增强数据分析工具和工作流检查功能

- 优化数据对比分析工具的准确性和性能
- 完善评估指标分析的算法和统计功能
- 改进医疗工作流分析的深度和覆盖范围
- 增强工作流完整性检查的全面性
- 新增工作流文件清理工具提升维护效率

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

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

analysis/data_comparison.py

@@ -45,7 +45,7 @@ COLORS = {
 QUALITY_DIMENSIONS = [
     'clinical_inquiry',
     'communication_quality',
-    'multi_round_consistency', 
+    'information_completeness', 
     'overall_professionalism'
 ]
 
@@ -64,7 +64,7 @@ DIMENSION_NAMES = {
     'clinical_inquiry': 'CI',
     'diagnostic_reasoning': 'DR',
     'communication_quality': 'CQ', 
-    'multi_round_consistency': 'MRC',
+    'information_completeness': 'IC',
     'overall_professionalism': 'OP',
     'present_illness_similarity': 'PHI Similarity',
     'past_history_similarity': 'HP Similarity',
@@ -134,8 +134,13 @@ class DataQualityComparisonAnalyzer:
                 
                 # 处理评估分数
                 for dimension in EVALUATION_DIMENSIONS:
-                    if dimension in evaluation_scores:
-                        score_info = evaluation_scores[dimension]
+                    # 向后兼容性处理：将旧的 multi_round_consistency 映射到新的 information_completeness
+                    actual_dimension = dimension
+                    if dimension == 'information_completeness' and dimension not in evaluation_scores and 'multi_round_consistency' in evaluation_scores:
+                        actual_dimension = 'multi_round_consistency'
+                    
+                    if actual_dimension in evaluation_scores:
+                        score_info = evaluation_scores[actual_dimension]
                         if isinstance(score_info, dict) and 'score' in score_info:
                             score = score_info['score']
                         elif isinstance(score_info, (int, float)):
@@ -536,7 +541,7 @@ def main():
         has_significant = False
         
         # 定义需要显示的维度顺序（四个质量指标 + 三个相似度指标）
-        target_dimensions = ['clinical_inquiry', 'multi_round_consistency', 'present_illness_similarity', 'past_history_similarity', 'chief_complaint_similarity']
+        target_dimensions = ['clinical_inquiry', 'information_completeness', 'present_illness_similarity', 'past_history_similarity', 'chief_complaint_similarity']
         
         for dimension in target_dimensions:
             if dimension in statistics['quality_statistics']['statistical_tests']:

analysis/evaluate_metrics_analysis.py

@@ -81,7 +81,7 @@ def extract_evaluate_scores(workflow: List[Dict]) -> List[Dict]:
             # 检查是否包含评估分数
             if any(key in output_data for key in [
                 'clinical_inquiry', 'communication_quality',
-                'multi_round_consistency', 'overall_professionalism',
+                'information_completeness', 'overall_professionalism',
                 'present_illness_similarity', 'past_history_similarity',
                 'chief_complaint_similarity'
             ]):
@@ -110,7 +110,7 @@ def calculate_metrics_by_step(workflow_data: List[List[Dict]]) -> Dict[str, List
     metrics_data = {
         'clinical_inquiry': [[] for _ in range(max_steps)],
         'communication_quality': [[] for _ in range(max_steps)],
-        'multi_round_consistency': [[] for _ in range(max_steps)],
+        'information_completeness': [[] for _ in range(max_steps)],
         'overall_professionalism': [[] for _ in range(max_steps)],
         'present_illness_similarity': [[] for _ in range(max_steps)],
         'past_history_similarity': [[] for _ in range(max_steps)],
@@ -124,8 +124,13 @@ def calculate_metrics_by_step(workflow_data: List[List[Dict]]) -> Dict[str, List
         for step_idx, score_data in enumerate(evaluate_scores):
             # 提取各维度分数
             for metric in metrics_data.keys():
-                if metric in score_data and isinstance(score_data[metric], dict):
-                    score = score_data[metric].get('score', 0.0)
+                # 向后兼容性处理：将旧的 multi_round_consistency 映射到新的 information_completeness
+                actual_metric = metric
+                if metric == 'information_completeness' and metric not in score_data and 'multi_round_consistency' in score_data:
+                    actual_metric = 'multi_round_consistency'
+                
+                if actual_metric in score_data and isinstance(score_data[actual_metric], dict):
+                    score = score_data[actual_metric].get('score', 0.0)
                     metrics_data[metric][step_idx].append(score)
     
