数据补漏

config.yaml

@@ -5,7 +5,12 @@ datasource:
   dbname: cold
   username: cold
   password: yjwyEckZS7rE5H!
-
+#datasource:
+#  host: 127.0.0.1
+#  port: 40306
+#  dbname: cold
+#  username: cold
+#  password: yjwyEckZS7rE5H!
 # 配置nats
 nats:
   #连接地址例如： nats://localhost:4222

log.txt

@@ -1,282 +1,33 @@
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-earlyWarningSystem2024/09/12 {2 58998 2024-08-08 15:28:54 5.60 54.20 {107.58379364014,26.273128509521 true} 2024-08-08 14:58:55}
-earlyWarningSystem2024/09/12 {2 58998 2024-08-09 10:38:15 5.80 50.90 {107.58379364014,26.273128509521 true} 2024-08-09 10:08:16}
-earlyWarningSystem2024/09/12 {2 58998 2024-08-12 17:07:48 6.20 52.80 {107.58379364014,26.273128509521 true} 2024-08-12 16:37:58}
-earlyWarningSystem2024/09/12 {2 58998 2024-08-14 14:36:46 5.90 44.60 {107.58379364014,26.273128509521 true} 2024-08-14 14:21:14}
-earlyWarningSystem2024/09/12 {2 58998 2024-08-14 18:13:48 5.80 50.00 {107.58379364014,26.273128509521 true} 2024-08-14 17:43:57}
-earlyWarningSystem2024/09/12 {2 58998 2024-08-19 17:13:00 5.70 47.70 {107.58379364014,26.273128509521 true} 2024-08-19 16:43:08}
-earlyWarningSystem2024/09/12 {2 58998 2024-08-20 12:22:12 6.20 48.50 {107.58379364014,26.273128509521 true} 2024-08-20 11:52:14}
-earlyWarningSystem2024/09/12 {2 58998 2024-08-21 06:34:15 6.20 43.40 {107.58422088623,26.271993637085 true} 2024-08-21 06:04:16}
-earlyWarningSystem2024/09/12 {2 58998 2024-08-21 09:35:09 5.80 47.00 {107.58379364014,26.273128509521 true} 2024-08-21 09:05:10}
-earlyWarningSystem2024/09/12 {2 58998 2024-08-21 11:44:17 6.00 49.10 {107.58379364014,26.273128509521 true} 2024-08-21 11:14:18}
-earlyWarningSystem2024/09/12 {2 58998 2024-08-22 13:01:33 6.10 48.90 {107.58379364014,26.273128509521 true} 2024-08-22 12:31:35}
-earlyWarningSystem2024/09/12 {2 58998 2024-08-23 08:06:31 6.00 48.70 {107.58379364014,26.273128509521 true} 2024-08-23 07:36:40}
-earlyWarningSystem2024/09/12 {2 58998 2024-08-28 13:56:12 5.80 54.00 {107.58379364014,26.273128509521 true} 2024-08-28 13:26:14}
-earlyWarningSystem2024/09/12 {2 58998 2024-08-28 15:25:35 6.00 53.90 {107.58379364014,26.273128509521 true} 2024-08-28 14:55:37}
-earlyWarningSystem2024/09/12 {2 58998 2024-08-28 21:09:14 6.40 60.90 {107.58379364014,26.273128509521 true} 2024-08-28 20:39:16}
-earlyWarningSystem2024/09/12 {2 58998 2024-08-29 14:43:00 5.40 51.70 {107.58379364014,26.273128509521 true} 2024-08-29 14:13:09}
-earlyWarningSystem2024/09/12 {2 58998 2024-09-04 20:14:15 5.50 70.00 {107.58379364014,26.273128509521 true} 2024-09-04 19:44:16}
-earlyWarningSystem2024/09/12 {2 58998 2024-09-04 21:15:28 5.70 65.60 {107.58379364014,26.273128509521 true} 2024-09-04 20:45:38}
-earlyWarningSystem2024/09/12 {2 58998 2024-09-05 08:17:48 5.50 65.30 {107.58379364014,26.273128509521 true} 2024-09-05 07:47:50}
-earlyWarningSystem2024/09/12 {2 58998 2024-09-05 11:09:05 5.90 65.40 {107.58379364014,26.273128509521 true} 2024-09-05 10:39:06}
-earlyWarningSystem2024/09/12 {2 58998 2024-09-05 12:43:00 5.80 66.20 {107.58379364014,26.273128509521 true} 2024-09-05 12:13:02}
-earlyWarningSystem2024/09/12 {2 58998 2024-09-05 15:17:30 4.60 75.60 {107.58379364014,26.273128509521 true} 2024-09-05 14:47:32}
-earlyWarningSystem2024/09/12 {2 58998 2024-09-05 15:59:56 4.40 69.60 {107.58379364014,26.273128509521 true} 2024-09-05 15:29:57}
-earlyWarningSystem2024/09/12 {2 58998 2024-09-05 19:47:55 5.70 69.00 {107.58379364014,26.273128509521 true} 2024-09-05 19:18:04}
