Merge branch 'master' into pre-master

ad-engine-commons/pom.xml

@@ -26,7 +26,7 @@
         <dependency>
             <groupId>com.tzld.piaoquan</groupId>
             <artifactId>recommend-feature-client</artifactId>
-            <version>1.1.36</version>
+            <version>1.1.37</version>
         </dependency>
         <dependency>
             <groupId>com.tzld.piaoquan</groupId>

ad-engine-commons/src/main/java/com/tzld/piaoquan/ad/engine/commons/dto/AdPlatformCreativeDTO.java

@@ -74,4 +74,6 @@ public class AdPlatformCreativeDTO {
     private String adSkuName;
 
     private Long agentId;
+
+    private String targetingConversion;
 }

ad-engine-commons/src/main/java/com/tzld/piaoquan/ad/engine/commons/helper/CopcV3DataHelper.java

@@ -0,0 +1,164 @@
+package com.tzld.piaoquan.ad.engine.commons.helper;
+
+import com.alibaba.fastjson.JSON;
+import com.alibaba.fastjson.JSONObject;
+import com.tzld.piaoquan.ad.engine.commons.redis.AlgorithmRedisHelper;
+import lombok.extern.slf4j.Slf4j;
+import org.springframework.beans.factory.annotation.Autowired;
+import org.springframework.scheduling.annotation.Scheduled;
+import org.springframework.stereotype.Component;
+
+import javax.annotation.PostConstruct;
+import java.util.Collections;
+import java.util.HashMap;
+import java.util.Map;
+
+/**
+ * COPC v3 系数数据缓存。
+ */
+@Slf4j
+@Component
+public class CopcV3DataHelper {
+
+    private static final String REDIS_KEY = "ad:engine:strategy:clib:copc_v3";
+    private static final long REFRESH_INTERVAL_MILLIS = 5 * 60 * 1000L;
+    private static final String EDGE_KEY_PREFIX = "edge:";
+    private static final String LANDING_KEY_PREFIX = "landing";
+    private static final String BUCKET_KEY_PREFIX = "bucket";
+    private static final String EVENT_KEY_PREFIX = "event";
+    private static final String GLOBAL_KEY = "global";
+    private static final int BUCKET_COUNT = 6;
+
+    @Autowired
+    protected AlgorithmRedisHelper algRedisHelper;
+
+    /**
+     * Redis value 中同时包含系数和版本等元数据，因此统一保留为字符串。
+     */
+    private static volatile Map<String, String> copcMap = Collections.emptyMap();
+
+    @PostConstruct
+    public void init() {
+        refreshCopcMap();
+    }
+
+    @Scheduled(fixedRate = REFRESH_INTERVAL_MILLIS)
+    public void scheduledUpdate() {
+        refreshCopcMap();
+    }
+
+    public static String getValue(String key) {
+        return key == null ? null : copcMap.get(key);
+    }
+
+    public static Map<String, String> getCopcMap() {
+        return copcMap;
+    }
+
+    /**
+     * 按分数所在分桶获取 COPC 加权系数。
+     *
+     * <p>分桶规则为：{@code score <= edge:1} 属于第 1 桶，依次类推；
+     * 大于 {@code edge:5} 的分数属于第 6 桶。查询顺序为
+     * {@code landing -> bucket -> event -> global}。</p>
+     */
+    public static Double getLandingCoefficient(Double score, Integer landingPageType, String targetingConversion) {
+        if (!isFinite(score)) {
+            return null;
+        }
+
+        int bucketNo = getBucketNo(score);
+        String event = targetingConversion == null ? null : targetingConversion.trim();
+        if (event != null && !event.isEmpty()) {
+            Double coefficient;
+            if (bucketNo > 0 && landingPageType != null) {
+                String landingKey = String.format("%s:%s:%s:%s", LANDING_KEY_PREFIX, event,
+                        landingPageType, bucketNo);
+                coefficient = getFiniteDoubleValue(landingKey);
+                if (coefficient != null) {
+                    return coefficient;
+                }
+            }
+            if (bucketNo > 0) {
+                String bucketKey = String.format("%s:%s:%s", BUCKET_KEY_PREFIX, event, bucketNo);
+                coefficient = getFiniteDoubleValue(bucketKey);
+                if (coefficient != null) {
+                    return coefficient;
+                }
+            }
+            coefficient = getFiniteDoubleValue(EVENT_KEY_PREFIX + ":" + event);
+            if (coefficient != null) {
+                return coefficient;
+            }
+        }
+        return getFiniteDoubleValue(GLOBAL_KEY);
+    }
+
+    private static int getBucketNo(double score) {
+        for (int bucketNo = 1; bucketNo < BUCKET_COUNT; bucketNo++) {
+            Double edge = getFiniteDoubleValue(EDGE_KEY_PREFIX + bucketNo);
+            if (edge == null) {
+                return 0;
+            }
+            if (score <= edge) {
+                return bucketNo;
+            }
+        }
+        return BUCKET_COUNT;
+    }
+
+    private static Double getFiniteDoubleValue(String key) {
+        String value = getValue(key);
+        if (value == null || value.trim().isEmpty()) {
+            return null;
+        }
+        try {
+            Double result = Double.valueOf(value);
+            return isFinite(result) ? result : null;
+        } catch (NumberFormatException e) {
+            return null;
+        }
+    }
+
+    private static boolean isFinite(Double value) {
+        return value != null && !value.isNaN() && !value.isInfinite();
+    }
+
+    private void refreshCopcMap() {
+        Map<String, String> map = loadCopcMap();
+        if (map == null) {
+            return;
+        }
+        copcMap = Collections.unmodifiableMap(map);
+        log.info("update COPC v3 data success size={}", copcMap.size());
+    }
+
+    /**
+     * @return 解析失败时返回 {@code null}，以保留上一次成功加载的数据。
+     */
+    private Map<String, String> loadCopcMap() {
+        try {
+            String value = algRedisHelper.get(REDIS_KEY);
+            if (value == null || value.trim().isEmpty()) {
+                return Collections.emptyMap();
+            }
+
+            JSONObject jsonObject = JSON.parseObject(value);
+            if (jsonObject == null) {
+                log.error("COPC v3 redis value is not a JSON object");
+                return null;
+            }
+
+            Map<String, String> map = new HashMap<>(jsonObject.size());
+            for (Map.Entry<String, Object> entry : jsonObject.entrySet()) {
+                if (entry.getValue() != null) {
+                    map.put(entry.getKey(), String.valueOf(entry.getValue()));
+                }
+            }
+            return map;
+        } catch (Exception e) {
+            log.error("update COPC v3 data error", e);
+            return null;
+        }
+    }
+}

ad-engine-commons/src/main/java/com/tzld/piaoquan/ad/engine/commons/helper/CreativeUserLayerDataHelperV2.java

@@ -0,0 +1,72 @@
+package com.tzld.piaoquan.ad.engine.commons.helper;
+
+import com.tzld.piaoquan.ad.engine.commons.redis.AlgorithmRedisHelper;
+import lombok.extern.slf4j.Slf4j;
+import org.springframework.beans.factory.annotation.Autowired;
+import org.springframework.scheduling.annotation.Scheduled;
+import org.springframework.stereotype.Component;
+
+import javax.annotation.PostConstruct;
+import java.util.Collections;
+import java.util.HashMap;
+import java.util.Map;
+
+@Slf4j
+@Component
+public class CreativeUserLayerDataHelperV2 {
+    @Autowired
+    protected AlgorithmRedisHelper algRedisHelper;
+
+    private static final String redisKey = "ad:engine:strategy:clib:v2:cid_layer";
+    private static final String keyFormat = "%s:%s:%s";
+
+    private volatile static Map<String, Double> cidMap = Collections.emptyMap();
+
+    // 服务启动时初始化数据
+    @PostConstruct
+    public void init() {
+        Map<String, Double> map = updateCidMap();
+        cidMap = Collections.unmodifiableMap(map);
+    }
+
+    // 每5分钟更新一次数据
+    @Scheduled(fixedRate = 5 * 60 * 1000)
+    public void scheduledUpdate() {
+        Map<String, Double> map = updateCidMap();
+        cidMap = Collections.unmodifiableMap(map);
+    }
+
+    public static Double getCopc(String model, String cid, String layer) {
+        if (null != cidMap) {
+            String key = String.format(keyFormat, model, cid, layer);
+            if (cidMap.containsKey(key)) {
+                return cidMap.get(key);
+            }
+            key = String.format(keyFormat, model, cid, "sum");
+            if (cidMap.containsKey(key)) {
+                return cidMap.get(key);
+            }
+        }
+        return null;
+    }
+
+    private Map<String, Double> updateCidMap() {
+        Map<String, Double> tmpCidSMap = new HashMap<>();
+        try {
+            String value = algRedisHelper.get(redisKey);
+            if (null != value && !value.isEmpty()) {
+                String[] cells = value.split(",");
+                for (String cell : cells) {
+                    String[] pair = cell.split("_");
+                    if (2 == pair.length) {
+                        tmpCidSMap.put(pair[0], Double.parseDouble(pair[1]));
+                    }
+                }
+            }
+            log.info("CreativeUserLayerDataHelperV2 update cid layer copc success size={}", tmpCidSMap.size());
+        } catch (Exception e) {
+            log.error("CreativeUserLayerDataHelperV2 update cid layer copc error", e);
+        }
+        return tmpCidSMap;
+    }
+}

ad-engine-commons/src/main/java/com/tzld/piaoquan/ad/engine/commons/param/RankRecommendRequestParam.java

@@ -13,6 +13,9 @@ import java.util.Map;
 @ToString(callSuper = true)
 public class RankRecommendRequestParam extends RecommendRequestParam{
      List<AdPlatformCreativeDTO> adIdList;
+     // 尾号(rootSessionId)实验命中列表，由 ad 服务解析后透传；与 adAbExpArr 来源隔离
+     // 值用 Object：tailIndex 透传过来是数字，Map<String,String> 反序列化可能踩 CCE
+     List<Map<String, Object>> tailExpArr;
      String sessionId;
      String subSessionId;
      String rootSessionId;

ad-engine-commons/src/main/java/com/tzld/piaoquan/ad/engine/commons/score/ScoreParam.java

@@ -26,6 +26,8 @@ public class ScoreParam {
 
     private Set<String> expCodeSet;
 
+    private Set<String> tailExpCodeSet;
+
     /**
      * 本次命中的实验
      */

ad-engine-commons/src/main/java/com/tzld/piaoquan/ad/engine/commons/score/model/PAIModelV2.java

@@ -6,7 +6,6 @@ import com.aliyun.openservices.eas.predict.request.TFDataType;
 import com.aliyun.openservices.eas.predict.request.TFRequest;
 import com.aliyun.openservices.eas.predict.response.TFResponse;
 import com.tzld.piaoquan.recommend.feature.domain.ad.base.AdRankItem;
-import org.apache.commons.collections4.MapUtils;
 import org.apache.commons.lang.math.NumberUtils;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
@@ -30,24 +29,16 @@ public class PAIModelV2 {
 
     private static final PredictClient client;
 
-    static {
-        model = new PAIModelV2();
-        client = new PredictClient(new HttpConfig());
-        client.setEndpoint("1894469520484605.vpc.cn-hangzhou.pai-eas.aliyuncs.com");
-        client.setToken("M2Y0ZGJlOGRiNDJiYmJlM2E2MGRiNzk4NTdhN2MxOWUzYWMxNzdjYg==");
-        client.setModelName("ad_rank_dnn_v11_easyrec_v2");
-    }
-
     private static final String[] sparseUserStrFeatures = {
             "brand", "region", "city", "cate1", "cate2", "user_cid_click_list", "user_cid_conver_list",
             "user_vid_return_tags_2h", "user_vid_return_tags_1d", "user_vid_return_tags_3d", "user_vid_return_tags_7d",
             "user_vid_return_tags_14d", "root_source_scene", "root_source_channel", "title_split", "user_vid_share_tags_1d",
             "user_vid_share_tags_14d", "user_vid_return_cate1_14d", "user_vid_return_cate2_14d", "user_vid_share_cate1_14d",
-            "user_vid_share_cate2_14d", "user_conver_ad_class"
+            "user_vid_share_cate2_14d", "user_conver_ad_class", "user_layer"
     };
 
     private static final String[] sparseUserLongFeatures = {
-            "vid", "apptype", "is_first_layer", "user_has_conver_1y"
+            "vid", "apptype", "is_first_layer", "user_has_conver_1y", "flag"
     };
 
     private static final String[] sparseSceneLongFeatures = {
@@ -65,14 +56,19 @@ public class PAIModelV2 {
     };
 
     private static final String[] sparseAdStrFeatures = {
-            "profession", "category_name"
+            "profession", "category_name", "customer", "landing",
     };
 
-    private final String[] userFeatures = {
+    private static final String[] userFeatures = {
             "viewAll", "clickAll", "converAll", "incomeAll", "ctr_all", "ctcvr_all", "cvr_all", "ecpm_all"
     };
 
-    private final String[] itemFeatures = {
+    /**
+     * 预计算的 userFeatures 小写映射，避免运行时重复调用 toLowerCase()
+     */
+    private static final String[] userFeaturesLowerCase;
+
+    private static final String[] itemFeatures = {
             "actionstatic_click", "actionstatic_ctcvr", "actionstatic_ctr", "actionstatic_view", "b2_12h_click", "b2_12h_conver",
             "b2_12h_conver_x_ctcvr", "b2_12h_conver_x_log_view", "b2_12h_ctcvr", "b2_12h_ctr", "b2_12h_cvr", "b2_12h_ecpm",
             "b2_1d_click", "b2_1d_conver", "b2_1d_conver_x_ctcvr", "b2_1d_conver_x_log_view", "b2_1d_ctcvr", "b2_1d_ctr",
@@ -119,16 +115,70 @@ public class PAIModelV2 {
             "e2_tags_3d_avgscore", "e2_tags_3d_maxscore", "e2_tags_7d_avgscore", "e2_tags_7d_maxscore", "timediff_conver",
             "timediff_view", "vid_rank_ctcvr_14d", "vid_rank_ctcvr_1d", "vid_rank_ctcvr_3d", "vid_rank_ctcvr_7d",
             "vid_rank_ctr_14d", "vid_rank_ctr_1d", "vid_rank_ctr_3d", "vid_rank_ctr_7d", "vid_rank_ecpm_14d", "vid_rank_ecpm_1d",
-            "vid_rank_ecpm_3d", "vid_rank_ecpm_7d"
+            "vid_rank_ecpm_3d", "vid_rank_ecpm_7d",
+            "j1_3d_conver_x_log_view", "j1_3d_conver", "j1_3d_ctcvr", "j1_3d_ctr",
+            "j2_3d_conver_x_log_view", "j2_3d_conver", "j2_3d_ctcvr", "j2_3d_ctr",
+            "j3_3h_conver_x_log_view", "j3_3h_conver", "j3_3h_ctcvr", "j3_3h_ctr", "j3_3d_conver_x_log_view", "j3_3d_conver", "j3_3d_ctcvr", "j3_3d_ctr",
+            "j4_3d_conver_x_log_view", "j4_3d_conver", "j4_3d_ctcvr", "j4_3d_ctr",
+            "j5_3d_conver_x_log_view", "j5_3d_conver", "j5_3d_ctcvr", "j5_3d_ctr",
+            "j6_1h_click", "j6_1h_conver", "j6_1h_conver_x_ctcvr", "j6_1h_conver_x_log_view", "j6_1h_ctcvr", "j6_1h_ctr", "j6_1h_cvr", "j6_1h_ecpm",
+            "j6_2h_click", "j6_2h_conver", "j6_2h_conver_x_ctcvr", "j6_2h_conver_x_log_view", "j6_2h_ctcvr", "j6_2h_ctr", "j6_2h_cvr", "j6_2h_ecpm",
+            "j6_3h_click", "j6_3h_conver", "j6_3h_conver_x_ctcvr", "j6_3h_conver_x_log_view", "j6_3h_ctcvr", "j6_3h_ctr", "j6_3h_cvr", "j6_3h_ecpm",
+            "j6_6h_ctr", "j6_6h_ecpm",
+            "j6_12h_click", "j6_12h_conver", "j6_12h_conver_x_ctcvr", "j6_12h_conver_x_log_view", "j6_12h_ctcvr", "j6_12h_ctr", "j6_12h_cvr", "j6_12h_ecpm",
+            "j6_3d_click", "j6_3d_conver", "j6_3d_conver_x_ctcvr", "j6_3d_conver_x_log_view", "j6_3d_ctcvr", "j6_3d_ctr", "j6_3d_cvr", "j6_3d_ecpm",
+            "j7_3d_conver_x_log_view", "j7_3d_conver", "j7_3d_ctcvr", "j7_3d_ctr",
+            "j8_3d_conver_x_log_view", "j8_3d_conver", "j8_3d_ctcvr", "j8_3d_ctr",
+            "j9_3d_conver_x_log_view", "j9_3d_conver", "j9_3d_ctcvr", "j9_3d_ctr", "j9_3h_conver_x_log_view", "j9_3h_conver", "j9_3h_ctcvr", "j9_3h_ctr",
+            "j10_3d_conver_x_log_view", "j10_3d_conver", "j10_3d_ctcvr", "j10_3d_ctr",
+            "j11_3d_conver_x_log_view", "j11_3d_conver", "j11_3d_ctcvr", "j11_3d_ctr"
     };
 
+    /**
+     * 预计算的 itemFeatures key 映射，避免运行时重复执行 replace 操作
+     * key: feature name, value: 转换后的 key（用于从 featureMap 中获取值）
+     */
+    private static final String[] itemFeatureKeys;
+
+    static {
+        model = new PAIModelV2();
+        client = new PredictClient(new HttpConfig());
+        client.setEndpoint("1894469520484605.vpc.cn-hangzhou.pai-eas.aliyuncs.com");
+        client.setToken("M2Y0ZGJlOGRiNDJiYmJlM2E2MGRiNzk4NTdhN2MxOWUzYWMxNzdjYg==");
+        client.setModelName("ad_rank_dnn_v11_easyrec_v2");
+
+
+        // 预计算 itemFeatures 的 key 映射
+        itemFeatureKeys = new String[itemFeatures.length];
+        for (int i = 0; i < itemFeatures.length; i++) {
+            itemFeatureKeys[i] = itemFeatures[i].replace("_x_", "*").replace("_view", "(view)");
+        }
+
+        // 预计算 userFeatures 的小写映射
+        userFeaturesLowerCase = new String[userFeatures.length];
+        for (int i = 0; i < userFeatures.length; i++) {
+            userFeaturesLowerCase[i] = userFeatures[i].toLowerCase();
+        }
+    }
+
+
+    public Map<String, List<Float>> score(final List<AdRankItem> items,
+                                          final Map<String, String> userFeatureMap,
+                                          final Map<String, String> sceneFeatureMap) {
+        long totalStart = System.currentTimeMillis();
+        long buildUserFeatureTime = 0;
+        long buildItemFeatureTime = 0;
+        long buildRequestTime = 0;
+        long predictTime = 0;
+        long parseResponseTime = 0;
+
+        final int size = items.size();
 
-    public List<Float> score(final List<AdRankItem> items,
-                             final Map<String, String> userFeatureMap,
-                             final Map<String, String> sceneFeatureMap) {
         try {
             TFRequest request = new TFRequest();
 
+            // 阶段1: 构建用户特征
+            long stageStart = System.currentTimeMillis();
             for (String feature : sparseUserStrFeatures) {
                 String v = userFeatureMap.getOrDefault(feature, "");
                 request.addFeed(feature, TFDataType.DT_STRING, new long[]{1}, new String[]{v});
@@ -144,68 +194,113 @@ public class PAIModelV2 {
                 request.addFeed(feature, TFDataType.DT_INT64, new long[]{1}, new long[]{v});
             }
 
+            for (int i = 0; i < userFeatures.length; i++) {
+                double v = NumberUtils.toDouble(userFeatureMap.getOrDefault(userFeatures[i], "0.0"), 0.0);
+                request.addFeed(userFeaturesLowerCase[i], TFDataType.DT_DOUBLE, new long[]{1}, new double[]{v});
+            }
+            buildUserFeatureTime = System.currentTimeMillis() - stageStart;
+
+            // 阶段2: 构建广告Item特征（优化版本）
+            stageStart = System.currentTimeMillis();
 
-            for (String feature : userFeatures) {
-                double v = NumberUtils.toDouble(userFeatureMap.getOrDefault(feature, "0.0"), 0.0);
-                request.addFeed(feature.toLowerCase(), TFDataType.DT_DOUBLE, new long[]{1}, new double[]{v});
+            // 预提取所有 item 的 featureMap 引用，避免重复调用 get 方法
+            Map[] featureMaps = new Map[size];
+            for (int i = 0; i < size; i++) {
+                featureMaps[i] = items.get(i).getFeatureMap();
             }
-            Map<String, double[]> doubleFeed = new HashMap<>();
-            Map<String, long[]> longFeed = new HashMap<>();
-            Map<String, String[]> strFeed = new HashMap<>();
-            for (int i = 0; i < items.size(); i++) {
-                for (String feature : itemFeatures) {
-                    String key = feature.replace("_x_", "*").replace("_view", "(view)");
-                    double[] doubles = doubleFeed.computeIfAbsent(feature, k -> new double[items.size()]);
-                    if (MapUtils.isEmpty(items.get(i).getFeatureMap())) {
+
+            // 预分配所有数组
+            double[][] doubleArrays = new double[itemFeatures.length][size];
+            long[][] longArrays = new long[sparseAdLongFeatures.length][size];
+            String[][] strArrays = new String[sparseAdStrFeatures.length][size];
+
+            // 按 feature 遍历（外层），提高缓存局部性
+            // 处理 double 类型特征
+            for (int f = 0; f < itemFeatures.length; f++) {
+                String key = itemFeatureKeys[f];
+                double[] doubles = doubleArrays[f];
+                for (int i = 0; i < size; i++) {
+                    Map<String, String> featureMap = featureMaps[i];
+                    if (featureMap == null || featureMap.isEmpty()) {
                         doubles[i] = 0.0;
-                        continue;
+                    } else {
+                        doubles[i] = NumberUtils.toDouble(featureMap.getOrDefault(key, "0.0"), 0.0);
                     }
-                    double v = NumberUtils.toDouble(items.get(i).getFeatureMap().getOrDefault(key, "0.0"), 0.0);
-                    doubles[i] = v;
                 }
+            }
 
