feat(power): 添加功率数据实体类字段

- 在DataHarmPowerP中新增totDpf、totHarmP和dpf字段 - 在DataHarmPowerQ中新增totHarmQ字段 - 在DataHarmPowerS中新增totHarmS字段 - 所有新增字段均为Double类型并配置相应数据库列映射
feat(influx): 添加数据实体类的时间列注解和新字段
2026-05-26 15:38:22 +08:00 · 2026-05-25 20:00:30 +08:00 · 2026-05-21 15:54:52 +08:00 · 2026-05-19 15:04:10 +08:00 · 2026-04-13 15:30:05 +08:00 · 2026-04-09 11:28:36 +08:00
22 changed files with 379 additions and 4 deletions
--- a/src/main/java/com/njcn/influx/imapper/DataHarmRateVCvtMapper.java
+++ b/src/main/java/com/njcn/influx/imapper/DataHarmRateVCvtMapper.java
@@ -0,0 +1,11 @@
+package com.njcn.influx.imapper;
+
+import com.njcn.influx.base.InfluxDbBaseMapper;
+import com.njcn.influx.pojo.po.DataHarmRateVCvt;
+
+/**
+ * @author xy
+ */
+public interface DataHarmRateVCvtMapper extends InfluxDbBaseMapper<DataHarmRateVCvt> {
+
+}
--- a/src/main/java/com/njcn/influx/pojo/bo/CommonQueryParam.java
+++ b/src/main/java/com/njcn/influx/pojo/bo/CommonQueryParam.java
@@ -14,6 +14,7 @@ public class CommonQueryParam {
    private String lineId;
    private String tableName;
    private String columnName;
+    private String resultName;
    private String phasic;
    private String startTime;
    private String endTime;
--- a/src/main/java/com/njcn/influx/pojo/constant/InfluxDBTableConstant.java
+++ b/src/main/java/com/njcn/influx/pojo/constant/InfluxDBTableConstant.java
@@ -196,5 +196,8 @@ public interface InfluxDBTableConstant {
    String NORMAL = "0";
    String UN_NORMAL = "1";

-
+    /**
+     * 数据清洗标志 0:正常 1:异常
+     */
+    String ABNORMAL_FLAG = "abnormal_flag";
 }
--- a/src/main/java/com/njcn/influx/pojo/po/DataFlicker.java
+++ b/src/main/java/com/njcn/influx/pojo/po/DataFlicker.java
@@ -58,4 +58,10 @@ public class DataFlicker {
    //是否是异常指标数据，0否1是
    @Column(name = "abnormal_flag")
    private Integer abnormalFlag;
+
+    @Column(name = "cl_did")
+    private String clDid;
+
+    @Column(name = "process")
+    private String process;
 }
--- a/src/main/java/com/njcn/influx/pojo/po/DataFluc.java
+++ b/src/main/java/com/njcn/influx/pojo/po/DataFluc.java
@@ -5,6 +5,7 @@ import com.njcn.influx.utils.InstantDateSerializer;
 import lombok.Data;
 import org.influxdb.annotation.Column;
 import org.influxdb.annotation.Measurement;
+import org.influxdb.annotation.TimeColumn;

 import java.time.Instant;

@@ -18,6 +19,7 @@ import java.time.Instant;
@Measurement(name = "data_fluc")
 public class DataFluc {

+    @TimeColumn
    @Column(name = "time")
    @JsonSerialize(using = InstantDateSerializer.class)
    private Instant time;
@@ -39,7 +41,14 @@ public class DataFluc {

    @Column(name = "value_type",tag = true)
    private String valueType;
+
    //是否是异常指标数据，0否1是
    @Column(name = "abnormal_flag")
    private Integer abnormalFlag;
+
+    @Column(name = "cl_did")
+    private String clDid;
+
+    @Column(name = "process")
+    private String process;
 }
--- a/src/main/java/com/njcn/influx/pojo/po/DataHarmPhasicI.java
+++ b/src/main/java/com/njcn/influx/pojo/po/DataHarmPhasicI.java
@@ -5,6 +5,7 @@ import com.njcn.influx.utils.InstantDateSerializer;
 import lombok.Data;
 import org.influxdb.annotation.Column;
 import org.influxdb.annotation.Measurement;
+import org.influxdb.annotation.TimeColumn;

 import java.time.Instant;

@@ -18,6 +19,7 @@ import java.time.Instant;
@Measurement(name = "data_harmphasic_i")
 public class DataHarmPhasicI {