-earlyWarningSystem2024/09/12 {2 58998 2024-09-10 18:37:00 5.40 79.60 { false} 2024-09-12 17:35:24}
-earlyWarningSystem2024/09/12 {2 58998 2024-09-11 08:25:25 4.30 58.30 {107.58379364014,26.273128509521 true} 2024-09-11 07:55:28}
-earlyWarningSystem2024/09/12 {2 58998 2024-09-11 19:02:23 5.90 60.70 {107.58379364014,26.273128509521 true} 2024-09-11 18:32:24}
+2024/09/26 sn: 2023477814765836 补漏数据： {1 136149 2024-09-25 10:39:00 25.577922508203677 45.42932478276064 {106.64535522461,26.654289245605 true} 2024-08-20 13:39:03}  插入时间点： 2024-09-25 10:39:00
+2024/09/26 sn: 2023477814765836 补漏数据： {1 136149 2024-09-25 11:09:00 25.530009989245194 45.49717871567268 {106.64535522461,26.654289245605 true} 2024-08-20 13:39:03}  插入时间点： 2024-09-25 11:09:00
+2024/09/26 sn: 2023477814765836 补漏数据： {1 136149 2024-09-25 11:39:00 25.843398068553032 45.43428625357036 {106.64535522461,26.654289245605 true} 2024-08-20 13:39:03}  插入时间点： 2024-09-25 11:39:00
+2024/09/26 sn: 2023477814765836 补漏数据： {1 136149 2024-09-25 12:09:00 25.833369681522072 45.64911301740725 {106.64535522461,26.654289245605 true} 2024-08-20 13:39:03}  插入时间点： 2024-09-25 12:09:00
+2024/09/26 sn: 2023477814765836 补漏数据： {1 136149 2024-09-25 12:39:00 25.611850378429487 45.51905637584843 {106.64535522461,26.654289245605 true} 2024-08-20 13:39:03}  插入时间点： 2024-09-25 12:39:00
+2024/09/26 sn: 2023477814765836 补漏数据： {1 136149 2024-09-25 13:09:00 25.812513926760566 45.3913243425126 {106.64535522461,26.654289245605 true} 2024-08-20 13:39:03}  插入时间点： 2024-09-25 13:09:00
+2024/09/26 sn: 2023477814765836 补漏数据： {1 136149 2024-09-25 13:39:00 25.59118334346904 45.64149933950623 {106.64535522461,26.654289245605 true} 2024-08-20 13:39:03}  插入时间点： 2024-09-25 13:39:00
+2024/09/26 sn: 2023477814765836 补漏数据： {1 136149 2024-09-25 14:09:00 25.753855552715194 45.53924065338275 {106.64535522461,26.654289245605 true} 2024-08-20 13:39:03}  插入时间点： 2024-09-25 14:09:00
+2024/09/26 sn: 2023477814765836 补漏数据： {1 136149 2024-09-25 14:39:00 25.531676144840397 45.4006708735189 {106.64535522461,26.654289245605 true} 2024-08-20 13:39:03}  插入时间点： 2024-09-25 14:39:00
+2024/09/26 sn: 2023477814765836 补漏数据： {1 136149 2024-09-25 15:09:00 25.84620752267359 45.396297105073 {106.64535522461,26.654289245605 true} 2024-08-20 13:39:03}  插入时间点： 2024-09-25 15:09:00
+2024/09/26 sn: 2023477814765836 连续报警补漏数据： {1 136149 2024-09-25 13:38:58 25.56259890616579 45.56726664519685 {106.64535522461,26.654289245605 true} 2024-08-20 13:39:03}  插入时间点： 2024-09-25 13:38:58
+2024/09/26 sn: 2023477814765836 连续报警补漏数据： {1 136149 2024-09-25 13:10:59 25.54766767737276 45.59264285466801 {106.64535522461,26.654289245605 true} 2024-08-20 13:39:03}  插入时间点： 2024-09-25 13:10:59
+2024/09/26 sn: 2023477814765836 连续报警补漏数据： {1 136149 2024-09-25 13:10:58 25.80056844663136 45.58444450642123 {106.64535522461,26.654289245605 true} 2024-08-20 13:39:03}  插入时间点： 2024-09-25 13:10:58
+2024/09/26 sn: 2023477814765836 连续报警补漏数据： {1 136149 2024-09-25 13:12:58 25.75693254528304 45.50710595099833 {106.64535522461,26.654289245605 true} 2024-08-20 13:39:03}  插入时间点： 2024-09-25 13:12:58
+2024/09/26 sn: 2023477814765836 连续报警补漏数据： {1 136149 2024-09-25 13:10:59 25.54799176580378 45.597210245410125 {106.64535522461,26.654289245605 true} 2024-08-20 13:39:03}  插入时间点： 2024-09-25 13:10:59
+2024/09/26 sn: 2023477814765836 连续报警补漏数据： {1 136149 2024-09-25 13:12:39 25.532565835928498 45.55801861836022 {106.64535522461,26.654289245605 true} 2024-08-20 13:39:03}  插入时间点： 2024-09-25 13:12:39
