创赢未来评分系统 - 初始化提交（移除大文件）

backend/ai_services/admin.py

@@ -0,0 +1,47 @@
+from django.contrib import admin
+from unfold.admin import ModelAdmin as UnfoldModelAdmin
+from unfold.admin import StackedInline as UnfoldStackedInline
+from .models import TranscriptionTask, AIEvaluation, AIEvaluationTemplate
+
+class AIEvaluationInline(UnfoldStackedInline):
+    model = AIEvaluation
+    extra = 0
+    can_delete = True
+    verbose_name = "AI评估结果"
+    verbose_name_plural = "AI评估结果"
+    readonly_fields = ['created_at', 'updated_at', 'raw_response', 'reasoning', 'template']
+    fields = ('template', 'score', 'evaluation', 'model_selection', 'prompt', 'reasoning', 'status', 'error_message')
+
+@admin.register(TranscriptionTask)
+class TranscriptionTaskAdmin(UnfoldModelAdmin):
+    list_display = ['id', 'status', 'task_id', 'created_at']
+    list_filter = ['status', 'created_at']
+    search_fields = ['id', 'task_id', 'transcription', 'summary']
+    readonly_fields = ['id', 'created_at', 'updated_at', 'task_id']
+    inlines = [AIEvaluationInline]
+
+@admin.register(AIEvaluationTemplate)
+class AIEvaluationTemplateAdmin(UnfoldModelAdmin):
+    list_display = ['name', 'model_selection', 'score_dimension', 'is_default', 'is_active', 'created_at']
+    list_filter = ['is_active', 'is_default', 'model_selection', 'created_at']
+    search_fields = ['name', 'prompt']
+
+@admin.register(AIEvaluation)
+class AIEvaluationAdmin(UnfoldModelAdmin):
+    list_display = ['id', 'task', 'template', 'score', 'status', 'model_selection', 'created_at']
+    list_filter = ['status', 'model_selection', 'created_at', 'template']
+    search_fields = ['task__id', 'evaluation', 'reasoning']
+    readonly_fields = ['id', 'created_at', 'updated_at', 'raw_response']
+    fieldsets = (
+        (None, {
+            'fields': ('task', 'template', 'status', 'score', 'evaluation')
+        }),
+        ('配置快照', {
+            'fields': ('model_selection', 'prompt'),
+            'classes': ('collapse',),
+        }),
+        ('调试信息', {
+            'fields': ('raw_response', 'reasoning', 'error_message'),
+            'classes': ('collapse',),
+        }),
+    )

backend/ai_services/apps.py

@@ -0,0 +1,5 @@
+from django.apps import AppConfig
+
+
+class AiServicesConfig(AppConfig):
+    name = 'ai_services'

backend/ai_services/bailian_service.py

@@ -0,0 +1,323 @@
+import logging
+import json
+import os
+from django.conf import settings
+from openai import OpenAI
+from .models import AIEvaluation
+
+logger = logging.getLogger(__name__)
+
+class BailianService:
+    def __init__(self):
+        self.api_key = getattr(settings, 'DASHSCOPE_API_KEY', None)
+        if not self.api_key:
+             self.api_key = os.environ.get("DASHSCOPE_API_KEY")
+        
+        if self.api_key:
+            self.client = OpenAI(
+                api_key=self.api_key,
+                base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"
+            )
+        else:
+            self.client = None
+            logger.warning("DASHSCOPE_API_KEY not configured.")
+
+    def evaluate_task(self, evaluation: AIEvaluation):
+        """
+        执行AI评估
+        """
+        if not self.client:
+            evaluation.status = AIEvaluation.Status.FAILED
+            evaluation.error_message = "服务未配置 (DASHSCOPE_API_KEY missing)"
+            evaluation.save()
+            return
+
+        task = evaluation.task
+        if not task.transcription:
+             evaluation.status = AIEvaluation.Status.FAILED
+             evaluation.error_message = "关联任务无逐字稿内容"
+             evaluation.save()
+             return
+
+        evaluation.status = AIEvaluation.Status.PROCESSING
+        evaluation.save()
+
+        try:
+            prompt = evaluation.prompt
+            content = task.transcription
+            
+            # 准备章节/时间戳数据以辅助分析发言节奏
+            chapter_context = ""
+            if task.auto_chapters_data:
+                try:
+                    chapters_str = ""
+                    # 处理特定的 AutoChapters 结构
+                    # 格式: {"AutoChapters": [{"Id": 1, "Start": 740, "End": 203436, "Headline": "...", "Summary": "..."}, ...]}
+                    if isinstance(task.auto_chapters_data, dict) and 'AutoChapters' in task.auto_chapters_data:
+                        chapters = task.auto_chapters_data['AutoChapters']
+                        if isinstance(chapters, list):
+                            chapter_lines = []
+                            for ch in chapters:
+                                # 毫秒转 MM:SS
+                                start_ms = ch.get('Start', 0)
+                                end_ms = ch.get('End', 0)
+                                start_str = f"{start_ms // 60000:02d}:{(start_ms // 1000) % 60:02d}"
+                                end_str = f"{end_ms // 60000:02d}:{(end_ms // 1000) % 60:02d}"
+                                
+                                headline = ch.get('Headline', '无标题')
+                                summary = ch.get('Summary', '')
+                                
+                                line = f"- [{start_str} - {end_str}] {headline}"
+                                if summary:
+                                    line += f"\n  摘要: {summary}"
+                                chapter_lines.append(line)
+                            
+                            chapters_str = "\n".join(chapter_lines)
+
+                    # 如果上面的解析为空（或者格式不匹配），回退到通用 JSON dump
+                    if not chapters_str:
+                        if isinstance(task.auto_chapters_data, (dict, list)):
+                            chapters_str = json.dumps(task.auto_chapters_data, ensure_ascii=False, indent=2)
+                        else:
+                            chapters_str = str(task.auto_chapters_data)
+                    
+                    chapter_context = f"\n\n【章节与时间戳信息】\n{chapters_str}\n\n(提示：请结合上述章节时间戳信息，分析发言者的语速、节奏变化及停顿情况。)"
+                except Exception as e:
+                    logger.warning(f"Failed to process auto_chapters_data: {e}")
+
+            # 截断过长的内容以防止超出Token限制 (简单处理，取前10000字)
+            if len(content) > 10000:
+                content = content[:10000] + "...(内容过长已截断)"
+
+            # Construct messages
+            messages = [
+                {'role': 'system', 'content': 'You are a helpful assistant designed to output JSON.'},
+                {'role': 'user', 'content': f"{prompt}\n\n以下是需要评估的内容：\n{content}{chapter_context}"}
+            ]
+
+            # 增加重试机制 (最多重试3次)
+            completion = None
+            last_error = None
+            import time
+            
+            for attempt in range(3):
+                try:
+                    completion = self.client.chat.completions.create(
+                        model=evaluation.model_selection,
+                        messages=messages,
+                        response_format={"type": "json_object"} 
+                    )
+                    break # 成功则跳出循环
+                except Exception as e:
+                    last_error = e
+                    logger.warning(f"AI Evaluation attempt {attempt+1}/3 failed for eval {evaluation.id}: {e}")
+                    if attempt < 2:
+                        time.sleep(2 * (attempt + 1)) # 简单的指数退避
+            
+            if not completion:
+                raise last_error or Exception("AI Service call failed after retries")
+
+            response_content = completion.choices[0].message.content
+            # Convert to dict for storage
+            raw_response = completion.model_dump()
+            
+            evaluation.raw_response = raw_response
+            
+            # Parse JSON
+            try:
+                result = json.loads(response_content)
+                evaluation.score = result.get('score')
+                evaluation.evaluation = result.get('evaluation') or result.get('comment')
+                
+                # 尝试获取推理过程（如果模型返回了）
+                evaluation.reasoning = result.get('reasoning') or result.get('analysis')
+                
+                if not evaluation.reasoning:
+                     # 如果JSON里没有，把整个JSON作为推理参考
+                     evaluation.reasoning = json.dumps(result, ensure_ascii=False, indent=2)
+
+                evaluation.status = AIEvaluation.Status.COMPLETED
+            except json.JSONDecodeError:
+                evaluation.status = AIEvaluation.Status.FAILED
+                evaluation.error_message = f"无法解析JSON响应: {response_content}"
+                evaluation.reasoning = response_content 
+            
+            evaluation.save()
+            
+            # 同步结果到参赛项目 (如果关联了)
+            self._sync_evaluation_to_project(evaluation)
+            
+            return evaluation
+
+        except Exception as e:
+            logger.error(f"AI Evaluation failed: {e}")
+            evaluation.status = AIEvaluation.Status.FAILED
+            evaluation.error_message = str(e)
+            evaluation.save()
+            return evaluation
+
+    def _sync_evaluation_to_project(self, evaluation: AIEvaluation):
+        """
+        将AI评估结果同步到关联的参赛项目（评分和评语）
+        """
+        try:
+            task = evaluation.task
+            if not task.project:
+                return
+
+            project = task.project
+            competition = project.competition
+            
+            # 1. 确定评委身份 (Based on Template)
+            # 用户要求：评委显示的是模板名称
+            template_name = evaluation.template.name if evaluation.template else "AI智能评委"
+            # 使用固定前缀 + template_id 确保唯一性，这样同一个模板在不同项目里是同一个评委
+            openid = f"ai_judge_{evaluation.template.id}" if evaluation.template else "ai_judge_default"
+            
+            # 延迟导入以避免循环依赖
+            from shop.models import WeChatUser
+            from competition.models import CompetitionEnrollment, Score, Comment, ScoreDimension
+
+            # 获取或创建虚拟评委用户
+            user, created = WeChatUser.objects.get_or_create(
+                openid=openid,
+                defaults={
+                    'nickname': template_name,
+                    'avatar_url': 'https://ui-avatars.com/api/?name=AI&background=random&color=fff'
+                }
+            )
+            
+            # 如果名字不匹配（比如模板改名了），更新它
+            if user.nickname != template_name:
+                user.nickname = template_name
+                user.save(update_fields=['nickname'])
+            
+            # 2. 确保评委已报名 (Enrollment)
+            enrollment, _ = CompetitionEnrollment.objects.get_or_create(
+                competition=competition,
+                user=user,
+                defaults={
+                    'role': 'judge',
+                    'status': 'approved'
+                }
+            )
+            
+            # 3. 同步评分 (Score)
+            if evaluation.score is not None:
+                # 尝试找到匹配的维度
+                dimensions = competition.score_dimensions.all()
+                target_dimension = None
+                
+                # 0. 优先使用模板配置的维度
+                if evaluation.template and evaluation.template.score_dimension:
+                    # 检查配置的维度是否属于当前比赛
+                    if evaluation.template.score_dimension.competition_id == competition.id:
+                        target_dimension = evaluation.template.score_dimension
+                    else:
+                        # 如果不属于当前比赛(跨比赛复用模板)，尝试查找同名维度
+                        target_dimension = dimensions.filter(name=evaluation.template.score_dimension.name).first()
+                
+                # 1. 如果未配置或未找到，尝试匹配 "AI Rating" (用户指定默认值)
+                if not target_dimension:
+                    target_dimension = dimensions.filter(name__iexact="AI Rating").first()
+                
+                # 2. 尝试匹配包含 "AI" 的维度
+                if not target_dimension:
+                    for dim in dimensions:
+                        if "AI" in dim.name.upper():
+                            target_dimension = dim
+                            break
+
+                # 3. 尝试匹配模板名称
+                if not target_dimension:
+                    target_dimension = dimensions.filter(name=template_name).first()
+                
+                # 4. 最后兜底：使用第一个维度
+                if not target_dimension and dimensions.exists():
+                    target_dimension = dimensions.first()
+                
+                if target_dimension:
+                    Score.objects.update_or_create(
+                        project=project,
+                        judge=enrollment,
+                        dimension=target_dimension,
+                        defaults={'score': evaluation.score}
+                    )
+                    logger.info(f"Synced AI score {evaluation.score} to project {project.id} dimension {target_dimension.name}")
+            
+            # 4. 同步评语 (Comment)
+            if evaluation.evaluation:
+                # 检查是否已存在该评委的评语，避免重复
+                comment = Comment.objects.filter(project=project, judge=enrollment).first()
+                if comment:
+                    comment.content = evaluation.evaluation
+                    comment.save()
+                else:
+                    Comment.objects.create(
+                        project=project,
+                        judge=enrollment,
+                        content=evaluation.evaluation
+                    )
+                logger.info(f"Synced AI comment to project {project.id}")
+                
+        except Exception as e:
+            logger.error(f"Failed to sync evaluation to project: {e}")
+
+    def summarize_task(self, task):
+        """
+        对转写任务进行总结
+        """
+        if not self.client:
+            logger.warning("BailianService not initialized, skipping summary.")
+            return
+
+        if not task.transcription:
+            logger.warning(f"Task {task.id} has no transcription, skipping summary.")
+            return
+
+        try:
+            content = task.transcription
+            # 简单截断防止过长
+            if len(content) > 15000:
+                content = content[:15000] + "...(内容过长已截断)"
+            
+            # 准备上下文数据
+            context_data = ""
+            if task.summary_data:
+                 context_data += f"\n\n【总结原始数据】\n{json.dumps(task.summary_data, ensure_ascii=False, indent=2)}"
+            
+            if task.auto_chapters_data:
+                 context_data += f"\n\n【章节原始数据】\n{json.dumps(task.auto_chapters_data, ensure_ascii=False, indent=2)}"
+
+            system_prompt = f"""你是一个专业的会议/内容总结助手。请根据提供的【转写文本】、【总结原始数据】和【章节原始数据】，生成一份结构清晰、内容详实的总结报告。
+
+请按照以下结构输出（Markdown格式）：
+1. **标题**：基于内容生成一个合适的标题。
+2. **核心摘要**：简要概括主要内容。
+3. **主要观点/话题**：结合思维导图数据，列出关键话题和层级。
+4. **章节速览**：结合章节数据，列出时间点和主要内容。[HH:MM:SS]格式来把章节列出来
+5. **问答精选**（如果有）：基于问答总结数据，列出重要问答。
+
+请确保语言通顺，重点突出，能够还原内容的逻辑结构。"""
+
+            user_content = f"以下是需要总结的内容：\n\n【转写文本】\n{content}{context_data}"
+
+            messages = [
+                {'role': 'system', 'content': system_prompt},
+                {'role': 'user', 'content': user_content}
+            ]
+
+            # 使用 qwen-plus 作为默认模型
+            completion = self.client.chat.completions.create(
+                model="qwen-plus",
+                messages=messages
+            )
+            
+            summary_content = completion.choices[0].message.content
+            task.summary = summary_content
+            task.save(update_fields=['summary'])
+            
+            logger.info(f"Task {task.id} summary generated successfully.")
+
+        except Exception as e:
+            logger.error(f"Failed to generate summary for task {task.id}: {e}")

