src/main/java/com/rf/psychological/scale/resultBusiness/scaleResult/AASScale.java

@@ -303,46 +303,46 @@ public class AASScale extends BaseScale {
             if (scoreA > 3 && scoreB <= 3) {
                 newResultDtos.add(new NewResultDto("总分", String.valueOf(scoreA + scoreB), "安全型", "亲密关系和相互依赖安心，乐观、好交际。在感情上很容易接近他人。不管是依赖他人还是被人依赖都感觉心安。不会担忧独处和不为人接纳。认为自己是值得爱的，他人也是值得爱和信任的。十分容易与其他人接近，总是放心地依赖他人和让别人依赖自己。不会过于担心被抛弃，也不怕别人在感情上与自己过于亲近。", "无",
                         "无", "亲近主要测量个体对接近和亲密感到舒适的程度。依赖主要测量个体感到当需要帮助时能有效依赖他人的程度以及拥有信赖他人的能力。焦虑主要测量一个人担心被抛弃或不被喜爱的程度，或者得不到爱与被人抛弃的无助。", "寻求专业心理治疗，如认知行为疗法或心理动力疗法，可以帮你认识并改变自己的行为模式。专业心理医生可以为你提供指导，增强自我意识，调整自己的行为。建立良好的依恋关系。", "是"));
-                resultMap0.put("总分症状", "安全型");
-                resultMap0.put("总分指导语", "亲密关系和相互依赖安心，乐观、好交际。在感情上很容易接近他人。不管是依赖他人还是被人依赖都感觉心安。不会担忧独处和不为人接纳。认为自己是值得爱的，他人也是值得爱和信任的。十分容易与其他人接近，总是放心地依赖他人和让别人依赖自己。不会过于担心被抛弃，也不怕别人在感情上与自己过于亲近。");
-                resultMap0.put("总分因子解释", "亲近主要测量个体对接近和亲密感到舒适的程度。依赖主要测量个体感到当需要帮助时能有效依赖他人的程度以及拥有信赖他人的能力。焦虑主要测量一个人担心被抛弃或不被喜爱的程度，或者得不到爱与被人抛弃的无助。");
+                resultMap0.put("总分解释", "安全型");
+                resultMap0.put("总分说明", "亲密关系和相互依赖安心，乐观、好交际。在感情上很容易接近他人。不管是依赖他人还是被人依赖都感觉心安。不会担忧独处和不为人接纳。认为自己是值得爱的，他人也是值得爱和信任的。十分容易与其他人接近，总是放心地依赖他人和让别人依赖自己。不会过于担心被抛弃，也不怕别人在感情上与自己过于亲近。");
+                resultMap0.put("总分解读", "亲近主要测量个体对接近和亲密感到舒适的程度。依赖主要测量个体感到当需要帮助时能有效依赖他人的程度以及拥有信赖他人的能力。焦虑主要测量一个人担心被抛弃或不被喜爱的程度，或者得不到爱与被人抛弃的无助。");
                 resultMap0.put("总分建议", "寻求专业心理治疗，如认知行为疗法或心理动力疗法，可以帮你认识并改变自己的行为模式。专业心理医生可以为你提供指导，增强自我意识，调整自己的行为。建立良好的依恋关系。");
-                resultJson.put("总分症状", "安全型");
-                resultJson.put("总分指导语", "亲密关系和相互依赖安心，乐观、好交际。在感情上很容易接近他人。不管是依赖他人还是被人依赖都感觉心安。不会担忧独处和不为人接纳。认为自己是值得爱的，他人也是值得爱和信任的。十分容易与其他人接近，总是放心地依赖他人和让别人依赖自己。不会过于担心被抛弃，也不怕别人在感情上与自己过于亲近。");
-                resultJson.put("总分因子解释", "亲近主要测量个体对接近和亲密感到舒适的程度。依赖主要测量个体感到当需要帮助时能有效依赖他人的程度以及拥有信赖他人的能力。焦虑主要测量一个人担心被抛弃或不被喜爱的程度，或者得不到爱与被人抛弃的无助。");
+                resultJson.put("总分解释", "安全型");
+                resultJson.put("总分说明", "亲密关系和相互依赖安心，乐观、好交际。在感情上很容易接近他人。不管是依赖他人还是被人依赖都感觉心安。不会担忧独处和不为人接纳。认为自己是值得爱的，他人也是值得爱和信任的。十分容易与其他人接近，总是放心地依赖他人和让别人依赖自己。不会过于担心被抛弃，也不怕别人在感情上与自己过于亲近。");
+                resultJson.put("总分解读", "亲近主要测量个体对接近和亲密感到舒适的程度。依赖主要测量个体感到当需要帮助时能有效依赖他人的程度以及拥有信赖他人的能力。焦虑主要测量一个人担心被抛弃或不被喜爱的程度，或者得不到爱与被人抛弃的无助。");
                 resultJson.put("总分建议", "寻求专业心理治疗，如认知行为疗法或心理动力疗法，可以帮你认识并改变自己的行为模式。专业心理医生可以为你提供指导，增强自我意识，调整自己的行为。建立良好的依恋关系。");
             } else if (scoreA > 3 && scoreB > 3) {
                 newResultDtos.add(new NewResultDto("总分", String.valueOf(scoreA + scoreB), "先占型", "对有损亲密关系的任何威胁都不安和警惕，贪婪、妒忌。希望在亲密关系中投入全部的感情，但经常发现他人并不乐意杷关系发展到如自己期望的那般亲密。没有亲密关系让你不安，有时还担心伴侣不会像你看重他一样看重你。", "无",
                         "无", "亲近主要测量个体对接近和亲密感到舒适的程度。依赖主要测量个体感到当需要帮助时能有效依赖他人的程度以及拥有信赖他人的能力。焦虑主要测量一个人担心被抛弃或不被喜爱的程度，或者得不到爱与被人抛弃的无助。", "寻求专业心理治疗，如认知行为疗法或心理动力疗法，可以帮你认识并改变自己的行为模式。专业心理医生可以为你提供指导，增强自我意识，调整自己的行为。建立良好的依恋关系。", "是"));
-                resultMap0.put("总分症状", "先占型");
-                resultMap0.put("总分指导语", "对有损亲密关系的任何威胁都不安和警惕，贪婪、妒忌。希望在亲密关系中投入全部的感情，但经常发现他人并不乐意杷关系发展到如自己期望的那般亲密。没有亲密关系让你不安，有时还担心伴侣不会像你看重他一样看重你。");
-                resultMap0.put("总分因子解释", "亲近主要测量个体对接近和亲密感到舒适的程度。依赖主要测量个体感到当需要帮助时能有效依赖他人的程度以及拥有信赖他人的能力。焦虑主要测量一个人担心被抛弃或不被喜爱的程度，或者得不到爱与被人抛弃的无助。");
+                resultMap0.put("总分解释", "先占型");
+                resultMap0.put("总分说明", "对有损亲密关系的任何威胁都不安和警惕，贪婪、妒忌。希望在亲密关系中投入全部的感情，但经常发现他人并不乐意杷关系发展到如自己期望的那般亲密。没有亲密关系让你不安，有时还担心伴侣不会像你看重他一样看重你。");
+                resultMap0.put("总分解读", "亲近主要测量个体对接近和亲密感到舒适的程度。依赖主要测量个体感到当需要帮助时能有效依赖他人的程度以及拥有信赖他人的能力。焦虑主要测量一个人担心被抛弃或不被喜爱的程度，或者得不到爱与被人抛弃的无助。");
                 resultMap0.put("总分建议", "寻求专业心理治疗，如认知行为疗法或心理动力疗法，可以帮你认识并改变自己的行为模式。专业心理医生可以为你提供指导，增强自我意识，调整自己的行为。建立良好的依恋关系。");
-                resultJson.put("总分症状", "先占型");
-                resultJson.put("总分指导语", "对有损亲密关系的任何威胁都不安和警惕，贪婪、妒忌。希望在亲密关系中投入全部的感情，但经常发现他人并不乐意杷关系发展到如自己期望的那般亲密。没有亲密关系让你不安，有时还担心伴侣不会像你看重他一样看重你。");
-                resultJson.put("总分因子解释", "亲近主要测量个体对接近和亲密感到舒适的程度。依赖主要测量个体感到当需要帮助时能有效依赖他人的程度以及拥有信赖他人的能力。焦虑主要测量一个人担心被抛弃或不被喜爱的程度，或者得不到爱与被人抛弃的无助。");
+                resultJson.put("总分解释", "先占型");
+                resultJson.put("总分说明", "对有损亲密关系的任何威胁都不安和警惕，贪婪、妒忌。希望在亲密关系中投入全部的感情，但经常发现他人并不乐意杷关系发展到如自己期望的那般亲密。没有亲密关系让你不安，有时还担心伴侣不会像你看重他一样看重你。");
+                resultJson.put("总分解读", "亲近主要测量个体对接近和亲密感到舒适的程度。依赖主要测量个体感到当需要帮助时能有效依赖他人的程度以及拥有信赖他人的能力。焦虑主要测量一个人担心被抛弃或不被喜爱的程度，或者得不到爱与被人抛弃的无助。");
                 resultJson.put("总分建议", "寻求专业心理治疗，如认知行为疗法或心理动力疗法，可以帮你认识并改变自己的行为模式。专业心理医生可以为你提供指导，增强自我意识，调整自己的行为。建立良好的依恋关系。");
             } else if (scoreA <= 3 && scoreB <= 3) {
                 newResultDtos.add(new NewResultDto("总分", String.valueOf(scoreA + scoreB), "拒绝型", "自立。漠视亲密关系，冷淡、独立。即使没有亲密关系也安心。对你而言，独立和自给自足更加重要，你不喜欢依赖别人或让人依赖。崇尚独立，对自我是积极的，否认渴望亲近，对他人是消极的。", "无",
                         "无", "亲近主要测量个体对接近和亲密感到舒适的程度。依赖主要测量个体感到当需要帮助时能有效依赖他人的程度以及拥有信赖他人的能力。焦虑主要测量一个人担心被抛弃或不被喜爱的程度，或者得不到爱与被人抛弃的无助。", "寻求专业心理治疗，如认知行为疗法或心理动力疗法，可以帮你认识并改变自己的行为模式。专业心理医生可以为你提供指导，增强自我意识，调整自己的行为。建立良好的依恋关系。", "是"));
-                resultMap0.put("总分症状", "拒绝型");
-                resultMap0.put("总分指导语", "自立。漠视亲密关系，冷淡、独立。即使没有亲密关系也安心。对你而言，独立和自给自足更加重要，你不喜欢依赖别人或让人依赖。崇尚独立，对自我是积极的，否认渴望亲近，对他人是消极的。");
-                resultMap0.put("总分因子解释", "亲近主要测量个体对接近和亲密感到舒适的程度。依赖主要测量个体感到当需要帮助时能有效依赖他人的程度以及拥有信赖他人的能力。焦虑主要测量一个人担心被抛弃或不被喜爱的程度，或者得不到爱与被人抛弃的无助。");
+                resultMap0.put("总分解释", "拒绝型");
+                resultMap0.put("总分说明", "自立。漠视亲密关系，冷淡、独立。即使没有亲密关系也安心。对你而言，独立和自给自足更加重要，你不喜欢依赖别人或让人依赖。崇尚独立，对自我是积极的，否认渴望亲近，对他人是消极的。");
+                resultMap0.put("总分解读", "亲近主要测量个体对接近和亲密感到舒适的程度。依赖主要测量个体感到当需要帮助时能有效依赖他人的程度以及拥有信赖他人的能力。焦虑主要测量一个人担心被抛弃或不被喜爱的程度，或者得不到爱与被人抛弃的无助。");
                 resultMap0.put("总分建议", "寻求专业心理治疗，如认知行为疗法或心理动力疗法，可以帮你认识并改变自己的行为模式。专业心理医生可以为你提供指导，增强自我意识，调整自己的行为。建立良好的依恋关系。");
-                resultJson.put("总分症状", "拒绝型");
-                resultJson.put("总分指导语", "自立。漠视亲密关系，冷淡、独立。即使没有亲密关系也安心。对你而言，独立和自给自足更加重要，你不喜欢依赖别人或让人依赖。崇尚独立，对自我是积极的，否认渴望亲近，对他人是消极的。");
-                resultJson.put("总分因子解释", "亲近主要测量个体对接近和亲密感到舒适的程度。依赖主要测量个体感到当需要帮助时能有效依赖他人的程度以及拥有信赖他人的能力。焦虑主要测量一个人担心被抛弃或不被喜爱的程度，或者得不到爱与被人抛弃的无助。");
+                resultJson.put("总分解释", "拒绝型");
+                resultJson.put("总分说明", "自立。漠视亲密关系，冷淡、独立。即使没有亲密关系也安心。对你而言，独立和自给自足更加重要，你不喜欢依赖别人或让人依赖。崇尚独立，对自我是积极的，否认渴望亲近，对他人是消极的。");
+                resultJson.put("总分解读", "亲近主要测量个体对接近和亲密感到舒适的程度。依赖主要测量个体感到当需要帮助时能有效依赖他人的程度以及拥有信赖他人的能力。焦虑主要测量一个人担心被抛弃或不被喜爱的程度，或者得不到爱与被人抛弃的无助。");
                 resultJson.put("总分建议", "寻求专业心理治疗，如认知行为疗法或心理动力疗法，可以帮你认识并改变自己的行为模式。专业心理医生可以为你提供指导，增强自我意识，调整自己的行为。建立良好的依恋关系。");
             } else if (scoreA <= 3 && scoreB > 3) {
                 newResultDtos.add(new NewResultDto("总分", String.valueOf(scoreA + scoreB), "恐惧型", "害怕被遗弃，不信任他人，猜忌多疑、害羞。和他人发生亲密接触使你不安。感情上你渴望亲密关系，但很难完全相信他人或依赖他人。担心自己和他人变得太亲密会受到伤害。对自己和他人的态度都是消极的，一旦建立了亲密关系，往往会过度担心伴侣会离开自己，整天提心吊胆。", "无",
                         "无", "亲近主要测量个体对接近和亲密感到舒适的程度。依赖主要测量个体感到当需要帮助时能有效依赖他人的程度以及拥有信赖他人的能力。焦虑主要测量一个人担心被抛弃或不被喜爱的程度，或者得不到爱与被人抛弃的无助。", "寻求专业心理治疗，如认知行为疗法或心理动力疗法，可以帮你认识并改变自己的行为模式。专业心理医生可以为你提供指导，增强自我意识，调整自己的行为。建立良好的依恋关系。", "是"));
-                resultMap0.put("总分症状", "恐惧型");
-                resultMap0.put("总分指导语", "害怕被遗弃，不信任他人，猜忌多疑、害羞。和他人发生亲密接触使你不安。感情上你渴望亲密关系，但很难完全相信他人或依赖他人。担心自己和他人变得太亲密会受到伤害。对自己和他人的态度都是消极的，一旦建立了亲密关系，往往会过度担心伴侣会离开自己，整天提心吊胆。");
-                resultMap0.put("总分因子解释", "亲近主要测量个体对接近和亲密感到舒适的程度。依赖主要测量个体感到当需要帮助时能有效依赖他人的程度以及拥有信赖他人的能力。焦虑主要测量一个人担心被抛弃或不被喜爱的程度，或者得不到爱与被人抛弃的无助。");
+                resultMap0.put("总分解释", "恐惧型");
+                resultMap0.put("总分说明", "害怕被遗弃，不信任他人，猜忌多疑、害羞。和他人发生亲密接触使你不安。感情上你渴望亲密关系，但很难完全相信他人或依赖他人。担心自己和他人变得太亲密会受到伤害。对自己和他人的态度都是消极的，一旦建立了亲密关系，往往会过度担心伴侣会离开自己，整天提心吊胆。");
+                resultMap0.put("总分解读", "亲近主要测量个体对接近和亲密感到舒适的程度。依赖主要测量个体感到当需要帮助时能有效依赖他人的程度以及拥有信赖他人的能力。焦虑主要测量一个人担心被抛弃或不被喜爱的程度，或者得不到爱与被人抛弃的无助。");
                 resultMap0.put("总分建议", "寻求专业心理治疗，如认知行为疗法或心理动力疗法，可以帮你认识并改变自己的行为模式。专业心理医生可以为你提供指导，增强自我意识，调整自己的行为。建立良好的依恋关系。");
-                resultJson.put("总分症状", "恐惧型");
-                resultJson.put("总分指导语", "害怕被遗弃，不信任他人，猜忌多疑、害羞。和他人发生亲密接触使你不安。感情上你渴望亲密关系，但很难完全相信他人或依赖他人。担心自己和他人变得太亲密会受到伤害。对自己和他人的态度都是消极的，一旦建立了亲密关系，往往会过度担心伴侣会离开自己，整天提心吊胆。");
-                resultJson.put("总分因子解释", "亲近主要测量个体对接近和亲密感到舒适的程度。依赖主要测量个体感到当需要帮助时能有效依赖他人的程度以及拥有信赖他人的能力。焦虑主要测量一个人担心被抛弃或不被喜爱的程度，或者得不到爱与被人抛弃的无助。");
+                resultJson.put("总分解释", "恐惧型");
+                resultJson.put("总分说明", "害怕被遗弃，不信任他人，猜忌多疑、害羞。和他人发生亲密接触使你不安。感情上你渴望亲密关系，但很难完全相信他人或依赖他人。担心自己和他人变得太亲密会受到伤害。对自己和他人的态度都是消极的，一旦建立了亲密关系，往往会过度担心伴侣会离开自己，整天提心吊胆。");
+                resultJson.put("总分解读", "亲近主要测量个体对接近和亲密感到舒适的程度。依赖主要测量个体感到当需要帮助时能有效依赖他人的程度以及拥有信赖他人的能力。焦虑主要测量一个人担心被抛弃或不被喜爱的程度，或者得不到爱与被人抛弃的无助。");
                 resultJson.put("总分建议", "寻求专业心理治疗，如认知行为疗法或心理动力疗法，可以帮你认识并改变自己的行为模式。专业心理医生可以为你提供指导，增强自我意识，调整自己的行为。建立良好的依恋关系。");
             }
             resultMapList.add(resultMap0);

