From 1571cd0f137ced4345fa8785e166a29dc31b6ad1 Mon Sep 17 00:00:00 2001
From: feiyu02 <risaku@163.com>
Date: 星期二, 13 五月 2025 17:42:39 +0800
Subject: [PATCH] 1. 新增动态污染溯源的数据异常判断逻辑 2. 新增动态污染溯源websocket连接功能
---
src/main/kotlin/com/flightfeather/uav/domain/entity/BaseRealTimeData.kt | 42 +++++++++++++++++++++++-------------------
1 files changed, 23 insertions(+), 19 deletions(-)
diff --git a/src/main/kotlin/com/flightfeather/uav/domain/entity/BaseRealTimeData.kt b/src/main/kotlin/com/flightfeather/uav/domain/entity/BaseRealTimeData.kt
index 8c9f9df..741a06a 100644
--- a/src/main/kotlin/com/flightfeather/uav/domain/entity/BaseRealTimeData.kt
+++ b/src/main/kotlin/com/flightfeather/uav/domain/entity/BaseRealTimeData.kt
@@ -1,7 +1,7 @@
package com.flightfeather.uav.domain.entity
-import com.flightfeather.uav.common.utils.DateUtil
import com.flightfeather.uav.biz.dataprocess.AvgPair
+import com.flightfeather.uav.common.utils.DateUtil
import com.flightfeather.uav.lightshare.bean.DataVo
import com.flightfeather.uav.socket.bean.AirData
import com.flightfeather.uav.socket.eunm.FactorType
@@ -10,14 +10,20 @@
import java.time.ZoneId
import java.util.*
import javax.persistence.Column
+import javax.persistence.GeneratedValue
+import javax.persistence.GenerationType
import javax.persistence.Id
-import kotlin.math.*
+import kotlin.math.atan
+import kotlin.math.cos
+import kotlin.math.round
+import kotlin.math.sin
/**
* 瀹炴椂鐩戞祴鏁版嵁鍩虹被
*/
open class BaseRealTimeData {
@Id
+ @GeneratedValue(strategy = GenerationType.IDENTITY)
var id: Int? = null
@Column(name = "device_code")
@@ -66,6 +72,9 @@
@Column(name = "NOI")
var noi: Float? = null
+ @Column(name = "NO")
+ var no: Float? = null
+
var velocity: Float? = null
@Column(name = "wind_speed")
@@ -105,20 +114,7 @@
add(AirData().apply { setData(FactorType.WIND_SPEED, windSpeed) })
add(AirData().apply { setData(FactorType.WIND_DIRECTION, windDirection) })
add(AirData().apply { setData(FactorType.HEIGHT, height) })
- }
- }
-
- fun getByFactorIndex(i: Int): Float? {
- return when (i) {
- 0 -> no2
- 1 -> co
- 2 -> h2s
- 3 -> so2
- 4 -> o3
- 5 -> pm25
- 6 -> pm10
- 7 -> voc
- else -> null
+ add(AirData().apply { setData(FactorType.NO, no) })
}
}
@@ -138,17 +134,18 @@
FactorType.LNG -> longitude?.toFloat()
FactorType.LAT -> latitude?.toFloat()
FactorType.VELOCITY -> velocity
-// FactorType.TIME -> noi
+// FactorType.TIME -> dataTime?.time?.toFloat()
FactorType.WIND_SPEED -> windSpeed
FactorType.WIND_DIRECTION -> windDirection
FactorType.HEIGHT -> height
+ FactorType.NO -> no
else -> null
}
}
}
-fun List<BaseRealTimeData>.avg(): RealTimeDataGridMin {
+fun List<BaseRealTimeData>.avg(): BaseRealTimeData {
//椋庡悜閲囩敤鍗曚綅鐭㈤噺娉曟眰鍙栧潎鍊�
var u = .0//涓滆タ鏂逛綅鍒嗛噺鎬诲拰
var v = .0//鍗楀寳鏂逛綅鍒嗛噺鎬诲拰
@@ -156,7 +153,7 @@
//闄ら鍚戝鐨勫叾浠栧洜瀛愰噰鐢ㄧ畻鏈钩鍧囨硶姹傚彇鍧囧��
val tmpList = mutableListOf<AvgPair>()
- repeat(17) {
+ repeat(18) {
tmpList.add(AvgPair(0f, 0))
}
@@ -271,6 +268,12 @@
this.c++
}
}
+ tmpList[17].apply {
+ it.no?.let {
+ t += it
+ this.c++
+ }
+ }
}
return RealTimeDataGridMin().apply {
@@ -295,6 +298,7 @@
velocity = tmpList[14].avg()
windSpeed = tmpList[15].avg()
height = tmpList[16].avg()
+ no = tmpList[17].avg()
if (c != 0) {
val avgU = u / c
--
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