backend/ai_services/management/commands/check_aliyun_config.py

@@ -0,0 +1,54 @@
+from django.core.management.base import BaseCommand
+from django.conf import settings
+import oss2
+from aliyunsdkcore.client import AcsClient
+
+class Command(BaseCommand):
+    help = 'Check Aliyun configuration status'
+
+    def handle(self, *args, **options):
+        self.stdout.write("Checking Aliyun Configuration...")
+        
+        configs = {
+            'ALIYUN_ACCESS_KEY_ID': settings.ALIYUN_ACCESS_KEY_ID,
+            'ALIYUN_ACCESS_KEY_SECRET': settings.ALIYUN_ACCESS_KEY_SECRET,
+            'ALIYUN_OSS_BUCKET_NAME': settings.ALIYUN_OSS_BUCKET_NAME,
+            'ALIYUN_OSS_ENDPOINT': settings.ALIYUN_OSS_ENDPOINT,
+            'ALIYUN_TINGWU_APP_KEY': settings.ALIYUN_TINGWU_APP_KEY,
+        }
+
+        all_valid = True
+        for key, value in configs.items():
+            if not value:
+                self.stdout.write(self.style.ERROR(f"[MISSING] {key} is not set or empty"))
+                all_valid = False
+            else:
+                masked_value = value[:4] + "****" + value[-4:] if len(value) > 8 else "****"
+                self.stdout.write(self.style.SUCCESS(f"[OK] {key}: {masked_value}"))
+
+        if not all_valid:
+            self.stdout.write(self.style.ERROR("\nConfiguration check FAILED. Some required settings are missing."))
+            return
+
+        # Test OSS Connection
+        self.stdout.write("\nTesting OSS Connection...")
+        try:
+            auth = oss2.Auth(configs['ALIYUN_ACCESS_KEY_ID'], configs['ALIYUN_ACCESS_KEY_SECRET'])
+            bucket = oss2.Bucket(auth, configs['ALIYUN_OSS_ENDPOINT'], configs['ALIYUN_OSS_BUCKET_NAME'])
+            bucket.get_bucket_info()
+            self.stdout.write(self.style.SUCCESS("[OK] OSS Connection successful"))
+        except Exception as e:
+            self.stdout.write(self.style.ERROR(f"[FAILED] OSS Connection failed: {e}"))
+            
+        # Test Tingwu Client Initialization
+        self.stdout.write("\nTesting Tingwu Client Initialization...")
+        try:
+            client = AcsClient(
+                configs['ALIYUN_ACCESS_KEY_ID'],
+                configs['ALIYUN_ACCESS_KEY_SECRET'],
+                "cn-beijing"
+            )
+            self.stdout.write(self.style.SUCCESS("[OK] Tingwu Client initialized"))
+        except Exception as e:
+             self.stdout.write(self.style.ERROR(f"[FAILED] Tingwu Client init failed: {e}"))
+

