fix: V536 补加 mergeAndSort + V562/V564/V565/V569 mergeAndSort 挪位与 V567 对齐

挪位: V562/V564/V565/V569 的 mergeAndSort 从 parseUserProfile 之前挪到
fetchCoarseRankScores 之前, 与 V567 方法顺序一致
(mergeAndRankRovRecall → mergeAndSort → fetchCoarseRankScores → parseUserProfile)。
方便日后 diff V567 时只看到本实验独有改动, 不被位置差异噪音干扰。

V536 补加: 上次叠加 V567 改动时遗漏 V536, 本次补上 3 imports + mergeAndSort
方法体, 位置与其他 4 个实验一致。

V563 不动 (无 fetchCoarseRankScores, mergeAndSort 紧跟 mergeAndRankRovRecall
已是正确位置)。

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

recommend-server-service/src/main/java/com/tzld/piaoquan/recommend/server/service/rank/strategy/RankStrategy4RegionMergeModelV536.java

@@ -7,7 +7,9 @@ import com.tzld.piaoquan.recommend.server.common.base.RankItem;
 import com.tzld.piaoquan.recommend.server.model.MachineInfo;
 import com.tzld.piaoquan.recommend.server.model.Video;
 import com.tzld.piaoquan.recommend.server.service.FeatureService;
+import com.tzld.piaoquan.recommend.server.service.funnel.FunnelContext;
 import com.tzld.piaoquan.recommend.server.service.rank.RankParam;
+import com.tzld.piaoquan.recommend.server.service.rank.RankResult;
 import com.tzld.piaoquan.recommend.server.service.rank.bo.UserShareReturnProfile;
 import com.tzld.piaoquan.recommend.server.service.rank.extractor.ExtractVideoMergeCate;
 import com.tzld.piaoquan.recommend.server.service.rank.tansform.FeatureV6;
@@ -17,6 +19,7 @@ import com.tzld.piaoquan.recommend.server.util.*;
 import lombok.extern.slf4j.Slf4j;
 import org.apache.commons.collections4.CollectionUtils;
 import org.apache.commons.collections4.MapUtils;
+import org.apache.commons.lang3.RandomUtils;
 import org.apache.commons.lang3.StringUtils;
 import org.springframework.beans.factory.annotation.Autowired;
 import org.springframework.stereotype.Service;
@@ -331,6 +334,77 @@ public class RankStrategy4RegionMergeModelV536 extends RankStrategy4RegionMergeM
         return result;
     }
 
+    /**
+     * 同 V567 极简 fusion：只保留"流量池相关 + 兜底相关"逻辑
+     *   1. rov 空兜底：rov 池为空时流量池直接顶上 (Basic 段 1)
+     *   7. 流量池按比例强插：topK 头部锁 rov + topK..size 按 flowPoolP / newFlowPoolSelectRate 概率门
+     *      混入 flowVideos / douHotFlowPoolVideos，否则用 rov 中段；一侧用光时另一侧兜底回填 (Basic 段 7)
+     *
+     * 删除（相对 Basic）：标签 filter / rov boost / 强插 / 品类降权 / 节日降权 / 密度控制
+     */
+    @Override
+    public RankResult mergeAndSort(RankParam param, List<Video> rovVideos, List<Video> flowVideos, List<Video> douHotFlowPoolVideos) {
+
+        // 1 兜底策略，rov池子不足时，用冷启池填补。直接返回。
+        if (CollectionUtils.isEmpty(rovVideos)) {
+            if (param.getSize() < flowVideos.size()) {
+                return new RankResult(flowVideos.subList(0, param.getSize()));
+            } else {
+                return new RankResult(flowVideos);
+            }
+        }
+
+        // 7 流量池按比例强插
+        FunnelContext funnelCtx = param.getFunnelContext();
+        List<Video> result = new ArrayList<>();
+        for (int i = 0; i < param.getTopK() && i < rovVideos.size(); i++) {
+            result.add(rovVideos.get(i));
+        }
+        double flowPoolP = getFlowPoolP(param);
+        int flowPoolIndex = 0;
+        int rovPoolIndex = param.getTopK();
+        for (int i = 0; i < param.getSize() - param.getTopK(); i++) {
+            double rand = RandomUtils.nextDouble(0, 1);
+            if (rand < flowPoolP) {
+                if (flowPoolIndex < flowVideos.size()) {
+                    Video v = flowVideos.get(flowPoolIndex++);
+                    result.add(v);
+                    markColdStartInserted(funnelCtx, v);
+                } else {
+                    break;
+                }
+            } else if (this.isInsertDouHotFlowPoolVideo()) {
+                if (flowPoolIndex < douHotFlowPoolVideos.size()) {
+                    Video v = douHotFlowPoolVideos.get(flowPoolIndex++);
+                    result.add(v);
+                    markColdStartInserted(funnelCtx, v);
+                } else {
+                    break;
+                }
+            } else {
+                if (rovPoolIndex < rovVideos.size()) {
+                    result.add(rovVideos.get(rovPoolIndex++));
+                } else {
+                    break;
+                }
+            }
+        }
+        if (rovPoolIndex >= rovVideos.size()) {
+            for (int i = flowPoolIndex; i < flowVideos.size() && result.size() < param.getSize(); i++) {
+                Video v = flowVideos.get(i);
+                result.add(v);
+                markColdStartInserted(funnelCtx, v);
+            }
+        }
+        if (flowPoolIndex >= flowVideos.size()) {
+            for (int i = rovPoolIndex; i < rovVideos.size() && result.size() < param.getSize(); i++) {
+                result.add(rovVideos.get(i));
+            }
+        }
+
+        return new RankResult(result);
+    }
+
     /**
      * V536 实验：拉取粗排分（按 vid → score 返回）。
      *

recommend-server-service/src/main/java/com/tzld/piaoquan/recommend/server/service/rank/strategy/RankStrategy4RegionMergeModelV562.java

