ow

Python/NamedEntity.cs

@@ -174,6 +174,17 @@ namespace CloudBuilder.Topshelf.Python
             return nodes?.Find(node => node.ItemId == self.FatherId);
         }
 
+        /// <summary>
+        /// 根据ID查子节点
+        /// </summary>
+        /// <param name="nodes">所有节点</param>
+        /// <param name="itemId">节点ID</param>
+        /// <returns>节点对象，如果未找到则返回null</returns>
+        public static HanlpConstituencyNode[] GetNodeSonById(List<HanlpConstituencyNode> nodes, int itemId)
+        {
+            return nodes?.Where(node => node.FatherId == itemId).ToArray();
+        }
+
         /// <summary>
         /// 根据ID查找等于label父节点
         /// </summary>
@@ -206,14 +217,15 @@ namespace CloudBuilder.Topshelf.Python
             if (nds == null || nds.Length == 0) return null;
             HanlpConstituencyNode father;
             List<string> list = new List<string>();
+            string names = string.Empty;
             foreach (HanlpConstituencyNode nd in nds)
             {
                 List<HanlpConstituencyNode> sons = GetChildrenNodes(nodes, nd.ItemId);
-                if (sons == null || sons.Count() == 0) continue;
-                return string.Join("", sons.Select(x => x.Label).ToArray());
+                if (sons == null || !sons.Where(x => x.Children == 0).Any()) continue;
+                names = names + string.Join("", sons.Where(x => x.Children == 0).Select(x => x.Label).ToArray());
             }
 
-            return null;
+            return names;
         }
 
         /// <summary>

Python/PythonCommand.cs

@@ -88,3 +88,754 @@ namespace CloudBuilder.Topshelf.Python
         }
     }
 }
+/*
+import os
+import json
+import logging
+
+# 配置日志
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+try:
+    import hanlp
+    from hanlp.components.mtl.multi_task_learning import MultiTaskLearning
+    from hanlp.components.mtl.tasks.tok.tag_tok import TaggingTokenization
+    from hanlp.components.mtl.tasks.ner.tag_ner import TaggingNamedEntityRecognition
+    
+    # 尝试导入tqdm，如果不存在则使用替代
+    try:
+        from tqdm import tqdm
+        HAS_TQDM = True
+    except ImportError:
+        logger.warning("tqdm未安装，将不显示进度条")
+        HAS_TQDM = False
+        
+        # 创建一个简单的tqdm替代
+        class tqdm:
+            def __init__(self, iterable, desc=None):
+                self.iterable = iterable
+                self.desc = desc
+            def __iter__(self):
+                return iter(self.iterable)
+except ImportError as e:
+    logger.error(f"导入HanLP失败: {e}")
+
+class HanLPProcessor:
+    """HanLP自然语言处理工具类"""
+    
+    def __init__(self, 
+                 hanlp_home='D:/Python38/hanlp/',
+                 hf_home='D:/Python38/models/',
+                 hf_endpoint='https://hf-mirror.com',
+                 hanlp_url='https://ftp.hankcs.com/hanlp/',
+                 surnames_path='D:/Python38/hanlp/dictionary/surnames.txt',
+                 titles_path='D:/Python38/hanlp/dictionary/titles.txt',
+                 words_path='D:/Python38/hanlp/dictionary/words.txt',
+                 salutations_path='D:/Python38/hanlp/dictionary/salutations.txt'):
+        # 初始化ID计数器和父ID跟踪
+        self.item_id_counter = 1
+        self.current_father_id = 0
+        """
+        初始化HanLP处理器
+        
+        参数:
+            hanlp_home: HanLP资源缓存目录
+            hf_home: HuggingFace模型缓存目录
+            hf_endpoint: HuggingFace下载端点
+            hanlp_url: HanLP下载URL
+            surnames_path: 姓氏词典路径
+            titles_path: 头衔词典路径
+            words_path: 专有名词词典路径
+            salutations_path: 称呼词典路径
+        """
+        # 存储配置信息
+        self.hanlp_home = hanlp_home
+        self.hf_home = hf_home
+        self.hf_endpoint = hf_endpoint
+        self.hanlp_url = hanlp_url
+        self.surnames_path = surnames_path
+        self.titles_path = titles_path
+        self.words_path = words_path