backend/ai_services/management/commands/poll_transcription_results.py

@@ -0,0 +1,63 @@
+import time
+import logging
+from django.core.management.base import BaseCommand
+from ai_services.models import TranscriptionTask
+from ai_services.services import AliyunTingwuService
+
+logger = logging.getLogger(__name__)
+
+class Command(BaseCommand):
+    help = 'Polls Aliyun Tingwu for transcription results every 10 seconds'
+
+    def handle(self, *args, **options):
+        self.stdout.write(self.style.SUCCESS('Starting polling service...'))
+        service = AliyunTingwuService()
+        
+        while True:
+            try:
+                # Find tasks that are PENDING or PROCESSING
+                # Include PENDING because create() might set it to PENDING initially 
+                # though usually it sets to PROCESSING if task_id is obtained.
+                # Just in case.
+                tasks = TranscriptionTask.objects.filter(
+                    status__in=[TranscriptionTask.Status.PENDING, TranscriptionTask.Status.PROCESSING]
+                ).exclude(task_id__isnull=True).exclude(task_id='')
+                
+                count = tasks.count()
+                if count > 0:
+                    self.stdout.write(f'Found {count} pending/processing tasks.')
+                
+                for task in tasks:
+                    self.stdout.write(f'Checking task {task.task_id} (Status: {task.status})...')
+                    try:
+                        result = service.get_task_info(task.task_id)
+                        
+                        # Store old status to check for changes
+                        old_status = task.status
+                        
+                        service.parse_and_update_task(task, result)
+                        
+                        # Re-fetch or check updated object
+                        if task.status != old_status:
+                            if task.status == TranscriptionTask.Status.SUCCEEDED:
+                                self.stdout.write(self.style.SUCCESS(f'Task {task.task_id} SUCCEEDED'))
+                            elif task.status == TranscriptionTask.Status.FAILED:
+                                self.stdout.write(self.style.ERROR(f'Task {task.task_id} FAILED: {task.error_message}'))
+                        else:
+                             # Still processing
+                             pass
+                            
+                    except Exception as e:
+                        logger.error(f"Error checking task {task.task_id}: {e}")
+                        self.stdout.write(self.style.ERROR(f"Error checking task {task.task_id}: {e}"))
+                        
+                # Wait for 10 seconds
+                time.sleep(10)
+                
+            except KeyboardInterrupt:
+                self.stdout.write(self.style.SUCCESS('Stopping polling service...'))
+                break
+            except Exception as e:
+                logger.error(f"Polling loop error: {e}")
+                self.stdout.write(self.style.ERROR(f"Polling loop error: {e}"))
+                time.sleep(10)

backend/ai_services/management/commands/test_tingwu_local.py

@@ -0,0 +1,102 @@
+import os
+import sys
+import django
+import json
+import logging
+from django.conf import settings
+
+# 设置 Django 环境
+# 添加项目根目录到 sys.path
+sys.path.append('/Volumes/data/Quant-Speed/market_page/backend')
+os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'config.settings') # 修正为正确的 settings 模块路径
+django.setup()
+
+from ai_services.services import AliyunTingwuService
+from ai_services.models import TranscriptionTask
+
+# 配置日志
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+def test_tingwu_transcription():
+    file_url = "https://tangledup-ai-staging.oss-cn-shanghai.aliyuncs.com/Video/%E6%95%99%E5%AD%A6.mp4"
+    
+    print(f"Testing transcription for: {file_url}")
+    
+    service = AliyunTingwuService()
+    
+    # 1. 创建任务
+    try:
+        print("Creating task...")
+        response = service.create_transcription_task(file_url)
+        print(f"Create task response: {json.dumps(response, indent=2, ensure_ascii=False)}")
+        
+        if 'Data' in response and isinstance(response['Data'], dict):
+            task_id = response['Data'].get('TaskId')
+        else:
+            task_id = response.get('TaskId')
+            
+        if not task_id:
+            print("Failed to get TaskId")
+            return
+            
+        print(f"Task created with ID: {task_id}")
+        
+        # 2. 轮询查询任务状态
+        import time
+        max_retries = 60 # 5 minutes
+        for i in range(max_retries):
+            print(f"Checking status (attempt {i+1}/{max_retries})...")
+            result = service.get_task_info(task_id)
+            
+            # 解析结果
+            if isinstance(result, str):
+                try:
+                    result = json.loads(result)
+                except:
+                    pass
+            
+            if isinstance(result, dict):
+                data_obj = result.get('Data', result)
+            else:
+                data_obj = result
+                
+            task_status = data_obj.get('TaskStatus')
+            if not task_status:
+                task_status = data_obj.get('Status')
+                
+            print(f"Current status: {task_status}")
+            
+            if task_status in ['COMPLETE', 'COMPLETED', 'SUCCEEDED']:
+                print("Task succeeded!")
+                print(f"Full Result: {json.dumps(data_obj, indent=2, ensure_ascii=False)}")
+                
+                # 尝试解析 Transcription
+                task_result = data_obj.get('Result', {})
+                transcription_data = task_result.get('Transcription', {})
+                
+                if isinstance(transcription_data, str) and transcription_data.startswith('http'):
+                    import requests
+                    print(f"Downloading transcription from {transcription_data}")
+                    t_resp = requests.get(transcription_data)
+                    if t_resp.status_code == 200:
+                        content = t_resp.json()
+                        print(f"Downloaded content structure keys: {content.keys()}")
+                        # print(f"Content sample: {json.dumps(content, indent=2, ensure_ascii=False)[:500]}...")
+                    else:
+                        print(f"Failed to download: {t_resp.status_code}")
+                
+                break
+            elif task_status == 'FAILED':
+                print(f"Task failed: {data_obj}")
+                break
+                
+            time.sleep(5)
+            
+    except Exception as e:
+        print(f"Error: {e}")
+        import traceback
+        traceback.print_exc()
+
+if __name__ == "__main__":
+    test_tingwu_transcription()

backend/ai_services/migrations/0001_initial.py

@@ -0,0 +1,34 @@
+# Generated by Django 6.0.1 on 2026-03-11 05:11
+
+import uuid
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    initial = True
+
+    dependencies = [
+    ]
+
+    operations = [
+        migrations.CreateModel(
+            name='TranscriptionTask',
+            fields=[
+                ('id', models.UUIDField(default=uuid.uuid4, editable=False, primary_key=True, serialize=False)),
+                ('file_url', models.URLField(max_length=1024, verbose_name='文件链接')),
+                ('task_id', models.CharField(blank=True, max_length=100, null=True, verbose_name='听悟任务ID')),
+                ('status', models.CharField(choices=[('PENDING', '等待中'), ('PROCESSING', '处理中'), ('SUCCEEDED', '成功'), ('FAILED', '失败')], default='PENDING', max_length=20, verbose_name='状态')),
+                ('transcription', models.TextField(blank=True, null=True, verbose_name='逐字稿')),
+                ('summary', models.TextField(blank=True, null=True, verbose_name='AI总结')),
+                ('error_message', models.TextField(blank=True, null=True, verbose_name='错误信息')),
+                ('created_at', models.DateTimeField(auto_now_add=True, verbose_name='创建时间')),
+                ('updated_at', models.DateTimeField(auto_now=True, verbose_name='更新时间')),
+            ],
+            options={
+                'verbose_name': '转写任务',
+                'verbose_name_plural': '转写任务',
+                'ordering': ['-created_at'],
+            },
+        ),
+    ]

backend/ai_services/migrations/0002_transcriptiontask_evaluation_transcriptiontask_score.py

@@ -0,0 +1,23 @@
+# Generated by Django 6.0.1 on 2026-03-11 05:12
+
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('ai_services', '0001_initial'),
+    ]
+
+    operations = [
+        migrations.AddField(
+            model_name='transcriptiontask',
+            name='evaluation',
+            field=models.TextField(blank=True, null=True, verbose_name='AI评语'),
+        ),
+        migrations.AddField(
+            model_name='transcriptiontask',
+            name='score',
+            field=models.IntegerField(blank=True, help_text='基于转写内容的评分', null=True, verbose_name='AI评分'),
+        ),
+    ]

backend/ai_services/migrations/0003_transcriptiontask_auto_chapters_data_and_more.py

@@ -0,0 +1,28 @@
+# Generated by Django 6.0.1 on 2026-03-11 12:30
+
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('ai_services', '0002_transcriptiontask_evaluation_transcriptiontask_score'),
+    ]
+
+    operations = [
+        migrations.AddField(
+            model_name='transcriptiontask',
+            name='auto_chapters_data',
+            field=models.JSONField(blank=True, help_text='阿里云返回的AutoChapters完整JSON', null=True, verbose_name='章节原始数据'),
+        ),
+        migrations.AddField(
+            model_name='transcriptiontask',
+            name='summary_data',
+            field=models.JSONField(blank=True, help_text='阿里云返回的Summarization完整JSON', null=True, verbose_name='总结原始数据'),
+        ),
+        migrations.AddField(
+            model_name='transcriptiontask',
+            name='transcription_data',
+            field=models.JSONField(blank=True, help_text='阿里云返回的Transcription完整JSON', null=True, verbose_name='转写原始数据'),
+        ),
+    ]

backend/ai_services/migrations/0004_remove_transcriptiontask_evaluation_and_more.py

