2025：当代码遇见智能——腾讯AI工具链重塑开发新范式

输入：开发一个带微信登录的国风五子棋PWA，支持实时对战和历史复盘
CodeBuddy解析：
- 技术栈：Vue3 + TypeScript + Express + SQLite + Socket.io
- 核心模块：AuthModule, GameModule, HistoryModule, RealtimeModule
- 关键算法：五子棋胜负判定（优化版）、JWT无状态认证、WebSocket房间管理

// CodeBuddy生成的优化版五子棋胜负判定算法
// 时间复杂度：O(1) 每次落子检测，空间复杂度：O(n²)

export interface BoardState {
  size: number;
  board: (0 | 1 | 2)[][];
  lastMove: [number, number] | null;
}

export class GameCore {
  private directions = [
    [0, 1], [1, 0], [1, 1], [1, -1]  // 四个方向向量
  ];

  /**
   * 增量式胜负判定：仅检测最新落子周围
   * @param boardState 当前棋盘状态
   * @returns 0:未结束, 1:黑胜, 2:白胜, 3:平局
   */
  checkWinner(boardState: BoardState): 0 | 1 | 2 | 3 {
    const { board, lastMove, size } = boardState;
    if (!lastMove) return 0;

    const [x, y] = lastMove;
    const currentPlayer = board[x][y];
    if (currentPlayer === 0) return 0;

    // 四方向计数优化：每个方向只需遍历4步
    for (const [dx, dy] of this.directions) {
      let count = 1;
      
      // 正向计数
      for (let i = 1; i < 5; i++) {
        const nx = x + dx * i, ny = y + dy * i;
        if (nx < 0 || nx >= size || ny < 0 || ny >= size || board[nx][ny] !== currentPlayer) break;
        count++;
      }
      
      // 反向计数
      for (let i = 1; i < 5; i++) {
        const nx = x - dx * i, ny = y - dy * i;
        if (nx < 0 || nx >= size || ny < 0 || ny >= size || board[nx][ny] !== currentPlayer) break;
        count++;
      }

      if (count >= 5) return currentPlayer as 1 | 2;
    }

    // 平局检测：统计空位
    const hasEmpty = board.some(row => row.some(cell => cell === 0));
    return hasEmpty ? 0 : 3;
  }

  /**
   * AI落子策略：基于评分函数的贪心算法
   */
  getAIMove(boardState: BoardState): [number, number] {
    const { board, size } = boardState;
    let bestScore = -Infinity;
    let bestMove: [number, number] = [7, 7]; // 默认天元
    
    // 遍历候选位置（仅空位周围一圈）
    const candidates = this.getCandidateMoves(board, size);
    
    for (const [x, y] of candidates) {
      board[x][y] = 2; // 假设AI执白
      
      const score = this.evaluatePosition(board, [x, y]);
      
      board[x][y] = 0; // 回溯
      
      if (score > bestScore) {
        bestScore = score;
        bestMove = [x, y];
      }
    }
    
    return bestMove;
  }

  private getCandidateMoves(board: number[][], size: number): [number, number][] {
    const candidates = new Set<string>();
    for (let i = 0; i < size; i++) {
      for (let j = 0; j < size; j++) {
        if (board[i][j] !== 0) {
          // 在已有棋子周围生成候选点
          for (let dx = -1; dx <= 1; dx++) {
            for (let dy = -1; dy <= 1; dy++) {
              const ni = i + dx, nj = j + dy;
              if (ni >= 0 && ni < size && nj >= 0 && nj < size && board[ni][nj] === 0) {
                candidates.add(`${ni},${nj}`);
              }
            }
          }
        }
      }
    }
    return Array.from(candidates).map(s => s.split(',').map(Number) as [number, number]);
  }

  private evaluatePosition(board: number[][], lastMove: [number, number]): number {
    // 评分函数：评估落子价值
    let score = 0;
    const player = board[lastMove[0]][lastMove[1]];
    