@@ -335,77 +335,6 @@ public class RankStrategy4RegionMergeModelV562 extends RankStrategy4RegionMergeM
         return result;
     }
 
-    /**
-     * V562 实验：拉取粗排分（按 vid → score 返回）。
-     *
-     * 数据源：alg_vid_recommend_exp_feature_20250212。
-     * 表里没有现成 rovn 字段，需要从原子字段 (return_n_uv_*, exp_*) 用 plusSmooth 算出来。
-     * 公式 = FeatureV6.oneTypeStatFeature 同口径：rovn = plusSmooth(return_n_uv, exp, plus, 1)
-     * 默认 plus=30 与 FeatureV6.largerSmoothPlus 对齐，AB 对比不会因口径不同污染结论。
-     *
-     * Apollo 可调维度：
-     *   - coarseRovn1hW / coarseRovn24hW：1h 和 24h 的加权（默认 0.5/0.5）
-     *   - coarseRovn1hSmoothPlus / coarseRovn24hSmoothPlus：贝叶斯平滑系数（默认 30/30）
-     *
-     * 缺失自动归一化：单值缺失时剩下的撑起全部权重；两值都缺失则 caller 兜底 RovScore。
-     */
-    private Map<Long, Double> fetchCoarseRankScores(RankParam param) {
-        if (param == null || param.getRecallResult() == null
-                || CollectionUtils.isEmpty(param.getRecallResult().getData())) {
-            return Collections.emptyMap();
-        }
-        Map<String, Double> mergeWeight = this.mergeWeight != null ? this.mergeWeight : Collections.emptyMap();
-        double w1h = mergeWeight.getOrDefault("coarseRovn1hW", 0.5);
-        double w24h = mergeWeight.getOrDefault("coarseRovn24hW", 0.5);
-        double plus1h = mergeWeight.getOrDefault("coarseRovn1hSmoothPlus", 30.0);
-        double plus24h = mergeWeight.getOrDefault("coarseRovn24hSmoothPlus", 30.0);
-        // 只对参与统一截断的 23 路 vid 拉粗排分（跳过流量池 3 路，省 proto + RPC 延迟）
-        List<String> vids = param.getRecallResult().getData().stream()
-                .filter(d -> d != null && CollectionUtils.isNotEmpty(d.getVideos()))
-                .filter(d -> ALL_ROV_PUSH_FROMS.contains(d.getPushFrom()))
-                .flatMap(d -> d.getVideos().stream())
-                .map(v -> String.valueOf(v.getVideoId()))
-                .distinct()
-                .collect(Collectors.toList());
-        if (vids.isEmpty()) return Collections.emptyMap();
-
-        Map<String, Map<String, Map<String, String>>> feats = featureService.getVideoCoarseRankFeature(vids);
-        Map<Long, Double> result = new HashMap<>(vids.size());
-        for (String vid : vids) {
-            Map<String, String> row = feats.getOrDefault(vid, Collections.emptyMap())
-                    .getOrDefault("alg_vid_recommend_exp_feature_20250212", Collections.emptyMap());
-            Double rovn1h = computeRovn(row, "1h", plus1h);
-            Double rovn24h = computeRovn(row, "24h", plus24h);
-            // 加权平均，缺失自动归一化
-            double sumW = (rovn1h != null ? w1h : 0) + (rovn24h != null ? w24h : 0);
-            if (sumW <= 0) continue;
-            double sumWS = (rovn1h != null ? rovn1h * w1h : 0) + (rovn24h != null ? rovn24h * w24h : 0);
-            try {
-                result.put(Long.parseLong(vid), sumWS / sumW);
-            } catch (NumberFormatException ignore) { }
-        }
-        return result;
-    }
-
-    /**
-     * 与 FeatureV6.oneTypeStatFeature 同口径：rovn = plusSmooth(return_n_uv, exp, plus, 1)
-     *
-     * 字段语义（区分 0 vs null）：
-     *   - exp 是 period 有效性 anchor：null 或 ≤0 → 整个 period 无效（return null）
-     *   - return_n_uv 缺失视为 0（真实信号"无回访"）：rovn=0，参与加权（不会让另一时段兜底）
-     */
-    private static Double computeRovn(Map<String, String> row, String period, double smoothPlus) {
-        Double exp = parseDoubleOrNull(row.get("exp_" + period));
-        if (exp == null || exp <= 0) return null;
-        Double returnNuv = parseDoubleOrNull(row.get("return_n_uv_" + period));
-        return FeatureUtils.plusSmooth(returnNuv != null ? returnNuv : 0, exp, smoothPlus, 1);
-    }
-
-    private static Double parseDoubleOrNull(String s) {
-        if (StringUtils.isBlank(s)) return null;
-        try { return Double.parseDouble(s); } catch (NumberFormatException e) { return null; }
-    }
-
     /**
      * 同 V567 极简 fusion：只保留"流量池相关 + 兜底相关"逻辑
      *   1. rov 空兜底：rov 池为空时流量池直接顶上 (Basic 段 1)
@@ -477,6 +406,77 @@ public class RankStrategy4RegionMergeModelV562 extends RankStrategy4RegionMergeM
         return new RankResult(result);
     }
 
