12/3/25About 17 min
第16章：高级特性和最新发展
学习目标
- 掌握Chrome扩展的高级特性和API
- 了解Manifest V3的最新特性
- 学会使用WebAssembly增强扩展性能
- 理解AI集成和机器学习应用
- 掌握跨浏览器兼容性开发
1. Manifest V3高级特性
1.1 Service Worker高级应用
Manifest V3引入了Service Worker替代Background Pages，提供了更强大的功能。
// src/background/advanced-service-worker.js
class AdvancedServiceWorker {
constructor() {
this.initializeModules();
this.setupAdvancedListeners();
this.startBackgroundTasks();
}
initializeModules() {
// 模块化初始化
this.modules = {
networking: new NetworkingModule(),
ai: new AIModule(),
analytics: new AdvancedAnalytics(),
sync: new CrossPlatformSync(),
security: new SecurityModule()
};
}
setupAdvancedListeners() {
// 高级事件监听
this.setupNetworkListeners();
this.setupTabGroupListeners();
this.setupDeclarativeNetRequestListeners();
this.setupUserScriptListeners();
}
setupNetworkListeners() {
// 使用Fetch API进行高级网络请求
self.addEventListener('fetch', (event) => {
if (event.request.url.includes('/api/')) {
event.respondWith(this.handleApiRequest(event.request));
}
});
// 监听网络状态变化
self.addEventListener('online', () => {
this.handleOnlineStatusChange(true);
});
self.addEventListener('offline', () => {
this.handleOnlineStatusChange(false);
});
}
async handleApiRequest(request) {
try {
// 添加认证头
const enhancedRequest = new Request(request.url, {
method: request.method,
headers: {
...Object.fromEntries(request.headers),
'Authorization': await this.getAuthToken(),
'X-Extension-Version': chrome.runtime.getManifest().version
},
body: request.body
});
// 发送增强的请求
const response = await fetch(enhancedRequest);
// 缓存响应
if (response.ok) {
await this.cacheResponse(request.url, response.clone());
}
return response;
} catch (error) {
console.error('API request failed:', error);
// 尝试从缓存返回
return this.getCachedResponse(request.url);
}
}
setupTabGroupListeners() {
// Chrome 89+ Tab Groups API
if (chrome.tabGroups) {
chrome.tabGroups.onCreated.addListener((group) => {
this.handleTabGroupCreated(group);
});
chrome.tabGroups.onUpdated.addListener((group) => {
this.handleTabGroupUpdated(group);
});
chrome.tabGroups.onRemoved.addListener((group) => {
this.handleTabGroupRemoved(group);
});
}
}
async handleTabGroupCreated(group) {
// 智能分组建议
const suggestions = await this.generateGroupSuggestions(group);
if (suggestions.length > 0) {
// 发送建议给用户
await chrome.notifications.create({
type: 'basic',
iconUrl: 'assets/icon48.png',
title: 'Smart Tab Grouping',
message: `We found ${suggestions.length} tabs that might belong to this group.`,
buttons: [
{ title: 'Apply Suggestions' },
{ title: 'Dismiss' }
]
});
}
}
setupDeclarativeNetRequestListeners() {
// Declarative Net Request API for advanced request blocking
chrome.declarativeNetRequest.onRuleMatchedDebug.addListener((info) => {
console.log('Rule matched:', info);
this.modules.analytics.trackRuleMatch(info);
});
}
setupUserScriptListeners() {
// User Scripts API (Chrome 102+)
if (chrome.userScripts) {
chrome.userScripts.onBeforeScript.addListener((details) => {
this.handleUserScriptExecution(details);
});
}
}
startBackgroundTasks() {
// 启动后台任务
this.startPeriodicTasks();
this.startIdleDetection();
this.startPerformanceMonitoring();
}
startPeriodicTasks() {
// 使用chrome.alarms进行定时任务
chrome.alarms.create('data-sync', { periodInMinutes: 30 });
chrome.alarms.create('cleanup', { periodInMinutes: 60 });
chrome.alarms.create('analytics-report', { periodInMinutes: 1440 }); // 24小时
chrome.alarms.onAlarm.addListener((alarm) => {
this.handleAlarm(alarm);
});
}
async handleAlarm(alarm) {
switch (alarm.name) {
case 'data-sync':
await this.modules.sync.performSync();
break;
case 'cleanup':
await this.performCleanup();
break;
case 'analytics-report':
await this.modules.analytics.generateReport();
break;
}
}
startIdleDetection() {
// 检测用户空闲状态
chrome.idle.setDetectionInterval(60); // 1分钟
chrome.idle.onStateChanged.addListener((state) => {
this.handleIdleStateChange(state);
});
}
handleIdleStateChange(state) {
switch (state) {
case 'idle':
// 用户空闲时执行低优先级任务
this.performIdleTasks();
break;
case 'active':
// 用户回到活跃状态
this.resumeActiveTasks();