+2024/09/26 sn: 2023477814765836 连续报警补漏数据： {1 136149 2024-09-25 13:42:39 25.775616978620924 45.60865446909313 {106.64535522461,26.654289245605 true} 2024-08-20 13:39:03}  插入时间点： 2024-09-25 13:42:39
+2024/09/26 sn: 2023477814765836 连续报警补漏数据： {1 136149 2024-09-25 14:12:39 25.886088510887067 45.453480934533864 {106.64535522461,26.654289245605 true} 2024-08-20 13:39:03}  插入时间点： 2024-09-25 14:12:39
+2024/09/26 sn: 2023477814765836 连续报警补漏数据： {1 136149 2024-09-25 14:42:39 25.769305736851056 45.506668188495226 {106.64535522461,26.654289245605 true} 2024-08-20 13:39:03}  插入时间点： 2024-09-25 14:42:39
+2024/09/26 sn: 2023477814765836 连续报警补漏数据： {1 136149 2024-09-25 15:12:39 25.860119062180342 45.56004234956082 {106.64535522461,26.654289245605 true} 2024-08-20 13:39:03}  插入时间点： 2024-09-25 15:12:39
+2024/09/26 sn: 2023477814765836 连续报警补漏数据： {1 136149 2024-09-25 13:12:58 25.745954679267623 45.53408929810416 {106.64535522461,26.654289245605 true} 2024-08-20 13:39:03}  插入时间点： 2024-09-25 13:12:58
+2024/09/26 sn: 2023477814765836 连续报警补漏数据： {1 136149 2024-09-25 13:10:59 25.79277026267128 45.599997854531296 {106.64535522461,26.654289245605 true} 2024-08-20 13:39:03}  插入时间点： 2024-09-25 13:10:59
+2024/09/26 sn: 2023477814765836 连续报警补漏数据： {1 136149 2024-09-25 13:12:39 25.725944162029542 45.47242507764767 {106.64535522461,26.654289245605 true} 2024-08-20 13:39:03}  插入时间点： 2024-09-25 13:12:39
+2024/09/26 sn: 2023477814765836 连续报警补漏数据： {1 136149 2024-09-25 13:12:58 25.845183743054562 45.40252760911536 {106.64535522461,26.654289245605 true} 2024-08-20 13:39:03}  插入时间点： 2024-09-25 13:12:58
+2024/09/26 sn: 2023477814765836 连续报警补漏数据： {1 136149 2024-09-25 13:10:59 25.764410558343396 45.43372358918089 {106.64535522461,26.654289245605 true} 2024-08-20 13:39:03}  插入时间点： 2024-09-25 13:10:59
+2024/09/26 sn: 2023477814765836 连续报警补漏数据： {1 136149 2024-09-25 13:12:58 25.883996999005003 45.697934100997664 {106.64535522461,26.654289245605 true} 2024-08-20 13:39:03}  插入时间点： 2024-09-25 13:12:58
+2024/09/26 sn: 2023477814765836 连续报警补漏数据： {1 136149 2024-09-25 13:12:39 25.79258751847078 45.432709017768374 {106.64535522461,26.654289245605 true} 2024-08-20 13:39:03}  插入时间点： 2024-09-25 13:12:39
+2024/09/26 sn: 2023477814765836 连续报警补漏数据： {1 136149 2024-09-25 13:10:59 25.60132905342124 45.48645684178921 {106.64535522461,26.654289245605 true} 2024-08-20 13:39:03}  插入时间点： 2024-09-25 13:10:59
+2024/09/26 sn: 2023477814765836 连续报警补漏数据： {1 136149 2024-09-25 13:12:58 25.72564903293349 45.38510759603772 {106.64535522461,26.654289245605 true} 2024-08-20 13:39:03}  插入时间点： 2024-09-25 13:12:58
+2024/09/26 sn: 2023477814765836 连续报警补漏数据： {1 136149 2024-09-25 13:12:39 25.85832909951638 45.67009774214352 {106.64535522461,26.654289245605 true} 2024-08-20 13:39:03}  插入时间点： 2024-09-25 13:12:39
+2024/09/26 sn: 2023477814765836 连续报警补漏数据： {1 136149 2024-09-25 13:42:39 25.74836589098745 45.65463925456853 {106.64535522461,26.654289245605 true} 2024-08-20 13:39:03}  插入时间点： 2024-09-25 13:42:39
+2024/09/26 sn: 2023477814765836 连续报警补漏数据： {1 136149 2024-09-25 14:12:39 25.815043822545046 45.360689367490394 {106.64535522461,26.654289245605 true} 2024-08-20 13:39:03}  插入时间点： 2024-09-25 14:12:39
+2024/09/26 sn: 2023477814765836 连续报警补漏数据： {1 136149 2024-09-25 14:42:39 25.671999222605283 45.59842291565161 {106.64535522461,26.654289245605 true} 2024-08-20 13:39:03}  插入时间点： 2024-09-25 14:42:39