+    @TimeColumn
    @Column(name = "time")
    @JsonSerialize(using = InstantDateSerializer.class)
    private Instant time;
@@ -186,4 +188,10 @@ public class DataHarmPhasicI {
    //是否是异常指标数据，0否1是
    @Column(name = "abnormal_flag")
    private Integer abnormalFlag;
+
+    @Column(name = "cl_did")
+    private String clDid;
+
+    @Column(name = "process")
+    private String process;
 }
--- a/src/main/java/com/njcn/influx/pojo/po/DataHarmPhasicV.java
+++ b/src/main/java/com/njcn/influx/pojo/po/DataHarmPhasicV.java
@@ -189,4 +189,10 @@ public class DataHarmPhasicV {
    //是否是异常指标数据，0否1是
    @Column(name = "abnormal_flag")
    private Integer abnormalFlag;
+
+    @Column(name = "cl_did")
+    private String clDid;
+
+    @Column(name = "process")
+    private String process;
 }
--- a/src/main/java/com/njcn/influx/pojo/po/DataHarmPowerP.java
+++ b/src/main/java/com/njcn/influx/pojo/po/DataHarmPowerP.java
@@ -199,4 +199,19 @@ public class DataHarmPowerP {
    //是否是异常指标数据，0否1是
    @Column(name = "abnormal_flag")
    private Integer abnormalFlag;
+
+    @Column(name = "cl_did")
+    private String clDid;
+
+    @Column(name = "process")
+    private String process;
+
+    @Column(name = "tot_dpf")
+    private Double totDpf;
+
+    @Column(name = "tot_harm_p")
+    private Double totHarmP;
+
+    @Column(name = "dpf")
+    private Double dpf;
 }
--- a/src/main/java/com/njcn/influx/pojo/po/DataHarmPowerQ.java
+++ b/src/main/java/com/njcn/influx/pojo/po/DataHarmPowerQ.java
@@ -5,6 +5,7 @@ import com.njcn.influx.utils.InstantDateSerializer;
 import lombok.Data;
 import org.influxdb.annotation.Column;
 import org.influxdb.annotation.Measurement;
+import org.influxdb.annotation.TimeColumn;

 import java.time.Instant;

@@ -19,6 +20,7 @@ import java.time.Instant;
@Measurement(name = "data_harmpower_q")
 public class DataHarmPowerQ {

+    @TimeColumn
    @Column(name = "time")
    @JsonSerialize(using = InstantDateSerializer.class)
    private Instant time;
@@ -193,4 +195,13 @@ public class DataHarmPowerQ {
    @Column(name = "abnormal_flag")
    private Integer abnormalFlag;

+    @Column(name = "cl_did")
+    private String clDid;
+
+    @Column(name = "process")
+    private String process;
+
+    @Column(name = "tot_harm_q")
+    private Double totHarmQ;
+
 }
--- a/src/main/java/com/njcn/influx/pojo/po/DataHarmPowerS.java
+++ b/src/main/java/com/njcn/influx/pojo/po/DataHarmPowerS.java
@@ -5,6 +5,7 @@ import com.njcn.influx.utils.InstantDateSerializer;
 import lombok.Data;
 import org.influxdb.annotation.Column;
 import org.influxdb.annotation.Measurement;
+import org.influxdb.annotation.TimeColumn;

 import java.time.Instant;

@@ -19,6 +20,7 @@ import java.time.Instant;
@Measurement(name = "data_harmpower_s")
 public class DataHarmPowerS {

+    @TimeColumn
    @Column(name = "time")
    @JsonSerialize(using = InstantDateSerializer.class)
    private Instant time;
@@ -192,4 +194,13 @@ public class DataHarmPowerS {
    @Column(name = "abnormal_flag")
    private Integer abnormalFlag;