break;
case 'locked':
// 屏幕锁定时暂停非关键任务
this.pauseNonCriticalTasks();
break;
}
}
async performIdleTasks() {
// 在空闲时执行的任务
await this.optimizeDatabase();
await this.preloadData();
await this.performMaintenance();
}
// 高级Web Streams API使用
async processLargeDataStream(dataUrl) {
try {
const response = await fetch(dataUrl);
const reader = response.body.getReader();
const stream = new ReadableStream({
start(controller) {
function pump() {
return reader.read().then(({ done, value }) => {
if (done) {
controller.close();
return;
}
// 处理数据块
const processedChunk = this.processDataChunk(value);
controller.enqueue(processedChunk);
return pump();
});
}
return pump();
}
});
return stream;
} catch (error) {
console.error('Stream processing failed:', error);
throw error;
}
}
processDataChunk(chunk) {
// 处理数据块的逻辑
return new TextDecoder().decode(chunk);
}
}
// 网络模块
class NetworkingModule {
constructor() {
this.requestQueue = [];
this.rateLimiter = new RateLimiter();
this.retryManager = new RetryManager();
}
async enhancedFetch(url, options = {}) {
// 增强的fetch包装器
const enhancedOptions = {
...options,
headers: {
'User-Agent': `Chrome Extension/${chrome.runtime.getManifest().version}`,
'Accept': 'application/json',
...options.headers
}
};
// 速率限制
await this.rateLimiter.waitForSlot();
// 重试机制
return this.retryManager.execute(async () => {
const response = await fetch(url, enhancedOptions);
if (!response.ok) {
throw new Error(`HTTP ${response.status}: ${response.statusText}`);
}
return response;
});
}
}
// 速率限制器
class RateLimiter {
constructor(maxRequests = 100, windowMs = 60000) {
this.maxRequests = maxRequests;
this.windowMs = windowMs;
this.requests = [];
}
async waitForSlot() {
const now = Date.now();
// 清理过期的请求记录
this.requests = this.requests.filter(time => now - time < this.windowMs);
if (this.requests.length >= this.maxRequests) {
const oldestRequest = Math.min(...this.requests);
const waitTime = this.windowMs - (now - oldestRequest);
if (waitTime > 0) {
await new Promise(resolve => setTimeout(resolve, waitTime));
}
}
this.requests.push(now);
}
}
// 重试管理器
class RetryManager {
constructor(maxRetries = 3, baseDelay = 1000) {
this.maxRetries = maxRetries;
this.baseDelay = baseDelay;
}
async execute(operation) {
let lastError;
for (let attempt = 0; attempt <= this.maxRetries; attempt++) {
try {
return await operation();
} catch (error) {
lastError = error;
if (attempt === this.maxRetries) {
break;
}
// 指数退避延迟
const delay = this.baseDelay * Math.pow(2, attempt);
await new Promise(resolve => setTimeout(resolve, delay));
}
}
throw lastError;
}
}
// 初始化高级Service Worker
new AdvancedServiceWorker();1.2 Python高级特性模拟器
import asyncio
import json
import time
import hashlib
from datetime import datetime, timedelta
from typing import Dict, List, Any, Optional
import concurrent.futures
from dataclasses import dataclass
@dataclass
class TabGroup:
"""标签组数据结构"""
id: int
color: str
title: str
collapsed: bool
windowId: int
@dataclass
class NetworkRequest:
"""网络请求数据结构"""
url: str
method: str
headers: Dict[str, str]
body: Optional[str] = None
timestamp: float = None
def __post_init__(self):
if self.timestamp is None:
self.timestamp = time.time()
class AdvancedChromeExtensionSimulator:
"""Chrome扩展高级特性模拟器"""
def __init__(self):
self.service_worker_active = False
self.tab_groups = {}
self.network_cache = {}
self.performance_metrics = {}
self.ai_models = {}
self.security_policies = {}
# 初始化高级功能
self._init_ai_capabilities()
self._init_performance_monitoring()
self._init_security_framework()
def _init_ai_capabilities(self):
"""初始化AI功能"""
self.ai_models = {
'content_classifier': ContentClassifier(),
'intent_detector': IntentDetector(),
'anomaly_detector': AnomalyDetector(),
'recommendation_engine': RecommendationEngine()
}
def _init_performance_monitoring(self):
"""初始化性能监控"""
self.performance_metrics = {
'memory_usage': [],
'cpu_usage': [],
'network_latency': [],
'user_interactions': []
}
def _init_security_framework(self):
"""初始化安全框架"""
self.security_policies = {
'csp_violations': [],