src/main/java/com/rf/psychological/scale/resultBusiness/scaleResult/ASASScale.java

@@ -917,8 +917,8 @@ public class ASASScale extends BaseScale {
                     if (scoreDecimal.compareTo(scoreEnd) <= 0 && scoreDecimal.compareTo(scoreStart) >= 0) {
                         newResultDtos.add(new NewResultDto("总分", df.format(score), tempTotalScore.getSymptom(), tempTotalScore.getImprovementSuggestions(),
                                 tempTotalScore.getFlag(), tempTotalScore.getReference(), tempTotalScore.getNameExplain(), tempTotalScore.getSuggestion(), tempTotalScore.getIsTotalScoreExplain()));
-                        resultMap0.put("总分症状", tempTotalScore.getSymptom());
-                        resultMap0.put("总分指导语", tempTotalScore.getImprovementSuggestions());
+                        resultMap0.put("总分解释", tempTotalScore.getSymptom());
+                        resultMap0.put("总分说明", tempTotalScore.getImprovementSuggestions());
                     }
                 }
             }
@@ -929,8 +929,8 @@ public class ASASScale extends BaseScale {
                 if (scoreDecimal.compareTo(scoreEnd) <= 0 && scoreDecimal.compareTo(scoreStart) >= 0) {
                     newResultDtos.add(new NewResultDto("总分", df.format(score), tempTotalScore.getSymptom(), tempTotalScore.getImprovementSuggestions(),
                             tempTotalScore.getFlag(), tempTotalScore.getReference(), tempTotalScore.getNameExplain(), tempTotalScore.getSuggestion(), tempTotalScore.getIsTotalScoreExplain()));
-                    resultMap0.put("总分症状", tempTotalScore.getSymptom());
-                    resultMap0.put("总分指导语", tempTotalScore.getImprovementSuggestions());
+                    resultMap0.put("总分解释", tempTotalScore.getSymptom());
+                    resultMap0.put("总分说明", tempTotalScore.getImprovementSuggestions());
                 }
             }
         }
@@ -1182,13 +1182,15 @@ public class ASASScale extends BaseScale {
 
 
     private static void putDimResult(Map<String, String> resultMap0, JSONObject resultJson, String dimensionName, NewResultDto newResultDto1, ScaleMarksEntity scaleMarksEntity) {
-        resultMap0.put(dimensionName + "常模参考值", scaleMarksEntity.getReference());
-        resultMap0.put(dimensionName + "症状", scaleMarksEntity.getSymptom());
-        resultJson.put(dimensionName + "结论", scaleMarksEntity.getSymptom());
-        resultMap0.put(dimensionName + "因子解释", scaleMarksEntity.getNameExplain());
-        resultMap0.put(dimensionName + "指导语", scaleMarksEntity.getImprovementSuggestions());
-        resultJson.put(dimensionName + "指导语", scaleMarksEntity.getImprovementSuggestions());
+        //resultMap0.put(dimensionName + "常模参考值", scaleMarksEntity.getReference());
+        resultMap0.put(dimensionName + "解释", scaleMarksEntity.getSymptom());
+        resultJson.put(dimensionName + "解释", scaleMarksEntity.getSymptom());
+        resultMap0.put(dimensionName + "解读", scaleMarksEntity.getNameExplain());
+        resultJson.put(dimensionName + "解读", scaleMarksEntity.getNameExplain());
+        resultMap0.put(dimensionName + "说明", scaleMarksEntity.getImprovementSuggestions());
+        resultJson.put(dimensionName + "说明", scaleMarksEntity.getImprovementSuggestions());
         resultMap0.put(dimensionName + "建议", scaleMarksEntity.getSuggestion());
+        resultJson.put(dimensionName + "建议", scaleMarksEntity.getSuggestion());
         BeanUtils.copyProperties(scaleMarksEntity, newResultDto1);
     }
 

src/main/java/com/rf/psychological/scale/resultBusiness/scaleResult/C16PFTScale.java