-                for (String feature : sparseAdLongFeatures) {
-                    long[] longs = longFeed.computeIfAbsent(feature, k -> new long[items.size()]);
-                    if (MapUtils.isEmpty(items.get(i).getFeatureMap())) {
+            // 处理 long 类型特征
+            for (int f = 0; f < sparseAdLongFeatures.length; f++) {
+                String feature = sparseAdLongFeatures[f];
+                long[] longs = longArrays[f];
+                for (int i = 0; i < size; i++) {
+                    Map<String, String> featureMap = featureMaps[i];
+                    if (featureMap == null || featureMap.isEmpty()) {
                         longs[i] = 0L;
-                        continue;
+                    } else {
+                        longs[i] = NumberUtils.toLong(featureMap.getOrDefault(feature, "0"), 0L);
                     }
-                    long v = NumberUtils.toLong(items.get(i).getFeatureMap().getOrDefault(feature, "0"), 0L);
-                    longs[i] = v;
                 }
+            }
 
-                for (String feature : sparseAdStrFeatures) {
-                    String[] strs = strFeed.computeIfAbsent(feature, k -> new String[items.size()]);
-                    if (MapUtils.isEmpty(items.get(i).getFeatureMap())) {
+            // 处理 String 类型特征
+            for (int f = 0; f < sparseAdStrFeatures.length; f++) {
+                String feature = sparseAdStrFeatures[f];
+                String[] strs = strArrays[f];
+                for (int i = 0; i < size; i++) {
+                    Map<String, String> featureMap = featureMaps[i];
+                    if (featureMap == null || featureMap.isEmpty()) {
                         strs[i] = "";
-                        continue;
+                    } else {
+                        strs[i] = featureMap.getOrDefault(feature, "");
                     }
-                    String v = items.get(i).getFeatureMap().getOrDefault(feature, "");
-                    strs[i] = v;
                 }
             }
-            for (Map.Entry<String, double[]> entry : doubleFeed.entrySet()) {
-                request.addFeed(entry.getKey(), TFDataType.DT_DOUBLE, new long[]{items.size()}, entry.getValue());
+            buildItemFeatureTime = System.currentTimeMillis() - stageStart;
+
+            // 阶段3: 构建请求体
+            stageStart = System.currentTimeMillis();
+            long[] shape = new long[]{size};
+
+            for (int f = 0; f < itemFeatures.length; f++) {
+                request.addFeed(itemFeatures[f], TFDataType.DT_DOUBLE, shape, doubleArrays[f]);
             }
 
-            for (Map.Entry<String, long[]> entry : longFeed.entrySet()) {
-                request.addFeed(entry.getKey(), TFDataType.DT_INT64, new long[]{items.size()}, entry.getValue());
+            for (int f = 0; f < sparseAdLongFeatures.length; f++) {
+                request.addFeed(sparseAdLongFeatures[f], TFDataType.DT_INT64, shape, longArrays[f]);
             }
 
-            for (Map.Entry<String, String[]> entry : strFeed.entrySet()) {
-                request.addFeed(entry.getKey(), TFDataType.DT_STRING, new long[]{items.size()}, entry.getValue());
+            for (int f = 0; f < sparseAdStrFeatures.length; f++) {
+                request.addFeed(sparseAdStrFeatures[f], TFDataType.DT_STRING, shape, strArrays[f]);
             }
             LOGGER.info("model request:{}", request.getRequest());
             request.addFetch("probs");
+            buildRequestTime = System.currentTimeMillis() - stageStart;
+
+            // 阶段4: PAI-EAS 远程调用
+            stageStart = System.currentTimeMillis();
             TFResponse response = client.predict(request);
-            List<Float> result = response.getFloatVals("probs");
-            if (!CollectionUtils.isEmpty(result)) {
-                return result;
-            }
+            predictTime = System.currentTimeMillis() - stageStart;
+
+            // 阶段5: 解析响应
+            stageStart = System.currentTimeMillis();
+            List<Float> scanResult = response.getFloatVals("probs_has_scan");
+            List<Float> addWechatResult = response.getFloatVals("probs_has_addwechat");
+            List<Float> conversionResult = response.getFloatVals("probs_has_conversion");
+            parseResponseTime = System.currentTimeMillis() - stageStart;
+            Map<String, List<Float>> result = new HashMap<>();
+            result.put("scanResult", scanResult);
+            result.put("addWechatResult", addWechatResult);
+            result.put("conversionResult", conversionResult);
+            return result;
         } catch (Exception e) {
-            LOGGER.error("PAIModel score error", e);
+            long totalTime = System.currentTimeMillis() - totalStart;
+            LOGGER.error("PAIModelV2.score error: total={}ms, itemSize={}, buildUserFeature={}ms, " +
+                            "buildItemFeature={}ms, buildRequest={}ms, predict={}ms, parseResponse={}ms",
+                    totalTime, items.size(), buildUserFeatureTime, buildItemFeatureTime,
+                    buildRequestTime, predictTime, parseResponseTime, e);
         }
-        return new ArrayList<>(Collections.nCopies(items.size(), 0.0f));
+        return new HashMap<>();
     }
 
 }

ad-engine-commons/src/main/java/com/tzld/piaoquan/ad/engine/commons/util/AbUtil.java

@@ -8,7 +8,7 @@ import java.util.stream.Collectors;
 
 public class AbUtil {
 
-    public static final List<String> adAlgExpCode = Arrays.asList("679", "680", "683", "687", "688", "833", "834", "840", "843", "847", "849", "851");
+    public static final List<String> adAlgExpCode = Arrays.asList("679", "680", "683", "687", "688", "833", "834", "840", "843", "847", "849", "851", "865", "872");
 
     public static Set<String> unfoldAllExpCode(List<Map<String, String>> adAbMap) {
         if (CollectionUtils.isEmpty(adAbMap)) {
@@ -46,4 +46,34 @@ public class AbUtil {
         }
         return "";
     }
+
+    /**
+     * ad-engine 尾号实验固定用「广告算法实验」层 = rootSessionId 倒数第6位(index 5)。
+     * 只认这一层,防止别的位的实验被 ID 误命中。
+     * ponytail: 固定常量;若要不停机切层,改成 Apollo 注入到调用处。
+     */
+    public static final int ENGINE_TAIL_INDEX = 5;
+
+    /**
+     * 展开尾号实验命中列表,只保留生效位(tailIndex)== wantIndex 的实验编号。
+     * tailIndex 透传过来可能是数字也可能是字符串,统一转字符串比较。
+     */
+    public static Set<String> unfoldTailExpCode(List<Map<String, Object>> tailArr, int wantIndex) {
+        if (CollectionUtils.isEmpty(tailArr)) {
+            return new HashSet<>();
+        }
+        return tailArr.stream()
+                .filter(m -> Objects.equals(String.valueOf(wantIndex), String.valueOf(m.get("tailIndex"))))
+                .map(m -> m.get("abExpCode"))
+                .filter(Objects::nonNull)
+                .map(Object::toString)
+                .collect(Collectors.toSet());
+    }
+
+    /**
+     * 尾号实验命中判断：与 AB 来源隔离，直接按实验编号匹配，不走新实验分组逻辑
+     */
+    public static boolean isInTailExp(Collection<String> tailExpCodeSet, String expCode) {
+        return CollectionUtils.isNotEmpty(tailExpCodeSet) && tailExpCodeSet.contains(expCode);
+    }
 }

ad-engine-server/src/test/java/com/tzld/piaoquan/ad/engine/service/FeatureServiceTest.java

@@ -1,26 +1,31 @@
-package com.tzld.piaoquan.ad.engine.service;
-
-import com.tzld.piaoquan.ad.engine.server.Application;
-import com.tzld.piaoquan.ad.engine.service.feature.FeatureService;
-import org.junit.jupiter.api.Test;
-import org.springframework.beans.factory.annotation.Autowired;
-import org.springframework.boot.test.context.SpringBootTest;
-
-@SpringBootTest(classes = Application.class)
-public class FeatureServiceTest {
-
-
-    @Autowired
-    private FeatureService featureService;
-
-    @Test
-    public void featureV3OptimizeTest() {
-
-        // List<FeatureKeyProto> protos = new ArrayList<>();
-        // protos.add(featureService.genWithKeyMap("o:%s:%s", "alg_videoid_feature", "10035427", ImmutableMap.of("videoid", "68781839")));
-
-        // System.out.println(featureService.invokeFeatureService(protos));
-    }
-
-
-}
+// package com.tzld.piaoquan.ad.engine.service;
+//
+// import com.google.common.collect.ImmutableMap;
+// import com.tzld.piaoquan.ad.engine.server.Application;
+// import com.tzld.piaoquan.ad.engine.service.feature.FeatureService;
+// import com.tzld.piaoquan.recommend.feature.model.feature.FeatureKeyProto;
+// import org.junit.jupiter.api.Test;
+// import org.springframework.beans.factory.annotation.Autowired;
+// import org.springframework.boot.test.context.SpringBootTest;
+//
+// import java.util.ArrayList;
+// import java.util.List;
+//
+// @SpringBootTest(classes = Application.class)
+// public class FeatureServiceTest {
+//
+//
+//     @Autowired
+//     private FeatureService featureService;
+//
+//     @Test
+//     public void featureV3OptimizeTest() {
+//
+//         List<FeatureKeyProto> protos = new ArrayList<>();
+//         protos.add(featureService.genWithKeyMap("o:%s:%s", "alg_videoid_feature", "10035427", ImmutableMap.of("videoid", "68781839")));
+//         //
+//         System.out.println(featureService.invokeFeatureService(protos));
+//     }
+//
+//
+// }

ad-engine-service/src/main/java/com/tzld/piaoquan/ad/engine/service/feature/FeatureService.java

@@ -22,9 +22,6 @@ import java.util.*;
 @Component
 public class FeatureService {
 
-    @Value("${feature.service.optimize.switch}")
-    private Boolean featureServiceOptimizeSwtich;
-
     @Autowired
     private FeatureV2RemoteService remoteService;
 
@@ -257,7 +254,6 @@ public class FeatureService {
             log.warn("invokeFeatureService: featureMap is null, return empty feature");
             return new Feature();
         }
-        featureMap = this.featureStrCover(featureMap);
 
         Feature feature = new Feature();
 
@@ -429,23 +425,4 @@ public class FeatureService {
                 .build();
     }
 
-    private Map<String, String> featureStrCover(Map<String, String> metaFeatureMap) {
-        Map<String, String> newFeatureMap = new HashMap<>();
-        for (Map.Entry<String, String> entry : metaFeatureMap.entrySet()) {
-            String key = entry.getKey();
-            try {
-                if (Boolean.TRUE.equals(featureServiceOptimizeSwtich)) {
-                    newFeatureMap.put(key, entry.getValue());
-                } else {
-                    Map<String, String> valueMap = JSONUtils.fromJson(entry.getValue(), new TypeToken<Map<String, String>>() {
-                    }, Collections.emptyMap());
-                    String value = valueMap.getOrDefault("feature", "{}");
-                    newFeatureMap.put(key, value);
-                }
-            } catch (Exception e) {
-                log.error("featureStrCover error: ", e);
-            }
-        }
-        return newFeatureMap;
-    }
 }

ad-engine-service/src/main/java/com/tzld/piaoquan/ad/engine/service/score/convert/RequestConvert.java

@@ -48,8 +48,14 @@ public class RequestConvert {
         String expCode = AbUtil.chooseAdAlgExpCode(expCodeSet, request.getAppType(), request.getNewExpGroup());
         scoreParam.setExpCode(expCode);
         if (request instanceof RankRecommendRequestParam) {
-            scoreParam.setRootSessionId(((RankRecommendRequestParam) request).getRootSessionId());
-            scoreParam.setEngineInfo(((RankRecommendRequestParam) request).getEngineInfo());
+            RankRecommendRequestParam rankReq = (RankRecommendRequestParam) request;
+            scoreParam.setRootSessionId(rankReq.getRootSessionId());
+            scoreParam.setEngineInfo(rankReq.getEngineInfo());
+
+            // 尾号实验：只认引擎这一层(倒数第6位/index 5)，单独存 tailExpCodeSet，与 AB 完全隔离。
+            // 业务侧用 AbUtil.isInTailExp(tailExpCodeSet, code) 单独判断，不和 AB 的 expCode 混用
+            Set<String> tailExpCodeSet = AbUtil.unfoldTailExpCode(rankReq.getTailExpArr(), AbUtil.ENGINE_TAIL_INDEX);
+            scoreParam.setTailExpCodeSet(tailExpCodeSet);
         }
         return scoreParam;
     }

ad-engine-service/src/main/java/com/tzld/piaoquan/ad/engine/service/score/impl/RankServiceImpl.java

@@ -6,6 +6,7 @@ import com.tzld.piaoquan.ad.engine.commons.dto.AdPlatformCreativeDTO;
 import com.tzld.piaoquan.ad.engine.commons.param.RankRecommendRequestParam;
 import com.tzld.piaoquan.ad.engine.commons.score.ScoreParam;
 import com.tzld.piaoquan.ad.engine.commons.score.ScorerUtils;
+import com.tzld.piaoquan.ad.engine.commons.util.AbUtil;
 import com.tzld.piaoquan.ad.engine.commons.util.RootSessionIdExpConfig;
 import com.tzld.piaoquan.ad.engine.commons.util.RootSessionIdTailUtil;
 import com.tzld.piaoquan.ad.engine.service.log.LogHubService;
@@ -26,6 +27,7 @@ import com.tzld.piaoquan.recommend.feature.domain.ad.base.AdItemFeature;
 import com.tzld.piaoquan.recommend.feature.domain.ad.base.AdRankItem;
 import com.tzld.piaoquan.recommend.feature.domain.ad.base.UserAdFeature;
 import org.apache.commons.collections4.CollectionUtils;
+import org.apache.commons.lang3.StringUtils;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 import org.springframework.beans.BeanUtils;
@@ -104,6 +106,10 @@ public class RankServiceImpl implements RankService {
     }
 
     public RankStrategy getRankStrategy(ScoreParam scoreParam) {
+        // 尾号实验 875(index 5)命中 → 走 872 排序策略；与 AB 的 expCode 完全隔离
+        if (AbUtil.isInTailExp(scoreParam.getTailExpCodeSet(), "875")) {
+            return ServiceBeanFactory.getBean(RankStrategyBy872.class);
+        }
         String rankStrategyCode = getRankStrategyCode(scoreParam);
         switch (rankStrategyCode) {
             case "687":
@@ -130,6 +136,10 @@ public class RankServiceImpl implements RankService {
                 return ServiceBeanFactory.getBean(RankStrategyBy849.class);
             case "851":
                 return ServiceBeanFactory.getBean(RankStrategyBy851.class);
+            case "865":
+                return ServiceBeanFactory.getBean(RankStrategyBy865.class);
+            case "872":
+                return ServiceBeanFactory.getBean(RankStrategyBy872.class);
             default:
                 return ServiceBeanFactory.getBean(RankStrategyByWeight.class);
         }

ad-engine-service/src/main/java/com/tzld/piaoquan/ad/engine/service/score/scorer/PAIScorerV2.java

@@ -130,12 +130,22 @@ public class PAIScorerV2 extends AbstractScorer {
                                   final Map<String, String> sceneFeatureMap,
                                   final PAIModelV2 model) {
 
-        List<Float> score = model.score(items, userFeatureMap, sceneFeatureMap);
-        LOGGER.debug("PAIScorer score={}", score);
+        Map<String, List<Float>> scores = model.score(items, userFeatureMap, sceneFeatureMap);
+        List<Float> scanResult = scores.get("scanResult");
+        List<Float> addWechatResult = scores.get("addWechatResult");
+        List<Float> conversionResult = scores.get("conversionResult");
+        LOGGER.debug("PAIScorer score={}", scores);
         for (int i = 0; i < items.size(); i++) {
-            Double pro = Double.valueOf(score.get(i));
-            items.get(i).setLrScore(pro);
-            items.get(i).getScoreMap().put("ctcvrScore", pro);
+            items.get(i).setLrScore(0.0);
+            if(CollectionUtils.isNotEmpty(scanResult)){
+                items.get(i).getScoreMap().put("scanScore", Double.valueOf(scanResult.get(i)));
+            }
+            if(CollectionUtils.isNotEmpty(addWechatResult)){
+                items.get(i).getScoreMap().put("addWechatScore", Double.valueOf(addWechatResult.get(i)));
+            }
+            if(CollectionUtils.isNotEmpty(conversionResult)){
+                items.get(i).getScoreMap().put("conversionScore", Double.valueOf(conversionResult.get(i)));
+            }
         }
     }
 

ad-engine-service/src/main/java/com/tzld/piaoquan/ad/engine/service/score/strategy/RankStrategyBy851.java

@@ -106,7 +106,6 @@ public class RankStrategyBy851 extends RankStrategyBasic {
         String userLayer = reqFeature.get("layer_l4");
         // feature1
         Feature feature = this.getFeatureV2(userLayer, scoreParam, request);
-        log.info("851 feature: {}", JSON.toJSONString(feature));
         if (feature == null) {
             log.warn("adItemRank: feature is null, skip processing. request={}", request);
             return new ArrayList<>();
@@ -940,7 +939,6 @@ public class RankStrategyBy851 extends RankStrategyBasic {
     }
 
     private Map<String, Map<String, Double>> parseD2FeatureMap(Map<String, String> d2Feature) {
-        log.info("parseD2FeatureMap d2Feature: {}", d2Feature);
         Map<String, Map<String, Double>> vidRankMaps = new HashMap<>();
         for (Map.Entry<String, String> entry : d2Feature.entrySet()) {
             String key = entry.getKey();

ad-engine-service/src/main/java/com/tzld/piaoquan/ad/engine/service/score/strategy/RankStrategyBy865.java