+    /**
+     * V562 实验：拉取粗排分（按 vid → score 返回）。
+     *
+     * 数据源：alg_vid_recommend_exp_feature_20250212。
+     * 表里没有现成 rovn 字段，需要从原子字段 (return_n_uv_*, exp_*) 用 plusSmooth 算出来。
+     * 公式 = FeatureV6.oneTypeStatFeature 同口径：rovn = plusSmooth(return_n_uv, exp, plus, 1)
+     * 默认 plus=30 与 FeatureV6.largerSmoothPlus 对齐，AB 对比不会因口径不同污染结论。
+     *
+     * Apollo 可调维度：
+     *   - coarseRovn1hW / coarseRovn24hW：1h 和 24h 的加权（默认 0.5/0.5）
+     *   - coarseRovn1hSmoothPlus / coarseRovn24hSmoothPlus：贝叶斯平滑系数（默认 30/30）
+     *
+     * 缺失自动归一化：单值缺失时剩下的撑起全部权重；两值都缺失则 caller 兜底 RovScore。
+     */
+    private Map<Long, Double> fetchCoarseRankScores(RankParam param) {
+        if (param == null || param.getRecallResult() == null
+                || CollectionUtils.isEmpty(param.getRecallResult().getData())) {
+            return Collections.emptyMap();
+        }
+        Map<String, Double> mergeWeight = this.mergeWeight != null ? this.mergeWeight : Collections.emptyMap();
+        double w1h = mergeWeight.getOrDefault("coarseRovn1hW", 0.5);
+        double w24h = mergeWeight.getOrDefault("coarseRovn24hW", 0.5);
+        double plus1h = mergeWeight.getOrDefault("coarseRovn1hSmoothPlus", 30.0);
+        double plus24h = mergeWeight.getOrDefault("coarseRovn24hSmoothPlus", 30.0);
+        // 只对参与统一截断的 23 路 vid 拉粗排分（跳过流量池 3 路，省 proto + RPC 延迟）
+        List<String> vids = param.getRecallResult().getData().stream()
+                .filter(d -> d != null && CollectionUtils.isNotEmpty(d.getVideos()))
+                .filter(d -> ALL_ROV_PUSH_FROMS.contains(d.getPushFrom()))
+                .flatMap(d -> d.getVideos().stream())
+                .map(v -> String.valueOf(v.getVideoId()))
+                .distinct()
+                .collect(Collectors.toList());
+        if (vids.isEmpty()) return Collections.emptyMap();
+
+        Map<String, Map<String, Map<String, String>>> feats = featureService.getVideoCoarseRankFeature(vids);
+        Map<Long, Double> result = new HashMap<>(vids.size());
+        for (String vid : vids) {
+            Map<String, String> row = feats.getOrDefault(vid, Collections.emptyMap())
+                    .getOrDefault("alg_vid_recommend_exp_feature_20250212", Collections.emptyMap());
+            Double rovn1h = computeRovn(row, "1h", plus1h);
+            Double rovn24h = computeRovn(row, "24h", plus24h);
+            // 加权平均，缺失自动归一化
+            double sumW = (rovn1h != null ? w1h : 0) + (rovn24h != null ? w24h : 0);
+            if (sumW <= 0) continue;
+            double sumWS = (rovn1h != null ? rovn1h * w1h : 0) + (rovn24h != null ? rovn24h * w24h : 0);
+            try {
+                result.put(Long.parseLong(vid), sumWS / sumW);
+            } catch (NumberFormatException ignore) { }
+        }
+        return result;
+    }
+
+    /**
+     * 与 FeatureV6.oneTypeStatFeature 同口径：rovn = plusSmooth(return_n_uv, exp, plus, 1)
+     *
+     * 字段语义（区分 0 vs null）：
+     *   - exp 是 period 有效性 anchor：null 或 ≤0 → 整个 period 无效（return null）
+     *   - return_n_uv 缺失视为 0（真实信号"无回访"）：rovn=0，参与加权（不会让另一时段兜底）
+     */
+    private static Double computeRovn(Map<String, String> row, String period, double smoothPlus) {
+        Double exp = parseDoubleOrNull(row.get("exp_" + period));
+        if (exp == null || exp <= 0) return null;
+        Double returnNuv = parseDoubleOrNull(row.get("return_n_uv_" + period));
+        return FeatureUtils.plusSmooth(returnNuv != null ? returnNuv : 0, exp, smoothPlus, 1);
+    }
+
+    private static Double parseDoubleOrNull(String s) {
+        if (StringUtils.isBlank(s)) return null;
+        try { return Double.parseDouble(s); } catch (NumberFormatException e) { return null; }
+    }
+
     private UserShareReturnProfile parseUserProfile(Map<String, Map<String, String>> userOriginInfo) {
         if (null != userOriginInfo) {
             Map<String, String> c9 = userOriginInfo.get("alg_recsys_feature_user_share_return_stat");

recommend-server-service/src/main/java/com/tzld/piaoquan/recommend/server/service/rank/strategy/RankStrategy4RegionMergeModelV564.java

@@ -332,77 +332,6 @@ public class RankStrategy4RegionMergeModelV564 extends RankStrategy4RegionMergeM
         return result;
     }
 
