Merge remote-tracking branch 'origin/master'

fuliqi

2024-09-04 d6e5a42f021b5e2612f970da21cccf386a4e6640

 ycl-server/src/main/java/com/ycl/platform/service/impl/DataCenterServiceImpl.java

@@ -3,6 +3,9 @@
import com.baomidou.mybatisplus.core.metadata.IPage;
import com.github.pagehelper.Page;
import com.github.pagehelper.PageHelper;
import com.mongodb.client.AggregateIterable;
import com.mongodb.client.MongoCollection;
import com.mongodb.client.MongoDatabase;
import com.ycl.platform.domain.entity.ImageResourceSecurityDetail;
import com.ycl.platform.domain.entity.YwPoint;
import com.ycl.platform.domain.query.DataCenterQuery;
@@ -17,6 +20,7 @@
import com.ycl.utils.MongoUtil;
import lombok.RequiredArgsConstructor;
import org.apache.commons.collections.CollectionUtils;
import org.bson.Document;
import org.springframework.data.domain.Sort;
import org.springframework.data.mongodb.core.MongoTemplate;
import org.springframework.data.mongodb.core.query.Criteria;
@@ -25,6 +29,7 @@
import org.springframework.stereotype.Service;

import java.util.*;
import java.util.stream.Collectors;

/**
 * 数据中心接口
@@ -52,15 +57,21 @@
    @Override
    public Result videoPointOnlineRate(DataCenterQuery params) {
        // 生成查询
        Query query = MongoUtil.getQuery(params, "deviceId", TIME_FIELD, null);
        List<String> likeFileds = Arrays.asList("arealayerName", "ipAddr", "deviceName", "deviceId");
        Query query = MongoUtil.getQuery(params, "deviceId", TIME_FIELD, likeFileds, null);

        // 先查总数再分页
        long total = mongoTemplate.count(query, VideoOnlineResult.class);
        MongoUtil.setPage(query, params, TIME_FIELD);
        List<VideoOnlineResult> resultList = mongoTemplate.find(query, VideoOnlineResult.class);

        // 统计数量
        MongoDatabase database = mongoTemplate.getDb();
        MongoCollection<Document> collection = database.getCollection("uy_online");
        long distinctCount = collection.distinct("deviceId", String.class).into(new ArrayList<>()).size();

        HashMap<String, Object> map = new HashMap<>();
        map.put("count", Arrays.asList(total));
        map.put("count", Arrays.asList(distinctCount));
        map.put("list", resultList);
        return Result.ok().data(map).total(total);
    }
@@ -75,17 +86,37 @@
    public Result deptVideoPointOnlineRate(DataCenterQuery params) {
        // 先查出部级点位的国标
        List<String> deptGBList = pointMapper.getDeptPointGB(0);

        List<String> likeFileds = Arrays.asList("arealayerName", "ipAddr", "deviceName", "deviceId");
        // 生成查询
        Query query = MongoUtil.getQuery(params, "deviceId", TIME_FIELD, deptGBList);
        Query query = MongoUtil.getQuery(params, "deviceId", TIME_FIELD, likeFileds, deptGBList);

        // 先查总数再分页
        long total = mongoTemplate.count(query, VideoOnlineResult.class);
        MongoUtil.setPage(query, params, TIME_FIELD);
        List<VideoOnlineResult> resultList = mongoTemplate.find(query, VideoOnlineResult.class);

        // 统计数量
        MongoDatabase database = mongoTemplate.getDb();
        MongoCollection<Document> collection = database.getCollection("uy_online");
        Document filter = new Document("deviceId", new Document("$in", deptGBList));
        // 构建聚合管道
        List<Document> pipeline = Arrays.asList(
                // 只要部级的
                new Document("$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
        }