@@ -0,0 +1,1157 @@
+package com.tzld.piaoquan.ad.engine.service.score.strategy;
+
+import com.alibaba.fastjson.JSONObject;
+import com.ctrip.framework.apollo.spring.annotation.ApolloJsonValue;
+import com.tzld.piaoquan.ad.engine.commons.dto.AdPlatformCreativeDTO;
+import com.tzld.piaoquan.ad.engine.commons.helper.CopcV3DataHelper;
+import com.tzld.piaoquan.ad.engine.commons.helper.CreativeUserLayerDataHelperV2;
+import com.tzld.piaoquan.ad.engine.commons.helper.DnnCidDataHelper;
+import com.tzld.piaoquan.ad.engine.commons.param.RankRecommendRequestParam;
+import com.tzld.piaoquan.ad.engine.commons.score.ScoreParam;
+import com.tzld.piaoquan.ad.engine.commons.score.ScorerUtils;
+import com.tzld.piaoquan.ad.engine.commons.thread.ThreadPoolFactory;
+import com.tzld.piaoquan.ad.engine.commons.util.*;
+import com.tzld.piaoquan.ad.engine.service.entity.CorrectCpaParam;
+import com.tzld.piaoquan.ad.engine.service.entity.GuaranteeView;
+import com.tzld.piaoquan.ad.engine.service.feature.Feature;
+import com.tzld.piaoquan.recommend.feature.domain.ad.base.AdRankItem;
+import lombok.extern.slf4j.Slf4j;
+import org.apache.commons.collections4.CollectionUtils;
+import org.apache.commons.collections4.MapUtils;
+import org.apache.commons.lang.math.NumberUtils;
+import org.apache.commons.lang3.StringUtils;
+import org.springframework.beans.factory.annotation.Value;
+import org.springframework.stereotype.Component;
+import org.xm.Similarity;
+
+import javax.annotation.PostConstruct;
+import java.io.BufferedReader;
+import java.io.IOException;
+import java.io.InputStream;
+import java.io.InputStreamReader;
+import java.util.*;
+import java.util.concurrent.ConcurrentHashMap;
+import java.util.concurrent.CountDownLatch;
+import java.util.concurrent.Future;
+import java.util.concurrent.TimeUnit;
+import java.util.stream.Collectors;
+
+import static com.tzld.piaoquan.ad.engine.commons.math.Const.*;
+
+@Slf4j
+@Component
+public class RankStrategyBy865 extends RankStrategyBasic {
+
+    /**
+     * 空 Map 常量，避免频繁创建空 HashMap
+    */
+    private static final Map<String, String> EMPTY_STRING_MAP = Collections.emptyMap();
+    private static final Map<String, Map<String, String>> EMPTY_NESTED_MAP = Collections.emptyMap();
+
+    private Map<String, double[]> bucketsMap = new HashMap<>();
+
+    private Map<String, Double> bucketsLen = new HashMap<>();
+
+    @Value("${word2vec.exp:694}")
+    private String word2vecExp;
+
+    @Value("${rank.score.copc.v3.switch:false}")
+    private Boolean copcV3Enabled;
+
+    @ApolloJsonValue("${rank.score.params.865:{}}")
+    private Map<String, String> paramsMap;
+
+    // FIXME(zhoutian): 可能需要独立配置
+    @ApolloJsonValue("${rank.score.weight.680:{}}")
+    private Map<String, Double> weightMap;
+
+    /**
+     * 人群分层&创意的权重
+     * 格式：{layer_creativeId: weight}
+     */
+    @ApolloJsonValue("${rank.score.weight.layer.and.creative:{}}")
+    private Map<String, Double> layerAndCreativeWeightMap;
+
+    @ApolloJsonValue("${rank.score.neg_sample_rate:0.01}")
+    Double negSampleRate;
+
+    Set<String> sparseFeatureSet;
+
+
+    @PostConstruct
+    public void afterInit() {
+        this.readBucketFile();
+        this.initSparseFeatureNames();
+    }
+
+
+    @Override
+    public List<AdRankItem> adItemRank(RankRecommendRequestParam request, ScoreParam scoreParam) {
+        Map<String, Double> weightParam = ObjUtil.nullOrDefault(weightMap, new HashMap<>());
+
+
+        Map<Long, Double> creativeScoreCoefficient = getCreativeScoreCoefficient();
+        Set<String> noApiAdVerIds = getNoApiAdVerIds();
+
+        long ts = System.currentTimeMillis() / 1000;
+
+        String brand = scoreParam.getRequestContext().getMachineinfoBrand();
+        if (StringUtils.isNotEmpty(brand)) {
+            scoreParam.getRequestContext().setMachineinfoBrand(brand + "-n");
+        }
+
+        long start = System.currentTimeMillis();
+        //过滤创意
+        filterRequestAdList(request, scoreParam);
+        // 特征处理
+
+        Map<String, String> reqFeature = this.getReqFeature(scoreParam, request);
+        String userLayer = reqFeature.get("layer_l4");
+        // feature1
+        Feature feature = this.getFeatureV2(userLayer, scoreParam, request);
+        if (feature == null) {
+            log.warn("adItemRank: feature is null, skip processing. request={}", request);
+            return new ArrayList<>();
+        }
+
+        Map<String, Map<String, String>> userFeature = feature.getUserFeature();
+        Map<String, Map<String, String>> videoFeature = feature.getVideoFeature();
+        Map<String, Map<String, Map<String, String>>> allAdVerFeature = feature.getAdVerFeature();
+        Map<String, Map<String, Map<String, String>>> allCidFeature = feature.getCidFeature();
+        Map<String, Map<String, Map<String, String>>> allSkuFeature = feature.getSkuFeature();
+        Map<String, Map<String, Map<String, String>>> otherFeature = feature.getOtherFeature();
+
+        Map<String, String> userFeatureMap = new HashMap<>();
+        Map<String, String> c1Feature = userFeature.getOrDefault("alg_mid_feature_ad_action", EMPTY_STRING_MAP);
+        List<TupleMapEntry<Tuple5>> midActionList = this.handleC1Feature(c1Feature, userFeatureMap);
+
+        Map<String, Double> midTimeDiffMap = this.parseC1FeatureListToTimeDiffMap(midActionList, ts);
+        Map<String, Double> actionStaticMap = this.parseC1FeatureListToActionStaticMap(midActionList);
+
+        Map<String, String> d2Feature = videoFeature.getOrDefault("alg_cid_feature_vid_cf_rank", EMPTY_STRING_MAP);
+        Map<String, String> d3Feature = videoFeature.getOrDefault("alg_vid_feature_basic_info", EMPTY_STRING_MAP);
+
+        Map<String, Map<String, Double>> vidRankMaps = this.parseD2FeatureMap(d2Feature);
+
+        Map<String, String> e1Feature = userFeature.getOrDefault("alg_mid_feature_return_tags", EMPTY_STRING_MAP);
+        Map<String, String> e2Feature = userFeature.getOrDefault("alg_mid_feature_share_tags", EMPTY_STRING_MAP);
+
+        Map<String, String> g1Feature = userFeature.getOrDefault("mid_return_video_cate", EMPTY_STRING_MAP);
+        Map<String, String> g2Feature = userFeature.getOrDefault("mid_share_video_cate", EMPTY_STRING_MAP);
+
+
+        userFeatureMap.put("brand", reqFeature.getOrDefault("brand", ""));
+        userFeatureMap.put("region", reqFeature.getOrDefault("region", ""));
+        userFeatureMap.put("city", reqFeature.getOrDefault("city", ""));
+        userFeatureMap.put("vid", reqFeature.getOrDefault("vid", ""));
+        userFeatureMap.put("apptype", reqFeature.getOrDefault("apptype", ""));
+        userFeatureMap.put("is_first_layer", reqFeature.getOrDefault("is_first_layer", ""));
+        userFeatureMap.put("root_source_scene", reqFeature.getOrDefault("root_source_scene", ""));
+        userFeatureMap.put("root_source_channel", reqFeature.getOrDefault("root_source_channel", ""));
+
+
+        userFeatureMap.put("cate1", d3Feature.get("merge_first_level_cate"));
+        userFeatureMap.put("cate2", d3Feature.get("merge_second_level_cate"));
+        userFeatureMap.put("user_vid_return_tags_2h", e1Feature.getOrDefault("tags_2h", null));
+        userFeatureMap.put("user_vid_return_tags_1d", e1Feature.getOrDefault("tags_1d", null));
+        userFeatureMap.put("user_vid_return_tags_3d", e1Feature.getOrDefault("tags_3d", null));
+        userFeatureMap.put("user_vid_return_tags_7d", e1Feature.getOrDefault("tags_7d", null));
+        userFeatureMap.put("user_vid_return_tags_14d", e1Feature.getOrDefault("tags_14d", null));
+        userFeatureMap.put("title_split", d3Feature.getOrDefault("title_split", null));
+        userFeatureMap.put("user_vid_share_tags_1d", e2Feature.getOrDefault("tags_1d", null));
+        userFeatureMap.put("user_vid_share_tags_14d", e2Feature.getOrDefault("tags_14d", null));
+        userFeatureMap.put("user_vid_return_cate1_14d", g1Feature.getOrDefault("cate1_14d", null));
+        userFeatureMap.put("user_vid_return_cate2_14d", g1Feature.getOrDefault("cate2_14d", null));
+        userFeatureMap.put("user_vid_share_cate1_14d", g2Feature.getOrDefault("cate1_14d", null));
+        userFeatureMap.put("user_vid_share_cate2_14d", g2Feature.getOrDefault("cate2_14d", null));
+        userFeatureMap.put("user_layer", reqFeature.getOrDefault("layer_l4", ""));
+        userFeatureMap.put("flag", "1");
+
+        Map<String, String> sceneFeatureMap = this.handleSceneFeature(ts);
+        long time1 = System.currentTimeMillis();
+
+        boolean isGuaranteedFlow = getIsGuaranteedFlow(scoreParam);
+        Map<String, GuaranteeView> map = getGuaranteeViewMap(request, isGuaranteedFlow);
+        Map<Long, CorrectCpaParam> correctCpaMap = getCorrectCpaParamMap(request, scoreParam, reqFeature);
+        List<AdRankItem> adRankItems = new ArrayList<>();
+        Random random = new Random();
+        List<Future<AdRankItem>> futures = new ArrayList<>();
+        CountDownLatch cdl1 = new CountDownLatch(request.getAdIdList().size());
+        for (AdPlatformCreativeDTO dto : request.getAdIdList()) {
+            Future<AdRankItem> future = ThreadPoolFactory.feature().submit(() -> {
+                AdRankItem adRankItem = new AdRankItem();
+                try {
+                    adRankItem.setAdId(dto.getCreativeId());
+                    adRankItem.setCreativeCode(dto.getCreativeCode());
+                    adRankItem.setAdVerId(dto.getAdVerId());
+                    adRankItem.setVideoId(request.getVideoId());
+                    adRankItem.setCpa(dto.getCpa());
+                    adRankItem.setId(dto.getAdId());
+                    adRankItem.setCampaignId(dto.getCampaignId());
+                    adRankItem.setCpm(ObjUtil.nullOrDefault(dto.getCpm(), 90).doubleValue());
+                    adRankItem.setSkuId(dto.getSkuId());
+                    adRankItem.setCustomerId(dto.getCustomerId());
+                    adRankItem.setAgentId(dto.getAgentId());
+                    adRankItem.setProfession(dto.getProfession());
+                    adRankItem.setLandingPageType(dto.getLandingPageType());
+                    adRankItem.setTargetingConversion(dto.getTargetingConversion());
+                    adRankItem.setRandom(random.nextInt(1000));
+                    if (noApiAdVerIds.contains(dto.getAdVerId())) {
+                        adRankItem.getExt().put("isApi", "0");
+                    } else {
+                        adRankItem.getExt().put("isApi", "1");
+                    }
+                    adRankItem.getExt().put("recallsources", dto.getRecallSources());
+                    fillAdRankItemExt(adRankItem, dto);
+                    adRankItem.getExt().put("correctCpaMap", JSONObject.toJSONString(correctCpaMap.get(dto.getAdId())));
+                    adRankItem.getExt().put("correctionFactor", correctCpaMap.get(dto.getAdId()).getCorrectionFactor());
+                    setGuaranteeWeight(map, dto.getAdVerId(), adRankItem.getExt(), isGuaranteedFlow, reqFeature);
+                    String cidStr = dto.getCreativeId().toString();
+                    Map<String, String> cidFeatureMap = adRankItem.getFeatureMap();
+                    Map<String, Map<String, String>> cidFeature = allCidFeature.getOrDefault(cidStr, EMPTY_NESTED_MAP);
+                    Map<String, String> b1Feature = cidFeature.getOrDefault("alg_cid_feature_basic_info", EMPTY_STRING_MAP);
+
+                    Map<String, Map<String, String>> adVerFeature = allAdVerFeature.getOrDefault(dto.getAdVerId(), EMPTY_NESTED_MAP);
+                    Map<String, Map<String, String>> skuFeature = allSkuFeature.getOrDefault(String.valueOf(dto.getSkuId()), EMPTY_NESTED_MAP);
+                    Map<String, String> d1Feature = cidFeature.getOrDefault("alg_cid_feature_vid_cf", EMPTY_STRING_MAP);
+
+                    this.handleB1Feature(b1Feature, cidFeatureMap, cidStr);
+                    this.handleB2ToB5AndB8ToB9Feature(cidFeature, adVerFeature, cidFeatureMap);
+                    this.handleB6ToB7Feature(cidFeature, cidFeatureMap);
+                    this.handleJ1ToJ11Feature(userLayer, dto, otherFeature, cidFeatureMap);
+                    this.handleC1UIFeature(midTimeDiffMap, actionStaticMap, cidFeatureMap, cidStr);
+                    this.handleD1Feature(d1Feature, cidFeatureMap);
+                    this.handleD2Feature(vidRankMaps, cidFeatureMap, cidStr);
+                    this.handleH1AndH2Feature(skuFeature, adVerFeature, cidFeatureMap);
+                    cidFeatureMap.put("cid", dto.getCreativeId() != null ? String.valueOf(dto.getCreativeId()) : "");
+                    cidFeatureMap.put("adid", dto.getAdId() != null ? String.valueOf(dto.getAdId()) : "");
+                    cidFeatureMap.put("adverid", dto.getAdVerId() != null ? dto.getAdVerId() : "");
+                    cidFeatureMap.put("profession", dto.getProfession() != null ? dto.getProfession() : "");
+                    cidFeatureMap.put("category_name", dto.getCategoryName() != null ? dto.getCategoryName() : "");
+                    cidFeatureMap.put("material_md5", dto.getMaterialMd5() != null ? dto.getMaterialMd5() : "");
+                    cidFeatureMap.put("ad_profession_id", dto.getAdProfessionId() != null ? String.valueOf(dto.getAdProfessionId()) : "");
+                    cidFeatureMap.put("ad_profession_name", dto.getAdProfessionName() != null ? dto.getAdProfessionName() : "");
+                    cidFeatureMap.put("ad_category_name", dto.getAdCategoryName() != null ? dto.getAdCategoryName() : "");
+                    cidFeatureMap.put("ad_category_id", dto.getAdCategoryId() != null ? String.valueOf(dto.getAdCategoryId()) : "");
+                    cidFeatureMap.put("ad_sku_id", dto.getAdSkuId() != null ? String.valueOf(dto.getAdSkuId()) : "");
+                    cidFeatureMap.put("ad_sku_code", dto.getAdSkuCode() != null ? dto.getAdSkuCode() : "");
+                    cidFeatureMap.put("ad_sku_name", dto.getAdSkuName() != null ? dto.getAdSkuName() : "");
+                    cidFeatureMap.put("customer", dto.getCustomerId() != null ? String.valueOf(dto.getCustomerId()) : "");
+                    cidFeatureMap.put("landing", dto.getLandingPageType() != null ? String.valueOf(dto.getLandingPageType()) : "");
+                    //DNN模型没训练过的cid才不传入广告相关的稀疏特征
+                    if (CollectionUtils.isNotEmpty(DnnCidDataHelper.getCidSet()) && !DnnCidDataHelper.getCidSet().contains(adRankItem.getAdId())) {
+                        cidFeatureMap.put("cid", "");
+                        cidFeatureMap.put("adid", "");
+                        cidFeatureMap.put("adverid", "");
+                        cidFeatureMap.put("customer", "");
+                    }
+                    return adRankItem;
+                } finally {
+                    cdl1.countDown();
+                }
+            });
+            futures.add(future);
+        }
+        try {
+            cdl1.await(300, TimeUnit.MILLISECONDS);
+        } catch (Exception e) {
+            log.error("handleE1AndE2Feature and handleD3AndB1Feature wait timeout", e);
+        }
+        for (Future<AdRankItem> future : futures) {
+            try {
+                if (future.isDone()) {
+                    adRankItems.add(future.get());
+                }
+            } catch (Exception e) {
+                log.error("Feature handle error", e);
+            }
+        }
+
+        long time2 = System.currentTimeMillis();
+        // feature3
+        // 请求级别的 tag 分词缓存，所有广告共享（同一用户的 tags 相同）
+        Map<String, List<String>> tagWordsCache = new ConcurrentHashMap<>();
+        CountDownLatch cdl2 = new CountDownLatch(adRankItems.size() * 2);
+        for (AdRankItem item : adRankItems) {
+            String cidStr = String.valueOf(item.getAdId());
+            Map<String, Map<String, String>> cidFeature = allCidFeature.getOrDefault(cidStr, EMPTY_NESTED_MAP);
+            Map<String, String> b1Feature = cidFeature.getOrDefault("alg_cid_feature_basic_info", EMPTY_STRING_MAP);
+            String title = b1Feature.getOrDefault("cidtitle", "");
+            ThreadPoolFactory.defaultPool().submit(() -> {
+                try {
+                    this.handleE1AndE2Feature(e1Feature, e2Feature, title, item.getFeatureMap(), scoreParam, tagWordsCache);
+                } finally {
+                    cdl2.countDown();
+                }
+            });
+            ThreadPoolFactory.defaultPool().submit(() -> {
+                try {
+                    this.handleD3AndB1Feature(d3Feature, title, item.getFeatureMap(), scoreParam);
+                } finally {
+                    cdl2.countDown();
+                }
+            });
+        }
+        try {
+            cdl2.await(150, TimeUnit.MILLISECONDS);
+        } catch (Exception e) {
+            log.error("handleE1AndE2Feature and handleD3AndB1Feature wait timeout", e);
+        }
+
+        long time3 = System.currentTimeMillis();
+        // 分桶
+        userFeatureMap = this.featureBucket(userFeatureMap);
+        CountDownLatch cdl4 = new CountDownLatch(adRankItems.size());
+        for (AdRankItem adRankItem : adRankItems) {
+            ThreadPoolFactory.feature().submit(() -> {
+                try {
+                    Map<String, String> featureMap = adRankItem.getFeatureMap();
+                    adRankItem.setFeatureMap(this.featureBucket(featureMap));
+                } finally {
+                    cdl4.countDown();
+                }
+            });
+        }
+        try {
+            cdl4.await(100, TimeUnit.MILLISECONDS);
+        } catch (Exception e) {
+            log.error("handleE1AndE2Feature and handleD3AndB1Feature wait timeout", e);
+        }
+        long time4 = System.currentTimeMillis();
+        // 打分排序
+        // getScorerPipeline
+
+        if (CollectionUtils.isEmpty(adRankItems)) {
+            log.error("adRankItems is empty");
+        }
+        List<AdRankItem> result = ScorerUtils.getScorerPipeline(ScorerUtils.PAI_SCORE_CONF_20250214).scoring(sceneFeatureMap, userFeatureMap, adRankItems);
+        if (CollectionUtils.isEmpty(result)) {
+            log.error("scoring result is empty");
+        }
+        long time5 = System.currentTimeMillis();
+        int viewLimit = NumberUtils.toInt(paramsMap.getOrDefault("viewLimit", "3000"));
+        // calibrate score for negative sampling or cold start
+        for (AdRankItem item : result) {
+            double originalScore = item.getLrScore();
+            double calibratedScore = originalScore / (originalScore + (1 - originalScore) / negSampleRate);
+            // 该创意尚未在模型中训练，打分不可靠
+            Map<String, Map<String, String>> cidFeature = allCidFeature.getOrDefault(String.valueOf(item.getAdId()), EMPTY_NESTED_MAP);
+            Map<String, String> b3Feature = cidFeature.getOrDefault("alg_cid_feature_cid_action", EMPTY_STRING_MAP);
+            double view3Day = Double.parseDouble(b3Feature.getOrDefault("ad_view_3d", "0"));
+            if ((CollectionUtils.isNotEmpty(DnnCidDataHelper.getCidSet()) && !DnnCidDataHelper.getCidSet().contains(item.getAdId()))
+                    || view3Day <= viewLimit) {
+                double view = Double.parseDouble(b3Feature.getOrDefault("ad_view_14d", "0"));
+                double conver = Double.parseDouble(b3Feature.getOrDefault("ad_conversion_14d", "0"));
+                double smoothCxr = NumUtil.divSmoothV1(conver, view, 1.64);
+                smoothCxr = this.getDefaultCxr(smoothCxr);
+                //模型打分和统计计算取打分更低的
+                item.getScoreMap().put("cvcvrItemValue", 1.0);
+                if (smoothCxr <= calibratedScore) {
+                    calibratedScore = smoothCxr;
+                    item.getScoreMap().put("cvcvrItemValue", 2.0);
+                }
+            }
+            item.setLrScore(calibratedScore);
+            item.getScoreMap().put("originCtcvrScore", originalScore);
+            item.getScoreMap().put("modelCtcvrScore", calibratedScore);
+            item.getScoreMap().put("ctcvrScore", calibratedScore);
+        }
+
+        String calibModelName = paramsMap.getOrDefault("calibModelName", "dnnV3");
+//        calculateCtcvrScore(result, request, scoreParam, calibModelName, reqFeature);
+
+        double minValidCopc = NumberUtils.toDouble(paramsMap.getOrDefault("minValidCopc", "0.8"));
+        double maxValidCopc = NumberUtils.toDouble(paramsMap.getOrDefault("maxValidCopc", "10"));
+        double minCopc = NumberUtils.toDouble(paramsMap.getOrDefault("minCopc", "0.2"));
+        double maxCopc = NumberUtils.toDouble(paramsMap.getOrDefault("maxCopc", "2.5"));
+        calibrationCidCtcvr(result, calibModelName, reqFeature, minValidCopc, maxValidCopc, minCopc, maxCopc);
+        if (CollectionUtils.isEmpty(result)) {
+            log.error("calculateCtcvrScore result is empty");
+        }
+        // loop
+        double cpmCoefficient = weightParam.getOrDefault("cpmCoefficient", 0.9);
+        boolean isGuaranteeType = false;
+        // 查询人群分层信息
+        String peopleLayer = Optional.of(reqFeature)
+                .map(f -> f.get("layer"))
+                .map(s -> s.replace("-炸", ""))
+                .orElse(null);
+
+        // 控制曝光参数
+        String expOldKey = paramsMap.getOrDefault("expOldKey", "ad_view_yesterday");
+        double expOldThreshold = NumberUtils.toDouble(paramsMap.getOrDefault("expOldThreshold", "1000"));
+        String expNewKey = paramsMap.getOrDefault("expNewKey", "ad_view_today");
+        double expNewThreshold = NumberUtils.toDouble(paramsMap.getOrDefault("expNewThreshold", "3000"));
+        double expLowerWeight = NumberUtils.toDouble(paramsMap.getOrDefault("expLowerWeight", "0.2"));
+        double expUpperWeight = NumberUtils.toDouble(paramsMap.getOrDefault("expUpperWeight", "1.0"));
