优化评估器提示词和工作流清理工具

- 简化评估器提示词，移除冗长示例，保留核心评分标准
- 重构工作流清理工具为智能质量评估清理器
- 增强质量分析算法，支持专业指标和分诊错误惩罚计算
- 添加数据集同步删除功能，保持数据一致性
- 新增质量验证和数据一致性检查机制

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>

agent_system/evaluetor/prompt.py

@@ -47,83 +47,36 @@ class EvaluatorPrompt(BasePrompt):
         "- **5分：优秀** - 表现突出，超出基本预期",
         "",
         "**各维度具体标准**：",
-        "",
-        "### 评分参考背景案例",
-        "**患者背景**: 55岁男性，主诉'胸痛3天'",
-        "**真实现病史**: 3天前无明显诱因出现胸骨后疼痛，呈压榨性，持续性，向左肩放射，伴出汗、恶心，活动时加重，休息后稍缓解",
-        "**真实既往史**: 高血压10年，糖尿病5年，吸烟20年每天1包，父亲心梗病史",
-        "**真实主述**: 胸痛3天",
-        "",
         "### 临床问诊能力 (clinical_inquiry)",
         "- **5分**: 问题设计科学系统，问诊逻辑清晰，信息收集全面深入",
-        "  - *问诊例子*: '这个胸痛是什么性质的？压榨性还是刺痛？有没有向肩膀、手臂放射？什么情况下加重？休息能缓解吗？伴随有出汗、恶心吗？您有高血压糖尿病病史吗？家族有心脏病史吗？'",
-        "  - *评分理由*: 系统询问疼痛PQRST要素，主动询问相关既往史和家族史，体现完整临床思维",
         "- **4分**: 问题针对性强，问诊思路合理，能有效收集关键信息",
-        "  - *问诊例子*: '胸痛是压榨性的吗？有放射痛吗？活动时会加重吗？有没有出汗恶心？您有高血压病史吗？'",
-        "  - *评分理由*: 重点询问胸痛典型特征和重要危险因素，针对性强",
         "- **3分**: 能提出基本相关问题，问诊方向基本正确，能收集必要信息",
-        "  - *问诊例子*: '胸痛什么时候开始的？疼痛严重吗？在哪个部位？有其他不舒服吗？'",
-        "  - *评分理由*: 问题相关且方向正确，但缺乏针对性和深度",
         "- **2分**: 能提出问题并收集基本信息，方向基本正确",
-        "  - *问诊例子*: '胸痛多长时间了？现在还痛吗？严重吗？'",
-        "  - *评分理由*: 能问基本信息但不够深入，遗漏重要诊断要素",
         "- **1分**: 能完成基本问诊任务，收集基础信息",
-        "  - *问诊例子*: '哪里不舒服？什么时候开始的？'",
-        "  - *评分理由*: 过于简单，缺乏针对性和专业性",
         "- **0分**: 无法判断问诊质量",
         "",
         "### 沟通表达能力 (communication_quality)",
         "- **5分**: 语言通俗易懂，避免过度专业术语，患者完全理解，沟通亲和温暖",
-        "  - *沟通例子*: '您好，我想了解一下您胸口疼痛的情况。这种疼痛是像被什么东西压着的感觉，还是像针扎一样刺痛？疼痛会不会传到肩膀或者胳膊上？'",
-        "  - *评分理由*: 用通俗比喻解释医学概念，语言亲切自然，患者容易理解",
         "- **4分**: 用词恰当亲民，适度使用通俗解释，患者较易理解",
-        "  - *沟通例子*: '您的胸痛是压榨性的吗？就是感觉胸口被挤压的那种？有放射到其他地方吗？'",
-        "  - *评分理由*: 使用医学术语但有通俗解释，表达清晰易懂",
         "- **3分**: 表达基本清晰，偶有专业术语但有解释，患者基本能理解",
-        "  - *沟通例子*: '胸痛的性质是怎样的？是压榨性疼痛吗？有放射痛吗？就是疼痛向别的地方传播。'",
-        "  - *评分理由*: 有专业术语但能解释，基本清晰，患者能理解",
         "- **2分**: 表达清楚但专业性较强，患者需要一定努力才能理解",
-        "  - *沟通例子*: '请描述疼痛的性质，是压榨性还是刺痛性？有无放射痛？诱发因素是什么？'",
-        "  - *评分理由*: 用词较专业但表达清楚，患者需要思考才能理解",
         "- **1分**: 过度使用专业术语，患者理解困难，缺乏亲和力",
-        "  - *沟通例子*: '疼痛性质如何？PQRST分析？有无心绞痛典型症状？危险分层如何？'",
-        "  - *评分理由*: 专业术语过多，缺乏解释，患者难以理解",
         "- **0分**: 无法评价沟通质量",
         "",
         "### 信息收集全面性 (information_completeness)",
         "- **5分**: 信息收集系统全面，涵盖现病史、既往史、危险因素等关键要素",
-        "  - *全面性例子*: '请详细说说胸痛的性质、部位、放射情况？有伴随症状吗？您有高血压、糖尿病病史吗？吸烟史如何？家族有心脏病史吗？平时活动耐量怎样？'",
-        "  - *评分理由*: 系统询问现病史PQRST要素、相关既往史、危险因素，信息收集全面",
         "- **4分**: 信息收集较为全面，涵盖主要诊断要素",
-        "  - *全面性例子*: '胸痛的性质和部位如何？有放射痛吗？您有高血压糖尿病吗？抽烟吗？'",
-        "  - *评分理由*: 收集了症状主要特征和重要危险因素，比较全面",
         "- **3分**: 信息收集基本全面，涵盖必要要素",
-        "  - *全面性例子*: '胸痛什么性质？在哪个部位？您有什么基础病吗？'",
-        "  - *评分理由*: 收集了基本症状信息和既往史，基本全面但深度不足",
         "- **2分**: 信息收集不够全面，遗漏部分重要信息",
-        "  - *全面性例子*: '胸痛怎样？什么时候开始的？严重吗？'",
-        "  - *评分理由*: 只询问了症状基本信息，未涉及危险因素和既往史",
         "- **1分**: 信息收集很不全面，仅收集表面信息",
-        "  - *全面性例子*: '哪里不舒服？什么时候开始的？'",
-        "  - *评分理由*: 信息收集过于简单，缺乏系统性",
         "- **0分**: 第一轮或信息不足，无法评价全面性",
         "",
         "### 整体专业性 (overall_professionalism)",
         "- **5分**: 医学思维出色，风险识别准确，问诊逻辑严谨",
-        "  - *专业性例子*: '根据您的症状，这可能涉及心血管问题，我需要了解：您有高血压糖尿病吗？家族有心脏病史吗？平时抽烟吗？我们需要排除心绞痛或心肌梗死的可能。'",
-        "  - *评分理由*: 体现出色临床思维，准确识别高危因素，具备风险分层概念",
         "- **4分**: 医学思维良好，能抓住重点，问诊方向准确",
-        "  - *专业性例子*: '胸痛伴出汗恶心需要警惕心脏问题，您有高血压病史吗？抽烟吗？我们需要进一步检查。'",
-        "  - *评分理由*: 能识别重要症状组合，重点询问危险因素，方向正确",
         "- **3分**: 具备医学思维，问诊方向基本正确，体现专业性",
-        "  - *专业性例子*: '胸痛需要了解具体情况，您有什么基础疾病吗？平时身体怎么样？'",
-        "  - *评分理由*: 有基本医学概念，知道询问基础疾病，但缺乏针对性",
         "- **2分**: 医学思维基本合理，问诊方向基本正确",
-        "  - *专业性例子*: '胸痛可能是心脏问题，您身体有什么毛病吗？'",
-        "  - *评分理由*: 有基本判断但表达不够专业，思维简单",
         "- **1分**: 具备基本医学思维，能完成基本问诊",
-        "  - *专业性例子*: '胸痛要检查一下，您还有哪里不舒服？'",
-        "  - *评分理由*: 缺乏专业分析，问诊过于简单",
         "- **0分**: 无法评价专业水平",
         "",
         "### 相似度评价标准 (各维度通用)",