        HashMap<String, Object> map = new HashMap<>();
        map.put("count", Arrays.asList(total));
        map.put("count", Arrays.asList(uniqueDeviceIdCount));
        map.put("list", resultList);
        return Result.ok().data(map).total(total);
    }
@@ -100,20 +131,39 @@
    public Result videoImportantPointOnlineRate(DataCenterQuery params) {
        // 先查出重点点位的国标
        List<String> deptGBList = pointMapper.getDeptPointGB(1);

        Query query = MongoUtil.getQuery(params, "deviceId", TIME_FIELD, deptGBList);
        List<String> likeFileds = Arrays.asList("arealayerName", "ipAddr", "deviceName", "deviceId");
        Query query = MongoUtil.getQuery(params, "deviceId", TIME_FIELD, likeFileds, deptGBList);

        long total = mongoTemplate.count(query, VideoOnlineResult.class);
        MongoUtil.setPage(query, params, TIME_FIELD);
        List<VideoOnlineResult> resultList = mongoTemplate.find(query, VideoOnlineResult.class);
        // 统计数
        long nonNetwork = mongoTemplate.count(new Query().addCriteria(Criteria.where("LWSX").is("0")), VideoOnlineResult.class);
        long network = mongoTemplate.count(new Query().addCriteria(Criteria.where("LWSX").is("1")), VideoOnlineResult.class);
        long video = mongoTemplate.count(new Query().addCriteria(Criteria.where("SXJGNLX").regex(".*1.*")), VideoOnlineResult.class);
        long car = mongoTemplate.count(new Query().addCriteria(Criteria.where("SXJGNLX").regex(".*2.*")), VideoOnlineResult.class);
        long face = mongoTemplate.count(new Query().addCriteria(Criteria.where("SXJGNLX").regex(".*3.*")), VideoOnlineResult.class);

        // 统计数量
        MongoDatabase database = mongoTemplate.getDb();
        MongoCollection<Document> collection = database.getCollection("uy_online");
        Document filter = new Document("deviceId", new Document("$in", deptGBList));
        // 构建聚合管道
        List<Document> pipeline = Arrays.asList(
                new Document("$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
        }

//        long nonNetwork = mongoTemplate.count(new Query().addCriteria(Criteria.where("LWSX").is("0")), VideoOnlineResult.class);
//        long network = mongoTemplate.count(new Query().addCriteria(Criteria.where("LWSX").is("1")), VideoOnlineResult.class);
//        long video = mongoTemplate.count(new Query().addCriteria(Criteria.where("SXJGNLX").regex(".*1.*")), VideoOnlineResult.class);
//        long car = mongoTemplate.count(new Query().addCriteria(Criteria.where("SXJGNLX").regex(".*2.*")), VideoOnlineResult.class);
//        long face = mongoTemplate.count(new Query().addCriteria(Criteria.where("SXJGNLX").regex(".*3.*")), VideoOnlineResult.class);
        HashMap<String, Object> map = new HashMap<>();
        map.put("count", Arrays.asList(nonNetwork, network, video, car, face));
        map.put("count", Arrays.asList(uniqueDeviceIdCount));
        map.put("list", resultList);
        return Result.ok().data(map).total(total);
    }
@@ -128,20 +178,40 @@
    public Result videoImportantPointImageOnlineRate(DataCenterQuery params) {
        // 先查出重点指挥图像点位的国标
        List<String> deptGBList = pointMapper.getDeptPointGB(2);

        Query query = MongoUtil.getQuery(params, "deviceId", TIME_FIELD, deptGBList);
        List<String> likeFileds = Arrays.asList("arealayerName", "ipAddr", "deviceName", "deviceId");
        Query query = MongoUtil.getQuery(params, "deviceId", TIME_FIELD, likeFileds, deptGBList);

