fix：预测指标动态环差

models/base_predict_from_calculate.go

@@ -251,7 +251,6 @@ func RefreshAllPredictCalculate(edbInfoIdList []*EdbInfo, edbInfoId, source int,
 			_ = to.Commit()
 		}
 	}()
-	fmt.Println(startDate)
 
 	latestDateStr, latestValue, err = refreshAllPredictCalculate(to, edbInfoIdList, edbInfoId, source, edbCode, formulaStr, startDate, edbInfoIdBytes)
 
@@ -260,7 +259,7 @@ func RefreshAllPredictCalculate(edbInfoIdList []*EdbInfo, edbInfoId, source int,
 
 // refreshAllPredictCalculate 刷新预测计算指标的全部数据
 func refreshAllPredictCalculate(to orm.TxOrmer, edbInfoIdList []*EdbInfo, edbInfoId, source int, edbCode, formulaStr, startDate string, edbInfoIdBytes []string) (latestDateStr string, latestValue float64, err error) {
-	fmt.Println(startDate)
+	fmt.Println("startDate:", startDate)
 
 	var minLatestDate time.Time
 	realSaveDataMap := make(map[string]map[int]float64)

models/edb_info.go

@@ -553,6 +553,8 @@ func GetChartPredictEdbInfoDataListByConfList(predictEdbConfList []*PredictEdbCo
 				return
 			}
 			predictEdbInfoData, tmpMinValue, tmpMaxValue = GetChartPredictEdbInfoDataListByRuleNLinearRegression(predictEdbConf.PredictEdbInfoId, nValue, startDate, dataEndTime, frequency, realPredictEdbInfoData, predictEdbInfoData, existMap)
+		case 9: //9：动态环差”预测规则；
+			predictEdbInfoData, tmpMinValue, tmpMaxValue = GetChartPredictEdbInfoDataListByRuleTrendsHC(predictEdbConf.PredictEdbInfoId, predictEdbConf.ConfigId, startDate, dataEndTime, frequency, realPredictEdbInfoData, predictEdbInfoData, existMap)
 		}
 		//startDate = dataEndTime.AddDate(0, 0, 1)
 		startDate = dataEndTime

models/predict_edb_info_rule.go

@@ -566,3 +566,75 @@ func getLinearResult(s []Coordinate) (gradient, intercept float64) {
 