'suspicious_activities': [],
'permission_usage': {},
'data_access_logs': []
}
async def start_service_worker(self):
"""启动Service Worker"""
self.service_worker_active = True
print("🚀 Advanced Service Worker started")
# 启动后台任务
await asyncio.gather(
self._background_sync_task(),
self._performance_monitoring_task(),
self._ai_processing_task(),
self._security_monitoring_task()
)
async def _background_sync_task(self):
"""后台同步任务"""
while self.service_worker_active:
try:
await self._perform_data_sync()
await asyncio.sleep(30) # 每30秒同步一次
except Exception as e:
print(f"❌ Background sync error: {e}")
await asyncio.sleep(60) # 错误时延长间隔
async def _perform_data_sync(self):
"""执行数据同步"""
# 模拟同步过程
sync_data = {
'timestamp': datetime.now().isoformat(),
'user_preferences': await self._get_user_preferences(),
'browsing_history': await self._get_recent_history(),
'extension_state': await self._get_extension_state()
}
# 上传到云端
await self._upload_to_cloud(sync_data)
print("☁️ Data synchronized successfully")
async def handle_tab_group_operation(self, operation: str, data: Dict):
"""处理标签组操作"""
if operation == 'create':
return await self._create_tab_group(data)
elif operation == 'update':
return await self._update_tab_group(data)
elif operation == 'remove':
return await self._remove_tab_group(data)
elif operation == 'suggest':
return await self._suggest_tab_grouping(data)
async def _create_tab_group(self, data: Dict) -> TabGroup:
"""创建标签组"""
group = TabGroup(
id=len(self.tab_groups) + 1,
color=data.get('color', 'blue'),
title=data.get('title', 'New Group'),
collapsed=data.get('collapsed', False),
windowId=data.get('windowId', 1)
)
self.tab_groups[group.id] = group
# AI智能建议
suggestions = await self._generate_group_suggestions(group)
return {
'group': group,
'suggestions': suggestions
}
async def _suggest_tab_grouping(self, data: Dict) -> List[Dict]:
"""智能标签分组建议"""
tabs = data.get('tabs', [])
# 使用AI进行内容分类
classifications = []
for tab in tabs:
classification = await self.ai_models['content_classifier'].classify(
tab.get('url', ''),
tab.get('title', '')
)
classifications.append({
'tab': tab,
'category': classification['category'],
'confidence': classification['confidence']
})
# 按类别分组
groups = {}
for item in classifications:
category = item['category']
if category not in groups:
groups[category] = []
groups[category].append(item['tab'])
# 生成建议
suggestions = []
for category, tab_list in groups.items():
if len(tab_list) >= 2: # 至少2个标签才建议分组
suggestions.append({
'title': f"{category.title()} Group",
'tabs': tab_list,
'color': self._get_category_color(category),
'confidence': sum(
item['confidence'] for item in classifications
if item['category'] == category
) / len(tab_list)
})
return suggestions
def _get_category_color(self, category: str) -> str:
"""为分类获取颜色"""
color_map = {
'work': 'blue',
'entertainment': 'red',
'shopping': 'green',
'social': 'pink',
'news': 'orange',
'development': 'purple',
'education': 'cyan'
}
return color_map.get(category, 'grey')
async def handle_declarative_net_request(self, request_data: Dict):
"""处理声明式网络请求"""
rules = request_data.get('rules', [])
for rule in rules:
await self._apply_network_rule(rule)
return {
'rules_applied': len(rules),
'timestamp': datetime.now().isoformat()
}
async def _apply_network_rule(self, rule: Dict):
"""应用网络规则"""
rule_type = rule.get('action', {}).get('type')
if rule_type == 'block':
await self._block_request(rule)
elif rule_type == 'redirect':
await self._redirect_request(rule)
elif rule_type == 'modifyHeaders':
await self._modify_headers(rule)
async def process_with_webassembly(self, data: bytes, wasm_module: str) -> bytes:
"""使用WebAssembly处理数据"""
# 模拟WebAssembly处理
print(f"🔧 Processing {len(data)} bytes with WASM module: {wasm_module}")
# 模拟高性能计算
processed_data = await self._simulate_wasm_processing(data, wasm_module)
return processed_data
async def _simulate_wasm_processing(self, data: bytes, module: str) -> bytes:
"""模拟WebAssembly处理"""
# 根据模块类型执行不同的处理
if module == 'image_processor':
return await self._process_image_data(data)