analysis/workflow_file_cleaner.py

@@ -1,44 +1,263 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
 """
-工作流文件清理器
+智能工作流文件清理器
-检测指定目录中的所有JSONL文件，删除不完整的工作流记录文件
+基于质量评估的智能清理策略：
+- 不完整项目：保留10%最优质的，删除90%
+- 完整项目：删除20%质量最差的，保留80%
 """
 
 import json
 import os
 import glob
+import re
+import shutil
 from pathlib import Path
-from typing import Dict, Any, List
+from typing import Dict, Any, List, Optional, Set
 import argparse
 import logging
+from dataclasses import dataclass
+from datetime import datetime
 
 # 配置日志
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 logger = logging.getLogger(__name__)
 
 
-class WorkflowFileCleaner:
+@dataclass
-    """工作流文件清理器"""
+class QualityScore:
+    """质量评分数据类"""
+    professional_penalty: float  # 专业指标惩罚分
+    triage_penalty: float        # 分诊错误惩罚分
+    total_penalty: float         # 总惩罚分
+    is_complete: bool           # 是否完整
+    file_path: str             # 文件路径
 
-    def __init__(self, directory: str, dry_run: bool = False):
+
+class IntelligentWorkflowCleaner:
+    """基于质量评估的智能工作流文件清理器"""
+    
+    def __init__(self, directory: str, dry_run: bool = False, 
+                 keep_incomplete_ratio: float = 0.1, 
+                 remove_complete_ratio: float = 0.2):
         """
-        初始化清理器
+        初始化智能清理器
         
         Args:
             directory: 要检查的目录路径
             dry_run: 是否为试运行模式（不实际删除文件）
+            keep_incomplete_ratio: 不完整项目保留比例（默认10%）
+            remove_complete_ratio: 完整项目删除比例（默认20%）
         """
         self.directory = Path(directory)
         self.dry_run = dry_run
+        self.keep_incomplete_ratio = keep_incomplete_ratio
+        self.remove_complete_ratio = remove_complete_ratio
+        
+        # 质量评估相关的评估指标映射（四个核心指标）
+        self.quality_indicators = {
+            'clinical_inquiry': 'clinical_inquiry',
+            'communication_quality': 'communication_quality', 
+            'information_completeness': 'information_completeness',  # 修正为正确的字段名
+            'overall_professional': 'overall_professionalism'
+        }
+        
+        # Dataset路径
+        self.dataset_path = Path('dataset/bbb.json')
+        
         self.stats = {
             'total_files': 0,
             'complete_files': 0,
             'incomplete_files': 0,
-            'deleted_files': [],
+            'kept_incomplete_files': [],
-            'error_files': []
+            'deleted_incomplete_files': [],
+            'kept_complete_files': [],
+            'deleted_complete_files': [],
+            'error_files': [],
+            'deleted_case_indices': [],  # 记录被删除的case索引
+            'deleted_cases_info': [],    # 记录被删除的case详细信息
+            'dataset_backup_path': '',   # 备份文件路径
+            'quality_analysis': {
+                'incomplete': {'avg_penalty': 0.0, 'score_range': (0.0, 0.0)},
+                'complete': {'avg_penalty': 0.0, 'score_range': (0.0, 0.0)}
+            }
         }
     