     # 计算平均值

analysis/medical_workflow_analysis.py

@@ -10,7 +10,7 @@ import os
 from collections import defaultdict
 import matplotlib.pyplot as plt
 from typing import Dict, List
-from file_filter_utils import filter_complete_files, print_filter_summary
+from file_filter_utils import load_incomplete_files
 
 
 class MedicalWorkflowAnalyzer:
@@ -30,23 +30,20 @@ class MedicalWorkflowAnalyzer:
         self.step_statistics = defaultdict(int)
         
     def load_workflow_data(self) -> None:
-        """加载所有工作流数据文件"""
+        """加载所有工作流数据文件（包括完成和未完成的）"""
         if not os.path.exists(self.results_dir):
             print(f"结果目录不存在: {self.results_dir}")
             return
             
         # 获取所有jsonl文件
-        all_files = [os.path.join(self.results_dir, f) for f in os.listdir(self.results_dir) 
-                    if f.endswith('.jsonl')]
+        all_files = [f for f in os.listdir(self.results_dir) if f.endswith('.jsonl')]
         
-        # 过滤出完成的文件
-        filtered_files = filter_complete_files(all_files, self.output_dir)
-        print_filter_summary(self.output_dir)
+        # 获取未完成文件列表
+        incomplete_files = load_incomplete_files(self.output_dir)
         
-        print(f"找到 {len(all_files)} 个数据文件，将处理 {len(filtered_files)} 个完成的文件")
+        print(f"找到 {len(all_files)} 个数据文件，将处理所有文件（包括未完成的）")
         
-        for filepath in sorted(filtered_files):
-            filename = os.path.basename(filepath)
+        for filename in sorted(all_files):
             filepath = os.path.join(self.results_dir, filename)
             try:
                 with open(filepath, 'r', encoding='utf-8') as f:
@@ -62,19 +59,28 @@ class MedicalWorkflowAnalyzer:
                                 continue
                     
                     if case_data:
+                        # 检查是否为未完成的文件
+                        is_incomplete = filename in incomplete_files
                         self.workflow_data.append({
                             'filename': filename,
-                            'data': case_data
+                            'data': case_data,
+                            'is_incomplete': is_incomplete
                         })
                         
             except Exception as e:
                 print(f"读取文件 {filename} 失败: {e}")
-                
+        
+        complete_count = len([case for case in self.workflow_data if not case.get('is_incomplete', False)])
+        incomplete_count = len([case for case in self.workflow_data if case.get('is_incomplete', False)])
+        
         print(f"成功加载 {len(self.workflow_data)} 个病例的数据")
+        print(f"  - 完成的病例: {complete_count} 个")
+        print(f"  - 未完成的病例: {incomplete_count} 个")
         
     def analyze_workflow_steps(self) -> Dict[str, List[int]]:
         """
         分析每个病例完成triage、hpi、ph三个阶段所需的step数量
+        包括未完成的样本（用-1表示未完成状态）
         
         Returns:
             Dict包含每个阶段所需的step数量列表
@@ -90,6 +96,7 @@ class MedicalWorkflowAnalyzer:
         
         for case_info in self.workflow_data:
             case_data = case_info['data']
+            is_incomplete = case_info.get('is_incomplete', False)
             
             # 按阶段分组step
             triage_steps = set()
@@ -97,6 +104,19 @@ class MedicalWorkflowAnalyzer:
             ph_steps = set()
             all_steps = set()
             