+        double expScale = NumberUtils.toDouble(paramsMap.getOrDefault("expScale", "10.0"));
+        int openH5 = NumberUtils.toInt(paramsMap.getOrDefault("openH5", "0"));
+
+        // 计算rerank权重
+        calRerankWeight(scoreParam, userLayer, result);
+        for (AdRankItem item : result) {
+            double bid = item.getCpa();
+            if (scoreParam.getExpCodeSet().contains(correctCpaExp1) || scoreParam.getExpCodeSet().contains(correctCpaExp2)) {
+                Double correctionFactor = (Double) item.getExt().get("correctionFactor");
+                item.getScoreMap().put("correctionFactor", correctionFactor);
+                bid = bid * correctionFactor;
+            }
+            item.getScoreMap().put("ecpm", item.getLrScore() * bid * 1000);
+            if (isGuaranteedFlow && item.getExt().get("isGuaranteed") != null && (boolean) item.getExt().get("isGuaranteed")) {
+                isGuaranteeType = true;
+            }
+
+            // h5 降权
+            double h5Weight = 1;
+            if (openH5 > 0) {
+                h5Weight = this.getH5SuppressWeight(item);
+            }
+
+            // 控制曝光权重
+            Map<String, Map<String, String>> cidFeature = allCidFeature.getOrDefault(String.valueOf(item.getAdId()), EMPTY_NESTED_MAP);
+            Map<String, String> b3Feature = cidFeature.getOrDefault("alg_cid_feature_cid_action", EMPTY_STRING_MAP);
+            double expWeight = getExpWeight(b3Feature,
+                    expOldKey, expOldThreshold,
+                    expNewKey, expNewThreshold,
+                    expLowerWeight, expUpperWeight, expScale);
+
+            // 控制流量权重
+            double flowCtlC = item.getScoreMap().getOrDefault("flowCtlC", 1.0);
+            double flowCtlA = item.getScoreMap().getOrDefault("flowCtlA", 1.0);
+            double kFinal = item.getScoreMap().getOrDefault("kFinal", 1.0);
+
+            String layerAndCreativeWeightMapKey = getLayerAndCreativeWeightMapKey(peopleLayer, String.valueOf(item.getAdId()));
+            // 人群分层&创意的权重
+            double layerAndCreativeWeight = getLayerAndCreativeWeight(layerAndCreativeWeightMapKey);
+            double scoreCoefficient = creativeScoreCoefficient.getOrDefault(item.getAdId(), 1d);
+            double guaranteeScoreCoefficient = getGuaranteeScoreCoefficient(isGuaranteedFlow, item.getExt());
+            double score = flowCtlC * flowCtlA * h5Weight * expWeight * item.getLrScore() * bid * scoreCoefficient * guaranteeScoreCoefficient * layerAndCreativeWeight * kFinal;
+            item.getScoreMap().put("guaranteeScoreCoefficient", guaranteeScoreCoefficient);
+            item.getScoreMap().put("cpa", item.getCpa());
+            item.getScoreMap().put("cpm", item.getCpm());
+            item.getScoreMap().put("bid", bid);
+            item.getScoreMap().put("cpmCoefficient", cpmCoefficient);
+            item.getScoreMap().put("scoreCoefficient", scoreCoefficient);
+            item.getScoreMap().put("h5", h5Weight);
+            item.getFeatureMap().putAll(userFeatureMap);
+            item.getFeatureMap().putAll(sceneFeatureMap);
+
+            // 没有转化回传的广告主，使用后台配置的CPM
+            if (noApiAdVerIds.contains(item.getAdVerId())) {
+                score = item.getCpm() * cpmCoefficient / 1000;
+            }
+            item.setScore(score);
+        }
+
+
+        result.sort(ComparatorUtil.equalsRandomComparator());
+
+        String logModelName = paramsMap.getOrDefault("logModelName", "dnnV3");
+        if (CollectionUtils.isNotEmpty(result)) {
+            AdRankItem top1Item = result.get(0);
+            List<String> participateCompetitionType = new ArrayList<>();
+            participateCompetitionType.add("engine");
+            top1Item.getExt().put("isGuaranteeType", isGuaranteeType);
+            if (isGuaranteeType) {
+                participateCompetitionType.add("guarantee");
+            }
+            top1Item.getExt().put("participateCompetitionType", StringUtils.join(participateCompetitionType, ","));
+            Double modelCtcvrScore = top1Item.getScoreMap().get("modelCtcvrScore");
+            Double ctcvrScore = top1Item.getScoreMap().get("ctcvrScore");
+            if (scoreParam.getExpCodeSet().contains(checkoutEcpmExp)) {
+                top1Item.getExt().put("ecpm", ctcvrScore * top1Item.getCpa() * 1000);
+                String filterEcpmValue = paramsMap.getOrDefault("filterEcpm", filterEcpm);
+                top1Item.getExt().put("filterEcpm", filterEcpmValue);
+                if (noApiAdVerIds.contains(top1Item.getAdVerId())) {
+                    top1Item.getExt().put("ecpm", top1Item.getCpm());
+                }
+            } else {
+                top1Item.getExt().put("ecpm", modelCtcvrScore * top1Item.getCpa() * 1000);
+            }
+            putMetaFeature(top1Item, feature, reqFeature, sceneFeatureMap, request);
+            top1Item.getExt().put("model", logModelName);
+            String coefficientRate = paramsMap.getOrDefault("coefficientRate", "1");
+            top1Item.getExt().put("coefficientRate", coefficientRate);
+        }
+        long time6 = System.currentTimeMillis();
+        log.info("cost={}, getFeature={}, handleFeature={},  similar={}, bucketFeature={}, getScorerPipeline={}, " +
+                        "other={}, adIdSize={}, adRankItemsSize={}",
+                time6 - start, time1 - start, time2 - time1, time3 - time2, time4 - time3,
+                time5 - time4, time6 - time5, request.getAdIdList().size(), adRankItems.size());
+
+        return result;
+    }
+
+    /**
+     * 获取人群分层和创意的权重
+     *
+     * @param key
+     * @return
+     */
+    private Double getLayerAndCreativeWeight(String key) {
+        if (StringUtils.isBlank(key)) {
+            return 1d;
+        }
+        return layerAndCreativeWeightMap.getOrDefault(key, 1d);
+    }
+
+    /**
+     * 获取人群分层和创意的权重key
+     *
+     * @param layer
+     * @param creativeId
+     * @return
+     */
+    private String getLayerAndCreativeWeightMapKey(String layer, String creativeId) {
+        if (StringUtils.isBlank(layer) || StringUtils.isBlank(creativeId)) {
+            return null;
+        }
+        return layer + "_" + creativeId;
+    }
+
+
+    private void handleB1Feature(Map<String, String> b1Feature, Map<String, String> cidFeatureMap, String cid) {
+        cidFeatureMap.put("cid_" + cid, "0.1");
+        // if (StringUtils.isNotBlank(b1Feature.get("adid"))) {
+        //     String adId = b1Feature.get("adid");
+        //     cidFeatureMap.put("adid_" + adId, idDefaultValue);
+        // }
+        if (StringUtils.isNotBlank(b1Feature.get("adverid"))) {
+            String adVerId = b1Feature.get("adverid");
+            cidFeatureMap.put("adverid_" + adVerId, "0.1");
+        }
+        // if (StringUtils.isNotBlank(b1Feature.get("targeting_conversion"))) {
+        //     String targetingConversion = b1Feature.get("targeting_conversion");
+        //     cidFeatureMap.put("targeting_conversion_" + targetingConversion, idDefaultValue);
+        // }
+        if (StringUtils.isNotBlank(b1Feature.get("cpa"))) {
+            String cpa = b1Feature.get("cpa");
+            cidFeatureMap.put("cpa", cpa);
+        }
+    }
+
+    private void handleB2ToB5AndB8ToB9Feature(Map<String, Map<String, String>> c1Feature, Map<String, Map<String, String>> adVerFeature, Map<String, String> cidFeatureMap) {
+        Map<String, String> b2Feature = adVerFeature.getOrDefault("alg_cid_feature_adver_action", EMPTY_STRING_MAP);
+        Map<String, String> b3Feature = c1Feature.getOrDefault("alg_cid_feature_cid_action", EMPTY_STRING_MAP);
+        Map<String, String> b4Feature = c1Feature.getOrDefault("alg_cid_feature_region_action", EMPTY_STRING_MAP);
+        Map<String, String> b5Feature = c1Feature.getOrDefault("alg_cid_feature_app_action", EMPTY_STRING_MAP);
+        Map<String, String> b8Feature = c1Feature.getOrDefault("alg_cid_feature_brand_action", EMPTY_STRING_MAP);
+        Map<String, String> b9Feature = c1Feature.getOrDefault("alg_cid_feature_weChatVersion_action", EMPTY_STRING_MAP);
+
+        List<String> timeList = Arrays.asList("1h", "2h", "3h", "6h", "12h", "1d", "3d", "7d", "yesterday", "today");
+        List<Tuple2<Map<String, String>, String>> featureList = Arrays.asList(
+                new Tuple2<>(b2Feature, "b2"),
+                new Tuple2<>(b3Feature, "b3"),
+                new Tuple2<>(b4Feature, "b4"),
+                new Tuple2<>(b5Feature, "b5"),
+                new Tuple2<>(b8Feature, "b8"),
+                new Tuple2<>(b9Feature, "b9")
+        );
+        for (Tuple2<Map<String, String>, String> tuple2 : featureList) {
+            Map<String, String> feature = tuple2.f1;
+            String prefix = tuple2.f2;
+            for (String time : timeList) {
+                double view = Double.parseDouble(feature.getOrDefault("ad_view_" + time, "0"));
+                double click = Double.parseDouble(feature.getOrDefault("ad_click_" + time, "0"));
+                double conver = Double.parseDouble(feature.getOrDefault("ad_conversion_" + time, "0"));
+                double income = Double.parseDouble(feature.getOrDefault("ad_income_" + time, "0"));
+                double cpc = NumUtil.div(income, click);
+                double ctr = NumUtil.divSmoothV2(click, view, CTR_SMOOTH_BETA_FACTOR);
+                double ctcvr = NumUtil.divSmoothV2(conver, view, CTCVR_SMOOTH_BETA_FACTOR);
+                double ecpm = ctr * cpc * 1000;
+                cidFeatureMap.put(prefix + "_" + time + "_ctr", String.valueOf(ctr));
+                cidFeatureMap.put(prefix + "_" + time + "_ctcvr", String.valueOf(ctcvr));
+                cidFeatureMap.put(prefix + "_" + time + "_cvr", String.valueOf(NumUtil.divSmoothV2(conver, click, CVR_SMOOTH_BETA_FACTOR)));
+                cidFeatureMap.put(prefix + "_" + time + "_conver", String.valueOf(conver));
+                cidFeatureMap.put(prefix + "_" + time + "_ecpm", String.valueOf(ecpm));
+
+                cidFeatureMap.put(prefix + "_" + time + "_click", String.valueOf(click));
+                cidFeatureMap.put(prefix + "_" + time + "_conver*log(view)", String.valueOf(conver * NumUtil.log(view)));
+                cidFeatureMap.put(prefix + "_" + time + "_conver*ctcvr", String.valueOf(conver * ctcvr));
+            }
+        }
+
+    }
+
+    private void handleB6ToB7Feature(Map<String, Map<String, String>> c1Feature, Map<String, String> cidFeatureMap) {
+        Map<String, String> b6Feature = c1Feature.getOrDefault("alg_cid_feature_week_action", EMPTY_STRING_MAP);
+        Map<String, String> b7Feature = c1Feature.getOrDefault("alg_cid_feature_hour_action", EMPTY_STRING_MAP);
+
+        List<String> timeList = Arrays.asList("7d", "14d");
+        List<Tuple2<Map<String, String>, String>> featureList = Arrays.asList(
+                new Tuple2<>(b6Feature, "b6"),
+                new Tuple2<>(b7Feature, "b7")
+        );
+        for (Tuple2<Map<String, String>, String> tuple2 : featureList) {
+            Map<String, String> feature = tuple2.f1;
+            String prefix = tuple2.f2;
+            for (String time : timeList) {
+                double view = Double.parseDouble(feature.getOrDefault("ad_view_" + time, "0"));
+                double click = Double.parseDouble(feature.getOrDefault("ad_click_" + time, "0"));
+                double conver = Double.parseDouble(feature.getOrDefault("ad_conversion_" + time, "0"));
+                double income = Double.parseDouble(feature.getOrDefault("ad_income_" + time, "0"));
+                double cpc = NumUtil.div(income, click);
+                double ctr = NumUtil.divSmoothV2(click, view, CTR_SMOOTH_BETA_FACTOR);
+                double ctcvr = NumUtil.divSmoothV2(conver, view, CTCVR_SMOOTH_BETA_FACTOR);
+                double ecpm = ctr * cpc * 1000;
+                cidFeatureMap.put(prefix + "_" + time + "_ctr", String.valueOf(ctr));
+                cidFeatureMap.put(prefix + "_" + time + "_ctcvr", String.valueOf(ctcvr));
+                cidFeatureMap.put(prefix + "_" + time + "_cvr", String.valueOf(NumUtil.divSmoothV2(conver, click, CVR_SMOOTH_BETA_FACTOR)));
+                cidFeatureMap.put(prefix + "_" + time + "_conver", String.valueOf(conver));
+                cidFeatureMap.put(prefix + "_" + time + "_ecpm", String.valueOf(ecpm));
+
+                cidFeatureMap.put(prefix + "_" + time + "_click", String.valueOf(click));
+                cidFeatureMap.put(prefix + "_" + time + "_conver*log(view)", String.valueOf(conver * NumUtil.log(view)));
+                cidFeatureMap.put(prefix + "_" + time + "_conver*ctcvr", String.valueOf(conver * ctcvr));
+            }
+        }
+
+    }
+
+    private void handleJ1ToJ11Feature(String userLayer, AdPlatformCreativeDTO dto, Map<String, Map<String, Map<String, String>>> otherFeature, Map<String, String> cidFeatureMap) {
+        String landing = dto.getLandingPageType() != null ? String.valueOf(dto.getLandingPageType()) : "";
+        String cust = dto.getCustomerId() != null ? String.valueOf(dto.getCustomerId()) : "";
+        String adv = dto.getAdVerId() != null ? dto.getAdVerId() : "";
+        String prof = dto.getProfession() != null ? dto.getProfession() : "";
+        String cate = dto.getCategoryName() != null ? dto.getCategoryName() : "";
+
+        String j1Key = CommonUtils.getFeatureUniqueKey("j1", userLayer);
+        Map<String, String> j1Feature = otherFeature.getOrDefault(j1Key, EMPTY_NESTED_MAP).getOrDefault("alg_cid_feature_level_action", EMPTY_STRING_MAP);
+        String j2Key = CommonUtils.getFeatureUniqueKey("j2", userLayer, adv);
+        Map<String, String> j2Feature = otherFeature.getOrDefault(j2Key, EMPTY_NESTED_MAP).getOrDefault("alg_cid_feature_level_advertiser_action", EMPTY_STRING_MAP);
+        String j3Key = CommonUtils.getFeatureUniqueKey("j3", userLayer, cust);
+        Map<String, String> j3Feature = otherFeature.getOrDefault(j3Key, EMPTY_NESTED_MAP).getOrDefault("alg_cid_feature_level_customer_action", EMPTY_STRING_MAP);
+        String j4Key = CommonUtils.getFeatureUniqueKey("j4", userLayer, prof);
+        Map<String, String> j4Feature = otherFeature.getOrDefault(j4Key, EMPTY_NESTED_MAP).getOrDefault("alg_cid_feature_level_profession_action", EMPTY_STRING_MAP);
+        String j5Key = CommonUtils.getFeatureUniqueKey("j5", userLayer, cate);
+        Map<String, String> j5Feature = otherFeature.getOrDefault(j5Key, EMPTY_NESTED_MAP).getOrDefault("alg_cid_feature_level_category_action", EMPTY_STRING_MAP);
+        String j6Key = CommonUtils.getFeatureUniqueKey("j6", userLayer, String.valueOf(dto.getCreativeId()));
+        Map<String, String> j6Feature = otherFeature.getOrDefault(j6Key, EMPTY_NESTED_MAP).getOrDefault("alg_cid_feature_level_cid_action", EMPTY_STRING_MAP);
+        String j7Key = CommonUtils.getFeatureUniqueKey("j7", landing);
+        Map<String, String> j7Feature = otherFeature.getOrDefault(j7Key, EMPTY_NESTED_MAP).getOrDefault("alg_cid_feature_landingpage_action", EMPTY_STRING_MAP);
+        String j8Key = CommonUtils.getFeatureUniqueKey("j8", landing, adv);
+        Map<String, String> j8Feature = otherFeature.getOrDefault(j8Key, EMPTY_NESTED_MAP).getOrDefault("alg_cid_feature_landingpage_advertiser_action", EMPTY_STRING_MAP);
+        String j9Key = CommonUtils.getFeatureUniqueKey("j9", landing, cust);
+        Map<String, String> j9Feature = otherFeature.getOrDefault(j9Key, EMPTY_NESTED_MAP).getOrDefault("alg_cid_feature_landingpage_customer_action", EMPTY_STRING_MAP);
+        String j10Key = CommonUtils.getFeatureUniqueKey("j10", landing, prof);
+        Map<String, String> j10Feature = otherFeature.getOrDefault(j10Key, EMPTY_NESTED_MAP).getOrDefault("alg_cid_feature_landingpage_profession_action", EMPTY_STRING_MAP);
+        String j11Key = CommonUtils.getFeatureUniqueKey("j11", landing, cate);
+        Map<String, String> j11Feature = otherFeature.getOrDefault(j11Key, EMPTY_NESTED_MAP).getOrDefault("alg_cid_feature_landingpage_category_action", EMPTY_STRING_MAP);
+
+        List<String> shortTimeList = Arrays.asList("3h", "3d");
+        List<String> longTimeList = Arrays.asList("1h", "2h", "3h", "6h", "12h", "1d", "3d");
+        List<Tuple3<Map<String, String>, String, List<String>>> featureList = Arrays.asList(
+                new Tuple3<>(j1Feature, "j1", shortTimeList),
+                new Tuple3<>(j2Feature, "j2", shortTimeList),
+                new Tuple3<>(j3Feature, "j3", shortTimeList),
+                new Tuple3<>(j4Feature, "j4", shortTimeList),
+                new Tuple3<>(j5Feature, "j5", shortTimeList),
+                new Tuple3<>(j6Feature, "j6", longTimeList),
+                new Tuple3<>(j7Feature, "j7", shortTimeList),
+                new Tuple3<>(j8Feature, "j8", shortTimeList),
+                new Tuple3<>(j9Feature, "j9", shortTimeList),
+                new Tuple3<>(j10Feature, "j10", shortTimeList),
+                new Tuple3<>(j11Feature, "j11", shortTimeList)
+        );
+        for (Tuple3<Map<String, String>, String, List<String>> tuple3 : featureList) {
+            Map<String, String> feature = tuple3.f1;
+            String prefix = tuple3.f2;
+            List<String> timeList = tuple3.f3;
+            for (String time : timeList) {
+                double view = Double.parseDouble(feature.getOrDefault("ad_view_" + time, "0"));
+                double click = Double.parseDouble(feature.getOrDefault("ad_click_" + time, "0"));
+                double conver = Double.parseDouble(feature.getOrDefault("ad_conversion_" + time, "0"));
+                double income = Double.parseDouble(feature.getOrDefault("ad_income_" + time, "0"));
+                double cpc = NumUtil.div(income, click);
+                double ctr = NumUtil.divSmoothV2(click, view, CTR_SMOOTH_BETA_FACTOR);
+                double ctcvr = NumUtil.divSmoothV2(conver, view, CTCVR_SMOOTH_BETA_FACTOR);
+                double ecpm = ctr * cpc * 1000;
+                cidFeatureMap.put(prefix + "_" + time + "_ctr", String.valueOf(ctr));
+                cidFeatureMap.put(prefix + "_" + time + "_ctcvr", String.valueOf(ctcvr));
+                cidFeatureMap.put(prefix + "_" + time + "_cvr", String.valueOf(NumUtil.divSmoothV2(conver, click, CVR_SMOOTH_BETA_FACTOR)));
+                cidFeatureMap.put(prefix + "_" + time + "_conver", String.valueOf(conver));
+                cidFeatureMap.put(prefix + "_" + time + "_ecpm", String.valueOf(ecpm));
+
+                cidFeatureMap.put(prefix + "_" + time + "_click", String.valueOf(click));
+                cidFeatureMap.put(prefix + "_" + time + "_conver*log(view)", String.valueOf(conver * NumUtil.log(view)));
+                cidFeatureMap.put(prefix + "_" + time + "_conver*ctcvr", String.valueOf(conver * ctcvr));
+            }
+        }
+    }
+
+    private List<TupleMapEntry<Tuple5>> handleC1Feature(Map<String, String> c1Feature, Map<String, String> featureMap) {
+
+        //用户近1年内是否有转化
+        if (c1Feature.containsKey("user_has_conver_1y") && c1Feature.get("user_has_conver_1y") != null) {
+            featureMap.put("user_has_conver_1y", c1Feature.get("user_has_conver_1y"));
+        }
+        //用户历史转化过品类
+        if (c1Feature.containsKey("user_conver_ad_class") && c1Feature.get("user_conver_ad_class") != null) {
+            featureMap.put("user_conver_ad_class", c1Feature.get("user_conver_ad_class"));
+        }
+
+        // 用户特征
+        List<TupleMapEntry<Tuple5>> midActionList = new ArrayList<>();
+        if (c1Feature.containsKey("action")) {
+            String action = c1Feature.get("action");
+            midActionList = Arrays.stream(action.split(","))
+                    .map(r -> {
+                        String[] rList = r.split(":");
+                        Tuple5 tuple5 = new Tuple5(rList[1], rList[2], rList[3], rList[4], rList[5]);
+                        return new TupleMapEntry<>(rList[0], tuple5);
+                    })
+                    // TODO 倒排
+                    .sorted((a, b) -> Integer.compare(Integer.parseInt(b.value.f1), Integer.parseInt(a.value.f1)))
+                    .collect(Collectors.toList());
+        }
+
+        double viewAll = midActionList.size();
+        double clickAll = midActionList.stream().mapToInt(e -> Integer.parseInt(e.value.f2)).sum();
+        double converAll = midActionList.stream().mapToInt(e -> Integer.parseInt(e.value.f3)).sum();
+        double incomeAll = midActionList.stream().mapToInt(e -> Integer.parseInt(e.value.f4)).sum();
+        featureMap.put("viewAll", String.valueOf(viewAll));
+        featureMap.put("clickAll", String.valueOf(clickAll));
+        featureMap.put("converAll", String.valueOf(converAll));
+        featureMap.put("incomeAll", String.valueOf(incomeAll));
+        featureMap.put("ctr_all", String.valueOf(NumUtil.div(clickAll, viewAll)));
+        featureMap.put("ctcvr_all", String.valueOf(NumUtil.div(converAll, viewAll)));
+        featureMap.put("cvr_all", String.valueOf(NumUtil.div(clickAll, converAll)));
+        featureMap.put("ecpm_all", String.valueOf(NumUtil.div(incomeAll * 1000, viewAll)));
+        if (CollectionUtils.isNotEmpty(midActionList)) {
+            List<String> cidClickList = new ArrayList<>();
+            List<String> cidConverList = new ArrayList<>();
+            for (TupleMapEntry<Tuple5> tupleMapEntry : midActionList) {
+                String cid = tupleMapEntry.key;
+                String click = tupleMapEntry.value.f2;
+                String conver = tupleMapEntry.value.f3;
+                if (Objects.equals(click, "1")) {
+                    cidClickList.add(cid);
+                }
+                if (Objects.equals(conver, "1")) {
+                    cidConverList.add(cid);
+                }
+            }