@@ -0,0 +1,44 @@
+# Generated by Django 6.0.1 on 2026-03-11 12:44
+
+import django.db.models.deletion
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('ai_services', '0003_transcriptiontask_auto_chapters_data_and_more'),
+    ]
+
+    operations = [
+        migrations.RemoveField(
+            model_name='transcriptiontask',
+            name='evaluation',
+        ),
+        migrations.RemoveField(
+            model_name='transcriptiontask',
+            name='score',
+        ),
+        migrations.CreateModel(
+            name='AIEvaluation',
+            fields=[
+                ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
+                ('score', models.IntegerField(blank=True, help_text='0-100分', null=True, verbose_name='AI评分')),
+                ('evaluation', models.TextField(blank=True, null=True, verbose_name='AI评语')),
+                ('model_selection', models.CharField(default='qwen-plus', help_text='例如: qwen-plus, qwen-turbo, qwen-max', max_length=50, verbose_name='模型选择')),
+                ('prompt', models.TextField(default='你是一个专业的评分助手。请根据提供的转写内容，对内容质量、逻辑清晰度、语言表达等方面进行综合评分（0-100分），并给出详细的评语。请以JSON格式返回，包含"score"和"evaluation"字段。', help_text='用于指导AI评分的提示词', verbose_name='评分提示词')),
+                ('raw_response', models.JSONField(blank=True, help_text='大模型返回的完整JSON', null=True, verbose_name='原始响应')),
+                ('reasoning', models.TextField(blank=True, help_text='AI的推理过程（如果有）', null=True, verbose_name='推理过程')),
+                ('status', models.CharField(choices=[('PENDING', '等待中'), ('PROCESSING', '生成中'), ('COMPLETED', '已完成'), ('FAILED', '失败')], default='PENDING', max_length=20, verbose_name='评估状态')),
+                ('error_message', models.TextField(blank=True, null=True, verbose_name='错误信息')),
+                ('created_at', models.DateTimeField(auto_now_add=True, verbose_name='创建时间')),
+                ('updated_at', models.DateTimeField(auto_now=True, verbose_name='更新时间')),
+                ('task', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, related_name='ai_evaluation', to='ai_services.transcriptiontask', verbose_name='关联任务')),
+            ],
+            options={
+                'verbose_name': 'AI智能评估',
+                'verbose_name_plural': 'AI智能评估',
+                'ordering': ['-created_at'],
+            },
+        ),
+    ]

backend/ai_services/migrations/0005_aievaluationtemplate_alter_aievaluation_options_and_more.py

@@ -0,0 +1,55 @@
+# Generated by Django 6.0.1 on 2026-03-11 13:00
+
+import django.db.models.deletion
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('ai_services', '0004_remove_transcriptiontask_evaluation_and_more'),
+    ]
+
+    operations = [
+        migrations.CreateModel(
+            name='AIEvaluationTemplate',
+            fields=[
+                ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
+                ('name', models.CharField(help_text='例如：销售话术评分、逻辑性分析', max_length=100, verbose_name='模板名称')),
+                ('model_selection', models.CharField(default='qwen-plus', help_text='例如: qwen-plus, qwen-turbo, qwen-max', max_length=50, verbose_name='模型选择')),
+                ('prompt', models.TextField(default='你是一个专业的评分助手。请根据提供的转写内容，对内容质量、逻辑清晰度、语言表达等方面进行综合评分（0-100分），并给出详细的评语。请以JSON格式返回，包含"score"和"evaluation"字段。', help_text='用于指导AI评分的提示词', verbose_name='评分提示词')),
+                ('is_active', models.BooleanField(default=True, help_text='启用后，新的转写任务完成后将自动使用此模板进行评估', verbose_name='是否启用')),
+                ('created_at', models.DateTimeField(auto_now_add=True, verbose_name='创建时间')),
+                ('updated_at', models.DateTimeField(auto_now=True, verbose_name='更新时间')),
+            ],
+            options={
+                'verbose_name': 'AI评估模板',
+                'verbose_name_plural': 'AI评估模板',
+                'ordering': ['-created_at'],
+            },
+        ),
+        migrations.AlterModelOptions(
+            name='aievaluation',
+            options={'ordering': ['-created_at'], 'verbose_name': 'AI评估结果', 'verbose_name_plural': 'AI评估结果'},
+        ),
+        migrations.AlterField(
+            model_name='aievaluation',
+            name='model_selection',
+            field=models.CharField(default='qwen-plus', max_length=50, verbose_name='模型选择'),
+        ),
+        migrations.AlterField(
+            model_name='aievaluation',
+            name='prompt',
+            field=models.TextField(verbose_name='评分提示词'),
+        ),
+        migrations.AlterField(
+            model_name='aievaluation',
+            name='task',
+            field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='ai_evaluations', to='ai_services.transcriptiontask', verbose_name='关联任务'),
+        ),
+        migrations.AddField(
+            model_name='aievaluation',
+            name='template',
+            field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='evaluations', to='ai_services.aievaluationtemplate', verbose_name='使用的模板'),
+        ),
+    ]

backend/ai_services/migrations/0006_transcriptiontask_project.py

@@ -0,0 +1,20 @@
+# Generated by Django 6.0.1 on 2026-03-11 14:10
+
+import django.db.models.deletion
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('ai_services', '0005_aievaluationtemplate_alter_aievaluation_options_and_more'),
+        ('competition', '0003_competition_project_visibility'),
+    ]
+
+    operations = [
+        migrations.AddField(
+            model_name='transcriptiontask',
+            name='project',
+            field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='transcription_tasks', to='competition.project', verbose_name='关联参赛项目'),
+        ),
+    ]

backend/ai_services/migrations/0007_aievaluationtemplate_score_dimension.py

@@ -0,0 +1,20 @@
+# Generated by Django 6.0.1 on 2026-03-11 15:03
+
+import django.db.models.deletion
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('ai_services', '0006_transcriptiontask_project'),
+        ('competition', '0003_competition_project_visibility'),
+    ]
+
+    operations = [
+        migrations.AddField(
+            model_name='aievaluationtemplate',
+            name='score_dimension',
+            field=models.ForeignKey(blank=True, help_text='如果同步到比赛评分，优先使用此维度。未填写则默认使用"AI Rating"或包含"AI"的维度', null=True, on_delete=django.db.models.deletion.SET_NULL, to='competition.scoredimension', verbose_name='关联评分维度'),
+        ),
+    ]

backend/ai_services/migrations/0008_add_is_default_to_template.py

@@ -0,0 +1,18 @@
+# Generated by Django 6.0.1 on 2026-03-17 15:55
+
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('ai_services', '0007_aievaluationtemplate_score_dimension'),
+    ]
+
+    operations = [
+        migrations.AddField(
+            model_name='aievaluationtemplate',
+            name='is_default',
+            field=models.BooleanField(default=False, help_text='默认模板会评价所有比赛，非默认模板且未关联评分维度时不会自动评价', verbose_name='是否为默认模板'),
+        ),
+    ]

backend/ai_services/migrations/0009_alter_aievaluation_id_alter_aievaluationtemplate_id.py

@@ -0,0 +1,23 @@
+# Generated by Django 4.2.29 on 2026-03-18 12:00
+
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('ai_services', '0008_add_is_default_to_template'),
+    ]
+
+    operations = [
+        migrations.AlterField(
+            model_name='aievaluation',
+            name='id',
+            field=models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID'),
+        ),
+        migrations.AlterField(
+            model_name='aievaluationtemplate',
+            name='id',
+            field=models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID'),
+        ),
+    ]

