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/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> {
+
+}

src/main/java/com/njcn/influx/imapper/day/GovernReportMapper.java

@@ -0,0 +1,13 @@
+package com.njcn.influx.imapper.day;
+
+import com.njcn.influx.base.InfluxDbBaseMapper;
+
+/**
+ * Description:
+ * Date: 2024/9/12  19:44【需求编号】
+ *
+ * @author clam
+ * @version V1.0.0
+ */
+public interface GovernReportMapper extends InfluxDbBaseMapper {
+}

src/main/java/com/njcn/influx/pojo/bo/CarryCapcityData.java

@@ -4,8 +4,11 @@ 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 java.time.Instant;
+import java.util.*;
+import java.util.stream.Collectors;
 
 /**
  * 类的介绍：
@@ -17,22 +20,62 @@ import java.time.Instant;
 @Data
 public class CarryCapcityData {
 
-    @Column(name = "time")
+    @Column(name = "time",tag = true)
     @JsonSerialize(using = InstantDateSerializer.class)
     private Instant time;
 
-    @Column(name = "line_id")
+    @Column(name = "line_id",tag = true)
     private String lineId;
 
-    @Column(name = "phasic_type")
+    @Column(name = "phasic_type",tag = true)
     private String phaseType;
 
     @Column(name = "quality_flag")
     private String qualityFlag;
 
-    @Column(name = "value_type")
+    @Column(name = "value_type",tag = true)
     private String valueType;
 
     private Double value;
 
+    public static void main(String[] args) {
+        String[] strs ={"eat","tea", "tan", "ate", "nat", "bat"};
+
+        CarryCapcityData.groupAnagrams(strs);
+    }
+
+    public static List<List<String>> groupAnagrams(String[] strs) {
+        Map<String, List<String>> collect = Arrays.stream(strs).collect(Collectors.groupingBy(temp -> {
+            char[] charArray = temp.toCharArray();
+            Arrays.sort(charArray);
+            return  new String(charArray);
+        }));
+        return new ArrayList<>(collect.values());
+
+    }
+
+    public int longestConsecutive(int[] nums) {
+        Set<Integer> num_set = new HashSet<>();
+        for (int num : nums) {
+            num_set.add(num);
+        }
+        int longestStreak = 0;
+        for (int num:
+        num_set) {
+            if (!num_set.contains(num - 1)) {
+                int currentNum = num;
+                int currentStreak = 1;
+
+                while (num_set.contains(currentNum + 1)) {
+                    currentNum += 1;
+                    currentStreak += 1;
+                }
+
+                longestStreak = Math.max(longestStreak, currentStreak);
+            }
+        }
+        return longestStreak;
+    }
+
+
 }

src/main/java/com/njcn/influx/pojo/bo/CommonQueryParam.java

@@ -14,11 +14,12 @@ public class CommonQueryParam {
     private String lineId;
     private String tableName;
     private String columnName;
+    private String resultName;
     private String phasic;
     private String startTime;
     private String endTime;
     private String dataType;
     private String process;
     private String clDid;
-
+    private String frequency;
 }

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/dto/StatisticalDataDTO.java

@@ -36,6 +36,10 @@ public class StatisticalDataDTO {
     private String clDid;
 
 
+    /**
+     * 谐波次数
+     */
+    private String frequency;
     /**
      * 指标值
      */

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

@@ -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_p")
 public class DataHarmPowerP {
 
+    @TimeColumn
     @Column(name = "time")
     @JsonSerialize(using = InstantDateSerializer.class)
     private Instant time;
@@ -197,4 +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

@@ -1,8 +1,14 @@
 package com.njcn.influx.pojo.po;
 
+import com.fasterxml.jackson.databind.annotation.JsonSerialize;
+import com.njcn.influx.utils.InstantDateSerializer;
 import lombok.Data;
 import lombok.EqualsAndHashCode;
+import org.influxdb.annotation.Column;
 import org.influxdb.annotation.Measurement;
+import org.influxdb.annotation.TimeColumn;
+
+import java.time.Instant;
 