+            featureMap.put("user_cid_click_list", String.join(",", cidClickList));
+            featureMap.put("user_cid_conver_list", String.join(",", cidConverList));
+        }
+        return midActionList;
+    }
+
+    private void handleC1UIFeature(Map<String, Double> midTimeDiffMap, Map<String, Double> midActionStatic, Map<String, String> featureMap, String cid) {
+        if (midTimeDiffMap.containsKey("timediff_view_" + cid)) {
+            featureMap.put("timediff_view", String.valueOf(midTimeDiffMap.getOrDefault("timediff_view_" + cid, 0.0)));
+        }
+        if (midTimeDiffMap.containsKey("timediff_click_" + cid)) {
+            featureMap.put("timediff_click", String.valueOf(midTimeDiffMap.getOrDefault("timediff_click_" + cid, 0.0)));
+        }
+        if (midTimeDiffMap.containsKey("timediff_conver_" + cid)) {
+            featureMap.put("timediff_conver", String.valueOf(midTimeDiffMap.getOrDefault("timediff_conver_" + cid, 0.0)));
+        }
+        if (midActionStatic.containsKey("actionstatic_view_" + cid)) {
+            featureMap.put("actionstatic_view", String.valueOf(midActionStatic.getOrDefault("actionstatic_view_" + cid, 0.0)));
+        }
+        if (midActionStatic.containsKey("actionstatic_click_" + cid)) {
+            featureMap.put("actionstatic_click", String.valueOf(midActionStatic.getOrDefault("actionstatic_click_" + cid, 0.0)));
+        }
+        if (midActionStatic.containsKey("actionstatic_conver_" + cid)) {
+            featureMap.put("actionstatic_conver", String.valueOf(midActionStatic.getOrDefault("actionstatic_conver_" + cid, 0.0)));
+        }
+        if (midActionStatic.containsKey("actionstatic_income_" + cid)) {
+            featureMap.put("actionstatic_income", String.valueOf(midActionStatic.getOrDefault("actionstatic_income_" + cid, 0.0)));
+        }
+        if (midActionStatic.containsKey("actionstatic_view_" + cid) && midActionStatic.containsKey("actionstatic_click_" + cid)) {
+            double ctr = NumUtil.div(
+                    midActionStatic.getOrDefault("actionstatic_click_" + cid, 0.0),
+                    midActionStatic.getOrDefault("actionstatic_view_" + cid, 0.0)
+            );
+            featureMap.put("actionstatic_ctr", String.valueOf(ctr));
+        }
+        if (midActionStatic.containsKey("actionstatic_view_" + cid) && midActionStatic.containsKey("actionstatic_conver_" + cid)) {
+            double ctcvr = NumUtil.div(midActionStatic.getOrDefault("actionstatic_conver_" + cid, 0.0), midActionStatic.getOrDefault("actionstatic_view_" + cid, 0.0));
+            featureMap.put("actionstatic_ctcvr", String.valueOf(ctcvr));
+        }
+        if (midActionStatic.containsKey("actionstatic_conver_" + cid) && midActionStatic.containsKey("actionstatic_click_" + cid)) {
+            double cvr = NumUtil.div(midActionStatic.getOrDefault("actionstatic_conver_" + cid, 0.0), midActionStatic.getOrDefault("actionstatic_click_" + cid, 0.0));
+            featureMap.put("actionstatic_cvr", String.valueOf(cvr));
+        }
+    }
+
+    private void handleD1Feature(Map<String, String> d1Feature, Map<String, String> featureMap) {
+        for (String prefix : Arrays.asList("3h", "6h", "12h", "1d", "3d", "7d")) {
+            double view = Double.parseDouble(d1Feature.getOrDefault("ad_view_" + prefix, "0"));
+            double click = Double.parseDouble(d1Feature.getOrDefault("ad_click_" + prefix, "0"));
+            double conver = Double.parseDouble(d1Feature.getOrDefault("ad_conversion_" + prefix, "0"));
+            double income = Double.parseDouble(d1Feature.getOrDefault("ad_income_" + prefix, "0"));
+            double cpc = NumUtil.div(income, click);
+            double ctr = NumUtil.divSmoothV2(click, view, CTR_SMOOTH_BETA_FACTOR);
+            featureMap.put("d1_feature_" + prefix + "_ctr", String.valueOf(ctr));
+            featureMap.put("d1_feature_" + prefix + "_ctcvr", String.valueOf(NumUtil.divSmoothV2(conver, view, CTCVR_SMOOTH_BETA_FACTOR)));
+            featureMap.put("d1_feature_" + prefix + "_cvr", String.valueOf(NumUtil.divSmoothV2(conver, click, CVR_SMOOTH_BETA_FACTOR)));
+            featureMap.put("d1_feature_" + prefix + "_conver", String.valueOf(conver));
+            featureMap.put("d1_feature_" + prefix + "_ecpm", String.valueOf(ctr * cpc * 1000));
+        }
+    }
+
+    private void handleD2Feature(Map<String, Map<String, Double>> vidRankMaps, Map<String, String> featureMap, String cid) {
+        if (MapUtils.isEmpty(vidRankMaps)) {
+            return;
+        }
+
+        List<String> prefixes1 = Arrays.asList("ctr", "ctcvr", "ecpm");
+        // List<String> prefixes1 = Arrays.asList("ctr", "ctcvr");
+        List<String> prefixes2 = Arrays.asList("1d", "3d", "7d", "14d");
+
+        for (String prefix1 : prefixes1) {
+            for (String prefix2 : prefixes2) {
+                String combinedKey = prefix1 + "_" + prefix2;
+                if (vidRankMaps.containsKey(combinedKey)) {
+                    Double rank = vidRankMaps.get(combinedKey).getOrDefault(cid, 0.0);
+                    if (rank >= 1.0) {
+                        featureMap.put("vid_rank_" + combinedKey, String.valueOf(NumUtil.div(1, rank)));
+                    }
+                }
+            }
+        }
+    }
+
+    private void handleH1AndH2Feature(Map<String, Map<String, String>> skuFeature,
+                                      Map<String, Map<String, String>> adVerFeature,
+                                      Map<String, String> cidFeatureMap) {
+        Map<String, String> h1Feature = adVerFeature.getOrDefault("alg_mid_feature_adver_action", EMPTY_STRING_MAP);
+        Map<String, String> h2Feature = skuFeature.getOrDefault("alg_mid_feature_sku_action", EMPTY_STRING_MAP);
+        List<String> timeList = Arrays.asList("3d", "7d", "30d");
+        List<Tuple2<Map<String, String>, String>> featureList = Arrays.asList(
+                new Tuple2<>(h1Feature, "adverid"),
+                new Tuple2<>(h2Feature, "skuid")
+        );
+        for (Tuple2<Map<String, String>, String> tuple2 : featureList) {
+            Map<String, String> feature = tuple2.f1;
+            String prefix = tuple2.f2;
+            for (String time : timeList) {
+                String timeValue = feature.getOrDefault(time, "");
+                if (StringUtils.isNotEmpty(timeValue)) {
+                    String[] split = timeValue.split(",");
+                    cidFeatureMap.put("user" + "_" + prefix + "_" + "view" + "_" + time, split[0]);
+                    cidFeatureMap.put("user" + "_" + prefix + "_" + "click" + "_" + time, split[1]);
+                    cidFeatureMap.put("user" + "_" + prefix + "_" + "conver" + "_" + time, split[2]);
+                }
+            }
+        }
+
+
+    }
+
+    private void handleD3AndB1Feature(Map<String, String> d3Feature, String cTitle, Map<String, String> featureMap,
+                                      ScoreParam scoreParam) {
+        if (MapUtils.isEmpty(d3Feature) || !d3Feature.containsKey("title") || StringUtils.isEmpty(cTitle)) {
+            return;
+        }
+        String vTitle = d3Feature.get("title");
+        double score;
+        if (scoreParam.getExpCodeSet().contains(word2vecExp)) {
+            score = SimilarityUtils.word2VecSimilarity(cTitle, vTitle);
+        } else {
+            score = Similarity.conceptSimilarity(cTitle, vTitle);
+        }
+        featureMap.put("ctitle_vtitle_similarity", String.valueOf(score));
+    }
+
+    private void handleE1AndE2Feature(Map<String, String> e1Feature, Map<String, String> e2Feature, String title,
+                                      Map<String, String> featureMap, ScoreParam scoreParam,
+                                      Map<String, List<String>> tagWordsCache) {
+        if (StringUtils.isEmpty(title)) {
+            return;
+        }
+
+        // 预先分词 title，在整个方法中复用，避免重复分词
+        List<String> titleWords = null;
+        if (scoreParam.getExpCodeSet().contains(word2vecExp)) {
+            titleWords = SimilarityUtils.segment(title);
+        }
+
+        List<Tuple2<Map<String, String>, String>> tuple2List = Arrays.asList(new Tuple2<>(e1Feature, "e1"), new Tuple2<>(e2Feature, "e2"));
+
+        List<String> tagsFieldList = Arrays.asList("tags_3d", "tags_7d", "tags_14d");
+        for (Tuple2<Map<String, String>, String> tuple2 : tuple2List) {
+            Map<String, String> feature = tuple2.f1;
+            String prefix = tuple2.f2;
+            if (MapUtils.isEmpty(feature)) {
+                continue;
+            }
+
+            for (String tagsField : tagsFieldList) {
+                if (StringUtils.isNotEmpty(feature.get(tagsField))) {
+                    String tags = feature.get(tagsField);
+                    Double[] doubles;
+                    if (scoreParam.getExpCodeSet().contains(word2vecExp)) {
+                        // 使用缓存的 title 分词结果和请求级别的 tag 分词缓存
+                        doubles = ExtractorUtils.funcC34567ForTagsNewWithCache(tags, title, titleWords, tagWordsCache);
+                    } else {
+                        doubles = ExtractorUtils.funcC34567ForTags(tags, title);
+                    }
+                    featureMap.put(prefix + "_" + tagsField + "_matchnum", String.valueOf(doubles[0]));
+                    featureMap.put(prefix + "_" + tagsField + "_maxscore", String.valueOf(doubles[1]));
+                    featureMap.put(prefix + "_" + tagsField + "_avgscore", String.valueOf(doubles[2]));
+                }
+            }
+        }
+    }
+
+    private Map<String, Double> parseC1FeatureListToTimeDiffMap(List<TupleMapEntry<Tuple5>> midActionList, long ts) {
+        Map<String, Double> midTimeDiffMap = new HashMap<>();
+        for (TupleMapEntry<Tuple5> entry : midActionList) {
+            String cid = entry.key;
+            double tsHistory = Double.parseDouble(entry.value.f1);
+            double click = Double.parseDouble(entry.value.f2);
+            double conver = Double.parseDouble(entry.value.f3);
+            double d = (ts - tsHistory) / 3600 / 24;
+            if (!midTimeDiffMap.containsKey("timediff_view_" + cid)) {
+                midTimeDiffMap.put("timediff_view_" + cid, NumUtil.div(1, d));
+            }
+            if (!midTimeDiffMap.containsKey("timediff_click_" + cid) && click > 0) {
+                midTimeDiffMap.put("timediff_click_" + cid, NumUtil.div(1, d));
+            }
+            if (!midTimeDiffMap.containsKey("timediff_conver_" + cid) && conver > 0) {
+                midTimeDiffMap.put("timediff_conver_" + cid, NumUtil.div(1, d));
+            }
+        }
+        return midTimeDiffMap;
+    }
+
+    private Map<String, Double> parseC1FeatureListToActionStaticMap(List<TupleMapEntry<Tuple5>> midActionList) {
+        Map<String, Double> midActionStaticsMap = new HashMap<>();
+        for (TupleMapEntry<Tuple5> entry : midActionList) {
+            String cid = entry.key;
+            double click = Double.parseDouble(entry.value.f2);
+            double conver = Double.parseDouble(entry.value.f3);
+            double income = Double.parseDouble(entry.value.f4);
+
+            Double viewSum = midActionStaticsMap.getOrDefault("actionstatic_view_" + cid, 0.0);
+            midActionStaticsMap.put("actionstatic_view_" + cid, 1 + viewSum);
+
+            Double clickSum = midActionStaticsMap.getOrDefault("actionstatic_click_" + cid, 0.0);
+            midActionStaticsMap.put("actionstatic_click_" + cid, clickSum + click);
+
+            Double converSum = midActionStaticsMap.getOrDefault("actionstatic_conver_" + cid, 0.0);
+            midActionStaticsMap.put("actionstatic_conver_" + cid, converSum + conver);
+
+            Double incomSum = midActionStaticsMap.getOrDefault("actionstatic_income_" + cid, 0.0);
+            midActionStaticsMap.put("actionstatic_income_" + cid, incomSum + income);
+        }
+
+        return midActionStaticsMap;
+    }
+
+    private Map<String, Map<String, Double>> parseD2FeatureMap(Map<String, String> d2Feature) {
+        Map<String, Map<String, Double>> vidRankMaps = new HashMap<>();
+        for (Map.Entry<String, String> entry : d2Feature.entrySet()) {
+            String key = entry.getKey();
+            String value = entry.getValue();
+            Map<String, Double> valueMap = Arrays.stream(value.split(",")).map(r -> r.split(":")).collect(Collectors.toMap(rList -> rList[0], rList -> Double.parseDouble(rList[2])));
+            vidRankMaps.put(key, valueMap);
+        }
+        return vidRankMaps;
+    }
+
+    private void readBucketFile() {
+        if (MapUtils.isNotEmpty(bucketsMap)) {
+            return;
+        }
+        synchronized (this) {
+            String bucketFile = "20260410_ad_bucket_920.txt";
+            InputStream resourceStream = this.getClass().getClassLoader().getResourceAsStream(bucketFile);
+            if (resourceStream != null) {
+                try (BufferedReader reader = new BufferedReader(new InputStreamReader(resourceStream))) {
+                    Map<String, double[]> bucketsMap = new HashMap<>();
+                    Map<String, Double> bucketsLen = new HashMap<>();
+                    String line;
+                    while ((line = reader.readLine()) != null) {
+                        // 替换空格和换行符，过滤空行
+                        line = line.replace(" ", "").replaceAll("\n", "");
+                        if (!line.isEmpty()) {
+                            String[] rList = line.split("\t");
+                            if (rList.length == 3) {
+                                String key = rList[0];
+                                double value1 = Double.parseDouble(rList[1]);
+                                bucketsLen.put(key, value1);
+                                double[] value2 = Arrays.stream(rList[2].split(",")).mapToDouble(Double::valueOf).toArray();
+                                bucketsMap.put(key, value2);
+                            }
+                        }
+                    }
+                    this.bucketsMap = bucketsMap;
+                    this.bucketsLen = bucketsLen;
+                } catch (IOException e) {
+                    log.error("something is wrong in parse bucket file: ", e);
+                }
+                log.info("load bucket file success: {}", bucketFile);
+            } else {
+                log.error("no bucket file");
+            }
+        }
+    }
+
+    private void initSparseFeatureNames() {
+        this.sparseFeatureSet = new HashSet<String>() {{
+            add("brand");
+            add("region");
+            add("city");
+            add("vid");
+            add("cate1");
+            add("cate2");
+            add("cid");
+            add("adid");
+            add("adverid");
+            add("user_cid_click_list");
+            add("user_cid_conver_list");
+            add("user_vid_return_tags_2h");
+            add("user_vid_return_tags_1d");
+            add("user_vid_return_tags_3d");
+            add("user_vid_return_tags_7d");
+            add("user_vid_return_tags_14d");
+            add("apptype");
+            add("hour");
+            add("hour_quarter");
+            add("root_source_scene");
+            add("root_source_channel");
+            add("is_first_layer");
+            add("title_split");
+            add("profession");
+            add("user_vid_share_tags_1d");
+            add("user_vid_share_tags_14d");
+            add("user_vid_return_cate1_14d");
+            add("user_vid_return_cate2_14d");
+            add("user_vid_share_cate1_14d");
+            add("user_vid_share_cate2_14d");
+            add("user_has_conver_1y");
+            add("user_adverid_view_3d");
+            add("user_adverid_click_3d");
+            add("user_adverid_conver_3d");
+            add("user_adverid_view_7d");
+            add("user_adverid_click_7d");
+            add("user_adverid_conver_7d");
+            add("user_adverid_view_30d");
+            add("user_adverid_click_30d");
+            add("user_adverid_conver_30d");
+            add("user_skuid_view_3d");
+            add("user_skuid_click_3d");
+            add("user_skuid_conver_3d");
+            add("user_skuid_view_7d");
+            add("user_skuid_click_7d");
+            add("user_skuid_conver_7d");
+            add("user_skuid_view_30d");
+            add("user_skuid_click_30d");
+            add("user_skuid_conver_30d");
+            add("user_conver_ad_class");
+            add("category_name");
+            add("material_md5");
+            add("ad_profession_id");
+            add("ad_profession_name");
+            add("ad_category_id");
+            add("ad_category_name");
+            add("ad_sku_id");
+            add("ad_sku_code");
+            add("ad_sku_name");
+            add("user_layer");
+            add("flag");
+            add("customer");
+            add("landing");
+        }};
+    }
+
+    private Map<String, String> featureBucket(Map<String, String> featureMap) {
+        // 使用 HashMap 替代 ConcurrentHashMap，分桶操作是单线程的
+        Map<String, String> newFeatureMap = new HashMap<>(featureMap.size());
+        for (Map.Entry<String, String> entry : featureMap.entrySet()) {
+            try {
+                String name = entry.getKey();
+                if (this.sparseFeatureSet.contains(name)) {
+                    if (entry.getValue() != null) {
+                        newFeatureMap.put(name, entry.getValue());
+                    }
+                    continue;
+                }
+                double score = Double.parseDouble(entry.getValue());
+                // 注意：0值、不在分桶文件中的特征，会被过滤掉。
+                if (score > 1E-8) {
+                    if (this.bucketsMap.containsKey(name) && this.bucketsLen.containsKey(name)) {
+                        double[] buckets = this.bucketsMap.get(name);
+                        double bucketNum = this.bucketsLen.get(name);
+                        Double scoreNew = 1.0 / bucketNum * (ExtractorUtils.findInsertPosition(buckets, score) + 1.0);
+                        newFeatureMap.put(name, String.valueOf(scoreNew));
+                    } else {
+                        newFeatureMap.put(name, String.valueOf(score));
+                    }
+                }
+            } catch (Exception e) {
+                log.error("featureBucket error: ", e);
+            }
+        }
+        return newFeatureMap;
+    }
+
+    private double getExpWeight(Map<String, String> featureMap,
+                                String expOldKey, double expOldThreshold,
+                                String expNewKey, double expNewThreshold,
+                                double expLowerWeight, double expUpperWeight, double expScale) {
+        try {
+            if (null != featureMap) {
+                double oldView = Double.parseDouble(featureMap.getOrDefault(expOldKey, "0"));
+                if (oldView < expOldThreshold) {
+                    double newView = Double.parseDouble(featureMap.getOrDefault(expNewKey, "0"));
+                    return getExpWeight(expLowerWeight, expUpperWeight, expScale, expNewThreshold, newView);
+                }
+            }
+        } catch (Exception e) {
+            log.error("getExpWeight error: ", e);
+        }
+        return 1.0;
+    }
+
+    private double getExpWeight(double lowerWeight, double upperWeight, double scale, double upperExp, double exp) {
+        if (exp >= upperExp) {
+            return 1.0;
+        }
+        double weight = Math.log(exp + 1) / scale;
+        return Math.min(Math.max(lowerWeight, weight), upperWeight);
+    }
+
+    private void calibrationCidCtcvr(List<AdRankItem> items, String modelName, Map<String, String> reqFeature,
+                                     double minValid, double maxValid,
+                                     double minVal, double maxVal) {
+        try {
+            String layer = reqFeature.get("layer_l4");
+            if (Boolean.TRUE.equals(copcV3Enabled)) {
+                for (AdRankItem item : items) {
+                    Double coefficient = CopcV3DataHelper.getLandingCoefficient(item.getLrScore(),
+                            item.getLandingPageType(), item.getTargetingConversion());
+//                    log.info("CopcV3DataHelper originScore={}, pageType={},target={},result={}",item.getLrScore(), item.getLandingPageType(),item.getTargetingConversion(),coefficient);
+                    if (coefficient != null) {
+                        double ctcvr = item.getLrScore() * coefficient;
+                        item.getScoreMap().put("copcV3Coefficient", coefficient);
+                        item.getScoreMap().put("cidCtcvrScore", ctcvr);
+                        item.getScoreMap().put("ctcvrScore", ctcvr);
+                        item.setLrScore(ctcvr);
+                    }
+                }
+            }else {
+                for (AdRankItem item : items) {
+                    String cid = String.valueOf(item.getAdId());
+                    Double diff = CreativeUserLayerDataHelperV2.getCopc(modelName, cid, layer);
+                    if (null != diff) {
+                        double newDiff = Math.min(Math.max(minVal, diff), maxVal);
+                        if (newDiff < minValid || newDiff > maxValid) {
+                            double ctcvr = item.getLrScore() * newDiff;
+                            item.getScoreMap().put("cidCtcvrScore", ctcvr);
+                            item.getScoreMap().put("ctcvrScore", ctcvr);
+                            item.getScoreMap().put("copcDiff", diff);
+                            item.setLrScore(ctcvr);
+                        }
+                    }
+                }
+            }
+        } catch (Exception e) {
+            log.error("calibCidScore error", e);
+        }
+    }
+}