    // 活四、冲四、活三等模式识别
    const patterns = this.analyzePatterns(board, lastMove, player);
    
    score += patterns.liveFour * 10000;
    score += patterns.rushFour * 1000;
    score += patterns.liveThree * 100;
    score += patterns.liveTwo * 10;
    
    return score;
  }

  private analyzePatterns(board: number[][], lastMove: [number, number], player: number) {
    // 模式识别逻辑（简化版）
    return { liveFour: 0, rushFour: 0, liveThree: 0, liveTwo: 0 };
  }
}

import { Server, Socket } from 'socket.io';
import jwt from 'jsonwebtoken';

interface Room {
  id: string;
  players: [Socket | null, Socket | null];
  boardState: BoardState;
  createdAt: number;
}

export class RoomManager {
  private rooms = new Map<string, Room>();
  private waitingQueue: Socket[] = [];

  constructor(private io: Server) {
    this.startMatchmaking();
  }

  /**
   * 快速匹配算法：基于Elo评分的动态匹配
   */
  private startMatchmaking() {
    setInterval(() => {
      while (this.waitingQueue.length >= 2) {
        const player1 = this.waitingQueue.shift()!;
        const player2 = this.waitingQueue.shift()!;
        
        // 简单的随机匹配（实际生产环境应基于Elo评分差）
        const roomId = `room_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`;
        
        const initialBoard: BoardState = {
          size: 15,
          board: Array(15).fill(null).map(() => Array(15).fill(0)),
          lastMove: null
        };

        this.rooms.set(roomId, {
          id: roomId,
          players: [player1, player2],
          boardState: initialBoard,
          createdAt: Date.now()
        });

        player1.join(roomId);
        player2.join(roomId);

        this.io.to(roomId).emit('gameStart', { 
          roomId, 
          players: [player1.id, player2.id],
          boardState: initialBoard 
        });
      }
    }, 3000); // 每3秒尝试匹配一次
  }

  /**
   * 处理用户落子：带状态机校验
   */
  handleMove(socket: Socket, data: { roomId: string; position: [number, number] }) {
    const { roomId, position } = data;
    const room = this.rooms.get(roomId);
    
    if (!room) {
      socket.emit('error', { message: '房间不存在' });
      return;
    }

    const playerIndex = room.players[0]?.id === socket.id ? 0 : 
                       room.players[1]?.id === socket.id ? 1 : -1;
    
    if (playerIndex === -1) {
      socket.emit('error', { message: '你不是房间玩家' });
      return;
    }

    // 校验当前是否是该玩家回合
    const currentPlayer = this.getCurrentPlayer(room.boardState);
    if (currentPlayer !== playerIndex + 1) {
      socket.emit('error', { message: '不是你的回合' });
      return;
    }

    // 更新棋盘状态
    const [x, y] = position;
    if (room.boardState.board[x][y] !== 0) {
      socket.emit('error', { message: '位置已被占用' });
      return;
    }

    room.boardState.board[x][y] = currentPlayer;
    room.boardState.lastMove = position;

    // 广播落子事件
    this.io.to(roomId).emit('move', { 
      player: currentPlayer, 
      position,
      boardState: room.boardState 
    });

    // 胜负判定
    const gameCore = new GameCore();
    const result = gameCore.checkWinner(room.boardState);
    
    if (result !== 0) {
      this.io.to(roomId).emit('gameEnd', { 
        winner: result === 3 ? 'draw' : `player${result}`,
        boardState: room.boardState 
      });
      this.rooms.delete(roomId);
    }
  }

  private getCurrentPlayer(boardState: BoardState): 1 | 2 {
    // 统计棋子数判断当前玩家
    let count1 = 0, count2 = 0;
    for (const row of boardState.board) {
      for (const cell of row) {
        if (cell === 1) count1++;
        if (cell === 2) count2++;
      }
    }
    return count1 === count2 ? 1 : 2;
  }
}

# video_script_generator.py
import requests
from openai import OpenAI
import json
from dataclasses import dataclass
from typing import List, Dict

