341 lines
9.4 KiB
Go
341 lines
9.4 KiB
Go
package dao
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import (
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"database/sql"
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"math"
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"sort"
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"time"
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"go_rain_dtu/internal/model"
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"go_rain_dtu/pkg/logger"
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)
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type SensorDAO struct {
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db *sql.DB
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}
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func NewSensorDAO(db *sql.DB) *SensorDAO {
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return &SensorDAO{db: db}
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}
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// 插入传感器数据
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func (dao *SensorDAO) Insert(data *model.SensorData) error {
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query := `
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INSERT INTO sensor_data (
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timestamp, wind_speed, wind_force, wind_direction_8,
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wind_direction_360, humidity, temperature, atm_pressure,
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solar_radiation, rainfall
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) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
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`
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_, err := dao.db.Exec(query,
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data.Timestamp, data.WindSpeed, data.WindForce, data.WindDirection8,
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data.WindDirection360, data.Humidity, data.Temperature, data.AtmPressure,
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data.SolarRadiation, data.OpticalRain,
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)
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if err != nil {
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logger.Logger.Printf("插入传感器数据失败: %v", err)
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return err
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}
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return nil
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}
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// 获取聚合数据
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func (dao *SensorDAO) GetAggregatedData(start, end time.Time, interval string) ([]model.AggregatedData, error) {
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// 参数化时间间隔
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var intervalMinutes int
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switch interval {
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case "1min":
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intervalMinutes = 1
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case "5min":
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intervalMinutes = 5
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case "30min":
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intervalMinutes = 30
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default: // 1hour
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intervalMinutes = 60
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}
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// 对于每个时间桶，我们需要找到该时间桶内最接近结束时间的记录
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// 首先，生成所有的时间桶
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var buckets []time.Time
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current := start
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for current.Before(end) || current.Equal(end) {
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buckets = append(buckets, current)
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// 移动到下一个时间桶
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current = current.Add(time.Duration(intervalMinutes) * time.Minute)
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}
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// 如果没有时间桶，直接返回空结果
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if len(buckets) == 0 {
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return []model.AggregatedData{}, nil
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}
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// 查询原始数据
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rawData, err := dao.GetRawData(start, end)
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if err != nil {
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return nil, err
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}
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// 按时间戳排序原始数据
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sort.Slice(rawData, func(i, j int) bool {
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return rawData[i].Timestamp.Before(rawData[j].Timestamp)
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})
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// 创建结果数组
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var result []model.AggregatedData
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// 对每个时间桶进行处理
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for i, bucketTime := range buckets {
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var nextBucketTime time.Time
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if i < len(buckets)-1 {
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nextBucketTime = buckets[i+1]
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} else {
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nextBucketTime = end.Add(time.Second)
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}
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// 收集该时间桶内的所有数据点
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var bucketData []model.AggregatedData
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for _, data := range rawData {
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if (data.Timestamp.Equal(bucketTime) || data.Timestamp.After(bucketTime)) &&
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data.Timestamp.Before(nextBucketTime) {
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bucketData = append(bucketData, data)
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}
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}
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if len(bucketData) == 0 {
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// 如果该时间桶内没有数据，尝试使用上一个时间桶的最后数据
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if i > 0 && len(result) > 0 {
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lastBucketData := result[len(result)-1]
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// 复制上一个时间桶的数据，但更新时间戳
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newData := lastBucketData
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newData.Timestamp = bucketTime
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result = append(result, newData)
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}
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continue
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}
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// 计算该时间桶的聚合数据
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var aggregated model.AggregatedData
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aggregated.Timestamp = bucketTime
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// 温度、湿度、风速、大气压和太阳辐射使用平均值
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var sumTemp, sumHumidity, sumWindSpeed, sumAtmPressure, sumSolarRadiation float64
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for _, data := range bucketData {
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sumTemp += data.AvgTemperature
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sumHumidity += data.AvgHumidity
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sumWindSpeed += data.AvgWindSpeed
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sumAtmPressure += data.AvgAtmPressure
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sumSolarRadiation += data.AvgSolarRadiation
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}
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count := float64(len(bucketData))
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aggregated.AvgTemperature = sumTemp / count
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aggregated.AvgHumidity = sumHumidity / count
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aggregated.AvgWindSpeed = sumWindSpeed / count
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aggregated.AvgAtmPressure = sumAtmPressure / count
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aggregated.AvgSolarRadiation = sumSolarRadiation / count
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// 对于雨量，使用时间桶内最后一个（最新的）数据点的值
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aggregated.Rainfall = bucketData[len(bucketData)-1].Rainfall
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// 计算新增雨量（相对于前一个时间桶）
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if i > 0 && len(result) > 0 {
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prevRainfall := result[len(result)-1].Rainfall
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// 如果当前雨量大于前一个时间桶的雨量，计算差值作为新增雨量
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if aggregated.Rainfall > prevRainfall {
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aggregated.Rainfall = aggregated.Rainfall - prevRainfall
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} else if aggregated.Rainfall == prevRainfall {
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// 如果雨量没有变化，表示没有新增雨量
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aggregated.Rainfall = 0
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}
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// 如果当前雨量小于前一个时间桶的雨量（可能是雨量计重置），保留当前值作为新增雨量
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}
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result = append(result, aggregated)
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}
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// 检查是否需要进行插值处理
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if intervalMinutes == 1 && len(result) > 0 {
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result = fillMissingMinutes(result, start, end)
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}
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logger.Logger.Printf("聚合查询成功: 返回%d条记录 (间隔=%s)", len(result), interval)
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return result, nil
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}
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// fillMissingMinutes 填充缺失的分钟数据点
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func fillMissingMinutes(data []model.AggregatedData, start, end time.Time) []model.AggregatedData {
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// 只有在数据点少于应有数量的一半时才进行插值