elif module == 'crypto_hasher':
return await self._compute_hash(data)
elif module == 'data_compressor':
return await self._compress_data(data)
else:
return data
async def _process_image_data(self, data: bytes) -> bytes:
"""处理图像数据"""
# 模拟图像处理
await asyncio.sleep(0.1) # 模拟处理时间
return data[:len(data)//2] # 模拟压缩
async def _compute_hash(self, data: bytes) -> bytes:
"""计算哈希"""
hash_obj = hashlib.sha256(data)
return hash_obj.digest()
async def _compress_data(self, data: bytes) -> bytes:
"""压缩数据"""
# 模拟压缩
import zlib
return zlib.compress(data)
async def integrate_machine_learning(self, task: str, input_data: Any) -> Dict:
"""集成机器学习功能"""
if task == 'content_analysis':
return await self._analyze_content_with_ml(input_data)
elif task == 'user_behavior_prediction':
return await self._predict_user_behavior(input_data)
elif task == 'anomaly_detection':
return await self._detect_anomalies(input_data)
elif task == 'recommendation_generation':
return await self._generate_recommendations(input_data)
else:
raise ValueError(f"Unsupported ML task: {task}")
async def _analyze_content_with_ml(self, content: Dict) -> Dict:
"""使用ML分析内容"""
analyzer = self.ai_models['content_classifier']
analysis = await analyzer.analyze({
'text': content.get('text', ''),
'url': content.get('url', ''),
'metadata': content.get('metadata', {})
})
return {
'sentiment': analysis.get('sentiment', 'neutral'),
'topics': analysis.get('topics', []),
'language': analysis.get('language', 'en'),
'quality_score': analysis.get('quality_score', 0.5),
'confidence': analysis.get('confidence', 0.8)
}
async def implement_cross_browser_compatibility(self, browser: str) -> Dict:
"""实现跨浏览器兼容性"""
compatibility_map = {
'firefox': await self._firefox_compatibility(),
'edge': await self._edge_compatibility(),
'safari': await self._safari_compatibility(),
'opera': await self._opera_compatibility()
}
if browser in compatibility_map:
return compatibility_map[browser]
else:
return {'supported': False, 'reason': 'Unsupported browser'}
async def _firefox_compatibility(self) -> Dict:
"""Firefox兼容性适配"""
return {
'supported': True,
'api_differences': {
'storage': 'browser.storage instead of chrome.storage',
'tabs': 'browser.tabs instead of chrome.tabs',
'runtime': 'browser.runtime instead of chrome.runtime'
},
'manifest_adjustments': {
'background': {
'scripts': ['background.js'], # Firefox使用scripts而非service_worker
'persistent': False
},
'browser_specific_settings': {
'gecko': {
'id': 'extension@example.com',
'strict_min_version': '57.0'
}
}
}
}
async def _edge_compatibility(self) -> Dict:
"""Edge兼容性适配"""
return {
'supported': True,
'api_differences': {
'notifications': 'Limited notification support',
'nativeMessaging': 'Requires additional permissions'
},
'manifest_adjustments': {
'minimum_edge_version': '79.0',
'ms_store_compatibility': True
}
}
async def implement_progressive_web_features(self) -> Dict:
"""实现PWA特性"""
features = {
'service_worker_registration': await self._register_service_worker(),
'offline_capabilities': await self._setup_offline_support(),
'push_notifications': await self._setup_push_notifications(),
'background_sync': await self._setup_background_sync(),
'install_prompt': await self._setup_install_prompt()
}
return features
async def setup_advanced_security(self) -> Dict:
"""设置高级安全功能"""
security_config = {
'content_security_policy': await self._setup_advanced_csp(),
'permission_management': await self._setup_permission_manager(),
'data_encryption': await self._setup_encryption(),
'audit_logging': await self._setup_audit_logging(),
'threat_detection': await self._setup_threat_detection()
}
return security_config
async def _setup_advanced_csp(self) -> Dict:
"""设置高级CSP"""
return {
'extension_pages': "script-src 'self' 'wasm-unsafe-eval'; object-src 'none';",
'content_scripts': "script-src 'self'; object-src 'none';",
'sandbox': "sandbox allow-scripts allow-forms allow-popups allow-modals;"
}
def get_performance_report(self) -> Dict:
"""获取性能报告"""
return {
'memory_usage': self._calculate_memory_stats(),
