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

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;

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";
 }

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;
 }

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;
 }

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;
 }

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;
 }

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;
 }

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;
+
 }

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;
+
 }

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;
 }

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;
+
 }

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;
+
 }

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;
+
 }

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;
 }

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;
+
 }

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;
 }

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;

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);
 }

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);
+    }
 }