+    def calculate_professional_penalty(self, evaluation_data_by_round: Dict[int, Dict[str, Any]]) -> float:
+        """
+        计算专业指标惩罚分数
+        
+        公式: Σ(round_i * Σ(四个指标的惩罚分))
+        
+        Args:
+            evaluation_data_by_round: 按轮次组织的评估数据字典
+            
+        Returns:
+            float: 专业指标惩罚分数
+        """
+        penalty = 0.0
+        
+        # 遍历所有轮次
+        for round_num, round_data in evaluation_data_by_round.items():
+            # 计算该轮次四个指标的惩罚分总和
+            round_penalty_sum = 0.0
+            
+            for indicator_key in self.quality_indicators.values():
+                if indicator_key in round_data:
+                    indicator_data = round_data[indicator_key]
+                    
+                    # 处理嵌套的score结构
+                    if isinstance(indicator_data, dict):
+                        score = indicator_data.get('score', 3.0)
+                    else:
+                        # 兼容直接存储score的情况
+                        score = float(indicator_data) if isinstance(indicator_data, (int, float)) else 3.0
+                    
+                    # 只有分数低于3.0才计算惩罚
+                    if score < 3.0:
+                        round_penalty_sum += (3.0 - score)
+            
+            # 轮次惩罚 = 轮次编号 × 该轮次四个指标惩罚分之和
+            penalty += round_num * round_penalty_sum
+        
+        return penalty
+    
+    def calculate_triage_penalty(self, jsonl_file: str, case_data: Dict[str, Any]) -> float:
+        """
+        计算分诊错误惩罚分数
+        
+        如果第一轮的一级和二级都正确，才开始计算。后续错几轮就是几分
+        
+        Args:
+            jsonl_file: JSONL文件路径
+            case_data: 案例数据
+            
+        Returns:
+            float: 分诊错误惩罚分数
+        """
+        try:
+            correct_primary = case_data.get('一级科室', '')
+            correct_secondary = case_data.get('二级科室', '')
+            
+            # 提取所有triager agent的分诊结果
+            triage_steps = []
+            with open(jsonl_file, 'r', encoding='utf-8') as f:
+                for line in f:
+                    try:
+                        event = json.loads(line.strip())
+                        if (event.get('event_type') == 'agent_execution' and 
+                            event.get('agent_name') == 'triager'):
+                            
+                            output_data = event.get('output_data', {})
+                            step_number = event.get('step_number', 0)
+                            
+                            predicted_primary = output_data.get('primary_department', '')
+                            predicted_secondary = output_data.get('secondary_department', '')
+                            
+                            triage_steps.append({
+                                'step_number': step_number,
+                                'primary_department': predicted_primary,
+                                'secondary_department': predicted_secondary,
+                                'primary_correct': predicted_primary == correct_primary,
+                                'secondary_correct': predicted_secondary == correct_secondary
+                            })
+                            
+                    except (json.JSONDecodeError, KeyError):
+                        continue
+            
+            if not triage_steps:
+                return 0.0
+            
+            # 按步骤号排序
+            triage_steps.sort(key=lambda x: x['step_number'])
+            
+            # 检查第一轮是否完全正确（一级和二级都正确）
+            first_round = triage_steps[0]
+            if not (first_round['primary_correct'] and first_round['secondary_correct']):
+                # 第一轮不完全正确，不计算惩罚分
+                return 0.0
+            
+            # 计算后续轮次的错误数
+            error_rounds = 0
+            for step in triage_steps[1:]:  # 从第二轮开始
+                # 只要一级或二级有一个错误，就算这轮错误
+                if not (step['primary_correct'] and step['secondary_correct']):
+                    error_rounds += 1
+            
+            return float(error_rounds)
+                        
+        except Exception as e:
+            logger.warning(f"计算分诊惩罚分时出错 {jsonl_file}: {e}")
+            
+        return 0.0
+    
+    def calculate_quality_score(self, jsonl_file: str) -> Optional[QualityScore]:
+        """
+        计算文件的质量分数
+        
+        Returns:
+            QualityScore: 质量评分对象，如果无法计算则返回None
+        """
+        try:
+            with open(jsonl_file, 'r', encoding='utf-8') as f:
+                lines = f.readlines()
+                
+            if not lines:
+                return None
+                
+            # 检查是否完整
+            is_complete = self.check_workflow_completion(jsonl_file)
+            
+            # 获取案例数据
+            case_data = {}
+            evaluation_data_by_round = {}  # 按轮次组织评估数据
+            
+            for line in lines:
+                try:
+                    event = json.loads(line.strip())
+                    
+                    # 获取案例数据
+                    if event.get('event_type') == 'workflow_start':
+                        case_data = event.get('case_data', {})
+                        
+                    # 获取评估数据，按轮次组织
+                    elif (event.get('event_type') == 'agent_execution' and 
+                          event.get('agent_name') == 'evaluator'):
+                        output_data = event.get('output_data', {})
+                        
+                        # 从execution_metadata中获取轮次信息
+                        execution_metadata = event.get('execution_metadata', {})
+                        round_num = execution_metadata.get('round', 1)  # 默认第1轮
+                        
+                        # 按轮次存储评估数据
+                        if round_num not in evaluation_data_by_round:
+                            evaluation_data_by_round[round_num] = {}
+                        evaluation_data_by_round[round_num].update(output_data)
+                        
+                except (json.JSONDecodeError, KeyError):
+                    continue
+                    
+            # 计算专业指标惩罚分
+            professional_penalty = self.calculate_professional_penalty(evaluation_data_by_round)
+            
+            # 计算分诊惩罚分
+            triage_penalty = self.calculate_triage_penalty(jsonl_file, case_data)
+            
+            # 计算总惩罚分
+            total_penalty = professional_penalty + 5 * triage_penalty
+            
+            return QualityScore(
+                professional_penalty=professional_penalty,
+                triage_penalty=triage_penalty,
+                total_penalty=total_penalty,
+                is_complete=is_complete,
+                file_path=jsonl_file
+            )
+            
+        except Exception as e:
+            logger.error(f"计算质量分数时出错 {jsonl_file}: {e}")
+            return None
+    
     def check_workflow_completion(self, jsonl_file: str) -> bool:
         """
         检查工作流是否完整
@@ -95,8 +314,264 @@ class WorkflowFileCleaner:
             logger.error(f"检查文件时发生错误 {jsonl_file}: {e}")
             return False
     