+        self.salutations_path = salutations_path
+        
+        # 初始化组件
+        self.mtl = None
+        self.tok = None
+        self.ner = None
+        
+        # 设置环境变量
+        self._set_environment_variables()
+        
+        # 加载模型和词典
+        self._load_model()
+        self._load_dictionaries()
+    
+    def _set_environment_variables(self):
+        """设置必要的环境变量"""
+        try:
+            os.environ['HANLP_HOME'] = self.hanlp_home
+            os.environ['HF_HOME'] = self.hf_home
+            os.environ['HF_ENDPOINT'] = self.hf_endpoint
+            os.environ['HANLP_URL'] = self.hanlp_url
+            logger.info("环境变量设置成功")
+        except Exception as e:
+            logger.error(f"设置环境变量失败: {e}")
+    
+    def _load_dict_from_file(self, file_path, default_tag='S-PERSON'):
+        """从文件加载词典"""
+        dict_tags = {}
+        try:
+            with open(file_path, 'r', encoding='utf-8') as f:
+                for word in f:
+                    word = word.strip()
+                    if word:  # 跳过空行
+                        dict_tags[(word,)] = (default_tag,)  # 保持元组形式
+        except Exception as e:
+            print(f"加载词典文件{file_path}失败: {e}")
+        return dict_tags
+    
+    def _load_names(self, surnames_path, titles_path):
+        """加载姓氏和头衔，生成姓名组合"""
+        try:
+            with open(surnames_path, 'r', encoding='utf-8') as f:
+                surnames = [line.strip() for line in f if line.strip()]
+            
+            with open(titles_path, 'r', encoding='utf-8') as f:
+                titles = [line.strip() for line in f if line.strip()]
+            
+            # 使用生成器表达式（节省内存）
+            full_names = (f'{surname}{title}' for surname in surnames for title in titles)
+            return full_names
+        except Exception as e:
+            print(f"加载姓名文件失败: {e}")
+            return []
+    
+    def _load_model(self):
+        """加载HanLP模型"""
+        try:
+            # CLOSE是自然语义标注的闭源语料库，BASE是中号模型，ZH中文
+            logger.info("开始加载HanLP模型...")
+            self.mtl = hanlp.load(hanlp.pretrained.mtl.CLOSE_TOK_POS_NER_SRL_DEP_SDP_CON_ELECTRA_SMALL_ZH)
+            self.tok = self.mtl['tok/coarse']
+            self.tok.dict_force = self.tok.dict_combine = None
+            self.ner = self.mtl['ner/msra']
+            logger.info("模型加载成功")
+        except Exception as e:
+            logger.error(f"加载模型失败: {e}")
+            self.mtl = None
+            self.tok = None
+            self.ner = None
+    
+    def _load_dictionaries(self):
+        """加载各种词典"""
+        try:
+            # 加载称呼词典
+            self.ner.dict_tags = self._load_dict_from_file(self.salutations_path)
+            
+            # 读取专有名词词典
+            with open(self.words_path, 'r', encoding='utf-8') as f:
+                names = [line.strip() for line in f if line.strip()]
+            
+            # 生成所有姓名组合
+            full_names = self._load_names(self.surnames_path, self.titles_path)
+            
+            # 构建强制分词词典（使用进度条）
+            dict_force = {}
+            for name in tqdm(full_names, desc='读取人名词典'):
+                dict_force[name] = [name]
+            
+            for name in names:
+                dict_force[name] = [name]
+            
+            self.tok.dict_force = dict_force
+        except Exception as e:
+            print(f"加载词典失败: {e}")
+    
+    def get_ner_json(self, text):
+        """识别文本中的实体并返回JSON格式"""
+        if not text or not isinstance(text, str):
+            logger.warning("无效的输入文本")
+            return json.dumps([])
+            
+        try:
+            if not self.mtl:
+                raise RuntimeError("模型未加载")
+            
+            result = self.mtl(text, tasks=['tok/coarse', 'ner/msra'], skip_tasks=['tok/fine'])
+            
+            # 检查结果格式
