeline);
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Integer uniqueDeviceIdCount = 0;
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for (Document doc : result) {
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uniqueDeviceIdCount = doc.getInteger("uniqueDeviceIds");
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break; // 不需要继续遍历，因为 $count 只会产生一个结果
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}
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Date now = new Date();
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List<CheckIndexVideo> videoList = new LambdaQueryChainWrapper<>(checkIndexVideoService.getBaseMapper())
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.select(CheckIndexVideo::getMinistrySiteOnline)
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.between(CheckIndexVideo::getCreateTime, DateUtils.getDayStart(now), DateUtils.getDayEnd(now))
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.list();
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BigDecimal onlineRate = BigDecimal.ZERO;
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if (CollectionUtils.isNotEmpty(videoList)) {
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BigDecimal sum = videoList.stream().map(CheckIndexVideo::getMinistrySiteOnline).reduce(BigDecimal.ZERO, BigDecimal::add);
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BigDecimal count = BigDecimal.valueOf(videoList.size());
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onlineRate = sum.divide(count, 4, RoundingMode.HALF_UP).multiply(BigDecimal.valueOf(100));
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}
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HashMap<String, Object> map = new HashMap<>();
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map.put("count", Arrays.asList(uniqueDeviceIdCount + "", onlineRate + "%"));
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map.put("list", resultList);
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return Result.ok().data(map).total(total);
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}
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/**
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* 视频：重点点位在线率
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*
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* @param params
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* @return
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*/
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@Override
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public Result videoImportantPointOnlineRate(DataCenterQuery params) {
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// 先查出重点点位的国标
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List<String> deptGBList = pointMapper.getDeptPointGB(1);
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List<String> likeFileds = Arrays.asList("arealayerName", "ipAddr", "deviceName", "deviceId");
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Query query = MongoUtil.getQuery(params, "deviceId", TIME_FIELD, likeFileds, deptGBList);
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long total = mongoTemplate.count(query, VideoOnlineResult.class);
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MongoUtil.setPage(query, params, TIME_FIELD);
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List<VideoOnlineResult> resultList = mongoTemplate.find(query, VideoOnlineResult.class);
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// 统计数量
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MongoDatabase database = mongoTemplate.getDb();
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MongoCollection<Document> collection = database.getCollection("uy_online");
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Document filter = new Document("deviceId", new Document("$in", deptGBList));
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// 构建聚合管道
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List<Document> pipeline = Arrays.asList(
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new Document("$match", filter),
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// $group 去重
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new Document("$group", new Document("_id", "$deviceId")),
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new Document("$count", "uniqueDeviceIds")
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);
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// 执行聚合查询并获取结果
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AggregateIterable<Document> result = collection.aggregate(pipeline);
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Integer uniqueDeviceIdCount = 0;
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for (Document doc : result) {
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uniqueDeviceIdCount = doc.getInteger("uniqueDeviceIds");
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break; // 不需要继续遍历，因为 $count 只会产生一个结果
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}
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Date now = new Date();
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List<CheckIndexVideo> videoList = new LambdaQueryChainWrapper<>(checkIndexVideoService.getBaseMapper())
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.select(CheckIndexVideo::getKeySiteOnline)
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.between(CheckIndexVideo::getCreateTime, DateUtils.getDayStart(now), DateUtils.getDayEnd(now))
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.list();
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BigDecimal onlineRate = BigDecimal.ZERO;
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if (CollectionUtils.isNotEmpty(videoList)) {
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BigDecimal sum = videoList.stream().map(CheckIndexVideo::getKeySiteOnline).reduce(BigDecimal.ZERO, BigDecimal::add);
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BigDecimal count = BigDecimal.valueOf(videoList.size());
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onlineRate = sum.divide(count, 4, RoundingMode.HALF_UP).multiply(BigDecimal.valueOf(100));
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}
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HashMap<String, Object> map = new HashMap<>();
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map.put("count", Arrays.asList(uniqueDeviceIdCount + "", onlineRate + "%"));
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map.put("list", resultList);
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return Result.ok().data(map).total(total);
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}
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/**
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* 视频：重点指挥图像在线率
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*
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* @param params
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* @return
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*/
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@Override
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public Result videoImportantPointImageOnlineRate(DataCenterQuery params) {
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// 先查出重点指挥图像点位的国标
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List<String> deptGBList = pointMapper.getDeptPointGB(2);
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List<String> likeFileds = Arrays.asList("arealayerName", "ipAddr", "deviceName", "deviceId");
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Query query = MongoUtil.getQuery(params, "deviceId", TIME_FIELD, likeFileds, deptGBList);
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long total = mongoTemplate.count(query, VideoOnlineResult.class);
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MongoUtil.setPage(query, params, TIME_FIELD);
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List<VideoOnlineResult> resultList = mongoTemplate.find(query, VideoOnlineResult.class);
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// 统计数量
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MongoDatabase database = mongoTemplate.getDb();
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MongoCollection<Document> collection = database.getCollection("uy_online");
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Document filter = new Document("deviceId", new Document("$in", deptGBList));
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// 构建聚合管道
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List<Document> pipeline = Arrays.asList(