backend/ai_services/models.py

@@ -0,0 +1,150 @@
+import uuid
+from django.db import models
+from django.utils.translation import gettext_lazy as _
+
+class TranscriptionTask(models.Model):
+    class Status(models.TextChoices):
+        PENDING = 'PENDING', _('等待中')
+        PROCESSING = 'PROCESSING', _('处理中')
+        SUCCEEDED = 'SUCCEEDED', _('成功')
+        FAILED = 'FAILED', _('失败')
+
+    id = models.UUIDField(primary_key=True, default=uuid.uuid4, editable=False)
+    file_url = models.URLField(verbose_name=_('文件链接'), max_length=1024)
+    task_id = models.CharField(verbose_name=_('听悟任务ID'), max_length=100, blank=True, null=True)
+    status = models.CharField(
+        verbose_name=_('状态'),
+        max_length=20,
+        choices=Status.choices,
+        default=Status.PENDING
+    )
+    # 存储阿里云听悟返回的原始 JSON 结构
+    transcription_data = models.JSONField(verbose_name=_('转写原始数据'), blank=True, null=True, help_text=_('阿里云返回的Transcription完整JSON'))
+    summary_data = models.JSONField(verbose_name=_('总结原始数据'), blank=True, null=True, help_text=_('阿里云返回的Summarization完整JSON'))
+    auto_chapters_data = models.JSONField(verbose_name=_('章节原始数据'), blank=True, null=True, help_text=_('阿里云返回的AutoChapters完整JSON'))
+    
+    project = models.ForeignKey(
+        'competition.Project', 
+        on_delete=models.SET_NULL, 
+        null=True, 
+        blank=True, 
+        related_name='transcription_tasks', 
+        verbose_name=_('关联参赛项目')
+    )
+
+    transcription = models.TextField(verbose_name=_('逐字稿'), blank=True, null=True)
+    summary = models.TextField(verbose_name=_('AI总结'), blank=True, null=True)
+    
+    # 已解耦到 AIEvaluation 模型
+    # score = models.IntegerField(verbose_name=_('AI评分'), blank=True, null=True, help_text=_('基于转写内容的评分'))
+    # evaluation = models.TextField(verbose_name=_('AI评语'), blank=True, null=True)
+    
+    error_message = models.TextField(verbose_name=_('错误信息'), blank=True, null=True)
+    created_at = models.DateTimeField(verbose_name=_('创建时间'), auto_now_add=True)
+    updated_at = models.DateTimeField(verbose_name=_('更新时间'), auto_now=True)
+
+    class Meta:
+        verbose_name = _('转写任务')
+        verbose_name_plural = _('转写任务')
+        ordering = ['-created_at']
+
+    def __str__(self):
+        return f"{self.id} - {self.get_status_display()}"
+
+
+class AIEvaluationTemplate(models.Model):
+    name = models.CharField(verbose_name=_('模板名称'), max_length=100, help_text=_('例如：销售话术评分、逻辑性分析'))
+    model_selection = models.CharField(
+        verbose_name=_('模型选择'), 
+        max_length=50, 
+        default='qwen-plus', 
+        help_text=_('例如: qwen-plus, qwen-turbo, qwen-max')
+    )
+    prompt = models.TextField(
+        verbose_name=_('评分提示词'), 
+        default='你是一个专业的评分助手。请根据提供的转写内容，对内容质量、逻辑清晰度、语言表达等方面进行综合评分（0-100分），并给出详细的评语。请以JSON格式返回，包含"score"和"evaluation"字段。',
+        help_text=_('用于指导AI评分的提示词')
+    )
+    score_dimension = models.ForeignKey(
+        'competition.ScoreDimension',
+        on_delete=models.SET_NULL,
+        null=True,
+        blank=True,
+        verbose_name=_('关联评分维度'),
+        help_text=_('如果同步到比赛评分，优先使用此维度。未填写则默认使用"AI Rating"或包含"AI"的维度')
+    )
+    is_default = models.BooleanField(
+        verbose_name=_('是否为默认模板'),
+        default=False,
+        help_text=_('默认模板会评价所有比赛，非默认模板且未关联评分维度时不会自动评价')
+    )
+    is_active = models.BooleanField(verbose_name=_('是否启用'), default=True, help_text=_('启用后，新的转写任务完成后将自动使用此模板进行评估'))
+    
+    created_at = models.DateTimeField(verbose_name=_('创建时间'), auto_now_add=True)
+    updated_at = models.DateTimeField(verbose_name=_('更新时间'), auto_now=True)
+
+    class Meta:
+        verbose_name = _('AI评估模板')
+        verbose_name_plural = _('AI评估模板')
+        ordering = ['-created_at']
+
+    def __str__(self):
+        return self.name
+
+
+class AIEvaluation(models.Model):
+    class Status(models.TextChoices):
+        PENDING = 'PENDING', _('等待中')
+        PROCESSING = 'PROCESSING', _('生成中')
+        COMPLETED = 'COMPLETED', _('已完成')
+        FAILED = 'FAILED', _('失败')
+
+    task = models.ForeignKey(
+        TranscriptionTask, 
+        on_delete=models.CASCADE, 
+        related_name='ai_evaluations',
+        verbose_name=_('关联任务')
+    )
+    template = models.ForeignKey(
+        AIEvaluationTemplate,
+        on_delete=models.SET_NULL,
+        null=True,
+        blank=True,
+        related_name='evaluations',
+        verbose_name=_('使用的模板')
+    )
+    
+    # 评分与评语
+    score = models.IntegerField(verbose_name=_('AI评分'), blank=True, null=True, help_text=_('0-100分'))
+    evaluation = models.TextField(verbose_name=_('AI评语'), blank=True, null=True)
+    
+    # 记录当时的配置 (快照)
+    model_selection = models.CharField(
+        verbose_name=_('模型选择'), 
+        max_length=50, 
+        default='qwen-plus'
+    )
+    prompt = models.TextField(verbose_name=_('评分提示词'))
+    
+    # 原始数据与推理
+    raw_response = models.JSONField(verbose_name=_('原始响应'), blank=True, null=True, help_text=_('大模型返回的完整JSON'))
+    reasoning = models.TextField(verbose_name=_('推理过程'), blank=True, null=True, help_text=_('AI的推理过程（如果有）'))
+    
+    status = models.CharField(
+        verbose_name=_('评估状态'), 
+        max_length=20, 
+        choices=Status.choices, 
+        default=Status.PENDING
+    )
+    error_message = models.TextField(verbose_name=_('错误信息'), blank=True, null=True)
+    
+    created_at = models.DateTimeField(verbose_name=_('创建时间'), auto_now_add=True)
+    updated_at = models.DateTimeField(verbose_name=_('更新时间'), auto_now=True)
+
+    class Meta:
+        verbose_name = _('AI评估结果')
+        verbose_name_plural = _('AI评估结果')
+        ordering = ['-created_at']
+
+    def __str__(self):
+        return f"Evaluation for Task {self.task.id} ({self.template.name if self.template else 'Custom'})"

backend/ai_services/serializers.py

@@ -0,0 +1,28 @@
+from rest_framework import serializers
+from .models import TranscriptionTask, AIEvaluation, AIEvaluationTemplate
+
+class AIEvaluationTemplateSerializer(serializers.ModelSerializer):
+    class Meta:
+        model = AIEvaluationTemplate
+        fields = ['id', 'name', 'model_selection', 'prompt', 'is_active', 'created_at']
+
+class AIEvaluationSerializer(serializers.ModelSerializer):
+    template = AIEvaluationTemplateSerializer(read_only=True)
+    
+    class Meta:
+        model = AIEvaluation
+        fields = ['id', 'template', 'score', 'evaluation', 'model_selection', 'prompt', 'reasoning', 'status', 'error_message', 'created_at', 'updated_at']
+
+class TranscriptionTaskSerializer(serializers.ModelSerializer):
+    ai_evaluations = AIEvaluationSerializer(many=True, read_only=True)
+    project_title = serializers.CharField(source='project.title', read_only=True)
+
+    class Meta:
+        model = TranscriptionTask
+        fields = ['id', 'file_url', 'task_id', 'status', 'transcription', 'summary', 'error_message', 'created_at', 'updated_at', 'transcription_data', 'summary_data', 'auto_chapters_data', 'ai_evaluations', 'project', 'project_title']
+        read_only_fields = ['id', 'file_url', 'task_id', 'status', 'transcription', 'summary', 'error_message', 'created_at', 'updated_at', 'transcription_data', 'summary_data', 'auto_chapters_data', 'ai_evaluations', 'project_title']
+
+class TranscriptionUploadSerializer(serializers.Serializer):
+    file = serializers.FileField(help_text="上传的音频文件", required=False)
+    file_url = serializers.URLField(help_text="音频文件的URL地址", required=False)
+    project_id = serializers.IntegerField(help_text="关联的参赛项目ID", required=False)