'cpu_usage': self._calculate_cpu_stats(),
'network_performance': self._calculate_network_stats(),
'user_experience_metrics': self._calculate_ux_metrics(),
'recommendations': self._generate_performance_recommendations()
}
def _calculate_memory_stats(self) -> Dict:
"""计算内存统计"""
memory_data = self.performance_metrics.get('memory_usage', [])
if not memory_data:
return {'status': 'no_data'}
return {
'average_mb': sum(memory_data) / len(memory_data),
'peak_mb': max(memory_data),
'trend': 'increasing' if memory_data[-1] > memory_data[0] else 'decreasing'
}
# AI模型类
class ContentClassifier:
"""内容分类器"""
async def classify(self, url: str, title: str) -> Dict:
"""分类内容"""
# 模拟AI分类
categories = ['work', 'entertainment', 'shopping', 'social', 'news', 'development']
# 简单的关键词匹配
text = f"{url} {title}".lower()
if any(word in text for word in ['github', 'stackoverflow', 'dev', 'code']):
return {'category': 'development', 'confidence': 0.9}
elif any(word in text for word in ['youtube', 'netflix', 'game']):
return {'category': 'entertainment', 'confidence': 0.8}
elif any(word in text for word in ['amazon', 'shop', 'buy']):
return {'category': 'shopping', 'confidence': 0.85}
else:
return {'category': 'general', 'confidence': 0.6}
async def analyze(self, content: Dict) -> Dict:
"""分析内容"""
text = content.get('text', '')
# 模拟情感分析
positive_words = ['good', 'great', 'excellent', 'amazing', 'wonderful']
negative_words = ['bad', 'terrible', 'awful', 'horrible', 'worst']
positive_count = sum(1 for word in positive_words if word in text.lower())
negative_count = sum(1 for word in negative_words if word in text.lower())
if positive_count > negative_count:
sentiment = 'positive'
elif negative_count > positive_count:
sentiment = 'negative'
else:
sentiment = 'neutral'
return {
'sentiment': sentiment,
'topics': ['technology', 'web development'], # 模拟话题检测
'language': 'en',
'quality_score': 0.7,
'confidence': 0.8
}
class IntentDetector:
"""意图检测器"""
async def detect_intent(self, user_action: str, context: Dict) -> Dict:
"""检测用户意图"""
# 模拟意图检测
intent_patterns = {
'search': ['find', 'search', 'look for'],
'purchase': ['buy', 'purchase', 'order'],
'learn': ['learn', 'study', 'understand'],
'entertainment': ['watch', 'play', 'enjoy']
}
for intent, patterns in intent_patterns.items():
if any(pattern in user_action.lower() for pattern in patterns):
return {'intent': intent, 'confidence': 0.8}
return {'intent': 'unknown', 'confidence': 0.3}
class AnomalyDetector:
"""异常检测器"""
async def detect_anomalies(self, data: List[Dict]) -> List[Dict]:
"""检测异常"""
anomalies = []
# 模拟异常检测逻辑
for item in data:
if item.get('value', 0) > 1000: # 简单的阈值检测
anomalies.append({
'item': item,
'anomaly_type': 'high_value',
'severity': 'medium',
'timestamp': datetime.now().isoformat()
})
return anomalies
class RecommendationEngine:
"""推荐引擎"""
async def generate_recommendations(self, user_data: Dict, context: Dict) -> List[Dict]:
"""生成推荐"""
# 模拟推荐生成
recommendations = [
{
'type': 'bookmark_organization',
'title': 'Organize your bookmarks',
'description': 'Group similar bookmarks together',
'confidence': 0.9
},
{
'type': 'productivity_tip',
'title': 'Use keyboard shortcuts',
'description': 'Speed up your browsing with shortcuts',
'confidence': 0.7
}
]
return recommendations
# 使用示例
async def main():
simulator = AdvancedChromeExtensionSimulator()
# 启动高级Service Worker
await simulator.start_service_worker()
# 测试标签组功能
group_result = await simulator.handle_tab_group_operation('create', {
'title': 'Work Tabs',
'color': 'blue'
})
print(f"Created tab group: {group_result}")
# 测试WebAssembly处理
test_data = b"Hello, World!" * 1000
processed = await simulator.process_with_webassembly(test_data, 'data_compressor')
print(f"Processed {len(test_data)} bytes to {len(processed)} bytes")
# 测试机器学习集成
ml_result = await simulator.integrate_machine_learning('content_analysis', {
'text': 'This is a great article about web development!',
'url': 'https://example.com/article'
})
print(f"ML analysis result: {ml_result}")
if __name__ == '__main__':