@dataclass
class ProductInfo:
    id: str
    name: str
    price: float
    features: List[str]
    category: str
    user_ratings: float

class ScriptGenerator:
    def __init__(self, deepseek_api_key: str):
        self.client = OpenAI(
            api_key=deepseek_api_key,
            base_url="https://api.deepseek.com/v1"
        )
    
    def fetch_product_data(self, product_id: str) -> ProductInfo:
        """从内部API获取商品信息"""
        response = requests.get(
            f"https://api.internal.com/products/{product_id}",
            headers={"Authorization": f"Bearer {INTERNAL_TOKEN}"}
        )
        data = response.json()
        
        # 并行爬取用户评论
        reviews = self.scrape_reviews(product_id)
        
        return ProductInfo(
            id=product_id,
            name=data["name"],
            price=data["price"],
            features=data["highlights"],
            category=data["category"],
            user_ratings=self.analyze_sentiment(reviews)
        )
    
    def generate_script(self, product: ProductInfo) -> Dict:
        """
        生成分镜脚本，包含：场景描述、景别、时长、旁白文案
        采用Few-shot Prompt提升输出稳定性
        """
        prompt = f"""
        你是一位资深短视频编导，请为以下商品生成30秒营销视频分镜脚本。
        商品信息：
        - 名称：{product.name}
        - 价格：{product.price}元
        - 核心卖点：{', '.join(product.features[:3])}
        - 用户评分：{product.user_ratings}
        
        输出JSON格式，包含4个分镜：
        {{
            "scenes": [
                {{
                    "scene_id": 1,
                    "description": "国风开场，水墨晕染",
                    "shot_type": "全景",
                    "duration": 5,
                    "voice_over": "千年技艺，今朝焕新",
                    "camera_movement": "缓慢推镜"
                }}
            ]
        }}
        
        示例脚本：
        [此处注入3个优秀案例]
        """
        
        response = self.client.chat.completions.create(
            model="deepseek-chat",
            messages=[
                {"role": "system", "content": "输出严格JSON格式"},
                {"role": "user", "content": prompt}
            ],
            temperature=0.7,
            response_format={"type": "json_object"}
        )
        
        return json.loads(response.choices[0].message.content)
    
    def scrape_reviews(self, product_id: str) -> List[str]:
        """爬取京东/淘宝用户评论"""
        # 使用Playwright模拟浏览器行为
        from playwright.sync_api import sync_playwright
        
        with sync_playwright() as p:
            browser = p.chromium.launch(headless=True)
            page = browser.new_page()
            page.goto(f"https://www.jd.com/product/{product_id}.html")
            
            # 模拟滚动加载评论
            page.evaluate("window.scrollTo(0, document.body.scrollHeight)")
            page.wait_for_timeout(2000)
            
            reviews = page.locator(".comment-item .comment-con").all_text_contents()
            browser.close()
            
            return reviews[:50]  # 取前50条
    
    def analyze_sentiment(self, reviews: List[str]) -> float:
        """简单情感分析计算平均评分"""
        # 实际生产环境应调用混元NLP接口
        positive_words = ['好', '满意', '推荐', '不错', '喜欢']
        total_score = 0
        
        for review in reviews:
            score = sum(1 for word in positive_words if word in review)
            total_score += min(score / 3, 1) * 5  # 映射到5分制
        
        return total_score / len(reviews) if reviews else 4.5

# hunyuan_video_client.py
import requests
import time
import os
from typing import Optional, Callable
from concurrent.futures import ThreadPoolExecutor, as_completed

class HunyuanVideoClient:
    def __init__(self, secret_id: str, secret_key: str):
        self.secret_id = secret_id
        self.secret_key = secret_key
        self.base_url = "https://hunyuan.tencentcloudapi.com"
        
    def generate_video(
        self, 
        prompt: str, 
        negative_prompt: str = "模糊,扭曲,低质量",
        height: int = 720,
        width: int = 1280,
        video_length: int = 5,
        seed: int = 42,
        callback: Optional[Callable] = None
    ) -> str:
        """
        调用混元Video API生成视频
        采用异步轮询机制，支持批量任务管理
        """
        # Step 1: 提交生成任务
        payload = {
            "Prompt": prompt,
            "NegativePrompt": negative_prompt,
            "Height": height,
            "Width": width,
            "VideoLength": video_length,
            "Seed": seed,
            "VaeDecoderTemp": 1.0,
            "MotionSpeed": 0.8,  # 控制画面运动幅度
            "CameraControl": "slow_zoom_in"  # 摄影机控制参数
        }
        
        headers = self._get_auth_headers()
        response = requests.post(
            f"{self.base_url}/v2/SubmitVideoGenerationTask",
            json=payload,
            headers=headers
        )
        
        task_id = response.json()["Response"]["TaskId"]
        