@@ -14,6 +14,8 @@ import java.text.DecimalFormat;
 import java.util.*;
 import java.util.stream.Collectors;
 
+import static com.rf.psychological.scale.resultBusiness.scaleResult.NEWCOMMONScale.commonComputeDimensionScore;
+
 /**
  * @Author: GaoYiXuan
  * @Description:卡特尔16PF测试
@@ -27,7 +29,7 @@ public class C16PFTScale extends BaseScale {
         super(jsonArray, resultJson);
     }
 
-    public  JSONObject scaleCalculate() throws Exception {
+    /*public  JSONObject scaleCalculate() throws Exception {
         //总分
         double score = 0;
         DecimalFormat df = new DecimalFormat("######0.00");
@@ -216,16 +218,139 @@ public class C16PFTScale extends BaseScale {
         returnJson.put(ScaleConstant.ResultEnum.RESULT_NEW_FIELD.getKeyword(), newResult);
 
         return returnJson;
-    }
+    }*/
+
+    public  JSONObject scaleCalculate() throws Exception {
+        //总分
+        double score = 0;
+        DecimalFormat df = new DecimalFormat("######0.00");
+
+        Map<String, String> resultMap0 = new LinkedHashMap<>();
+        List<Map<String, String>> resultMapList = new ArrayList<>();
+        //新版本数据格式
+        Map<String, Object> newResult = new LinkedHashMap<>();
 
-    private static void putDimResult(Map<String, String> resultMap0, JSONObject resultJson, DimensionEntity dimensionEntity, NewResultDto newResultDto1, ScaleMarksEntity scaleMarksEntity) {
-        resultMap0.put(dimensionEntity.getName() + "常模参考值", scaleMarksEntity.getReference());
-        resultMap0.put(dimensionEntity.getName() + "症状", scaleMarksEntity.getSymptom());
-        resultJson.put(dimensionEntity.getName() + "结论", scaleMarksEntity.getSymptom());
-        resultMap0.put(dimensionEntity.getName() + "因子解释", scaleMarksEntity.getNameExplain());
-        resultMap0.put(dimensionEntity.getName() + "指导语", scaleMarksEntity.getImprovementSuggestions());
-        resultJson.put(dimensionEntity.getName() + "指导语", scaleMarksEntity.getImprovementSuggestions());
-        resultMap0.put(dimensionEntity.getName() + "建议", scaleMarksEntity.getSuggestion());
-        BeanUtils.copyProperties(scaleMarksEntity, newResultDto1);
+        //获取答案列表
+        List<AnswerEntity> answerEntities = (List<AnswerEntity>) resultJson.get("answerEntities");
+        //获取评分规则列表
+        List<ScaleMarksEntity> scaleMarksEntities = (List<ScaleMarksEntity>) resultJson.get("scaleMarksEntities");
+        //获取维度信息列表
+        List<DimensionEntity> dimensionEntities = (List<DimensionEntity>) resultJson.get("dimensionEntities");
+        //计算每个维度得分dimensionEntities
+        double[] dimensionScore = new double[dimensionEntities.size()];
+        Map<String,Double> dimensionScoreMap = new HashMap<>();
+        //jsonArray:用户自己选择的题目选项
+        for (int i = 0; i < jsonArray.size(); i++) {
+            JSONObject jsonObject1 = jsonArray.getJSONObject(i);
+            //计算总分
+            for (AnswerEntity answerEntity : answerEntities) {
+                if (answerEntity.getQuestionNo().equals(jsonObject1.getString("questionNo"))) {
+                    if (answerEntity.getName().replaceAll("\\s", "").equals(jsonObject1.getString("checkItems").replaceAll("\\s", ""))) {
+                        score += Double.parseDouble(answerEntity.getScore());
+                        break;
+                    }
+                }
+            }
+            //计算维度分数
+            for (int y = 0; y < dimensionEntities.size(); y++) {
+                //获取维度信息
+                DimensionEntity dimensionEntity = dimensionEntities.get(y);
+                //获取该维度下有哪些选项
+                String questionNos = dimensionEntity.getQuestionNo();
+                //删除空白字符后得到进行划分得到选择题号
+                String[] questionNo = questionNos.replaceAll("\\s", "").split("；");
+                //计算每道题的得分
+                for (String question : questionNo) {
+                    if (question.equals(jsonObject1.getString("questionNo"))) {
+                        //获取所有答案中本道题目的答案list数组
+                        List<AnswerEntity> nowQuestionAnswerList = answerEntities.stream().filter(answerEntity -> answerEntity.getQuestionNo().equals(question)).collect(Collectors.toList());
+                        //本题目的正确答案与自己的选项进行对比，正确的话相应分数进行相加
+                        for (AnswerEntity answerEntity : nowQuestionAnswerList) {
+                            if (answerEntity.getName().replaceAll("\\s", "").equals(jsonObject1.getString("checkItems").replaceAll("\\s", ""))) {
+                                dimensionScoreMap.put(dimensionEntity.getId(),dimensionScoreMap.get(dimensionEntity.getId())==null?Double.parseDouble(answerEntity.getScore()):(dimensionScoreMap.get(dimensionEntity.getId()) +Double.parseDouble(answerEntity.getScore())));
+                                dimensionScore[y] += Double.parseDouble(answerEntity.getScore());
+                                break;
+                            }
+                        }
+                    }
+                }
+            }
+            resultJson.put("dimensionScoreMap",dimensionScoreMap);
+        }
+        //计算标准分
+        for (int y = 0; y < dimensionEntities.size(); y++) {
+            for (int i = 0; i < scaleMarksEntities.size(); i++) {
+                if (dimensionEntities.get(y).getName().equals(scaleMarksEntities.get(i).getName())) {
+                    String[] splits = scaleMarksEntities.get(i).getReference().split("±");
+                    double a = Double.valueOf(splits[0]);
+                    double b = Double.valueOf(splits[1]);
+                    dimensionScore[y] = Math.floor(((dimensionScore[y] - a)/b)*1.5 + 5.5);
+                    break;
+                }
+            }
+        }
+
+        //返回值以及存数据库格式
+        List<NewResultDto> newResultDtos = new ArrayList<>();
+
+        //判断最高分
+        double[] dimensionScores = new double[dimensionScore.length];
+        for (int y = 0; y < dimensionScore.length; y++) {
+            dimensionScores[y] = dimensionScore[y];
+        }
+        Arrays.sort(dimensionScores);
+        JSONObject jsonObject = new JSONObject(true);
+        for (int y = 0; y < dimensionScore.length; y++) {
+            if (dimensionScore[y] == dimensionScores[dimensionScores.length-1]){
+                jsonObject.put(dimensionEntities.get(y).getName(),dimensionScores[dimensionScores.length-1]);
+            }
+        }
+        Set<String> keys = jsonObject.keySet();
+        List<DimensionEntity> dimensionEntitieList = new ArrayList<>();
+        int b = 0;
+        for (String key : keys) {
+            for (DimensionEntity dimensionEntity : dimensionEntities) {
+                if (dimensionEntity.getName().equals(key)){
+                    dimensionEntitieList.add(dimensionEntity);
+                    dimensionScore[b] = jsonObject.getInteger(key);
+                    b++;
+                }
+            }
+        }
+
+
+        //将量表总维度根据维度名称进行分组
+        Map<String, List<ScaleMarksEntity>> scaleMarksMap = scaleMarksEntities.stream().collect(Collectors.groupingBy(ScaleMarksEntity::getName));
+        JSONObject iconInfo = new JSONObject();
+        //雷达图需要的维度以及最大值
+        List<Map<String, Object>> indicator = new LinkedList<>();
+        //雷达图所需要的常模参考值
+        LinkedList<String> reference = new LinkedList<>();
+        //雷达图需要的分数
+        LinkedList<String> scoreList = new LinkedList<>();
+        //计算维度所在评分区间
+        commonComputeDimensionScore(df, resultMap0, resultJson, dimensionEntitieList, dimensionScore, scaleMarksMap, newResultDtos, indicator, reference, scoreList);
+        //需要图表展示的
+        iconInfo.put("indicator", indicator);
+        //判断是否有常模参考值，即reference的内容如果全为0，则不进行返回
+        if (!reference.stream().allMatch("0"::equals)) {
+            iconInfo.put("reference", reference);
+        }
+        iconInfo.put("scoreList", scoreList);
+        if (indicator.size() == 0 && reference.size() == 0) {
+            newResult.put("iconInfo", "");
+        } else {
+            newResult.put("iconInfo", iconInfo);
+        }
+
+        newResult.put("result", newResultDtos);
+        resultMapList.add(resultMap0);
+        JSONObject returnJson = new JSONObject(true);
+        returnJson.put("resultMapList", resultMapList);
+        returnJson.put("resultJson", resultJson);
+        returnJson.put(ScaleConstant.ResultEnum.RESULT_NEW_FIELD.getKeyword(), newResult);
+
+
+        return returnJson;
     }
 }

src/main/java/com/rf/psychological/scale/resultBusiness/scaleResult/CMACDScale.java