-    /**
-     * V564 实验：拉取粗排分（按 vid → score 返回）。
-     *
-     * 数据源：alg_vid_recommend_exp_feature_20250212。
-     * 表里没有现成 rovn 字段，需要从原子字段 (return_n_uv_*, exp_*) 用 plusSmooth 算出来。
-     * 公式 = FeatureV6.oneTypeStatFeature 同口径：rovn = plusSmooth(return_n_uv, exp, plus, 1)
-     * 默认 plus=30 与 FeatureV6.largerSmoothPlus 对齐，AB 对比不会因口径不同污染结论。
-     *
-     * Apollo 可调维度：
-     *   - coarseRovn1hW / coarseRovn24hW：1h 和 24h 的加权（默认 0.5/0.5）
-     *   - coarseRovn1hSmoothPlus / coarseRovn24hSmoothPlus：贝叶斯平滑系数（默认 30/30）
-     *
-     * 缺失自动归一化：单值缺失时剩下的撑起全部权重；两值都缺失则 caller 兜底 RovScore。
-     */
-    private Map<Long, Double> fetchCoarseRankScores(RankParam param) {
-        if (param == null || param.getRecallResult() == null
-                || CollectionUtils.isEmpty(param.getRecallResult().getData())) {
-            return Collections.emptyMap();
-        }
-        Map<String, Double> mergeWeight = this.mergeWeight != null ? this.mergeWeight : Collections.emptyMap();
-        double w1h = mergeWeight.getOrDefault("coarseRovn1hW", 0.5);
-        double w24h = mergeWeight.getOrDefault("coarseRovn24hW", 0.5);
-        double plus1h = mergeWeight.getOrDefault("coarseRovn1hSmoothPlus", 30.0);
-        double plus24h = mergeWeight.getOrDefault("coarseRovn24hSmoothPlus", 30.0);
-        // 只对参与统一截断的 23 路 vid 拉粗排分（跳过流量池 3 路，省 proto + RPC 延迟）
-        List<String> vids = param.getRecallResult().getData().stream()
-                .filter(d -> d != null && CollectionUtils.isNotEmpty(d.getVideos()))
-                .filter(d -> ALL_ROV_PUSH_FROMS.contains(d.getPushFrom()))
-                .flatMap(d -> d.getVideos().stream())
-                .map(v -> String.valueOf(v.getVideoId()))
-                .distinct()
-                .collect(Collectors.toList());
-        if (vids.isEmpty()) return Collections.emptyMap();
-
-        Map<String, Map<String, Map<String, String>>> feats = featureService.getVideoCoarseRankFeature(vids);
-        Map<Long, Double> result = new HashMap<>(vids.size());
-        for (String vid : vids) {
-            Map<String, String> row = feats.getOrDefault(vid, Collections.emptyMap())
-                    .getOrDefault("alg_vid_recommend_exp_feature_20250212", Collections.emptyMap());
-            Double rovn1h = computeRovn(row, "1h", plus1h);
-            Double rovn24h = computeRovn(row, "24h", plus24h);
-            // 加权平均，缺失自动归一化
-            double sumW = (rovn1h != null ? w1h : 0) + (rovn24h != null ? w24h : 0);
-            if (sumW <= 0) continue;
-            double sumWS = (rovn1h != null ? rovn1h * w1h : 0) + (rovn24h != null ? rovn24h * w24h : 0);
-            try {
-                result.put(Long.parseLong(vid), sumWS / sumW);
-            } catch (NumberFormatException ignore) { }
-        }
-        return result;
-    }
-
-    /**
-     * 与 FeatureV6.oneTypeStatFeature 同口径：rovn = plusSmooth(return_n_uv, exp, plus, 1)
-     *
-     * 字段语义（区分 0 vs null）：
-     *   - exp 是 period 有效性 anchor：null 或 ≤0 → 整个 period 无效（return null）
-     *   - return_n_uv 缺失视为 0（真实信号"无回访"）：rovn=0，参与加权（不会让另一时段兜底）
-     */
-    private static Double computeRovn(Map<String, String> row, String period, double smoothPlus) {
-        Double exp = parseDoubleOrNull(row.get("exp_" + period));
-        if (exp == null || exp <= 0) return null;
-        Double returnNuv = parseDoubleOrNull(row.get("return_n_uv_" + period));
-        return FeatureUtils.plusSmooth(returnNuv != null ? returnNuv : 0, exp, smoothPlus, 1);
-    }
-
-    private static Double parseDoubleOrNull(String s) {
-        if (StringUtils.isBlank(s)) return null;
-        try { return Double.parseDouble(s); } catch (NumberFormatException e) { return null; }
-    }
-
     /**
      * 同 V567 极简 fusion：只保留"流量池相关 + 兜底相关"逻辑
      *   1. rov 空兜底：rov 池为空时流量池直接顶上 (Basic 段 1)
@@ -474,6 +403,77 @@ public class RankStrategy4RegionMergeModelV564 extends RankStrategy4RegionMergeM
         return new RankResult(result);
     }
 
