出不出 v4

ad-engine-service/src/main/java/com/tzld/piaoquan/ad/engine/service/predict/impl/PredictModelServiceImpl.java

@@ -246,12 +246,6 @@ public class PredictModelServiceImpl implements PredictModelService {
                 }
             }
 
-            // fission_rate策略数据填充
-            Map<String, Object> userFissionRatePredict = predictStrategyByFissionRate.predict(predictContext);
-
-            // fission_rate_copy策略数据填充
-            Map<String, Object> userFissionRatePredictCopy = predictStrategyByFissionRateCopy.predict(predictContext);
-
             // ror行为策略
             Map<String, Object> userRorPredict = predictStrategyByRor.predict(predictContext);
             if (MapUtils.isNotEmpty(userRorPredict)) {
@@ -259,14 +253,6 @@ public class PredictModelServiceImpl implements PredictModelService {
                 if (MapUtils.isNotEmpty(predictExtInfo)) {
                     userRorPredict.putAll(predictExtInfo);
                 }
-                // fission_rate策略数据填充
-                if (MapUtils.isNotEmpty(userFissionRatePredict)) {
-                    userRorPredict.putAll(userFissionRatePredict);
-                }
-                // fission_rate_copy策略数据填充
-                if (MapUtils.isNotEmpty(userFissionRatePredictCopy)) {
-                    userRorPredict.putAll(userFissionRatePredictCopy);
-                }
                 return userRorPredict;
             }
 
@@ -277,17 +263,31 @@ public class PredictModelServiceImpl implements PredictModelService {
                 if (MapUtils.isNotEmpty(predictExtInfo)) {
                     userRorPredictCopy.putAll(predictExtInfo);
                 }
-                // fission_rate策略数据填充
-                if (MapUtils.isNotEmpty(userFissionRatePredict)) {
-                    userRorPredictCopy.putAll(userFissionRatePredict);
+                return userRorPredictCopy;
+            }
+
+
+            // fission_rate行为策略
+            Map<String, Object> userFissionRatePredict = predictStrategyByFissionRateCopy.predict(predictContext);
+            if (MapUtils.isNotEmpty(userFissionRatePredict)) {
+                // 填充 819 参数
+                if (MapUtils.isNotEmpty(predictExtInfo)) {
+                    userFissionRatePredict.putAll(predictExtInfo);
                 }
-                // fission_rate_copy策略数据填充
-                if (MapUtils.isNotEmpty(userFissionRatePredictCopy)) {
-                    userRorPredict.putAll(userFissionRatePredictCopy);
+                return userFissionRatePredict;
+            }
+
+            // fission_rate_copy行为策略
+            Map<String, Object> userFissionRatePredictCopy = predictStrategyByRorCopy.predict(predictContext);
+            if (MapUtils.isNotEmpty(userFissionRatePredictCopy)) {
+                // 填充 819 参数
+                if (MapUtils.isNotEmpty(predictExtInfo)) {
+                    userFissionRatePredictCopy.putAll(predictExtInfo);
                 }
-                return userRorPredictCopy;
+                return userFissionRatePredictCopy;
             }
 
+
             Map<String, Object> predictResult;
             if (expCodes.contains("599")){
                 predictResult = predictStrategyBy599.predict(predictContext);
@@ -300,14 +300,6 @@ public class PredictModelServiceImpl implements PredictModelService {
             if (MapUtils.isNotEmpty(predictResult) && MapUtils.isNotEmpty(predictExtInfo)) {
                 predictResult.putAll(predictExtInfo);
             }
-            // fission_rate策略数据填充
-            if (MapUtils.isNotEmpty(predictResult) && MapUtils.isNotEmpty(userFissionRatePredict)) {
-                predictResult.putAll(userFissionRatePredict);
-            }
-            // fission_rate_copy策略数据填充
-            if (MapUtils.isNotEmpty(userFissionRatePredictCopy)) {
-                predictResult.putAll(userFissionRatePredictCopy);
-            }
             return predictResult;
             
         } catch (Exception e) {

ad-engine-service/src/main/java/com/tzld/piaoquan/ad/engine/service/predict/v2/PredictStrategyByFissionRate.java

@@ -132,6 +132,8 @@ public class PredictStrategyByFissionRate extends BasicPredict {
             }
 
             // 记录决策相关的特征和参数，用于日志分析和效果追踪
+            rtnMap.putAll(rtnAdPredict(ctx));
+            rtnMap.put("model", this.name());
             rtnMap.put("launchs", launchs);
             rtnMap.put("ror", ror);
             rtnMap.put("ad_level", adLevel);

ad-engine-service/src/main/java/com/tzld/piaoquan/ad/engine/service/predict/v2/PredictStrategyByFissionRateCopy.java

@@ -132,6 +132,8 @@ public class PredictStrategyByFissionRateCopy extends BasicPredict {
             }
 
             // 记录决策相关的特征和参数，用于日志分析和效果追踪
+            rtnMap.putAll(rtnAdPredict(ctx));
+            rtnMap.put("model", this.name());
             rtnMap.put("launchs", launchs);
             rtnMap.put("ror", ror);
             rtnMap.put("ad_level", adLevel);

ad-engine-service/src/main/java/com/tzld/piaoquan/ad/engine/service/predict/v2/PredictStrategyByRorCopy.java

@@ -94,20 +94,20 @@ public class PredictStrategyByRorCopy extends BasicPredict {
             String appType = ctx.getAppType();
 