 	return
 }
+
+//	GetChartPredictEdbInfoDataListByRuleTrendsHC 根据动态环比增加值的计算规则获取预测数据
+// 研究员有对预测指标进行动态环差计算的需求，即预测指标使用环差规则进行预测时，环比增加值不是固定值，而是由几个预测指标计算得出的动态变化的值；
+//需求说明：
+//1、增加“动态环差”预测规则；
+//2、环比增加值在弹窗设置；
+//3、动态环差预测举例：
+//指标A实际最新数据为2022-10-27（100）；
+//预测指标B预测数据为2022-10-28（240）、2022-10-29（300）；
+//预测指标C预测数据为2022-10-28（260）、2022-10-29（310）；
+//计算公式为B-C；
+//则指标A至2022-10-29的预测值为2022-10-28（100+（240-260）=80）、2022-10-29（80+（300-310）=90）；
+//注：动态环比增加值的计算遵从计算指标的计算规则，即用于计算的指标若有部分指标缺少部分日期数据，则这部分日期数据不做计算，为空；若动态环比增加值某一天为空，则往前追溯最近一期有值的环比增加值作为该天的数值参与计算；
+func GetChartPredictEdbInfoDataListByRuleTrendsHC(edbInfoId, configId int, startDate, endDate time.Time, frequency string, realPredictEdbInfoData, predictEdbInfoData []*EdbInfoSearchData, existMap map[string]float64) (newPredictEdbInfoData []*EdbInfoSearchData, minValue, maxValue float64) {
+	allDataList := make([]*EdbInfoSearchData, 0)
+	allDataList = append(allDataList, realPredictEdbInfoData...)
+	allDataList = append(allDataList, predictEdbInfoData...)
+	newPredictEdbInfoData = predictEdbInfoData
+
+	lenAllData := len(allDataList)
+	if lenAllData <= 0 {
+		return
+	}
+
+	hcDataMap := make(map[string]float64) //规则计算的环差值map
+
+	tmpPredictEdbRuleDataList, err := GetPredictEdbRuleDataItemList(edbInfoId, configId, startDate.Format(utils.FormatDate), endDate.Format(utils.FormatDate))
+	if err != nil {
+		return
+	}
+	for _, v := range tmpPredictEdbRuleDataList {
+		hcDataMap[v.DataTime] = v.Value
+	}
+
+	dayList := getPredictEdbDayList(startDate, endDate, frequency)
+	for k, currentDate := range dayList {
+		// 最近一条数据
+		tmpLenAllDataList := len(allDataList)
+		lastValue := allDataList[tmpLenAllDataList-1].Value
+
+		// 动态环差值数据
+		currentDateStr := currentDate.Format(utils.FormatDate)
+		hcVal, ok := hcDataMap[currentDateStr]
+		if !ok {
+			continue
+		}
+		lastValueDecimal := decimal.NewFromFloat(lastValue)
+		hcValDecimal := decimal.NewFromFloat(hcVal)
+
+		val, _ := lastValueDecimal.Add(hcValDecimal).RoundCeil(4).Float64()
+
+		tmpData := &EdbInfoSearchData{
+			EdbDataId: edbInfoId + 10000000000 + lenAllData + k,
+			//EdbInfoId:     edbInfoId,
+			DataTime: currentDateStr,
+			Value:    val,
+			//DataTimestamp: (currentDate.UnixNano() / 1e6) + 1000, //前端需要让加1s，说是2022-09-01 00:00:00 这样的整点不合适
+		}
+		newPredictEdbInfoData = append(newPredictEdbInfoData, tmpData)
+		allDataList = append(allDataList, tmpData)
+		existMap[currentDateStr] = val
+
+		// 最大最小值
+		if val < minValue {
+			minValue = val
+		}
+		if val > maxValue {
+			maxValue = val
+		}
+	}
+	return
+}

models/predict_edb_rule_data.go

@@ -1,6 +1,9 @@
 package models
 
-import "time"
+import (
+	"github.com/beego/beego/v2/client/orm"
+	"time"
+)
 
 // PredictEdbRuleData 预测指标，动态规则的计算数据
 type PredictEdbRuleData struct {
@@ -13,3 +16,33 @@ type PredictEdbRuleData struct {
 	ModifyTime           time.Time
 	DataTimestamp        int64
 }
+
+// PredictEdbRuleDataItem 预测指标，动态规则的计算数据
+type PredictEdbRuleDataItem struct {
+	PredictEdbRuleDataId int `orm:"column(predict_edb_rule_data_id);pk"`
+	EdbInfoId            int
+	ConfigId             int
+	DataTime             string
+	Value                float64
+	CreateTime           time.Time
+	ModifyTime           time.Time
+	DataTimestamp        int64
+}
+
+// GetPredictEdbRuleDataItemList 根据基础预测指标id集合 获取 所有的普通指标列表数据
+func GetPredictEdbRuleDataItemList(edbInfoId, configId int, startDate, endDate string) (list []*PredictEdbRuleDataItem, err error) {
+	o := orm.NewOrm()
+	var pars []interface{}
+	sql := ` SELECT * FROM predict_edb_rule_data WHERE edb_info_id = ? AND config_id = ? `
+	if startDate != "" {
+		sql += ` AND data_time>=? `
+		pars = append(pars, startDate)
+	}
+	if endDate != "" {
+		sql += ` AND data_time<=? `
+		pars = append(pars, endDate)
+	}
+	sql += ` ORDER BY data_time ASC `
+	_, err = o.Raw(sql, edbInfoId, configId, pars).QueryRows(&list)
+	return
+}