@@ -106,8 +106,8 @@ public class CMACDScale extends BaseScale {
                 if (scoreDecimal.compareTo(scoreEnd) <= 0 && scoreDecimal.compareTo(scoreStart) >= 0) {
                     newResultDtos.add(new NewResultDto("总分", df.format(score), tempTotalScore.getSymptom(), tempTotalScore.getImprovementSuggestions(),
                             tempTotalScore.getFlag(), tempTotalScore.getReference(), tempTotalScore.getNameExplain(), tempTotalScore.getSuggestion(), tempTotalScore.getIsTotalScoreExplain()));
-                    resultMap0.put("总分症状", tempTotalScore.getSymptom());
-                    resultMap0.put("总分指导语", tempTotalScore.getImprovementSuggestions());
+                    resultMap0.put("总分解释", tempTotalScore.getSymptom());
+                    resultMap0.put("总分说明", tempTotalScore.getImprovementSuggestions());
                 }
             }
         }
@@ -286,13 +286,14 @@ public class CMACDScale extends BaseScale {
 
 
     private static void putDimResult(Map<String, String> resultMap0, JSONObject resultJson, String dimensionName, NewResultDto newResultDto1, ScaleMarksEntity scaleMarksEntity) {
-        resultMap0.put(dimensionName + "常模参考值", scaleMarksEntity.getReference());
-        resultMap0.put(dimensionName + "症状", scaleMarksEntity.getSymptom());
-        resultJson.put(dimensionName + "结论", scaleMarksEntity.getSymptom());
-        resultMap0.put(dimensionName + "因子解释", scaleMarksEntity.getNameExplain());
-        resultMap0.put(dimensionName + "指导语", scaleMarksEntity.getImprovementSuggestions());
-        resultJson.put(dimensionName + "指导语", scaleMarksEntity.getImprovementSuggestions());
+        resultMap0.put(dimensionName + "解释", scaleMarksEntity.getSymptom());
+        resultJson.put(dimensionName + "解释", scaleMarksEntity.getSymptom());
+        resultMap0.put(dimensionName + "解读", scaleMarksEntity.getNameExplain());
+        resultJson.put(dimensionName + "解读", scaleMarksEntity.getNameExplain());
+        resultMap0.put(dimensionName + "说明", scaleMarksEntity.getImprovementSuggestions());
+        resultJson.put(dimensionName + "说明", scaleMarksEntity.getImprovementSuggestions());
         resultMap0.put(dimensionName + "建议", scaleMarksEntity.getSuggestion());
+        resultJson.put(dimensionName + "建议", scaleMarksEntity.getSuggestion());
         BeanUtils.copyProperties(scaleMarksEntity, newResultDto1);
     }
 

src/main/java/com/rf/psychological/scale/resultBusiness/scaleResult/ESLIScale.java

@@ -38,34 +38,34 @@ public class ESLIScale extends BaseScale{
         resultMap.put("总分", String.valueOf(score));
         resultJson.put("总分", String.valueOf(score));
         if (score < 6){
-            resultMap.put("孤立症状", "无或几无孤立");
-            resultJson.put("孤立症状", "无或几无孤立");
-            resultMap.put("孤独症状", "无或几无孤独");
-            resultJson.put("孤独症状", "无或几无孤独");
+            resultMap.put("孤立解释", "无或几无孤立");
+            resultJson.put("孤立解释", "无或几无孤立");
+            resultMap.put("孤独解释", "无或几无孤独");
+            resultJson.put("孤独解释", "无或几无孤独");
         }
         if (score >= 6 && score <= 8){
-            resultMap.put("孤立症状", "一般的孤立");
-            resultJson.put("孤立症状", "一般的孤立");
+            resultMap.put("孤立解释", "一般的孤立");
+            resultJson.put("孤立解释", "一般的孤立");
         }
         if (score >= 9 && score <= 12){
-            resultMap.put("孤立症状", "属于一般人的孤立");
-            resultJson.put("孤立症状", "属于一般人的孤立");
+            resultMap.put("孤立解释", "属于一般人的孤立");
+            resultJson.put("孤立解释", "属于一般人的孤立");
         }
         if (score >= 13){
-            resultMap.put("孤立症状", "孤立问题严重");
-            resultJson.put("孤立症状", "孤立问题严重");
+            resultMap.put("孤立解释", "孤立问题严重");
+            resultJson.put("孤立解释", "孤立问题严重");
         }
         if (score >= 6 && score <= 10){
-            resultMap.put("孤独症状", "一般的情绪孤独");
-            resultJson.put("孤独症状", "一般的情绪孤独");
+            resultMap.put("孤独解释", "一般的情绪孤独");
+            resultJson.put("孤独解释", "一般的情绪孤独");
         }
         if (score >= 11 && score <= 14){
-            resultMap.put("孤独症状", "高于一般人的孤独");
-            resultJson.put("孤独症状", "高于一般人的孤独");
+            resultMap.put("孤独解释", "高于一般人的孤独");
+            resultJson.put("孤独解释", "高于一般人的孤独");
         }
         if (score >= 15){
-            resultMap.put("孤独症状", "情绪孤独问题严重");
-            resultJson.put("孤独症状", "情绪孤独问题严重");
+            resultMap.put("孤独解释", "情绪孤独问题严重");
+            resultJson.put("孤独解释", "情绪孤独问题严重");
         }
 
         List<Map<String,String>> resultMapList = new ArrayList<>();

src/main/java/com/rf/psychological/scale/resultBusiness/scaleResult/FACESIIScale.java

@@ -283,13 +283,13 @@ public class FACESIIScale extends BaseScale {
 
 
     private static void putDimResult(Map<String, String> resultMap0, JSONObject resultJson, String dimensionName, NewResultDto newResultDto1, ScaleMarksEntity scaleMarksEntity) {
-        resultMap0.put(dimensionName + "常模参考值", scaleMarksEntity.getReference());
-        resultMap0.put(dimensionName + "症状", scaleMarksEntity.getSymptom());
-        resultJson.put(dimensionName + "结论", scaleMarksEntity.getSymptom());
-        resultMap0.put(dimensionName + "因子解释", scaleMarksEntity.getNameExplain());
-        resultJson.put(dimensionName + "因子解释", scaleMarksEntity.getNameExplain());
-        resultMap0.put(dimensionName + "指导语", scaleMarksEntity.getImprovementSuggestions());
-        resultJson.put(dimensionName + "指导语", scaleMarksEntity.getImprovementSuggestions());
+        //resultMap0.put(dimensionName + "常模参考值", scaleMarksEntity.getReference());
+        resultMap0.put(dimensionName + "解释", scaleMarksEntity.getSymptom());
+        resultJson.put(dimensionName + "解释", scaleMarksEntity.getSymptom());
+        resultMap0.put(dimensionName + "解读", scaleMarksEntity.getNameExplain());
+        resultJson.put(dimensionName + "解读", scaleMarksEntity.getNameExplain());
+        resultMap0.put(dimensionName + "说明", scaleMarksEntity.getImprovementSuggestions());
+        resultJson.put(dimensionName + "说明", scaleMarksEntity.getImprovementSuggestions());
         resultMap0.put(dimensionName + "建议", scaleMarksEntity.getSuggestion());
         resultJson.put(dimensionName + "建议", scaleMarksEntity.getSuggestion());
         BeanUtils.copyProperties(scaleMarksEntity, newResultDto1);

src/main/java/com/rf/psychological/scale/resultBusiness/scaleResult/FESCVScale.java

@@ -246,13 +246,13 @@ public class FESCVScale extends BaseScale{
                     if (scoreDecimal.compareTo(scoreEnd) <= 0 && scoreDecimal.compareTo(scoreStart) >= 0) {
                         newResultDtos.add(new NewResultDto("总分", df.format(score), tempTotalScore.getSymptom(), tempTotalScore.getImprovementSuggestions(),
                                 tempTotalScore.getFlag(), tempTotalScore.getReference(), tempTotalScore.getNameExplain(), tempTotalScore.getSuggestion(), tempTotalScore.getIsTotalScoreExplain()));
-                        resultMap0.put("总分症状", tempTotalScore.getSymptom());
-                        resultMap0.put("总分指导语", tempTotalScore.getImprovementSuggestions());
-                        resultMap0.put("总分因子解释", tempTotalScore.getNameExplain());
+                        resultMap0.put("总分解释", tempTotalScore.getSymptom());
+                        resultMap0.put("总分说明", tempTotalScore.getImprovementSuggestions());
+                        resultMap0.put("总分解读", tempTotalScore.getNameExplain());
                         resultMap0.put("总分建议", tempTotalScore.getSuggestion());
-                        resultJson.put("总分症状", tempTotalScore.getSymptom());
-                        resultJson.put("总分指导语", tempTotalScore.getImprovementSuggestions());
-                        resultJson.put("总分因子解释", tempTotalScore.getNameExplain());
+                        resultJson.put("总分解释", tempTotalScore.getSymptom());
+                        resultJson.put("总分说明", tempTotalScore.getImprovementSuggestions());
+                        resultJson.put("总分解读", tempTotalScore.getNameExplain());
                         resultJson.put("总分建议", tempTotalScore.getSuggestion());
                     }
                 }
@@ -264,13 +264,13 @@ public class FESCVScale extends BaseScale{
                 if (scoreDecimal.compareTo(scoreEnd) <= 0 && scoreDecimal.compareTo(scoreStart) >= 0) {
                     newResultDtos.add(new NewResultDto("总分", df.format(score), tempTotalScore.getSymptom(), tempTotalScore.getImprovementSuggestions(),
                             tempTotalScore.getFlag(), tempTotalScore.getReference(), tempTotalScore.getNameExplain(), tempTotalScore.getSuggestion(), tempTotalScore.getIsTotalScoreExplain()));
-                    resultMap0.put("总分症状", tempTotalScore.getSymptom());
-                    resultMap0.put("总分指导语", tempTotalScore.getImprovementSuggestions());
-                    resultMap0.put("总分因子解释", tempTotalScore.getNameExplain());
+                    resultMap0.put("总分解释", tempTotalScore.getSymptom());
+                    resultMap0.put("总分说明", tempTotalScore.getImprovementSuggestions());
+                    resultMap0.put("总分解读", tempTotalScore.getNameExplain());
                     resultMap0.put("总分建议", tempTotalScore.getSuggestion());
-                    resultJson.put("总分症状", tempTotalScore.getSymptom());
-                    resultJson.put("总分指导语", tempTotalScore.getImprovementSuggestions());
-                    resultJson.put("总分因子解释", tempTotalScore.getNameExplain());
+                    resultJson.put("总分解释", tempTotalScore.getSymptom());
+                    resultJson.put("总分说明", tempTotalScore.getImprovementSuggestions());
+                    resultJson.put("总分解读", tempTotalScore.getNameExplain());
                     resultJson.put("总分建议", tempTotalScore.getSuggestion());
                 }
             }
@@ -604,13 +604,14 @@ public class FESCVScale extends BaseScale{
 