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new Document("$match", filter),
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// $group 去重
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new Document("$group", new Document("_id", "$deviceId")),
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new Document("$count", "uniqueDeviceIds")
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);
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// 执行聚合查询并获取结果
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AggregateIterable<Document> result = collection.aggregate(pipeline);
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Integer uniqueDeviceIdCount = 0;
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for (Document doc : result) {
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uniqueDeviceIdCount = doc.getInteger("uniqueDeviceIds");
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break; // 不需要继续遍历，因为 $count 只会产生一个结果
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}
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Date now = new Date();
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List<CheckIndexVideo> videoList = new LambdaQueryChainWrapper<>(checkIndexVideoService.getBaseMapper())
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.select(CheckIndexVideo::getKeyCommandImageOnline)
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.between(CheckIndexVideo::getCreateTime, DateUtils.getDayStart(now), DateUtils.getDayEnd(now))
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.list();
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BigDecimal onlineRate = BigDecimal.ZERO;
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if (CollectionUtils.isNotEmpty(videoList)) {
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BigDecimal sum = videoList.stream().map(CheckIndexVideo::getKeyCommandImageOnline).reduce(BigDecimal.ZERO, BigDecimal::add);
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BigDecimal count = BigDecimal.valueOf(videoList.size());
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onlineRate = sum.divide(count, 4, RoundingMode.HALF_UP).multiply(BigDecimal.valueOf(100));
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}
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HashMap<String, Object> map = new HashMap<>();
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map.put("count", Arrays.asList(uniqueDeviceIdCount + "", onlineRate + "%"));
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map.put("list", resultList);
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return Result.ok().data(map).total(total);
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}
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/**
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* 视频：一机一档注册率
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*
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* @param params
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* @return
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*/
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@Override
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public Result videoOneMachineDocumentRegister(DataCenterQuery params) {
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List<String> likeFileds = Arrays.asList("ip.showValue", "name.showValue", "serialNumber.showValue");
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Query query = MongoUtil.getQuery(params, "serialNumber.showValue", TIME_FIELD, likeFileds, null);
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long total = mongoTemplate.count(query, MonitorQualifyResult.class);
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MongoUtil.setPage(query, params, TIME_FIELD);
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List<MonitorQualifyResult> resultList = mongoTemplate.find(query, MonitorQualifyResult.class);
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// 统计数量
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MongoDatabase database = mongoTemplate.getDb();
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MongoCollection<Document> collection = database.getCollection("uy_monitor_qualify");
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Document ipErrFilter = new Document("ip.error", true);
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Document macdzErrFilter = new Document("macdz.error", true);
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Document latitudeErrFilter = new Document("latitude.error", true);
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Document longitudeErrFilter = new Document("longitude.error", true);
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List<Document> lists = Arrays.asList(ipErrFilter, macdzErrFilter, latitudeErrFilter, longitudeErrFilter);
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List<String> rList = lists.stream().map(filter -> {
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// 构建聚合管道
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List<Document> pipeline = Arrays.asList(
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new Document("$match", filter),
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// $group 去重
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new Document("$group", new Document("_id", "$serialNumber.showValue")),
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new Document("$count", "uniqueDeviceIds")
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);
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// 执行聚合查询并获取结果
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AggregateIterable<Document> result = collection.aggregate(pipeline);
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Integer uniqueDeviceIdCount = 0;
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for (Document doc : result) {
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uniqueDeviceIdCount = doc.getInteger("uniqueDeviceIds");
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break; // 不需要继续遍历，因为 $count 只会产生一个结果
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}
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return uniqueDeviceIdCount + "";
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}).collect(Collectors.toList());
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Date now = new Date();
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List<CheckIndexVideo> videoList = new LambdaQueryChainWrapper<>(checkIndexVideoService.getBaseMapper())
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.select(CheckIndexVideo::getMonitorRegistration)
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.between(CheckIndexVideo::getCreateTime, DateUtils.getDayStart(now), DateUtils.getDayEnd(now))
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.list();
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BigDecimal onlineRate = BigDecimal.ZERO;
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if (CollectionUtils.isNotEmpty(videoList)) {
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BigDecimal sum = videoList.stream().map(CheckIndexVideo::getMonitorRegistration).reduce(BigDecimal.ZERO, BigDecimal::add);
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BigDecimal count = BigDecimal.valueOf(videoList.size());
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onlineRate = sum.divide(count, 4, RoundingMode.HALF_UP).multiply(BigDecimal.valueOf(100));
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}
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rList.add(onlineRate + "%");
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HashMap<String, Object> map = new HashMap<>();
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map.put("count", rList);
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map.put("list", resultList);
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return Result.ok().data(map).total(total);
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}
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/**
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* 视频：一机一档合格率
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*
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* @param params
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* @return
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*/
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@Override
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public Result videoOneMachineDocumentQualified(DataCenterQuery params) {