ad-engine-service/src/main/java/com/tzld/piaoquan/ad/engine/service/score/strategy/RankStrategyBy872.java

@@ -0,0 +1,1133 @@
+package com.tzld.piaoquan.ad.engine.service.score.strategy;
+
+import com.alibaba.fastjson.JSONObject;
+import com.ctrip.framework.apollo.spring.annotation.ApolloJsonValue;
+import com.tzld.piaoquan.ad.engine.commons.dto.AdPlatformCreativeDTO;
+import com.tzld.piaoquan.ad.engine.commons.helper.CreativeUserLayerDataHelper;
+import com.tzld.piaoquan.ad.engine.commons.helper.DnnCidDataHelper;
+import com.tzld.piaoquan.ad.engine.commons.param.RankRecommendRequestParam;
+import com.tzld.piaoquan.ad.engine.commons.score.ScoreParam;
+import com.tzld.piaoquan.ad.engine.commons.score.ScorerUtils;
+import com.tzld.piaoquan.ad.engine.commons.thread.ThreadPoolFactory;
+import com.tzld.piaoquan.ad.engine.commons.util.*;
+import com.tzld.piaoquan.ad.engine.service.entity.CorrectCpaParam;
+import com.tzld.piaoquan.ad.engine.service.entity.GuaranteeView;
+import com.tzld.piaoquan.ad.engine.service.feature.Feature;
+import com.tzld.piaoquan.recommend.feature.domain.ad.base.AdRankItem;
+import lombok.extern.slf4j.Slf4j;
+import org.apache.commons.collections4.CollectionUtils;
+import org.apache.commons.collections4.MapUtils;
+import org.apache.commons.lang.math.NumberUtils;
+import org.apache.commons.lang3.StringUtils;
+import org.springframework.beans.factory.annotation.Value;
+import org.springframework.stereotype.Component;
+import org.xm.Similarity;
+
+import javax.annotation.PostConstruct;
+import java.io.BufferedReader;
+import java.io.IOException;
+import java.io.InputStream;
+import java.io.InputStreamReader;
+import java.util.*;
+import java.util.concurrent.ConcurrentHashMap;
+import java.util.concurrent.CountDownLatch;
+import java.util.concurrent.Future;
+import java.util.concurrent.TimeUnit;
+import java.util.stream.Collectors;
+
+import static com.tzld.piaoquan.ad.engine.commons.math.Const.*;
+
+@Slf4j
+@Component
+public class RankStrategyBy872 extends RankStrategyBasic {
+
+    /**
+     * 空 Map 常量，避免频繁创建空 HashMap
+     */
+    private static final Map<String, String> EMPTY_STRING_MAP = Collections.emptyMap();
+    private static final Map<String, Map<String, String>> EMPTY_NESTED_MAP = Collections.emptyMap();
+
+    private Map<String, double[]> bucketsMap = new HashMap<>();
+
+    private Map<String, Double> bucketsLen = new HashMap<>();
+
+    @Value("${word2vec.exp:694}")
+    private String word2vecExp;
+
+    @ApolloJsonValue("${rank.score.params.851:{}}")
+    private Map<String, String> paramsMap;
+
+    // FIXME(zhoutian): 可能需要独立配置
+    @ApolloJsonValue("${rank.score.weight.680:{}}")
+    private Map<String, Double> weightMap;
+
+    /**
+     * 人群分层&创意的权重
+     * 格式：{layer_creativeId: weight}
+     */
+    @ApolloJsonValue("${rank.score.weight.layer.and.creative:{}}")
+    private Map<String, Double> layerAndCreativeWeightMap;
+
+    @ApolloJsonValue("${rank.score.neg_sample_rate:0.01}")
+    Double negSampleRate;
+
+    Set<String> sparseFeatureSet;
+
+
+    @PostConstruct
+    public void afterInit() {
+        this.readBucketFile();
+        this.initSparseFeatureNames();
+    }
+
+
+    @Override
+    public List<AdRankItem> adItemRank(RankRecommendRequestParam request, ScoreParam scoreParam) {
+
+        Map<String, Double> weightParam = ObjUtil.nullOrDefault(weightMap, new HashMap<>());
+
+
+        Map<Long, Double> creativeScoreCoefficient = getCreativeScoreCoefficient();
+        Set<String> noApiAdVerIds = getNoApiAdVerIds();
+
+        long ts = System.currentTimeMillis() / 1000;
+
+        String brand = scoreParam.getRequestContext().getMachineinfoBrand();
+        if (StringUtils.isNotEmpty(brand)) {
+            scoreParam.getRequestContext().setMachineinfoBrand(brand + "-n");
+        }
+
+        long start = System.currentTimeMillis();
+        //过滤创意
+        filterRequestAdList(request, scoreParam);
+        // 特征处理
+
+        Map<String, String> reqFeature = this.getReqFeature(scoreParam, request);
+        String userLayer = reqFeature.get("layer_l4");
+        // feature1
+        Feature feature = this.getFeatureV2(userLayer, scoreParam, request);
+        if (feature == null) {
+            log.warn("adItemRank: feature is null, skip processing. request={}", request);
+            return new ArrayList<>();
+        }
+
+        Map<String, Map<String, String>> userFeature = feature.getUserFeature();
+        Map<String, Map<String, String>> videoFeature = feature.getVideoFeature();
+        Map<String, Map<String, Map<String, String>>> allAdVerFeature = feature.getAdVerFeature();
+        Map<String, Map<String, Map<String, String>>> allCidFeature = feature.getCidFeature();
+        Map<String, Map<String, Map<String, String>>> allSkuFeature = feature.getSkuFeature();
+        Map<String, Map<String, Map<String, String>>> otherFeature = feature.getOtherFeature();
+
+        Map<String, String> userFeatureMap = new HashMap<>();
+        Map<String, String> c1Feature = userFeature.getOrDefault("alg_mid_feature_ad_action", EMPTY_STRING_MAP);
+        List<TupleMapEntry<Tuple5>> midActionList = this.handleC1Feature(c1Feature, userFeatureMap);
+
+        Map<String, Double> midTimeDiffMap = this.parseC1FeatureListToTimeDiffMap(midActionList, ts);
+        Map<String, Double> actionStaticMap = this.parseC1FeatureListToActionStaticMap(midActionList);
+
+        Map<String, String> d2Feature = videoFeature.getOrDefault("alg_cid_feature_vid_cf_rank", EMPTY_STRING_MAP);
+        Map<String, String> d3Feature = videoFeature.getOrDefault("alg_vid_feature_basic_info", EMPTY_STRING_MAP);
+
+        Map<String, Map<String, Double>> vidRankMaps = this.parseD2FeatureMap(d2Feature);
+
+        Map<String, String> e1Feature = userFeature.getOrDefault("alg_mid_feature_return_tags", EMPTY_STRING_MAP);
+        Map<String, String> e2Feature = userFeature.getOrDefault("alg_mid_feature_share_tags", EMPTY_STRING_MAP);
+
+        Map<String, String> g1Feature = userFeature.getOrDefault("mid_return_video_cate", EMPTY_STRING_MAP);
+        Map<String, String> g2Feature = userFeature.getOrDefault("mid_share_video_cate", EMPTY_STRING_MAP);
+
+
+        userFeatureMap.put("brand", reqFeature.getOrDefault("brand", ""));
+        userFeatureMap.put("region", reqFeature.getOrDefault("region", ""));
+        userFeatureMap.put("city", reqFeature.getOrDefault("city", ""));
+        userFeatureMap.put("vid", reqFeature.getOrDefault("vid", ""));
+        userFeatureMap.put("apptype", reqFeature.getOrDefault("apptype", ""));
+        userFeatureMap.put("is_first_layer", reqFeature.getOrDefault("is_first_layer", ""));
+        userFeatureMap.put("root_source_scene", reqFeature.getOrDefault("root_source_scene", ""));
+        userFeatureMap.put("root_source_channel", reqFeature.getOrDefault("root_source_channel", ""));
+
+
+        userFeatureMap.put("cate1", d3Feature.get("merge_first_level_cate"));
+        userFeatureMap.put("cate2", d3Feature.get("merge_second_level_cate"));
+        userFeatureMap.put("user_vid_return_tags_2h", e1Feature.getOrDefault("tags_2h", null));
+        userFeatureMap.put("user_vid_return_tags_1d", e1Feature.getOrDefault("tags_1d", null));
+        userFeatureMap.put("user_vid_return_tags_3d", e1Feature.getOrDefault("tags_3d", null));
+        userFeatureMap.put("user_vid_return_tags_7d", e1Feature.getOrDefault("tags_7d", null));
+        userFeatureMap.put("user_vid_return_tags_14d", e1Feature.getOrDefault("tags_14d", null));
+        userFeatureMap.put("title_split", d3Feature.getOrDefault("title_split", null));
+        userFeatureMap.put("user_vid_share_tags_1d", e2Feature.getOrDefault("tags_1d", null));
+        userFeatureMap.put("user_vid_share_tags_14d", e2Feature.getOrDefault("tags_14d", null));
+        userFeatureMap.put("user_vid_return_cate1_14d", g1Feature.getOrDefault("cate1_14d", null));
+        userFeatureMap.put("user_vid_return_cate2_14d", g1Feature.getOrDefault("cate2_14d", null));
+        userFeatureMap.put("user_vid_share_cate1_14d", g2Feature.getOrDefault("cate1_14d", null));
+        userFeatureMap.put("user_vid_share_cate2_14d", g2Feature.getOrDefault("cate2_14d", null));
+        userFeatureMap.put("user_layer", reqFeature.getOrDefault("layer_l4", ""));
+        userFeatureMap.put("flag", "1");
+
+        Map<String, String> sceneFeatureMap = this.handleSceneFeature(ts);
+        long time1 = System.currentTimeMillis();
+
+        boolean isGuaranteedFlow = getIsGuaranteedFlow(scoreParam);
+        Map<String, GuaranteeView> map = getGuaranteeViewMap(request, isGuaranteedFlow);
+        Map<Long, CorrectCpaParam> correctCpaMap = getCorrectCpaParamMap(request, scoreParam, reqFeature);
+        List<AdRankItem> adRankItems = new ArrayList<>();
+        Random random = new Random();
+        List<Future<AdRankItem>> futures = new ArrayList<>();
+        CountDownLatch cdl1 = new CountDownLatch(request.getAdIdList().size());
+        for (AdPlatformCreativeDTO dto : request.getAdIdList()) {
+            Future<AdRankItem> future = ThreadPoolFactory.feature().submit(() -> {
+                AdRankItem adRankItem = new AdRankItem();
+                try {
+                    adRankItem.setAdId(dto.getCreativeId());
+                    adRankItem.setCreativeCode(dto.getCreativeCode());
+                    adRankItem.setAdVerId(dto.getAdVerId());
+                    adRankItem.setVideoId(request.getVideoId());
+                    adRankItem.setCpa(dto.getCpa());
+                    adRankItem.setId(dto.getAdId());
+                    adRankItem.setCampaignId(dto.getCampaignId());
+                    adRankItem.setCpm(ObjUtil.nullOrDefault(dto.getCpm(), 90).doubleValue());
+                    adRankItem.setSkuId(dto.getSkuId());
+                    adRankItem.setCustomerId(dto.getCustomerId());
+                    adRankItem.setAgentId(dto.getAgentId());
+                    adRankItem.setProfession(dto.getProfession());
+                    adRankItem.setLandingPageType(dto.getLandingPageType());
+                    adRankItem.setTargetingConversion(dto.getTargetingConversion());
+                    adRankItem.setRandom(random.nextInt(1000));
+                    if (noApiAdVerIds.contains(dto.getAdVerId())) {
+                        adRankItem.getExt().put("isApi", "0");
+                    } else {
+                        adRankItem.getExt().put("isApi", "1");
+                    }
+                    adRankItem.getExt().put("recallsources", dto.getRecallSources());
+                    fillAdRankItemExt(adRankItem, dto);
+                    adRankItem.getExt().put("correctCpaMap", JSONObject.toJSONString(correctCpaMap.get(dto.getAdId())));
+                    adRankItem.getExt().put("correctionFactor", correctCpaMap.get(dto.getAdId()).getCorrectionFactor());
+                    setGuaranteeWeight(map, dto.getAdVerId(), adRankItem.getExt(), isGuaranteedFlow, reqFeature);
+                    String cidStr = dto.getCreativeId().toString();
+                    Map<String, String> cidFeatureMap = adRankItem.getFeatureMap();
+                    Map<String, Map<String, String>> cidFeature = allCidFeature.getOrDefault(cidStr, EMPTY_NESTED_MAP);
+                    Map<String, String> b1Feature = cidFeature.getOrDefault("alg_cid_feature_basic_info", EMPTY_STRING_MAP);
+
+                    Map<String, Map<String, String>> adVerFeature = allAdVerFeature.getOrDefault(dto.getAdVerId(), EMPTY_NESTED_MAP);
+                    Map<String, Map<String, String>> skuFeature = allSkuFeature.getOrDefault(String.valueOf(dto.getSkuId()), EMPTY_NESTED_MAP);
+                    Map<String, String> d1Feature = cidFeature.getOrDefault("alg_cid_feature_vid_cf", EMPTY_STRING_MAP);
+
+                    this.handleB1Feature(b1Feature, cidFeatureMap, cidStr);
+                    this.handleB2ToB5AndB8ToB9Feature(cidFeature, adVerFeature, cidFeatureMap);
+                    this.handleB6ToB7Feature(cidFeature, cidFeatureMap);
+                    this.handleJ1ToJ11Feature(userLayer, dto, otherFeature, cidFeatureMap);
+                    this.handleC1UIFeature(midTimeDiffMap, actionStaticMap, cidFeatureMap, cidStr);
+                    this.handleD1Feature(d1Feature, cidFeatureMap);
+                    this.handleD2Feature(vidRankMaps, cidFeatureMap, cidStr);
+                    this.handleH1AndH2Feature(skuFeature, adVerFeature, cidFeatureMap);
+                    cidFeatureMap.put("cid", dto.getCreativeId() != null ? String.valueOf(dto.getCreativeId()) : "");
+                    cidFeatureMap.put("adid", dto.getAdId() != null ? String.valueOf(dto.getAdId()) : "");
+                    cidFeatureMap.put("adverid", dto.getAdVerId() != null ? dto.getAdVerId() : "");
+                    cidFeatureMap.put("profession", dto.getProfession() != null ? dto.getProfession() : "");
+                    cidFeatureMap.put("category_name", dto.getCategoryName() != null ? dto.getCategoryName() : "");
+                    cidFeatureMap.put("material_md5", dto.getMaterialMd5() != null ? dto.getMaterialMd5() : "");
+                    cidFeatureMap.put("ad_profession_id", dto.getAdProfessionId() != null ? String.valueOf(dto.getAdProfessionId()) : "");
+                    cidFeatureMap.put("ad_profession_name", dto.getAdProfessionName() != null ? dto.getAdProfessionName() : "");
+                    cidFeatureMap.put("ad_category_name", dto.getAdCategoryName() != null ? dto.getAdCategoryName() : "");
+                    cidFeatureMap.put("ad_category_id", dto.getAdCategoryId() != null ? String.valueOf(dto.getAdCategoryId()) : "");
+                    cidFeatureMap.put("ad_sku_id", dto.getAdSkuId() != null ? String.valueOf(dto.getAdSkuId()) : "");
+                    cidFeatureMap.put("ad_sku_code", dto.getAdSkuCode() != null ? dto.getAdSkuCode() : "");
+                    cidFeatureMap.put("ad_sku_name", dto.getAdSkuName() != null ? dto.getAdSkuName() : "");
+                    cidFeatureMap.put("customer", dto.getCustomerId() != null ? String.valueOf(dto.getCustomerId()) : "");
+                    cidFeatureMap.put("landing", dto.getLandingPageType() != null ? String.valueOf(dto.getLandingPageType()) : "");
+                    //DNN模型没训练过的cid才不传入广告相关的稀疏特征
+                    if (CollectionUtils.isNotEmpty(DnnCidDataHelper.getCidSet()) && !DnnCidDataHelper.getCidSet().contains(adRankItem.getAdId())) {
+                        cidFeatureMap.put("cid", "");
+                        cidFeatureMap.put("adid", "");
+                        cidFeatureMap.put("adverid", "");
+                        cidFeatureMap.put("customer", "");
+                    }
+                    return adRankItem;
+                } finally {
+                    cdl1.countDown();
+                }
+            });
+            futures.add(future);
+        }
+        try {
+            cdl1.await(300, TimeUnit.MILLISECONDS);
+        } catch (Exception e) {
+            log.error("handleE1AndE2Feature and handleD3AndB1Feature wait timeout", e);
+        }
+        for (Future<AdRankItem> future : futures) {
+            try {
+                if (future.isDone()) {
+                    adRankItems.add(future.get());
+                }
+            } catch (Exception e) {
+                log.error("Feature handle error", e);
+            }
+        }
+
+        long time2 = System.currentTimeMillis();
+        // feature3
+        // 请求级别的 tag 分词缓存，所有广告共享（同一用户的 tags 相同）
+        Map<String, List<String>> tagWordsCache = new ConcurrentHashMap<>();
+        CountDownLatch cdl2 = new CountDownLatch(adRankItems.size() * 2);
+        for (AdRankItem item : adRankItems) {
+            String cidStr = String.valueOf(item.getAdId());
+            Map<String, Map<String, String>> cidFeature = allCidFeature.getOrDefault(cidStr, EMPTY_NESTED_MAP);
+            Map<String, String> b1Feature = cidFeature.getOrDefault("alg_cid_feature_basic_info", EMPTY_STRING_MAP);
+            String title = b1Feature.getOrDefault("cidtitle", "");
+            ThreadPoolFactory.defaultPool().submit(() -> {
+                try {
+                    this.handleE1AndE2Feature(e1Feature, e2Feature, title, item.getFeatureMap(), scoreParam, tagWordsCache);
+                } finally {
+                    cdl2.countDown();
+                }
+            });
+            ThreadPoolFactory.defaultPool().submit(() -> {
+                try {
+                    this.handleD3AndB1Feature(d3Feature, title, item.getFeatureMap(), scoreParam);
+                } finally {
+                    cdl2.countDown();
+                }
+            });
+        }
+        try {
+            cdl2.await(150, TimeUnit.MILLISECONDS);
+        } catch (Exception e) {
+            log.error("handleE1AndE2Feature and handleD3AndB1Feature wait timeout", e);
+        }
+
+        long time3 = System.currentTimeMillis();
+        // 分桶
+        userFeatureMap = this.featureBucket(userFeatureMap);
+        CountDownLatch cdl4 = new CountDownLatch(adRankItems.size());
+        for (AdRankItem adRankItem : adRankItems) {
+            ThreadPoolFactory.feature().submit(() -> {
+                try {
+                    Map<String, String> featureMap = adRankItem.getFeatureMap();
+                    adRankItem.setFeatureMap(this.featureBucket(featureMap));
+                } finally {
+                    cdl4.countDown();
+                }
+            });
+        }
+        try {
+            cdl4.await(100, TimeUnit.MILLISECONDS);
+        } catch (Exception e) {
+            log.error("handleE1AndE2Feature and handleD3AndB1Feature wait timeout", e);
+        }
+        long time4 = System.currentTimeMillis();
+        // 打分排序
+        // getScorerPipeline
+
+        if (CollectionUtils.isEmpty(adRankItems)) {
+            log.error("adRankItems is empty");
+        }
+        List<AdRankItem> result = ScorerUtils.getScorerPipeline(ScorerUtils.PAI_SCORE_CONF_20250804).scoring(sceneFeatureMap, userFeatureMap, adRankItems);
+        if (CollectionUtils.isEmpty(result)) {
+            log.error("scoring result is empty");
+        }
+        long time5 = System.currentTimeMillis();
+        int viewLimit = NumberUtils.toInt(paramsMap.getOrDefault("viewLimit", "3000"));
+        // calibrate score for negative sampling or cold start
+        for (AdRankItem item : result) {
+            double scanScore = item.getScoreMap().getOrDefault("scanScore", 0.0);
+            double addWechatScore = item.getScoreMap().getOrDefault("addWechatScore", 0.0);
+            double conversionScore = item.getScoreMap().getOrDefault("conversionScore", 0.0);
+            double calibratedScanScore = scanScore / (scanScore + (1 - scanScore) / negSampleRate);
+            double calibratedConversionScore = conversionScore / (conversionScore + (1 - conversionScore) / negSampleRate);
+            item.getScoreMap().put("calibratedScanScore", calibratedScanScore);
+            item.getScoreMap().put("calibratedConversionScore", calibratedConversionScore);
+            double calibratedScore = 0.0;
+            if (Objects.equals(item.getLandingPageType(), 3) && Objects.equals(item.getTargetingConversion(), "10019")) {
+                calibratedScore = calibratedScanScore;
+            } else if (Objects.equals(item.getLandingPageType(), 3) && Objects.equals(item.getTargetingConversion(), "10004")) {
+                calibratedScore = calibratedScanScore * addWechatScore;
+            } else {
+                calibratedScore = calibratedConversionScore;
+            }
+            item.getScoreMap().put("modelCtcvrScore", calibratedScore);
+            item.getScoreMap().put("ctcvrScore", calibratedScore);
+            item.setLrScore(calibratedScore);
+        }
+
+//        String calibModelName = paramsMap.getOrDefault("calibModelName", "dnnV3");
+//        calculateCtcvrScore(result, request, scoreParam, calibModelName, reqFeature);
+//
+//        double minValidCopc = NumberUtils.toDouble(paramsMap.getOrDefault("minValidCopc", "0.8"));
+//        double maxValidCopc = NumberUtils.toDouble(paramsMap.getOrDefault("maxValidCopc", "10"));
+//        double minCopc = NumberUtils.toDouble(paramsMap.getOrDefault("minCopc", "0.2"));
+//        double maxCopc = NumberUtils.toDouble(paramsMap.getOrDefault("maxCopc", "2.5"));
+//        // Copc校准 人群校准
+//        calibrationCidCtcvr(result, calibModelName, reqFeature, minValidCopc, maxValidCopc, minCopc, maxCopc);
+//        if (CollectionUtils.isEmpty(result)) {
+//            log.error("calculateCtcvrScore result is empty");
+//        }
+//        // loop
+        double cpmCoefficient = weightParam.getOrDefault("cpmCoefficient", 0.9);
+        boolean isGuaranteeType = false;
+//        // 查询人群分层信息
+//        String peopleLayer = Optional.of(reqFeature)
+//                .map(f -> f.get("layer"))
+//                .map(s -> s.replace("-炸", ""))
+//                .orElse(null);
+//
+//        // 控制曝光参数
+//        String expOldKey = paramsMap.getOrDefault("expOldKey", "ad_view_yesterday");
+//        double expOldThreshold = NumberUtils.toDouble(paramsMap.getOrDefault("expOldThreshold", "1000"));
+//        String expNewKey = paramsMap.getOrDefault("expNewKey", "ad_view_today");
+//        double expNewThreshold = NumberUtils.toDouble(paramsMap.getOrDefault("expNewThreshold", "3000"));