        long total = mongoTemplate.count(query, VideoOnlineResult.class);
        MongoUtil.setPage(query, params, TIME_FIELD);
        List<VideoOnlineResult> resultList = mongoTemplate.find(query, VideoOnlineResult.class);
        // 统计数
        long nonNetwork = mongoTemplate.count(new Query().addCriteria(Criteria.where("LWSX").is("0")), VideoOnlineResult.class);
        long network = mongoTemplate.count(new Query().addCriteria(Criteria.where("LWSX").is("1")), VideoOnlineResult.class);
        long video = mongoTemplate.count(new Query().addCriteria(Criteria.where("SXJGNLX").regex(".*1.*")), VideoOnlineResult.class);
        long car = mongoTemplate.count(new Query().addCriteria(Criteria.where("SXJGNLX").regex(".*2.*")), VideoOnlineResult.class);
        long face = mongoTemplate.count(new Query().addCriteria(Criteria.where("SXJGNLX").regex(".*3.*")), VideoOnlineResult.class);

        // 统计数量
        MongoDatabase database = mongoTemplate.getDb();
        MongoCollection<Document> collection = database.getCollection("uy_online");
        Document filter = new Document("deviceId", new Document("$in", deptGBList));
        // 构建聚合管道
        List<Document> pipeline = Arrays.asList(
                new Document("$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
        }

//        // 统计数
//        long nonNetwork = mongoTemplate.count(new Query().addCriteria(Criteria.where("LWSX").is("0")), VideoOnlineResult.class);
//        long network = mongoTemplate.count(new Query().addCriteria(Criteria.where("LWSX").is("1")), VideoOnlineResult.class);
//        long video = mongoTemplate.count(new Query().addCriteria(Criteria.where("SXJGNLX").regex(".*1.*")), VideoOnlineResult.class);
//        long car = mongoTemplate.count(new Query().addCriteria(Criteria.where("SXJGNLX").regex(".*2.*")), VideoOnlineResult.class);
//        long face = mongoTemplate.count(new Query().addCriteria(Criteria.where("SXJGNLX").regex(".*3.*")), VideoOnlineResult.class);
        HashMap<String, Object> map = new HashMap<>();
        map.put("count", Arrays.asList(nonNetwork, network, video, car, face));
        map.put("count", Arrays.asList(uniqueDeviceIdCount));
        map.put("list", resultList);
        return Result.ok().data(map).total(total);
    }
@@ -154,20 +224,43 @@
     */
    @Override
    public Result videoOneMachineDocumentRegister(DataCenterQuery params) {

        Query query = MongoUtil.getQuery(params, "serialNumber.showValue", TIME_FIELD, null);
        List<String> likeFileds = Arrays.asList("ip.showValue", "name.showValue", "serialNumber.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
        long nonNetwork = mongoTemplate.count(new Query().addCriteria(Criteria.where("LWSX").is("0")), MonitorQualifyResult.class);
        long network = mongoTemplate.count(new Query().addCriteria(Criteria.where("LWSX").is("1")), MonitorQualifyResult.class);
        long video = mongoTemplate.count(new Query().addCriteria(Criteria.where("SXJGNLX").regex(".*1.*")), MonitorQualifyResult.class);
        long car = mongoTemplate.count(new Query().addCriteria(Criteria.where("SXJGNLX").regex(".*2.*")), MonitorQualifyResult.class);
        long face = mongoTemplate.count(new Query().addCriteria(Criteria.where("SXJGNLX").regex(".*3.*")), 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", Arrays.asList(nonNetwork, network, video, car, face));
        map.put("count", rList);
        map.put("list", resultList);
        return Result.ok().data(map).total(total);
    }
@@ -180,15 +273,43 @@
     */
    @Override
    public Result videoOneMachineDocumentQualified(DataCenterQuery params) {

        Query query = MongoUtil.getQuery(params, "serialNumber.showValue", TIME_FIELD, null);
        List<String> likeFileds = Arrays.asList("ip.showValue", "name.showValue", "serialNumber.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