 /**
  * 类的介绍：
@@ -11,8 +17,182 @@ import org.influxdb.annotation.Measurement;
  * @version 1.0.0
  * @createTime 2022/5/12 11:27
  */
-@EqualsAndHashCode(callSuper = true)
 @Data
 @Measurement(name = "data_harmrate_v")
-public class DataHarmRateV extends DataHarmPhasicV{
+public class DataHarmRateV{
+    @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;
+
+    @Column(name = "cl_did")
+    private String clDid;
+
+    @Column(name = "process")
+    private String process;
 }

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;
+
+}

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/EntData.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;
 
@@ -18,12 +19,12 @@ import java.time.Instant;
 @Data
 @Measurement(name = "evt_data")
 public class EntData {
-
-    @Column(name = "time")
+    @TimeColumn
+    @Column(name = "time",tag = true)
     @JsonSerialize(using = InstantDateSerializer.class)
     private Instant time;
 
-    @Column(name = "uuid")
+    @Column(name = "uuid", tag = true)
     private String uuid;
 
     @Column(name = "Evt_Param_Phase")

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/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;

src/main/java/com/njcn/influx/service/CommonService.java

@@ -33,7 +33,7 @@ public interface CommonService {
     List<StatisticalDataDTO> getDeviceRtData(List<CommonQueryParam> commonQueryParams);
 
     /**
-
+     * 此方法和getDeviceRtDataByTime方法逻辑一致,只是允许部分查询参数为空,也即可以不带入查询
      * @Description: getDeviceRtDataByTime
      * @return: java.util.List<com.njcn.influx.pojo.dto.StatisticalDataDTO>
      * @Author: clam
@@ -41,6 +41,15 @@ public interface CommonService {
      */
     List<StatisticalDataDTO> getDeviceRtDataByTime(List<CommonQueryParam> commonQueryParams);
 
+    /**
+
+     * @Description: getNewDeviceRtDataByTime
+     * @return: java.util.List<com.njcn.influx.pojo.dto.StatisticalDataDTO>
+     * @Author: guofeihu
+     * @Date: 2024/8/27
+     */
+    List<StatisticalDataDTO> getNewDeviceRtDataByTime(List<CommonQueryParam> commonQueryParams);
+
     /**
      * 根据条件获取监测点时间范围内的最大最小值
      *
@@ -68,4 +77,16 @@ public interface CommonService {
      * @return
      */
     List<StatisticalDataDTO> getTopTemperature(String lineId,String tableName, String columnName,String process);
+
+    /**
+     * 获取监测点的指标数量，用来计算完整率
+     *
+     */
+    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/DataFlucService.java

@@ -1,9 +1,13 @@
 package com.njcn.influx.service;
 
+import com.njcn.influx.pojo.po.DataFluc;
+import java.util.List;
+
 /**
  * @author hongawen
  * @version 1.0.0
  * @date 2023年05月05日 09:10
  */
 public interface DataFlucService {
+    List<DataFluc> getNewDataFluc(String lineIndex, String startTime, String endTime);
 }

src/main/java/com/njcn/influx/service/DataHarmPowerPService.java

@@ -12,4 +12,5 @@ import java.util.List;
  */
 public interface DataHarmPowerPService {
     List<DataHarmPowerP> getHarmonicPowerP(String lineIndex, String startTime, String endTime);
+
 }

src/main/java/com/njcn/influx/service/DataHarmRateVService.java

@@ -2,6 +2,7 @@ package com.njcn.influx.service;
 
 import com.njcn.influx.pojo.po.DataHarmRateV;
 import com.njcn.influx.query.InfluxQueryWrapper;
+import java.util.List;
 
 /**
  * @author hongawen
@@ -10,4 +11,5 @@ import com.njcn.influx.query.InfluxQueryWrapper;
  */
 public interface DataHarmRateVService {
     DataHarmRateV getMeanAllTimesData(InfluxQueryWrapper influxQueryWrapper);
+    List<DataHarmRateV> getNewDataHarmRateV(String lineIndex, String startTime, String endTime);
 }

src/main/java/com/njcn/influx/service/DataInHarmVService.java

@@ -0,0 +1,13 @@
+package com.njcn.influx.service;
+
+import com.njcn.influx.pojo.po.DataInHarmV;
+import java.util.List;
+
+/**
+ * @author guofeihu
+ * @version 1.0.0
+ * @date 2024年09月13日 11:01
+ */
+public interface DataInHarmVService {
+    List<DataInHarmV> getNewDataInHarmV(String lineIndex, String startTime, String endTime);
+}

