Merge branch 'gray0520' into wn_cutdown_price_for_0530

@@ -27,7 +27,7 @@ public class ToolsController {
 
     @RequestMapping(value = "/vectorVersion")
     public Map<String, Object> vectorVersion() {
-        Map<String, Object> results = new HashMap<String, Object>();
+        Map<String, Object> results = new HashMap<>();
         //大数据目前推荐的版本
         String bigDataRecomDateStr = personalVectorVersionManager.getBigDataRecomDateStr();
         results.put("bigDataRecomDateStr", bigDataRecomDateStr == null ? "" : bigDataRecomDateStr);
@@ -42,14 +42,23 @@ public class ToolsController {
 
     @RequestMapping(value = "/bigdataServiceTest")
     public Map<String, Object> bigdataServiceTets(Integer uid) {
-        Map<String, Object> results = new HashMap<String, Object>();
+        Map<String, Object> results = new HashMap<>();
+
         //大数据目前推荐的版本
-        String bigDataRecomDateStr = bidataServiceCaller.getBigDataRecomDateStr();
+        String bigDataRecomDateStr = personalVectorVersionManager.getBigDataRecomDateStr();
         results.put("bigDataRecomDateStr", bigDataRecomDateStr == null ? "" : bigDataRecomDateStr);
-        results.put("userFavoriteSizes", bidataServiceCaller.getUserFavoriteSizes(uid.toString()));
+        results.put("bigDataRecomDateStrFeatures", bidataServiceCaller.getUserVectorFeature(uid.toString(), bigDataRecomDateStr));
+
+        //zk中目前的版本
+        String currentVersionInZk = personalVectorVersionManager.getCurrentVersionInZk();
+        results.put("currentVersionInZk", currentVersionInZk == null ? "" : currentVersionInZk);
+        results.put("currentVersionInZkFeatures", bidataServiceCaller.getUserVectorFeature(uid.toString(), currentVersionInZk));
+
+        //用户性别偏好
         results.put("userGenderFeature", bidataServiceCaller.getUserGenderFeature(uid.toString()));
+        //用户尺码偏好
         results.put("userFavoriteSizes", bidataServiceCaller.getUserFavoriteSizes(uid.toString()));
-        results.put("userVectorFeature", bidataServiceCaller.getUserVectorFeature(uid.toString(), bigDataRecomDateStr));
+        //用户个性化因子
         results.put("userPersionalFactor", bidataServiceCaller.queryUserPersionalFactor(uid, null, null));
         return results;
     }