        # Step 2: 轮询任务状态
        while True:
            status = self._query_task_status(task_id)
            
            if callback:
                callback(task_id, status)
            
            if status["Status"] == "SUCCESS":
                return status["VideoUrl"]
            elif status["Status"] == "FAILED":
                raise Exception(f"Video generation failed: {status['ErrorMessage']}")
            
            time.sleep(5)  # 每5秒查询一次
    
    def batch_generate(
        self, 
        scripts: List[Dict], 
        max_workers: int = 3
    ) -> List[str]:
        """
        批量生成视频，支持并发控制和错误重试
        """
        video_urls = []
        
        with ThreadPoolExecutor(max_workers=max_workers) as executor:
            future_to_scene = {
                executor.submit(self._generate_single_scene, scene): scene 
                for scene in scripts["scenes"]
            }
            
            for future in as_completed(future_to_scene):
                scene = future_to_scene[future]
                try:
                    url = future.result()
                    video_urls.append(url)
                except Exception as e:
                    print(f"场景{scene['scene_id']}生成失败: {e}")
                    # 失败场景使用占位视频
                    video_urls.append("https://placeholder.video/default.mp4")
        
        return video_urls
    
    def _generate_single_scene(self, scene: Dict) -> str:
        """生成单个分镜视频"""
        # 构建Prompt：融合商品特征与国风元素
        enhanced_prompt = f"""
        (masterpiece, best quality, cinematic lighting)
        {scene['description']}, 
        商品展示: {scene.get('product_feature', '')},
        国风美学, 水墨渲染, 8K超清,
        camera movement: {scene['camera_movement']}
        """
        
        # 重试机制：失败时自动降低分辨率重试
        for attempt in range(3):
            try:
                resolution = 1080 if attempt == 0 else 720
                return self.generate_video(
                    prompt=enhanced_prompt,
                    height=resolution,
                    width=int(resolution * 16/9),
                    video_length=scene["duration"]
                )
            except Exception as e:
                if attempt == 2:
                    raise
                time.sleep(2 ** attempt)  # 指数退避
    
    def _get_auth_headers(self) -> Dict:
        """腾讯云API签名生成"""
        import hmac
        import hashlib
        import base64
        
        timestamp = str(int(time.time()))
        sign_str = f"POSThunyuan.tencentcloudapi.com/?{timestamp}"
        
        sign = base64.b64encode(
            hmac.new(
                self.secret_key.encode(),
                sign_str.encode(),
                hashlib.sha256
            ).digest()
        ).decode()
        
        return {
            "Authorization": f"TC3-HMAC-SHA256 Credential={self.secret_id}/{timestamp}/hunyuan/request, SignedHeaders=content-type, Signature={sign}",
            "Content-Type": "application/json",
            "X-TC-Version": "2025-11-20"
        }

# 使用示例
if __name__ == "__main__":
    client = HunyuanVideoClient(
        secret_id=os.getenv("TX_SECRET_ID"),
        secret_key=os.getenv("TX_SECRET_KEY")
    )
    
    # 批量生成4个分镜
    script = {
        "scenes": [
            {
                "scene_id": 1,
                "description": "水墨风格开场，产品若隐若现",
                "duration": 5,
                "camera_movement": "slow_zoom_in"
            },
            # ... 更多分镜
        ]
    }
    
    urls = client.batch_generate(script, max_workers=4)
    print(f"生成完成，视频URL: {urls}")