+    /**
+     * V564 实验：拉取粗排分（按 vid → score 返回）。
+     *
+     * 数据源：alg_vid_recommend_exp_feature_20250212。
+     * 表里没有现成 rovn 字段，需要从原子字段 (return_n_uv_*, exp_*) 用 plusSmooth 算出来。
+     * 公式 = FeatureV6.oneTypeStatFeature 同口径：rovn = plusSmooth(return_n_uv, exp, plus, 1)
+     * 默认 plus=30 与 FeatureV6.largerSmoothPlus 对齐，AB 对比不会因口径不同污染结论。
+     *
+     * Apollo 可调维度：
+     *   - coarseRovn1hW / coarseRovn24hW：1h 和 24h 的加权（默认 0.5/0.5）
+     *   - coarseRovn1hSmoothPlus / coarseRovn24hSmoothPlus：贝叶斯平滑系数（默认 30/30）
+     *
+     * 缺失自动归一化：单值缺失时剩下的撑起全部权重；两值都缺失则 caller 兜底 RovScore。
+     */
+    private Map<Long, Double> fetchCoarseRankScores(RankParam param) {
+        if (param == null || param.getRecallResult() == null
+                || CollectionUtils.isEmpty(param.getRecallResult().getData())) {
+            return Collections.emptyMap();
+        }
+        Map<String, Double> mergeWeight = this.mergeWeight != null ? this.mergeWeight : Collections.emptyMap();
+        double w1h = mergeWeight.getOrDefault("coarseRovn1hW", 0.5);
+        double w24h = mergeWeight.getOrDefault("coarseRovn24hW", 0.5);
+        double plus1h = mergeWeight.getOrDefault("coarseRovn1hSmoothPlus", 30.0);
+        double plus24h = mergeWeight.getOrDefault("coarseRovn24hSmoothPlus", 30.0);
+        // 只对参与统一截断的 23 路 vid 拉粗排分（跳过流量池 3 路，省 proto + RPC 延迟）
+        List<String> vids = param.getRecallResult().getData().stream()
+                .filter(d -> d != null && CollectionUtils.isNotEmpty(d.getVideos()))
+                .filter(d -> ALL_ROV_PUSH_FROMS.contains(d.getPushFrom()))
+                .flatMap(d -> d.getVideos().stream())
+                .map(v -> String.valueOf(v.getVideoId()))
+                .distinct()
+                .collect(Collectors.toList());
+        if (vids.isEmpty()) return Collections.emptyMap();
+
+        Map<String, Map<String, Map<String, String>>> feats = featureService.getVideoCoarseRankFeature(vids);
+        Map<Long, Double> result = new HashMap<>(vids.size());
+        for (String vid : vids) {
+            Map<String, String> row = feats.getOrDefault(vid, Collections.emptyMap())
+                    .getOrDefault("alg_vid_recommend_exp_feature_20250212", Collections.emptyMap());
+            Double rovn1h = computeRovn(row, "1h", plus1h);
+            Double rovn24h = computeRovn(row, "24h", plus24h);
+            // 加权平均，缺失自动归一化
+            double sumW = (rovn1h != null ? w1h : 0) + (rovn24h != null ? w24h : 0);
+            if (sumW <= 0) continue;
+            double sumWS = (rovn1h != null ? rovn1h * w1h : 0) + (rovn24h != null ? rovn24h * w24h : 0);
+            try {
+                result.put(Long.parseLong(vid), sumWS / sumW);
+            } catch (NumberFormatException ignore) { }
+        }
+        return result;
+    }
+
+    /**
+     * 与 FeatureV6.oneTypeStatFeature 同口径：rovn = plusSmooth(return_n_uv, exp, plus, 1)
+     *
+     * 字段语义（区分 0 vs null）：
+     *   - exp 是 period 有效性 anchor：null 或 ≤0 → 整个 period 无效（return null）
+     *   - return_n_uv 缺失视为 0（真实信号"无回访"）：rovn=0，参与加权（不会让另一时段兜底）
+     */
+    private static Double computeRovn(Map<String, String> row, String period, double smoothPlus) {
+        Double exp = parseDoubleOrNull(row.get("exp_" + period));
+        if (exp == null || exp <= 0) return null;
+        Double returnNuv = parseDoubleOrNull(row.get("return_n_uv_" + period));
+        return FeatureUtils.plusSmooth(returnNuv != null ? returnNuv : 0, exp, smoothPlus, 1);
+    }
+
+    private static Double parseDoubleOrNull(String s) {
+        if (StringUtils.isBlank(s)) return null;
+        try { return Double.parseDouble(s); } catch (NumberFormatException e) { return null; }
+    }
+
     private UserShareReturnProfile parseUserProfile(Map<String, Map<String, String>> userOriginInfo) {
         if (null != userOriginInfo) {
             Map<String, String> c9 = userOriginInfo.get("alg_recsys_feature_user_share_return_stat");

recommend-server-service/src/main/java/com/tzld/piaoquan/recommend/server/service/rank/strategy/RankStrategy4RegionMergeModelV565.java

@@ -335,77 +335,6 @@ public class RankStrategy4RegionMergeModelV565 extends RankStrategy4RegionMergeM
         return result;
     }
 
-    /**
-     * V565 实验：拉取粗排分（按 vid → score 返回）。
-     *
-     * 数据源：alg_vid_recommend_exp_feature_20250212。
-     * 表里没有现成 rovn 字段，需要从原子字段 (return_n_uv_*, exp_*) 用 plusSmooth 算出来。
-     * 公式 = FeatureV6.oneTypeStatFeature 同口径：rovn = plusSmooth(return_n_uv, exp, plus, 1)
-     * 默认 plus=30 与 FeatureV6.largerSmoothPlus 对齐，AB 对比不会因口径不同污染结论。
-     *
-     * Apollo 可调维度：
-     *   - coarseRovn1hW / coarseRovn24hW：1h 和 24h 的加权（默认 0.5/0.5）
-     *   - coarseRovn1hSmoothPlus / coarseRovn24hSmoothPlus：贝叶斯平滑系数（默认 30/30）
-     *
-     * 缺失自动归一化：单值缺失时剩下的撑起全部权重；两值都缺失则 caller 兜底 RovScore。
-     */
-    private Map<Long, Double> fetchCoarseRankScores(RankParam param) {
-        if (param == null || param.getRecallResult() == null
-                || CollectionUtils.isEmpty(param.getRecallResult().getData())) {
-            return Collections.emptyMap();
-        }
-        Map<String, Double> mergeWeight = this.mergeWeight != null ? this.mergeWeight : Collections.emptyMap();
-        double w1h = mergeWeight.getOrDefault("coarseRovn1hW", 0.5);
-        double w24h = mergeWeight.getOrDefault("coarseRovn24hW", 0.5);
-        double plus1h = mergeWeight.getOrDefault("coarseRovn1hSmoothPlus", 30.0);
-        double plus24h = mergeWeight.getOrDefault("coarseRovn24hSmoothPlus", 30.0);
-        // 只对参与统一截断的 23 路 vid 拉粗排分（跳过流量池 3 路，省 proto + RPC 延迟）
-        List<String> vids = param.getRecallResult().getData().stream()
-                .filter(d -> d != null && CollectionUtils.isNotEmpty(d.getVideos()))
-                .filter(d -> ALL_ROV_PUSH_FROMS.contains(d.getPushFrom()))
-                .flatMap(d -> d.getVideos().stream())
-                .map(v -> String.valueOf(v.getVideoId()))
-                .distinct()
-                .collect(Collectors.toList());
-        if (vids.isEmpty()) return Collections.emptyMap();
-
-        Map<String, Map<String, Map<String, String>>> feats = featureService.getVideoCoarseRankFeature(vids);
-        Map<Long, Double> result = new HashMap<>(vids.size());
-        for (String vid : vids) {
-            Map<String, String> row = feats.getOrDefault(vid, Collections.emptyMap())
-                    .getOrDefault("alg_vid_recommend_exp_feature_20250212", Collections.emptyMap());
-            Double rovn1h = computeRovn(row, "1h", plus1h);
-            Double rovn24h = computeRovn(row, "24h", plus24h);
-            // 加权平均，缺失自动归一化
-            double sumW = (rovn1h != null ? w1h : 0) + (rovn24h != null ? w24h : 0);
-            if (sumW <= 0) continue;
-            double sumWS = (rovn1h != null ? rovn1h * w1h : 0) + (rovn24h != null ? rovn24h * w24h : 0);
-            try {
-                result.put(Long.parseLong(vid), sumWS / sumW);
-            } catch (NumberFormatException ignore) { }
-        }
-        return result;
-    }
-
-    /**
-     * 与 FeatureV6.oneTypeStatFeature 同口径：rovn = plusSmooth(return_n_uv, exp, plus, 1)
-     *
-     * 字段语义（区分 0 vs null）：
-     *   - exp 是 period 有效性 anchor：null 或 ≤0 → 整个 period 无效（return null）
-     *   - return_n_uv 缺失视为 0（真实信号"无回访"）：rovn=0，参与加权（不会让另一时段兜底）
-     */
-    private static Double computeRovn(Map<String, String> row, String period, double smoothPlus) {
-        Double exp = parseDoubleOrNull(row.get("exp_" + period));
-        if (exp == null || exp <= 0) return null;
-        Double returnNuv = parseDoubleOrNull(row.get("return_n_uv_" + period));
-        return FeatureUtils.plusSmooth(returnNuv != null ? returnNuv : 0, exp, smoothPlus, 1);
-    }
-
-    private static Double parseDoubleOrNull(String s) {
-        if (StringUtils.isBlank(s)) return null;
-        try { return Double.parseDouble(s); } catch (NumberFormatException e) { return null; }
-    }
-
     /**
      * 同 V567 极简 fusion：只保留"流量池相关 + 兜底相关"逻辑
      *   1. rov 空兜底：rov 池为空时流量池直接顶上 (Basic 段 1)
@@ -477,6 +406,77 @@ public class RankStrategy4RegionMergeModelV565 extends RankStrategy4RegionMergeM
         return new RankResult(result);
     }
 