+    @Column(name = "cl_did")
+    private String clDid;
+
+    @Column(name = "process")
+    private String process;
+
+    @Column(name = "tot_harm_s")
+    private Double totHarmS;
+
 }
--- a/src/main/java/com/njcn/influx/pojo/po/DataHarmRateV.java
+++ b/src/main/java/com/njcn/influx/pojo/po/DataHarmRateV.java
@@ -189,4 +189,10 @@ public class DataHarmRateV{
    //是否是异常指标数据，0否1是
    @Column(name = "abnormal_flag")
    private Integer abnormalFlag;
+
+    @Column(name = "cl_did")
+    private String clDid;
+
+    @Column(name = "process")
+    private String process;
 }
--- a/src/main/java/com/njcn/influx/pojo/po/DataHarmRateVCvt.java
+++ b/src/main/java/com/njcn/influx/pojo/po/DataHarmRateVCvt.java
@@ -0,0 +1,193 @@
+package com.njcn.influx.pojo.po;
+
+import com.fasterxml.jackson.databind.annotation.JsonSerialize;
+import com.njcn.influx.utils.InstantDateSerializer;
+import lombok.Data;
+import org.influxdb.annotation.Column;
+import org.influxdb.annotation.Measurement;
+import org.influxdb.annotation.TimeColumn;
+
+import java.time.Instant;
+
+
+/**
+ * 类的介绍：
+ *
+ * @author xuyang
+ * @version 1.0.0
+ * @createTime 2022/5/12 11:27
+ */
+@Data
+@Measurement(name = "data_harmrate_v_cvt")
+public class DataHarmRateVCvt {
+    @TimeColumn
+    @Column(name = "time", tag = true)
+    @JsonSerialize(using = InstantDateSerializer.class)
+    private Instant time;
+
+    @Column(name = "line_id", tag = true)
+    private String lineId;
+
+    @Column(name = "phasic_type", tag = true)
+    private String phaseType;
+
+    @Column(name = "quality_flag", tag = true)
+    private String qualityFlag;
+
+    @Column(name = "value_type", tag = true)
+    private String valueType;
+
+    @Column(name = "v_1")
+    private Double v1;
+
+    @Column(name = "v_2")
+    private Double v2;
+
+    @Column(name = "v_3")
+    private Double v3;
+
+    @Column(name = "v_4")
+    private Double v4;
+
+    @Column(name = "v_5")
+    private Double v5;
+
+    @Column(name = "v_6")
+    private Double v6;
+
+    @Column(name = "v_7")
+    private Double v7;
+
+    @Column(name = "v_8")
+    private Double v8;
+
+    @Column(name = "v_9")
+    private Double v9;
+
+    @Column(name = "v_10")
+    private Double v10;
+
+    @Column(name = "v_11")
+    private Double v11;
+
+    @Column(name = "v_12")
+    private Double v12;
+
+    @Column(name = "v_13")
+    private Double v13;
+
+    @Column(name = "v_14")
+    private Double v14;
+
+    @Column(name = "v_15")
+    private Double v15;
+
+    @Column(name = "v_16")
+    private Double v16;
+
+    @Column(name = "v_17")
+    private Double v17;
+
+    @Column(name = "v_18")
+    private Double v18;
+
+    @Column(name = "v_19")
+    private Double v19;
+
+    @Column(name = "v_20")
+    private Double v20;
+
+    @Column(name = "v_21")
+    private Double v21;
+
+    @Column(name = "v_22")
+    private Double v22;
+
+    @Column(name = "v_23")
+    private Double v23;
+
+    @Column(name = "v_24")
+    private Double v24;
+
+    @Column(name = "v_25")
+    private Double v25;
+
+    @Column(name = "v_26")
+    private Double v26;
+
+    @Column(name = "v_27")
+    private Double v27;
+
+    @Column(name = "v_28")
+    private Double v28;
+
+    @Column(name = "v_29")
+    private Double v29;
+
+    @Column(name = "v_30")
+    private Double v30;
+
+    @Column(name = "v_31")
+    private Double v31;
+
+    @Column(name = "v_32")
+    private Double v32;
+
+    @Column(name = "v_33")
+    private Double v33;
+
+    @Column(name = "v_34")
+    private Double v34;
+
+    @Column(name = "v_35")
+    private Double v35;
+
+    @Column(name = "v_36")
+    private Double v36;
+
+    @Column(name = "v_37")
+    private Double v37;
+
+    @Column(name = "v_38")
+    private Double v38;
+
+    @Column(name = "v_39")
+    private Double v39;
+
+    @Column(name = "v_40")
+    private Double v40;
+
+    @Column(name = "v_41")
+    private Double v41;
+
+    @Column(name = "v_42")
+    private Double v42;
+
+    @Column(name = "v_43")
+    private Double v43;
+
+    @Column(name = "v_44")
+    private Double v44;
+
+    @Column(name = "v_45")
+    private Double v45;
+
+    @Column(name = "v_46")
+    private Double v46;
+
+    @Column(name = "v_47")
+    private Double v47;
+
+    @Column(name = "v_48")
+    private Double v48;
+
+    @Column(name = "v_49")
+    private Double v49;
+
+    @Column(name = "v_50")
+    private Double v50;
+    //是否是异常指标数据，0否1是
+    @Column(name = "abnormal_flag")
+    private Integer abnormalFlag;
+
+}
--- a/src/main/java/com/njcn/influx/pojo/po/DataI.java
+++ b/src/main/java/com/njcn/influx/pojo/po/DataI.java
@@ -209,4 +209,10 @@ public class DataI {
    @Column(name = "abnormal_flag")
    private Integer abnormalFlag;