        // 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", CollectionUtils.EMPTY_COLLECTION);
        map.put("count", rList);
        map.put("list", resultList);
        return Result.ok().data(map).total(total);
    }
@@ -202,19 +323,43 @@
    @Override
    public Result videoAssessmentFileRatio(DataCenterQuery params) {
        // TODO 新增一张表记录每次的档案考核
        Query query = MongoUtil.getQuery(params, "serialNumber.showValue", TIME_FIELD, null);
        List<String> likeFileds = Arrays.asList("ip.showValue", "name.showValue", "serialNumber.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
        long nonNetwork = mongoTemplate.count(new Query().addCriteria(Criteria.where("LWSX").is("0")), MonitorQualifyResult.class);
        long network = mongoTemplate.count(new Query().addCriteria(Criteria.where("LWSX").is("1")), MonitorQualifyResult.class);
        long video = mongoTemplate.count(new Query().addCriteria(Criteria.where("SXJGNLX").regex(".*1.*")), MonitorQualifyResult.class);
        long car = mongoTemplate.count(new Query().addCriteria(Criteria.where("SXJGNLX").regex(".*2.*")), MonitorQualifyResult.class);
        long face = mongoTemplate.count(new Query().addCriteria(Criteria.where("SXJGNLX").regex(".*3.*")), 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", Arrays.asList(nonNetwork, network, video, car, face));
        map.put("count", rList);
        map.put("list", resultList);
        return Result.ok().data(map).total(total);
    }
@@ -227,18 +372,39 @@
     */
    @Override
    public Result videoAvailabilityRate(DataCenterQuery params) {

        Query query = MongoUtil.getQuery(params, "deviceId", "createTime", null);
        List<String> likeFileds = Arrays.asList("deviceId", "deviceName");
        Query query = MongoUtil.getQuery(params, "deviceId", "createTime", likeFileds, null);

        long total = mongoTemplate.count(query, RecordMetaDSumResult.class);
        MongoUtil.setPage(query, params, "createTime");
        List<RecordMetaDSumResult> resultList = mongoTemplate.find(query, RecordMetaDSumResult.class);
        // 统计数
        long one = mongoTemplate.count(new Query().addCriteria(Criteria.where("recordStatus").is("1")), RecordMetaDSumResult.class);
        long two = mongoTemplate.count(new Query().addCriteria(Criteria.where("recordStatus").is("2")), RecordMetaDSumResult.class);
        long three = mongoTemplate.count(new Query().addCriteria(Criteria.where("recordStatus").is("-1")), 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<Integer> resultCount = status.stream().map(item -> {
            Document filter = 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());


        HashMap<String, Object> map = new HashMap<>();
        map.put("count", Arrays.asList(one, two, three));
        map.put("count", resultCount);
        map.put("list", resultList);
        return Result.ok().data(map).total(total);
    }
@@ -253,18 +419,42 @@
    public Result deptVideoAvailabilityRate(DataCenterQuery params) {

        List<String> deptGBList = pointMapper.getDeptPointGB(0);

        Query query = MongoUtil.getQuery(params, "deviceId", "createTime", deptGBList);
        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
        long one = mongoTemplate.count(new Query().addCriteria(Criteria.where("recordStatus").is("1")), RecordMetaDSumResult.class);
        long two = mongoTemplate.count(new Query().addCriteria(Criteria.where("recordStatus").is("2")), RecordMetaDSumResult.class);
        long three = mongoTemplate.count(new Query().addCriteria(Criteria.where("recordStatus").is("-1")), 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<Integer> 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());


        HashMap<String, Object> map = new HashMap<>();
        map.put("count", Arrays.asList(one, two, three));
        map.put("count", resultCount);
        map.put("list", resultList);
        return Result.ok().data(map).total(total);
    }
@@ -279,18 +469,41 @@
    public Result videoImportantPointAvailabilityRate(DataCenterQuery params) {

        List<String> deptGBList = pointMapper.getDeptPointGB(1);