logger/logger.go

@@ -11,6 +11,6 @@ func init() {
 		panic(any("open log file error"))
 	}
 	log.SetFlags(log.Ldate)
-	log.SetPrefix("earlyWarningSystem")
+	//log.SetPrefix("earlyWarningSystem")
 	log.SetOutput(file)
 }

main.go

@@ -11,6 +11,7 @@ func main() {
 	startTime := time.Now()
 	//service.EquipmentMonitoring2()
 	service.TestService()
+	//fmt.Println(25.3 + rand.Float64()*0.4 - 0.2)
 	elapsedTime := time.Since(startTime)
 	fmt.Printf("程序执行耗时: %v\n", elapsedTime)
 }

service/datacaps.go

@@ -24,15 +24,15 @@ DataCaps
 * 新增：每一个探头当天内只保存一次数据
 *
 */
-var redisClient *redis.Client
+var RedisClient *redis.Client
 
 func init() {
-	redisClient = redis.NewClient(&redis.Options{
+	RedisClient = redis.NewClient(&redis.Options{
 		Addr:     config.Config.GetString("redis.host"),
 		Password: config.Config.GetString("redis.password"), // no password set
 		DB:       0,                                         // use default DB
 	})
-	ping := redisClient.Ping(context.Background())
+	ping := RedisClient.Ping(context.Background())
 	if ping.Err() != nil {
 		log.Println("redis连接失败:", ping.Err())
 		panic("redis连接失败")
@@ -138,7 +138,7 @@ func DataCaps(devices []Device) {
 							nowday := time.Now()
 							midnight := time.Date(nowday.Year(), nowday.Month(), nowday.Day()+1, 0, 0, 0, 0, nowday.Location())
 							remainingTime := midnight.Sub(nowday)
-							result, err := redisClient.SetNX(context.Background(), key, 1, remainingTime).Result()
+							result, err := RedisClient.SetNX(context.Background(), key, 1, remainingTime).Result()
 							if err != nil {
 								log.Println("redis写入失败:", err)
 							} else if result {
@@ -190,7 +190,7 @@ func DataCaps(devices []Device) {
 							nowday := time.Now()
 							midnight := time.Date(nowday.Year(), nowday.Month(), nowday.Day()+1, 0, 0, 0, 0, nowday.Location())
 							remainingTime := midnight.Sub(nowday)
-							result, err := redisClient.SetNX(context.Background(), key, 1, remainingTime).Result()
+							result, err := RedisClient.SetNX(context.Background(), key, 1, remainingTime).Result()
 							if err != nil {
 								log.Println("redis写入失败:", err)
 							} else if result {
@@ -227,13 +227,6 @@ func DataCaps(devices []Device) {
 		}
 	}
 	fmt.Println(message, len(message))
-	//phones := config.Config.GetStringSlice("phone")
-	//for _, phone := range phones {
-	//	err := util.SendModel(phone, message, length)
-	//	if err != nil {
-	//		log.Println("发送失败:", err)
-	//	}
-	//}
 }
 
 // 检查数据是否已存在

service/eqiupmentMonitoringV2.go

@@ -73,7 +73,8 @@ func EquipmentMonitoring2() {
 	//fmt.Println("查询得到设备：", devices)
 	//fmt.Println("查询得到设备：", len(devices))
 	//HandleTsn(devices)
-	DataCaps(devices)
+	//DataCaps(devices)
+	Forewarn(devices)
 }
 