+//        double expLowerWeight = NumberUtils.toDouble(paramsMap.getOrDefault("expLowerWeight", "0.2"));
+//        double expUpperWeight = NumberUtils.toDouble(paramsMap.getOrDefault("expUpperWeight", "1.0"));
+//        double expScale = NumberUtils.toDouble(paramsMap.getOrDefault("expScale", "10.0"));
+//        int openH5 = NumberUtils.toInt(paramsMap.getOrDefault("openH5", "0"));
+//
+//        // 计算rerank权重 打压客户
+//        calRerankWeight(scoreParam, userLayer, result);
+        for (AdRankItem item : result) {
+            double bid = item.getCpa();
+            if (scoreParam.getExpCodeSet().contains(correctCpaExp1) || scoreParam.getExpCodeSet().contains(correctCpaExp2)) {
+                Double correctionFactor = (Double) item.getExt().get("correctionFactor");
+                item.getScoreMap().put("correctionFactor", correctionFactor);
+                bid = bid * correctionFactor;
+            }
+            item.getScoreMap().put("ecpm", item.getLrScore() * bid * 1000);
+            if (isGuaranteedFlow && item.getExt().get("isGuaranteed") != null && (boolean) item.getExt().get("isGuaranteed")) {
+                isGuaranteeType = true;
+            }
+//
+//            // h5 降权
+//            double h5Weight = 1;
+//            if (openH5 > 0) {
+//                h5Weight = this.getH5SuppressWeight(item);
+//            }
+//
+//            // 控制曝光权重
+//            Map<String, Map<String, String>> cidFeature = allCidFeature.getOrDefault(String.valueOf(item.getAdId()), EMPTY_NESTED_MAP);
+//            Map<String, String> b3Feature = cidFeature.getOrDefault("alg_cid_feature_cid_action", EMPTY_STRING_MAP);
+//            double expWeight = getExpWeight(b3Feature,
+//                    expOldKey, expOldThreshold,
+//                    expNewKey, expNewThreshold,
+//                    expLowerWeight, expUpperWeight, expScale);
+//
+//            // 控制流量权重
+//            double flowCtlC = item.getScoreMap().getOrDefault("flowCtlC", 1.0);
+//            double flowCtlA = item.getScoreMap().getOrDefault("flowCtlA", 1.0);
+//            double kFinal = item.getScoreMap().getOrDefault("kFinal", 1.0);
+//
+//            String layerAndCreativeWeightMapKey = getLayerAndCreativeWeightMapKey(peopleLayer, String.valueOf(item.getAdId()));
+//            // 人群分层&创意的权重
+//            double layerAndCreativeWeight = getLayerAndCreativeWeight(layerAndCreativeWeightMapKey);
+//            double scoreCoefficient = creativeScoreCoefficient.getOrDefault(item.getAdId(), 1d);
+//            double guaranteeScoreCoefficient = getGuaranteeScoreCoefficient(isGuaranteedFlow, item.getExt());
+            double score = item.getLrScore() * bid;
+//            item.getScoreMap().put("guaranteeScoreCoefficient", guaranteeScoreCoefficient);
+            item.getScoreMap().put("cpa", item.getCpa());
+            item.getScoreMap().put("cpm", item.getCpm());
+            item.getScoreMap().put("bid", bid);
+            item.getScoreMap().put("cpmCoefficient", cpmCoefficient);
+//            item.getScoreMap().put("scoreCoefficient", scoreCoefficient);
+//            item.getScoreMap().put("h5", h5Weight);
+            item.getFeatureMap().putAll(userFeatureMap);
+            item.getFeatureMap().putAll(sceneFeatureMap);
+
+            // 没有转化回传的广告主，使用后台配置的CPM
+            if (noApiAdVerIds.contains(item.getAdVerId())) {
+                score = item.getCpm() * cpmCoefficient / 1000;
+            }
+            item.setScore(score);
+        }
+
+
+        result.sort(ComparatorUtil.equalsRandomComparator());
+
+        String logModelName = paramsMap.getOrDefault("logModelName", "dnnV3");
+        if (CollectionUtils.isNotEmpty(result)) {
+            AdRankItem top1Item = result.get(0);
+            List<String> participateCompetitionType = new ArrayList<>();
+            participateCompetitionType.add("engine");
+            top1Item.getExt().put("isGuaranteeType", isGuaranteeType);
+            if (isGuaranteeType) {
+                participateCompetitionType.add("guarantee");
+            }
+            top1Item.getExt().put("participateCompetitionType", StringUtils.join(participateCompetitionType, ","));
+            Double modelCtcvrScore = top1Item.getScoreMap().get("modelCtcvrScore");
+            Double ctcvrScore = top1Item.getScoreMap().get("ctcvrScore");
+            if (scoreParam.getExpCodeSet().contains(checkoutEcpmExp)) {
+                top1Item.getExt().put("ecpm", ctcvrScore * top1Item.getCpa() * 1000);
+                String filterEcpmValue = paramsMap.getOrDefault("filterEcpm", filterEcpm);
+                top1Item.getExt().put("filterEcpm", filterEcpmValue);
+                if (noApiAdVerIds.contains(top1Item.getAdVerId())) {
+                    top1Item.getExt().put("ecpm", top1Item.getCpm());
+                }
+            } else {
+                top1Item.getExt().put("ecpm", modelCtcvrScore * top1Item.getCpa() * 1000);
+            }
+            putMetaFeature(top1Item, feature, reqFeature, sceneFeatureMap, request);
+            top1Item.getExt().put("model", logModelName);
+            String coefficientRate = paramsMap.getOrDefault("coefficientRate", "1");
+            top1Item.getExt().put("coefficientRate", coefficientRate);
+        }
+        long time6 = System.currentTimeMillis();
+        log.info("cost={}, getFeature={}, handleFeature={},  similar={}, bucketFeature={}, getScorerPipeline={}, " +
+                        "other={}, adIdSize={}, adRankItemsSize={}",
+                time6 - start, time1 - start, time2 - time1, time3 - time2, time4 - time3,
+                time5 - time4, time6 - time5, request.getAdIdList().size(), adRankItems.size());
+        return result;
+    }
+
+    /**
+     * 获取人群分层和创意的权重
+     *
+     * @param key
+     * @return
+     */
+    private Double getLayerAndCreativeWeight(String key) {
+        if (StringUtils.isBlank(key)) {
+            return 1d;
+        }
+        return layerAndCreativeWeightMap.getOrDefault(key, 1d);
+    }
+
+    /**
+     * 获取人群分层和创意的权重key
+     *
+     * @param layer
+     * @param creativeId
+     * @return
+     */
+    private String getLayerAndCreativeWeightMapKey(String layer, String creativeId) {
+        if (StringUtils.isBlank(layer) || StringUtils.isBlank(creativeId)) {
+            return null;
+        }
+        return layer + "_" + creativeId;
+    }
+
+
+    private void handleB1Feature(Map<String, String> b1Feature, Map<String, String> cidFeatureMap, String cid) {
+        cidFeatureMap.put("cid_" + cid, "0.1");
+        // if (StringUtils.isNotBlank(b1Feature.get("adid"))) {
+        //     String adId = b1Feature.get("adid");
+        //     cidFeatureMap.put("adid_" + adId, idDefaultValue);
+        // }
+        if (StringUtils.isNotBlank(b1Feature.get("adverid"))) {
+            String adVerId = b1Feature.get("adverid");
+            cidFeatureMap.put("adverid_" + adVerId, "0.1");
+        }
+        // if (StringUtils.isNotBlank(b1Feature.get("targeting_conversion"))) {
+        //     String targetingConversion = b1Feature.get("targeting_conversion");
+        //     cidFeatureMap.put("targeting_conversion_" + targetingConversion, idDefaultValue);
+        // }
+        if (StringUtils.isNotBlank(b1Feature.get("cpa"))) {
+            String cpa = b1Feature.get("cpa");
+            cidFeatureMap.put("cpa", cpa);
+        }
+    }
+
+    private void handleB2ToB5AndB8ToB9Feature(Map<String, Map<String, String>> c1Feature, Map<String, Map<String, String>> adVerFeature, Map<String, String> cidFeatureMap) {
+        Map<String, String> b2Feature = adVerFeature.getOrDefault("alg_cid_feature_adver_action", EMPTY_STRING_MAP);
+        Map<String, String> b3Feature = c1Feature.getOrDefault("alg_cid_feature_cid_action", EMPTY_STRING_MAP);
+        Map<String, String> b4Feature = c1Feature.getOrDefault("alg_cid_feature_region_action", EMPTY_STRING_MAP);
+        Map<String, String> b5Feature = c1Feature.getOrDefault("alg_cid_feature_app_action", EMPTY_STRING_MAP);
+        Map<String, String> b8Feature = c1Feature.getOrDefault("alg_cid_feature_brand_action", EMPTY_STRING_MAP);
+        Map<String, String> b9Feature = c1Feature.getOrDefault("alg_cid_feature_weChatVersion_action", EMPTY_STRING_MAP);
+
+        List<String> timeList = Arrays.asList("1h", "2h", "3h", "6h", "12h", "1d", "3d", "7d", "yesterday", "today");
+        List<Tuple2<Map<String, String>, String>> featureList = Arrays.asList(
+                new Tuple2<>(b2Feature, "b2"),
+                new Tuple2<>(b3Feature, "b3"),
+                new Tuple2<>(b4Feature, "b4"),
+                new Tuple2<>(b5Feature, "b5"),
+                new Tuple2<>(b8Feature, "b8"),
+                new Tuple2<>(b9Feature, "b9")
+        );
+        for (Tuple2<Map<String, String>, String> tuple2 : featureList) {
+            Map<String, String> feature = tuple2.f1;
+            String prefix = tuple2.f2;
+            for (String time : timeList) {
+                double view = Double.parseDouble(feature.getOrDefault("ad_view_" + time, "0"));
+                double click = Double.parseDouble(feature.getOrDefault("ad_click_" + time, "0"));
+                double conver = Double.parseDouble(feature.getOrDefault("ad_conversion_" + time, "0"));
+                double income = Double.parseDouble(feature.getOrDefault("ad_income_" + time, "0"));
+                double cpc = NumUtil.div(income, click);
+                double ctr = NumUtil.divSmoothV2(click, view, CTR_SMOOTH_BETA_FACTOR);
+                double ctcvr = NumUtil.divSmoothV2(conver, view, CTCVR_SMOOTH_BETA_FACTOR);
+                double ecpm = ctr * cpc * 1000;
+                cidFeatureMap.put(prefix + "_" + time + "_ctr", String.valueOf(ctr));
+                cidFeatureMap.put(prefix + "_" + time + "_ctcvr", String.valueOf(ctcvr));
+                cidFeatureMap.put(prefix + "_" + time + "_cvr", String.valueOf(NumUtil.divSmoothV2(conver, click, CVR_SMOOTH_BETA_FACTOR)));
+                cidFeatureMap.put(prefix + "_" + time + "_conver", String.valueOf(conver));
+                cidFeatureMap.put(prefix + "_" + time + "_ecpm", String.valueOf(ecpm));
+
+                cidFeatureMap.put(prefix + "_" + time + "_click", String.valueOf(click));
+                cidFeatureMap.put(prefix + "_" + time + "_conver*log(view)", String.valueOf(conver * NumUtil.log(view)));
+                cidFeatureMap.put(prefix + "_" + time + "_conver*ctcvr", String.valueOf(conver * ctcvr));
+            }
+        }
+
+    }
+
+    private void handleB6ToB7Feature(Map<String, Map<String, String>> c1Feature, Map<String, String> cidFeatureMap) {
+        Map<String, String> b6Feature = c1Feature.getOrDefault("alg_cid_feature_week_action", EMPTY_STRING_MAP);
+        Map<String, String> b7Feature = c1Feature.getOrDefault("alg_cid_feature_hour_action", EMPTY_STRING_MAP);
+
+        List<String> timeList = Arrays.asList("7d", "14d");
+        List<Tuple2<Map<String, String>, String>> featureList = Arrays.asList(
+                new Tuple2<>(b6Feature, "b6"),
+                new Tuple2<>(b7Feature, "b7")
+        );
+        for (Tuple2<Map<String, String>, String> tuple2 : featureList) {
+            Map<String, String> feature = tuple2.f1;
+            String prefix = tuple2.f2;
+            for (String time : timeList) {
+                double view = Double.parseDouble(feature.getOrDefault("ad_view_" + time, "0"));
+                double click = Double.parseDouble(feature.getOrDefault("ad_click_" + time, "0"));
+                double conver = Double.parseDouble(feature.getOrDefault("ad_conversion_" + time, "0"));
+                double income = Double.parseDouble(feature.getOrDefault("ad_income_" + time, "0"));
+                double cpc = NumUtil.div(income, click);
+                double ctr = NumUtil.divSmoothV2(click, view, CTR_SMOOTH_BETA_FACTOR);
+                double ctcvr = NumUtil.divSmoothV2(conver, view, CTCVR_SMOOTH_BETA_FACTOR);
+                double ecpm = ctr * cpc * 1000;
+                cidFeatureMap.put(prefix + "_" + time + "_ctr", String.valueOf(ctr));
+                cidFeatureMap.put(prefix + "_" + time + "_ctcvr", String.valueOf(ctcvr));
+                cidFeatureMap.put(prefix + "_" + time + "_cvr", String.valueOf(NumUtil.divSmoothV2(conver, click, CVR_SMOOTH_BETA_FACTOR)));
+                cidFeatureMap.put(prefix + "_" + time + "_conver", String.valueOf(conver));
+                cidFeatureMap.put(prefix + "_" + time + "_ecpm", String.valueOf(ecpm));
+
+                cidFeatureMap.put(prefix + "_" + time + "_click", String.valueOf(click));
+                cidFeatureMap.put(prefix + "_" + time + "_conver*log(view)", String.valueOf(conver * NumUtil.log(view)));
+                cidFeatureMap.put(prefix + "_" + time + "_conver*ctcvr", String.valueOf(conver * ctcvr));
+            }
+        }
+
+    }
+
+    private void handleJ1ToJ11Feature(String userLayer, AdPlatformCreativeDTO dto, Map<String, Map<String, Map<String, String>>> otherFeature, Map<String, String> cidFeatureMap) {
+        String landing = dto.getLandingPageType() != null ? String.valueOf(dto.getLandingPageType()) : "";
+        String cust = dto.getCustomerId() != null ? String.valueOf(dto.getCustomerId()) : "";
+        String adv = dto.getAdVerId() != null ? dto.getAdVerId() : "";
+        String prof = dto.getProfession() != null ? dto.getProfession() : "";
+        String cate = dto.getCategoryName() != null ? dto.getCategoryName() : "";
+
+        String j1Key = CommonUtils.getFeatureUniqueKey("j1", userLayer);
+        Map<String, String> j1Feature = otherFeature.getOrDefault(j1Key, EMPTY_NESTED_MAP).getOrDefault("alg_cid_feature_level_action", EMPTY_STRING_MAP);
+        String j2Key = CommonUtils.getFeatureUniqueKey("j2", userLayer, adv);
+        Map<String, String> j2Feature = otherFeature.getOrDefault(j2Key, EMPTY_NESTED_MAP).getOrDefault("alg_cid_feature_level_advertiser_action", EMPTY_STRING_MAP);
+        String j3Key = CommonUtils.getFeatureUniqueKey("j3", userLayer, cust);
+        Map<String, String> j3Feature = otherFeature.getOrDefault(j3Key, EMPTY_NESTED_MAP).getOrDefault("alg_cid_feature_level_customer_action", EMPTY_STRING_MAP);
+        String j4Key = CommonUtils.getFeatureUniqueKey("j4", userLayer, prof);
+        Map<String, String> j4Feature = otherFeature.getOrDefault(j4Key, EMPTY_NESTED_MAP).getOrDefault("alg_cid_feature_level_profession_action", EMPTY_STRING_MAP);
+        String j5Key = CommonUtils.getFeatureUniqueKey("j5", userLayer, cate);
+        Map<String, String> j5Feature = otherFeature.getOrDefault(j5Key, EMPTY_NESTED_MAP).getOrDefault("alg_cid_feature_level_category_action", EMPTY_STRING_MAP);
+        String j6Key = CommonUtils.getFeatureUniqueKey("j6", userLayer, String.valueOf(dto.getCreativeId()));
+        Map<String, String> j6Feature = otherFeature.getOrDefault(j6Key, EMPTY_NESTED_MAP).getOrDefault("alg_cid_feature_level_cid_action", EMPTY_STRING_MAP);
+        String j7Key = CommonUtils.getFeatureUniqueKey("j7", landing);
+        Map<String, String> j7Feature = otherFeature.getOrDefault(j7Key, EMPTY_NESTED_MAP).getOrDefault("alg_cid_feature_landingpage_action", EMPTY_STRING_MAP);
+        String j8Key = CommonUtils.getFeatureUniqueKey("j8", landing, adv);
+        Map<String, String> j8Feature = otherFeature.getOrDefault(j8Key, EMPTY_NESTED_MAP).getOrDefault("alg_cid_feature_landingpage_advertiser_action", EMPTY_STRING_MAP);
+        String j9Key = CommonUtils.getFeatureUniqueKey("j9", landing, cust);
+        Map<String, String> j9Feature = otherFeature.getOrDefault(j9Key, EMPTY_NESTED_MAP).getOrDefault("alg_cid_feature_landingpage_customer_action", EMPTY_STRING_MAP);
+        String j10Key = CommonUtils.getFeatureUniqueKey("j10", landing, prof);
+        Map<String, String> j10Feature = otherFeature.getOrDefault(j10Key, EMPTY_NESTED_MAP).getOrDefault("alg_cid_feature_landingpage_profession_action", EMPTY_STRING_MAP);
+        String j11Key = CommonUtils.getFeatureUniqueKey("j11", landing, cate);
+        Map<String, String> j11Feature = otherFeature.getOrDefault(j11Key, EMPTY_NESTED_MAP).getOrDefault("alg_cid_feature_landingpage_category_action", EMPTY_STRING_MAP);
+
+        List<String> shortTimeList = Arrays.asList("3h", "3d");
+        List<String> longTimeList = Arrays.asList("1h", "2h", "3h", "6h", "12h", "1d", "3d");
+        List<Tuple3<Map<String, String>, String, List<String>>> featureList = Arrays.asList(
+                new Tuple3<>(j1Feature, "j1", shortTimeList),
+                new Tuple3<>(j2Feature, "j2", shortTimeList),
+                new Tuple3<>(j3Feature, "j3", shortTimeList),
+                new Tuple3<>(j4Feature, "j4", shortTimeList),
+                new Tuple3<>(j5Feature, "j5", shortTimeList),
+                new Tuple3<>(j6Feature, "j6", longTimeList),
+                new Tuple3<>(j7Feature, "j7", shortTimeList),
+                new Tuple3<>(j8Feature, "j8", shortTimeList),
+                new Tuple3<>(j9Feature, "j9", shortTimeList),
+                new Tuple3<>(j10Feature, "j10", shortTimeList),
+                new Tuple3<>(j11Feature, "j11", shortTimeList)
+        );
+        for (Tuple3<Map<String, String>, String, List<String>> tuple3 : featureList) {
+            Map<String, String> feature = tuple3.f1;
+            String prefix = tuple3.f2;
+            List<String> timeList = tuple3.f3;
+            for (String time : timeList) {
+                double view = Double.parseDouble(feature.getOrDefault("ad_view_" + time, "0"));
+                double click = Double.parseDouble(feature.getOrDefault("ad_click_" + time, "0"));
+                double conver = Double.parseDouble(feature.getOrDefault("ad_conversion_" + time, "0"));
+                double income = Double.parseDouble(feature.getOrDefault("ad_income_" + time, "0"));
+                double cpc = NumUtil.div(income, click);
+                double ctr = NumUtil.divSmoothV2(click, view, CTR_SMOOTH_BETA_FACTOR);
+                double ctcvr = NumUtil.divSmoothV2(conver, view, CTCVR_SMOOTH_BETA_FACTOR);
+                double ecpm = ctr * cpc * 1000;
+                cidFeatureMap.put(prefix + "_" + time + "_ctr", String.valueOf(ctr));
+                cidFeatureMap.put(prefix + "_" + time + "_ctcvr", String.valueOf(ctcvr));
+                cidFeatureMap.put(prefix + "_" + time + "_cvr", String.valueOf(NumUtil.divSmoothV2(conver, click, CVR_SMOOTH_BETA_FACTOR)));
+                cidFeatureMap.put(prefix + "_" + time + "_conver", String.valueOf(conver));
+                cidFeatureMap.put(prefix + "_" + time + "_ecpm", String.valueOf(ecpm));
+
+                cidFeatureMap.put(prefix + "_" + time + "_click", String.valueOf(click));
+                cidFeatureMap.put(prefix + "_" + time + "_conver*log(view)", String.valueOf(conver * NumUtil.log(view)));
+                cidFeatureMap.put(prefix + "_" + time + "_conver*ctcvr", String.valueOf(conver * ctcvr));
+            }
+        }
+    }
+
+    private List<TupleMapEntry<Tuple5>> handleC1Feature(Map<String, String> c1Feature, Map<String, String> featureMap) {
+
+        //用户近1年内是否有转化
+        if (c1Feature.containsKey("user_has_conver_1y") && c1Feature.get("user_has_conver_1y") != null) {
+            featureMap.put("user_has_conver_1y", c1Feature.get("user_has_conver_1y"));
+        }
+        //用户历史转化过品类
+        if (c1Feature.containsKey("user_conver_ad_class") && c1Feature.get("user_conver_ad_class") != null) {
+            featureMap.put("user_conver_ad_class", c1Feature.get("user_conver_ad_class"));
+        }
+
+        // 用户特征
+        List<TupleMapEntry<Tuple5>> midActionList = new ArrayList<>();
+        if (c1Feature.containsKey("action")) {
+            String action = c1Feature.get("action");
+            midActionList = Arrays.stream(action.split(","))
+                    .map(r -> {
+                        String[] rList = r.split(":");
+                        Tuple5 tuple5 = new Tuple5(rList[1], rList[2], rList[3], rList[4], rList[5]);
+                        return new TupleMapEntry<>(rList[0], tuple5);
+                    })
+                    // TODO 倒排
+                    .sorted((a, b) -> Integer.compare(Integer.parseInt(b.value.f1), Integer.parseInt(a.value.f1)))
+                    .collect(Collectors.toList());
+        }
+
+        double viewAll = midActionList.size();
+        double clickAll = midActionList.stream().mapToInt(e -> Integer.parseInt(e.value.f2)).sum();
+        double converAll = midActionList.stream().mapToInt(e -> Integer.parseInt(e.value.f3)).sum();
+        double incomeAll = midActionList.stream().mapToInt(e -> Integer.parseInt(e.value.f4)).sum();
+        featureMap.put("viewAll", String.valueOf(viewAll));
+        featureMap.put("clickAll", String.valueOf(clickAll));
+        featureMap.put("converAll", String.valueOf(converAll));
+        featureMap.put("incomeAll", String.valueOf(incomeAll));
+        featureMap.put("ctr_all", String.valueOf(NumUtil.div(clickAll, viewAll)));
+        featureMap.put("ctcvr_all", String.valueOf(NumUtil.div(converAll, viewAll)));
+        featureMap.put("cvr_all", String.valueOf(NumUtil.div(clickAll, converAll)));
+        featureMap.put("ecpm_all", String.valueOf(NumUtil.div(incomeAll * 1000, viewAll)));
+        if (CollectionUtils.isNotEmpty(midActionList)) {