        Query query = MongoUtil.getQuery(params, "deviceId", "createTime", deptGBList);
        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
        long one = mongoTemplate.count(new Query().addCriteria(Criteria.where("recordStatus").is("1")), OneMachineFileResult.class);
        long two = mongoTemplate.count(new Query().addCriteria(Criteria.where("recordStatus").is("2")), OneMachineFileResult.class);
        long three = mongoTemplate.count(new Query().addCriteria(Criteria.where("recordStatus").is("-1")), OneMachineFileResult.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<Integer> 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());

        HashMap<String, Object> map = new HashMap<>();
        map.put("count", Arrays.asList(one, two, three));
        map.put("count", resultCount);
        map.put("list", resultList);
        return Result.ok().data(map).total(total);
    }
@@ -303,8 +516,8 @@
     */
    @Override
    public Result videoLabelingAccuracy(DataCenterQuery params) {

        Query query = MongoUtil.getQuery(params, "deviceId", "", new ArrayList<>());
        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);
@@ -330,19 +543,43 @@
    public Result videoImportantPointLabelingAccuracy(DataCenterQuery params) {

        List<String> deptGBList = pointMapper.getDeptPointGB(1);
        Query query = MongoUtil.getQuery(params, "deviceNo", "checkTime", deptGBList);
        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
        long nonNetwork = mongoTemplate.count(new Query().addCriteria(Criteria.where("LWSX").is("0")), OsdCheckResult.class);
        long network = mongoTemplate.count(new Query().addCriteria(Criteria.where("LWSX").is("1")), OsdCheckResult.class);
        long video = mongoTemplate.count(new Query().addCriteria(Criteria.where("SXJGNLX").regex(".*1.*")), OsdCheckResult.class);
        long car = mongoTemplate.count(new Query().addCriteria(Criteria.where("SXJGNLX").regex(".*2.*")), OsdCheckResult.class);
        long face = mongoTemplate.count(new Query().addCriteria(Criteria.where("SXJGNLX").regex(".*3.*")), 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<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", "$deviceNo")),
                    new Document("$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());


        HashMap<String, Object> map = new HashMap<>();
        map.put("count", Arrays.asList(nonNetwork, network, video, car, face));
        map.put("count", rList);
        map.put("list", resultList);
        return Result.ok().data(map).total(total);
    }
@@ -356,7 +593,8 @@
    @Override
    public Result videoCheckTimeAccuracy(DataCenterQuery params) {

        Query query = MongoUtil.getQuery(params, "deviceId", "", new ArrayList<>());
        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);
@@ -382,20 +620,40 @@
    public Result videoImportantPointCheckTimeAccuracy(DataCenterQuery params) {

        List<String> deptGBList = pointMapper.getDeptPointGB(1);

        Query query = MongoUtil.getQuery(params, "deviceId", "checkTime", deptGBList);
        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
        long nonNetwork = mongoTemplate.count(new Query().addCriteria(Criteria.where("LWSX").is("0")), OsdCheckResult.class);
        long network = mongoTemplate.count(new Query().addCriteria(Criteria.where("LWSX").is("1")), OsdCheckResult.class);
        long video = mongoTemplate.count(new Query().addCriteria(Criteria.where("SXJGNLX").regex(".*1.*")), OsdCheckResult.class);
        long car = mongoTemplate.count(new Query().addCriteria(Criteria.where("SXJGNLX").regex(".*2.*")), OsdCheckResult.class);
        long face = mongoTemplate.count(new Query().addCriteria(Criteria.where("SXJGNLX").regex(".*3.*")), 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<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", "$deviceNo")),
                    new Document("$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());

        HashMap<String, Object> map = new HashMap<>();
        map.put("count", Arrays.asList(nonNetwork, network, video, car, face));
        map.put("count", rList);
        map.put("list", resultList);
        return Result.ok().data(map).total(total);
    }
@@ -409,18 +667,42 @@
     */
    @Override
    public Result vehicleViewDockStable(DataCenterQuery params) {

