create git

internal/server/diff/diff.go

@@ -0,0 +1,170 @@
+// diff.go
+package diff
+
+import (
+	"fmt"
+	"math"
+	"strconv"
+	"strings"
+)
+
+type KalmanVarDiff struct {
+	Kf *KalmanFilter
+
+	StartDiff float64
+	MinDiff   float64
+	MaxDiff   float64
+
+	TargetInterval float64
+
+	MhsEst  float64
+	DiffEst float64
+}
+
+type KalmanFilter struct {
+	X float64 // 估计的状态
+	P float64 // 状态协方差
+	F float64 // 状态转移矩阵
+	H float64 // 观测矩阵
+	Q float64 // 过程噪声协方差
+	R float64 // 观测噪声协方差
+	K float64 // 卡尔曼增益
+}
+
+func NewKalmanFilter(init_mhs float64, init_state_p float64) *KalmanFilter {
+	x := init_mhs
+	p := init_state_p
+	f := 1.0
+	h := 1.0
+	q := 0.01
+	r := init_mhs * 3
+	//r := 1.0
+	return &KalmanFilter{
+		X: x,
+		P: p,
+		F: f,
+		H: h,
+		Q: q,
+		R: r,
+	}
+	/*
+		return &KalmanFilter{
+			X: 1.0,
+			P: 1.0,
+			F: 1.0,
+			H: 1.0,
+			Q: 0.1,
+			R: 1.0,
+		}*/
+}
+
+func (kf *KalmanFilter) Update(measurement float64) (float64, float64) {
+	kf.R = measurement * 2
+
+	// 预测
+	p := kf.X*kf.Q + kf.P
+
+	// 计算卡尔曼增益
+	kf.K = p / (p + kf.R + 1)
+
+	// 更新状态估计
+	if measurement >= kf.X {
+		kf.X = kf.X + kf.K*(measurement-kf.X)
+	} else {
+		kf.X = kf.X - kf.K*(kf.X-measurement)
+	}
+
+	// 更新协方差矩阵
+	kf.P = (1 - kf.K) * p
+
+	// 自适应调整过程噪声和观测噪声
+	//kf.adapt()
+
+	return kf.X, kf.P
+}
+
+func (kf *KalmanFilter) adapt() {
+	// 自适应调整参数
+	if kf.P > 10.0 {
+		kf.Q *= 1.1 // 增加过程噪声
+	} else {
+		kf.Q *= 0.9 // 减少过程噪声
+	}
+	if kf.K > 0.5 {
+		kf.R *= 1.1 // 增加观测噪声
+	} else {
+		kf.R *= 0.9 // 减少观测噪声
+	}
+}
+
+func (kd *KalmanVarDiff) Init(startDiff float64, minDiff float64, maxDiff float64, targetTime float64) {
+	kd.StartDiff = startDiff
+	kd.MinDiff = minDiff
+	kd.MaxDiff = maxDiff
+	kd.TargetInterval = targetTime
+	kd.DiffEst = startDiff
+	kd.MhsEst = startDiff / targetTime
+	kd.Kf = NewKalmanFilter(startDiff/targetTime, 1.0)
+}
+
+func (kd *KalmanVarDiff) DeInit() {
+
+}
+
+// 提取科学计数法有效数字（整数部分和一位小数）及指数，并合并为新的浮点数
+func extractAndCombine(num float64) float64 {
+	// 将浮点数格式化为科学计数法
+	scientificStr := fmt.Sprintf("%.10e", num)
+
+	// 分离小数部分和指数部分
+	parts := strings.Split(scientificStr, "e")
+	if len(parts) != 2 {
+		fmt.Println("Error: unexpected scientific notation format")
+		return 0
+	}
+
+	// 处理小数部分
+	decimalPart := parts[0]
+	exponentPart := parts[1]
+
+	// 去除小数部分前的 "0."
+	decimalPart = strings.TrimPrefix(decimalPart, "0.")
+
+	// 提取整数部分和一位小数
+	decimalParts := strings.Split(decimalPart, ".")
+	if len(decimalParts) < 2 {
+		decimalPart = decimalParts[0] + ".0" // 没有小数部分时，添加 ".0"
+	} else {
+		decimalPart = decimalParts[0] + "." + decimalParts[1][:1] // 只取一位小数
+		//decimalPart = decimalParts[0]
+	}
+
+	// 将指数部分转换为整数
+	exponent, err := strconv.Atoi(exponentPart)
+	if err != nil {
+		fmt.Println("Error parsing exponent:", err)
+		return 0
+	}
+
+	// 计算新的浮点数
+	newNumber := (func() float64 {
+		digit, err := strconv.ParseFloat(decimalPart, 64)
+		if err != nil {
+			fmt.Println("Error parsing decimal part:", err)
+			return 0
+		}
+		return digit * math.Pow(10, float64(exponent))
+	})()
+
+	return newNumber
+}
+
+func (kd *KalmanVarDiff) Handler(diff float64, interval float64) (float64, float64) {
+	//newx, newp := kd.Kf.Update(kd.DiffEst / interval)
+	newx, newp := kd.Kf.Update(diff / interval)
+	kd.MhsEst = newx
+	newdiff := newx * kd.TargetInterval
+	kd.DiffEst = newdiff
+	newdiff2 := extractAndCombine(newdiff)
+	return newdiff2, newp
+}