+            if 'ner/msra' not in result or not isinstance(result['ner/msra'], (list, tuple)):
+                logger.warning("模型返回结果格式异常")
+                return json.dumps([])
+                
+            json_data = []
+            for item in result['ner/msra']:
+                # 确保item是正确的格式
+                if isinstance(item, (list, tuple)) and len(item) >= 4:
+                    json_data.append({
+                        'Text': str(item[0]),
+                        'Type': str(item[1]),
+                        'Start': int(item[2]),
+                        'End': int(item[3])
+                    })
+                
+            return json.dumps(json_data, ensure_ascii=False)
+        except Exception as e:
+            logger.error(f"实体识别失败: {e}")
+            return json.dumps([])
+    
+    def get_srl_json(self, text):
+        """识别文本中的人物角色并返回JSON格式"""
+        if not text or not isinstance(text, str):
+            logger.warning("无效的输入文本")
+            return json.dumps([])
+            
+        try:
+            if not self.mtl:
+                raise RuntimeError("模型未加载")
+            
+            results = self.mtl(text, tasks=['tok/coarse', 'srl'], skip_tasks=['tok/fine'])
+            result = []
+            
+            # 检查结果格式
+            if 'srl' not in results:
+                logger.warning("模型返回结果中未找到SRL数据")
+                return json.dumps([])
+                
+            data = results['srl']
+            if not data:
+                return json.dumps(result)
+            
+            for sentence in data:
+                if not isinstance(sentence, (list, tuple)):
+                    continue
+                    
+                sentence_data = []
+                for item in sentence:
+                    if not isinstance(item, (list, tuple)):
+                        continue
+                        
+                    # 确保至少有2个元素
+                    if len(item) < 2:
+                        continue
+                        
+                    entry = {
+                        'Text': str(item[0]),
+                        'Type': str(item[1]),
+                        'Start': int(item[2]) if len(item) > 2 else -1,
+                        'End': int(item[3]) if len(item) > 3 else -1
+                    }
+                    sentence_data.append(entry)
+                
+                if sentence_data:  # 只添加非空的句子数据
+                    result.append(sentence_data)
+            
+            return json.dumps(result, ensure_ascii=False, indent=2)
+        except Exception as e:
+            logger.error(f"人物角色识别失败: {e}")
+            return json.dumps([])
+    
+    def get_con(self, text):
+        """解析文本的句法结构并返回结果"""
+        if not text or not isinstance(text, str):
+            logger.warning("无效的输入文本")
+            return None
+            
+        try:
+            if not self.mtl:
+                raise RuntimeError("模型未加载")
+            
+            # logger.info("开始句法分析...")
+            result = self.mtl(text, tasks=['con'])
+            # logger.info("句法分析完成")
+            return result
+        except Exception as e:
+            logger.error(f"句法分析失败: {e}")
+            return None
+
+    def get_all(self,text):
+        result = self.mtl(text)
+        return result;
+
+    def get_con_json(self, text):
+        """解析文本的句法结构并返回JSON格式字符串，便于C#对象处理"""
+        # 重置ID计数器和父ID，确保每次调用都从1开始
+        self.item_id_counter = 0
+        self.current_father_id = 0
+        
+        # 初始化JSON数组用于存储所有解析结果元素
+        result_array = []
+        # 保存结果数组的引用，供packLabel方法使用
+        self.result_array = result_array
+        
+        if not text or not isinstance(text, str):
+            logger.warning("无效的输入文本")
+            return json.dumps([])
+            
+        if not self.mtl:
+            raise RuntimeError("模型未加载")
+        
+        # logger.info("开始句法分析...")