        Query query = MongoUtil.getQuery(params, "deviceId", "", new ArrayList<>());
        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
        long one = mongoTemplate.count(new Query().addCriteria(Criteria.where("resultType").is("1")), SnapshotDataMonitorResult.class);
        long two = mongoTemplate.count(new Query().addCriteria(Criteria.where("resultType").is("2")), SnapshotDataMonitorResult.class);
        long three = mongoTemplate.count(new Query().addCriteria(Criteria.where("resultType").is("3")), SnapshotDataMonitorResult.class);
        long four = mongoTemplate.count(new Query().addCriteria(Criteria.where("resultType").is("4")), 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", Arrays.asList(one, two, three, four));
        map.put("count", rList);
        map.put("list", resultList);
        return Result.ok().data(map).total(total);
    }
@@ -433,18 +715,20 @@
     */
    @Override
    public Result vehiclePointOnlineRate(DataCenterQuery params) {

        Query query = MongoUtil.getQuery(params, "deviceId", "", new ArrayList<>());
        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
        long one = mongoTemplate.count(new Query().addCriteria(Criteria.where("resultType").is("1")), SnapshotDataMonitorResult.class);
        long two = mongoTemplate.count(new Query().addCriteria(Criteria.where("resultType").is("2")), SnapshotDataMonitorResult.class);
        long three = mongoTemplate.count(new Query().addCriteria(Criteria.where("resultType").is("3")), SnapshotDataMonitorResult.class);
        long four = mongoTemplate.count(new Query().addCriteria(Criteria.where("resultType").is("4")), 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(one, two, three, four));
        map.put("count", Arrays.asList(distinctCount));
        map.put("list", resultList);
        return Result.ok().data(map).total(total);
    }
@@ -457,19 +741,43 @@
     */
    @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);

        Query query = MongoUtil.getQuery(params, "deviceId", "", new ArrayList<>());
        long total = mongoTemplate.count(query, MonitorQualifyResult.class);
        MongoUtil.setPage(query, params, TIME_FIELD);
        List<MonitorQualifyResult> resultList = mongoTemplate.find(query, MonitorQualifyResult.class);

        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);
        // 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", Arrays.asList(nonNetwork, network, video, car, face));
        map.put("count", rList);
        map.put("list", resultList);
        return Result.ok().data(map).total(total);
    }
@@ -482,18 +790,43 @@
     */
    @Override
    public Result vehicleCollectionConsistency(DataCenterQuery params) {

        Query query = MongoUtil.getQuery(params, "deviceId", "", new ArrayList<>());
        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
        long one = mongoTemplate.count(new Query().addCriteria(Criteria.where("lalType").is("1")), CrossDetailResult.class);
        long two = mongoTemplate.count(new Query().addCriteria(Criteria.where("lalType").is("2")), CrossDetailResult.class);
        long three = mongoTemplate.count(new Query().addCriteria(Criteria.where("lalType").is("3")), CrossDetailResult.class);
        long four = mongoTemplate.count(new Query().addCriteria(Criteria.where("lalType").is("4")), 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", Arrays.asList(one, two, three, four));
        map.put("count", rList);
        map.put("list", resultList);
        return Result.ok().data(map).total(total);
    }
@@ -506,12 +839,17 @@
     */
    @Override
    public Result vehicleCollectionDataIntegrity(DataCenterQuery params) {

        Query query = MongoUtil.getQuery(params, "deviceId", "", new ArrayList<>());
        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

        // 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);
@@ -526,10 +864,11 @@
     */
    @Override
    public Result vehicleCollectionDataCaptured(DataCenterQuery params) {

        Query query = MongoUtil.getQuery(params, "deviceId", "", new ArrayList<>());
        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<>();
@@ -546,17 +885,43 @@
     */
    @Override
    public Result vehicleClockAccuracy(DataCenterQuery params) {