+    /**
+     * V565 实验：拉取粗排分（按 vid → score 返回）。
+     *
+     * 数据源：alg_vid_recommend_exp_feature_20250212。
+     * 表里没有现成 rovn 字段，需要从原子字段 (return_n_uv_*, exp_*) 用 plusSmooth 算出来。
+     * 公式 = FeatureV6.oneTypeStatFeature 同口径：rovn = plusSmooth(return_n_uv, exp, plus, 1)
+     * 默认 plus=30 与 FeatureV6.largerSmoothPlus 对齐，AB 对比不会因口径不同污染结论。
+     *
+     * Apollo 可调维度：
+     *   - coarseRovn1hW / coarseRovn24hW：1h 和 24h 的加权（默认 0.5/0.5）
+     *   - coarseRovn1hSmoothPlus / coarseRovn24hSmoothPlus：贝叶斯平滑系数（默认 30/30）
+     *
+     * 缺失自动归一化：单值缺失时剩下的撑起全部权重；两值都缺失则 caller 兜底 RovScore。
+     */
+    private Map<Long, Double> fetchCoarseRankScores(RankParam param) {
+        if (param == null || param.getRecallResult() == null
+                || CollectionUtils.isEmpty(param.getRecallResult().getData())) {
+            return Collections.emptyMap();
+        }
+        Map<String, Double> mergeWeight = this.mergeWeight != null ? this.mergeWeight : Collections.emptyMap();
+        double w1h = mergeWeight.getOrDefault("coarseRovn1hW", 0.5);
+        double w24h = mergeWeight.getOrDefault("coarseRovn24hW", 0.5);
+        double plus1h = mergeWeight.getOrDefault("coarseRovn1hSmoothPlus", 30.0);
+        double plus24h = mergeWeight.getOrDefault("coarseRovn24hSmoothPlus", 30.0);
+        // 只对参与统一截断的 23 路 vid 拉粗排分（跳过流量池 3 路，省 proto + RPC 延迟）
+        List<String> vids = param.getRecallResult().getData().stream()
+                .filter(d -> d != null && CollectionUtils.isNotEmpty(d.getVideos()))
+                .filter(d -> ALL_ROV_PUSH_FROMS.contains(d.getPushFrom()))
+                .flatMap(d -> d.getVideos().stream())
+                .map(v -> String.valueOf(v.getVideoId()))
+                .distinct()
+                .collect(Collectors.toList());
+        if (vids.isEmpty()) return Collections.emptyMap();
+
+        Map<String, Map<String, Map<String, String>>> feats = featureService.getVideoCoarseRankFeature(vids);
+        Map<Long, Double> result = new HashMap<>(vids.size());
+        for (String vid : vids) {
+            Map<String, String> row = feats.getOrDefault(vid, Collections.emptyMap())
+                    .getOrDefault("alg_vid_recommend_exp_feature_20250212", Collections.emptyMap());
+            Double rovn1h = computeRovn(row, "1h", plus1h);
+            Double rovn24h = computeRovn(row, "24h", plus24h);
+            // 加权平均，缺失自动归一化
+            double sumW = (rovn1h != null ? w1h : 0) + (rovn24h != null ? w24h : 0);
+            if (sumW <= 0) continue;
+            double sumWS = (rovn1h != null ? rovn1h * w1h : 0) + (rovn24h != null ? rovn24h * w24h : 0);
+            try {
+                result.put(Long.parseLong(vid), sumWS / sumW);
+            } catch (NumberFormatException ignore) { }
+        }
+        return result;
+    }
+
+    /**
+     * 与 FeatureV6.oneTypeStatFeature 同口径：rovn = plusSmooth(return_n_uv, exp, plus, 1)
+     *
+     * 字段语义（区分 0 vs null）：
+     *   - exp 是 period 有效性 anchor：null 或 ≤0 → 整个 period 无效（return null）
+     *   - return_n_uv 缺失视为 0（真实信号"无回访"）：rovn=0，参与加权（不会让另一时段兜底）
+     */
+    private static Double computeRovn(Map<String, String> row, String period, double smoothPlus) {
+        Double exp = parseDoubleOrNull(row.get("exp_" + period));
+        if (exp == null || exp <= 0) return null;
+        Double returnNuv = parseDoubleOrNull(row.get("return_n_uv_" + period));
+        return FeatureUtils.plusSmooth(returnNuv != null ? returnNuv : 0, exp, smoothPlus, 1);
+    }
+
+    private static Double parseDoubleOrNull(String s) {
+        if (StringUtils.isBlank(s)) return null;
+        try { return Double.parseDouble(s); } catch (NumberFormatException e) { return null; }
+    }
+
     private UserShareReturnProfile parseUserProfile(Map<String, Map<String, String>> userOriginInfo) {
         if (null != userOriginInfo) {
             Map<String, String> c9 = userOriginInfo.get("alg_recsys_feature_user_share_return_stat");