+        results = self.mtl(text, tasks=['con'])
+        # logger.info("句法分析完成")
+        
+        # 检查结果格式
+        if 'con' not in results:
+            logger.warning("模型返回结果中未找到CON数据")
+            return json.dumps([])
+            
+        #从results中截取"con": [截取字符串]}
+        # 将results转为字符串并提取con字段值
+        results_str = str(results)
+        # logger.info(results_str)
+        # 只取出top的内容
+        new_results = self.get_con_top('"con": [',results_str)
+        level=0
+
+        self.item_id_counter += 1
+        
+        # 调用pack_childen处理所有节点，所有生成的元素将通过packLabel方法添加到result_array
+        try:
+            self.pack_childen(new_results, level)
+        except Exception as e:
+            logger.warning(f"pack_childen处理时出错: {e}")
+            
+        # 清理引用，避免内存泄漏
+        delattr(self, 'result_array')
+            
+        # 将结果数组转换为JSON字符串返回，便于C#交互
+        return json.dumps(result_array, ensure_ascii=False)
+
+    def pack_childen(self, text, level, father_id=None):
+        # 如果没有提供father_id，则使用当前类的current_father_id
+        if father_id is None:
+            father_id = self.current_father_id
+        
+        childen_json = []
+        json_temp = self.split(text, ',')
+        # 去除前后的[]
+        childen = self.substring_before_after_one(json_temp[1])
+        # 分隔元素
+        childens = self.split(childen, ',')
+        label = json_temp[0]
+        
+        if json_temp[0] == '_':
+            # "_", ["二愣子"] - 提取列表中的值
+            # 当标签为"_"时，提取childen中的值并直接返回值字符串
+            label = self.substring_before_after_one(childen)
+            # 保存当前的current_father_id
+            current_father = self.current_father_id
+            # 设置当前元素的父ID
+            self.current_father_id = father_id
+            # 创建节点
+            top_json = self.packLabel(label, level, 0)
+            # 恢复原来的current_father_id
+            self.current_father_id = current_father
+            # logger.info(top_json)
+            return
+        
+        # 保存当前的current_father_id
+        current_father = self.current_father_id
+        # 设置当前元素的父ID
+        self.current_father_id = father_id
+        # 创建当前节点
+        top_json = self.packLabel(label, level, len(childens))
+        # 获取当前节点的ID作为子节点的父ID
+        current_node_id = top_json['ItemId']
+        # 恢复原来的current_father_id
+        self.current_father_id = current_father
+        
+        # logger.info(top_json)
+        
+        # 递归处理子节点，传递当前节点的ID作为父ID
+        for i, item in enumerate(childens):
+            childen_temp = self.substring_before_after_one(item)
+            childen_json.append(self.pack_childen(childen_temp, level + 1, current_node_id))
+        
+    def get_root(self, text):
+        json_str = self.get_task(text,'dep')
+        
+        for index, dep in enumerate(json_str["dep"]):
+            if('root'==dep[1]):
+                return json_str["tok/fine"][index];
+        return ''
+
+    def get_task(self, text, task):
+        '''
+        执行指定的任务并跳过fine分词
+        
+        参数:
+            text: 输入文本
+            task: 要执行的任务名称
+            
+        返回:
+            任务执行结果
+        '''
+        if not text or not isinstance(text, str):
+            logger.warning('无效的输入文本')
+            return None
+            
+        try:
+            if not self.mtl:
+                raise RuntimeError('模型未加载')
+            
+            # 跳过tok/fine任务
+            result = self.mtl(text, tasks=[task])
+            return result
+        except Exception as e:
+            logger.error(f'执行任务{task}失败: {e}')
+            return None
+
+    def get_tok_fine(self, text):
+        """细粒度分词，返回C#可JsonSerializer的数组JSON"""