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expectedPoints := int(end.Sub(start).Minutes()) + 1
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if len(data) > expectedPoints/2 {
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return data // 数据点足够多，不需要插值
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}
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// 创建一个映射，用于快速查找已有数据点
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dataMap := make(map[int64]model.AggregatedData)
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for _, point := range data {
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// 将时间转换为Unix时间戳（秒），便于比较
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key := point.Timestamp.Unix()
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dataMap[key] = point
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}
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// 初始化结果数组，预分配足够的空间
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result := make([]model.AggregatedData, 0, expectedPoints)
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// 创建按时间排序的索引
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var timestamps []int64
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for k := range dataMap {
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timestamps = append(timestamps, k)
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}
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sort.Slice(timestamps, func(i, j int) bool {
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return timestamps[i] < timestamps[j]
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})
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// 如果没有数据点，直接返回
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if len(timestamps) == 0 {
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return data
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}
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// 获取第一个和最后一个数据点
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firstPointTime := time.Unix(timestamps[0], 0)
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lastPointTime := time.Unix(timestamps[len(timestamps)-1], 0)
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// 调整开始和结束时间，确保它们不会超出实际数据范围
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if start.Before(firstPointTime) {
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start = firstPointTime
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}
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if end.After(lastPointTime) {
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end = lastPointTime
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}
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// 将start向下取整到整分钟
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start = time.Date(start.Year(), start.Month(), start.Day(), start.Hour(), start.Minute(), 0, 0, start.Location())
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// 按分钟迭代时间范围
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for t := start; !t.After(end); t = t.Add(time.Minute) {
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key := t.Unix()
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if point, exists := dataMap[key]; exists {
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// 使用现有数据点
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result = append(result, point)
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} else {
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// 使用最近的有效数据点进行插值
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nearestPoint := findNearestPoint(t, timestamps, dataMap)
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if !nearestPoint.Timestamp.IsZero() {
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// 创建新的数据点
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newPoint := nearestPoint
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newPoint.Timestamp = t
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result = append(result, newPoint)
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}
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}
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}
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return result
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}
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// findNearestPoint 查找最接近指定时间的数据点
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func findNearestPoint(t time.Time, timestamps []int64, dataMap map[int64]model.AggregatedData) model.AggregatedData {
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target := t.Unix()
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nearest := int64(0)
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minDiff := int64(math.MaxInt64)
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for _, ts := range timestamps {
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diff := int64(math.Abs(float64(ts - target)))
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if diff < minDiff {
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minDiff = diff
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nearest = ts
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}
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}
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// 如果最近的点超过10分钟，返回空值
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if minDiff > 10*60 {
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return model.AggregatedData{}
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}
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return dataMap[nearest]
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}
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// GetLatestSensorData 获取最新的一条传感器数据
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func (dao *SensorDAO) GetLatestSensorData() (*model.SensorData, error) {
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query := `
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SELECT id, timestamp, wind_speed, wind_force, wind_direction_8,
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wind_direction_360, humidity, temperature, atm_pressure,
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solar_radiation, rainfall
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FROM sensor_data
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ORDER BY timestamp DESC
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LIMIT 1
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`
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var data model.SensorData
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err := dao.db.QueryRow(query).Scan(
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&data.ID, &data.Timestamp, &data.WindSpeed, &data.WindForce, &data.WindDirection8,
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&data.WindDirection360, &data.Humidity, &data.Temperature, &data.AtmPressure,
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&data.SolarRadiation, &data.OpticalRain,
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)
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if err != nil {
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if err == sql.ErrNoRows {
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logger.Logger.Printf("未找到传感器数据")
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return nil, nil
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}
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logger.Logger.Printf("获取最新传感器数据失败: %v", err)
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return nil, err
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}
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return &data, nil
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}
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// GetRawData 获取指定时间范围内的原始传感器数据
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func (dao *SensorDAO) GetRawData(start, end time.Time) ([]model.AggregatedData, error) {
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query := `
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SELECT
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timestamp,
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temperature/10 AS temperature,
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rainfall/10 AS rainfall,
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humidity/10 AS humidity,
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wind_speed/100 AS wind_speed,
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atm_pressure/10 AS atm_pressure,
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solar_radiation
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FROM sensor_data
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WHERE timestamp BETWEEN ? AND ?
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ORDER BY timestamp DESC
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`
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// 执行查询
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rows, err := dao.db.Query(query, start, end)
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if err != nil {
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logger.Logger.Printf("查询原始数据失败: %v (开始=%s, 结束=%s)",
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err, start.Format(time.RFC3339), end.Format(time.RFC3339))
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return nil, err
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}
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defer rows.Close()
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var result []model.AggregatedData
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for rows.Next() {
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var data model.AggregatedData
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err := rows.Scan(
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&data.Timestamp,
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&data.AvgTemperature,
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&data.Rainfall,
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&data.AvgHumidity,
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&data.AvgWindSpeed,
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&data.AvgAtmPressure,
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&data.AvgSolarRadiation)
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if err != nil {
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logger.Logger.Printf("扫描数据行失败: %v", err)
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continue
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}
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result = append(result, data)
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}
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// 检查是否有游标错误
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if err = rows.Err(); err != nil {
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logger.Logger.Printf("查询游标错误: %v", err)
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return nil, err
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}
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logger.Logger.Printf("原始数据查询成功: 返回%d条记录", len(result))
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return result, nil
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}
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