AI 直播智能体：24 小时不间断的虚拟主播

┌─────────────────────────────────────────────────────┐
│                  AI 直播智能体                        │
│                                                      │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐          │
│  │ 感知模块   │  │ 决策模块   │  │ 执行模块   │          │
│  │ 弹幕监听   │→ │ 对话引擎   │→ │ 语音合成   │          │
│  │ 礼物检测   │  │ 情感计算   │  │ 数字人驱动  │          │
│  │ 人流统计   │  │ 行为规划   │  │ 画面渲染   │          │
│  └──────────┘  └──────────┘  └──────────┘          │
│                                                      │
│  ┌──────────────────────────────────┐               │
│  │           知识与数据层             │               │
│  │  商品库 │ 话术库 │ FAQ库 │ 销售策略 │               │
│  └──────────────────────────────────┘               │
│                                                      │
│  ┌──────────────────────────────────┐               │
│  │           推流与分发层             │               │
│  │  RTMP推流 │ 多平台分发 │ 录制存档   │               │
│  └──────────────────────────────────┘               │
└─────────────────────────────────────────────────────┘

class LiveStreamDialogueEngine:
    """AI 直播对话引擎"""
    
    def __init__(self):
        self.llm = OpenAI()  # GPT-4o
        self.rag = RAGEngine()  # 商品知识检索
        self.emotion_analyzer = EmotionAnalyzer()
        self.behavior_planner = BehaviorPlanner()
        self.conversation_buffer = []
        
        # 直播人设
        self.persona = """
        你是「小美」，一位时尚电商主播。
        - 热情开朗，说话有感染力
        - 熟悉所有商品信息
        - 善于引导下单，不强行推销
        - 回答简洁，每句不超过50字
        - 适当使用表情和语气词
        """
    
    async def process_danmu(self, danmu_list):
        """处理弹幕"""
        # 1. 弹幕分类
        categorized = self._categorize_danmu(danmu_list)
        
        # 2. 优先级排序
        prioritized = self._prioritize(categorized)
        
        # 3. 生成回复
        responses = []
        for item in prioritized[:3]:  # 每轮最多回复3条
            reply = await self._generate_reply(item)
            responses.append(reply)
        
        # 4. 主动行为（无弹幕时）
        if not responses:
            responses = await self._proactive_behavior()
        
        return responses
    
    def _categorize_danmu(self, danmu_list):
        """弹幕分类"""
        categories = {
            "product_question": [],   # 商品问题
            "price_inquiry": [],      # 价格咨询
            "order_issue": [],        # 订单问题
            "chat": [],               # 闲聊
            "spam": [],               # 垃圾信息
        }
        
        for danmu in danmu_list:
            # 使用轻量分类模型
            category = self._classify(danmu["text"])
            if category != "spam":
                categories[category].append(danmu)
        
        return categories
    
    async def _generate_reply(self, item):
        """生成回复"""
        # RAG 检索相关知识
        context = await self.rag.search(item["text"])
        
        # LLM 生成回复
        reply = await self.llm.chat.completions.create(
            model="gpt-4o",
            messages=[
                {"role": "system", "content": self.persona},
                {"role": "system", "content": f"知识库信息：{context}"},
                {"role": "user", "content": item["text"]}
            ],
            max_tokens=100,
            temperature=0.7
        )
        
        return {
            "type": item["category"],
            "user": item["user"],
            "reply": reply.choices[0].message.content,
            "emotion": self.emotion_analyzer.analyze(reply.choices[0].message.content)
        }

├── 开场话术
│   ├── 早上开场.json
│   ├── 下午开场.json
│   └── 晚间开场.json
├── 商品讲解
│   ├── 产品A_详细讲解.json
│   ├── 产品A_卖点提炼.json
│   ├── 产品A_常见问题.json
│   └── ...
├── 互动话术
│   ├── 欢迎新粉丝.json
│   ├── 感谢关注.json
│   ├── 感谢礼物.json
│   └── 引导分享.json
├── 逼单话术
│   ├── 限时优惠.json
│   ├── 库存紧张.json
│   └── 最后冲刺.json
└── 结束话术
    └── 下播告别.json

async def generate_product_pitch(product_info):
    """根据商品信息自动生成讲解话术"""
    
    prompt = f"""
    商品信息：
    - 名称：{product_info['name']}
    - 价格：{product_info['price']}
    - 卖点：{product_info['selling_points']}
    - 适用人群：{product_info['target_audience']}
    