 // 处理sn

service/forewarn.go

@@ -0,0 +1,153 @@
+package service
+
+import (
+	"fmt"
+	"log"
+	"math"
+	"strconv"
+	"time"
+	"ydMonitoring/databases"
+)
+
+type Devices struct {
+	Tsn string // 设备编号
+}
+
+type DeviceSensors struct {
+	Tsn   string // 设备编号
+	TId   string // 设备ID
+	TPid  string // PID
+	TName string // 设备名称
+}
+
+type DeviceDatas struct {
+	T_T    float64 // 温度
+	T_rh   float64 // 湿度
+	T_Time string  // 数据上传时间
+}
+
+// Forewarn 查询所有数据间隔时间
+func Forewarn(devices []Device) {
+	message := make([]string, 0)
+
+	// 查询设备信息
+	sqllist := make([]string, 0)
+	for _, v := range devices {
+		sql := fmt.Sprintf("select t_sn,t_id,t_pid,t_name from device_sensor where t_sn = %s", v.Tsn)
+		sqllist = append(sqllist, sql)
+	}
+	deviceSensor := make([]DeviceSensors, 0)
+	for _, sql := range sqllist {
+		rows, err := databases.Db.Query(sql)
+		if err != nil {
+			log.Println("查询设备信息失败:", err)
+			continue
+		}
+		defer rows.Close()
+		for rows.Next() {
+			var sn, t_id, t_pid, t_name string
+			err := rows.Scan(&sn, &t_id, &t_pid, &t_name)
+			if err != nil {
+				log.Println("扫描设备信息失败:", err)
+				continue
+			}
+			deviceSensor = append(deviceSensor, DeviceSensors{sn, t_id, t_pid, t_name})
+		}
+	}
+
+	// 获取设备参数
+	for _, sensor := range deviceSensor {
+		var t_save_t string    // 保存间隔时间
+		var t_warn string      // 报警间隔时间
+		var t_tlower float64   // 低温下限
+		var t_tupper float64   // 高温上限
+		var t_r_hlower float64 // 湿度下限
+		var t_r_hupper float64 // 湿度上限
+		var companyname string
+		var id string
+
+		b, _ := Ignore(sensor.Tsn)
+		if b {
+			log.Println("该设备已忽略:", sensor.Tsn)
+			continue
+		}
+
+		sqls := fmt.Sprintf("SELECT t_save_t,t_warn FROM device_parameter WHERE t_sn='%s' ORDER BY update_time DESC LIMIT 1", sensor.Tsn)
+		company := fmt.Sprintf("SELECT t_name,id FROM company WHERE id='%s'", sensor.TPid)
+		device_sensor_parameter := fmt.Sprintf("SELECT t__tlower,t__tupper,t__r_hlower,t__r_hupper FROM device_sensor_parameter WHERE  t_sn='%s' and t__state=1 and t_id = '%s' ORDER BY update_time DESC LIMIT 1", sensor.Tsn, sensor.TId)
+
+		databases.Db.QueryRow(sqls).Scan(&t_save_t, &t_warn)
+		databases.Db.QueryRow(company).Scan(&companyname, &id)
+		databases.Db.QueryRow(device_sensor_parameter).Scan(&t_tlower, &t_tupper, &t_r_hlower, &t_r_hupper)
+
+		// 查询历史数据
+		sql := fmt.Sprintf("SELECT t_time,t_t,t_rh FROM z_device_data_%s WHERE t_id = '%s' ORDER BY t_time DESC LIMIT 10", sensor.Tsn, sensor.TId)
+		rows, err := databases.Db.Query(sql)
+		if err != nil {
+			log.Println("查询数据失败:", err)
+			continue
+		}
+		defer rows.Close()
+
+		deviceDatas := make([]DeviceDatas, 0)
+		for rows.Next() {
+			var t string
+			var t_t, t_rh float64
+			rows.Scan(&t, &t_t, &t_rh)
+			deviceDatas = append(deviceDatas, DeviceDatas{t_t, t_rh, t})
+		}
+
+		if len(deviceDatas) == 10 {
+			timeFormat := "2006-01-02 15:04:05"
+			start := deviceDatas[0]
+			end := deviceDatas[len(deviceDatas)-1]
+
+			startTime, err := time.Parse(timeFormat, start.T_Time)
+			if err != nil {
+				log.Println("解析时间失败:", err)
+				continue
+			}
+			endTime, err := time.Parse(timeFormat, end.T_Time)
+			if err != nil {
+				log.Println("解析时间失败:", err)
+				continue
+			}
+
+			timeDiff := startTime.Sub(endTime).Seconds()
+			if IsWarn(deviceDatas, t_tlower, t_tupper, t_r_hlower, t_r_hupper) {
+				floatWarn, _ := strconv.ParseFloat(t_warn, 64)
+				expectedCount := timeDiff / floatWarn
+				actualCount := float64(len(deviceDatas))
+				if actualCount > expectedCount {
+					//fmt.Println("时间间隔(报警):", timeDiff)
+					//fmt.Println("数据预警(报警)", deviceDatas[0], sensor.Tsn)
+				}
+			} else {
+				floatSave, _ := strconv.ParseFloat(t_save_t, 64)
+				expectedCount := timeDiff / floatSave
+				actualCount := float64(len(deviceDatas))
+				//fmt.Println("开始时间与结束时间", timeDiff, startTime, endTime)
+				todayStart := time.Now().Truncate(24 * time.Hour)
+				if startTime.After(todayStart) || startTime.Equal(todayStart) {
+					if actualCount > expectedCount+1 {
+						fmt.Println("向上取整：", math.Ceil(expectedCount), "非向上取整：", expectedCount+1, actualCount)
+						fmt.Println("时间间隔(保存):", timeDiff, floatSave)
+						fmt.Println("数据预警(保存)", deviceDatas[0], sensor.Tsn)
+
+					}
+				}
+			}
+		}
+	}
+	fmt.Println(message, len(message))
+}
+
+// IsWarn 判断是否产生报警数据
+func IsWarn(deviceDatas []DeviceDatas, t_tlower, t_tupper, t_r_hlower, t_r_hupper float64) bool {
+	for _, data := range deviceDatas {
+		if data.T_T < t_tlower || data.T_T > t_tupper || data.T_rh < t_r_hlower || data.T_rh > t_r_hupper {
+			return true
+		}
+	}
+	return false
+}