+            List<String> cidClickList = new ArrayList<>();
+            List<String> cidConverList = new ArrayList<>();
+            for (TupleMapEntry<Tuple5> tupleMapEntry : midActionList) {
+                String cid = tupleMapEntry.key;
+                String click = tupleMapEntry.value.f2;
+                String conver = tupleMapEntry.value.f3;
+                if (Objects.equals(click, "1")) {
+                    cidClickList.add(cid);
+                }
+                if (Objects.equals(conver, "1")) {
+                    cidConverList.add(cid);
+                }
+            }
+            featureMap.put("user_cid_click_list", String.join(",", cidClickList));
+            featureMap.put("user_cid_conver_list", String.join(",", cidConverList));
+        }
+        return midActionList;
+    }
+
+    private void handleC1UIFeature(Map<String, Double> midTimeDiffMap, Map<String, Double> midActionStatic, Map<String, String> featureMap, String cid) {
+        if (midTimeDiffMap.containsKey("timediff_view_" + cid)) {
+            featureMap.put("timediff_view", String.valueOf(midTimeDiffMap.getOrDefault("timediff_view_" + cid, 0.0)));
+        }
+        if (midTimeDiffMap.containsKey("timediff_click_" + cid)) {
+            featureMap.put("timediff_click", String.valueOf(midTimeDiffMap.getOrDefault("timediff_click_" + cid, 0.0)));
+        }
+        if (midTimeDiffMap.containsKey("timediff_conver_" + cid)) {
+            featureMap.put("timediff_conver", String.valueOf(midTimeDiffMap.getOrDefault("timediff_conver_" + cid, 0.0)));
+        }
+        if (midActionStatic.containsKey("actionstatic_view_" + cid)) {
+            featureMap.put("actionstatic_view", String.valueOf(midActionStatic.getOrDefault("actionstatic_view_" + cid, 0.0)));
+        }
+        if (midActionStatic.containsKey("actionstatic_click_" + cid)) {
+            featureMap.put("actionstatic_click", String.valueOf(midActionStatic.getOrDefault("actionstatic_click_" + cid, 0.0)));
+        }
+        if (midActionStatic.containsKey("actionstatic_conver_" + cid)) {
+            featureMap.put("actionstatic_conver", String.valueOf(midActionStatic.getOrDefault("actionstatic_conver_" + cid, 0.0)));
+        }
+        if (midActionStatic.containsKey("actionstatic_income_" + cid)) {
+            featureMap.put("actionstatic_income", String.valueOf(midActionStatic.getOrDefault("actionstatic_income_" + cid, 0.0)));
+        }
+        if (midActionStatic.containsKey("actionstatic_view_" + cid) && midActionStatic.containsKey("actionstatic_click_" + cid)) {
+            double ctr = NumUtil.div(
+                    midActionStatic.getOrDefault("actionstatic_click_" + cid, 0.0),
+                    midActionStatic.getOrDefault("actionstatic_view_" + cid, 0.0)
+            );
+            featureMap.put("actionstatic_ctr", String.valueOf(ctr));
+        }
+        if (midActionStatic.containsKey("actionstatic_view_" + cid) && midActionStatic.containsKey("actionstatic_conver_" + cid)) {
+            double ctcvr = NumUtil.div(midActionStatic.getOrDefault("actionstatic_conver_" + cid, 0.0), midActionStatic.getOrDefault("actionstatic_view_" + cid, 0.0));
+            featureMap.put("actionstatic_ctcvr", String.valueOf(ctcvr));
+        }
+        if (midActionStatic.containsKey("actionstatic_conver_" + cid) && midActionStatic.containsKey("actionstatic_click_" + cid)) {
+            double cvr = NumUtil.div(midActionStatic.getOrDefault("actionstatic_conver_" + cid, 0.0), midActionStatic.getOrDefault("actionstatic_click_" + cid, 0.0));
+            featureMap.put("actionstatic_cvr", String.valueOf(cvr));
+        }
+    }
+
+    private void handleD1Feature(Map<String, String> d1Feature, Map<String, String> featureMap) {
+        for (String prefix : Arrays.asList("3h", "6h", "12h", "1d", "3d", "7d")) {
+            double view = Double.parseDouble(d1Feature.getOrDefault("ad_view_" + prefix, "0"));
+            double click = Double.parseDouble(d1Feature.getOrDefault("ad_click_" + prefix, "0"));
+            double conver = Double.parseDouble(d1Feature.getOrDefault("ad_conversion_" + prefix, "0"));
+            double income = Double.parseDouble(d1Feature.getOrDefault("ad_income_" + prefix, "0"));
+            double cpc = NumUtil.div(income, click);
+            double ctr = NumUtil.divSmoothV2(click, view, CTR_SMOOTH_BETA_FACTOR);
+            featureMap.put("d1_feature_" + prefix + "_ctr", String.valueOf(ctr));
+            featureMap.put("d1_feature_" + prefix + "_ctcvr", String.valueOf(NumUtil.divSmoothV2(conver, view, CTCVR_SMOOTH_BETA_FACTOR)));
+            featureMap.put("d1_feature_" + prefix + "_cvr", String.valueOf(NumUtil.divSmoothV2(conver, click, CVR_SMOOTH_BETA_FACTOR)));
+            featureMap.put("d1_feature_" + prefix + "_conver", String.valueOf(conver));
+            featureMap.put("d1_feature_" + prefix + "_ecpm", String.valueOf(ctr * cpc * 1000));
+        }
+    }
+
+    private void handleD2Feature(Map<String, Map<String, Double>> vidRankMaps, Map<String, String> featureMap, String cid) {
+        if (MapUtils.isEmpty(vidRankMaps)) {
+            return;
+        }
+
+        List<String> prefixes1 = Arrays.asList("ctr", "ctcvr", "ecpm");
+        // List<String> prefixes1 = Arrays.asList("ctr", "ctcvr");
+        List<String> prefixes2 = Arrays.asList("1d", "3d", "7d", "14d");
+
+        for (String prefix1 : prefixes1) {
+            for (String prefix2 : prefixes2) {
+                String combinedKey = prefix1 + "_" + prefix2;
+                if (vidRankMaps.containsKey(combinedKey)) {
+                    Double rank = vidRankMaps.get(combinedKey).getOrDefault(cid, 0.0);
+                    if (rank >= 1.0) {
+                        featureMap.put("vid_rank_" + combinedKey, String.valueOf(NumUtil.div(1, rank)));
+                    }
+                }
+            }
+        }
+    }
+
+    private void handleH1AndH2Feature(Map<String, Map<String, String>> skuFeature,
+                                      Map<String, Map<String, String>> adVerFeature,
+                                      Map<String, String> cidFeatureMap) {
+        Map<String, String> h1Feature = adVerFeature.getOrDefault("alg_mid_feature_adver_action", EMPTY_STRING_MAP);
+        Map<String, String> h2Feature = skuFeature.getOrDefault("alg_mid_feature_sku_action", EMPTY_STRING_MAP);
+        List<String> timeList = Arrays.asList("3d", "7d", "30d");
+        List<Tuple2<Map<String, String>, String>> featureList = Arrays.asList(
+                new Tuple2<>(h1Feature, "adverid"),
+                new Tuple2<>(h2Feature, "skuid")
+        );
+        for (Tuple2<Map<String, String>, String> tuple2 : featureList) {
+            Map<String, String> feature = tuple2.f1;
+            String prefix = tuple2.f2;
+            for (String time : timeList) {
+                String timeValue = feature.getOrDefault(time, "");
+                if (StringUtils.isNotEmpty(timeValue)) {
+                    String[] split = timeValue.split(",");
+                    cidFeatureMap.put("user" + "_" + prefix + "_" + "view" + "_" + time, split[0]);
+                    cidFeatureMap.put("user" + "_" + prefix + "_" + "click" + "_" + time, split[1]);
+                    cidFeatureMap.put("user" + "_" + prefix + "_" + "conver" + "_" + time, split[2]);
+                }
+            }
+        }
+
+
+    }
+
+    private void handleD3AndB1Feature(Map<String, String> d3Feature, String cTitle, Map<String, String> featureMap,
+                                      ScoreParam scoreParam) {
+        if (MapUtils.isEmpty(d3Feature) || !d3Feature.containsKey("title") || StringUtils.isEmpty(cTitle)) {
+            return;
+        }
+        String vTitle = d3Feature.get("title");
+        double score;
+        if (scoreParam.getExpCodeSet().contains(word2vecExp)) {
+            score = SimilarityUtils.word2VecSimilarity(cTitle, vTitle);
+        } else {
+            score = Similarity.conceptSimilarity(cTitle, vTitle);
+        }
+        featureMap.put("ctitle_vtitle_similarity", String.valueOf(score));
+    }
+
+    private void handleE1AndE2Feature(Map<String, String> e1Feature, Map<String, String> e2Feature, String title,
+                                      Map<String, String> featureMap, ScoreParam scoreParam,
+                                      Map<String, List<String>> tagWordsCache) {
+        if (StringUtils.isEmpty(title)) {
+            return;
+        }
+
+        // 预先分词 title，在整个方法中复用，避免重复分词
+        List<String> titleWords = null;
+        if (scoreParam.getExpCodeSet().contains(word2vecExp)) {
+            titleWords = SimilarityUtils.segment(title);
+        }
+
+        List<Tuple2<Map<String, String>, String>> tuple2List = Arrays.asList(new Tuple2<>(e1Feature, "e1"), new Tuple2<>(e2Feature, "e2"));
+
+        List<String> tagsFieldList = Arrays.asList("tags_3d", "tags_7d", "tags_14d");
+        for (Tuple2<Map<String, String>, String> tuple2 : tuple2List) {
+            Map<String, String> feature = tuple2.f1;
+            String prefix = tuple2.f2;
+            if (MapUtils.isEmpty(feature)) {
+                continue;
+            }
+
+            for (String tagsField : tagsFieldList) {
+                if (StringUtils.isNotEmpty(feature.get(tagsField))) {
+                    String tags = feature.get(tagsField);
+                    Double[] doubles;
+                    if (scoreParam.getExpCodeSet().contains(word2vecExp)) {
+                        // 使用缓存的 title 分词结果和请求级别的 tag 分词缓存
+                        doubles = ExtractorUtils.funcC34567ForTagsNewWithCache(tags, title, titleWords, tagWordsCache);
+                    } else {
+                        doubles = ExtractorUtils.funcC34567ForTags(tags, title);
+                    }
+                    featureMap.put(prefix + "_" + tagsField + "_matchnum", String.valueOf(doubles[0]));
+                    featureMap.put(prefix + "_" + tagsField + "_maxscore", String.valueOf(doubles[1]));
+                    featureMap.put(prefix + "_" + tagsField + "_avgscore", String.valueOf(doubles[2]));
+                }
+            }
+        }
+    }
+
+    private Map<String, Double> parseC1FeatureListToTimeDiffMap(List<TupleMapEntry<Tuple5>> midActionList, long ts) {
+        Map<String, Double> midTimeDiffMap = new HashMap<>();
+        for (TupleMapEntry<Tuple5> entry : midActionList) {
+            String cid = entry.key;
+            double tsHistory = Double.parseDouble(entry.value.f1);
+            double click = Double.parseDouble(entry.value.f2);
+            double conver = Double.parseDouble(entry.value.f3);
+            double d = (ts - tsHistory) / 3600 / 24;
+            if (!midTimeDiffMap.containsKey("timediff_view_" + cid)) {
+                midTimeDiffMap.put("timediff_view_" + cid, NumUtil.div(1, d));
+            }
+            if (!midTimeDiffMap.containsKey("timediff_click_" + cid) && click > 0) {
+                midTimeDiffMap.put("timediff_click_" + cid, NumUtil.div(1, d));
+            }
+            if (!midTimeDiffMap.containsKey("timediff_conver_" + cid) && conver > 0) {
+                midTimeDiffMap.put("timediff_conver_" + cid, NumUtil.div(1, d));
+            }
+        }
+        return midTimeDiffMap;
+    }
+
+    private Map<String, Double> parseC1FeatureListToActionStaticMap(List<TupleMapEntry<Tuple5>> midActionList) {
+        Map<String, Double> midActionStaticsMap = new HashMap<>();
+        for (TupleMapEntry<Tuple5> entry : midActionList) {
+            String cid = entry.key;
+            double click = Double.parseDouble(entry.value.f2);
+            double conver = Double.parseDouble(entry.value.f3);
+            double income = Double.parseDouble(entry.value.f4);
+
+            Double viewSum = midActionStaticsMap.getOrDefault("actionstatic_view_" + cid, 0.0);
+            midActionStaticsMap.put("actionstatic_view_" + cid, 1 + viewSum);
+
+            Double clickSum = midActionStaticsMap.getOrDefault("actionstatic_click_" + cid, 0.0);
+            midActionStaticsMap.put("actionstatic_click_" + cid, clickSum + click);
+
+            Double converSum = midActionStaticsMap.getOrDefault("actionstatic_conver_" + cid, 0.0);
+            midActionStaticsMap.put("actionstatic_conver_" + cid, converSum + conver);
+
+            Double incomSum = midActionStaticsMap.getOrDefault("actionstatic_income_" + cid, 0.0);
+            midActionStaticsMap.put("actionstatic_income_" + cid, incomSum + income);
+        }
+
+        return midActionStaticsMap;
+    }
+
+    private Map<String, Map<String, Double>> parseD2FeatureMap(Map<String, String> d2Feature) {
+        Map<String, Map<String, Double>> vidRankMaps = new HashMap<>();
+        for (Map.Entry<String, String> entry : d2Feature.entrySet()) {
+            String key = entry.getKey();
+            String value = entry.getValue();
+            Map<String, Double> valueMap = Arrays.stream(value.split(",")).map(r -> r.split(":")).collect(Collectors.toMap(rList -> rList[0], rList -> Double.parseDouble(rList[2])));
+            vidRankMaps.put(key, valueMap);
+        }
+        return vidRankMaps;
+    }
+
+    private void readBucketFile() {
+        if (MapUtils.isNotEmpty(bucketsMap)) {
+            return;
+        }
+        synchronized (this) {
+            String bucketFile = "20260410_ad_bucket_920.txt";
+            InputStream resourceStream = this.getClass().getClassLoader().getResourceAsStream(bucketFile);
+            if (resourceStream != null) {
+                try (BufferedReader reader = new BufferedReader(new InputStreamReader(resourceStream))) {
+                    Map<String, double[]> bucketsMap = new HashMap<>();
+                    Map<String, Double> bucketsLen = new HashMap<>();
+                    String line;
+                    while ((line = reader.readLine()) != null) {
+                        // 替换空格和换行符，过滤空行
+                        line = line.replace(" ", "").replaceAll("\n", "");
+                        if (!line.isEmpty()) {
+                            String[] rList = line.split("\t");
+                            if (rList.length == 3) {
+                                String key = rList[0];
+                                double value1 = Double.parseDouble(rList[1]);
+                                bucketsLen.put(key, value1);
+                                double[] value2 = Arrays.stream(rList[2].split(",")).mapToDouble(Double::valueOf).toArray();
+                                bucketsMap.put(key, value2);
+                            }
+                        }
+                    }
+                    this.bucketsMap = bucketsMap;
+                    this.bucketsLen = bucketsLen;
+                } catch (IOException e) {
+                    log.error("something is wrong in parse bucket file: ", e);
+                }
+                log.info("load bucket file success: {}", bucketFile);
+            } else {
+                log.error("no bucket file");
+            }
+        }
+    }
+
+    private void initSparseFeatureNames() {
+        this.sparseFeatureSet = new HashSet<String>() {{
+            add("brand");
+            add("region");
+            add("city");
+            add("vid");
+            add("cate1");
+            add("cate2");
+            add("cid");
+            add("adid");
+            add("adverid");
+            add("user_cid_click_list");
+            add("user_cid_conver_list");
+            add("user_vid_return_tags_2h");
+            add("user_vid_return_tags_1d");
+            add("user_vid_return_tags_3d");
+            add("user_vid_return_tags_7d");
+            add("user_vid_return_tags_14d");
+            add("apptype");
+            add("hour");
+            add("hour_quarter");
+            add("root_source_scene");
+            add("root_source_channel");
+            add("is_first_layer");
+            add("title_split");
+            add("profession");
+            add("user_vid_share_tags_1d");
+            add("user_vid_share_tags_14d");
+            add("user_vid_return_cate1_14d");
+            add("user_vid_return_cate2_14d");
+            add("user_vid_share_cate1_14d");
+            add("user_vid_share_cate2_14d");
+            add("user_has_conver_1y");
+            add("user_adverid_view_3d");
+            add("user_adverid_click_3d");
+            add("user_adverid_conver_3d");
+            add("user_adverid_view_7d");
+            add("user_adverid_click_7d");
+            add("user_adverid_conver_7d");
+            add("user_adverid_view_30d");
+            add("user_adverid_click_30d");
+            add("user_adverid_conver_30d");
+            add("user_skuid_view_3d");
+            add("user_skuid_click_3d");
+            add("user_skuid_conver_3d");
+            add("user_skuid_view_7d");
+            add("user_skuid_click_7d");
+            add("user_skuid_conver_7d");
+            add("user_skuid_view_30d");
+            add("user_skuid_click_30d");
+            add("user_skuid_conver_30d");
+            add("user_conver_ad_class");
+            add("category_name");
+            add("material_md5");
+            add("ad_profession_id");
+            add("ad_profession_name");
+            add("ad_category_id");
+            add("ad_category_name");
+            add("ad_sku_id");
+            add("ad_sku_code");
+            add("ad_sku_name");
+            add("user_layer");
+            add("flag");
+            add("customer");
+            add("landing");
+        }};
+    }
+
+    private Map<String, String> featureBucket(Map<String, String> featureMap) {
+        // 使用 HashMap 替代 ConcurrentHashMap，分桶操作是单线程的
+        Map<String, String> newFeatureMap = new HashMap<>(featureMap.size());
+        for (Map.Entry<String, String> entry : featureMap.entrySet()) {
+            try {
+                String name = entry.getKey();
+                if (this.sparseFeatureSet.contains(name)) {
+                    if (entry.getValue() != null) {
+                        newFeatureMap.put(name, entry.getValue());
+                    }
+                    continue;
+                }
+                double score = Double.parseDouble(entry.getValue());
+                // 注意：0值、不在分桶文件中的特征，会被过滤掉。
+                if (score > 1E-8) {
+                    if (this.bucketsMap.containsKey(name) && this.bucketsLen.containsKey(name)) {
+                        double[] buckets = this.bucketsMap.get(name);
+                        double bucketNum = this.bucketsLen.get(name);
+                        Double scoreNew = 1.0 / bucketNum * (ExtractorUtils.findInsertPosition(buckets, score) + 1.0);
+                        newFeatureMap.put(name, String.valueOf(scoreNew));
+                    } else {
+                        newFeatureMap.put(name, String.valueOf(score));
+                    }
+                }
+            } catch (Exception e) {
+                log.error("featureBucket error: ", e);
+            }
+        }
+        return newFeatureMap;
+    }
+
+    private double getExpWeight(Map<String, String> featureMap,
+                                String expOldKey, double expOldThreshold,
+                                String expNewKey, double expNewThreshold,
+                                double expLowerWeight, double expUpperWeight, double expScale) {
+        try {
+            if (null != featureMap) {
+                double oldView = Double.parseDouble(featureMap.getOrDefault(expOldKey, "0"));
+                if (oldView < expOldThreshold) {
+                    double newView = Double.parseDouble(featureMap.getOrDefault(expNewKey, "0"));
+                    return getExpWeight(expLowerWeight, expUpperWeight, expScale, expNewThreshold, newView);
+                }
+            }
+        } catch (Exception e) {
+            log.error("getExpWeight error: ", e);
+        }
+        return 1.0;
+    }
+
+    private double getExpWeight(double lowerWeight, double upperWeight, double scale, double upperExp, double exp) {
+        if (exp >= upperExp) {
+            return 1.0;
+        }
+        double weight = Math.log(exp + 1) / scale;
+        return Math.min(Math.max(lowerWeight, weight), upperWeight);
+    }
+
+    private void calibrationCidCtcvr(List<AdRankItem> items, String modelName, Map<String, String> reqFeature,
+                                     double minValid, double maxValid,
+                                     double minVal, double maxVal) {
+        try {
+            String layer = reqFeature.get("layer_l4");
+            for (AdRankItem item : items) {
+                String cid = String.valueOf(item.getAdId());
+                Double diff = CreativeUserLayerDataHelper.getCopc(modelName, cid, layer);
+                if (null != diff) {
+                    double newDiff = Math.min(Math.max(minVal, diff), maxVal);
+                    if (newDiff < minValid || newDiff > maxValid) {
+                        double ctcvr = item.getLrScore() * newDiff;
+                        item.getScoreMap().put("cidCtcvrScore", ctcvr);
+                        item.getScoreMap().put("ctcvrScore", ctcvr);
+                        item.setLrScore(ctcvr);
+                    }
+                }
+            }
+        } catch (Exception e) {
+            log.error("calibCidScore error", e);
+        }
+    }
+}