        Query query = MongoUtil.getQuery(params, "deviceId", "", new ArrayList<>());
        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
        long one = mongoTemplate.count(new Query().addCriteria(Criteria.where("snapResult").is("1")), VehicleDeviceInspectionResult.class);
        long two = mongoTemplate.count(new Query().addCriteria(Criteria.where("snapResult").is("2")), VehicleDeviceInspectionResult.class);
        long four = mongoTemplate.count(new Query().addCriteria(Criteria.where("snapResult").is("4")), 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", Arrays.asList(one, two, four));
        map.put("count", rList);
        map.put("list", resultList);
        return Result.ok().data(map).total(total);
    }
@@ -569,10 +934,11 @@
     */
    @Override
    public Result vehicleTimelyUploadAccuracy(DataCenterQuery params) {

        Query query = MongoUtil.getQuery(params, "deviceId", "", new ArrayList<>());
        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<>();
@@ -589,10 +955,11 @@
     */
    @Override
    public Result vehicleUrlAccuracy(DataCenterQuery params) {

        Query query = MongoUtil.getQuery(params, "deviceId", "", new ArrayList<>());
        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<>();
@@ -609,10 +976,11 @@
     */
    @Override
    public Result vehicleBigImgAccuracy(DataCenterQuery params) {

        Query query = MongoUtil.getQuery(params, "deviceId", "", new ArrayList<>());
        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<>();
@@ -629,18 +997,42 @@
     */
    @Override
    public Result faceViewDockStable(DataCenterQuery params) {

        Query query = MongoUtil.getQuery(params, "deviceId", "", new ArrayList<>());
        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
        long one = mongoTemplate.count(new Query().addCriteria(Criteria.where("resultType").is("1")), SnapshotDataMonitorResult.class);
        long two = mongoTemplate.count(new Query().addCriteria(Criteria.where("resultType").is("2")), SnapshotDataMonitorResult.class);
        long three = mongoTemplate.count(new Query().addCriteria(Criteria.where("resultType").is("3")), SnapshotDataMonitorResult.class);
        long four = mongoTemplate.count(new Query().addCriteria(Criteria.where("resultType").is("4")), 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", Arrays.asList(one, two, three, four));
        map.put("count", rList);
        map.put("list", resultList);
        return Result.ok().data(map).total(total);
    }
@@ -653,18 +1045,42 @@
     */
    @Override
    public Result facePointOnlineRate(DataCenterQuery params) {

        Query query = MongoUtil.getQuery(params, "deviceId", "", new ArrayList<>());
        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
        long one = mongoTemplate.count(new Query().addCriteria(Criteria.where("resultType").is("1")), SnapshotDataMonitorResult.class);
        long two = mongoTemplate.count(new Query().addCriteria(Criteria.where("resultType").is("2")), SnapshotDataMonitorResult.class);
        long three = mongoTemplate.count(new Query().addCriteria(Criteria.where("resultType").is("3")), SnapshotDataMonitorResult.class);
        long four = mongoTemplate.count(new Query().addCriteria(Criteria.where("resultType").is("4")), 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", Arrays.asList(one, two, three, four));
        map.put("count", rList);
        map.put("list", resultList);
        return Result.ok().data(map).total(total);
    }
@@ -677,19 +1093,43 @@
     */
    @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);

        Query query = MongoUtil.getQuery(params, "deviceId", "", new ArrayList<>());
        long total = mongoTemplate.count(query, MonitorQualifyResult.class);
        MongoUtil.setPage(query, params, TIME_FIELD);
        List<MonitorQualifyResult> resultList = mongoTemplate.find(query, MonitorQualifyResult.class);

        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);
        // 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", Arrays.asList(nonNetwork, network, video, car, face));
        map.put("count", rList);
        map.put("list", resultList);
        return Result.ok().data(map).total(total);
    }
@@ -702,18 +1142,43 @@
     */
    @Override
    public Result faceCollectionConsistency(DataCenterQuery params) {