asyncio.run(main())2. WebAssembly集成
2.1 高性能计算模块
// src/modules/wasm-integration.js
class WebAssemblyManager {
constructor() {
this.modules = new Map();
this.initialized = false;
}
async initialize() {
if (this.initialized) return;
try {
// 加载各种WASM模块
await Promise.all([
this.loadModule('image-processor', '/wasm/image_processor.wasm'),
this.loadModule('crypto-hasher', '/wasm/crypto_hasher.wasm'),
this.loadModule('data-compressor', '/wasm/data_compressor.wasm'),
this.loadModule('ml-inference', '/wasm/ml_inference.wasm')
]);
this.initialized = true;
console.log('✅ WebAssembly modules loaded successfully');
} catch (error) {
console.error('❌ Failed to load WebAssembly modules:', error);
throw error;
}
}
async loadModule(name, wasmPath) {
try {
const wasmModule = await WebAssembly.instantiateStreaming(
fetch(chrome.runtime.getURL(wasmPath)),
{
env: {
memory: new WebAssembly.Memory({ initial: 256, maximum: 512 }),
table: new WebAssembly.Table({ initial: 1, element: 'anyfunc' }),
memoryBase: 0,
tableBase: 0,
// 提供JavaScript函数给WASM调用
jsLog: (ptr, len) => {
const message = this.readString(ptr, len);
console.log(`[WASM:${name}] ${message}`);
},
jsError: (ptr, len) => {
const message = this.readString(ptr, len);
console.error(`[WASM:${name}] ${message}`);
}
}
}
);
this.modules.set(name, {
instance: wasmModule.instance,
exports: wasmModule.instance.exports,
memory: wasmModule.instance.exports.memory
});
console.log(`📦 Loaded WASM module: ${name}`);
} catch (error) {
console.error(`Failed to load WASM module ${name}:`, error);
throw error;
}
}
readString(ptr, len) {
const memory = this.modules.get('current')?.memory;
if (!memory) return '';
const bytes = new Uint8Array(memory.buffer, ptr, len);
return new TextDecoder().decode(bytes);
}
writeString(moduleName, str) {
const module = this.modules.get(moduleName);
if (!module) throw new Error(`Module ${moduleName} not found`);
const bytes = new TextEncoder().encode(str);
const ptr = module.exports.allocate(bytes.length);
const memory = new Uint8Array(module.memory.buffer);
memory.set(bytes, ptr);
return { ptr, len: bytes.length };
}
async processImage(imageData, operations) {
const module = this.modules.get('image-processor');
if (!module) throw new Error('Image processor module not loaded');
try {
// 将图像数据写入WASM内存
const imagePtr = this.writeImageData(module, imageData);
// 调用WASM函数处理图像
const resultPtr = module.exports.process_image(
imagePtr,
imageData.width,
imageData.height,
JSON.stringify(operations)
);
// 读取处理结果
const processedData = this.readImageData(module, resultPtr, imageData.width, imageData.height);
// 清理内存
module.exports.deallocate(imagePtr);
module.exports.deallocate(resultPtr);
return processedData;
} catch (error) {
console.error('Image processing error:', error);
throw error;
}
}
writeImageData(module, imageData) {
const totalSize = imageData.width * imageData.height * 4; // RGBA
const ptr = module.exports.allocate(totalSize);
const memory = new Uint8ClampedArray(module.memory.buffer);
// 复制图像数据到WASM内存
memory.set(imageData.data, ptr);
return ptr;
}
readImageData(module, ptr, width, height) {
const totalSize = width * height * 4;
const memory = new Uint8ClampedArray(module.memory.buffer, ptr, totalSize);
return new ImageData(
new Uint8ClampedArray(memory),
width,
height
);
}
async computeHash(data, algorithm = 'sha256') {
const module = this.modules.get('crypto-hasher');
if (!module) throw new Error('Crypto hasher module not loaded');
try {
// 写入数据
const dataPtr = module.exports.allocate(data.length);
const memory = new Uint8Array(module.memory.buffer);
memory.set(new Uint8Array(data), dataPtr);
// 计算哈希
const hashPtr = module.exports.compute_hash(dataPtr, data.length, algorithm);
// 读取哈希结果
const hashLength = module.exports.get_hash_length(algorithm);
const hashData = new Uint8Array(module.memory.buffer, hashPtr, hashLength);
// 转换为十六进制字符串
const hexHash = Array.from(hashData)
.map(b => b.toString(16).padStart(2, '0'))
.join('');
// 清理内存
module.exports.deallocate(dataPtr);