    请生成3分钟的商品讲解话术，包含：
    1. 痛点引入（30秒）
    2. 产品介绍（60秒）
    3. 卖点演示（60秒）
    4. 逼单引导（30秒）
    """
    
    pitch = await llm.generate(prompt)
    return pitch

class DigitalHuman2D:
    """2D 数字人直播驱动"""
    
    def __init__(self):
        self.tts = CosyVoice2("pretrained_model")
        self.lip_sync = Wav2LipPlusPlus()
        self.base_image = "anchor_base.jpg"
        
    async def generate_frame(self, text, emotion="happy"):
        """生成一帧直播画面"""
        # 1. TTS 合成语音
        audio = self.tts.synthesize(
            text=text,
            voice_id="anchor_voice",
            emotion=emotion
        )
        
        # 2. 唇形同步
        video_frame = self.lip_sync.drive(
            source_image=self.base_image,
            audio=audio
        )
        
        return video_frame, audio
    
    async def live_stream(self, dialogue_engine):
        """直播主循环"""
        while True:
            # 获取弹幕
            danmu = await self._get_danmu()
            
            # 生成回复
            responses = await dialogue_engine.process_danmu(danmu)
            
            for resp in responses:
                # 生成画面和音频
                frame, audio = await self.generate_frame(
                    resp["reply"], 
                    resp["emotion"]
                )
                
                # 推流
                await self._push_stream(frame, audio)

class DigitalHuman3D:
    """3D 数字人直播驱动（Unreal Engine）"""
    
    def __init__(self):
        self.ue_connection = UE5Connection()
        self.tts = CosyVoice2("pretrained_model")
        
    async def drive(self, text, emotion, action=None):
        """驱动 3D 数字人"""
        # 1. TTS
        audio = self.tts.synthesize(text=text, emotion=emotion)
        
        # 2. 音频到 blendshape
        blendshapes = self.audio_to_blendshape(audio)
        
        # 3. 情感 blendshape
        emotion_bs = self.emotion_to_blendshape(emotion)
        
        # 4. 动作
        if action:
            animation = self.get_animation(action)
        else:
            animation = self.idle_animation()
        
        # 5. 发送到 UE5
        self.ue_connection.send_command(
            "DriveDigitalHuman",
            {
                "blendshapes": blendshapes + emotion_bs,
                "animation": animation,
                "audio": audio
            }
        )

import subprocess
import asyncio

class StreamPusher:
    """RTMP 推流器"""
    
    def __init__(self, rtmp_url):
        self.rtmp_url = rtmp_url
        self.ffmpeg_process = None
    
    def start(self):
        """启动推流"""
        self.ffmpeg_process = subprocess.Popen([
            "ffmpeg",
            "-y",
            "-re",
            "-f", "rawvideo",
            "-pix_fmt", "bgr24",
            "-s", "1920x1080",
            "-r", "30",
            "-i", "-",              # 视频输入
            "-f", "s16le",
            "-ar", "48000",
            "-ac", "2",
            "-i", "-",              # 音频输入
            "-c:v", "libx264",
            "-preset", "veryfast",
            "-tune", "zerolatency",
            "-b:v", "4000k",
            "-c:a", "aac",
            "-b:a", "128k",
            "-f", "flv",
            self.rtmp_url
        ], stdin=subprocess.PIPE)
    
    def push_frame(self, frame, audio):
        """推送一帧"""
        self.ffmpeg_process.stdin.write(frame.tobytes())
        self.ffmpeg_process.stdin.write(audio.tobytes())
    
    def stop(self):
        """停止推流"""
        if self.ffmpeg_process:
            self.ffmpeg_process.stdin.close()
            self.ffmpeg_process.terminate()