+            # 如果是未完成的样本，检查任务完成状态
+            incomplete_phases = set()
+            if is_incomplete:
+                # 查找倒数第二行的task_completion_summary
+                for entry in reversed(case_data):
+                    if 'task_completion_summary' in entry:
+                        phases = entry.get('task_completion_summary', {}).get('phases', {})
+                        for phase_name in ['triage', 'hpi', 'ph']:
+                            phase_info = phases.get(phase_name, {})
+                            if not phase_info.get('is_completed', False):
+                                incomplete_phases.add(phase_name)
+                        break
+            
             for entry in case_data:
                 if entry.get('event_type') == 'step_start' and 'current_phase' in entry:
                     step_num = entry.get('step_number', 0)
@@ -111,18 +131,18 @@ class MedicalWorkflowAnalyzer:
                     elif phase == 'ph':
                         ph_steps.add(step_num)
             
-            # 计算每个阶段的step数量
-            triage_count = len(triage_steps)
-            hpi_count = len(hpi_steps)
-            ph_count = len(ph_steps)
+            # 计算每个阶段的step数量，对于未完成的阶段使用-1
+            triage_count = -1 if 'triage' in incomplete_phases else len(triage_steps)
+            hpi_count = -1 if 'hpi' in incomplete_phases else len(hpi_steps)
+            ph_count = -1 if 'ph' in incomplete_phases else len(ph_steps)
             final_step = max(all_steps) if all_steps else 0
             
-            # 只添加有数据的阶段
-            if triage_count > 0:
+            # 添加数据（包括-1表示的未完成状态）
+            if triage_count != 0:  # 包括-1和正数
                 stage_steps['triage'].append(triage_count)
-            if hpi_count > 0:
+            if hpi_count != 0:  # 包括-1和正数
                 stage_steps['hpi'].append(hpi_count)
-            if ph_count > 0:
+            if ph_count != 0:  # 包括-1和正数
                 stage_steps['ph'].append(ph_count)
             if final_step > 0:
                 stage_steps['final_step'].append(final_step)
@@ -156,7 +176,7 @@ class MedicalWorkflowAnalyzer:
     def plot_step_distribution_subplots(self, stage_stats: Dict[str, Dict[int, int]], 
                                       output_file: str = "step_distribution_subplots.png") -> None:
         """
-        绘制四个子图的step数量分布柱形图
+        绘制四个子图的step数量分布柱形图（包括未完成的数据）
         
         Args:
             stage_stats: 各阶段的step数量统计数据
@@ -166,9 +186,10 @@ class MedicalWorkflowAnalyzer:
             print("没有数据可供绘制")
             return
             
-        # 设置英文显示
-        plt.rcParams['font.family'] = 'DejaVu Sans'
-        plt.rcParams['axes.unicode_minus'] = False
+        # 设置字体支持中文
+        import matplotlib
+        matplotlib.rcParams['font.sans-serif'] = ['SimHei', 'Arial Unicode MS', 'WenQuanYi Micro Hei', 'sans-serif']
+        matplotlib.rcParams['axes.unicode_minus'] = False
         
         # 创建四个子图
         fig, axes = plt.subplots(2, 2, figsize=(16, 12))
@@ -190,39 +211,70 @@ class MedicalWorkflowAnalyzer:
             ax = axes[row, col]
             
             if stage in stage_stats and stage_stats[stage]:
-                steps = sorted(stage_stats[stage].keys())
-                counts = [stage_stats[stage][step] for step in steps]
+                # 分离完成和未完成的数据
+                completed_data = {k: v for k, v in stage_stats[stage].items() if k != -1}
+                incomplete_count = stage_stats[stage].get(-1, 0)
                 
-                # 绘制柱形图
-                bars = ax.bar(steps, counts, color=['#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4'][stages_order.index(stage) % 4], 
-                             alpha=0.7, edgecolor='black', linewidth=0.5)
+                # 准备x轴数据和标签
+                if completed_data:
+                    steps = sorted(completed_data.keys())
+                    counts = [completed_data[step] for step in steps]
+                    x_labels = [str(step) for step in steps]
+                else:
+                    steps = []
+                    counts = []
+                    x_labels = []
                 