module.exports.deallocate(hashPtr);
return hexHash;
} catch (error) {
console.error('Hash computation error:', error);
throw error;
}
}
async compressData(data, compressionLevel = 6) {
const module = this.modules.get('data-compressor');
if (!module) throw new Error('Data compressor module not loaded');
try {
// 写入原始数据
const inputPtr = module.exports.allocate(data.length);
const memory = new Uint8Array(module.memory.buffer);
memory.set(new Uint8Array(data), inputPtr);
// 压缩数据
const result = module.exports.compress_data(inputPtr, data.length, compressionLevel);
const compressedSize = module.exports.get_compressed_size(result);
const compressedPtr = module.exports.get_compressed_data(result);
// 读取压缩结果
const compressedData = new Uint8Array(module.memory.buffer, compressedPtr, compressedSize);
// 清理内存
module.exports.deallocate(inputPtr);
module.exports.free_compression_result(result);
return {
data: new Uint8Array(compressedData),
originalSize: data.length,
compressedSize: compressedSize,
ratio: compressedSize / data.length
};
} catch (error) {
console.error('Data compression error:', error);
throw error;
}
}
async runMLInference(model, inputData) {
const module = this.modules.get('ml-inference');
if (!module) throw new Error('ML inference module not loaded');
try {
// 加载模型
const modelId = await this.loadMLModel(module, model);
// 准备输入数据
const inputPtr = this.writeMLInput(module, inputData);
// 运行推理
const outputPtr = module.exports.run_inference(modelId, inputPtr);
// 读取输出
const output = this.readMLOutput(module, outputPtr);
// 清理资源
module.exports.deallocate(inputPtr);
module.exports.deallocate(outputPtr);
return output;
} catch (error) {
console.error('ML inference error:', error);
throw error;
}
}
async loadMLModel(module, modelConfig) {
const configStr = JSON.stringify(modelConfig);
const { ptr, len } = this.writeString('ml-inference', configStr);
const modelId = module.exports.load_model(ptr, len);
module.exports.deallocate(ptr);
return modelId;
}
writeMLInput(module, inputData) {
const serialized = new Float32Array(inputData.flat());
const ptr = module.exports.allocate(serialized.byteLength);
const memory = new Uint8Array(module.memory.buffer);
memory.set(new Uint8Array(serialized.buffer), ptr);
return ptr;
}
readMLOutput(module, outputPtr) {
// 获取输出维度
const dims = module.exports.get_output_dims(outputPtr);
const totalSize = module.exports.get_output_size(outputPtr);
// 读取输出数据
const dataPtr = module.exports.get_output_data(outputPtr);
const outputData = new Float32Array(module.memory.buffer, dataPtr, totalSize);
return {
data: Array.from(outputData),
dimensions: dims
};
}
getModuleStats() {
const stats = {};
for (const [name, module] of this.modules) {
const memory = module.memory;
stats[name] = {
memorySize: memory.buffer.byteLength,
memoryPages: memory.buffer.byteLength / 65536,
exports: Object.keys(module.exports)
};
}
return stats;
}
async benchmark(moduleName, operation, iterations = 1000) {
const module = this.modules.get(moduleName);
if (!module) throw new Error(`Module ${moduleName} not found`);
const startTime = performance.now();
for (let i = 0; i < iterations; i++) {
await operation(module);
}
const endTime = performance.now();
const totalTime = endTime - startTime;
return {
totalTime,
averageTime: totalTime / iterations,
operationsPerSecond: 1000 / (totalTime / iterations)
};
}
}
// 使用示例
class WAMSImageProcessor {
constructor() {
this.wasmManager = new WebAssemblyManager();
}
async initialize() {
await this.wasmManager.initialize();
}
async processScreenshot(canvas) {
const ctx = canvas.getContext('2d');
const imageData = ctx.getImageData(0, 0, canvas.width, canvas.height);
// 使用WASM进行高性能图像处理
const operations = {
brightness: 1.2,
contrast: 1.1,
blur: 2,
noise_reduction: true
};
const processedData = await this.wasmManager.processImage(imageData, operations);
// 将处理结果绘制回画布
ctx.putImageData(processedData, 0, 0);
return canvas;
}
async compressImageData(imageData) {
// 将ImageData转换为字节数组
const rawData = imageData.data.buffer;
// 使用WASM压缩