# video_composer.py
from moviepy.editor import VideoFileClip, concatenate_videoclips, TextClip, CompositeVideoClip
import requests
from io import BytesIO

class VideoComposer:
    def __init__(self):
        self.font_path = "/path/to/chinese_font.ttf"  # 中文字体路径
    
    def compose_final_video(
        self, 
        scene_urls: List[str], 
        script: Dict,
        output_path: str
    ):
        """
        将分镜视频合并为最终成品，添加转场和字幕
        """
        clips = []
        
        for i, url in enumerate(scene_urls):
            # 下载视频片段
            resp = requests.get(url)
            temp_path = f"/tmp/scene_{i}.mp4"
            with open(temp_path, "wb") as f:
                f.write(resp.content)
            
            scene = script["scenes"][i]
            clip = VideoFileClip(temp_path)
            
            # 添加旁白字幕
            if scene.get("voice_over"):
                txt_clip = TextClip(
                    scene["voice_over"],
                    fontsize=48,
                    font=self.font_path,
                    color='white',
                    stroke_color='black',
                    stroke_width=2
                ).set_position(('center', 'bottom')).set_duration(clip.duration)
                
                clip = CompositeVideoClip([clip, txt_clip])
            
            clips.append(clip)
        
        # 合并所有片段
        final_clip = concatenate_videoclips(clips, method="compose")
        
        # 添加背景音乐（国风）
        from moviepy.audio.io.AudioFileClip import AudioFileClip
        bgm = AudioFileClip("/path/to/guzheng_bgm.mp3").subclip(0, final_clip.duration)
        final_clip = final_clip.set_audio(bgm)
        
        # 输出最终视频
        final_clip.write_videofile(
            output_path,
            fps=30,
            codec='libx264',
            audio_codec='aac',
            threads=4
        )
        
        # 清理临时文件
        for i in range(len(scene_urls)):
            os.remove(f"/tmp/scene_{i}.mp4")

# 交互式创建全栈项目
tcb ai init --name shop-admin --template fullstack-ts

# AI自动识别需求，生成以下结构：
# .
# ├── cloudbaserc.js          # 云环境配置
# ├── functions/              # 云函数目录
# │   ├── product/
# │   │   ├── index.ts        # 商品CRUD
# │   │   └── model.ts        # 数据模型
# │   └── order/
# │       ├── index.ts        # 订单处理
# │       └── payment.ts      # 支付对接
# ├── web/                    # 前端Vue3项目
# │   ├── src/api/            # 自动生成的API调用
# │   └── src/views/          # 基础页面模板
# └── ai-instructions.md      # AI生成指令缓存

// functions/order/index.ts - AI生成代码
import { FunctionContext } from '@cloudbase/node-sdk';
import { OrderModel } from './model';
import { PaymentService } from './payment';
import { NotificationService } from '../notification/service';

/**
 * CloudBase AI CLI生成的订单创建云函数
 * 自动集成：参数校验、库存锁定、支付发起、消息通知
 */

interface CreateOrderRequest {
  userId: string;
  productId: string;
  quantity: number;
  addressId: string;
  couponId?: string;
}

export async function createOrder(ctx: FunctionContext) {
  const { userId, productId, quantity, addressId, couponId } = ctx.body as CreateOrderRequest;
  
  // AI自动注入的校验逻辑
  if (!userId || !productId || quantity <= 0) {
    return { code: 400, message: '参数错误' };
  }

  try {
    // Step 1: 库存预扣减（分布式锁防止超卖）
    const stockLock = await acquireDistributedLock(`stock:${productId}`, 5000);
    const product = await ctx.db.collection('products').doc(productId).get();
    
    if (product.stock < quantity) {
      await releaseDistributedLock(stockLock);
      return { code: 409, message: '库存不足' };
    }
    
    await ctx.db.collection('products').doc(productId).update({
      stock: product.stock - quantity,
      lockedStock: product.lockedStock + quantity
    });
    
    // Step 2: 计算订单金额（AI自动识别优惠券逻辑）
    let totalAmount = product.price * quantity;
    if (couponId) {
      const coupon = await ctx.db.collection('coupons').doc(couponId).get();
      if (coupon && coupon.userId === userId && !coupon.used) {
        totalAmount = Math.max(0, totalAmount - coupon.discount);
      }
    }
    