recommend-server-service/src/main/java/com/tzld/piaoquan/recommend/server/service/rank/strategy/RankStrategy4RegionMergeModelV569.java

@@ -335,77 +335,6 @@ public class RankStrategy4RegionMergeModelV569 extends RankStrategy4RegionMergeM
         return result;
     }
 
-    /**
-     * V569 实验：拉取粗排分（按 vid → score 返回）。
-     *
-     * 数据源：alg_vid_recommend_exp_feature_20250212。
-     * 表里没有现成 rovn 字段，需要从原子字段 (return_n_uv_*, exp_*) 用 plusSmooth 算出来。
-     * 公式 = FeatureV6.oneTypeStatFeature 同口径：rovn = plusSmooth(return_n_uv, exp, plus, 1)
-     * 默认 plus=30 与 FeatureV6.largerSmoothPlus 对齐，AB 对比不会因口径不同污染结论。
-     *
-     * Apollo 可调维度：
-     *   - coarseRovn1hW / coarseRovn24hW：1h 和 24h 的加权（默认 0.5/0.5）
-     *   - coarseRovn1hSmoothPlus / coarseRovn24hSmoothPlus：贝叶斯平滑系数（默认 30/30）
-     *
-     * 缺失自动归一化：单值缺失时剩下的撑起全部权重；两值都缺失则 caller 兜底 RovScore。
-     */
-    private Map<Long, Double> fetchCoarseRankScores(RankParam param) {
-        if (param == null || param.getRecallResult() == null
-                || CollectionUtils.isEmpty(param.getRecallResult().getData())) {
-            return Collections.emptyMap();
-        }
-        Map<String, Double> mergeWeight = this.mergeWeight != null ? this.mergeWeight : Collections.emptyMap();
-        double w1h = mergeWeight.getOrDefault("coarseRovn1hW", 0.5);
-        double w24h = mergeWeight.getOrDefault("coarseRovn24hW", 0.5);
-        double plus1h = mergeWeight.getOrDefault("coarseRovn1hSmoothPlus", 30.0);
-        double plus24h = mergeWeight.getOrDefault("coarseRovn24hSmoothPlus", 30.0);
-        // 只对参与统一截断的 23 路 vid 拉粗排分（跳过流量池 3 路，省 proto + RPC 延迟）
-        List<String> vids = param.getRecallResult().getData().stream()
-                .filter(d -> d != null && CollectionUtils.isNotEmpty(d.getVideos()))
-                .filter(d -> ALL_ROV_PUSH_FROMS.contains(d.getPushFrom()))
-                .flatMap(d -> d.getVideos().stream())
-                .map(v -> String.valueOf(v.getVideoId()))
-                .distinct()
-                .collect(Collectors.toList());
-        if (vids.isEmpty()) return Collections.emptyMap();
-
-        Map<String, Map<String, Map<String, String>>> feats = featureService.getVideoCoarseRankFeature(vids);
-        Map<Long, Double> result = new HashMap<>(vids.size());
-        for (String vid : vids) {
-            Map<String, String> row = feats.getOrDefault(vid, Collections.emptyMap())
-                    .getOrDefault("alg_vid_recommend_exp_feature_20250212", Collections.emptyMap());
-            Double rovn1h = computeRovn(row, "1h", plus1h);
-            Double rovn24h = computeRovn(row, "24h", plus24h);
-            // 加权平均，缺失自动归一化
-            double sumW = (rovn1h != null ? w1h : 0) + (rovn24h != null ? w24h : 0);
-            if (sumW <= 0) continue;
-            double sumWS = (rovn1h != null ? rovn1h * w1h : 0) + (rovn24h != null ? rovn24h * w24h : 0);
-            try {
-                result.put(Long.parseLong(vid), sumWS / sumW);
-            } catch (NumberFormatException ignore) { }
-        }
-        return result;
-    }
-
-    /**
-     * 与 FeatureV6.oneTypeStatFeature 同口径：rovn = plusSmooth(return_n_uv, exp, plus, 1)
-     *
-     * 字段语义（区分 0 vs null）：
-     *   - exp 是 period 有效性 anchor：null 或 ≤0 → 整个 period 无效（return null）
-     *   - return_n_uv 缺失视为 0（真实信号"无回访"）：rovn=0，参与加权（不会让另一时段兜底）
-     */
-    private static Double computeRovn(Map<String, String> row, String period, double smoothPlus) {
-        Double exp = parseDoubleOrNull(row.get("exp_" + period));
-        if (exp == null || exp <= 0) return null;
-        Double returnNuv = parseDoubleOrNull(row.get("return_n_uv_" + period));
-        return FeatureUtils.plusSmooth(returnNuv != null ? returnNuv : 0, exp, smoothPlus, 1);
-    }
-
-    private static Double parseDoubleOrNull(String s) {
-        if (StringUtils.isBlank(s)) return null;
-        try { return Double.parseDouble(s); } catch (NumberFormatException e) { return null; }
-    }
-
     /**
      * 同 V567 极简 fusion：只保留"流量池相关 + 兜底相关"逻辑
      *   1. rov 空兜底：rov 池为空时流量池直接顶上 (Basic 段 1)
@@ -477,6 +406,77 @@ public class RankStrategy4RegionMergeModelV569 extends RankStrategy4RegionMergeM
         return new RankResult(result);
     }
 