+        if not text or not isinstance(text, str):
+            logger.warning("无效的输入文本")
+            return json.dumps([], ensure_ascii=False)
+
+        try:
+            if not self.mtl:
+                raise RuntimeError("模型未加载")
+
+            results = self.mtl(text, tasks=['tok/fine'], skip_tasks=['tok/coarse'])
+
+            if 'tok/fine' not in results or not isinstance(results['tok/fine'], (list, tuple)):
+                logger.warning("模型返回结果格式异常")
+                return json.dumps([], ensure_ascii=False)
+
+            return json.dumps(results['tok/fine'], ensure_ascii=False)
+        except Exception as e:
+            logger.error(f"细粒度分词失败: {e}")
+            return json.dumps([], ensure_ascii=False)
+
+    def get_tok_coarse(self, text):
+        """粗粒度分词，返回C#可JsonSerializer的数组JSON"""
+        if not text or not isinstance(text, str):
+            logger.warning("无效的输入文本")
+            return json.dumps([], ensure_ascii=False)
+
+        try:
+            if not self.mtl:
+                raise RuntimeError("模型未加载")
+
+            results = self.mtl(text, tasks=['tok/coarse'], skip_tasks=['tok/fine'])
+
+            if 'tok/coarse' not in results or not isinstance(results['tok/coarse'], (list, tuple)):
+                logger.warning("模型返回结果格式异常")
+                return json.dumps([], ensure_ascii=False)
+
+            return json.dumps(results['tok/coarse'], ensure_ascii=False)
+        except Exception as e:
+            logger.error(f"粗粒度分词失败: {e}")
+            return json.dumps([], ensure_ascii=False)
+
+    def get_con_top(self,from_text,text):
+        if '"con": [' in text:
+            start = text.find(from_text) + len(from_text)
+            # 寻找对应的结束括号
+            end = start
+            bracket_count = 1
+            while end < len(text) and bracket_count > 0:
+                if text[end] == '[':
+                    bracket_count += 1
+                elif text[end] == ']':
+                    bracket_count -= 1
+                end += 1
+            new_results = text[start:end-1]  # -1 是为了去掉最后的']'
+        else:
+            new_results = ''
+        return new_results
+
+    def substring_before_after_one(self, text):
+        if not text or not isinstance(text, str):
+            return text
+        if len(text) < 2:
+            return text
+        return text[1:-1]
+
+    def split(self, text, char):
+        if not text:
+            return []
+            
+        result = []
+        current = []
+        in_quotes = False  # 是否在引号内
+        bracket_level = 0  # 括号嵌套级别
+        
+        i = 0
+        while i < len(text):
+            c = text[i]
+            
+            # 处理引号
+            if c == '"':
+                in_quotes = not in_quotes
+                current.append(c)
+            
+            # 处理括号
+            elif c == '[' and not in_quotes:
+                bracket_level += 1
+                current.append(c)
+            elif c == ']' and not in_quotes:
+                if bracket_level > 0:
+                    bracket_level -= 1
+                current.append(c)
+            
+            # 遇到分隔符且不在括号内和引号内
+            elif c == char and bracket_level == 0 and not in_quotes:
+                # 添加当前部分到结果
+                part = ''.join(current).strip()
+                # 清理引号（如果有）
+                if part.startswith('"') and part.endswith('"'):
+                    part = part[1:-1]
+                result.append(part)
+                current = []
+            
+            # 处理转义字符
+            elif c == '\\' and i + 1 < len(text):
+                current.append(c)
+                current.append(text[i + 1])