+    @Column(name = "cl_did")
+    private String clDid;
+
+    @Column(name = "process")
+    private String process;
+
 }
--- a/src/main/java/com/njcn/influx/pojo/po/DataInHarmRateI.java
+++ b/src/main/java/com/njcn/influx/pojo/po/DataInHarmRateI.java
@@ -1,8 +1,11 @@
 package com.njcn.influx.pojo.po;

+import com.fasterxml.jackson.databind.annotation.JsonSerialize;
+import com.njcn.influx.utils.InstantDateSerializer;
 import lombok.Data;
 import org.influxdb.annotation.Column;
 import org.influxdb.annotation.Measurement;
+import org.influxdb.annotation.TimeColumn;

 import java.time.Instant;

@@ -17,7 +20,9 @@ import java.time.Instant;
@Measurement(name = "data_inharmrate_i")
 public class DataInHarmRateI {

+    @TimeColumn
    @Column(name = "time")
+    @JsonSerialize(using = InstantDateSerializer.class)
    private Instant time;

    @Column(name = "line_id",tag = true)
@@ -185,4 +190,10 @@ public class DataInHarmRateI {
    @Column(name = "abnormal_flag")
    private Integer abnormalFlag;

+    @Column(name = "cl_did")
+    private String clDid;
+
+    @Column(name = "process")
+    private String process;
+
 }
--- a/src/main/java/com/njcn/influx/pojo/po/DataInHarmRateV.java
+++ b/src/main/java/com/njcn/influx/pojo/po/DataInHarmRateV.java
@@ -191,4 +191,10 @@ public class DataInHarmRateV {
    @Column(name = "abnormal_flag")
    private Integer abnormalFlag;

+    @Column(name = "cl_did")
+    private String clDid;
+
+    @Column(name = "process")
+    private String process;
+
 }
--- a/src/main/java/com/njcn/influx/pojo/po/DataInHarmV.java
+++ b/src/main/java/com/njcn/influx/pojo/po/DataInHarmV.java
@@ -189,4 +189,10 @@ public class DataInHarmV {
    //是否是异常指标数据，0否1是
    @Column(name = "abnormal_flag")
    private Integer abnormalFlag;
+
+    @Column(name = "cl_did")
+    private String clDid;
+
+    @Column(name = "process")
+    private String process;
 }
--- a/src/main/java/com/njcn/influx/pojo/po/DataPlt.java
+++ b/src/main/java/com/njcn/influx/pojo/po/DataPlt.java
@@ -51,4 +51,10 @@ public class DataPlt {
    @Column(name = "abnormal_flag")
    private Integer abnormalFlag;

+    @Column(name = "cl_did")
+    private String clDid;
+
+    @Column(name = "process")
+    private String process;
+
 }
--- a/src/main/java/com/njcn/influx/pojo/po/DataV.java
+++ b/src/main/java/com/njcn/influx/pojo/po/DataV.java
@@ -232,7 +232,14 @@ public class DataV {
    //自定义字段
    @Column(name = "count")
    private Integer count;
+
    //是否是异常指标数据，0否1是
    @Column(name = "abnormal_flag")
    private Integer abnormalFlag;
+
+    @Column(name = "cl_did")
+    private String clDid;
+
+    @Column(name = "process")
+    private String process;
 }
--- a/src/main/java/com/njcn/influx/pojo/po/cs/PqdData.java
+++ b/src/main/java/com/njcn/influx/pojo/po/cs/PqdData.java
@@ -39,6 +39,9 @@ public class PqdData implements Serializable {
    @Column(name = "abnormal_flag")
    private Integer abnormalFlag;

+    @Column(name = "quality_flag",tag = true)
+    private String qualityFlag;
+

    @Column(name = "Pq_DF")
    private Double pq_DF;
--- a/src/main/java/com/njcn/influx/pojo/po/day/DayV.java
+++ b/src/main/java/com/njcn/influx/pojo/po/day/DayV.java
@@ -215,7 +215,7 @@ public class DayV {
    private String lineId;