-                # 在柱形上标注数值
-                for bar, count in zip(bars, counts):
-                    height = bar.get_height()
-                    ax.text(bar.get_x() + bar.get_width()/2., height + max(counts)*0.01,
-                           f'{count}', ha='center', va='bottom', fontsize=9, fontweight='bold')
+                # 如果有未完成数据，添加到最后
+                if incomplete_count > 0:
+                    steps.append(len(steps))  # 位置索引
+                    counts.append(incomplete_count)
+                    x_labels.append('未完成')
                 
-                # 设置子图属性
-                ax.set_title(f'{subplot_titles[stage]}\n(n={sum(counts)})', fontsize=12, fontweight='bold')
-                ax.set_xlabel('Number of Steps', fontsize=10)
-                ax.set_ylabel('Number of Cases', fontsize=10)
-                ax.grid(True, alpha=0.3, linestyle='--')
-                
-                # 设置x轴刻度
-                if steps:
-                    ax.set_xticks(steps)
-                    ax.set_xticklabels(steps, rotation=45)
-                
-                # 添加统计信息文本
-                if counts:
-                    mean_val = sum(s*c for s, c in zip(steps, counts)) / sum(counts)
-                    max_val = max(steps)
-                    min_val = min(steps)
+                if steps and counts:
+                    # 绘制柱形图
+                    bars = ax.bar(range(len(steps)), counts, 
+                                 color=['#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4'][stages_order.index(stage) % 4], 
+                                 alpha=0.7, edgecolor='black', linewidth=0.5)
                     
-                    stats_text = f'Mean: {mean_val:.1f}\nRange: {min_val}-{max_val}'
-                    ax.text(0.02, 0.98, stats_text, transform=ax.transAxes, fontsize=9, 
-                           verticalalignment='top', bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5))
+                    # 在柱形上标注数值
+                    for i, (bar, count) in enumerate(zip(bars, counts)):
+                        height = bar.get_height()
+                        ax.text(bar.get_x() + bar.get_width()/2., height + max(counts)*0.01,
+                               f'{count}', ha='center', va='bottom', fontsize=9, fontweight='bold')
+                    
+                    # 设置子图属性
+                    ax.set_title(f'{subplot_titles[stage]}\n(n={sum(counts)})', fontsize=12, fontweight='bold')
+                    ax.set_xlabel('Number of Steps', fontsize=10)
+                    ax.set_ylabel('Number of Cases', fontsize=10)
+                    ax.grid(True, alpha=0.3, linestyle='--')
+                    
+                    # 设置x轴刻度和标签
+                    ax.set_xticks(range(len(steps)))
+                    ax.set_xticklabels(x_labels, rotation=45)
+                    
+                    # 添加统计信息文本（只针对完成的数据）
+                    if completed_data:
+                        completed_steps = list(completed_data.keys())
+                        completed_counts = list(completed_data.values())
+                        mean_val = sum(s*c for s, c in zip(completed_steps, completed_counts)) / sum(completed_counts)
+                        max_val = max(completed_steps)
+                        min_val = min(completed_steps)
+                        
+                        stats_text = f'Completed Mean: {mean_val:.1f}\nCompleted Range: {min_val}-{max_val}'
+                        if incomplete_count > 0:
+                            stats_text += f'\nIncomplete: {incomplete_count}'
+                        
+                        ax.text(0.02, 0.98, stats_text, transform=ax.transAxes, fontsize=9, 
+                               verticalalignment='top', bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5))
+                    elif incomplete_count > 0:
+                        stats_text = f'All Incomplete: {incomplete_count}'
+                        ax.text(0.02, 0.98, stats_text, transform=ax.transAxes, fontsize=9, 
+                               verticalalignment='top', bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5))
+                else:
+                    ax.text(0.5, 0.5, 'No Data Available', ha='center', va='center', 
+                           transform=ax.transAxes, fontsize=12)
+                    ax.set_title(f'{subplot_titles[stage]}\n(n=0)', fontsize=12, fontweight='bold')
             else:
                 ax.text(0.5, 0.5, 'No Data Available', ha='center', va='center', 
                        transform=ax.transAxes, fontsize=12)
@@ -242,7 +294,7 @@ class MedicalWorkflowAnalyzer:
         print(f"Four-subplot chart saved to: {output_path}")
         
     def print_statistics_summary(self, stage_steps: Dict[str, List[int]]) -> None:
-        """打印统计摘要"""
+        """打印统计摘要（包括未完成数据）"""
         print("\n=== Medical Workflow Step Statistics Summary ===")
         