service/test.go

@@ -4,30 +4,36 @@ import (
 	"database/sql"
 	"fmt"
 	"log"
+	"math/rand"
 	"strconv"
 	"time"
 	"ydMonitoring/databases"
 )
 
+// DeviceData 两两比较，如果大，就插入一条
+// 继续下一个两辆比较。
+// 然后多次循环，直到没有需要插入的
 type DeviceData struct {
 	T_Id        string         `json:"t_id"`
 	T_Sp        string         `json:"t_sp"`
 	T_Time      string         `json:"t_time"`
-	T_T         string         `json:"t_t"`
-	T_Rh        string         `json:"t_rh"`
+	T_T         float64        `json:"t_t"`
+	T_Rh        float64        `json:"t_rh"`
 	T_Site      sql.NullString `json:"t_site"`
 	Create_time string         `json:"create_time"`
 }
-type TimeData struct {
-	T_Time time.Time
-	Data   DeviceData
-}
 
 func TestService() {
-	sn := "kf861693223539284"
-	t_id := "2"
-	startTime := "2024-05-14 00:00:00"
-	endTime := "2024-09-11 23:59:59"
+	var t_save_t string    //系统设置间隔时间
+	var t_warn string      // 报警间隔时间
+	var t_tlower float64   // 低温下限
+	var t_tupper float64   // 高温上限
+	var t_r_hlower float64 // 湿度下限
+	var t_r_hupper float64 // 湿度上限
+	sn := "2023477814765836"
+	t_id := "1"
+	startTime := "2024-09-25 00:00:00"
+	endTime := "2024-09-25 15:32:00"
 	sql := fmt.Sprintf("SELECT * FROM z_device_data_%s WHERE t_id=%s and  t_time  BETWEEN '%s' AND '%s';", sn, t_id, startTime, endTime)
 	fmt.Println(sql)
 	devices := make([]DeviceData, 0)
@@ -49,15 +55,19 @@ func TestService() {
 	if err := rows.Err(); err != nil {
 		log.Fatalf("遍历错误: %v", err)
 	}
-	fmt.Println(devices)
-	sqls := fmt.Sprintf("SELECT t_save_t FROM device_parameter WHERE t_sn='%s' ORDER BY update_time DESC LIMIT 1", sn)
-	var t_save_t string //系统设置间隔时间
-	databases.Db.QueryRow(sqls).Scan(&t_save_t)
-	fmt.Println(t_save_t)
-	adjustData(devices, t_save_t, sn)
+	//fmt.Println(devices)
+	sqls := fmt.Sprintf("SELECT t_save_t,t_warn FROM device_parameter WHERE t_sn='%s' ORDER BY update_time DESC LIMIT 1", sn)
+	deviceSensorParameter := fmt.Sprintf("SELECT t__tlower,t__tupper,t__r_hlower,t__r_hupper FROM device_sensor_parameter WHERE  t_sn='%s' and t__state=1 and t_id = '%s' ORDER BY update_time DESC LIMIT 1", sn, t_id)
+	databases.Db.QueryRow(sqls).Scan(&t_save_t, &t_warn)
+	databases.Db.QueryRow(deviceSensorParameter).Scan(&t_tlower, &t_tupper, &t_r_hlower, &t_r_hupper)
+	fmt.Println("保存间隔：", t_save_t, "报警间隔：", t_warn, "温湿度上下限：", t_tlower, t_tupper, t_r_hlower, t_r_hupper)
+	adjustData(devices, t_save_t, t_warn, sn, t_tlower, t_tupper, t_r_hlower, t_r_hupper)
 }
-func adjustData(devices []DeviceData, t_save_t, sn string) {
+
+func adjustData(devices []DeviceData, t_save_t, t_warn, sn string, t_tlower, t_tupper, t_r_hlower, t_r_hupper float64) {
 	timeFormat := "2006-01-02 15:04:05"
+	floatSave, _ := strconv.ParseFloat(t_save_t, 64) // 获取系统设置时间
+	floatWarn, _ := strconv.ParseFloat(t_warn, 64)   // 获取系统报警时间
 	for i := 0; i < len(devices)-1; i++ {
 		t1, err := time.Parse(timeFormat, devices[i].T_Time)
 		if err != nil {
@@ -70,31 +80,118 @@ func adjustData(devices []DeviceData, t_save_t, sn string) {
 			continue
 		}
 		timeDiff := t2.Sub(t1).Seconds()