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List<String> likeFileds = Arrays.asList("ip.showValue", "name.showValue", "serialNumber.showValue");
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Query query = MongoUtil.getQuery(params, "serialNumber.showValue", TIME_FIELD, likeFileds, null);
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long total = mongoTemplate.count(query, MonitorQualifyResult.class);
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MongoUtil.setPage(query, params, TIME_FIELD);
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List<MonitorQualifyResult> resultList = mongoTemplate.find(query, MonitorQualifyResult.class);
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// 统计数量
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MongoDatabase database = mongoTemplate.getDb();
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MongoCollection<Document> collection = database.getCollection("uy_monitor_qualify");
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Document ipErrFilter = new Document("ip.error", true);
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Document macdzErrFilter = new Document("macdz.error", true);
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Document latitudeErrFilter = new Document("latitude.error", true);
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Document longitudeErrFilter = new Document("longitude.error", true);
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List<Document> lists = Arrays.asList(ipErrFilter, macdzErrFilter, latitudeErrFilter, longitudeErrFilter);
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List<String> rList = lists.stream().map(filter -> {
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// 构建聚合管道
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List<Document> pipeline = Arrays.asList(
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new Document("$match", filter),
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// $group 去重
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new Document("$group", new Document("_id", "$serialNumber.showValue")),
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new Document("$count", "uniqueDeviceIds")
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);
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// 执行聚合查询并获取结果
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AggregateIterable<Document> result = collection.aggregate(pipeline);
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Integer uniqueDeviceIdCount = 0;
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for (Document doc : result) {
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uniqueDeviceIdCount = doc.getInteger("uniqueDeviceIds");
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break; // 不需要继续遍历，因为 $count 只会产生一个结果
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}
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return uniqueDeviceIdCount + "";
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}).collect(Collectors.toList());
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Date now = new Date();
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List<CheckIndexVideo> videoList = new LambdaQueryChainWrapper<>(checkIndexVideoService.getBaseMapper())
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.select(CheckIndexVideo::getMonitorQualification)
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.between(CheckIndexVideo::getCreateTime, DateUtils.getDayStart(now), DateUtils.getDayEnd(now))
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.list();
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BigDecimal onlineRate = BigDecimal.ZERO;
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if (CollectionUtils.isNotEmpty(videoList)) {
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BigDecimal sum = videoList.stream().map(CheckIndexVideo::getMonitorQualification).reduce(BigDecimal.ZERO, BigDecimal::add);
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BigDecimal count = BigDecimal.valueOf(videoList.size());
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onlineRate = sum.divide(count, 4, RoundingMode.HALF_UP).multiply(BigDecimal.valueOf(100));
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}
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rList.add(onlineRate + "%");
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HashMap<String, Object> map = new HashMap<>();
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map.put("count", rList);
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map.put("list", resultList);
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return Result.ok().data(map).total(total);
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}
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/**
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* 视频：档案考核比
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*
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* @param params
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* @return
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*/
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@Override
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public Result videoAssessmentFileRatio(DataCenterQuery params) {
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// TODO 新增一张表记录每次的档案考核
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List<String> likeFileds = Arrays.asList("ip.showValue", "name.showValue", "serialNumber.showValue");
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Query query = MongoUtil.getQuery(params, "serialNumber.showValue", TIME_FIELD, likeFileds, null);
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long total = mongoTemplate.count(query, MonitorQualifyResult.class);
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MongoUtil.setPage(query, params, TIME_FIELD);
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List<MonitorQualifyResult> resultList = mongoTemplate.find(query, MonitorQualifyResult.class);
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// 统计数量
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MongoDatabase database = mongoTemplate.getDb();
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MongoCollection<Document> collection = database.getCollection("uy_monitor_qualify");
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Document ipErrFilter = new Document("ip.error", true);
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Document macdzErrFilter = new Document("macdz.error", true);
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Document latitudeErrFilter = new Document("latitude.error", true);
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Document longitudeErrFilter = new Document("longitude.error", true);
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List<Document> lists = Arrays.asList(ipErrFilter, macdzErrFilter, latitudeErrFilter, longitudeErrFilter);
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List<String> rList = lists.stream().map(filter -> {
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// 构建聚合管道
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List<Document> pipeline = Arrays.asList(
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new Document("$match", filter),
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// $group 去重
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new Document("$group", new Document("_id", "$serialNumber.showValue")),
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new Document("$count", "uniqueDeviceIds")
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);
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// 执行聚合查询并获取结果
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AggregateIterable<Document> result = collection.aggregate(pipeline);
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Integer uniqueDeviceIdCount = 0;
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for (Document doc : result) {
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uniqueDeviceIdCount = doc.getInteger("uniqueDeviceIds");
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break; // 不需要继续遍历，因为 $count 只会产生一个结果
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}
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return uniqueDeviceIdCount + "";
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}).collect(Collectors.toList());
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rList.add("0%");
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HashMap<String, Object> map = new HashMap<>();
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map.put("count", rList);