 
     private static void putDimResult(Map<String, String> resultMap0, JSONObject resultJson, String dimensionName, NewResultDto newResultDto1, ScaleMarksEntity scaleMarksEntity) {
-        resultMap0.put(dimensionName + "常模参考值", scaleMarksEntity.getReference());
-        resultMap0.put(dimensionName + "症状", scaleMarksEntity.getSymptom());
-        resultJson.put(dimensionName + "结论", scaleMarksEntity.getSymptom());
-        resultMap0.put(dimensionName + "因子解释", scaleMarksEntity.getNameExplain());
-        resultMap0.put(dimensionName + "指导语", scaleMarksEntity.getImprovementSuggestions());
-        resultJson.put(dimensionName + "指导语", scaleMarksEntity.getImprovementSuggestions());
+        resultMap0.put(dimensionName + "解释", scaleMarksEntity.getSymptom());
+        resultJson.put(dimensionName + "解释", scaleMarksEntity.getSymptom());
+        resultMap0.put(dimensionName + "解读", scaleMarksEntity.getNameExplain());
+        resultJson.put(dimensionName + "解读", scaleMarksEntity.getNameExplain());
+        resultMap0.put(dimensionName + "说明", scaleMarksEntity.getImprovementSuggestions());
+        resultJson.put(dimensionName + "说明", scaleMarksEntity.getImprovementSuggestions());
         resultMap0.put(dimensionName + "建议", scaleMarksEntity.getSuggestion());
+        resultJson.put(dimensionName + "建议", scaleMarksEntity.getSuggestion());
         BeanUtils.copyProperties(scaleMarksEntity, newResultDto1);
     }
 

src/main/java/com/rf/psychological/scale/resultBusiness/scaleResult/IOWBScale.java

@@ -192,13 +192,15 @@ public class IOWBScale extends BaseScale {
     }
 
     private static void putDimResult(Map<String, String> resultMap0, JSONObject resultJson, DimensionEntity dimensionEntity, NewResultDto newResultDto1, ScaleMarksEntity scaleMarksEntity) {
-        resultMap0.put(dimensionEntity.getName() + "常模参考值", scaleMarksEntity.getReference());
-        resultMap0.put(dimensionEntity.getName() + "症状", scaleMarksEntity.getSymptom());
-        resultJson.put(dimensionEntity.getName() + "结论", scaleMarksEntity.getSymptom());
-        resultMap0.put(dimensionEntity.getName() + "因子解释", scaleMarksEntity.getNameExplain());
-        resultMap0.put(dimensionEntity.getName() + "指导语", scaleMarksEntity.getImprovementSuggestions());
-        resultJson.put(dimensionEntity.getName() + "指导语", scaleMarksEntity.getImprovementSuggestions());
+        resultMap0.put(dimensionEntity.getName() + "解释", scaleMarksEntity.getSymptom());
+        resultJson.put(dimensionEntity.getName() + "解释", scaleMarksEntity.getSymptom());
+        resultMap0.put(dimensionEntity.getName() + "解读", scaleMarksEntity.getNameExplain());
+        resultJson.put(dimensionEntity.getName() + "解读", scaleMarksEntity.getNameExplain());
+        resultMap0.put(dimensionEntity.getName() + "说明", scaleMarksEntity.getImprovementSuggestions());
+        resultJson.put(dimensionEntity.getName() + "说明", scaleMarksEntity.getImprovementSuggestions());
         resultMap0.put(dimensionEntity.getName() + "建议", scaleMarksEntity.getSuggestion());
+        resultJson.put(dimensionEntity.getName() + "建议", scaleMarksEntity.getSuggestion());
+
         BeanUtils.copyProperties(scaleMarksEntity, newResultDto1);
     }
 }

src/main/java/com/rf/psychological/scale/resultBusiness/scaleResult/MMPI566Scale.java

@@ -135,13 +135,13 @@ public class MMPI566Scale extends BaseScale {
                     if (scoreDecimal.compareTo(scoreEnd) <= 0 && scoreDecimal.compareTo(scoreStart) >= 0) {
                         newResultDtos.add(new NewResultDto("总分", df.format(score), tempTotalScore.getSymptom(), tempTotalScore.getImprovementSuggestions(),
                                 tempTotalScore.getFlag(), tempTotalScore.getReference(), tempTotalScore.getNameExplain(), tempTotalScore.getSuggestion(), tempTotalScore.getIsTotalScoreExplain()));
-                        resultMap0.put("总分症状", tempTotalScore.getSymptom());
-                        resultMap0.put("总分指导语", tempTotalScore.getImprovementSuggestions());
-                        resultMap0.put("总分因子解释", tempTotalScore.getNameExplain());
+                        resultMap0.put("总分解释", tempTotalScore.getSymptom());
+                        resultMap0.put("总分说明", tempTotalScore.getImprovementSuggestions());
+                        resultMap0.put("总分解读", tempTotalScore.getNameExplain());
                         resultMap0.put("总分建议", tempTotalScore.getSuggestion());
-                        resultJson.put("总分症状", tempTotalScore.getSymptom());
-                        resultJson.put("总分指导语", tempTotalScore.getImprovementSuggestions());
-                        resultJson.put("总分因子解释", tempTotalScore.getNameExplain());
+                        resultJson.put("总分解释", tempTotalScore.getSymptom());
+                        resultJson.put("总分说明", tempTotalScore.getImprovementSuggestions());
+                        resultJson.put("总分解读", tempTotalScore.getNameExplain());
                         resultJson.put("总分建议", tempTotalScore.getSuggestion());
                     }
                 }
@@ -153,13 +153,13 @@ public class MMPI566Scale extends BaseScale {
                 if (scoreDecimal.compareTo(scoreEnd) <= 0 && scoreDecimal.compareTo(scoreStart) >= 0) {
                     newResultDtos.add(new NewResultDto("总分", df.format(score), tempTotalScore.getSymptom(), tempTotalScore.getImprovementSuggestions(),
                             tempTotalScore.getFlag(), tempTotalScore.getReference(), tempTotalScore.getNameExplain(), tempTotalScore.getSuggestion(), tempTotalScore.getIsTotalScoreExplain()));
-                    resultMap0.put("总分症状", tempTotalScore.getSymptom());
-                    resultMap0.put("总分指导语", tempTotalScore.getImprovementSuggestions());
-                    resultMap0.put("总分因子解释", tempTotalScore.getNameExplain());
+                    resultMap0.put("总分解释", tempTotalScore.getSymptom());
+                    resultMap0.put("总分说明", tempTotalScore.getImprovementSuggestions());
+                    resultMap0.put("总分解读", tempTotalScore.getNameExplain());
                     resultMap0.put("总分建议", tempTotalScore.getSuggestion());
-                    resultJson.put("总分症状", tempTotalScore.getSymptom());
-                    resultJson.put("总分指导语", tempTotalScore.getImprovementSuggestions());
-                    resultJson.put("总分因子解释", tempTotalScore.getNameExplain());
+                    resultJson.put("总分解释", tempTotalScore.getSymptom());
+                    resultJson.put("总分说明", tempTotalScore.getImprovementSuggestions());
+                    resultJson.put("总分解读", tempTotalScore.getNameExplain());
                     resultJson.put("总分建议", tempTotalScore.getSuggestion());
                 }
             }

src/main/java/com/rf/psychological/scale/resultBusiness/scaleResult/MMPIScale.java

@@ -595,13 +595,14 @@ public class MMPIScale extends BaseScale {
 
 
     private static void putDimResult(Map<String, String> resultMap0, JSONObject resultJson, String dimensionName, NewResultDto newResultDto1, ScaleMarksEntity scaleMarksEntity) {
-        resultMap0.put(dimensionName + "常模参考值", scaleMarksEntity.getReference());
-        resultMap0.put(dimensionName + "症状", scaleMarksEntity.getSymptom());
-        resultJson.put(dimensionName + "结论", scaleMarksEntity.getSymptom());
-        resultMap0.put(dimensionName + "因子解释", scaleMarksEntity.getNameExplain());
-        resultMap0.put(dimensionName + "指导语", scaleMarksEntity.getImprovementSuggestions());
-        resultJson.put(dimensionName + "指导语", scaleMarksEntity.getImprovementSuggestions());
+        resultMap0.put(dimensionName + "解释", scaleMarksEntity.getSymptom());
+        resultJson.put(dimensionName + "解释", scaleMarksEntity.getSymptom());
+        resultMap0.put(dimensionName + "解读", scaleMarksEntity.getNameExplain());
+        resultJson.put(dimensionName + "解读", scaleMarksEntity.getNameExplain());
+        resultMap0.put(dimensionName + "说明", scaleMarksEntity.getImprovementSuggestions());
+        resultJson.put(dimensionName + "说明", scaleMarksEntity.getImprovementSuggestions());
         resultMap0.put(dimensionName + "建议", scaleMarksEntity.getSuggestion());
+        resultJson.put(dimensionName + "建议", scaleMarksEntity.getSuggestion());
         BeanUtils.copyProperties(scaleMarksEntity, newResultDto1);
     }
 

src/main/java/com/rf/psychological/scale/resultBusiness/scaleResult/MUNSHScale.java