             // 获取默认概率阈值（基于 rootSessionId 尾号和 appType 匹配配置）
-            Boolean matchResult = getDefaultProbability(rootSessionId, appType);
+            Double defaultProbability = getDefaultProbability(rootSessionId, appType);
 
             // 用户不在实验分桶内，跳过该策略
-            if (!matchResult) {
+            if (defaultProbability == null) {
                 return null;
             }
 
             Map<String, Object> rtnMap = new HashMap<>();
 
             // 用户行为特征变量（来自离线特征表 alg_mid_history_behavior_1month）
-            String launchs = null;   // 启动次数分桶（如 "0-5", "5-10" 等）
-            String ror = null;       // 留存率分桶
-            String adLevel = null;   // 广告等级（用户对广告的敏感度分层）
-            String return30day = null;   // 用户回流率分桶
+            String launchs = "-999";   // 启动次数分桶（如 "0-5", "5-10" 等）
+            String ror = "-999";       // 留存率分桶
+            String adLevel = "无转化";   // 广告等级（用户对广告的敏感度分层）
+            String return30day = "r_0_8";   // 用户回流率分桶
 
             // 根据 mid 获取用户近一个月的历史行为特征
             Feature feature = featureService.getMidBehaviorFeature(TABLE_NAME, ctx.getMid());
@@ -117,15 +117,15 @@ public class PredictStrategyByRorCopy extends BasicPredict {
             // 安全地提取特征值（多层 null 检查）
             if (feature != null && feature.getUserFeature() != null && feature.getUserFeature().get(TABLE_NAME) != null) {
                 Map<String, String> algMidHistoryBehavior1month = feature.getUserFeature().get(TABLE_NAME);
-                launchs = algMidHistoryBehavior1month.get("launchs");
-                ror = algMidHistoryBehavior1month.get("ror");
-                adLevel = algMidHistoryBehavior1month.get("ad_level");
-                return30day = algMidHistoryBehavior1month.get("return_30day");
+                launchs = StringUtils.isBlank(algMidHistoryBehavior1month.get("launchs")) ? launchs : algMidHistoryBehavior1month.get("launchs");
+                ror = StringUtils.isBlank(algMidHistoryBehavior1month.get("ror")) ? ror : algMidHistoryBehavior1month.get("ror");
+                adLevel = StringUtils.isBlank(algMidHistoryBehavior1month.get("ad_level")) ? adLevel : algMidHistoryBehavior1month.get("ad_level");
+                return30day = StringUtils.isBlank(algMidHistoryBehavior1month.get("return_30day")) ? return30day : algMidHistoryBehavior1month.get("return_30day");
             }
 
             // 计算最终的广告展示概率阈值
             // 优先使用 Redis 中基于用户特征的精细化阈值，否则使用默认阈值
-            Double showAdProbability = getShowAdProbability(launchs, ror, adLevel,return30day);
+            Double showAdProbability = getShowAdProbability(launchs, ror, adLevel,return30day,defaultProbability);
 
             if (showAdProbability == null) {
                 return null;
@@ -172,10 +172,10 @@ public class PredictStrategyByRorCopy extends BasicPredict {
      *
      * @return 广告展示概率阈值 [0, 1]
      */
-    private Double getShowAdProbability(String launchs, String ror, String ad_level,String return30day) {
+    private Double getShowAdProbability(String launchs, String ror, String ad_level,String return30day, Double defaultProbability) {
         // 任一特征为空，使用默认概率
         if (StringUtils.isAnyBlank(launchs, ror, ad_level,return30day)) {
-            return null;
+            return defaultProbability;
         }
         try {
             // 构建 Redis key：格式为 "ad_level:launchs:ror"，例如 "有转化:10:000"
@@ -186,11 +186,11 @@ public class PredictStrategyByRorCopy extends BasicPredict {
             String probability = adRedisHelper.get(key);
 
             // 解析概率值，如果为 null 则使用默认值
-            return StringUtils.isBlank(probability) ? null : Double.parseDouble(probability);
+            return StringUtils.isBlank(probability) ? defaultProbability : Double.parseDouble(probability);
         } catch (Exception e) {
             // 解析失败（如非数字字符串）或 Redis 异常，记录错误并使用默认值
             log.error("getShowAdProbability error, launchs: {}, ror: {}, ad_level: {}, e = ", launchs, ror, ad_level, e);
-            return null;
+            return defaultProbability;
         }
     }
 
@@ -203,10 +203,10 @@ public class PredictStrategyByRorCopy extends BasicPredict {
      * @param appType       应用类型
      * @return 默认概率，如果不匹配任何配置则返回 null
      */
-    private Boolean getDefaultProbability(String rootSessionId, String appType) {
+    private Double getDefaultProbability(String rootSessionId, String appType) {
         // 前置校验
         if (CollectionUtils.isEmpty(configItems) || StringUtils.isAnyBlank(rootSessionId) || appType == null) {
-            return false;
+            return null;
         }
 
         // 提取 rootSessionId 的最后一个字符作为尾号，用于流量分桶
@@ -216,15 +216,14 @@ public class PredictStrategyByRorCopy extends BasicPredict {
         for (RootSessionIdTailConfigItem item : configItems) {
             if (item.getAppType() != null && item.getTail() != null) {
                 if (item.getAppType().contains(appType) && item.getTail().contains(tail)) {
-//                    return item.getConfig().get("default_probability");
-                    return true;
+                    return item.getConfig().get("default_probability");
                 }
             }
 
         }
 
         // 未匹配到任何配置
-        return false;
+        return null;
     }
 
 }