+                i += 1
+            
+            else:
+                current.append(c)
+            
+            i += 1
+        
+        # 处理最后一个元素
+        if current:
+            part = ''.join(current).strip()
+            # 清理引号（如果有）
+            if part.startswith('"') and part.endswith('"'):
+                part = part[1:-1]
+            result.append(part)
+            
+        return result
+        
+    def packLabel(self, label, level, children):
+        # 保存当前ID作为返回值
+        current_id = self.item_id_counter
+        
+        # 创建包含ID和父ID的字典
+        result = {
+            "ItemId": current_id,
+            "FatherId": self.current_father_id,
+            "Label": label,
+            "Level": level,
+            "Children": children
+        }
+        
+        # 将生成的元素添加到result_array（如果存在）
+        if hasattr(self, 'result_array'):
+            self.result_array.append(result)
+        
+        # 更新计数器
+        self.item_id_counter += 1
+        
+        return result
+    
+    def extract_underscore_value(self, text):
+        """
+        从"_", ["值"]格式的文本中提取值，并直接返回字符串值
+        
+        Args:
+            text: 格式为"_", ["值"]的字符串
+            
+        Returns:
+            提取的字符串值
+        """
+        # 分割文本以获取值部分
+        parts = self.split(text, ',')
+        if len(parts) < 2:
+            return ""
+            
+        # 获取第二个部分（包含值的列表）
+        value_part = parts[1]
+        # 去除前后的[]和可能的引号
+        value = self.substring_before_after_one(value_part).strip('"')
+        
+        # 直接返回提取的值
+        return value
+        
+processor=HanLPProcessor()
+
+# 测试split函数
+def test_split_function():
+    # 测试用例1: 简单的逗号分隔
+    text1 = ' "TOP",[["IP", [["NP", [["_", ["二愣子"]]]], ["VP", [["VP", [["_", ["姓"]], ["NP", [["_", ["韩"]]]]]], ["VP", [["_", ["名"]], ["NP", [["_", ["立"]]]]]]]], ["_", ["。"]]]]]'
+    result1 = processor.split(text1, ',')
+    print("测试用例1结果:")
+    for i, item in enumerate(result1):
+        print(f"  {i}: {item}")
+    
+    text1='二愣子姓韩名立。'
+    print(processor.get_con_json(text1))
+    # 测试用例2: 括号内包含逗号
+    # text2 = '"NP",[["NN",["_", "张三"]],["NN",["_", "李四"]]]'
+    # result2 = processor.split(text2, ',')
+    # print("\n测试用例2结果:")
+
+def test_extract_underscore_value():
+    """
+    测试新添加的extract_underscore_value函数
+    """
+    print("\n测试extract_underscore_value函数:")
+    
+    # 测试用例1: 基本格式
+    test_text1 = '"_", ["二愣子"]'
+    result1 = processor.extract_underscore_value(test_text1)
+    print(f"输入: {test_text1}")
+    print(f"输出: {result1}")
+    
+    # 测试用例2: 其他值
+    test_text2 = '"_", ["张三"]'
+    result2 = processor.extract_underscore_value(test_text2)
+    print(f"\n输入: {test_text2}")
+    print(f"输出: {result2}")
+    
+    # 测试用例3: 空值情况
+    test_text3 = '"_", []'
+    result3 = processor.extract_underscore_value(test_text3)
+    print(f"\n输入: {test_text3}")
+    print(f"输出: {result3}")
+
+# 测试修复后的pack_childen方法对"_", ["二愣子"]格式的处理
+def test_pack_childen_with_underscore():
+    """
+    测试pack_childen方法对"_", ["值"]格式的处理
+    """
+    print("\n测试pack_childen方法对'_', ['二愣子']格式的处理:")
+    # 测试用例：直接调用pack_childen处理"_", ["二愣子"]格式
+    test_input = '"_", ["二愣子"]'
+    print(f"输入: {test_input}")
+    try:
+        # 调用pack_childen方法，使用level=0
+        result = processor.pack_childen(test_input, 0)
+        # 由于pack_childen在处理"_"标签时会直接返回，我们需要查看logger输出
+        print("测试完成，请检查日志输出")
+    except Exception as e:
+        print(f"测试出错: {e}")
+    # for i, item in enumerate(result2):
+    #     print(f"  {i}: {item}")
+
+# 如果直接运行此文件，则执行测试
+def test_id_generation():
+    """
+    测试ItemId和FatherId的生成逻辑
+    """
+    print("开始测试ID生成...")