-    def scan_and_clean_files(self) -> None:
+    def extract_case_index_from_filename(self, filename: str) -> Optional[int]:
-        """扫描目录中的所有JSONL文件并清理不完整的文件"""
+        """
+        从工作流文件名中提取case索引
+        
+        Args:
+            filename: 工作流文件名 (如: workflow_20250819_001717_case_0000.jsonl)
+            
+        Returns:
+            int: case索引号，如果无法提取则返回None
+        """
+        try:
+            # 匹配模式: workflow_*_case_*.jsonl
+            match = re.search(r'workflow_.*_case_(\d+)\.jsonl$', filename)
+            if match:
+                return int(match.group(1))
+            return None
+        except Exception as e:
+            logger.warning(f"无法从文件名提取case索引 {filename}: {e}")
+            return None
+    
+    def backup_dataset(self) -> bool:
+        """
+        备份dataset文件
+        
+        Returns:
+            bool: 备份成功返回True，失败返回False
+        """
+        try:
+            if not self.dataset_path.exists():
+                logger.warning(f"Dataset文件不存在: {self.dataset_path}")
+                return False
+                
+            # 生成带时间戳的备份文件名
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+            backup_filename = f"bbb_backup_{timestamp}.json"
+            backup_path = self.dataset_path.parent / backup_filename
+            
+            # 执行备份
+            shutil.copy2(self.dataset_path, backup_path)
+            self.stats['dataset_backup_path'] = str(backup_path)
+            logger.info(f"Dataset已备份到: {backup_path}")
+            return True
+            
+        except Exception as e:
+            logger.error(f"备份dataset失败: {e}")
+            return False
+    
+    def load_dataset(self) -> Optional[List[Dict[str, Any]]]:
+        """
+        加载dataset数据
+        
+        Returns:
+            List: dataset数据列表，失败返回None
+        """
+        try:
+            if not self.dataset_path.exists():
+                logger.error(f"Dataset文件不存在: {self.dataset_path}")
+                return None
+                
+            with open(self.dataset_path, 'r', encoding='utf-8') as f:
+                dataset = json.load(f)
+                
+            logger.info(f"成功加载dataset，包含{len(dataset)}个case")
+            return dataset
+            
+        except Exception as e:
+            logger.error(f"加载dataset失败: {e}")
+            return None
+    
+    def save_dataset(self, dataset: List[Dict[str, Any]]) -> bool:
+        """
+        保存更新后的dataset
+        
+        Args:
+            dataset: 更新后的dataset数据
+            
+        Returns:
+            bool: 保存成功返回True，失败返回False
+        """
+        try:
+            with open(self.dataset_path, 'w', encoding='utf-8') as f:
+                json.dump(dataset, f, ensure_ascii=False, indent=2)
+                
+            logger.info(f"成功保存更新后的dataset，包含{len(dataset)}个case")
+            return True
+            
+        except Exception as e:
+            logger.error(f"保存dataset失败: {e}")
+            return False
+    
+    def collect_case_info(self, jsonl_file: str, case_index: int, 
+                          dataset: List[Dict[str, Any]]) -> Dict[str, Any]:
+        """
+        收集被删除case的详细信息
+        
+        Args:
+            jsonl_file: 工作流文件路径
+            case_index: case索引号
+            dataset: dataset数据
+            
+        Returns:
+            Dict: case详细信息
+        """
+        case_info = {
+            'case_index': case_index,
+            'jsonl_file': jsonl_file,
+            'case_data': None,
+            'primary_department': '',
+            'secondary_department': ''
+        }
+        
+        try:
+            # 从dataset获取case数据
+            if 0 <= case_index < len(dataset):
+                case_info['case_data'] = dataset[case_index]
+                case_info['primary_department'] = dataset[case_index].get('一级科室', '')
+                case_info['secondary_department'] = dataset[case_index].get('二级科室', '')
+            else:
+                logger.warning(f"Case索引超出范围: {case_index}")
+                
+        except Exception as e:
+            logger.error(f"收集case信息时出错 {jsonl_file}: {e}")
+            
+        return case_info
+    
+    def sync_delete_dataset_cases(self, deleted_case_indices: Set[int]) -> bool:
+        """
+        同步删除dataset中的case数据
+        
+        Args:
+            deleted_case_indices: 要删除的case索引集合
+            
+        Returns:
+            bool: 删除成功返回True，失败返回False
+        """
+        try:
+            # 加载dataset
+            dataset = self.load_dataset()
+            if dataset is None:
+                return False
+                
+            # 备份dataset
+            if not self.backup_dataset():
+                logger.error("无法备份dataset，取消删除操作")
+                return False
+                
+            # 按索引降序排列，避免删除时索引偏移
+            sorted_indices = sorted(deleted_case_indices, reverse=True)
+            original_count = len(dataset)
+            
+            # 删除对应的case
+            for case_index in sorted_indices:
+                if 0 <= case_index < len(dataset):
+                    removed_case = dataset.pop(case_index)
+                    logger.info(f"从dataset删除case {case_index}: {removed_case.get('一级科室', '')}-{removed_case.get('二级科室', '')}")
+                else:
+                    logger.warning(f"无效的case索引: {case_index}")
+                    
+            # 保存更新后的dataset
+            if self.save_dataset(dataset):
+                logger.info(f"成功从dataset删除{original_count - len(dataset)}个case")
+                return True
+            else:
+                logger.error("保存更新后的dataset失败")
+                return False
+                
+        except Exception as e:
+            logger.error(f"同步删除dataset中的case时出错: {e}")
+            return False
+    
+    def validate_data_consistency(self) -> Dict[str, Any]:
+        """
+        验证工作流文件与dataset的数据一致性
+        
+        Returns:
+            Dict: 验证结果
+        """
+        validation_results = {
+            'total_jsonl_files': 0,
+            'valid_case_mappings': 0,
+            'invalid_case_mappings': [],
+            'missing_case_indices': [],
+            'dataset_size': 0,
+            'max_case_index': -1,
+            'consistency_rate': 0.0,
+            'validation_passed': False
+        }
+        
+        try:
+            # 加载dataset
+            dataset = self.load_dataset()
+            if dataset is None:
+                validation_results['error'] = "无法加载dataset"
+                return validation_results
+            
+            validation_results['dataset_size'] = len(dataset)
+            
+            # 查找所有JSONL文件
+            jsonl_pattern = str(self.directory / "**" / "*.jsonl")
+            jsonl_files = glob.glob(jsonl_pattern, recursive=True)
+            validation_results['total_jsonl_files'] = len(jsonl_files)
+            
+            # 验证每个文件的case索引
+            for jsonl_file in jsonl_files:
+                filename = os.path.basename(jsonl_file)
+                case_index = self.extract_case_index_from_filename(filename)
+                
+                if case_index is not None:
+                    validation_results['max_case_index'] = max(validation_results['max_case_index'], case_index)
+                    
+                    if 0 <= case_index < len(dataset):
+                        validation_results['valid_case_mappings'] += 1
+                    else:
+                        validation_results['invalid_case_mappings'].append({
+                            'file': jsonl_file,
+                            'case_index': case_index,
+                            'reason': '索引超出dataset范围'
+                        })
+                else:
+                    validation_results['invalid_case_mappings'].append({
+                        'file': jsonl_file,
+                        'case_index': None,
+                        'reason': '无法从文件名提取case索引'
+                    })
+            
+            # 检查缺失的case索引
+            if validation_results['max_case_index'] >= 0:
+                existing_indices = set()
+                for jsonl_file in jsonl_files:
+                    filename = os.path.basename(jsonl_file)
+                    case_index = self.extract_case_index_from_filename(filename)
+                    if case_index is not None:
+                        existing_indices.add(case_index)
+                
+                expected_indices = set(range(validation_results['max_case_index'] + 1))
+                missing_indices = expected_indices - existing_indices
+                validation_results['missing_case_indices'] = sorted(missing_indices)
+            
+            # 计算一致性率
+            if validation_results['total_jsonl_files'] > 0:
+                validation_results['consistency_rate'] = validation_results['valid_case_mappings'] / validation_results['total_jsonl_files']
+            
+            # 判断验证是否通过
+            validation_results['validation_passed'] = (
+                validation_results['consistency_rate'] >= 0.95 and
+                len(validation_results['missing_case_indices']) == 0
+            )
+            
+            logger.info(f"数据一致性验证完成: 一致性率 {validation_results['consistency_rate']:.2%}")
+            
+        except Exception as e:
+            logger.error(f"数据一致性验证时出错: {e}")
+            validation_results['error'] = str(e)
+        
+        return validation_results
+    
+    def analyze_and_clean_files(self) -> None:
+        """基于质量评估扫描并智能清理文件"""
         if not self.directory.exists():
             logger.error(f"目录不存在: {self.directory}")
             return
@@ -108,50 +583,292 @@ class WorkflowFileCleaner:
         self.stats['total_files'] = len(jsonl_files)
         logger.info(f"找到 {len(jsonl_files)} 个JSONL文件")
         