    @Column(name = "phasic_type",tag = true)
-    private String phasicType;
+    private String phaseType;

    @Column(name = "value_type",tag = true)
    private String valueType;
--- a/src/main/java/com/njcn/influx/service/CommonService.java
+++ b/src/main/java/com/njcn/influx/service/CommonService.java
@@ -84,5 +84,9 @@ public interface CommonService {
     */
    StatisticalDataDTO getCounts(String lineId, String tableName, String columnName,String resultName, String phasic, String dataType, String clDid, String process,String startTime, String endTime);

+    List<StatisticalDataDTO> getEachModule(CommonQueryParam param);

+    StatisticalDataDTO getDataCounts(String lineId, String tableName, String columnName,String resultName, String phasic, String dataType, String clDid, String process,String startTime, String endTime);
+
+    List<StatisticalDataDTO> getModuleData(CommonQueryParam param);
 }
--- a/src/main/java/com/njcn/influx/service/impl/CommonServiceImpl.java
+++ b/src/main/java/com/njcn/influx/service/impl/CommonServiceImpl.java
@@ -1,5 +1,6 @@
 package com.njcn.influx.service.impl;

+import cn.hutool.core.date.DatePattern;
 import com.njcn.influx.imapper.CommonMapper;
 import com.njcn.influx.pojo.bo.CommonQueryParam;
 import com.njcn.influx.pojo.constant.InfluxDBTableConstant;
@@ -9,6 +10,8 @@ import com.njcn.influx.service.CommonService;
 import lombok.RequiredArgsConstructor;
 import org.springframework.stereotype.Service;

+import java.time.LocalDateTime;
+import java.time.format.DateTimeFormatter;
 import java.util.ArrayList;
 import java.util.List;

@@ -32,6 +35,7 @@ public class CommonServiceImpl implements CommonService {
                .select(StatisticalDataDTO::getPhaseType)
                .select(StatisticalDataDTO::getValueType)
                .last(columnName)
+                .le(InfluxDBTableConstant.TIME, LocalDateTime.now().format(DateTimeFormatter.ofPattern(DatePattern.NORM_DATETIME_PATTERN)))
                .eq(InfluxDBTableConstant.LINE_ID,lineId)
                .eq(InfluxDBTableConstant.PHASIC_TYPE,phasic)
                .eq(InfluxDBTableConstant.VALUE_TYPE,dataType)
@@ -141,7 +145,7 @@ public class CommonServiceImpl implements CommonService {
                .select(StatisticalDataDTO::getClDid)
                .last(columnName,InfluxDBTableConstant.VALUE)
                .eq(InfluxDBTableConstant.LINE_ID,lineId)
-                .eq(InfluxDBTableConstant.PHASIC_TYPE, "M")
+                .eq(InfluxDBTableConstant.PHASIC_TYPE, "T")
                .eq(InfluxDBTableConstant.PROCESS,process)
                .groupBy(InfluxDBTableConstant.CL_DID);
        return commonMapper.getTopTemperature(influxQueryWrapper);
@@ -157,9 +161,51 @@ public class CommonServiceImpl implements CommonService {
                .eq(InfluxDBTableConstant.VALUE_TYPE,dataType)
                .eq(InfluxDBTableConstant.CL_DID,clDid)
                .eq(InfluxDBTableConstant.PROCESS,process)
-                .between(InfluxDBTableConstant.TIME, startTime, endTime);;
+                .between(InfluxDBTableConstant.TIME, startTime, endTime);
        return commonMapper.getLineRtData(influxQueryWrapper);
    }

+    @Override
+    public List<StatisticalDataDTO> getEachModule(CommonQueryParam param) {
+        InfluxQueryWrapper influxQueryWrapper = new InfluxQueryWrapper(param.getTableName(),StatisticalDataDTO.class);
+        influxQueryWrapper.select(param.getColumnName(),param.getResultName())
+                .between(InfluxDBTableConstant.TIME, param.getStartTime(), param.getEndTime())
+                .eq(InfluxDBTableConstant.LINE_ID,param.getLineId())
+                .eq(InfluxDBTableConstant.PHASIC_TYPE, "T")
+                .eq(InfluxDBTableConstant.VALUE_TYPE,"AVG")
+                .eq(param.getDataType(),0)
+                .eq(InfluxDBTableConstant.PROCESS,param.getProcess())
+                .eq(InfluxDBTableConstant.CL_DID,param.getClDid());
+        return commonMapper.getDeviceRtDataByTime(influxQueryWrapper);
+    }