class MultiPlatformStreamer:
    """多平台直播分发"""
    
    def __init__(self):
        self.platforms = {
            "douyin": "rtmp://push.douyin.com/live/{key}",
            "kuaishou": "rtmp://push.kuaishou.com/live/{key}",
            "taobao": "rtmp://push.taobao.com/live/{key}",
            "bilibili": "rtmp://live-push.bilivideo.com/live-bvc/{key}"
        }
    
    def start_multi_stream(self, video_source, platform_keys):
        """同时推流到多个平台"""
        processes = []
        for platform, key in platform_keys.items():
            rtmp_url = self.platforms[platform].format(key=key)
            
            # 使用 tee 模式或 nginx-rtmp 分发
            cmd = f"""
            ffmpeg -i {video_source} 
            -c copy -f flv "{rtmp_url}"
            """
            p = subprocess.Popen(cmd, shell=True)
            processes.append(p)
        
        return processes

class DanmuPrioritizer:
    """弹幕优先级管理"""
    
    PRIORITY_RULES = {
        "gift_high": 100,      # 大额礼物
        "order_question": 90,   # 订单问题
        "product_question": 80, # 商品问题
        "price_inquiry": 70,    # 价格咨询
        "gift_low": 60,         # 小额礼物
        "follow": 50,           # 关注
        "chat": 30,             # 闲聊
        "repeat": 10,           # 重复问题
    }
    
    def prioritize(self, danmu_list):
        """对弹幕排序"""
        scored = []
        for d in danmu_list:
            category = self._classify(d)
            score = self.PRIORITY_RULES.get(category, 20)
            
            # 紧急程度加分
            if self._is_urgent(d):
                score += 20
            
            # 新用户加分
            if d["is_new_user"]:
                score += 10
            
            scored.append((d, score))
        
        scored.sort(key=lambda x: x[1], reverse=True)
        return [d for d, _ in scored]

class AntiSpamFilter:
    """防刷屏过滤"""
    
    def __init__(self):
        self.user_cooldown = {}  # 用户冷却
        self.recent_messages = []  # 最近消息
    
    def filter(self, danmu_list):
        """过滤刷屏"""
        filtered = []
        for d in danmu_list:
            # 1. 用户冷却（5秒内不重复回复同一用户）
            if self._in_cooldown(d["user_id"]):
                continue
            
            # 2. 重复内容检测
            if self._is_duplicate(d["text"]):
                continue
            
            # 3. 敏感词过滤
            if self._has_sensitive_words(d["text"]):
                continue
            
            # 4. 机器人检测
            if self._is_bot(d):
                continue
            
            filtered.append(d)
        
        return filtered

class StreamPacingController:
    """直播节奏控制"""
    
    SCHEDULE = {
        "00:00-05:00": {"action": "opening", "energy": "high"},
        "05:00-20:00": {"action": "product_intro", "energy": "high"},
        "20:00-25:00": {"action": "interaction", "energy": "medium"},
        "25:00-30:00": {"action": "promotion", "energy": "high"},
        "30:00-35:00": {"action": "qa_session", "energy": "medium"},
        "35:00-40:00": {"action": "flash_sale", "energy": "high"},
        "40:00-45:00": {"action": "product_intro_2", "energy": "medium"},
        "45:00-50:00": {"action": "interaction", "energy": "medium"},
        "50:00-55:00": {"action": "final_push", "energy": "high"},
        "55:00-60:00": {"action": "closing", "energy": "medium"},
    }
    
    def get_current_action(self, stream_time):
        """根据直播时间获取当前动作"""
        minute = stream_time % 60
        for period, action in self.SCHEDULE.items():
            start, end = map(int, period.split("-")[0].split(":")[0:2] + 
                           period.split("-")[1].split(":")[0:2])
            if start <= minute < end:
                return action
        return {"action": "idle", "energy": "low"}

AI 直播 → AI 简化模式 → 录播循环 → 静态画面
    ↑           ↑            ↑
  正常运行    API 限流     服务故障