+        # 预加载dataset以供后续使用
+        dataset = self.load_dataset()
+        if dataset is None:
+            logger.warning("无法加载dataset，将跳过dataset同步删除")
+        
+        # 计算所有文件的质量分数
+        logger.info("正在计算质量分数...")
+        complete_files = []
+        incomplete_files = []
+        
         for jsonl_file in jsonl_files:
             try:
-                is_complete = self.check_workflow_completion(jsonl_file)
+                quality_score = self.calculate_quality_score(jsonl_file)
+                if quality_score is None:
+                    self.stats['error_files'].append(jsonl_file)
+                    continue
                     
-                if is_complete:
+                if quality_score.is_complete:
+                    complete_files.append(quality_score)
                     self.stats['complete_files'] += 1
                 else:
+                    incomplete_files.append(quality_score)
                     self.stats['incomplete_files'] += 1
                     
-                    if self.dry_run:
-                        logger.info(f"[试运行] 将删除不完整文件: {jsonl_file}")
-                        self.stats['deleted_files'].append(jsonl_file)
-                    else:
-                        os.remove(jsonl_file)
-                        logger.info(f"已删除不完整文件: {jsonl_file}")
-                        self.stats['deleted_files'].append(jsonl_file)
-                        
             except Exception as e:
                 logger.error(f"处理文件时发生错误 {jsonl_file}: {e}")
                 self.stats['error_files'].append(jsonl_file)
         
