mongo统计

xiangpei

2024-09-04 bf4261a3ec8165506e4b627b0711b6586d8ca23e

mongo统计

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

@@ -841,7 +841,11 @@
        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);
@@ -883,12 +887,37 @@
        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);
    }
@@ -970,13 +999,36 @@
        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);
    }
@@ -995,13 +1047,36 @@
        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);
    }
@@ -1020,14 +1095,37 @@
        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);
    }
@@ -1046,13 +1144,37 @@
        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);
    }
@@ -1066,18 +1188,42 @@
    @Override
    public Result faceImgQualification(DataCenterQuery params) {

        Query query = MongoUtil.getQuery(params, "deviceId", TIME_FIELD, null);
        Query query = MongoUtil.getQuery(params, "externalIndexCode", TIME_FIELD, 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);
    }
@@ -1096,12 +1242,36 @@
        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);
    }
@@ -1120,12 +1290,36 @@
        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);
    }

 ycl-server/src/main/resources/mapper/zgyw/TMonitorMapper.xml

@@ -300,12 +300,9 @@
        IFNULL(SUM(IF(on_state = 2, 1, 0)), 0) AS postsPercentage,
        IFNULL(ROUND(SUM(IF(on_state = 1, 1, 0)) / count(*) * 100, 2), 0) as viewsPercentage
        FROM t_monitor m
        left join t_yw_point p on m.serial_number = p.serial_number
        left join sys_dept d on p.dept_id = d.dept_id
        <where>
            camera_fun_type like concat('%', #{cameraFunType}, '%')
        </where>
        ${params.dataScope}
    </select>

    <select id="recoveryException" resultType="java.util.Map">