@@ -158,8 +158,8 @@ public class MUNSHScale extends BaseScale{
                     if (scoreDecimal.compareTo(scoreEnd) <= 0 && scoreDecimal.compareTo(scoreStart) >= 0) {
                         newResultDtos.add(new NewResultDto("总分", df.format(score), tempTotalScore.getSymptom(), tempTotalScore.getImprovementSuggestions(),
                                 tempTotalScore.getFlag(), tempTotalScore.getReference(), tempTotalScore.getNameExplain(), tempTotalScore.getSuggestion(), tempTotalScore.getIsTotalScoreExplain()));
-                        resultMap0.put("总分症状", tempTotalScore.getSymptom());
-                        resultMap0.put("总分指导语", tempTotalScore.getImprovementSuggestions());
+                        resultMap0.put("总分解释", tempTotalScore.getSymptom());
+                        resultMap0.put("总分说明", tempTotalScore.getImprovementSuggestions());
                     }
                 }
             }
@@ -170,8 +170,8 @@ public class MUNSHScale extends BaseScale{
                 if (scoreDecimal.compareTo(scoreEnd) <= 0 && scoreDecimal.compareTo(scoreStart) >= 0) {
                     newResultDtos.add(new NewResultDto("总分", df.format(score), tempTotalScore.getSymptom(), tempTotalScore.getImprovementSuggestions(),
                             tempTotalScore.getFlag(), tempTotalScore.getReference(), tempTotalScore.getNameExplain(), tempTotalScore.getSuggestion(), tempTotalScore.getIsTotalScoreExplain()));
-                    resultMap0.put("总分症状", tempTotalScore.getSymptom());
-                    resultMap0.put("总分指导语", tempTotalScore.getImprovementSuggestions());
+                    resultMap0.put("总分解释", tempTotalScore.getSymptom());
+                    resultMap0.put("总分说明", tempTotalScore.getImprovementSuggestions());
                 }
             }
         }
@@ -310,13 +310,15 @@ public class MUNSHScale extends BaseScale{
     }
 
     private static void putDimResult(Map<String, String> resultMap0, JSONObject resultJson, String dimensionName, NewResultDto newResultDto1, ScaleMarksEntity scaleMarksEntity) {
-        resultMap0.put(dimensionName + "常模参考值", scaleMarksEntity.getReference());
-        resultMap0.put(dimensionName + "症状", scaleMarksEntity.getSymptom());
-        resultJson.put(dimensionName + "结论", scaleMarksEntity.getSymptom());
-        resultMap0.put(dimensionName + "因子解释", scaleMarksEntity.getNameExplain());
-        resultMap0.put(dimensionName + "指导语", scaleMarksEntity.getImprovementSuggestions());
-        resultJson.put(dimensionName + "指导语", scaleMarksEntity.getImprovementSuggestions());
+        //resultMap0.put(dimensionName + "常模参考值", scaleMarksEntity.getReference());
+        resultMap0.put(dimensionName + "解释", scaleMarksEntity.getSymptom());
+        resultJson.put(dimensionName + "解释", scaleMarksEntity.getSymptom());
+        resultMap0.put(dimensionName + "解读", scaleMarksEntity.getNameExplain());
+        resultJson.put(dimensionName + "解读", scaleMarksEntity.getNameExplain());
+        resultMap0.put(dimensionName + "说明", scaleMarksEntity.getImprovementSuggestions());
+        resultJson.put(dimensionName + "说明", scaleMarksEntity.getImprovementSuggestions());
         resultMap0.put(dimensionName + "建议", scaleMarksEntity.getSuggestion());
+        resultJson.put(dimensionName + "建议", scaleMarksEntity.getSuggestion());
         BeanUtils.copyProperties(scaleMarksEntity, newResultDto1);
     }
 

src/main/java/com/rf/psychological/scale/resultBusiness/scaleResult/NEWCOMMONScale.java

@@ -143,13 +143,13 @@ public class NEWCOMMONScale extends BaseScale {
                     if (scoreDecimal.compareTo(scoreEnd) <= 0 && scoreDecimal.compareTo(scoreStart) >= 0) {
                         newResultDtos.add(new NewResultDto("总分", df.format(score), tempTotalScore.getSymptom(), tempTotalScore.getImprovementSuggestions(),
                                 tempTotalScore.getFlag(), tempTotalScore.getReference(), tempTotalScore.getNameExplain(), tempTotalScore.getSuggestion(), tempTotalScore.getIsTotalScoreExplain()));
-                        resultMap0.put("总分症状", tempTotalScore.getSymptom());
-                        resultMap0.put("总分指导语", tempTotalScore.getImprovementSuggestions());
-                        resultMap0.put("总分因子解释", tempTotalScore.getNameExplain());
+                        resultMap0.put("总分解释", tempTotalScore.getSymptom());
+                        resultMap0.put("总分说明", tempTotalScore.getImprovementSuggestions());
+                        resultMap0.put("总分解读", tempTotalScore.getNameExplain());
                         resultMap0.put("总分建议", tempTotalScore.getSuggestion());
-                        resultJson.put("总分症状", tempTotalScore.getSymptom());
-                        resultJson.put("总分指导语", tempTotalScore.getImprovementSuggestions());
-                        resultJson.put("总分因子解释", tempTotalScore.getNameExplain());
+                        resultJson.put("总分解释", tempTotalScore.getSymptom());
+                        resultJson.put("总分说明", tempTotalScore.getImprovementSuggestions());
+                        resultJson.put("总分解读", tempTotalScore.getNameExplain());
                         resultJson.put("总分建议", tempTotalScore.getSuggestion());
                     }
                 }
@@ -161,13 +161,13 @@ public class NEWCOMMONScale extends BaseScale {
                 if (scoreDecimal.compareTo(scoreEnd) <= 0 && scoreDecimal.compareTo(scoreStart) >= 0) {
                     newResultDtos.add(new NewResultDto("总分", df.format(score), tempTotalScore.getSymptom(), tempTotalScore.getImprovementSuggestions(),
                             tempTotalScore.getFlag(), tempTotalScore.getReference(), tempTotalScore.getNameExplain(), tempTotalScore.getSuggestion(), tempTotalScore.getIsTotalScoreExplain()));
-                    resultMap0.put("总分症状", tempTotalScore.getSymptom());
-                    resultMap0.put("总分指导语", tempTotalScore.getImprovementSuggestions());
-                    resultMap0.put("总分因子解释", tempTotalScore.getNameExplain());
+                    resultMap0.put("总分解释", tempTotalScore.getSymptom());
+                    resultMap0.put("总分说明", tempTotalScore.getImprovementSuggestions());
+                    resultMap0.put("总分解读", tempTotalScore.getNameExplain());
                     resultMap0.put("总分建议", tempTotalScore.getSuggestion());
-                    resultJson.put("总分症状", tempTotalScore.getSymptom());
-                    resultJson.put("总分指导语", tempTotalScore.getImprovementSuggestions());
-                    resultJson.put("总分因子解释", tempTotalScore.getNameExplain());
+                    resultJson.put("总分解释", tempTotalScore.getSymptom());
+                    resultJson.put("总分说明", tempTotalScore.getImprovementSuggestions());
+                    resultJson.put("总分解读", tempTotalScore.getNameExplain());
                     resultJson.put("总分建议", tempTotalScore.getSuggestion());
                 }
             }
@@ -348,12 +348,12 @@ public class NEWCOMMONScale extends BaseScale {
 
     private static void putDimResult(Map<String, String> resultMap0, JSONObject resultJson, String dimensionName, NewResultDto newResultDto1, ScaleMarksEntity scaleMarksEntity) {
         //resultMap0.put(dimensionName + "常模参考值", scaleMarksEntity.getReference());
-        resultMap0.put(dimensionName + "症状", scaleMarksEntity.getSymptom());
-        resultJson.put(dimensionName + "结论", scaleMarksEntity.getSymptom());
-        resultMap0.put(dimensionName + "因子解释", scaleMarksEntity.getNameExplain());
-        resultJson.put(dimensionName + "因子解释", scaleMarksEntity.getNameExplain());
-        resultMap0.put(dimensionName + "指导语", scaleMarksEntity.getImprovementSuggestions());
-        resultJson.put(dimensionName + "指导语", scaleMarksEntity.getImprovementSuggestions());
+        resultMap0.put(dimensionName + "解释", scaleMarksEntity.getSymptom());
+        resultJson.put(dimensionName + "解释", scaleMarksEntity.getSymptom());
+        resultMap0.put(dimensionName + "解读", scaleMarksEntity.getNameExplain());
+        resultJson.put(dimensionName + "解读", scaleMarksEntity.getNameExplain());
+        resultMap0.put(dimensionName + "说明", scaleMarksEntity.getImprovementSuggestions());
+        resultJson.put(dimensionName + "说明", scaleMarksEntity.getImprovementSuggestions());
         resultMap0.put(dimensionName + "建议", scaleMarksEntity.getSuggestion());
         resultJson.put(dimensionName + "建议", scaleMarksEntity.getSuggestion());
         BeanUtils.copyProperties(scaleMarksEntity, newResultDto1);

src/main/java/com/rf/psychological/scale/resultBusiness/scaleResult/PSQIScale.java