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map.put("list", resultList);
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return Result.ok().data(map).total(total);
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}
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/**
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* 视频：录像可用率
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*
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* @param params
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* @return
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*/
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@Override
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public Result videoAvailabilityRate(DataCenterQuery params) {
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List<String> likeFileds = Arrays.asList("deviceId", "deviceName");
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Query query = MongoUtil.getQuery(params, "deviceId", "createTime", likeFileds, null);
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long total = mongoTemplate.count(query, RecordMetaDSumResult.class);
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MongoUtil.setPage(query, params, "createTime");
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List<RecordMetaDSumResult> resultList = mongoTemplate.find(query, RecordMetaDSumResult.class);
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// 统计数量
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MongoDatabase database = mongoTemplate.getDb();
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MongoCollection<Document> collection = database.getCollection("uy_record_meta_d_sum");
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List<Integer> status = Arrays.asList(1, 0, -1);
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List<String> resultCount = status.stream().map(item -> {
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Document filter = new Document("recordStatus", item);
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// 构建聚合管道
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List<Document> pipeline = Arrays.asList(
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new Document("$match", filter),
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// $group 去重
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new Document("$group", new Document("_id", "$deviceId")),
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new Document("$count", "uniqueDeviceIds")
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);
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// 执行聚合查询并获取结果
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AggregateIterable<Document> result = collection.aggregate(pipeline);
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Integer uniqueDeviceIdCount = 0;
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for (Document doc : result) {
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uniqueDeviceIdCount = doc.getInteger("uniqueDeviceIds");
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break; // 不需要继续遍历，因为 $count 只会产生一个结果
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}
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return uniqueDeviceIdCount + "";
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}).collect(Collectors.toList());
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Date now = new Date();
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List<CheckIndexVideo> videoList = new LambdaQueryChainWrapper<>(checkIndexVideoService.getBaseMapper())
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.select(CheckIndexVideo::getVideoAvailable)
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.between(CheckIndexVideo::getCreateTime, DateUtils.getDayStart(now), DateUtils.getDayEnd(now))
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.list();
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BigDecimal onlineRate = BigDecimal.ZERO;
|
if (CollectionUtils.isNotEmpty(videoList)) {
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BigDecimal sum = videoList.stream().map(CheckIndexVideo::getVideoAvailable).reduce(BigDecimal.ZERO, BigDecimal::add);
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BigDecimal count = BigDecimal.valueOf(videoList.size());
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onlineRate = sum.divide(count, 4, RoundingMode.HALF_UP).multiply(BigDecimal.valueOf(100));
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}
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resultCount.add(onlineRate + "%");
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HashMap<String, Object> map = new HashMap<>();
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map.put("count", resultCount);
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map.put("list", resultList);
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return Result.ok().data(map).total(total);
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}
|
|
/**
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* 视频：部级录像可用率
|
*
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* @param params
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* @return
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*/
|
@Override
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public Result deptVideoAvailabilityRate(DataCenterQuery params) {
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List<String> deptGBList = pointMapper.getDeptPointGB(0);
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List<String> likeFileds = Arrays.asList("deviceId", "deviceName");
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Query query = MongoUtil.getQuery(params, "deviceId", "createTime", likeFileds, deptGBList);
|
|
long total = mongoTemplate.count(query, RecordMetaDSumResult.class);
|
MongoUtil.setPage(query, params, "createTime");
|
List<RecordMetaDSumResult> resultList = mongoTemplate.find(query, RecordMetaDSumResult.class);
|
|
// 统计数量
|
MongoDatabase database = mongoTemplate.getDb();
|
MongoCollection<Document> collection = database.getCollection("uy_record_meta_d_sum");
|
List<Integer> status = Arrays.asList(1, 0, -1);
|
List<String> resultCount = status.stream().map(item -> {
|
Document filter = new Document("$and", Arrays.asList(
|
new Document("deviceId", new Document("$in", deptGBList)), // $in 条件
|
new Document("recordStatus", item)
|
));
|
// 构建聚合管道
|
List<Document> pipeline = Arrays.asList(
|
new Document("$match", filter),
|
// $group 去重
|
new Document("$group", new Document("_id", "$deviceId")),
|
new Document("$count", "uniqueDeviceIds")
|
);
|
// 执行聚合查询并获取结果
|
AggregateIterable<Document> result = collection.aggregate(pipeline);
|
Integer uniqueDeviceIdCount = 0;
|
for (Document doc : result) {
|
uniqueDeviceIdCount = doc.getInteger("uniqueDeviceIds");
|
break; // 不需要继续遍历，因为 $count 只会产生一个结果
|
}
|
return uniqueDeviceIdCount + "";
|
}).collect(Collectors.toList());
|
|
Date now = new Date();
|
List<CheckIndexVideo> videoList = new LambdaQueryChainWrapper<>(checkIndexVideoService.getBaseMapper())
|
.select(CheckIndexVideo::getMinistryVideoAvailable)
|
.between(CheckIndexVideo::getCreateTime, DateUtils.getDayStart(now), DateUtils.getDayEnd(now))
|
.list();
|
BigDecimal onlineRate = BigDecimal.ZERO;
|
if (CollectionUtils.isNotEmpty(videoList)) {
|
BigDecimal sum = videoList.stream().map(CheckIndexVideo::getMinistryVideoAvailable).reduce(BigDecimal.ZERO, BigDecimal::add);
|
BigDecimal count = BigDecimal.valueOf(videoList.size());
|
onlineRate = sum.divide(count, 4, RoundingMode.HALF_UP).multiply(BigDecimal.valueOf(100));
|
}
|
resultCount.add(onlineRate + "%");
|
HashMap<String, Object> map = new HashMap<>();
|
map.put("count", resultCount);
|
map.put("list", resultList);
|
return Result.ok().data(map).total(total);
|
}
|
|
/**
|
* 视频：重点点位录像可用率
|
*
|
* @param params
|
* @return
|
*/
|
@Override
|
public Result videoImportantPointAvailabilityRate(DataCenterQuery params) {
|
|
List<String> deptGBList = pointMapper.getDeptPointGB(1);
|
List<String> likeFileds = Arrays.asList("deviceId", "deviceName");
|
Query query = MongoUtil.getQuery(params, "deviceId", "createTime", likeFileds, deptGBList);
|
|
long total = mongoTemplate.count(query, RecordMetaDSumResult.class);
|
MongoUtil.setPage(query, params, "createTime");
|
List<RecordMetaDSumResult> resultList = mongoTemplate.find(query, RecordMetaDSumResult.class);
|
|
// 统计数量
|
MongoDatabase database = mongoTemplate.getDb();
|
MongoCollection<Document> collection = database.getCollection("uy_record_meta_d_sum");
|
List<Integer> status = Arrays.asList(1, 0, -1);
|
List<String> resultCount = status.stream().map(item -> {