backend/ai_services/services.py

@@ -0,0 +1,420 @@
+import json
+import logging
+import time
+import uuid
+import oss2
+from aliyunsdkcore.client import AcsClient
+from aliyunsdkcore.acs_exception.exceptions import ClientException, ServerException
+# 尝试导入最新的 API 版本，如果有问题可能需要调整
+try:
+    from aliyunsdktingwu.request.v20230930 import CreateTaskRequest, GetTaskInfoRequest
+except ImportError:
+    # Fallback or error handling if version differs
+    pass
+    
+from django.conf import settings
+
+logger = logging.getLogger(__name__)
+
+from .models import TranscriptionTask, AIEvaluation, AIEvaluationTemplate
+
+class AliyunTingwuService:
+    def __init__(self):
+        self.access_key_id = settings.ALIYUN_ACCESS_KEY_ID
+        self.access_key_secret = settings.ALIYUN_ACCESS_KEY_SECRET
+        self.oss_bucket_name = settings.ALIYUN_OSS_BUCKET_NAME
+        self.oss_endpoint = settings.ALIYUN_OSS_ENDPOINT
+        self.tingwu_app_key = settings.ALIYUN_TINGWU_APP_KEY
+        self.region_id = "cn-shanghai" # 听悟服务区域，根据文档应与OSS区域一致，或者使用 'cn-beijing'
+
+        # 初始化 OSS Bucket
+        if self.access_key_id and self.access_key_secret and self.oss_endpoint:
+             auth = oss2.Auth(self.access_key_id, self.access_key_secret)
+             self.bucket = oss2.Bucket(auth, self.oss_endpoint, self.oss_bucket_name)
+        else:
+            self.bucket = None
+            logger.warning("Aliyun OSS configuration missing.")
+        
+        # 初始化听悟 Client
+        if self.access_key_id and self.access_key_secret:
+            self.client = AcsClient(
+                self.access_key_id,
+                self.access_key_secret,
+                self.region_id
+            )
+            # 显式添加听悟服务的 Endpoint 映射，解决 EndpointResolvingError
+            # 听悟 API 的服务接入点通常是 tingwu.cn-beijing.aliyuncs.com
+            # 但新版听悟 API (tingwu.aliyuncs.com) 可能不同，需根据实际情况添加
+            # 这里添加一个通用的 Endpoint 映射
+            try:
+                # 尝试为 tingwu 产品设置 Endpoint
+                # 注意：听悟服务主要部署在北京，Endpoint 通常为 tingwu.cn-beijing.aliyuncs.com
+                # 如果您的服务在上海，也可能需要连接到北京的接入点
+                self.client.add_endpoint(self.region_id, "tingwu", "tingwu.cn-beijing.aliyuncs.com")
+            except Exception as e:
+                logger.warning(f"Failed to add endpoint: {e}")
+
+        else:
+            self.client = None
+            logger.warning("Aliyun AccessKey configuration missing.")
+
+    def upload_to_oss(self, file_obj, file_name, day=7):
+        """
+        上传文件到 OSS 并返回带签名的 URL
+        默认生成有效期为 7 天 (3600 * 24 * day) 的签名URL，方便评委在一段时间内都能播放。
+        """
+        if not self.bucket:
+            raise Exception("OSS Client not initialized")
+            
+        try:
+            # 上传文件
+            # file_obj 应该是打开的文件对象或字节流
+            self.bucket.put_object(file_name, file_obj)
+            
+            # 生成签名 URL，有效期 7 天 (3600 * 24 * 7 = 604800 秒)
+            url = self.bucket.sign_url('GET', file_name, 3600 * 24 * day)
+            return url
+        except Exception as e:
+            logger.error(f"OSS Upload failed: {e}")
+            raise e
+
+    def create_transcription_task(self, file_url, language="cn"):
+        """
+        创建听悟转写任务
+        """
+        if not self.client:
+             raise Exception("Tingwu Client not initialized")
+
+        request = CreateTaskRequest.CreateTaskRequest()
+        
+        # 针对阿里云 SDK 不同版本的兼容性处理
+        # "type" 参数是听悟 API (ROA 风格) 的必填项，用于指定任务类型
+        # 根据官方文档，离线任务的 type 通常就是 'offline'
+        request.add_query_param('type', 'offline') 
+        
+        # 构造请求体 (Body)
+        # 根据听悟 API 文档，AppKey, Input, Parameters 应位于 JSON Body 中
+        # 而不是 Query Parameter
+        body = {
+            "AppKey": self.tingwu_app_key,
+            "Input": {
+                "FileUrl": file_url,
+                "SourceLanguage": language,
+                "TaskKey": str(uuid.uuid4())
+            },
+            "Parameters": {
+                "Transcoding": {
+                    "TargetAudioFormat": "mp3"
+                },
+                "Transcription": {
+                    "DiarizationEnabled": True, 
+                    "ChannelId": 0
+                },
+                "TranscriptionEnabled": True,
+                "AutoChaptersEnabled": True,
+                "SummarizationEnabled": True,
+                "Summarization": {
+                    "Types": ["Paragraph", "Conversational", "QuestionsAnswering", "MindMap"]
+                } 
+            }
+        }
+        
+        # 设置 Body 内容
+        request.set_content(json.dumps(body))
+        request.add_header('Content-Type', 'application/json')
+        
+        # 强制设置 Endpoint，避免 SDK.EndpointResolvingError
+        # 听悟目前主要服务点在北京
+        request.set_endpoint("tingwu.cn-beijing.aliyuncs.com")
+
+        # 显式设置 Method 为 PUT
+        request.set_method('PUT')
+
+        try:
+            response = self.client.do_action_with_exception(request)
+            return json.loads(response)
+        except (ClientException, ServerException) as e:
+            logger.error(f"Tingwu CreateTask failed: {e}")
+            raise e
+
+    def get_task_info(self, task_id):
+        """
+        查询任务状态和结果
+        """
+        if not self.client:
+             raise Exception("Tingwu Client not initialized")
+
+        request = GetTaskInfoRequest.GetTaskInfoRequest()
+        request.set_TaskId(task_id)
+        
+        try:
+            response = self.client.do_action_with_exception(request)
+            return json.loads(response)
+        except (ClientException, ServerException) as e:
+            logger.error(f"Tingwu GetTaskInfo failed: {e}")
+            raise e
+
+    def parse_and_update_task(self, task, result):
+        """
+        解析听悟结果并更新任务
+        :param task: TranscriptionTask 实例
+        :param result: get_task_info 返回的完整 JSON (或 Data 部分)
+        """
+        # 记录之前的状态，用于判断是否是首次完成
+        previous_status = task.status
+
+        # 1. 提取 Data 对象
+        if isinstance(result, dict):
+            data_obj = result.get('Data', result)
+        else:
+            data_obj = result
+            
+        if not isinstance(data_obj, dict):
+            logger.error(f"Unexpected data format: {type(data_obj)}")
+            return
+
+        # 2. 更新状态
+        task_status = data_obj.get('TaskStatus') or data_obj.get('Status')
+        if task_status in ['COMPLETE', 'COMPLETED', 'SUCCEEDED']:
+            task.status = 'SUCCEEDED' # 使用字符串引用，避免导入模型循环引用
+        elif task_status == 'FAILED':
+            task.status = 'FAILED'
+            task.error_message = data_obj.get('TaskStatusText', data_obj.get('Message', 'Unknown error'))
+            task.save()
+            return
+        else:
+            # 仍在处理中，不更新内容
+            return
+
+        # 3. 解析结果
+        task_result = data_obj.get('Result', {})
+        
+        # 兼容处理：如果 Result 为空，或者不存在，尝试直接使用 data_obj 作为结果源
+        # 某些情况下，Summarization/AutoChapters 可能直接位于 Data 层级
+        if not task_result:
+            task_result = data_obj
+
+        # 辅助函数：从源字典或其 Result 子字典中获取字段
+        def get_data_field(source, key):
+            # 1. 尝试直接从 task_result 获取 (如果 task_result 就是 Data 本身，这里也会生效)
+            if isinstance(source, dict) and key in source:
+                return source[key]
+            # 2. 如果 source 是 Data，尝试从 source['Result'] 获取
+            if isinstance(source, dict) and 'Result' in source and isinstance(source['Result'], dict):
+                if key in source['Result']:
+                    return source['Result'][key]
+            return None
+        
+        # --- A. 处理逐字稿 (Transcription) ---
+        transcription_data = get_data_field(task_result, 'Transcription') or get_data_field(data_obj, 'Transcription') or {}
+        
+        # 处理 URL 下载
+        if isinstance(transcription_data, str) and transcription_data.startswith('http'):
+            try:
+                import requests
+                t_resp = requests.get(transcription_data)
+                if t_resp.status_code == 200:
+                    transcription_data = t_resp.json()
+            except Exception as e:
+                logger.error(f"Download transcription failed: {e}")
+                transcription_data = {}
+        elif isinstance(transcription_data, dict) and 'TranscriptionUrl' in transcription_data:
+             try:
+                import requests
+                t_resp = requests.get(transcription_data['TranscriptionUrl'])