@@ -124,46 +124,46 @@ public class PSQIScale extends BaseScale{
             if (score >= 0 && score <= 12) {
                 newResultDtos.add(new NewResultDto("总分", String.valueOf(score), "睡眠质量很好。", "睡眠质量，很好，入睡速度非常快，夜间不会有频繁醒来的情况，早上起床之后精神状态良好", "无",
                         "无", "无", "无", "是"));
-                resultMap0.put("总分症状", "睡眠质量很好。");
-                resultMap0.put("总分指导语", "睡眠质量，很好，入睡速度非常快，夜间不会有频繁醒来的情况，早上起床之后精神状态良好");
-                resultMap0.put("总分因子解释", "无");
+                resultMap0.put("总分解释", "睡眠质量很好。");
+                resultMap0.put("总分说明", "睡眠质量，很好，入睡速度非常快，夜间不会有频繁醒来的情况，早上起床之后精神状态良好");
+                resultMap0.put("总分解读", "无");
                 resultMap0.put("总分建议", "无");
-                resultJson.put("总分症状", "睡眠质量很好。");
-                resultJson.put("总分指导语", "睡眠质量，很好，入睡速度非常快，夜间不会有频繁醒来的情况，早上起床之后精神状态良好");
-                resultJson.put("总分因子解释", "无");
+                resultJson.put("总分解释", "睡眠质量很好。");
+                resultJson.put("总分说明", "睡眠质量，很好，入睡速度非常快，夜间不会有频繁醒来的情况，早上起床之后精神状态良好");
+                resultJson.put("总分解读", "无");
                 resultJson.put("总分建议", "无");
             } else if (score >= 13 && score <= 26) {
                 newResultDtos.add(new NewResultDto("总分", String.valueOf(score), "睡眠质量还行", "睡眠质量一般,健康状况有所下降，如果再不引起足够的重视，您的状况会向一般发展。", "无",
                         "无", "无", "坚持有规律的作息时间，在周末不要睡得太晚，睡前不要暴饮暴食，也不要喝太多的水。", "是"));
-                resultMap0.put("总分症状", "睡眠质量还行");
-                resultMap0.put("总分指导语", "睡眠质量一般,健康状况有所下降，如果再不引起足够的重视，您的状况会向一般发展。");
-                resultMap0.put("总分因子解释", "无");
+                resultMap0.put("总分解释", "睡眠质量还行");
+                resultMap0.put("总分说明", "睡眠质量一般,健康状况有所下降，如果再不引起足够的重视，您的状况会向一般发展。");
+                resultMap0.put("总分解读", "无");
                 resultMap0.put("总分建议", "坚持有规律的作息时间，在周末不要睡得太晚，睡前不要暴饮暴食，也不要喝太多的水。");
-                resultJson.put("总分症状", "睡眠质量还行");
-                resultJson.put("总分指导语", "睡眠质量一般,健康状况有所下降，如果再不引起足够的重视，您的状况会向一般发展。");
-                resultJson.put("总分因子解释", "无");
+                resultJson.put("总分解释", "睡眠质量还行");
+                resultJson.put("总分说明", "睡眠质量一般,健康状况有所下降，如果再不引起足够的重视，您的状况会向一般发展。");
+                resultJson.put("总分解读", "无");
                 resultJson.put("总分建议", "坚持有规律的作息时间，在周末不要睡得太晚，睡前不要暴饮暴食，也不要喝太多的水。");
             } else if (score >= 27 && score <= 40) {
                 newResultDtos.add(new NewResultDto("总分", String.valueOf(score), "睡眠质量一般", "睡眠质量比较糟糕,健康状况明显受损,容易被一些声音吵醒，饮食起居失去规律。在不引起重视的话,会发展到更严重的地步", "无",
                         "无", "无", "舒缓的心情就可以让患者早日睡眠，还有就是患者要积极锻炼，在肌体劳累之时就可以很快入眠。", "是"));
-                resultMap0.put("总分症状", "睡眠质量一般");
-                resultMap0.put("总分指导语", "睡眠质量比较糟糕,健康状况明显受损,容易被一些声音吵醒，饮食起居失去规律。在不引起重视的话,会发展到更严重的地步");
-                resultMap0.put("总分因子解释", "无");
+                resultMap0.put("总分解释", "睡眠质量一般");
+                resultMap0.put("总分说明", "睡眠质量比较糟糕,健康状况明显受损,容易被一些声音吵醒，饮食起居失去规律。在不引起重视的话,会发展到更严重的地步");
+                resultMap0.put("总分解读", "无");
                 resultMap0.put("总分建议", "舒缓的心情就可以让患者早日睡眠，还有就是患者要积极锻炼，在肌体劳累之时就可以很快入眠。");
-                resultJson.put("总分症状", "睡眠质量一般");
-                resultJson.put("总分指导语", "睡眠质量比较糟糕,健康状况明显受损,容易被一些声音吵醒，饮食起居失去规律。在不引起重视的话,会发展到更严重的地步");
-                resultJson.put("总分因子解释", "无");
+                resultJson.put("总分解释", "睡眠质量一般");
+                resultJson.put("总分说明", "睡眠质量比较糟糕,健康状况明显受损,容易被一些声音吵醒，饮食起居失去规律。在不引起重视的话,会发展到更严重的地步");
+                resultJson.put("总分解读", "无");
                 resultJson.put("总分建议", "舒缓的心情就可以让患者早日睡眠，还有就是患者要积极锻炼，在肌体劳累之时就可以很快入眠。");
             } else if (score >= 41 && score <= 54) {
                 newResultDtos.add(new NewResultDto("总分", String.valueOf(score), "睡眠质量很差", "睡眠质量已经到了令您非常头痛的地步,长期的睡眠不足,精神状态较差，难以入睡，导致健康状况的严重恶化,应付工作力不从心,情绪不稳定。", "无",
                         "无", "无", "建议您及时就医了，必要时采用药物治疗。", "是"));
-                resultMap0.put("总分症状", "睡眠质量很差");
-                resultMap0.put("总分指导语", "睡眠质量已经到了令您非常头痛的地步,长期的睡眠不足,精神状态较差，难以入睡，导致健康状况的严重恶化,应付工作力不从心,情绪不稳定。");
-                resultMap0.put("总分因子解释", "无");
+                resultMap0.put("总分解释", "睡眠质量很差");
+                resultMap0.put("总分说明", "睡眠质量已经到了令您非常头痛的地步,长期的睡眠不足,精神状态较差，难以入睡，导致健康状况的严重恶化,应付工作力不从心,情绪不稳定。");
+                resultMap0.put("总分解读", "无");
                 resultMap0.put("总分建议", "建议您及时就医了，必要时采用药物治疗。");
-                resultJson.put("总分症状", "睡眠质量很差");
-                resultJson.put("总分指导语", "睡眠质量已经到了令您非常头痛的地步,长期的睡眠不足,精神状态较差，难以入睡，导致健康状况的严重恶化,应付工作力不从心,情绪不稳定。");
-                resultJson.put("总分因子解释", "无");
+                resultJson.put("总分解释", "睡眠质量很差");
+                resultJson.put("总分说明", "睡眠质量已经到了令您非常头痛的地步,长期的睡眠不足,精神状态较差，难以入睡，导致健康状况的严重恶化,应付工作力不从心,情绪不稳定。");
+                resultJson.put("总分解读", "无");
                 resultJson.put("总分建议", "建议您及时就医了，必要时采用药物治疗。");
             }
 

src/main/java/com/rf/psychological/scale/resultBusiness/scaleResult/SCL90Scale.java

@@ -113,10 +113,10 @@ public class SCL90Scale extends BaseScale {
         for (ScaleMarksEntity scaleMarksEntity : gpaScale) {
             if (GPA <= Double.parseDouble(df.format(Double.parseDouble(scaleMarksEntity.getScoreEnd()))) && GPA >= Double.parseDouble(df.format(Double.parseDouble(scaleMarksEntity.getScoreStart())))) {
                 resultMap0.put("总均分常模参考值", scaleMarksEntity.getReference());
-                resultMap0.put("总均分症状", scaleMarksEntity.getSymptom());
-                resultJson.put("总均分症状", scaleMarksEntity.getSymptom());
-                resultMap0.put("总均分因子解释", scaleMarksEntity.getNameExplain());
-                resultMap0.put("总均分指导语", scaleMarksEntity.getImprovementSuggestions());
+                resultMap0.put("总均分解释", scaleMarksEntity.getSymptom());
+                resultJson.put("总均分解释", scaleMarksEntity.getSymptom());
+                resultMap0.put("总均分解读", scaleMarksEntity.getNameExplain());
+                resultMap0.put("总均分说明", scaleMarksEntity.getImprovementSuggestions());
                 resultMap0.put("总均分建议", scaleMarksEntity.getSuggestion());
 
                 NewResultDto newResultDto = new NewResultDto();
@@ -270,10 +270,10 @@ public class SCL90Scale extends BaseScale {
 
     private static void putDimResult(Map<String, String> resultMap0, JSONObject oldResult, DimensionEntity dimensionEntity, NewResultDto newResultDto1, ScaleMarksEntity scaleMarksEntity) {
         resultMap0.put(dimensionEntity.getName() + "常模参考值", scaleMarksEntity.getReference());
-        resultMap0.put(dimensionEntity.getName() + "症状", scaleMarksEntity.getSymptom());
-        oldResult.put(dimensionEntity.getName() + "症状", scaleMarksEntity.getSymptom());
-        resultMap0.put(dimensionEntity.getName() + "因子解释", scaleMarksEntity.getNameExplain());
-        resultMap0.put(dimensionEntity.getName() + "指导语", scaleMarksEntity.getImprovementSuggestions());
+        resultMap0.put(dimensionEntity.getName() + "解释", scaleMarksEntity.getSymptom());
+        oldResult.put(dimensionEntity.getName() + "解释", scaleMarksEntity.getSymptom());
+        resultMap0.put(dimensionEntity.getName() + "解读", scaleMarksEntity.getNameExplain());
+        resultMap0.put(dimensionEntity.getName() + "说明", scaleMarksEntity.getImprovementSuggestions());
         resultMap0.put(dimensionEntity.getName() + "建议", scaleMarksEntity.getSuggestion());
         BeanUtils.copyProperties(scaleMarksEntity, newResultDto1);
     }

src/main/java/com/rf/psychological/scale/resultBusiness/scaleResult/SCSQScale.java

@@ -96,13 +96,13 @@ public class SCSQScale extends BaseScale{
                     if (scoreDecimal.compareTo(scoreEnd) <= 0 && scoreDecimal.compareTo(scoreStart) >= 0) {
                         newResultDtos.add(new NewResultDto("总分", df.format(v), tempTotalScore.getSymptom(), tempTotalScore.getImprovementSuggestions(),
                                 tempTotalScore.getFlag(), tempTotalScore.getReference(), tempTotalScore.getNameExplain(), tempTotalScore.getSuggestion(), tempTotalScore.getIsTotalScoreExplain()));
-                        resultMap1.put("总分症状", tempTotalScore.getSymptom());
-                        resultMap1.put("总分指导语", tempTotalScore.getImprovementSuggestions());
-                        resultMap1.put("总分因子解释", tempTotalScore.getNameExplain());
+                        resultMap1.put("总分解释", tempTotalScore.getSymptom());
+                        resultMap1.put("总分说明", tempTotalScore.getImprovementSuggestions());
+                        resultMap1.put("总分解读", tempTotalScore.getNameExplain());
                         resultMap1.put("总分建议", tempTotalScore.getSuggestion());
-                        resultJson.put("总分症状", tempTotalScore.getSymptom());
-                        resultJson.put("总分指导语", tempTotalScore.getImprovementSuggestions());
-                        resultJson.put("总分因子解释", tempTotalScore.getNameExplain());
+                        resultJson.put("总分解释", tempTotalScore.getSymptom());
+                        resultJson.put("总分说明", tempTotalScore.getImprovementSuggestions());
+                        resultJson.put("总分解读", tempTotalScore.getNameExplain());
                         resultJson.put("总分建议", tempTotalScore.getSuggestion());
                     }
                 }
@@ -114,13 +114,13 @@ public class SCSQScale extends BaseScale{
                 if (scoreDecimal.compareTo(scoreEnd) <= 0 && scoreDecimal.compareTo(scoreStart) >= 0) {
                     newResultDtos.add(new NewResultDto("总分", df.format(v), tempTotalScore.getSymptom(), tempTotalScore.getImprovementSuggestions(),
                             tempTotalScore.getFlag(), tempTotalScore.getReference(), tempTotalScore.getNameExplain(), tempTotalScore.getSuggestion(), tempTotalScore.getIsTotalScoreExplain()));
-                    resultMap1.put("总分症状", tempTotalScore.getSymptom());
-                    resultMap1.put("总分指导语", tempTotalScore.getImprovementSuggestions());
-                    resultMap1.put("总分因子解释", tempTotalScore.getNameExplain());
+                    resultMap1.put("总分解释", tempTotalScore.getSymptom());
+                    resultMap1.put("总分说明", tempTotalScore.getImprovementSuggestions());
+                    resultMap1.put("总分解读", tempTotalScore.getNameExplain());
                     resultMap1.put("总分建议", tempTotalScore.getSuggestion());
-                    resultJson.put("总分症状", tempTotalScore.getSymptom());
-                    resultJson.put("总分指导语", tempTotalScore.getImprovementSuggestions());
-                    resultJson.put("总分因子解释", tempTotalScore.getNameExplain());
+                    resultJson.put("总分解释", tempTotalScore.getSymptom());
+                    resultJson.put("总分说明", tempTotalScore.getImprovementSuggestions());
+                    resultJson.put("总分解读", tempTotalScore.getNameExplain());
                     resultJson.put("总分建议", tempTotalScore.getSuggestion());
                 }
             }

src/main/java/com/rf/psychological/scale/resultBusiness/scaleResult/SITScale.java

