云原生CAD：浏览器中的工业设计革命与AI协同范式

# 云原生CAD技术栈示例
tech_stack = {
    "前端框架": ["React", "WebComponents", "Three.js"],
    "几何内核": ["OpenCascade.wasm", "CGAL.js"],
    "协同协议": ["CRDT", "Operational Transformation"],
    "AI引擎": ["TensorFlow.js", "ONNX Runtime Web"],
    "部署平台": ["Kubernetes", "Istio", "Cloudflare Workers"]
}
print(json.dumps(tech_stack, indent=2))

{
  "前端框架": ["React", "WebComponents", "Three.js"],
  "几何内核": ["OpenCascade.wasm", "CGAL.js"],
  "协同协议": ["CRDT", "Operational Transformation"],
  "AI引擎": ["TensorFlow.js", "ONNX Runtime Web"],
  "部署平台": ["Kubernetes", "Istio", "Cloudflare Workers"]
}

// emscripten绑定示例（C++ -> JS）
EMSCRIPTEN_BINDINGS(brep_module) {
    class_<BRepModel>("BRepModel")
        .constructor<>()
        .function("extrude", &BRepModel::extrude)
        .function("fillet", &BRepModel::fillet);
        
    value_array<std::array<double, 3>>("array_double_3")
        .element(emscripten::index<0>())
        .element(emscripten::index<1>())
        .element(emscripten::index<2>());
        
    register_vector<Face*>("VectorFace");
}

class OTController {
  constructor(doc) {
    this.doc = doc; // 当前文档状态
    this.ops = [];  // 操作记录
  }

  applyOperation(op) {
    // 转换函数处理并发冲突
    const transformed = this.ops.reduce((acc, prevOp) => {
      return transformOperation(acc, prevOp);
    }, op);
    
    // 应用转换后的操作
    this.doc = applyOp(this.doc, transformed);
    this.ops.push(transformed);
    
    return transformed;
  }

  // 操作转换核心算法
  transformOperation(op1, op2) {
    if (op1.target !== op2.target) return op1;
    
    // 移动操作冲突解决
    if (op1.type === 'MOVE' && op2.type === 'MOVE') {
      return {
        ...op1,
        vec: vectorAdd(op1.vec, op2.vec)
      };
    }
    
    // 旋转与移动的转换
    if (op1.type === 'ROTATE' && op2.type === 'MOVE') {
      const rotatedVec = applyRotation(op2.vec, op1.axis, op1.angle);
      return {
        type: 'MOVE',
        target: op1.target,
        vec: rotatedVec
      };
    }
    
    // 其他转换规则...
  }
}

# 基于TensorFlow.js的浏览器端特征提取
import * as tf from '@tensorflow/tfjs';
import {loadGraphModel} from '@tensorflow/tfjs-converter';

class DesignFeatureExtractor {
  constructor() {
    this.model = null;
  }

  async loadModel() {
    this.model = await loadGraphModel('web_models/feature_extractor/model.json');
  }

  extract(canvas) {
    const img = tf.browser.fromPixels(canvas);
    const normalized = img.div(255.0).expandDims(0);
    
    // 使用WebGL加速推理
    const features = this.model.execute(normalized);
    
    return features.dataSync();
  }
}

// 应用示例
const extractor = new DesignFeatureExtractor();
await extractor.loadModel();

canvasElement.addEventListener('mousemove', () => {
  const features = extractor.extract(canvasElement);
  // 实时生成设计建议
  generateSuggestion(features);
});

# 几何计算服务HPA配置
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: geometry-service-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: geometry-service
  minReplicas: 3
  maxReplicas: 50
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 60
  - type: Pods
    pods:
      metric:
        name: gpu_utilization
      target:
        type: AverageValue
        averageValue: 70

// 使用Web Crypto API保护设计数据
async function encryptDesignData(data, password) {
  // 生成密钥
  const keyMaterial = await window.crypto.subtle.importKey(
    'raw',
    new TextEncoder().encode(password),
    {name: 'PBKDF2'},
    false,
    ['deriveKey']
  );
  
  const salt = window.crypto.getRandomValues(new Uint8Array(16));
  const key = await window.crypto.subtle.deriveKey(
    {
      name: 'PBKDF2',
      salt,
      iterations: 100000,
      hash: 'SHA-256'
    },
    keyMaterial,
    {name: 'AES-GCM', length: 256},
    false,
    ['encrypt', 'decrypt']
  );
  
  const iv = window.crypto.getRandomValues(new Uint8Array(12));
  const encrypted = await window.crypto.subtle.encrypt(
    {name: 'AES-GCM', iv},
    key,
    new TextEncoder().encode(JSON.stringify(data))
  );
  
  return {
    salt: Array.from(salt),
    iv: Array.from(iv),
    data: Array.from(new Uint8Array(encrypted))
  };
}

// 参数化设计+AI优化示例
class GearDesigner {
  constructor() {
    this.params = {
      module: 2,
      teeth: 20,
      pressureAngle: 20
    };
  }

  generateGear() {
    // 调用WASM几何内核
    const gear = wasmModule.generateInvoluteGear(
      this.params.module,
      this.params.teeth,
      this.params.pressureAngle
    );
    return gear;
  }

  async optimizeForStrength() {
    // 调用AI优化服务
    const response = await fetch('/api/ai/optimize', {
      method: 'POST',
      headers: {'Content-Type': 'application/json'},
      body: JSON.stringify({
        type: 'GEAR',
        params: this.params,
        constraints: {
          maxStress: 200, // MPa
          minEfficiency: 0.95
        }
      })
    });
    
    const result = await response.json();
    this.params = result.optimizedParams;
    
    return this.generateGear();
  }
}

gantt
    title 云原生CAD实施路线图
    dateFormat  YYYY-MM-DD
    section 基础架构
    几何内核移植       ：active,  a1, 2023-08-01, 90d
    协同框架开发       ：         a2, after a1, 60d
    GPU加速服务部署    ：         a3, after a2, 45d
    
    section AI集成
    草图识别模型训练   ：         b1, 2023-10-01, 120d
    参数优化引擎      ：         b2, after b1, 90d
    制造性分析模块    ：         b3, after b2, 60d
    
    section 生产部署
    压力测试         ：         c1, 2024-04-01, 30d
    用户验收测试     ：         c2, after c1, 45d
    正式上线         ：         c3, after c2, 15d

云原生CAD价值指数 = (协作效率增益 × 0.3) 
                 + (创新速度提升 × 0.4) 
                 + (总拥有成本下降 × 0.3)
                 × 数据资产系数