+    # 创建处理器实例
+    processor = HanLPProcessor()
+    
+    # 测试基本的packLabel方法
+    print("\n测试基本的packLabel方法:")
+    result1 = processor.packLabel("TOP", 0, 1)
+    print(result1)
+    
+    # 测试生成第二个节点，应该有正确的父子关系
+    print("\n测试生成第二个节点:")
+    # 对于第二个节点，我们手动设置它的父ID为第一个节点的ID
+    processor.current_father_id = result1['ItemId']
+    result2 = processor.packLabel("TOP", 0, 1)
+    print(result2)
+    
+    # 测试pack_childen方法
+    print("\n测试pack_childen方法:")
+    # 重置处理器的ID计数器
+    processor.item_id_counter = 1
+    processor.current_father_id = 0
+    
+    # 使用一个简单的测试字符串来模拟解析结果，确保格式正确
+    test_input = "TOP, [IP, [NP, [NN, 你]], [VP, [VV, 好]]]"
+    try:
+        processor.pack_childen(test_input, 0)
+        print("pack_childen测试成功")
+    except Exception as e:
+        print(f"pack_childen测试失败: {e}")
+        # 为了演示，我们可以直接测试packLabel的组合
+        print("\n直接测试ID和父子关系:")
+        processor.item_id_counter = 1
+        processor.current_father_id = 0
+        
+        # 模拟用户要求的输出格式
+        result1 = processor.packLabel("TOP", 0, 1)
+        print(f"{{'ItemId':{result1['ItemId']},'FatherId':{result1['FatherId']},'Label': '{result1['Label']}', 'Level': {result1['Level']}, 'Children': {result1['Children']}}}")
+        
+        # 第二个节点的父ID是第一个节点的ID
+        processor.current_father_id = result1['ItemId']
+        result2 = processor.packLabel("TOP", 0, 1)
+        print(f"{{'ItemId':{result2['ItemId']},'FatherId':{result2['FatherId']},'Label': '{result2['Label']}', 'Level': {result2['Level']}, 'Children': {result2['Children']}}}")
+    
+    print("\nID生成测试完成")
+
+def test_con_json():
+    """
+    测试get_con_json方法返回的JSON格式字符串
+    """
+    print("\n开始测试get_con_json方法...")
+    processor = HanLPProcessor()
+    
+    # 测试空输入
+    empty_result = processor.get_con_json("")
+    print(f"空输入测试: {empty_result}")
+    print(f"空输入结果类型: {type(empty_result)}")
+    # 验证可以解析为空数组
+    try:
+        parsed_empty = json.loads(empty_result)
+        print(f"空输入解析结果: {parsed_empty}, 类型: {type(parsed_empty)}")
+    except json.JSONDecodeError as e:
+        print(f"空输入JSON解析失败: {e}")
+    
+    # 测试None输入
+    none_result = processor.get_con_json(None)
+    print(f"None输入测试: {none_result}")
+    print(f"None输入结果类型: {type(none_result)}")
+    # 验证可以解析为空数组
+    try:
+        parsed_none = json.loads(none_result)
+        print(f"None输入解析结果: {parsed_none}, 类型: {type(parsed_none)}")
+    except json.JSONDecodeError as e:
+        print(f"None输入JSON解析失败: {e}")
+    
+    # 尝试测试有效输入（注意：由于可能没有加载模型，这里可能会抛出异常）
+    try:
+        # 尝试简单的中文文本
+        text = "二愣子姓韩名立。"
+        print(f"测试有效输入: {text}")
+        result = processor.get_con_json(text)
+        print(f"返回结果类型: {type(result)}")
+        print(f"返回结果字符串长度: {len(result)}")
+        
+        # 验证返回的是字符串
+        assert isinstance(result, str), "返回结果应该是字符串类型"
+        print("✓ 验证通过：返回结果是字符串类型")
+        
+        # 尝试解析JSON字符串
+        try:
+            parsed_result = json.loads(result)
+            print(f"✓ JSON解析成功，解析后类型: {type(parsed_result)}")
+            print(f"解析后数组长度: {len(parsed_result)}")
+            
+            # 如果解析成功且有元素，显示部分内容
+            if parsed_result:
+                print(f"解析后第一个元素: {parsed_result[0]}")
+                if len(parsed_result) > 1:
+                    print(f"解析后第二个元素: {parsed_result[1]}")
+        except json.JSONDecodeError as e:
+            print(f"✗ JSON解析失败: {e}")
+            
+        print(f"完整返回结果: {result}")
+    except Exception as e:
+        print(f"有效输入测试出错（可能是模型未加载）: {e}")
+    
+    print("get_con_json方法测试完成")
+
+if __name__ == "__main__":
+    # test_split_function()
+    # test_id_generation()
+    # test_extract_underscore_value()
+    # test_pack_childen_with_underscore()
+    # test_con_json()
+    # processor=HanLPProcessor()
+    print("")
+
+*/