+                if t_resp.status_code == 200:
+                    transcription_data = t_resp.json()
+             except Exception as e:
+                 logger.error(f"Download transcription url failed: {e}")
+
+        # 保存原始数据
+        task.transcription_data = transcription_data
+        
+        # 提取文本
+        # 结构: {"Transcription": {"Paragraphs": [{"Words": [{"Text": "..."}]}]}}
+        # 或直接 {"Paragraphs": ...}
+        content_source = transcription_data
+        if 'Transcription' in content_source and isinstance(content_source['Transcription'], dict):
+            content_source = content_source['Transcription']
+            
+        paragraphs = content_source.get('Paragraphs', [])
+        full_text_lines = []
+        
+        if paragraphs and isinstance(paragraphs, list):
+            for p in paragraphs:
+                # 尝试从 Words 中提取
+                words = p.get('Words', [])
+                if words:
+                    line_text = "".join([str(w.get('Text', '')) for w in words])
+                    full_text_lines.append(line_text)
+                # 兼容旧结构或直接 Text
+                elif 'Text' in p:
+                    full_text_lines.append(p['Text'])
+        
+        if full_text_lines:
+            task.transcription = "\n".join(full_text_lines)
+        
+        # --- B. 处理 AI 总结 (Summarization) ---
+        summarization = get_data_field(task_result, 'Summarization') or get_data_field(data_obj, 'Summarization') or {}
+        
+        # 处理 URL 下载
+        if isinstance(summarization, str) and summarization.startswith('http'):
+            try:
+                import requests
+                s_resp = requests.get(summarization)
+                if s_resp.status_code == 200:
+                    summarization = s_resp.json()
+            except Exception as e:
+                logger.error(f"Download summarization failed: {e}")
+                summarization = {}
+
+        # 保存原始数据
+        task.summary_data = summarization
+        
+        # 提取文本 (MindMapSummary)
+        # 结构: {"MindMapSummary": [{"Title": "...", "Topic": [...]}]}
+        # 移除了原先的 summary_text 拼接逻辑
+        
+        # --- C. 处理章节 (AutoChapters) ---
+        auto_chapters = get_data_field(task_result, 'AutoChapters') or get_data_field(data_obj, 'AutoChapters') or []
+        
+        # 处理 URL 下载
+        if isinstance(auto_chapters, str) and auto_chapters.startswith('http'):
+            try:
+                import requests
+                ac_resp = requests.get(auto_chapters)
+                if ac_resp.status_code == 200:
+                    auto_chapters = ac_resp.json()
+            except Exception as e:
+                logger.error(f"Download auto chapters failed: {e}")
+                auto_chapters = []
+
+        # 保存原始数据
+        task.auto_chapters_data = auto_chapters
+        
+        # 保存任务，确保原始数据已写入数据库
+        task.save()
+
+        # 调用大模型生成总结 (如果 summary_data 或 auto_chapters_data 存在)
+        if task.summary_data or task.auto_chapters_data:
+            try:
+                # 设置占位状态
+                task.summary = "AI总结生成当中..."
+                task.save(update_fields=['summary'])
+
+                # 异步执行总结
+                import threading
+                from .bailian_service import BailianService
+                
+                def async_summarize_in_service(task_id):
+                    try:
+                        # 重新获取 task 以避免线程安全问题
+                        from .models import TranscriptionTask
+                        t = TranscriptionTask.objects.get(id=task_id)
+                        bailian_service = BailianService()
+                        bailian_service.summarize_task(t)
+                    except Exception as e:
+                        logger.error(f"Async summary generation failed in service: {e}")
+
+                threading.Thread(target=async_summarize_in_service, args=(task.id,)).start()
+                logger.info(f"Triggered async summary generation for task {task.id}")
+
+            except Exception as e:
+                logger.error(f"Failed to trigger AI summarization: {e}")
+
+        # 4. 自动触发 AI 评估 (如果任务首次成功且有启用的模板)
+        if previous_status != 'SUCCEEDED' and task.status == 'SUCCEEDED' and task.transcription:
+            # 同样改为异步触发，传递 task.id 以避免线程中的对象状态问题
+            import threading
+            threading.Thread(target=self.trigger_ai_evaluations, args=(task.id,)).start()
+
+    def trigger_ai_evaluations(self, task_id):
+        """
+        根据启用的模板自动触发 AI 评估
+        
+        逻辑：
+        1. 如果模板关联了评分维度(s score_dimension)，只对关联了相同维度的比赛进行评估
+        2. 如果模板未关联评分维度：
+           - 如果是默认模板(is_default=True)，评价所有比赛
+           - 否则不进行自动评价
+        """
+        try:
+            # 在线程中重新获取 task 对象，并预加载 project，避免懒加载导致的线程数据库连接问题
+            from .models import TranscriptionTask
+            task = TranscriptionTask.objects.select_related('project', 'project__competition').get(id=task_id)
+        except Exception as e:
+            # 兼容处理：如果 task_id 其实是 task 对象（虽然我们上面改了，但防止其他地方调用传错）
+            if hasattr(task_id, 'id'):
+                try:
+                    from .models import TranscriptionTask
+                    task = TranscriptionTask.objects.select_related('project', 'project__competition').get(id=task_id.id)
+                except:
+                    task = task_id
+            else:
+                logger.error(f"Failed to retrieve task {task_id}: {e}")
+                return
+
+        active_templates = AIEvaluationTemplate.objects.filter(is_active=True)
+        if not active_templates.exists():
+            logger.info("No active AI evaluation templates found.")
+            return
+
+        from .bailian_service import BailianService
+        service = BailianService()
+
+        for template in active_templates:
+            # 检查是否已经存在相同的评估，避免重复创建
+            if AIEvaluation.objects.filter(task=task, template=template).exists():
+                logger.info(f"Evaluation for task {task.id} and template {template.name} already exists.")
+                continue
+            
+            # 获取任务关联的比赛
+            task_competition = None
+            if task.project and task.project.competition:
+                task_competition = task.project.competition
+            
+            # 判断是否应该对此任务进行评估
+            should_evaluate = False
+            
+            if template.score_dimension:
+                # 模板关联了评分维度，只对关联了相同维度的比赛进行评估
+                if task_competition:
+                    # 获取该比赛下所有关联了相同评分维度的比赛ID列表
+                    from competition.models import ScoreDimension
+                    related_competition_ids = ScoreDimension.objects.filter(
+                        id=template.score_dimension.id
+                    ).values_list('competition_id', flat=True)
+                    
+                    if task_competition.id in related_competition_ids:
+                        should_evaluate = True
+                        logger.info(f"Template '{template.name}' is linked to score_dimension, task's competition matches.")
+                    else:
+                        logger.info(f"Template '{template.name}' is linked to score_dimension, but task's competition does not match. Skipping.")
+                else:
+                    logger.info(f"Task {task.id} has no associated competition. Skipping template '{template.name}'.")
+            else:
+                # 模板未关联评分维度，只有默认模板才评价所有比赛
+                if template.is_default:
+                    should_evaluate = True
+                    logger.info(f"Template '{template.name}' is default template, evaluating all competitions.")
+                else:
+                    logger.info(f"Template '{template.name}' is not linked to score_dimension and is not default. Skipping.")
+            
+            if not should_evaluate:
+                continue
+
+            # 创建评估记录
+            evaluation = AIEvaluation.objects.create(
+                task=task,
+                template=template,
+                model_selection=template.model_selection,
+                prompt=template.prompt,
+                status=AIEvaluation.Status.PENDING
+            )
+            
+            # 触发评估
+            try:
+                service.evaluate_task(evaluation)
+                logger.info(f"Triggered evaluation {evaluation.id} for template {template.name}")
+            except Exception as e:
+                logger.error(f"Failed to trigger evaluation {evaluation.id}: {e}")

