feat(server-go): 风险评分引擎（#9，公式/分级/阶段与环境系数）接入巡检
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weijuesen
@@ -112,6 +112,23 @@ func createInspection(db *gorm.DB, s3 *service.S3Service, ai *service.AIClient,
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} else {
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raw, _ := json.Marshal(detRes.Detections)
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rec.Detections = raw
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// 风险评分（#9）：AI 置信度取检测结果最大值；环境/阶段系数在有 roomId 时按房间数据计算
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aiConf := 0.0
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for _, d := range detRes.Detections {
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if d.Confidence > aiConf {
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aiConf = d.Confidence
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}
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}
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stageCoef, envCoef := loadRoomRisk(db, roomID)
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score := service.ComputeRiskScore(service.RiskInput{
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AI: aiConf,
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Env: envCoef,
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Stage: stageCoef,
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})
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rec.RiskScore = &score
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level := service.RiskLevel(score)
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rec.RiskLevel = &level
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}
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if err := db.Create(&rec).Error; err != nil {
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@@ -130,6 +147,44 @@ func createInspection(db *gorm.DB, s3 *service.S3Service, ai *service.AIClient,
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}
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}
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// loadRoomRisk 加载房间阶段系数与环境系数（无房间/无数据时返回 0）
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func loadRoomRisk(db *gorm.DB, roomID string) (stageCoef, envCoef float64) {
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if roomID == "" {
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return 0, 0
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}
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var room model.Room
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if db.Where("id = ?", roomID).First(&room).Error != nil {
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return 0, 0
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}
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if room.Stage != nil {
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stageCoef = service.StageCoefficient(*room.Stage)
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}
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var humidity, temperature *float64
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var h model.Telemetry
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if err := db.Table("telemetry").
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Select("telemetry.value").
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Joins("JOIN devices ON devices.device_key = telemetry.device_key AND devices.room_id = ?", roomID).
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Where("telemetry.metric = ?", "humidity").
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Order("telemetry.timestamp DESC").
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Limit(1).
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First(&h).Error; err == nil {
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humidity = &h.Value
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}
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var t model.Telemetry
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if err := db.Table("telemetry").
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Select("telemetry.value").
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Joins("JOIN devices ON devices.device_key = telemetry.device_key AND devices.room_id = ?", roomID).
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Where("telemetry.metric = ?", "temperature").
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Order("telemetry.timestamp DESC").
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Limit(1).
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First(&t).Error; err == nil {
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temperature = &t.Value
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}
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envCoef = service.EnvCoefficient(temperature, humidity)
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return stageCoef, envCoef
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}
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// listInspections 巡检记录列表（roomId/limit 过滤）
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func listInspections(db *gorm.DB) gin.HandlerFunc {
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return func(c *gin.Context) {
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@@ -0,0 +1,69 @@
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package service
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import "math"
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// RiskInput 风险评分输入（各系数取值 0~1）
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type RiskInput struct {
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AI float64 // AI 识别置信度（权重 0.5）
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Env float64 // 环境风险系数（权重 0.2）
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Stage float64 // 饲养阶段风险系数（权重 0.15）
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Uniformity float64 // 群体整齐度偏离度（权重 0.15）
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}
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// ComputeRiskScore 按规格书 3.1.4 公式计算 0~100 风险分：
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// 风险分 = 0.5×AI置信度 + 0.2×环境系数 + 0.15×阶段系数 + 0.15×整齐度偏离度
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func ComputeRiskScore(in RiskInput) float64 {
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score := 0.5*in.AI + 0.2*in.Env + 0.15*in.Stage + 0.15*in.Uniformity
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score = math.Max(0, math.Min(1, score))
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return score * 100
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}
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// RiskLevel 按规格书 3.1.4 分级：绿 0-30 / 黄 31-60 / 橙 61-80 / 红 81-100
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func RiskLevel(score float64) string {
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switch {
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case score <= 30:
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return "green"
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case score <= 60:
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return "yellow"
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case score <= 80:
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return "orange"
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default:
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return "red"
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}
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}
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// StageCoefficient 蚕房阶段 → 阶段风险系数（Room.Stage 粗粒度映射；
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// 待 #7 蚕匾/批次管理的龄期字段落地后细化）
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func StageCoefficient(stage string) float64 {
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switch stage {
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case "pupa":
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return 0.5 // 核型多角体病 5龄后期至蛹期高发