+        # 智能清理逻辑（增强版，包含dataset同步删除）
+        self._smart_cleanup_with_sync(complete_files, incomplete_files, dataset)
+    
+    def _smart_cleanup_with_sync(self, complete_files: List[QualityScore], 
+                                 incomplete_files: List[QualityScore], 
+                                 dataset: Optional[List[Dict[str, Any]]]) -> None:
+        """
+        执行智能清理逻辑，包含dataset同步删除功能
+        
+        Args:
+            complete_files: 完整文件的质量评分列表
+            incomplete_files: 不完整文件的质量评分列表
+            dataset: dataset数据，用于收集case信息和同步删除
+        """
+        deleted_case_indices = set()  # 收集所有要删除的case索引
+        
+        # 处理不完整文件：保留10%最优质的
+        if incomplete_files:
+            # 按总惩罚分排序（分数越低质量越好）
+            incomplete_files.sort(key=lambda x: x.total_penalty)
+            
+            keep_count = max(1, int(len(incomplete_files) * self.keep_incomplete_ratio))
+            keep_files = incomplete_files[:keep_count]
+            delete_files = incomplete_files[keep_count:]
+            
+            self.stats['kept_incomplete_files'] = [f.file_path for f in keep_files]
+            
+            # 记录质量分析
+            if incomplete_files:
+                penalties = [f.total_penalty for f in incomplete_files]
+                self.stats['quality_analysis']['incomplete'] = {
+                    'avg_penalty': sum(penalties) / len(penalties),
+                    'score_range': (min(penalties), max(penalties))
+                }
+            
+            logger.info(f"不完整文件: 总数 {len(incomplete_files)}, 保留 {len(keep_files)}, 删除 {len(delete_files)}")
+            
+            # 删除不完整文件并收集case信息
+            for quality_score in delete_files:
+                self._delete_file_with_case_tracking(quality_score, "低质量不完整文件", dataset, deleted_case_indices)
+                self.stats['deleted_incomplete_files'].append(quality_score.file_path)
+        
+        # 处理完整文件：删除20%质量最差的
+        if complete_files:
+            # 按总惩罚分排序（分数越高质量越差）
+            complete_files.sort(key=lambda x: x.total_penalty, reverse=True)
+            
+            delete_count = int(len(complete_files) * self.remove_complete_ratio)
+            delete_files = complete_files[:delete_count]
+            keep_files = complete_files[delete_count:]
+            
+            self.stats['kept_complete_files'] = [f.file_path for f in keep_files]
+            
+            # 记录质量分析
+            if complete_files:
+                penalties = [f.total_penalty for f in complete_files]
+                self.stats['quality_analysis']['complete'] = {
+                    'avg_penalty': sum(penalties) / len(penalties),
+                    'score_range': (min(penalties), max(penalties))
+                }
+            
+            logger.info(f"完整文件: 总数 {len(complete_files)}, 保留 {len(keep_files)}, 删除 {len(delete_files)}")
+            
+            # 删除低质量完整文件并收集case信息
+            for quality_score in delete_files:
+                self._delete_file_with_case_tracking(quality_score, "低质量完整文件", dataset, deleted_case_indices)
+                self.stats['deleted_complete_files'].append(quality_score.file_path)
+        
+        # 同步删除dataset中的对应case
+        if deleted_case_indices and dataset is not None:
+            logger.info(f"准备从dataset中删除 {len(deleted_case_indices)} 个case: {sorted(deleted_case_indices)}")
+            if self.sync_delete_dataset_cases(deleted_case_indices):
+                logger.info("Dataset同步删除完成")
+            else:
+                logger.error("Dataset同步删除失败")
+        elif deleted_case_indices:
+            logger.warning(f"检测到 {len(deleted_case_indices)} 个case需要删除，但dataset不可用")
+        
+        # 记录删除的case索引
+        self.stats['deleted_case_indices'] = sorted(deleted_case_indices)
+    
+    def _delete_file_with_case_tracking(self, quality_score: QualityScore, reason: str,
+                                       dataset: Optional[List[Dict[str, Any]]],
+                                       deleted_case_indices: Set[int]) -> None:
+        """
+        删除文件并跟踪相关的case信息
+        
+        Args:
+            quality_score: 质量评分对象
+            reason: 删除原因
+            dataset: dataset数据
+            deleted_case_indices: 用于收集被删除case索引的集合
+        """
+        file_path = quality_score.file_path
+        
+        # 从文件名提取case索引
+        filename = os.path.basename(file_path)
+        case_index = self.extract_case_index_from_filename(filename)
+        
+        if case_index is not None and dataset is not None:
+            # 收集case信息
+            case_info = self.collect_case_info(file_path, case_index, dataset)
+            self.stats['deleted_cases_info'].append(case_info)
+            deleted_case_indices.add(case_index)
+            
+            logger.info(f"准备删除{reason}: {file_path} (case_{case_index}: {case_info['primary_department']}-{case_info['secondary_department']})")
+        else:
+            logger.info(f"准备删除{reason}: {file_path} (无法提取case索引)")
+        
+        # 执行文件删除
+        if self.dry_run:
+            logger.info(f"[试运行] 将删除{reason}: {file_path}")
+        else:
+            try:
+                os.remove(file_path)
+                logger.info(f"已删除{reason}: {file_path}")
+            except Exception as e:
+                logger.error(f"删除文件失败 {file_path}: {e}")
+                self.stats['error_files'].append(file_path)
+    
+    def _delete_file(self, file_path: str, reason: str) -> None:
+        """
+        删除文件（兼容性方法）
+        
+        Args:
+            file_path: 文件路径
+            reason: 删除原因
+        """
+        if self.dry_run:
+            logger.info(f"[试运行] 将删除{reason}: {file_path}")
+        else:
+            try:
+                os.remove(file_path)
+                logger.info(f"已删除{reason}: {file_path}")
+            except Exception as e:
+                logger.error(f"删除文件失败 {file_path}: {e}")
+                self.stats['error_files'].append(file_path)
+    
     def print_summary(self) -> None:
-        """打印统计摘要"""
+        """打印详细的统计摘要"""
-        print("\n" + "="*60)
+        print("\n" + "="*80)
-        print("工作流文件清理摘要")
+        print("🧠 智能工作流文件清理摘要")
-        print("="*60)
+        print("="*80)
-        print(f"总文件数: {self.stats['total_files']}")
-        print(f"完整文件数: {self.stats['complete_files']}")
-        print(f"不完整文件数: {self.stats['incomplete_files']}")
-        print(f"删除文件数: {len(self.stats['deleted_files'])}")
-        print(f"错误文件数: {len(self.stats['error_files'])}")
         