|
Document filter = new Document("$and", Arrays.asList(
|
new Document("deviceId", new Document("$in", deptGBList)), // $in 条件
|
new Document("recordStatus", item)
|
));
|
// 构建聚合管道
|
List<Document> pipeline = Arrays.asList(
|
new Document("$match", filter),
|
// $group 去重
|
new Document("$group", new Document("_id", "$deviceId")),
|
new Document("$count", "uniqueDeviceIds")
|
);
|
// 执行聚合查询并获取结果
|
AggregateIterable<Document> result = collection.aggregate(pipeline);
|
Integer uniqueDeviceIdCount = 0;
|
for (Document doc : result) {
|
uniqueDeviceIdCount = doc.getInteger("uniqueDeviceIds");
|
break; // 不需要继续遍历，因为 $count 只会产生一个结果
|
}
|
return uniqueDeviceIdCount + "%";
|
}).collect(Collectors.toList());
|
|
Date now = new Date();
|
List<CheckIndexVideo> videoList = new LambdaQueryChainWrapper<>(checkIndexVideoService.getBaseMapper())
|
.select(CheckIndexVideo::getKeyVideoAvailable)
|
.between(CheckIndexVideo::getCreateTime, DateUtils.getDayStart(now), DateUtils.getDayEnd(now))
|
.list();
|
BigDecimal onlineRate = BigDecimal.ZERO;
|
if (CollectionUtils.isNotEmpty(videoList)) {
|
BigDecimal sum = videoList.stream().map(CheckIndexVideo::getKeyVideoAvailable).reduce(BigDecimal.ZERO, BigDecimal::add);
|
BigDecimal count = BigDecimal.valueOf(videoList.size());
|
onlineRate = sum.divide(count, 4, RoundingMode.HALF_UP).multiply(BigDecimal.valueOf(100));
|
}
|
resultCount.add(onlineRate + "%");
|
HashMap<String, Object> map = new HashMap<>();
|
map.put("count", resultCount);
|
map.put("list", resultList);
|
return Result.ok().data(map).total(total);
|
}
|
|
/**
|
* 视频：标注正确率
|
*
|
* @param params
|
* @return
|
*/
|
@Override
|
public Result videoLabelingAccuracy(DataCenterQuery params) {
|
List<String> likeFileds = Arrays.asList("deviceId", "deviceName");
|
Query query = MongoUtil.getQuery(params, "deviceId", TIME_FIELD, likeFileds, null);
|
|
long total = mongoTemplate.count(query, RecordMetaDSumResult.class);
|
List<RecordMetaDSumResult> resultList = mongoTemplate.find(query, RecordMetaDSumResult.class);
|
// 统计数
|
long nonNetwork = mongoTemplate.count(new Query().addCriteria(Criteria.where("LWSX").is("0")), RecordMetaDSumResult.class);
|
long network = mongoTemplate.count(new Query().addCriteria(Criteria.where("LWSX").is("1")), RecordMetaDSumResult.class);
|
long video = mongoTemplate.count(new Query().addCriteria(Criteria.where("SXJGNLX").regex(".*1.*")), RecordMetaDSumResult.class);
|
long car = mongoTemplate.count(new Query().addCriteria(Criteria.where("SXJGNLX").regex(".*2.*")), RecordMetaDSumResult.class);
|
long face = mongoTemplate.count(new Query().addCriteria(Criteria.where("SXJGNLX").regex(".*3.*")), RecordMetaDSumResult.class);
|
HashMap<String, Object> map = new HashMap<>();
|
map.put("count", Arrays.asList(nonNetwork, network, video, car, face));
|
map.put("list", resultList);
|
return Result.ok().data(map).total(total);
|
}
|
|
/**
|
* 视频：重点点位标注正确率
|
*
|
* @param params
|
* @return
|
*/
|
@Override
|
public Result videoImportantPointLabelingAccuracy(DataCenterQuery params) {
|
|
List<String> deptGBList = pointMapper.getDeptPointGB(1);
|
List<String> likeFileds = Arrays.asList("deviceNo", "osdName");
|
Query query = MongoUtil.getQuery(params, "deviceNo", "checkTime", likeFileds, deptGBList);
|
|
long total = mongoTemplate.count(query, OsdCheckResult.class);
|
MongoUtil.setPage(query, params, "checkTime");
|
List<OsdCheckResult> resultList = mongoTemplate.find(query, OsdCheckResult.class);
|
|
// 统计数量
|
MongoDatabase database = mongoTemplate.getDb();
|
MongoCollection<Document> collection = database.getCollection("osd_check_result");
|
|
Document osdNameFilter = new Document("osdNameCorrect", 1);
|
Document osdNameErrFilter = new Document("osdNameCorrect", -1);
|
Document osdTimeFilter = new Document("osdTimeCorrect", 1);
|
Document osdTimeErrFilter = new Document("osdTimeCorrect", -1);
|
List<Document> lists = Arrays.asList(osdNameFilter, osdNameErrFilter, osdTimeFilter, osdTimeErrFilter);
|
List<String> rList = lists.stream().map(filter -> {
|
// 构建聚合管道
|
List<Document> pipeline = Arrays.asList(
|
new Document("$match", filter),
|
// $group 去重
|
new Document("$group", new Document("_id", "$deviceNo")),
|
new Document("$count", "uniqueDeviceIds")
|
);
|
// 执行聚合查询并获取结果
|
AggregateIterable<Document> result = collection.aggregate(pipeline);
|
Integer uniqueDeviceIdCount = 0;
|
for (Document doc : result) {
|
uniqueDeviceIdCount = doc.getInteger("uniqueDeviceIds");
|
break; // 不需要继续遍历，因为 $count 只会产生一个结果
|
}
|
return uniqueDeviceIdCount + "";
|
}).collect(Collectors.toList());
|
|
Date now = new Date();
|
List<CheckIndexVideo> videoList = new LambdaQueryChainWrapper<>(checkIndexVideoService.getBaseMapper())
|
.select(CheckIndexVideo::getKeyAnnotationAccuracy)
|
.between(CheckIndexVideo::getCreateTime, DateUtils.getDayStart(now), DateUtils.getDayEnd(now))
|
.list();
|
BigDecimal onlineRate = BigDecimal.ZERO;
|
if (CollectionUtils.isNotEmpty(videoList)) {
|
BigDecimal sum = videoList.stream().map(CheckIndexVideo::getKeyAnnotationAccuracy).reduce(BigDecimal.ZERO, BigDecimal::add);
|
BigDecimal count = BigDecimal.valueOf(videoList.size());
|
onlineRate = sum.divide(count, 4, RoundingMode.HALF_UP).multiply(BigDecimal.valueOf(100));
|
}
|
rList.add(onlineRate + "%");
|
HashMap<String, Object> map = new HashMap<>();
|
map.put("count", rList);
|
map.put("list", resultList);
|
return Result.ok().data(map).total(total);
|
}
|
|
/**
|
* 视频：校时正确率
|
*
|
* @param params
|
* @return
|
*/
|
@Override
|
public Result videoCheckTimeAccuracy(DataCenterQuery params) {
|
|
List<String> likeFileds = Arrays.asList("deviceId");
|
Query query = MongoUtil.getQuery(params, "deviceId", TIME_FIELD, likeFileds, null);
|
|
long total = mongoTemplate.count(query, OneMachineFileResult.class);
|
List<OneMachineFileResult> resultList = mongoTemplate.find(query, OneMachineFileResult.class);
|
// 统计数
|
long nonNetwork = mongoTemplate.count(new Query().addCriteria(Criteria.where("LWSX").is("0")), OneMachineFileResult.class);
|
long network = mongoTemplate.count(new Query().addCriteria(Criteria.where("LWSX").is("1")), OneMachineFileResult.class);
|
long video = mongoTemplate.count(new Query().addCriteria(Criteria.where("SXJGNLX").regex(".*1.*")), OneMachineFileResult.class);
|
long car = mongoTemplate.count(new Query().addCriteria(Criteria.where("SXJGNLX").regex(".*2.*")), OneMachineFileResult.class);
|
long face = mongoTemplate.count(new Query().addCriteria(Criteria.where("SXJGNLX").regex(".*3.*")), OneMachineFileResult.class);
|
HashMap<String, Object> map = new HashMap<>();
|
map.put("count", Arrays.asList(nonNetwork, network, video, car, face));
|
map.put("list", resultList);
|
return Result.ok().data(map).total(total);
|
}
|
|
/**
|
* 视频：重点点位校时正确率
|
*
|
* @param params
|
* @return
|
*/
|
@Override
|
public Result videoImportantPointCheckTimeAccuracy(DataCenterQuery params) {
|
|
List<String> deptGBList = pointMapper.getDeptPointGB(1);
|
List<String> likeFileds = Arrays.asList("deviceNo", "osdName");
|
Query query = MongoUtil.getQuery(params, "deviceNo", "checkTime", likeFileds, deptGBList);
|
|
long total = mongoTemplate.count(query, OsdCheckResult.class);
|
MongoUtil.setPage(query, params, "checkTime");
|
List<OsdCheckResult> resultList = mongoTemplate.find(query, OsdCheckResult.class);
|
|
// 统计数量
|
MongoDatabase database = mongoTemplate.getDb();
|
MongoCollection<Document> collection = database.getCollection("osd_check_result");
|
|
Document osdTimeFilter = new Document("osdTimeCorrect", 1);
|
Document osdTimeErrFilter = new Document("osdTimeCorrect", -1);
|
List<Document> lists = Arrays.asList(osdTimeFilter, osdTimeErrFilter);
|
List<String> rList = lists.stream().map(filter -> {
|
// 构建聚合管道
|
List<Document> pipeline = Arrays.asList(
|
new Document("$match", filter),
|
// $group 去重
|
new Document("$group", new Document("_id", "$deviceNo")),
|
new Document("$count", "uniqueDeviceIds")
|
);
|
// 执行聚合查询并获取结果
|
AggregateIterable<Document> result = collection.aggregate(pipeline);
|
Integer uniqueDeviceIdCount = 0;
|
for (Document doc : result) {
|
uniqueDeviceIdCount = doc.getInteger("uniqueDeviceIds");
|
break; // 不需要继续遍历，因为 $count 只会产生一个结果
|
}
|
return uniqueDeviceIdCount + "";
|
}).collect(Collectors.toList());
|
|
Date now = new Date();
|
List<CheckIndexVideo> videoList = new LambdaQueryChainWrapper<>(checkIndexVideoService.getBaseMapper())
|
.select(CheckIndexVideo::getKeyTimingAccuracy)
|
.between(CheckIndexVideo::getCreateTime, DateUtils.getDayStart(now), DateUtils.getDayEnd(now))
|
.list();
|
BigDecimal onlineRate = BigDecimal.ZERO;
|
if (CollectionUtils.isNotEmpty(videoList)) {
|
BigDecimal sum = videoList.stream().map(CheckIndexVideo::getKeyTimingAccuracy).reduce(BigDecimal.ZERO, BigDecimal::add);