+    /**
+     * V569 实验：拉取粗排分（按 vid → score 返回）。
+     *
+     * 数据源：alg_vid_recommend_exp_feature_20250212。
+     * 表里没有现成 rovn 字段，需要从原子字段 (return_n_uv_*, exp_*) 用 plusSmooth 算出来。
+     * 公式 = FeatureV6.oneTypeStatFeature 同口径：rovn = plusSmooth(return_n_uv, exp, plus, 1)
+     * 默认 plus=30 与 FeatureV6.largerSmoothPlus 对齐，AB 对比不会因口径不同污染结论。
+     *
+     * Apollo 可调维度：
+     *   - coarseRovn1hW / coarseRovn24hW：1h 和 24h 的加权（默认 0.5/0.5）
+     *   - coarseRovn1hSmoothPlus / coarseRovn24hSmoothPlus：贝叶斯平滑系数（默认 30/30）
+     *
+     * 缺失自动归一化：单值缺失时剩下的撑起全部权重；两值都缺失则 caller 兜底 RovScore。
+     */
+    private Map<Long, Double> fetchCoarseRankScores(RankParam param) {
+        if (param == null || param.getRecallResult() == null
+                || CollectionUtils.isEmpty(param.getRecallResult().getData())) {
+            return Collections.emptyMap();
+        }
+        Map<String, Double> mergeWeight = this.mergeWeight != null ? this.mergeWeight : Collections.emptyMap();
+        double w1h = mergeWeight.getOrDefault("coarseRovn1hW", 0.5);
+        double w24h = mergeWeight.getOrDefault("coarseRovn24hW", 0.5);
+        double plus1h = mergeWeight.getOrDefault("coarseRovn1hSmoothPlus", 30.0);
+        double plus24h = mergeWeight.getOrDefault("coarseRovn24hSmoothPlus", 30.0);
+        // 只对参与统一截断的 23 路 vid 拉粗排分（跳过流量池 3 路，省 proto + RPC 延迟）
+        List<String> vids = param.getRecallResult().getData().stream()
+                .filter(d -> d != null && CollectionUtils.isNotEmpty(d.getVideos()))
+                .filter(d -> ALL_ROV_PUSH_FROMS.contains(d.getPushFrom()))
+                .flatMap(d -> d.getVideos().stream())
+                .map(v -> String.valueOf(v.getVideoId()))
+                .distinct()
+                .collect(Collectors.toList());
+        if (vids.isEmpty()) return Collections.emptyMap();
+
+        Map<String, Map<String, Map<String, String>>> feats = featureService.getVideoCoarseRankFeature(vids);
+        Map<Long, Double> result = new HashMap<>(vids.size());
+        for (String vid : vids) {
+            Map<String, String> row = feats.getOrDefault(vid, Collections.emptyMap())
+                    .getOrDefault("alg_vid_recommend_exp_feature_20250212", Collections.emptyMap());
+            Double rovn1h = computeRovn(row, "1h", plus1h);
+            Double rovn24h = computeRovn(row, "24h", plus24h);
+            // 加权平均，缺失自动归一化
+            double sumW = (rovn1h != null ? w1h : 0) + (rovn24h != null ? w24h : 0);
+            if (sumW <= 0) continue;
+            double sumWS = (rovn1h != null ? rovn1h * w1h : 0) + (rovn24h != null ? rovn24h * w24h : 0);
+            try {
+                result.put(Long.parseLong(vid), sumWS / sumW);
+            } catch (NumberFormatException ignore) { }
+        }
+        return result;
+    }
+
+    /**
+     * 与 FeatureV6.oneTypeStatFeature 同口径：rovn = plusSmooth(return_n_uv, exp, plus, 1)
+     *
+     * 字段语义（区分 0 vs null）：
+     *   - exp 是 period 有效性 anchor：null 或 ≤0 → 整个 period 无效（return null）
+     *   - return_n_uv 缺失视为 0（真实信号"无回访"）：rovn=0，参与加权（不会让另一时段兜底）
+     */
+    private static Double computeRovn(Map<String, String> row, String period, double smoothPlus) {
+        Double exp = parseDoubleOrNull(row.get("exp_" + period));
+        if (exp == null || exp <= 0) return null;
+        Double returnNuv = parseDoubleOrNull(row.get("return_n_uv_" + period));
+        return FeatureUtils.plusSmooth(returnNuv != null ? returnNuv : 0, exp, smoothPlus, 1);
+    }
+
+    private static Double parseDoubleOrNull(String s) {
+        if (StringUtils.isBlank(s)) return null;
+        try { return Double.parseDouble(s); } catch (NumberFormatException e) { return null; }
+    }
+
     private UserShareReturnProfile parseUserProfile(Map<String, Map<String, String>> userOriginInfo) {
         if (null != userOriginInfo) {
             Map<String, String> c9 = userOriginInfo.get("alg_recsys_feature_user_share_return_stat");