录像可用率判断方式，海康的接口调整

zxl

2025-04-24 9d0d134447c84abcf34ddfda1f31099af7037c18

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

@@ -23,6 +23,7 @@
import com.ycl.platform.mapper.YwPointMapper;
import com.ycl.platform.service.*;
import com.ycl.system.Result;
import com.ycl.system.service.ISysConfigService;
import com.ycl.utils.DateUtils;
import com.ycl.utils.MongoUtil;
import com.ycl.utils.StringUtils;
@@ -1259,6 +1260,7 @@
        return Result.ok().data(map).total(total);
    }

    private final ISysConfigService configService;
    /**
     * 视频：录像可用率
     *
@@ -1268,6 +1270,17 @@
    @Override

    public Result videoAvailabilityRate(DataCenterQuery params) {
        //获取系统参数
        String dictLabel =  configService.selectConfigByKey("recording_min_time");

        double recordingMinTime;
        try {
            recordingMinTime = Double.parseDouble(dictLabel) / 60; // 如果 dictLabel 是以小时为单位，则无需除以 60
        } catch (Exception e) {
            log.error("配置的删除时间范围格式不正确: {}", dictLabel, e);
            recordingMinTime = 12.0; // 默认 12 小时（以小时为单位）
        }

        List<String> likeFileds = Arrays.asList("deviceId", "deviceName");
        Query query = MongoUtil.getQuery(params, "createTime", likeFileds, null);
        //下拉框录像情况查询条件
@@ -1315,6 +1328,32 @@
            }
            return uniqueDeviceIdCount + "";
        }).collect(Collectors.toList());

        //计算录像可用率
        MongoDatabase databaes2 = mongoTemplate.getDb();
        MongoCollection<Document> collection2 = databaes2.getCollection("uy_record_meta_d_sum");

        double finalRecordingMinTime = recordingMinTime;

        List<Document> documentList = new ArrayList<>(3);
        setTag(params, documentList);
        Document recording = new Document("recordDuration",new Document("$gte", finalRecordingMinTime));
        documentList.add(recording);
        Document filter = new Document("$and", documentList);
        // 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")
        );
        AggregateIterable<Document> result = collection2.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
        }
        log.error("打印：{}",uniqueDeviceIdCount);
//        List<CheckIndexVideo> videoList = new LambdaQueryChainWrapper<>(checkIndexVideoService.getBaseMapper())
//                .select(CheckIndexVideo::getVideoAvailable)
//                .eq(params.getDataType().equals(1), CheckIndexVideo::getExamineTag, CheckConstants.Examine_Tag_Province)
@@ -1338,12 +1377,11 @@
//        1：完整 0：间歇 -1：异常 |
        if (!StringUtils.isEmpty(resultCount.get(0)) && !"0".equals(resultCount.get(0))) {
            //resultCount.get(0)
            onlineRate = new BigDecimal(resultCount.get(1)).divide(new BigDecimal(resultCount.get(0)), 3,RoundingMode.DOWN).multiply(new BigDecimal("100"));
            onlineRate = new BigDecimal(resultCount.get(1)).divide(new BigDecimal(uniqueDeviceIdCount), 3,RoundingMode.DOWN).multiply(new BigDecimal("100"));
        }
        resultCount.add(this.remove0(onlineRate));

        HashMap<String, Object> map = new HashMap<>();
        log.error("resultCount:{}",resultCount.get(3));
        map.put("count", resultCount);
        map.put("list", resultList);
        return Result.ok().data(map).total(total);