src/main/java/com/njcn/influx/service/DataPltService.java

@@ -0,0 +1,13 @@
+package com.njcn.influx.service;
+
+import com.njcn.influx.pojo.po.DataPlt;
+import java.util.List;
+
+/**
+ * @author guofeihu
+ * @version 1.0.0
+ * @date 2024年09月13日 11:01
+ */
+public interface DataPltService {
+    List<DataPlt> getNewDataPlt(String lineIndex, String startTime, String endTime);
+}

src/main/java/com/njcn/influx/service/IDataIService.java

@@ -21,4 +21,5 @@ public interface IDataIService {
     * @Date: 2024/2/27
     */
     List<DataI> getWeekDataI(String lineIndex, String startTime, String endTime);
+    List<DataI> getNewDataI(String lineIndex, String startTime, String endTime);
 }

src/main/java/com/njcn/influx/service/IDataVService.java

@@ -1,12 +1,11 @@
 package com.njcn.influx.service;
 
 import com.njcn.influx.pojo.po.DataV;
-
 import java.util.List;
 
 public interface IDataVService {
 
     List<DataV> getDataV(String lineIndex, String startTime, String endTime);
     List<DataV> getHarmonicDataV(String lineIndex, String startTime, String endTime);
-
+    List<DataV> getNewDataV(String lineIndex, String startTime, String endTime);
 }

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)
@@ -80,6 +84,43 @@ public class CommonServiceImpl implements CommonService {
         return resultList;
     }
 
+    @Override
+    public List<StatisticalDataDTO> getNewDeviceRtDataByTime(List<CommonQueryParam> commonQueryParams) {
+        List<StatisticalDataDTO> resultList = new ArrayList<>();
+        for (CommonQueryParam commonQueryParam: commonQueryParams) {
+            InfluxQueryWrapper influxQueryWrapper = new InfluxQueryWrapper(commonQueryParam.getTableName(), StatisticalDataDTO.class);
+            influxQueryWrapper.select(StatisticalDataDTO::getLineId)
+                    .select(StatisticalDataDTO::getPhaseType)
+                    .select(StatisticalDataDTO::getValueType)
+                    .select(commonQueryParam.getColumnName(), InfluxDBTableConstant.VALUE)
+                    //查询条件开始和结束时间是必须的
+                    .between(InfluxDBTableConstant.TIME, commonQueryParam.getStartTime(), commonQueryParam.getEndTime());
+                    //此方法和getDeviceRtDataByTime方法逻辑一致,只是在以下条件判断中允许部分查询参数为空,也即可以不带入查询
+                    if(commonQueryParam.getLineId() != null) {
+                        influxQueryWrapper.eq(InfluxDBTableConstant.LINE_ID, commonQueryParam.getLineId());
+                    }
+                    if(commonQueryParam.getPhasic() != null) {
+                        influxQueryWrapper.eq(InfluxDBTableConstant.PHASIC_TYPE, commonQueryParam.getPhasic());
+                    }
+                    if(commonQueryParam.getProcess() != null) {
+                        influxQueryWrapper.eq(InfluxDBTableConstant.PROCESS, commonQueryParam.getProcess());
+                    }
+                    if(commonQueryParam.getDataType() != null) {
+                        influxQueryWrapper.eq(InfluxDBTableConstant.VALUE_TYPE, commonQueryParam.getDataType());
+                    }
+                    if(commonQueryParam.getClDid() != null) {
+                        influxQueryWrapper.eq(InfluxDBTableConstant.CL_DID, commonQueryParam.getClDid());
+                    }
+            List<StatisticalDataDTO> deviceRtData = commonMapper.getDeviceRtDataByTime(influxQueryWrapper);
+            for(StatisticalDataDTO statisticalDataDTO : deviceRtData){
+                statisticalDataDTO.setFrequency(commonQueryParam.getFrequency());
+            }
+            resultList.addAll(deviceRtData);
+        }
+
+        return resultList;
+    }
+
     @Override
     public StatisticalDataDTO getLineHistoryData(String lineId, String tableName, String columnName, String startTime, String endTime, String clDid) {
         InfluxQueryWrapper influxQueryWrapper = new InfluxQueryWrapper(tableName,StatisticalDataDTO.class);
@@ -104,11 +145,67 @@ public class CommonServiceImpl implements CommonService {
                 .select(StatisticalDataDTO::getClDid)
                 .last(columnName,InfluxDBTableConstant.VALUE)
                 .eq(InfluxDBTableConstant.LINE_ID,lineId)
+                .eq(InfluxDBTableConstant.PHASIC_TYPE, "T")
                 .eq(InfluxDBTableConstant.PROCESS,process)
                 .groupBy(InfluxDBTableConstant.CL_DID);
         return commonMapper.getTopTemperature(influxQueryWrapper);
     }
 