|
BigDecimal count = BigDecimal.valueOf(videoList.size());
|
onlineRate = sum.divide(count, 4, RoundingMode.HALF_UP).multiply(BigDecimal.valueOf(100));
|
}
|
rList.add(onlineRate + "%");
|
HashMap<String, Object> map = new HashMap<>();
|
map.put("count", rList);
|
map.put("list", resultList);
|
return Result.ok().data(map).total(total);
|
}
|
|
|
/**
|
* 车辆：视图库对接稳定性
|
*
|
* @param params
|
* @return
|
*/
|
@Override
|
public Result vehicleViewDockStable(DataCenterQuery params) {
|
List<String> likeFileds = Arrays.asList("externalIndexCode", "deviceName");
|
Query query = MongoUtil.getQuery(params, "externalIndexCode", TIME_FIELD, likeFileds, null);
|
|
long total = mongoTemplate.count(query, SnapshotDataMonitorResult.class);
|
MongoUtil.setPage(query, params, TIME_FIELD);
|
List<SnapshotDataMonitorResult> resultList = mongoTemplate.find(query, SnapshotDataMonitorResult.class);
|
|
// 统计数量
|
MongoDatabase database = mongoTemplate.getDb();
|
MongoCollection<Document> collection = database.getCollection("hk_snapshot_data_monitor");
|
|
Document normalFilter = new Document("resultType", 1);
|
Document noDataFilter = new Document("resultType", 2);
|
Document trFilter = new Document("resultType", 3);
|
Document littleFilter = new Document("resultType", 4);
|
List<Document> lists = Arrays.asList(normalFilter, noDataFilter, trFilter, littleFilter);
|
List<Integer> rList = lists.stream().map(filter -> {
|
// 构建聚合管道
|
List<Document> pipeline = Arrays.asList(
|
new Document("$match", filter),
|
// $group 去重
|
new Document("$group", new Document("_id", "$externalIndexCode")),
|
new Document("$count", "uniqueDeviceIds")
|
);
|
// 执行聚合查询并获取结果
|
AggregateIterable<Document> result = collection.aggregate(pipeline);
|
Integer uniqueDeviceIdCount = 0;
|
for (Document doc : result) {
|
uniqueDeviceIdCount = doc.getInteger("uniqueDeviceIds");
|
break; // 不需要继续遍历，因为 $count 只会产生一个结果
|
}
|
return uniqueDeviceIdCount;
|
}).collect(Collectors.toList());
|
|
HashMap<String, Object> map = new HashMap<>();
|
map.put("count", rList);
|
map.put("list", resultList);
|
return Result.ok().data(map).total(total);
|
}
|
|
/**
|
* 车辆：点位在线率
|
*
|
* @param params
|
* @return
|
*/
|
@Override
|
public Result vehiclePointOnlineRate(DataCenterQuery params) {
|
List<String> likeFileds = Arrays.asList("externalIndexCode", "deviceName");
|
Query query = MongoUtil.getQuery(params, "externalIndexCode", TIME_FIELD, likeFileds, null);
|
|
long total = mongoTemplate.count(query, SnapshotDataMonitorResult.class);
|
MongoUtil.setPage(query, params, TIME_FIELD);
|
List<SnapshotDataMonitorResult> resultList = mongoTemplate.find(query, SnapshotDataMonitorResult.class);
|
|
// 统计数量
|
MongoDatabase database = mongoTemplate.getDb();
|
MongoCollection<Document> collection = database.getCollection("hk_snapshot_data_monitor");
|
long distinctCount = collection.distinct("externalIndexCode", String.class).into(new ArrayList<>()).size();
|
|
HashMap<String, Object> map = new HashMap<>();
|
map.put("count", Arrays.asList(distinctCount));
|
map.put("list", resultList);
|
return Result.ok().data(map).total(total);
|
}
|
|
/**
|
* 车辆：互联网卡口设备目录一致性
|
*
|
* @param params
|
* @return
|
*/
|
@Override
|
public Result vehicleNetDeviceDirectoryConsistency(DataCenterQuery params) {
|
List<String> likeFileds = Arrays.asList("ip.showValue", "name.showValue", "serialNumber.showValue");
|
Query query = MongoUtil.getQuery(params, "externalIndexCode", TIME_FIELD, likeFileds, null);
|
|
long total = mongoTemplate.count(query, MonitorQualifyResult.class);
|
MongoUtil.setPage(query, params, TIME_FIELD);
|
List<MonitorQualifyResult> resultList = mongoTemplate.find(query, MonitorQualifyResult.class);
|
|
// 统计数量
|
MongoDatabase database = mongoTemplate.getDb();
|
MongoCollection<Document> collection = database.getCollection("uy_monitor_qualify");
|
|
Document ipErrFilter = new Document("ip.error", true);
|
Document macdzErrFilter = new Document("macdz.error", true);
|
Document latitudeErrFilter = new Document("latitude.error", true);
|
Document longitudeErrFilter = new Document("longitude.error", true);
|
|
List<Document> lists = Arrays.asList(ipErrFilter, macdzErrFilter, latitudeErrFilter, longitudeErrFilter);
|
List<Integer> rList = lists.stream().map(filter -> {
|
// 构建聚合管道
|
List<Document> pipeline = Arrays.asList(
|
new Document("$match", filter),
|
// $group 去重
|
new Document("$group", new Document("_id", "$serialNumber.showValue")),
|
new Document("$count", "uniqueDeviceIds")
|
);
|
// 执行聚合查询并获取结果
|
AggregateIterable<Document> result = collection.aggregate(pipeline);
|
Integer uniqueDeviceIdCount = 0;
|
for (Document doc : result) {
|
uniqueDeviceIdCount = doc.getInteger("uniqueDeviceIds");
|
break; // 不需要继续遍历，因为 $count 只会产生一个结果
|
}
|
return uniqueDeviceIdCount;
|
}).collect(Collectors.toList());
|
|
HashMap<String, Object> map = new HashMap<>();
|
map.put("count", rList);
|
map.put("list", resultList);
|
return Result.ok().data(map).total(total);
|
}
|
|
/**
|
* 车辆：车辆卡口信息采集准确率
|
*
|
* @param params
|
* @return
|
*/
|
@Override
|
public Result vehicleCollectionConsistency(DataCenterQuery params) {
|
List<String> likeFileds = Arrays.asList("externalIndexCode", "crossName");
|
Query query = MongoUtil.getQuery(params, "externalIndexCode", TIME_FIELD, likeFileds, null);
|
|
long total = mongoTemplate.count(query, CrossDetailResult.class);
|
MongoUtil.setPage(query, params, TIME_FIELD);
|
List<CrossDetailResult> resultList = mongoTemplate.find(query, CrossDetailResult.class);
|
|
// 统计数量
|
MongoDatabase database = mongoTemplate.getDb();
|
MongoCollection<Document> collection = database.getCollection("hk_cross_detail");
|
|
Document ipErrFilter = new Document("lalType", 1);
|
Document macdzErrFilter = new Document("lalType", 2);
|
Document latitudeErrFilter = new Document("lalType", 3);
|
Document longitudeErrFilter = new Document("lalType", 4);
|
|
List<Document> lists = Arrays.asList(ipErrFilter, macdzErrFilter, latitudeErrFilter, longitudeErrFilter);
|
List<Integer> rList = lists.stream().map(filter -> {
|
// 构建聚合管道
|
List<Document> pipeline = Arrays.asList(
|
new Document("$match", filter),
|
// $group 去重
|
new Document("$group", new Document("_id", "$externalIndexCode")),
|
new Document("$count", "uniqueDeviceIds")
|
);
|
// 执行聚合查询并获取结果
|
AggregateIterable<Document> result = collection.aggregate(pipeline);
|
Integer uniqueDeviceIdCount = 0;
|
for (Document doc : result) {
|
uniqueDeviceIdCount = doc.getInteger("uniqueDeviceIds");
|
break; // 不需要继续遍历，因为 $count 只会产生一个结果
|
}
|
return uniqueDeviceIdCount;
|
}).collect(Collectors.toList());
|
|
HashMap<String, Object> map = new HashMap<>();
|
map.put("count", rList);
|
map.put("list", resultList);
|
return Result.ok().data(map).total(total);
|
}
|
|
/**
|
* 车辆：车辆卡口设备抓拍数据完整性
|
*
|
* @param params
|
* @return
|
*/
|
@Override
|
public Result vehicleCollectionDataIntegrity(DataCenterQuery params) {
|
List<String> likeFileds = Arrays.asList("externalIndexCode", "deviceName");
|
Query query = MongoUtil.getQuery(params, "externalIndexCode", TIME_FIELD, likeFileds, null);
|
|
long total = mongoTemplate.count(query, DataIntegrityMonitoringResult.class);
|
MongoUtil.setPage(query, params, TIME_FIELD);
|
List<DataIntegrityMonitoringResult> resultList = mongoTemplate.find(query, DataIntegrityMonitoringResult.class);
|
|
// 统计数量
|
MongoDatabase database = mongoTemplate.getDb();
|
MongoCollection<Document> collection = database.getCollection("hk_data_integrity_monitoring");
|
|
HashMap<String, Object> map = new HashMap<>();
|
map.put("count", CollectionUtils.EMPTY_COLLECTION);
|
map.put("list", resultList);
|
return Result.ok().data(map).total(total);
|
}
|
|
/**
|
* 车辆：车辆卡口设备抓拍数据准确性
|
*
|
* @param params
|
* @return
|
*/
|
@Override
|
public Result vehicleCollectionDataCaptured(DataCenterQuery params) {
|
List<String> likeFileds = Arrays.asList("externalIndexCode", "deviceName");
|
Query query = MongoUtil.getQuery(params, "externalIndexCode", TIME_FIELD, likeFileds, null);
|
|
long total = mongoTemplate.count(query, AttrRecognitionMonitorResult.class);
|
MongoUtil.setPage(query, params, TIME_FIELD);
|
List<AttrRecognitionMonitorResult> resultList = mongoTemplate.find(query, AttrRecognitionMonitorResult.class);
|
// 统计数
|
HashMap<String, Object> map = new HashMap<>();
|
map.put("count", CollectionUtils.EMPTY_COLLECTION);
|
map.put("list", resultList);
|
return Result.ok().data(map).total(total);
|
}
|
|
/**
|
* 车辆：车辆卡口设备时钟准确性
|
*
|
* @param params
|
* @return
|
*/
|
@Override
|
public Result vehicleClockAccuracy(DataCenterQuery params) {
|
List<String> likeFileds = Arrays.asList("externalIndexCode", "deviceName");
|
