1、传入知识图谱的特征不进行截取

2、传入大数据只截取前六个特征

bigdata-web/src/main/java/org/diagbot/bigdata/work/ParamsDataProxy.java

@@ -259,7 +259,10 @@ public class ParamsDataProxy {
                 map.put("concept", String.valueOf(featureMap.get("concept")));
                 if (Constants.default_negative.equals(featureMap.get("negative"))) {
                     if (searchData.getInputs().get(map.get("feature_name")) == null) {
-                        searchData.getInputs().put(map.get("feature_name"), map);
+                        if (i < 6) {
+                            searchData.getInputs().put(map.get("feature_name"), map);
+                        }
+                        searchData.getGraphInputs().put(map.get("feature_name"), map);
                     }
                 } else {
                     searchData.getFilters().put(map.get("feature_name"), map);

common-service/src/main/java/org/diagbot/common/work/SearchData.java

@@ -44,6 +44,8 @@ public class SearchData {
     protected String algorithmClassifyValue;
     //推送条件
     private Map<String, Map<String, String>> inputs = new HashMap<>(10, 0.8f);
+    //知识图谱推送条件
+    private Map<String, Map<String, String>> graphInputs = new HashMap<>(10, 0.8f);
     //阴性 页面录入数据需要对结果过滤的集合
     private Map<String, Map<String, String>> filters = new HashMap<>(10, 0.8f);
 
@@ -252,4 +254,12 @@ public class SearchData {
     public void setIndications(String indications) {
         this.indications = indications;
     }
+
+    public Map<String, Map<String, String>> getGraphInputs() {
+        return graphInputs;
+    }
+
+    public void setGraphInputs(Map<String, Map<String, String>> graphInputs) {
+        this.graphInputs = graphInputs;
+    }
 }

nlp/src/main/java/org/diagbot/nlp/feature/extract/CaseToken.java

@@ -100,7 +100,7 @@ public abstract class CaseToken {
             }
         }
         if (!hasFeature) {
-            if (sn <= 6) {
+//            if (sn <= 6) {
                 Map<String, Object> fMap = new HashMap<>(10);
                 fMap.put("feature_name", lexeme.getText());
                 fMap.put("feature_type", featureType);
@@ -109,7 +109,7 @@ public abstract class CaseToken {
                 fMap.put("property", lexeme.getProperty());
                 fMap.put("concept", lexeme.getConcept());
                 featuresList.add(fMap);
-            }
+//            }
         }
     }
 }