         # 英文阶段名称映射
@@ -256,12 +308,25 @@ class MedicalWorkflowAnalyzer:
         for stage, steps in stage_steps.items():
             stage_name = stage_names.get(stage, stage.upper())
             if steps:
+                # 分离完成和未完成的数据
+                completed_steps = [s for s in steps if s != -1]
+                incomplete_count = steps.count(-1)
+                
                 print(f"\n{stage_name}:")
                 print(f"  Total Cases: {len(steps)}")
-                print(f"  Mean Steps: {sum(steps)/len(steps):.2f}")
-                print(f"  Min Steps: {min(steps)}")
-                print(f"  Max Steps: {max(steps)}")
-                print(f"  Step Distribution: {dict(sorted({s: steps.count(s) for s in set(steps)}.items()))}")
+                
+                if completed_steps:
+                    print(f"  Mean Steps: {sum(completed_steps)/len(completed_steps):.2f}")
+                    print(f"  Min Steps: {min(completed_steps)}")
+                    print(f"  Max Steps: {max(completed_steps)}")
+                    
+                    # 构建分布字典
+                    distribution = dict(sorted({s: completed_steps.count(s) for s in set(completed_steps)}.items()))
+                    if incomplete_count > 0:
+                        distribution['未完成'] = incomplete_count
+                    print(f"  Step Distribution: {distribution}")
+                else:
+                    print(f"  All cases incomplete: {incomplete_count}")
             else:
                 print(f"\n{stage_name}: No Data")
                 

analysis/workflow_completeness_checker.py

@@ -41,19 +41,32 @@ class WorkflowCompletenessChecker:
         """
         try:
             with open(filepath, 'r', encoding='utf-8') as f:
-                # 读取最后一行
                 lines = f.readlines()
-                if not lines:
+                if len(lines) < 2:  # 需要至少两行：倒数第二行和最后一行
                     return False
                 
-                last_line = lines[-1].strip()
-                if not last_line:
+                # 检查倒数第二行的task_completion_summary
+                second_to_last_line = lines[-2].strip()
+                if not second_to_last_line:
                     return False
                 
-                # 解析最后一行JSON
                 try:
-                    last_event = json.loads(last_line)
-                    return last_event.get('event_type') == 'workflow_complete'
+                    second_to_last_event = json.loads(second_to_last_line)
+                    # 检查是否有task_completion_summary字段
+                    task_summary = second_to_last_event.get('task_completion_summary', {})
+                    if not task_summary:
+                        return False
+                    
+                    # 检查三个阶段的完成状态
+                    phases = task_summary.get('phases', {})
+                    required_phases = ['triage', 'hpi', 'ph']
+                    
+                    for phase in required_phases:
+                        phase_info = phases.get(phase, {})
+                        if not phase_info.get('is_completed', False):
+                            return False
+                    
+                    return True
                 except json.JSONDecodeError:
                     return False
                     