Query query = MongoUtil.getQuery(params, "externalIndexCode", TIME_FIELD, likeFileds, null);
|
|
long total = mongoTemplate.count(query, VehicleDeviceInspectionResult.class);
|
MongoUtil.setPage(query, params, TIME_FIELD);
|
List<VehicleDeviceInspectionResult> resultList = mongoTemplate.find(query, VehicleDeviceInspectionResult.class);
|
|
|
// 统计数量
|
MongoDatabase database = mongoTemplate.getDb();
|
MongoCollection<Document> collection = database.getCollection("hk_vehicle_device_inspection");
|
|
Document ipErrFilter = new Document("snapResult", 1);
|
Document macdzErrFilter = new Document("snapResult", 2);
|
Document longitudeErrFilter = new Document("snapResult", 4);
|
|
List<Document> lists = Arrays.asList(ipErrFilter, macdzErrFilter, longitudeErrFilter);
|
List<Integer> rList = lists.stream().map(filter -> {
|
// 构建聚合管道
|
List<Document> pipeline = Arrays.asList(
|
new Document("$match", filter),
|
// $group 去重
|
new Document("$group", new Document("_id", "$externalIndexCode")),
|
new Document("$count", "uniqueDeviceIds")
|
);
|
// 执行聚合查询并获取结果
|
AggregateIterable<Document> result = collection.aggregate(pipeline);
|
Integer uniqueDeviceIdCount = 0;
|
for (Document doc : result) {
|
uniqueDeviceIdCount = doc.getInteger("uniqueDeviceIds");
|
break; // 不需要继续遍历，因为 $count 只会产生一个结果
|
}
|
return uniqueDeviceIdCount;
|
}).collect(Collectors.toList());
|
|
HashMap<String, Object> map = new HashMap<>();
|
map.put("count", rList);
|
map.put("list", resultList);
|
return Result.ok().data(map).total(total);
|
}
|
|
/**
|
* 车辆：车辆卡口设备抓拍数据上传及时性
|
*
|
* @param params
|
* @return
|
*/
|
@Override
|
public Result vehicleTimelyUploadAccuracy(DataCenterQuery params) {
|
List<String> likeFileds = Arrays.asList("externalIndexCode", "deviceName");
|
Query query = MongoUtil.getQuery(params, "externalIndexCode", TIME_FIELD, likeFileds, null);
|
|
long total = mongoTemplate.count(query, SnapshotDelayMonitorResult.class);
|
MongoUtil.setPage(query, params, TIME_FIELD);
|
List<SnapshotDelayMonitorResult> resultList = mongoTemplate.find(query, SnapshotDelayMonitorResult.class);
|
// 统计数
|
HashMap<String, Object> map = new HashMap<>();
|
map.put("count", CollectionUtils.EMPTY_COLLECTION);
|
map.put("list", resultList);
|
return Result.ok().data(map).total(total);
|
}
|
|
/**
|
* 车辆：车辆卡口设备url可用性
|
*
|
* @param params
|
* @return
|
*/
|
@Override
|
public Result vehicleUrlAccuracy(DataCenterQuery params) {
|
List<String> likeFileds = Arrays.asList("externalIndexCode", "deviceName");
|
Query query = MongoUtil.getQuery(params, "externalIndexCode", TIME_FIELD, likeFileds, null);
|
|
long total = mongoTemplate.count(query, PicAccessResult.class);
|
MongoUtil.setPage(query, params, TIME_FIELD);
|
List<PicAccessResult> resultList = mongoTemplate.find(query, PicAccessResult.class);
|
// 统计数
|
HashMap<String, Object> map = new HashMap<>();
|
map.put("count", CollectionUtils.EMPTY_COLLECTION);
|
map.put("list", resultList);
|
return Result.ok().data(map).total(total);
|
}
|
|
/**
|
* 车辆：车辆卡口设备抓拍数据大图可用性
|
*
|
* @param params
|
* @return
|
*/
|
@Override
|
public Result vehicleBigImgAccuracy(DataCenterQuery params) {
|
List<String> likeFileds = Arrays.asList("externalIndexCode", "deviceName");
|
Query query = MongoUtil.getQuery(params, "externalIndexCode", TIME_FIELD, likeFileds, null);
|
|
long total = mongoTemplate.count(query, VehicleDeviceSamplingResult.class);
|
MongoUtil.setPage(query, params, TIME_FIELD);
|
List<VehicleDeviceSamplingResult> resultList = mongoTemplate.find(query, VehicleDeviceSamplingResult.class);
|
// 统计数
|
HashMap<String, Object> map = new HashMap<>();
|
map.put("count", CollectionUtils.EMPTY_COLLECTION);
|
map.put("list", resultList);
|
return Result.ok().data(map).total(total);
|
}
|
|
/**
|
* 人脸：视图库对接稳定性
|
*
|
* @param params
|
* @return
|
*/
|
@Override
|
public Result faceViewDockStable(DataCenterQuery params) {
|
List<String> likeFileds = Arrays.asList("externalIndexCode", "deviceName");
|
Query query = MongoUtil.getQuery(params, "externalIndexCode", TIME_FIELD, likeFileds, null);
|
|
long total = mongoTemplate.count(query, SnapshotDataMonitorResult.class);
|
MongoUtil.setPage(query, params, TIME_FIELD);
|
List<SnapshotDataMonitorResult> resultList = mongoTemplate.find(query, SnapshotDataMonitorResult.class);
|
|
// 统计数量
|
MongoDatabase database = mongoTemplate.getDb();
|
MongoCollection<Document> collection = database.getCollection("hk_snapshot_data_monitor");
|
|
Document normalFilter = new Document("resultType", 1);
|
Document noDataFilter = new Document("resultType", 2);
|
Document trFilter = new Document("resultType", 3);
|
Document littleFilter = new Document("resultType", 4);
|
List<Document> lists = Arrays.asList(normalFilter, noDataFilter, trFilter, littleFilter);
|
List<Integer> rList = lists.stream().map(filter -> {
|
// 构建聚合管道
|
List<Document> pipeline = Arrays.asList(
|
new Document("$match", filter),
|
// $group 去重
|
new Document("$group", new Document("_id", "$externalIndexCode")),
|
new Document("$count", "uniqueDeviceIds")
|
);
|
// 执行聚合查询并获取结果
|
AggregateIterable<Document> result = collection.aggregate(pipeline);
|
Integer uniqueDeviceIdCount = 0;
|
for (Document doc : result) {
|
uniqueDeviceIdCount = doc.getInteger("uniqueDeviceIds");
|
break; // 不需要继续遍历，因为 $count 只会产生一个结果
|
}
|
return uniqueDeviceIdCount;
|
}).collect(Collectors.toList());
|
|
HashMap<String, Object> map = new HashMap<>();
|
map.put("count", rList);
|
map.put("list", resultList);
|
return Result.ok().data(map).total(total);
|
}
|
|
/**
|
* 人脸：点位在线率
|
*
|
* @param params
|
* @return
|
*/
|
@Override
|
public Result facePointOnlineRate(DataCenterQuery params) {
|
List<String> likeFileds = Arrays.asList("externalIndexCode", "deviceName");
|
Query query = MongoUtil.getQuery(params, "externalIndexCode", TIME_FIELD, likeFileds, null);
|
|
long total = mongoTemplate.count(query, SnapshotDataMonitorResult.class);
|
MongoUtil.setPage(query, params, TIME_FIELD);
|
List<SnapshotDataMonitorResult> resultList = mongoTemplate.find(query, SnapshotDataMonitorResult.class);
|
|
// 统计数量
|
MongoDatabase database = mongoTemplate.getDb();
|
MongoCollection<Document> collection = database.getCollection("hk_snapshot_data_monitor");
|
|
Document normalFilter = new Document("resultType", 1);
|
Document noDataFilter = new Document("resultType", 2);
|
Document trFilter = new Document("resultType", 3);
|
Document littleFilter = new Document("resultType", 4);
|
List<Document> lists = Arrays.asList(normalFilter, noDataFilter, trFilter, littleFilter);
|
List<Integer> rList = lists.stream().map(filter -> {
|
// 构建聚合管道
|
List<Document> pipeline = Arrays.asList(
|
new Document("$match", filter),
|
// $group 去重
|
new Document("$group", new Document("_id", "$externalIndexCode")),
|
new Document("$count", "uniqueDeviceIds")
|
);
|
// 执行聚合查询并获取结果
|
AggregateIterable<Document> result = collection.aggregate(pipeline);
|
Integer uniqueDeviceIdCount = 0;
|
for (Document doc : result) {
|
uniqueDeviceIdCount = doc.getInteger("uniqueDeviceIds");
|
break; // 不需要继续遍历，因为 $count 只会产生一个结果
|
}
|
return uniqueDeviceIdCount;
|
}).collect(Collectors.toList());
|
|
HashMap<String, Object> map = new HashMap<>();
|
map.put("count", rList);
|
map.put("list", resultList);
|
return Result.ok().data(map).total(total);
|
}
|
|
/**
|
* 人脸：目录一致率
|
*
|
* @param params
|
* @return
|
*/
|
@Override
|
public Result faceDirectoryConsistency(DataCenterQuery params) {
|
List<String> likeFileds = Arrays.asList("serialNumber.showValue", "ip.showValue", "name.showValue");
|
Query query = MongoUtil.getQuery(params, "serialNumber.showValue", TIME_FIELD, likeFileds, null);
|
|
long total = mongoTemplate.count(query, MonitorQualifyResult.class);
|
MongoUtil.setPage(query, params, TIME_FIELD);
|
List<MonitorQualifyResult> resultList = mongoTemplate.find(query, MonitorQualifyResult.class);
|
|
// 统计数量
|
MongoDatabase database = mongoTemplate.getDb();
|
MongoCollection<Document> collection = database.getCollection("uy_monitor_qualify");
|
|
Document ipErrFilter = new Document("ip.error", true);
|
Document macdzErrFilter = new Document("macdz.error", true);
|
Document latitudeErrFilter = new Document("latitude.error", true);
|
Document longitudeErrFilter = new Document("longitude.error", true);
|
|
List<Document> lists = Arrays.asList(ipErrFilter, macdzErrFilter, latitudeErrFilter, longitudeErrFilter);
|
List<Integer> rList = lists.stream().map(filter -> {
|
// 构建聚合管道
|
List<Document> pipeline = Arrays.asList(
|
new Document("$match", filter),
|
// $group 去重
|
new Document("$group", new Document("_id", "$serialNumber.showValue")),
|
new Document("$count", "uniqueDeviceIds")
|
);
|
// 执行聚合查询并获取结果
|
AggregateIterable<Document> result = collection.aggregate(pipeline);