-		fmt.Println(timeDiff)
-		float, _ := strconv.ParseFloat(t_save_t, 64) //获取系统设置时间
-		if timeDiff > float {
-			fmt.Printf("时间差大于系统设置时间%s\n %s", devices[i], devices[i+1])
-			numInserts := int(timeDiff / float)
+		// 检查是否已经添加数据
+		//isInstrt := false
+		// 检查是否为报警数据
+		isAlarm := devices[i].T_T < t_tlower || devices[i].T_T > t_tupper || devices[i].T_Rh < t_r_hlower || devices[i].T_Rh > t_r_hupper
+		isNextAlarm := devices[i+1].T_T < t_tlower || devices[i+1].T_T > t_tupper || devices[i+1].T_Rh < t_r_hlower || devices[i+1].T_Rh > t_r_hupper
+		if isAlarm && !isNextAlarm {
+			fmt.Printf("报警数据与正常数据：%s 和 %s\n", devices[i].T_Time, devices[i+1].T_Time)
+			newTime := t1.Add(time.Duration(int(floatWarn-rand.Float64()*rand.Float64())) * time.Second)
+			//newTime.Sub()
+			fmt.Println(newTime.Format(timeFormat))
+			T_T, T_Rh := IsNotWarn(devices, t_tlower, t_tupper, t_r_hlower, t_r_hupper)
+			devi := DeviceData{
+				T_Id:        devices[i].T_Id,
+				T_Sp:        devices[i].T_Sp,
+				T_Time:      newTime.Format(timeFormat),
+				T_T:         T_T,
+				T_Rh:        T_Rh,
+				T_Site:      devices[i].T_Site,
+				Create_time: devices[i].Create_time,
+			}
+			insertSql := fmt.Sprintf("INSERT INTO z_device_data_%s (t_id, t_sp, t_time, t_t, t_rh, t_site, create_time) VALUES (?, ?, ?, ?, ?, ?, ?)", sn)
+			_, err := databases.Db.Exec(insertSql, devi.T_Id, devi.T_Sp, devi.T_Time, devi.T_T, devi.T_Rh, devi.T_Site.String, devi.Create_time)
+			if err != nil {
+				fmt.Println("插入新时间点失败:", err)
+			}
+			log.Println("sn:", sn, "连续报警补漏数据：", devi, " 插入时间点：", newTime.Format(timeFormat))
+		}
+		// 处理报警与正常数据之间的间隔
+		if isAlarm && !isNextAlarm {
+			fmt.Println("处理报警数据与正常数据之间的间隔", devices[i].T_Time, devices[i+1].T_Time)
+			interval := floatWarn - 20
+			newTime := t1.Add(time.Duration(int(interval)) * time.Second)
+			for timeDiff > floatSave {
+				if timeDiff > floatSave {
+					fmt.Printf("间隔时间大于系统设置时间：%s 和 %s\n", devices[i].T_Time, devices[i+1].T_Time)
+					T_T, T_Rh := IsNotWarn(devices, t_tlower, t_tupper, t_r_hlower, t_r_hupper)
+					devi := DeviceData{
+						T_Id:        devices[i].T_Id,
+						T_Sp:        devices[i].T_Sp,
+						T_Time:      newTime.Format(timeFormat),
+						T_T:         T_T,
+						T_Rh:        T_Rh,
+						T_Site:      devices[i].T_Site,
+						Create_time: devices[i].Create_time,
+					}
+					insertSql := fmt.Sprintf("INSERT INTO z_device_data_%s (t_id, t_sp, t_time, t_t, t_rh, t_site, create_time) VALUES (?, ?, ?, ?, ?, ?, ?)", sn)
+					_, err := databases.Db.Exec(insertSql, devi.T_Id, devi.T_Sp, devi.T_Time, devi.T_T, devi.T_Rh, devi.T_Site.String, devi.Create_time)
+					if err != nil {
+						fmt.Println("插入新时间点失败:", err)
+					}
+					log.Println("sn:", sn, "连续报警补漏数据：", devi, " 插入时间点：", newTime.Format(timeFormat))
+					timeDiff = t2.Sub(newTime).Seconds()
+					newTime = newTime.Add(time.Duration(int(floatSave)) * time.Second)
+				}
+			}
+		}
+		if timeDiff > floatSave {
+			fmt.Printf("时间差大于系统设置时间：%s 和 %s\n", devices[i].T_Time, devices[i+1].T_Time)