+    @Override
+    public StatisticalDataDTO getDataCounts(String lineId, String tableName, String columnName, String resultName, String phasic, String dataType, String clDid, String process, String startTime, String endTime) {
+        InfluxQueryWrapper influxQueryWrapper = new InfluxQueryWrapper(tableName,StatisticalDataDTO.class);
+        influxQueryWrapper.count(columnName,resultName)
+                .eq(InfluxDBTableConstant.LINE_ID,lineId)
+                .eq(InfluxDBTableConstant.PHASIC_TYPE,phasic)
+                .eq(InfluxDBTableConstant.VALUE_TYPE,dataType)
+                .eq(InfluxDBTableConstant.CL_DID,clDid)
+                .eq(InfluxDBTableConstant.PROCESS,process)
+                .between(InfluxDBTableConstant.TIME, startTime, endTime);
+        return commonMapper.getLineRtData(influxQueryWrapper);
+    }
+
+    @Override
+    public List<StatisticalDataDTO> getModuleData(CommonQueryParam param) {
+        InfluxQueryWrapper influxQueryWrapper = new InfluxQueryWrapper(param.getTableName(),StatisticalDataDTO.class);
+        influxQueryWrapper
+                .select(StatisticalDataDTO::getLineId)
+                .select(StatisticalDataDTO::getPhaseType)
+                .select("Apf_RmsI_ModOut","value")
+                .select("Apf_RmsI_Load","avgValue")
+                .select("Apf_Temp_Env","minValue")
+                .between(InfluxDBTableConstant.TIME, param.getStartTime(), param.getEndTime())
+                .eq(InfluxDBTableConstant.LINE_ID,param.getLineId())
+                .eq(InfluxDBTableConstant.VALUE_TYPE,"avg")
+                .eq(InfluxDBTableConstant.PROCESS,param.getProcess())
+                .eq(InfluxDBTableConstant.CL_DID,param.getClDid());
+        return commonMapper.getDeviceRtDataByTime(influxQueryWrapper);
+    }
 }
Author	SHA1	Message	Date
xy	97fdd6bc34	feat(power): 添加功率数据实体类字段 - 在DataHarmPowerP中新增totDpf、totHarmP和dpf字段 - 在DataHarmPowerQ中新增totHarmQ字段 - 在DataHarmPowerS中新增totHarmS字段 - 所有新增字段均为Double类型并配置相应数据库列映射	2026-05-26 15:38:22 +08:00
xy	c5b300abbf	feat(influx): 添加数据实体类的时间列注解和新字段 - 为多个数据实体类添加 @TimeColumn 注解以标识时间列 - 在多个数据实体类中新增 cl_did 和 process 字段 - 为 PqdData 类添加 quality_flag 字段作为标签 - 更新 DataInHarmRateI 类的时间序列化配置 - 统一数据实体类中的异常标志字段定义	2026-05-25 20:00:30 +08:00
xy	30492cffa1	fix(influx): 修正相位类型和值类型查询条件 - 将 PHASIC_TYPE 从 "M" 修改为 "T" - 将 VALUE_TYPE 从 "avg" 修改为 "AVG" - 统一查询条件的数据类型匹配规则	2026-05-21 15:54:52 +08:00
xy	df01e39a58	feat(influx): 添加数据清洗标志常量 - 新增 abnormal_flag 常量用于标识数据清洗状态 - 添加注释说明数据清洗标志含义（0:正常 1:异常）	2026-05-19 15:04:10 +08:00
xy	6375115d77	微调	2026-04-13 15:30:05 +08:00
xy	bce83bd8e8	查询最新一条数据方法优化。添加时间条件，在当前时间之前的最新一条数据	2026-04-09 11:28:36 +08:00
xy	20b0c44874	微调	2025-07-17 15:34:03 +08:00
xy	e0af988ce4	微调	2025-07-09 16:05:26 +08:00
xy	c3ac9b12f7	新增查询治理各模块数据的方法	2025-07-09 11:51:19 +08:00
xy	3b136b0c5b	新增查询治理各模块数据的方法	2025-06-30 11:16:54 +08:00
hzj	20287f02bc	添加cvt中间表	2025-03-10 16:22:03 +08:00
hzj	fcbb327c4a	添加cvt中间表	2025-03-05 14:07:41 +08:00
chendaofei	5ca85f69e9	字段修改	2024-12-06 11:19:42 +08:00