|
Integer uniqueDeviceIdCount = 0;
|
for (Document doc : result) {
|
uniqueDeviceIdCount = doc.getInteger("uniqueDeviceIds");
|
break; // 不需要继续遍历，因为 $count 只会产生一个结果
|
}
|
return uniqueDeviceIdCount;
|
}).collect(Collectors.toList());
|
|
HashMap<String, Object> map = new HashMap<>();
|
map.put("count", rList);
|
map.put("list", resultList);
|
return Result.ok().data(map).total(total);
|
}
|
|
/**
|
* 人脸：人脸卡口信息采集准确率
|
*
|
* @param params
|
* @return
|
*/
|
@Override
|
public Result faceCollectionConsistency(DataCenterQuery params) {
|
List<String> likeFileds = Arrays.asList("externalIndexCode", "crossName");
|
Query query = MongoUtil.getQuery(params, "externalIndexCode", TIME_FIELD, likeFileds, null);
|
|
long total = mongoTemplate.count(query, CrossDetailResult.class);
|
MongoUtil.setPage(query, params, TIME_FIELD);
|
List<CrossDetailResult> resultList = mongoTemplate.find(query, CrossDetailResult.class);
|
|
// 统计数量
|
MongoDatabase database = mongoTemplate.getDb();
|
MongoCollection<Document> collection = database.getCollection("hk_cross_detail");
|
|
Document ipErrFilter = new Document("lalType", 1);
|
Document macdzErrFilter = new Document("lalType", 2);
|
Document latitudeErrFilter = new Document("lalType", 3);
|
Document longitudeErrFilter = new Document("lalType", 4);
|
|
List<Document> lists = Arrays.asList(ipErrFilter, macdzErrFilter, latitudeErrFilter, longitudeErrFilter);
|
List<Integer> rList = lists.stream().map(filter -> {
|
// 构建聚合管道
|
List<Document> pipeline = Arrays.asList(
|
new Document("$match", filter),
|
// $group 去重
|
new Document("$group", new Document("_id", "$externalIndexCode")),
|
new Document("$count", "uniqueDeviceIds")
|
);
|
// 执行聚合查询并获取结果
|
AggregateIterable<Document> result = collection.aggregate(pipeline);
|
Integer uniqueDeviceIdCount = 0;
|
for (Document doc : result) {
|
uniqueDeviceIdCount = doc.getInteger("uniqueDeviceIds");
|
break; // 不需要继续遍历，因为 $count 只会产生一个结果
|
}
|
return uniqueDeviceIdCount;
|
}).collect(Collectors.toList());
|
|
HashMap<String, Object> map = new HashMap<>();
|
map.put("count", rList);
|
map.put("list", resultList);
|
return Result.ok().data(map).total(total);
|
}
|
|
/**
|
* 人脸：设备抓拍图片合格性
|
*
|
* @param params
|
* @return
|
*/
|
@Override
|
public Result faceImgQualification(DataCenterQuery params) {
|
List<String> likeFileds = Arrays.asList("externalIndexCode", "cameraName");
|
Query query = MongoUtil.getQuery(params, "externalIndexCode", TIME_FIELD, likeFileds, null);
|
|
long total = mongoTemplate.count(query, MonitoringDetailResult.class);
|
MongoUtil.setPage(query, params, TIME_FIELD);
|
List<MonitoringDetailResult> resultList = mongoTemplate.find(query, MonitoringDetailResult.class);
|
|
// 统计数量
|
MongoDatabase database = mongoTemplate.getDb();
|
MongoCollection<Document> collection = database.getCollection("hk_monitoring_detail");
|
|
Document ipErrFilter = new Document("lalType", 1);
|
Document macdzErrFilter = new Document("lalType", 2);
|
Document latitudeErrFilter = new Document("lalType", 3);
|
Document longitudeErrFilter = new Document("lalType", 4);
|
|
List<Document> lists = Arrays.asList(ipErrFilter, macdzErrFilter, latitudeErrFilter, longitudeErrFilter);
|
List<Integer> rList = lists.stream().map(filter -> {
|
// 构建聚合管道
|
List<Document> pipeline = Arrays.asList(
|
new Document("$match", filter),
|
// $group 去重
|
new Document("$group", new Document("_id", "$externalIndexCode")),
|
new Document("$count", "uniqueDeviceIds")
|
);
|
// 执行聚合查询并获取结果
|
AggregateIterable<Document> result = collection.aggregate(pipeline);
|
Integer uniqueDeviceIdCount = 0;
|
for (Document doc : result) {
|
uniqueDeviceIdCount = doc.getInteger("uniqueDeviceIds");
|
break; // 不需要继续遍历，因为 $count 只会产生一个结果
|
}
|
return uniqueDeviceIdCount;
|
}).collect(Collectors.toList());
|
|
HashMap<String, Object> map = new HashMap<>();
|
map.put("count", rList);
|
map.put("list", resultList);
|
return Result.ok().data(map).total(total);
|
}
|
|
/**
|
* 人脸：设备抓拍图片时钟准确性
|
*
|
* @param params
|
* @return
|
*/
|
@Override
|
public Result faceCapturesImagesAccuracy(DataCenterQuery params) {
|
List<String> likeFileds = Arrays.asList("externalIndexCode", "deviceName");
|
Query query = MongoUtil.getQuery(params, "externalIndexCode", TIME_FIELD, likeFileds, null);
|
|
long total = mongoTemplate.count(query, FaceDeviceInspectionResult.class);
|
MongoUtil.setPage(query, params, TIME_FIELD);
|
List<FaceDeviceInspectionResult> resultList = mongoTemplate.find(query, FaceDeviceInspectionResult.class);
|
|
// 统计数量
|
MongoDatabase database = mongoTemplate.getDb();
|
MongoCollection<Document> collection = database.getCollection("hk_vehicle_device_inspection");
|
|
Document ipErrFilter = new Document("snapResult", 1);
|
Document macdzErrFilter = new Document("snapResult", 2);
|
Document longitudeErrFilter = new Document("snapResult", 4);
|
|
List<Document> lists = Arrays.asList(ipErrFilter, macdzErrFilter, longitudeErrFilter);
|
List<Integer> rList = lists.stream().map(filter -> {
|
// 构建聚合管道
|
List<Document> pipeline = Arrays.asList(
|
new Document("$match", filter),
|
// $group 去重
|
new Document("$group", new Document("_id", "$externalIndexCode")),
|
new Document("$count", "uniqueDeviceIds")
|
);
|
// 执行聚合查询并获取结果
|
AggregateIterable<Document> result = collection.aggregate(pipeline);
|
Integer uniqueDeviceIdCount = 0;
|
for (Document doc : result) {
|
uniqueDeviceIdCount = doc.getInteger("uniqueDeviceIds");
|
break; // 不需要继续遍历，因为 $count 只会产生一个结果
|
}
|
return uniqueDeviceIdCount;
|
}).collect(Collectors.toList());
|
|
HashMap<String, Object> map = new HashMap<>();
|
map.put("count", rList);
|
map.put("list", resultList);
|
return Result.ok().data(map).total(total);
|
}
|
|
/**
|
* 人脸：抓拍人脸数据上传及时性
|
*
|
* @param params
|
* @return
|
*/
|
@Override
|
public Result faceTimelyUpload(DataCenterQuery params) {
|
List<String> likeFileds = Arrays.asList("externalIndexCode", "deviceName");
|
Query query = MongoUtil.getQuery(params, "externalIndexCode", TIME_FIELD, likeFileds, null);
|
|
long total = mongoTemplate.count(query, FaceDeviceInspectionResult.class);
|
MongoUtil.setPage(query, params, TIME_FIELD);
|
List<FaceDeviceInspectionResult> resultList = mongoTemplate.find(query, FaceDeviceInspectionResult.class);
|
|
// 统计数量
|
MongoDatabase database = mongoTemplate.getDb();
|
MongoCollection<Document> collection = database.getCollection("hk_vehicle_device_inspection");
|
|
Document ipErrFilter = new Document("snapResult", 1);
|
Document macdzErrFilter = new Document("snapResult", 2);
|
Document longitudeErrFilter = new Document("snapResult", 4);
|
|
List<Document> lists = Arrays.asList(ipErrFilter, macdzErrFilter, longitudeErrFilter);
|
List<Integer> rList = lists.stream().map(filter -> {
|
// 构建聚合管道
|
List<Document> pipeline = Arrays.asList(
|
new Document("$match", filter),
|
// $group 去重
|
new Document("$group", new Document("_id", "$externalIndexCode")),
|
new Document("$count", "uniqueDeviceIds")
|
);
|
// 执行聚合查询并获取结果
|
AggregateIterable<Document> result = collection.aggregate(pipeline);
|
Integer uniqueDeviceIdCount = 0;
|
for (Document doc : result) {
|
uniqueDeviceIdCount = doc.getInteger("uniqueDeviceIds");
|
break; // 不需要继续遍历，因为 $count 只会产生一个结果
|
}
|
return uniqueDeviceIdCount;
|
}).collect(Collectors.toList());
|
|
HashMap<String, Object> map = new HashMap<>();
|
map.put("count", rList);
|
map.put("list", resultList);
|
return Result.ok().data(map).total(total);
|
}
|
|
/**
|
* 人脸：人脸卡口设备抓拍数据大图可用性
|
*
|
* @param params
|
* @return
|
*/
|
@Override
|
public Result faceAvailabilityOfLargeImg(DataCenterQuery params) {
|
List<String> likeFileds = Arrays.asList("externalIndexCode", "deviceName");
|
Query query = MongoUtil.getQuery(params, "externalIndexCode", TIME_FIELD, likeFileds, null);
|
|
long total = mongoTemplate.count(query, FaceDeviceSamplingResult.class);
|
MongoUtil.setPage(query, params, TIME_FIELD);
|
List<FaceDeviceSamplingResult> resultList = mongoTemplate.find(query, FaceDeviceSamplingResult.class);
|
// 统计数
|
HashMap<String, Object> map = new HashMap<>();
|
map.put("count", CollectionUtils.EMPTY_COLLECTION);
|
map.put("list", resultList);
|
return Result.ok().data(map).total(total);
|
}
|
/**
|
* 视频：视频图像资源安全管理
|
*
|
* @param query
|
* @return
|
*/
|
@Override
|
public Result videoImageResourceSecurity(DataCenterQuery query) {
|
Page<ImageResourceSecurityDetail> page = PageHelper.startPage(query.getPageNum(), query.getPageSize());
|
securityDetailMapper.selectImageResourceSecurityDetailList(query);
|
|
// 统计数
|
HashMap<String, Object> map = new HashMap<>();
|
map.put("count", CollectionUtils.EMPTY_COLLECTION);
|
map.put("list", page);
|
return Result.ok().data(map).total(page.getTotal());
|
|
}
|
}
|