    // Step 3: 创建订单文档
    const orderModel = new OrderModel();
    const order = orderModel.create({
      orderNo: generateOrderNo(),
      userId,
      productId,
      quantity,
      totalAmount,
      status: 'pending_payment',
      createTime: Date.now()
    });
    
    await ctx.db.collection('orders').add(order);
    
    // Step 4: 发起支付（AI自动识别微信支付配置）
    const paymentService = new PaymentService(ctx);
    const paymentResult = await paymentService.createWechatPayment({
      orderNo: order.orderNo,
      amount: totalAmount,
      openId: await getUserOpenId(userId)
    });
    
    // Step 5: 发送通知（AI自动注入消息队列）
    const notifier = new NotificationService(ctx);
    await notifier.sendTemplateMessage({
      userId,
      templateId: 'ORDER_CREATED',
      data: {
        orderNo: order.orderNo,
        amount: totalAmount,
        productName: product.name
      }
    });
    
    await releaseDistributedLock(stockLock);
    
    return {
      code: 200,
      data: {
        orderId: order.id,
        paymentParams: paymentResult
      }
    };
    
  } catch (error) {
    console.error('订单创建失败:', error);
    await releaseDistributedLock(stockLock);
    return { code: 500, message: '系统错误' };
  }
}

// AI自动生成的辅助函数（简化展示）
async function acquireDistributedLock(key: string, ttl: number): Promise<string> {
  const redis = require('@cloudbase/redis');
  const lockValue = Math.random().toString(36);
  const acquired = await redis.set(key, lockValue, 'PX', ttl, 'NX');
  if (!acquired) throw new Error('获取锁失败');
  return lockValue;
}

function generateOrderNo(): string {
  return `ORD${Date.now()}${Math.random().toString(36).substr(2, 6).toUpperCase()}`;
}

# AI自动生成单元测试并执行
tcb ai deploy functions/order --auto-test --coverage-threshold 80

# 输出：
# ✅ 生成测试用例：functions/order/index.test.ts (12个测试点)
# ✅ 本地测试通过：覆盖率82%
# ✅ 部署到云函数：order-prod-xxx
# ✅ 配置API网关：/api/v1/orders
# ✅ 设置监控告警：错误率>5%时通知
# ✅ 预估月费用：约¥120（1万次调用/天）

# 查看实时日志和AI分析
tcb ai logs functions/order --tail --ai-insight

# AI自动识别异常模式：
# ⚠️ 预警：函数超时率3.2%（平均执行时间4.2s，接近5s阈值）
# 💡 建议：1) 将图片上传逻辑迁移至客户端 2) 增加Redis缓存类目数据

// .cloudbase/ai-plugins/internal-ui/index.js
module.exports = {
  name: 'internal-ui',
  version: '1.0.0',
  
  // 注入自定义Prompt上下文
  promptEnhancer(context) {
    return `
      你正在使用腾讯内部UI组件库(TDesign Mobile)。
      优先使用以下组件：
      - 按钮：<t-button theme="primary" size="large">按钮</t-button>
      - 表单：<t-input v-model="value" label="用户名" />
      - 列表：<t-cell-group><t-cell title="订单" /></t-cell-group>
      
      样式变量：
      - 主色：--td-brand-color: #0052d9;
      - 字体：--td-font-family: PingFang SC;
      
      必须遵循移动端适配原则，使用rpx单位。
    `;
  },
  
  // 后置处理器：替换生成代码中的通用组件
  postProcessor(generatedCode) {
    return generatedCode
      .replace(/<el-button/g, '<t-button')
      .replace(/<van-button/g, '<t-button');
  },
  
  // 自定义验证规则
  validators: [
    {
      name: 'check-mobile-adaptation',
      validate(code) {
        if (code.includes('px') && !code.includes('rpx')) {
          return {
            level: 'warning',
            message: '检测到px单位，建议在移动端使用rpx'
          };
        }
        return null;
      }
    }
  ]
};

// 在cloudbaserc.js中注册插件
{
  "ai": {
    "plugins": ["./.cloudbase/ai-plugins/internal-ui"],
    "model": "hunyuan-pro",  // 指定使用混元大模型
    "timeout": 120000
  }
}