@@ -123,83 +123,83 @@ public class SITScale extends BaseScale{
         resultMap1.put("前庭失衡得分", String.valueOf(score1));
         resultJson.put("前庭失衡得分", String.valueOf(score1));
         if (score1 < 20){
-            resultMap1.put("前庭失衡症状", "重度失调");
-            resultJson.put("前庭失衡症状", "重度失调");
+            resultMap1.put("前庭失衡解释", "重度失调");
+            resultJson.put("前庭失衡解释", "重度失调");
         }else if (score1 >= 20 && score1 < 30){
-            resultMap1.put("前庭失衡症状", "中度失调");
-            resultJson.put("前庭失衡症状", "中度失调");
+            resultMap1.put("前庭失衡解释", "中度失调");
+            resultJson.put("前庭失衡解释", "中度失调");
         }else if (score1 >= 30 && score1 < 40){
-            resultMap1.put("前庭失衡症状", "轻度失调");
-            resultJson.put("前庭失衡症状", "轻度失调");
+            resultMap1.put("前庭失衡解释", "轻度失调");
+            resultJson.put("前庭失衡解释", "轻度失调");
         }else {
-            resultMap1.put("前庭失衡症状", "正常");
-            resultJson.put("前庭失衡症状", "正常");
+            resultMap1.put("前庭失衡解释", "正常");
+            resultJson.put("前庭失衡解释", "正常");
         }
         //得分列表
         Map<String, String> resultMap2 = new LinkedHashMap<>();
         resultMap2.put("触觉过分防御得分", String.valueOf(score2));
         resultJson.put("触觉过分防御得分", String.valueOf(score2));
         if (score2 < 20){
-            resultMap2.put("触觉过分防御症状", "重度失调");
-            resultJson.put("触觉过分防御症状", "重度失调");
+            resultMap2.put("触觉过分防御解释", "重度失调");
+            resultJson.put("触觉过分防御解释", "重度失调");
         }else if (score2 >= 20 && score2 < 30){
-            resultMap2.put("触觉过分防御症状", "中度失调");
-            resultJson.put("触觉过分防御症状", "中度失调");
+            resultMap2.put("触觉过分防御解释", "中度失调");
+            resultJson.put("触觉过分防御解释", "中度失调");
         }else if (score2 >= 30 && score2 < 40){
-            resultMap2.put("触觉过分防御症状", "轻度失调");
-            resultJson.put("触觉过分防御症状", "轻度失调");
+            resultMap2.put("触觉过分防御解释", "轻度失调");
+            resultJson.put("触觉过分防御解释", "轻度失调");
         }else {
-            resultMap2.put("触觉过分防御症状", "正常");
-            resultJson.put("触觉过分防御症状", "正常");
+            resultMap2.put("触觉过分防御解释", "正常");
+            resultJson.put("触觉过分防御解释", "正常");
         }
         //得分列表
         Map<String, String> resultMap3 = new LinkedHashMap<>();
         resultMap3.put("本体感失调得分", String.valueOf(score3));
         resultJson.put("本体感失调得分", String.valueOf(score3));
         if (score3 < 20){
-            resultMap3.put("本体感失调症状", "重度失调");
-            resultJson.put("本体感失调症状", "重度失调");
+            resultMap3.put("本体感失调解释", "重度失调");
+            resultJson.put("本体感失调解释", "重度失调");
         }else if (score3 >= 20 && score3 < 30){
-            resultMap3.put("本体感失调症状", "中度失调");
-            resultJson.put("本体感失调症状", "中度失调");
+            resultMap3.put("本体感失调解释", "中度失调");
+            resultJson.put("本体感失调解释", "中度失调");
         }else if (score3 >= 30 && score3 < 40){
-            resultMap3.put("本体感失调症状", "轻度失调");
-            resultJson.put("本体感失调症状", "轻度失调");
+            resultMap3.put("本体感失调解释", "轻度失调");
+            resultJson.put("本体感失调解释", "轻度失调");
         }else {
-            resultMap3.put("本体感失调症状", "正常");
-            resultJson.put("本体感失调症状", "正常");
+            resultMap3.put("本体感失调解释", "正常");
+            resultJson.put("本体感失调解释", "正常");
         }
         Map<String, String> resultMap4 = new LinkedHashMap<>();
         resultMap4.put("学习能力发展不足得分", String.valueOf(score4));
         resultJson.put("学习能力发展不足得分", String.valueOf(score4));
         if (score4 < 20){
-            resultMap4.put("学习能力发展不足症状", "重度失调");
-            resultJson.put("学习能力发展不足症状", "重度失调");
+            resultMap4.put("学习能力发展不足解释", "重度失调");
+            resultJson.put("学习能力发展不足解释", "重度失调");
         }else if (score4 >= 20 && score4 < 30){
-            resultMap4.put("学习能力发展不足症状", "中度失调");
-            resultJson.put("学习能力发展不足症状", "中度失调");
+            resultMap4.put("学习能力发展不足解释", "中度失调");
+            resultJson.put("学习能力发展不足解释", "中度失调");
         }else if (score4 >= 30 && score4 < 40){
-            resultMap4.put("学习能力发展不足症状", "轻度失调");
-            resultJson.put("学习能力发展不足症状", "轻度失调");
+            resultMap4.put("学习能力发展不足解释", "轻度失调");
+            resultJson.put("学习能力发展不足解释", "轻度失调");
         }else {
-            resultMap4.put("学习能力发展不足症状", "正常");
-            resultJson.put("学习能力发展不足症状", "正常");
+            resultMap4.put("学习能力发展不足解释", "正常");
+            resultJson.put("学习能力发展不足解释", "正常");
         }
         Map<String, String> resultMap5 = new LinkedHashMap<>();
         resultMap5.put("大年龄的特殊问题得分", String.valueOf(score5));
         resultJson.put("大年龄的特殊问题得分", String.valueOf(score5));
         if (score5 < 20){
-            resultMap5.put("大年龄的特殊问题症状", "重度失调");
-            resultJson.put("大年龄的特殊问题症状", "重度失调");
+            resultMap5.put("大年龄的特殊问题解释", "重度失调");
+            resultJson.put("大年龄的特殊问题解释", "重度失调");
         }else if (score5 >= 20 && score5 < 30){
-            resultMap5.put("大年龄的特殊问题症状", "中度失调");
-            resultJson.put("大年龄的特殊问题症状", "中度失调");
+            resultMap5.put("大年龄的特殊问题解释", "中度失调");
+            resultJson.put("大年龄的特殊问题解释", "中度失调");
         }else if (score5 >= 30 && score5 < 40){
-            resultMap5.put("大年龄的特殊问题症状", "轻度失调");
-            resultJson.put("大年龄的特殊问题症状", "轻度失调");
+            resultMap5.put("大年龄的特殊问题解释", "轻度失调");
+            resultJson.put("大年龄的特殊问题解释", "轻度失调");
         }else {
-            resultMap5.put("大年龄的特殊问题症状", "正常");
-            resultJson.put("大年龄的特殊问题症状", "正常");
+            resultMap5.put("大年龄的特殊问题解释", "正常");
+            resultJson.put("大年龄的特殊问题解释", "正常");
         }
 
         List<Map<String,String>> resultMapList = new ArrayList<>();

src/main/java/com/rf/psychological/scale/resultBusiness/scaleResult/TBScale.java

@@ -193,13 +193,14 @@ public class TBScale extends BaseScale {
     }
 
     private static void putDimResult(Map<String, String> resultMap0, JSONObject resultJson, DimensionEntity dimensionEntity, NewResultDto newResultDto1, ScaleMarksEntity scaleMarksEntity) {
-        resultMap0.put(dimensionEntity.getName() + "常模参考值", scaleMarksEntity.getReference());
-        resultMap0.put(dimensionEntity.getName() + "症状", scaleMarksEntity.getSymptom());
-        resultJson.put(dimensionEntity.getName() + "结论", scaleMarksEntity.getSymptom());
-        resultMap0.put(dimensionEntity.getName() + "因子解释", scaleMarksEntity.getNameExplain());
-        resultMap0.put(dimensionEntity.getName() + "指导语", scaleMarksEntity.getImprovementSuggestions());
-        resultJson.put(dimensionEntity.getName() + "指导语", scaleMarksEntity.getImprovementSuggestions());
+        resultMap0.put(dimensionEntity.getName() + "解释", scaleMarksEntity.getSymptom());
+        resultJson.put(dimensionEntity.getName() + "解释", scaleMarksEntity.getSymptom());
+        resultMap0.put(dimensionEntity.getName() + "解读", scaleMarksEntity.getNameExplain());
+        resultJson.put(dimensionEntity.getName() + "解读", scaleMarksEntity.getNameExplain());
+        resultMap0.put(dimensionEntity.getName() + "说明", scaleMarksEntity.getImprovementSuggestions());
+        resultJson.put(dimensionEntity.getName() + "说明", scaleMarksEntity.getImprovementSuggestions());
         resultMap0.put(dimensionEntity.getName() + "建议", scaleMarksEntity.getSuggestion());
+        resultJson.put(dimensionEntity.getName() + "建议", scaleMarksEntity.getSuggestion());
         BeanUtils.copyProperties(scaleMarksEntity, newResultDto1);
     }
 }

src/main/java/com/rf/psychological/scale/resultBusiness/scaleResult/TTTScale.java

@@ -164,9 +164,9 @@ public class TTTScale extends BaseScale{
         }
         DecimalFormat df = new DecimalFormat("######0.00");
 
-        int totalScore = score1 + score2 + score3 + score4;
-        resultMap0.put("总分", String.valueOf(totalScore));
-        resultJson.put("总分", String.valueOf(totalScore));
+        //int totalScore = score1 + score2 + score3 + score4;
+        //resultMap0.put("总分", String.valueOf(totalScore));
+        //resultJson.put("总分", String.valueOf(totalScore));
 
 
         //将量表总维度根据维度名称进行分组