+    @Override
+    public StatisticalDataDTO getCounts(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.select(StatisticalDataDTO::getLineId)
+                .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> 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);
+    }
 }

src/main/java/com/njcn/influx/service/impl/DataFlucServiceImpl.java

@@ -1,6 +1,12 @@
 package com.njcn.influx.service.impl;
 
+import com.njcn.influx.imapper.DataFlucMapper;
+import com.njcn.influx.pojo.po.DataFluc;
+import com.njcn.influx.query.InfluxQueryWrapper;
+import com.njcn.influx.service.DataFlucService;
+import lombok.RequiredArgsConstructor;
 import org.springframework.stereotype.Service;
+import java.util.List;
 
 /**
  * @author hongawen
@@ -8,5 +14,17 @@ import org.springframework.stereotype.Service;
  * @date 2023年05月05日 09:10
  */
 @Service
-public class DataFlucServiceImpl {
+@RequiredArgsConstructor
+public class DataFlucServiceImpl implements DataFlucService {
+
+    private final DataFlucMapper dataFlucMapper;
+
+    @Override
+    public List<DataFluc> getNewDataFluc(String lineIndex, String startTime, String endTime) {
+        InfluxQueryWrapper influxQueryWrapper = new InfluxQueryWrapper(DataFluc.class);
+        influxQueryWrapper.eq(DataFluc::getLineId, lineIndex)
+                .between(DataFluc::getTime, startTime, endTime);
+        return dataFlucMapper.getStatisticsByWraper(influxQueryWrapper);
+    }
+
 }

src/main/java/com/njcn/influx/service/impl/DataHarmPowerPServiceImpl.java

@@ -29,4 +29,5 @@ public class DataHarmPowerPServiceImpl implements DataHarmPowerPService {
         result1 = dataHarmPowerPMapper.getStatisticsByWraper(influxQueryWrapper);
         return result1;
     }
+
 }

src/main/java/com/njcn/influx/service/impl/DataHarmRateVServiceImpl.java

@@ -2,11 +2,14 @@ package com.njcn.influx.service.impl;
 
 import com.njcn.influx.imapper.DataHarmRateVMapper;
 import com.njcn.influx.pojo.po.DataHarmRateV;
+import com.njcn.influx.pojo.po.DataV;
 import com.njcn.influx.query.InfluxQueryWrapper;
 import com.njcn.influx.service.DataHarmRateVService;
 import lombok.RequiredArgsConstructor;
 import org.springframework.stereotype.Service;
 
+import java.util.List;
+
 /**
  * @author hongawen
  * @version 1.0.0
@@ -23,4 +26,12 @@ public class DataHarmRateVServiceImpl implements DataHarmRateVService {
     public DataHarmRateV getMeanAllTimesData(InfluxQueryWrapper influxQueryWrapper) {
         return dataHarmRateVMapper.getMeanAllTimesData(influxQueryWrapper);
     }
+
+    @Override
+    public List<DataHarmRateV> getNewDataHarmRateV(String lineIndex, String startTime, String endTime) {
+        InfluxQueryWrapper influxQueryWrapper = new InfluxQueryWrapper(DataHarmRateV.class);
+        influxQueryWrapper.eq(DataHarmRateV::getLineId, lineIndex)
+                .between(DataHarmRateV::getTime, startTime, endTime);;
+        return dataHarmRateVMapper.getStatisticsByWraper(influxQueryWrapper);
+    }
 }