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case "larva":
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return 0.4 // 软化病 5 龄集中暴发等
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case "moth":
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return 0.2
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case "egg":
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return 0.1
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default:
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return 0
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}
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}
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// EnvCoefficient 由最新温湿度计算环境风险系数（规则取自规格书 3.2.3）
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func EnvCoefficient(temp, humidity *float64) float64 {
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coef := 0.0
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if humidity != nil {
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switch {
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case *humidity >= 80:
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coef = math.Max(coef, 0.8) // 白僵病等真菌病高湿条件
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case *humidity >= 75:
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coef = math.Max(coef, 0.5)
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}
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}
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if temp != nil {
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if *temp > 30 || *temp < 20 {
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coef = math.Max(coef, 0.5) // 温度突变诱发核型多角体病
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}
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}
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return coef
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}
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@@ -0,0 +1,85 @@
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package service
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import "testing"
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func f(v float64) *float64 { return &v }
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func TestComputeRiskScoreWeights(t *testing.T) {
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// 全 1：0.5*1 + 0.2*1 + 0.15*1 + 0.15*1 = 1 → 100
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if s := ComputeRiskScore(RiskInput{AI: 1, Env: 1, Stage: 1, Uniformity: 1}); s != 100 {
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t.Errorf("全 1 应得 100，实际 %.2f", s)
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}
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// 仅 AI 置信度 1：0.5*1 = 0.5 → 50
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if s := ComputeRiskScore(RiskInput{AI: 1}); s != 50 {
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t.Errorf("仅 AI=1 应得 50，实际 %.2f", s)
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}
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// 0.5*0.8 + 0.2*0.5 = 0.5
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if s := ComputeRiskScore(RiskInput{AI: 0.8, Env: 0.5}); s != 50 {
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t.Errorf("0.8/0.5 应得 50，实际 %.2f", s)
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}
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}
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func TestComputeRiskScoreClamps(t *testing.T) {
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if s := ComputeRiskScore(RiskInput{AI: 2, Env: 2, Stage: 2, Uniformity: 2}); s > 100 {
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t.Errorf("应钳制到 100，实际 %.2f", s)
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}
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if s := ComputeRiskScore(RiskInput{AI: -1}); s < 0 {
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t.Errorf("应钳制到 0，实际 %.2f", s)
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}
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}
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func TestRiskLevelBoundaries(t *testing.T) {
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cases := map[float64]string{
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0: "green",
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30: "green",
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30.5: "yellow",
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60: "yellow",
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61: "orange",
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80: "orange",
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81: "red",
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100: "red",
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}
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for score, want := range cases {
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if got := RiskLevel(score); got != want {
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t.Errorf("RiskLevel(%.1f) = %s, want %s", score, got, want)
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}
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}
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}
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func TestStageCoefficientMapping(t *testing.T) {
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if StageCoefficient("pupa") <= 0 {
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t.Error("蛹期应有风险系数")
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}
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if StageCoefficient("") != 0 {
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t.Error("空阶段应为 0")
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}
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if StageCoefficient("unknown") != 0 {
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t.Error("未知阶段应为 0")
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}
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if StageCoefficient("pupa") <= StageCoefficient("egg") {
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t.Error("蛹期系数应高于卵期")
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}
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}
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func TestEnvCoefficientRules(t *testing.T) {
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// 湿度 >=80 → 高（真菌病）
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if c := EnvCoefficient(f(25), f(85)); c < 0.7 {
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t.Errorf("湿度 85 应 ≥0.7，实际 %.2f", c)
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}
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// 湿度 75-80 → 中
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if c := EnvCoefficient(f(25), f(78)); c < 0.3 {
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t.Errorf("湿度 78 应 ≥0.3，实际 %.2f", c)
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}
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// 温度突变 >30 → 中（核型多角体病诱发）
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if c := EnvCoefficient(f(32), f(60)); c < 0.3 {
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t.Errorf("温度 32 应 ≥0.3，实际 %.2f", c)
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}
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// 舒适环境 → 0
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if c := EnvCoefficient(f(25), f(60)); c != 0 {
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t.Errorf("舒适环境应为 0，实际 %.2f", c)
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}
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// 缺数据 → 0
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if c := EnvCoefficient(nil, nil); c != 0 {
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t.Errorf("无数据应为 0，实际 %.2f", c)
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}
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}
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