backend/ai_services/tests.py

@@ -0,0 +1,3 @@
+from django.test import TestCase
+
+# Create your tests here.

backend/ai_services/urls.py

@@ -0,0 +1,11 @@
+from django.urls import path, include
+from rest_framework.routers import DefaultRouter
+from .views import TranscriptionTaskViewSet, tingwu_callback
+
+router = DefaultRouter()
+router.register(r'transcriptions', TranscriptionTaskViewSet)
+
+urlpatterns = [
+    path('callback/', tingwu_callback, name='tingwu-callback'),
+    path('', include(router.urls)),
+]

backend/ai_services/views.py

@@ -0,0 +1,364 @@
+import logging
+import uuid
+from rest_framework import viewsets, status
+from rest_framework.decorators import action, api_view, permission_classes, parser_classes
+from rest_framework.response import Response
+from rest_framework.parsers import MultiPartParser, FormParser, JSONParser
+from rest_framework.permissions import AllowAny
+from django.conf import settings
+from drf_spectacular.utils import extend_schema, OpenApiParameter, OpenApiTypes
+from .models import TranscriptionTask, AIEvaluation
+from .serializers import TranscriptionTaskSerializer, TranscriptionUploadSerializer, AIEvaluationSerializer
+from .services import AliyunTingwuService
+
+logger = logging.getLogger(__name__)
+
+@api_view(['POST'])
+@permission_classes([AllowAny])
+def tingwu_callback(request):
+    """
+    处理阿里云听悟的回调消息
+    """
+    data = request.data
+    logger.info(f"收到听悟回调: {data}")
+    
+    # 1. 处理连通性测试消息
+    # 格式: {"Code": "0", "Data": {"Test": "..."}, "Message": "success", "RequestId": "..."}
+    if isinstance(data, dict) and 'Data' in data and 'Test' in data['Data']:
+        logger.info("收到听悟连通性测试请求")
+        return Response({'message': 'success'}, status=status.HTTP_200_OK)
+
+    # 2. 处理任务完成消息 (根据实际文档或后续调试完善)
+    # 通常会包含 TaskId 和 Status
+    # 注意：阿里云听悟回调的结构可能在 Header 或 Body 中不同，需根据实际情况调整
+    # 这里是一个通用的处理逻辑
+    task_id = data.get('TaskId')
+    task_status = data.get('Status')
+    
+    if task_id:
+        try:
+            task = TranscriptionTask.objects.filter(task_id=task_id).first()
+            if task:
+                if task_status == 'COMPLETE':
+                    logger.info(f"任务 {task_id} 完成，等待下一次查询刷新")
+                    # 可以在这里直接调用 get_task_info 刷新数据，但要注意超时
+                elif task_status == 'FAILED':
+                    task.status = TranscriptionTask.Status.FAILED
+                    task.error_message = data.get('StatusText', 'Callback reported failure')
+                    task.save()
+            else:
+                logger.warning(f"回调收到未知任务ID: {task_id}")
+        except Exception as e:
+            logger.error(f"处理回调异常: {e}")
+
+    return Response({'message': 'success'}, status=status.HTTP_200_OK)
+
+class TranscriptionTaskViewSet(viewsets.ModelViewSet):
+    queryset = TranscriptionTask.objects.all()
+    serializer_class = TranscriptionTaskSerializer
+    parser_classes = (MultiPartParser, FormParser)
+
+    @extend_schema(
+        request={
+            'multipart/form-data': {
+                'type': 'object',
+                'properties': {
+                    'file': {
+                        'type': 'string',
+                        'format': 'binary'
+                    },
+                    'file_url': {
+                        'type': 'string',
+                        'description': '音频文件的URL地址'
+                    },
+                    'project_id': {
+                        'type': 'integer',
+                        'description': '关联的参赛项目ID'
+                    }
+                }
+            }
+        },
+        responses={201: TranscriptionTaskSerializer}
+    )
+    def create(self, request, *args, **kwargs):
+        """
+        上传音频文件并创建听悟转写任务
+        """
+        file_obj = request.FILES.get('file')
+        file_url = request.data.get('file_url')
+        project_id = request.data.get('project_id')
+        
+        if not file_obj and not file_url:
+            return Response({'error': '请提供文件或文件URL'}, status=status.HTTP_400_BAD_REQUEST)
+
+        service = AliyunTingwuService()
+        if not service.bucket or not service.client:
+            return Response({'error': '阿里云服务未配置'}, status=status.HTTP_503_SERVICE_UNAVAILABLE)
+
+        try:
+            oss_url = None
+            if file_obj:
+                # 1. 上传文件到 OSS
+                file_extension = file_obj.name.split('.')[-1]
+                file_name = f"transcription/{uuid.uuid4()}.{file_extension}"
+                
+                # 使用服务上传
+                oss_url = service.upload_to_oss(file_obj, file_name)
+            else:
+                # 使用提供的 URL
+                oss_url = file_url
+            
+            # 2. 创建数据库记录
+            task_data = {
+                'file_url': oss_url,
+                'status': TranscriptionTask.Status.PENDING
+            }
+            if project_id:
+                try:
+                    p_id = int(project_id)
+                    # 只有当 ID > 0 时才认为是有效的项目 ID
+                    # 避免前端传递 0 或 Swagger 默认值导致的外键约束错误
+                    if p_id > 0:
+                        task_data['project_id'] = p_id
+                except (ValueError, TypeError):
+                    pass # Ignore invalid project_id
+
+            task_record = TranscriptionTask.objects.create(**task_data)
+            logger.info(f"Created TranscriptionTask {task_record.id} with project_id={project_id}")
+
+            # 3. 调用听悟接口创建任务
+            try:
+                tingwu_response = service.create_transcription_task(oss_url)
+                
+                # 兼容处理响应结构，通常为 {"Data": {"TaskId": "...", ...}}
+                if 'Data' in tingwu_response and isinstance(tingwu_response['Data'], dict):
+                    task_id = tingwu_response['Data'].get('TaskId')
+                else:
+                    task_id = tingwu_response.get('TaskId')
+                
+                if task_id:
+                    task_record.task_id = task_id
+                    task_record.status = TranscriptionTask.Status.PROCESSING
+                    task_record.save()
+                else:
+                    task_record.status = TranscriptionTask.Status.FAILED
+                    task_record.error_message = "未能获取 TaskId"
+                    task_record.save()
+                    return Response({'error': '未能获取 TaskId'}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)
+
+            except Exception as e:
+                task_record.status = TranscriptionTask.Status.FAILED
+                task_record.error_message = str(e)
+                task_record.save()
+                logger.error(f"创建听悟任务失败: {e}")
+                return Response({'error': f"创建听悟任务失败: {str(e)}"}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)
+
+            serializer = self.get_serializer(task_record)
+            return Response(serializer.data, status=status.HTTP_201_CREATED)
+
+        except Exception as e:
+            logger.error(f"处理上传请求失败: {e}")
+            return Response({'error': str(e)}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)
+
+    @action(detail=True, methods=['post'])
+    @extend_schema(
+        request={
+            'application/json': {
+                'type': 'object',
+                'properties': {
+                    'model_selection': {'type': 'string', 'description': '模型选择'},
+                    'prompt': {'type': 'string', 'description': '评分提示词'},
+                }
+            }
+        },
+        responses={200: AIEvaluationSerializer(many=True)}
+    )
+    def evaluate(self, request, pk=None):
+        """
+        触发AI评估
+        """
+        task = self.get_object()
+        
+        # 1. 如果有 active template，触发所有 active template
+        # 2. 如果请求体提供了 custom prompt，则创建一个 custom evaluation (no template)
+        
+        from .models import AIEvaluationTemplate
+        from .bailian_service import BailianService
+        service = BailianService()
+        
+        evaluations_to_process = []
+        
+        # A. 如果指定了 Prompt/Model，视为手动单次评估
+        model_selection = request.data.get('model_selection')
+        prompt = request.data.get('prompt')
+        
+        if prompt:
+             # 创建一个不关联 Template 的评估
+            eval, _ = AIEvaluation.objects.get_or_create(
+                task=task,
+                template=None,
+                defaults={
+                    'model_selection': model_selection or 'qwen-plus',
+                    'prompt': prompt
+                }
+            )
+            # 更新配置
+            eval.model_selection = model_selection or eval.model_selection
+            eval.prompt = prompt
+            eval.save()
+            evaluations_to_process.append(eval)
+        else:
+            # B. 否则触发所有 Active Templates
+            active_templates = AIEvaluationTemplate.objects.filter(is_active=True)
+            if not active_templates.exists():
+                return Response({'message': 'No active templates and no custom prompt provided'}, status=status.HTTP_400_BAD_REQUEST)
+                
+            for t in active_templates:
+                eval, _ = AIEvaluation.objects.get_or_create(
+                    task=task,
+                    template=t,
+                    defaults={
+                        'model_selection': t.model_selection,
+                        'prompt': t.prompt
+                    }
+                )
+                # 始终更新为模板最新配置? 或者保留历史? 用户意图似乎是"模版搭好...启用...生成几份"
+                # 这里假设触发时应用模板当前配置
+                eval.model_selection = t.model_selection
+                eval.prompt = t.prompt
+                eval.save()
+                evaluations_to_process.append(eval)
+                
+        # 执行评估 (改为异步并发执行)
+        # 提取ID列表，避免传递模型对象导致可能的线程问题
+        eval_ids = [e.id for e in evaluations_to_process]
+        
+        if eval_ids:
+            import threading
+            from concurrent.futures import ThreadPoolExecutor
+            
+            def run_evaluations_background(ids):
+                # 在后台线程中重新引入依赖
+                from .models import AIEvaluation
+                from .bailian_service import BailianService
+                
+                # 为该线程创建独立的服务实例
+                local_service = BailianService()
+                
+                # 获取最新的对象
+                target_evals = AIEvaluation.objects.filter(id__in=ids)
+                
+                # 使用线程池并发执行
+                # max_workers=4 可以同时处理4个评估请求
+                with ThreadPoolExecutor(max_workers=4) as executor:
+                    executor.map(local_service.evaluate_task, target_evals)
+            
+            # 启动后台线程，不阻塞当前 HTTP 请求
+            thread = threading.Thread(target=run_evaluations_background, args=(eval_ids,))
+            thread.daemon = True # 设置为守护线程
+            thread.start()
+            
+        # 返回该任务的所有评估结果
+        all_evals = AIEvaluation.objects.filter(task=task)
+        serializer = AIEvaluationSerializer(all_evals, many=True)
+        return Response(serializer.data)
+
+    @action(detail=True, methods=['get'])
+    @extend_schema(
+        parameters=[
+            OpenApiParameter("id", OpenApiTypes.UUID, OpenApiParameter.PATH, description="Task ID"),
+        ],
+        responses={200: TranscriptionTaskSerializer}
+    )
+    def refresh_status(self, request, pk=None):
+        """
+        刷新任务状态并获取结果
+        """
+        task = self.get_object()
+        
+        # 允许刷新的条件：
+        # 1. 任务未完成 (PENDING, PROCESSING)
+        # 2. 任务已完成但逐字稿 (transcription) 为空
+        # 3. 任务已完成但 AI总结 (summary) 为空 (新增)
+        
+        should_refresh = False
+        if task.status not in [TranscriptionTask.Status.SUCCEEDED, TranscriptionTask.Status.FAILED]:
+            should_refresh = True
+        elif task.status == TranscriptionTask.Status.SUCCEEDED:
+            if not task.transcription or not task.summary:
+                should_refresh = True
+        
+        if not should_refresh:
+            serializer = self.get_serializer(task)
+            return Response(serializer.data)
+
+        if not task.task_id:
+            return Response({'error': '任务ID不存在'}, status=status.HTTP_400_BAD_REQUEST)
+
+        service = AliyunTingwuService()
+        try:
+            result = service.get_task_info(task.task_id)
+            
+            # 兼容处理响应结构 {"Data": {"TaskStatus": "...", "Result": ...}}
+            # 有些情况下 SDK 返回的是 JSON 字符串，需要二次解析
+            if isinstance(result, str):
+                import json
+                try:
+                    result = json.loads(result)
+                except:
+                    pass
+
+            if isinstance(result, dict):
+                data_obj = result.get('Data', result)
+            else:
+                data_obj = result
+            if not isinstance(data_obj, dict):
+                # 如果 Data 不是字典，可能它本身就是字符串，或者 result 结构更平铺
+                data_obj = result
+            
+            # 防御性编程：确保 data_obj 是字典
+            if not isinstance(data_obj, dict):
+                 logger.error(f"Unexpected response format: {type(data_obj)} - {data_obj}")
+                 return Response({'error': f"Unexpected response format: {type(data_obj)}"}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)
+
+            # 调用 Service 进行解析和更新
+            service.parse_and_update_task(task, result)
+            
+            # 如果任务成功但 AI 总结仍为空 (可能之前解析没触发，或者大模型调用失败)
+            # 再次尝试强制触发 summarize_task (如果原始数据存在)
+            # 注意：service.parse_and_update_task 内部已经尝试异步触发，这里作为补救措施
+            if task.status == TranscriptionTask.Status.SUCCEEDED and not task.summary:
+                if task.summary_data or task.auto_chapters_data:
+                     try:
+                         # 先设置状态为 "AI总结生成当中..."
+                         task.summary = "AI总结生成当中..."
+                         task.save(update_fields=['summary'])
+                         
+                         # 异步触发总结生成
+                         import threading
+                         from .bailian_service import BailianService
+                         
+                         def async_summarize(task_id):
+                             try:
+                                 # 重新获取 task 对象以避免线程问题
+                                 from .models import TranscriptionTask
+                                 task_obj = TranscriptionTask.objects.get(id=task_id)
+                                 bailian_service = BailianService()
+                                 bailian_service.summarize_task(task_obj)
+                             except Exception as e:
+                                 logger.error(f"Async summary generation failed: {e}")
+                                 
+                         threading.Thread(target=async_summarize, args=(task.id,)).start()
+                         
+                     except Exception as e:
+                         logger.error(f"Force trigger AI summarization failed: {e}")
+
+            # 重新获取 task 以包含更新后的关联字段
+            task.refresh_from_db()
+            
+            serializer = self.get_serializer(task)
+            return Response(serializer.data)
+            
+        except Exception as e:
+            logger.error(f"刷新任务状态失败: {e}")
+            return Response({'error': str(e)}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)