        Query query = MongoUtil.getQuery(params, "deviceId", "", new ArrayList<>());
        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
        long one = mongoTemplate.count(new Query().addCriteria(Criteria.where("lalType").is("1")), CrossDetailResult.class);
        long two = mongoTemplate.count(new Query().addCriteria(Criteria.where("lalType").is("2")), CrossDetailResult.class);
        long three = mongoTemplate.count(new Query().addCriteria(Criteria.where("lalType").is("3")), CrossDetailResult.class);
        long four = mongoTemplate.count(new Query().addCriteria(Criteria.where("lalType").is("4")), 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", Arrays.asList(one, two, three, four));
        map.put("count", rList);
        map.put("list", resultList);
        return Result.ok().data(map).total(total);
    }
@@ -726,18 +1191,43 @@
     */
    @Override
    public Result faceImgQualification(DataCenterQuery params) {

        Query query = MongoUtil.getQuery(params, "deviceId", "", new ArrayList<>());
        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
        long one = mongoTemplate.count(new Query().addCriteria(Criteria.where("lalType").is("1")), MonitoringDetailResult.class);
        long two = mongoTemplate.count(new Query().addCriteria(Criteria.where("lalType").is("2")), MonitoringDetailResult.class);
        long three = mongoTemplate.count(new Query().addCriteria(Criteria.where("lalType").is("3")), MonitoringDetailResult.class);
        long four = mongoTemplate.count(new Query().addCriteria(Criteria.where("lalType").is("4")), 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", Arrays.asList(one, two, three, four));
        map.put("count", rList);
        map.put("list", resultList);
        return Result.ok().data(map).total(total);
    }
@@ -750,17 +1240,42 @@
     */
    @Override
    public Result faceCapturesImagesAccuracy(DataCenterQuery params) {

        Query query = MongoUtil.getQuery(params, "deviceId", "", new ArrayList<>());
        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
        long one = mongoTemplate.count(new Query().addCriteria(Criteria.where("snapResult").is("1")), FaceDeviceInspectionResult.class);
        long two = mongoTemplate.count(new Query().addCriteria(Criteria.where("snapResult").is("2")), FaceDeviceInspectionResult.class);
        long four = mongoTemplate.count(new Query().addCriteria(Criteria.where("snapResult").is("4")), 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", Arrays.asList(one, two, four));
        map.put("count", rList);
        map.put("list", resultList);
        return Result.ok().data(map).total(total);
    }
@@ -773,17 +1288,42 @@
     */
    @Override
    public Result faceTimelyUpload(DataCenterQuery params) {

        Query query = MongoUtil.getQuery(params, "deviceId", "", new ArrayList<>());
        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
        long one = mongoTemplate.count(new Query().addCriteria(Criteria.where("snapResult").is("1")), FaceDeviceInspectionResult.class);
        long two = mongoTemplate.count(new Query().addCriteria(Criteria.where("snapResult").is("2")), FaceDeviceInspectionResult.class);
        long four = mongoTemplate.count(new Query().addCriteria(Criteria.where("snapResult").is("4")), 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", Arrays.asList(one, two, four));
        map.put("count", rList);
        map.put("list", resultList);
        return Result.ok().data(map).total(total);
    }
@@ -796,10 +1336,11 @@
     */
    @Override
    public Result faceAvailabilityOfLargeImg(DataCenterQuery params) {

        Query query = MongoUtil.getQuery(params, "deviceId", "", new ArrayList<>());
        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<>();
@@ -815,9 +1356,8 @@
     */
    @Override
    public Result videoImageResourceSecurity(DataCenterQuery query) {
        ImageResourceSecurityDetail imageResourceSecurityDetail = new ImageResourceSecurityDetail();
        Page<ImageResourceSecurityDetail> page = PageHelper.startPage(query.getPageNum(), query.getPageSize());
        securityDetailMapper.selectImageResourceSecurityDetailList(imageResourceSecurityDetail);
        securityDetailMapper.selectImageResourceSecurityDetailList(query);

        // 统计数
        HashMap<String, Object> map = new HashMap<>();