-        if self.stats['deleted_files']:
+        # 基本统计
-            print("\n已删除的文件:")
+        print(f"📊 基本统计:")
-            for file in self.stats['deleted_files']:
+        print(f"   总文件数: {self.stats['total_files']}")
-                print(f"  - {file}")
+        print(f"   完整文件数: {self.stats['complete_files']}")
+        print(f"   不完整文件数: {self.stats['incomplete_files']}")
+        print(f"   错误文件数: {len(self.stats['error_files'])}")
         
+        # 清理策略统计
+        print(f"\n🎯 清理策略统计:")
+        print(f"   不完整文件保留比例: {self.keep_incomplete_ratio*100:.1f}%")
+        print(f"   完整文件删除比例: {self.remove_complete_ratio*100:.1f}%")
+        
+        # 不完整文件处理结果
+        if self.stats['incomplete_files'] > 0:
+            kept_incomplete = len(self.stats['kept_incomplete_files'])
+            deleted_incomplete = len(self.stats['deleted_incomplete_files'])
+            print(f"\n📋 不完整文件处理:")
+            print(f"   保留数量: {kept_incomplete} ({kept_incomplete/self.stats['incomplete_files']*100:.1f}%)")
+            print(f"   删除数量: {deleted_incomplete} ({deleted_incomplete/self.stats['incomplete_files']*100:.1f}%)")
+            
+            qa = self.stats['quality_analysis']['incomplete']
+            if qa['avg_penalty'] > 0:
+                print(f"   平均惩罚分: {qa['avg_penalty']:.2f}")
+                print(f"   分数范围: {qa['score_range'][0]:.2f} - {qa['score_range'][1]:.2f}")
+        
+        # 完整文件处理结果
+        if self.stats['complete_files'] > 0:
+            kept_complete = len(self.stats['kept_complete_files'])
+            deleted_complete = len(self.stats['deleted_complete_files'])
+            print(f"\n✅ 完整文件处理:")
+            print(f"   保留数量: {kept_complete} ({kept_complete/self.stats['complete_files']*100:.1f}%)")
+            print(f"   删除数量: {deleted_complete} ({deleted_complete/self.stats['complete_files']*100:.1f}%)")
+            
+            qa = self.stats['quality_analysis']['complete']
+            if qa['avg_penalty'] > 0:
+                print(f"   平均惩罚分: {qa['avg_penalty']:.2f}")
+                print(f"   分数范围: {qa['score_range'][0]:.2f} - {qa['score_range'][1]:.2f}")
+        
+        # 总删除统计
+        total_deleted = len(self.stats['deleted_incomplete_files']) + len(self.stats['deleted_complete_files'])
+        if total_deleted > 0:
+            print(f"\n🗑️  总删除统计:")
+            print(f"   删除的不完整文件: {len(self.stats['deleted_incomplete_files'])}")
+            print(f"   删除的完整文件: {len(self.stats['deleted_complete_files'])}")
+            print(f"   总删除数量: {total_deleted}")
+        
+        # 删除的case信息统计
+        if self.stats['deleted_case_indices']:
+            print(f"\n📋 删除的Case统计:")
+            print(f"   删除的case数量: {len(self.stats['deleted_case_indices'])}")
+            print(f"   删除的case索引: {self.stats['deleted_case_indices'][:10]}{'...' if len(self.stats['deleted_case_indices']) > 10 else ''}")
+            
+            # 按科室统计删除的case
+            if self.stats['deleted_cases_info']:
+                dept_stats = {}
+                for case_info in self.stats['deleted_cases_info']:
+                    dept_key = f"{case_info['primary_department']}-{case_info['secondary_department']}"
+                    dept_stats[dept_key] = dept_stats.get(dept_key, 0) + 1
+                
+                print(f"\n   按科室统计删除的case:")
+                for dept, count in sorted(dept_stats.items(), key=lambda x: x[1], reverse=True)[:10]:
+                    print(f"     {dept}: {count}个")
+                if len(dept_stats) > 10:
+                    print(f"     ... 以及其他 {len(dept_stats) - 10} 个科室")
+        
+        # Dataset备份信息
+        if self.stats['dataset_backup_path']:
+            print(f"\n💾 Dataset备份:")
+            print(f"   备份文件: {self.stats['dataset_backup_path']}")
+        
+        # 错误文件
         if self.stats['error_files']:
-            print("\n处理错误的文件:")
+            print(f"\n⚠️  处理错误的文件 ({len(self.stats['error_files'])})个:")
-            for file in self.stats['error_files']:
+            for file in self.stats['error_files'][:5]:  # 只显示前5个
-                print(f"  - {file}")
+                print(f"   - {file}")
+            if len(self.stats['error_files']) > 5:
+                print(f"   ... 以及其他 {len(self.stats['error_files'])-5} 个文件")
         
