出不出 摸底

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

@@ -87,6 +87,8 @@ public class PredictModelServiceImpl implements PredictModelService {
     private PredictStrategyByRorCopy predictStrategyByRorCopy;
     @Autowired
     private PredictStrategyByFissionRateCopy predictStrategyByFissionRateCopy;
+    @Autowired
+    private PredictStrategyChubuchuModi predictStrategyChubuchuModi;
 
     @Autowired
     private UserService userService;
@@ -207,6 +209,13 @@ public class PredictModelServiceImpl implements PredictModelService {
             PredictContext predictContext = ConvertUtil.predictParam2Context(requestParam);
 
             if(in817time){
+
+                // 摸底熔断
+                Map<String, Object> predictModi = predictStrategyChubuchuModi.predict(predictContext);
+                if (MapUtils.isNotEmpty(predictModi)) {
+                    return predictModi;
+                }
+
                 // 早间ror熔断
                 Map<String, Object> userRorPredictMorning = predictStrategyByRorMorning.predict(predictContext);
                 if (MapUtils.isNotEmpty(userRorPredictMorning)) {
@@ -226,6 +235,12 @@ public class PredictModelServiceImpl implements PredictModelService {
             //     predictExtInfo = predictStrategyBy819.predict(predictContext);
             // }
 
+            // 摸底熔断
+            Map<String, Object> predictModi = predictStrategyChubuchuModi.predict(predictContext);
+            if (MapUtils.isNotEmpty(predictModi)) {
+                return predictModi;
+            }
+
             Map<String, Object> userLayerPredict = userLayerRootSessionIdPredict.predict(predictContext);
             if (MapUtils.isNotEmpty(userLayerPredict)) {
                 // 填充 819 参数

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

@@ -0,0 +1,98 @@
+package com.tzld.piaoquan.ad.engine.service.predict.v2;
+
+import com.alibaba.fastjson.JSON;
+import com.alibaba.fastjson.JSONObject;
+import com.ctrip.framework.apollo.spring.annotation.ApolloJsonValue;
+import com.tzld.piaoquan.ad.engine.commons.enums.RedisPrefixEnum;
+import com.tzld.piaoquan.ad.engine.commons.redis.AdRedisHelper;
+import com.tzld.piaoquan.ad.engine.commons.util.JSONUtils;
+import com.tzld.piaoquan.ad.engine.service.feature.Feature;
+import com.tzld.piaoquan.ad.engine.service.feature.FeatureService;
+import lombok.extern.slf4j.Slf4j;
+import org.apache.commons.collections4.CollectionUtils;
+import org.apache.commons.lang3.StringUtils;
+import org.springframework.beans.factory.annotation.Autowired;
+import org.springframework.beans.factory.annotation.Value;
+import org.springframework.stereotype.Service;
+
+import java.util.*;
+
+/**
+ * 基于 ROR的广告预测策略
+ * <p>
+ * 核心逻辑：
+ * 1. 根据用户的历史行为特征（启动次数launchs、留存率ror、人群分层ad_level）计算展示广告的概率阈值
+ * 2. 通过 mid 的 hash 值生成伪随机分数
+ * 3. 如果分数 <= 阈值，则展示广告；否则不展示
+ * <p>
+ * 用于控制不同用户群体的广告曝光频率，实现精细化运营
+ */
+@Slf4j
+@Service
+public class PredictStrategyChubuchuModi extends BasicPredict {
+    /**
+     * Apollo 动态配置：根据 rootSessionId 尾号和 appType 进行流量分桶
+     * <p>
+     * 配置格式示例：
+     * <pre>
+     * [
+     *   {
+     *     "appType": ["0", "3"],
+     *     "tail": ["0", "1", "2"]
+     *   }
+     * ]
+     * </pre>
+     */
+    @ApolloJsonValue("${experiment.chubuchu.modi.root.session.id.tail.config:[]}")
+    private List<RootSessionIdTailConfigItem> configItems;
+
+    /**
+     * 策略名称标识
+     */
+    @Override
+
+public String name() {
+        return "chubuchu_modi";
+    }
+
+
+    @Override
+    public Map<String, Object> predict(PredictContext ctx) {
+
+        try {
+            String rootSessionId = ctx.getRootSessionId();
+            if (CollectionUtils.isEmpty(configItems) || StringUtils.isAnyBlank(rootSessionId)) {
+                return Collections.emptyMap();
+            }
+            String appType = ctx.getAppType();
+            String tail = rootSessionId.substring(rootSessionId.length() - 1);
+
+            for (RootSessionIdTailConfigItem item : configItems) {
+                if (item.getAppType().contains(appType) && item.getTail().contains(tail)) {
+                    Map<String, Object> returnMap = new HashMap<>();
+                    double threshold = 0.5;
+                    // 取随机，各50%概率
+                    double score = this.calcScoreByMid(ctx.getMid());
+                    if (score < threshold) {
+                        returnMap.putAll(rtnAdPredict(ctx));
+                        returnMap.put("model", this.name());
+                    } else {
+                        returnMap.putAll(rtnNoAdPredict(ctx));
+                        returnMap.put("model", this.name());
+                        returnMap.put("no_ad_strategy", this.name());
+                    }
+                    returnMap.put("score", score);
+                    returnMap.put("threshold", threshold);
+
+                    return returnMap;
+                }
+            }
+
+            return Collections.emptyMap();
+        } catch (Exception e) {
+            log.error("[PredictStrategyChubuchuModi] predict error, ctx: {}", ctx, e);
+            return null;
+        }
+    }
+
+}