analysis/workflow_file_cleaner.py

@@ -0,0 +1,188 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+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
+"""
+
+import json
+import os
+import glob
+from pathlib import Path
+from typing import Dict, Any, List
+import argparse
+import logging
+
+# 配置日志
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+
+class WorkflowFileCleaner:
+    """工作流文件清理器"""
+    
+    def __init__(self, directory: str, dry_run: bool = False):
+        """
+        初始化清理器
+        
+        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）
+        """
+        self.directory = Path(directory)
+        self.dry_run = dry_run
+        self.stats = {
+            'total_files': 0,
+            'complete_files': 0,
+            'incomplete_files': 0,
+            'deleted_files': [],
+            'error_files': []
+        }
+    
+    def check_workflow_completion(self, jsonl_file: str) -> bool:
+        """
+        检查工作流是否完整
+        
+        Args:
+            jsonl_file: JSONL文件路径
+            
+        Returns:
+            bool: True表示工作流完整，False表示不完整
+        """
+        try:
+            with open(jsonl_file, 'r', encoding='utf-8') as f:
+                lines = f.readlines()
+            
+            if not lines:
+                logger.warning(f"文件为空: {jsonl_file}")
+                return False
+            
+            # 获取最后一行
+            last_line = lines[-1].strip()
+            if not last_line:
+                logger.warning(f"文件最后一行为空: {jsonl_file}")
+                return False
+            
+            try:
+                last_event = json.loads(last_line)
+            except json.JSONDecodeError as e:
+                logger.error(f"解析最后一行JSON失败 {jsonl_file}: {e}")
+                return False
+            
+            # 检查是否包含workflow_complete事件
+            if last_event.get('event_type') != 'workflow_complete':
+                logger.info(f"工作流未完成 - 缺少workflow_complete事件: {jsonl_file}")
+                return False
+            
+            # 检查final_summary中的phases完成状态
+            final_summary = last_event.get('final_summary', {})
+            phases = final_summary.get('phases', {})
+            
+            required_phases = ['triage', 'hpi', 'ph']
+            for phase in required_phases:
+                phase_info = phases.get(phase, {})
+                is_completed = phase_info.get('is_completed', False)
+                completion_rate = phase_info.get('completion_rate', 0.0)
+                
+                if not is_completed or completion_rate != 1.0:
+                    logger.info(f"工作流未完成 - 阶段 {phase} 未完成: {jsonl_file}")
+                    return False
+            
+            logger.info(f"工作流完整: {jsonl_file}")
+            return True
+            
+        except Exception as e:
+            logger.error(f"检查文件时发生错误 {jsonl_file}: {e}")
+            return False
+    
+    def scan_and_clean_files(self) -> None:
+        """扫描目录中的所有JSONL文件并清理不完整的文件"""
+        if not self.directory.exists():
+            logger.error(f"目录不存在: {self.directory}")
+            return
+        
+        # 查找所有JSONL文件
+        jsonl_pattern = str(self.directory / "**" / "*.jsonl")
+        jsonl_files = glob.glob(jsonl_pattern, recursive=True)
+        
+        self.stats['total_files'] = len(jsonl_files)
+        logger.info(f"找到 {len(jsonl_files)} 个JSONL文件")
+        
+        for jsonl_file in jsonl_files:
+            try:
+                is_complete = self.check_workflow_completion(jsonl_file)
+                
+                if is_complete:
+                    self.stats['complete_files'] += 1
+                else:
+                    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)
+    
+    def print_summary(self) -> None:
+        """打印统计摘要"""
+        print("\n" + "="*60)
+        print("工作流文件清理摘要")
+        print("="*60)
+        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']:
+            print("\n已删除的文件:")
+            for file in self.stats['deleted_files']:
+                print(f"  - {file}")
+        
+        if self.stats['error_files']:
+            print("\n处理错误的文件:")
+            for file in self.stats['error_files']:
+                print(f"  - {file}")
+        
+        if self.dry_run and self.stats['deleted_files']:
+            print(f"\n注意: 这是试运行模式，实际上没有删除任何文件")
+    
+    def run(self) -> Dict[str, Any]:
+        """
+        运行清理器
+        
+        Returns:
+            Dict: 包含统计信息的字典
+        """
+        logger.info(f"开始检查目录: {self.directory}")
+        if self.dry_run:
+            logger.info("运行在试运行模式")
+        
+        self.scan_and_clean_files()
+        self.print_summary()
+        
+        return self.stats
+
+
+def main():
+    """主函数"""
+    parser = argparse.ArgumentParser(description='工作流文件清理器')
+    parser.add_argument('directory', nargs='?', default='results/results0903', 
+                       help='要检查的目录路径 (默认: results)')
+    parser.add_argument('--dry-run', action='store_true', 
+                       help='试运行模式，不实际删除文件')
+    
+    args = parser.parse_args()
+    
+    cleaner = WorkflowFileCleaner(args.directory, args.dry_run)
+    cleaner.run()
+
+
+if __name__ == "__main__":
+    main()