src/main/java/com/njcn/influx/service/impl/DataInHarmVServiceImpl.java

@@ -0,0 +1,29 @@
+package com.njcn.influx.service.impl;
+
+import com.njcn.influx.imapper.DataInHarmVMapper;
+import com.njcn.influx.pojo.po.DataInHarmV;
+import com.njcn.influx.query.InfluxQueryWrapper;
+import com.njcn.influx.service.DataInHarmVService;
+import lombok.RequiredArgsConstructor;
+import org.springframework.stereotype.Service;
+import java.util.List;
+
+/**
+ * @author guofeihu
+ * @version 1.0.0
+ * @date 2024年09月13日 11:01
+ */
+@Service
+@RequiredArgsConstructor
+public class DataInHarmVServiceImpl implements DataInHarmVService {
+
+    private final DataInHarmVMapper dataInHarmVMapper;
+
+    @Override
+    public List<DataInHarmV> getNewDataInHarmV(String lineIndex, String startTime, String endTime) {
+        InfluxQueryWrapper influxQueryWrapper = new InfluxQueryWrapper(DataInHarmV.class);
+        influxQueryWrapper.eq(DataInHarmV::getLineId, lineIndex)
+                .between(DataInHarmV::getTime, startTime, endTime);;
+        return dataInHarmVMapper.getStatisticsByWraper(influxQueryWrapper);
+    }
+}

src/main/java/com/njcn/influx/service/impl/DataPltServiceImpl.java

@@ -0,0 +1,29 @@
+package com.njcn.influx.service.impl;
+
+import com.njcn.influx.imapper.DataPltMapper;
+import com.njcn.influx.pojo.po.*;
+import com.njcn.influx.query.InfluxQueryWrapper;
+import com.njcn.influx.service.DataPltService;
+import lombok.RequiredArgsConstructor;
+import org.springframework.stereotype.Service;
+import java.util.List;
+
+/**
+ * @author guofeihu
+ * @version 1.0.0
+ * @date 2024年09月13日 11:01
+ */
+@Service
+@RequiredArgsConstructor
+public class DataPltServiceImpl implements DataPltService {
+
+    private final DataPltMapper dataPltMapper;
+
+    @Override
+    public List<DataPlt> getNewDataPlt(String lineIndex, String startTime, String endTime) {
+        InfluxQueryWrapper influxQueryWrapper = new InfluxQueryWrapper(DataPlt.class);
+        influxQueryWrapper.eq(DataPlt::getLineId, lineIndex)
+                .between(DataPlt::getTime, startTime, endTime);;
+        return dataPltMapper.getStatisticsByWraper(influxQueryWrapper);
+    }
+}

src/main/java/com/njcn/influx/service/impl/DataVServiceImpl.java

@@ -61,6 +61,14 @@ public class DataVServiceImpl implements IDataVService {
         return result1;
     }
 
+    @Override
+    public List<DataV> getNewDataV(String lineIndex, String startTime, String endTime) {
+        InfluxQueryWrapper influxQueryWrapper = new InfluxQueryWrapper(DataV.class);
+        influxQueryWrapper.eq(DataV::getLineId, lineIndex)
+                .between(DataV::getTime, startTime, endTime);;
+        return dataVMapper.getStatisticsByWraper(influxQueryWrapper);
+    }
+
 
     /***
      * 自定义需要查询的谐波次数

src/main/java/com/njcn/influx/service/impl/IDataIServiceImpl.java

@@ -37,4 +37,12 @@ public class IDataIServiceImpl implements IDataIService {
         result1 = dataIMapper.getStatisticsByWraper(influxQueryWrapper);
         return result1;
     }
+
+    @Override
+    public List<DataI> getNewDataI(String lineIndex, String startTime, String endTime) {
+        InfluxQueryWrapper influxQueryWrapper = new InfluxQueryWrapper(DataI.class);
+        influxQueryWrapper.eq(DataI::getLineId, lineIndex)
+                .between(DataI::getTime, startTime, endTime);;
+        return dataIMapper.getStatisticsByWraper(influxQueryWrapper);
+    }
 }