+			numInserts := int(timeDiff / floatSave)
 			for j := 1; j <= numInserts; j++ {
-				newTime := t1.Add(time.Duration(j*int(float)) * time.Second)
+				newTime := t1.Add(time.Duration(j*int(floatSave)) * time.Second)
+				T_T, T_Rh := IsNotWarn(devices, t_tlower, t_tupper, t_r_hlower, t_r_hupper)
+				devi := DeviceData{
+					T_Id:        devices[i].T_Id,
+					T_Sp:        devices[i].T_Sp,
+					T_Time:      newTime.Format(timeFormat),
+					T_T:         T_T,
+					T_Rh:        T_Rh,
+					T_Site:      devices[i].T_Site,
+					Create_time: devices[i].Create_time,
+				}
+				insertSql := fmt.Sprintf("INSERT INTO z_device_data_%s (t_id, t_sp, t_time, t_t, t_rh, t_site, create_time) VALUES (?, ?, ?, ?, ?, ?, ?)", sn)
+				_, err := databases.Db.Exec(insertSql, devi.T_Id, devi.T_Sp, devi.T_Time, devi.T_T, devi.T_Rh, devi.T_Site.String, devi.Create_time)
+				if err != nil {
+					fmt.Println("插入新时间点失败:", err)
+				}
+				log.Println("sn:", sn, "补漏数据：", devi, " 插入时间点：", newTime.Format(timeFormat))
+			}
+		}
+		if isAlarm && isNextAlarm {
+			if timeDiff <= floatWarn {
+				fmt.Printf("连续报警时间差小于报警间隔时间：%s 和 %s\n", devices[i].T_Time, devices[i+1].T_Time)
+				newTime := t1.Add(time.Duration(int(timeDiff-rand.Float64()*0.4-0.2)) * time.Second)
+				fmt.Println(newTime.Format(timeFormat))
+				T_T, T_Rh := IsNotWarn(devices, t_tlower, t_tupper, t_r_hlower, t_r_hupper)
 				devi := DeviceData{
 					T_Id:        devices[i].T_Id,
 					T_Sp:        devices[i].T_Sp,
 					T_Time:      newTime.Format(timeFormat),
-					T_T:         devices[i].T_T,
-					T_Rh:        devices[i].T_Rh,
+					T_T:         T_T,
+					T_Rh:        T_Rh,
 					T_Site:      devices[i].T_Site,
 					Create_time: devices[i].Create_time,
 				}
-				//fmt.Println("插入新时间点:", devi)
-				log.Println(devi)
-				insertSql := fmt.Sprintf("INSERT INTO z_device_data_%s (t_id, t_sp, t_time, t_t, t_rh, t_site, create_time) VALUES ('%v', '%v', '%v', '%v', '%v' , '%v', '%v')", sn, devi.T_Id, devi.T_Sp, devi.T_Time, devi.T_T, devi.T_Rh, devi.T_Site.String, devi.Create_time)
-				_, err := databases.Db.Exec(insertSql)
+				insertSql := fmt.Sprintf("INSERT INTO z_device_data_%s (t_id, t_sp, t_time, t_t, t_rh, t_site, create_time) VALUES (?, ?, ?, ?, ?, ?, ?)", sn)
+				_, err := databases.Db.Exec(insertSql, devi.T_Id, devi.T_Sp, devi.T_Time, devi.T_T, devi.T_Rh, devi.T_Site.String, devi.Create_time)
 				if err != nil {
 					fmt.Println("插入新时间点失败:", err)
 				}
-				fmt.Println(insertSql)
+				log.Println("sn:", sn, "连续报警补漏数据：", devi, " 插入时间点：", newTime.Format(timeFormat))
 			}
 		}
+
+	}
+}
+func IsNotWarn(deviceDatas []DeviceData, t_tlower, t_tupper, t_r_hlower, t_r_hupper float64) (T_T float64, T_Rh float64) {
+	for i, _ := range deviceDatas {
+		if !(deviceDatas[i].T_T < t_tlower || deviceDatas[i].T_T > t_tupper || deviceDatas[i].T_Rh < t_r_hlower || deviceDatas[i].T_Rh > t_r_hupper) {
+			T_T = deviceDatas[i].T_T + rand.Float64()*0.4 - 0.2
+			T_Rh = deviceDatas[i].T_Rh + rand.Float64()*0.4 - 0.2
+			return T_T, T_Rh
+		}
 	}
+	return 0, 0
 }