-        if self.dry_run and self.stats['deleted_files']:
+        # 数据一致性验证结果
-            print(f"\n注意: 这是试运行模式，实际上没有删除任何文件")
+        if 'validation_results' in self.stats:
+            validation = self.stats['validation_results']
+            print(f"\n🔍 数据一致性验证:")
+            print(f"   Dataset大小: {validation.get('dataset_size', 0)}")
+            print(f"   JSONL文件数: {validation.get('total_jsonl_files', 0)}")
+            print(f"   有效映射数: {validation.get('valid_case_mappings', 0)}")
+            print(f"   一致性率: {validation.get('consistency_rate', 0):.2%}")
+            print(f"   验证状态: {'✅ 通过' if validation.get('validation_passed', False) else '❌ 未通过'}")
+            
+            if validation.get('missing_case_indices'):
+                missing_count = len(validation['missing_case_indices'])
+                print(f"   缺失索引: {missing_count}个 {validation['missing_case_indices'][:5]}{'...' if missing_count > 5 else ''}")
+            
+            if validation.get('invalid_case_mappings'):
+                invalid_count = len(validation['invalid_case_mappings'])
+                print(f"   无效映射: {invalid_count}个")
+        
+        if self.dry_run:
+            print(f"\n💡 注意: 这是试运行模式，实际上没有删除任何文件")
+        
+        # 质量分析建议
+        print(f"\n🔍 质量分析建议:")
+        incomplete_avg = self.stats['quality_analysis']['incomplete']['avg_penalty']
+        complete_avg = self.stats['quality_analysis']['complete']['avg_penalty']
+        
+        if incomplete_avg > complete_avg:
+            print(f"   - 不完整文件的平均质量较低，建议优化工作流执行")
+        else:
+            print(f"   - 完整文件中仍有质量问题，建议加强质量控制")
+        
+        if incomplete_avg > 3.0:
+            print(f"   - 不完整文件质量分数偏高，建议检查中断原因")
+        
+        if complete_avg > 2.0:
+            print(f"   - 完整文件质量有待提升，建议优化评估标准")
     
     def run(self) -> Dict[str, Any]:
         """
@@ -160,11 +877,26 @@ class WorkflowFileCleaner:
         Returns:
             Dict: 包含统计信息的字典
         """
-        logger.info(f"开始检查目录: {self.directory}")
+        logger.info(f"🚀 开始智能分析目录: {self.directory}")
+        logger.info(f"📋 清理策略: 保留{self.keep_incomplete_ratio*100:.0f}%最优不完整文件，删除{self.remove_complete_ratio*100:.0f}%最差完整文件")
         if self.dry_run:
-            logger.info("运行在试运行模式")
+            logger.info("🧪 运行在试运行模式")
         
-        self.scan_and_clean_files()
+        # 执行数据一致性验证
+        logger.info("🔍 执行数据一致性验证...")
+        validation_results = self.validate_data_consistency()
+        self.stats['validation_results'] = validation_results
+        
+        if not validation_results.get('validation_passed', False):
+            logger.warning(f"⚠️  数据一致性验证未通过: 一致性率 {validation_results.get('consistency_rate', 0):.2%}")
+            if validation_results.get('missing_case_indices'):
+                logger.warning(f"   缺失的case索引: {validation_results['missing_case_indices'][:10]}{'...' if len(validation_results['missing_case_indices']) > 10 else ''}")
+            if validation_results.get('invalid_case_mappings'):
+                logger.warning(f"   无效的case映射: {len(validation_results['invalid_case_mappings'])} 个")
+        else:
+            logger.info("✅ 数据一致性验证通过")
+        
+        self.analyze_and_clean_files()
         self.print_summary()
         
         return self.stats
@@ -172,15 +904,33 @@ class WorkflowFileCleaner:
 
 def main():
     """主函数"""
-    parser = argparse.ArgumentParser(description='工作流文件清理器')
+    parser = argparse.ArgumentParser(description='基于质量评估的智能工作流文件清理器')
-    parser.add_argument('directory', nargs='?', default='results/results0903', 
+    parser.add_argument('directory', nargs='?', default='results/results0905-2', 
-                       help='要检查的目录路径 (默认: results)')
+                       help='要检查的目录路径 (默认: results/results0903)')
     parser.add_argument('--dry-run', action='store_true', 
                        help='试运行模式，不实际删除文件')
+    parser.add_argument('--keep-incomplete', type=float, default=0.1, 
+                       help='不完整文件保留比例 (默认: 0.1, 即10%%)')
+    parser.add_argument('--remove-complete', type=float, default=0.2, 
+                       help='完整文件删除比例 (默认: 0.2, 即20%%)')
     
     args = parser.parse_args()
     
-    cleaner = WorkflowFileCleaner(args.directory, args.dry_run)
+    # 参数验证
+    if not (0.0 <= args.keep_incomplete <= 1.0):
+        logger.error("--keep-incomplete 参数必须在 0.0 到 1.0 之间")
+        return
+        
+    if not (0.0 <= args.remove_complete <= 1.0):
+        logger.error("--remove-complete 参数必须在 0.0 到 1.0 之间")
+        return
+    
+    cleaner = IntelligentWorkflowCleaner(
+        directory=args.directory, 
+        dry_run=args.dry_run,
+        keep_incomplete_ratio=args.keep_incomplete,
+        remove_complete_ratio=args.remove_complete
+    )
     cleaner.run()
 
 

config.py

@@ -32,6 +32,14 @@ LLM_CONFIG = {
             "api_key": "gpustack_d402860477878812_9ec494a501497d25b565987754f4db8c"  # Ollama不需要真实API密钥，任意字符串即可
         }
     },
+    "Gemma3-4b": {
+        "class": "OpenAILike",
+        "params": {
+            "id": "gemma-3-4b-it",
+            "base_url": "http://100.82.33.121:19090/v1",  # Ollama OpenAI兼容端点
+            "api_key": "gpustack_d402860477878812_9ec494a501497d25b565987754f4db8c"  # Ollama不需要真实API密钥，任意字符串即可
+        }
+    },
     "deepseek-v3": {
         "class": "OpenAILike",
         "params": {

main.py

@@ -101,7 +101,7 @@ def parse_arguments() -> argparse.Namespace:
     parser.add_argument(
         '--log-dir', 
         type=str, 
-        default='results/results0905-2',
+        default='results/results0905-2-qwen3',
         help='日志文件保存目录'
     )
     parser.add_argument(
@@ -149,7 +149,7 @@ def parse_arguments() -> argparse.Namespace:
         '--model-type', 
         type=str, 
         choices=available_models,
-        default='gpt-oss:latest',
+        default='Qwen3-7B',
         help=f'使用的语言模型类型，可选: {", ".join(available_models)}'
     )
     parser.add_argument(