const compressed = await this.wasmManager.compressData(rawData, 9);
console.log(`Compression ratio: ${compressed.ratio.toFixed(2)}`);
return compressed;
}
}
// 初始化WASM管理器
const wasmManager = new WebAssemblyManager();3. AI和机器学习集成
3.1 浏览器内机器学习
// src/modules/ai-integration.js
class AIManager {
constructor() {
this.models = new Map();
this.tfjs = null;
this.initialized = false;
}
async initialize() {
if (this.initialized) return;
try {
// 动态加载TensorFlow.js
await this.loadTensorFlowJS();
// 加载预训练模型
await this.loadModels();
this.initialized = true;
console.log('🤖 AI Manager initialized successfully');
} catch (error) {
console.error('❌ Failed to initialize AI Manager:', error);
throw error;
}
}
async loadTensorFlowJS() {
// 动态加载TensorFlow.js
const script = document.createElement('script');
script.src = 'https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@4.0.0/dist/tf.min.js';
return new Promise((resolve, reject) => {
script.onload = () => {
this.tfjs = window.tf;
resolve();
};
script.onerror = reject;
document.head.appendChild(script);
});
}
async loadModels() {
const modelConfigs = [
{
name: 'toxicity',
url: 'https://storage.googleapis.com/tfjs-models/tfjs/toxicity_classifier/model.json',
type: 'text-classification'
},
{
name: 'universal-sentence-encoder',
url: 'https://storage.googleapis.com/tfjs-models/tfjs/universal_sentence_encoder/model.json',
type: 'text-embedding'
},
{
name: 'mobilenet',
url: 'https://storage.googleapis.com/tfjs-models/tfjs/mobilenet_v2_1.0_224/model.json',
type: 'image-classification'
}
];
for (const config of modelConfigs) {
try {
const model = await this.tfjs.loadLayersModel(config.url);
this.models.set(config.name, {
model,
type: config.type,
loaded: true
});
console.log(`📦 Loaded AI model: ${config.name}`);
} catch (error) {
console.warn(`⚠️ Failed to load model ${config.name}:`, error);
}
}
}
async analyzeText(text, analysisType = 'sentiment') {
switch (analysisType) {
case 'toxicity':
return await this.detectToxicity(text);
case 'sentiment':
return await this.analyzeSentiment(text);
case 'embedding':
return await this.getTextEmbedding(text);
case 'intent':
return await this.detectIntent(text);
default:
throw new Error(`Unsupported analysis type: ${analysisType}`);
}
}
async detectToxicity(text) {
const toxicityModel = this.models.get('toxicity');
if (!toxicityModel) {
throw new Error('Toxicity model not loaded');
}
try {
const predictions = await toxicityModel.model.predict([text]);
const results = {};
const labels = ['identity_attack', 'insult', 'obscene', 'severe_toxicity', 'sexual_explicit', 'threat', 'toxicity'];
predictions.forEach((prediction, index) => {
results[labels[index]] = {
probability: prediction.dataSync()[0],
match: prediction.dataSync()[0] > 0.7
};
});
return {
toxic: results.toxicity.match,
confidence: results.toxicity.probability,
categories: results
};
} catch (error) {
console.error('Toxicity detection error:', error);
return { toxic: false, confidence: 0, error: error.message };
}
}
async analyzeSentiment(text) {
// 使用简单的词典方法进行情感分析
// 在实际应用中，这里会使用更复杂的模型
const positiveWords = [
'good', 'great', 'excellent', 'amazing', 'wonderful', 'fantastic',
'awesome', 'brilliant', 'outstanding', 'perfect', 'love', 'like'
];
const negativeWords = [
'bad', 'terrible', 'awful', 'horrible', 'hate', 'dislike',
'worst', 'disgusting', 'annoying', 'frustrating', 'disappointed'
];
const words = text.toLowerCase().match(/\b\w+\b/g) || [];
let positiveScore = 0;
let negativeScore = 0;
words.forEach(word => {
if (positiveWords.includes(word)) positiveScore++;
if (negativeWords.includes(word)) negativeScore++;
});
const totalWords = words.length;
const sentimentScore = (positiveScore - negativeScore) / Math.max(totalWords, 1);
let sentiment;
if (sentimentScore > 0.1) sentiment = 'positive';
else if (sentimentScore < -0.1) sentiment = 'negative';
else sentiment = 'neutral';
return {
sentiment,
score: sentimentScore,
confidence: Math.abs(sentimentScore),
details: {
positive_words: positiveScore,
negative_words: negativeScore,
total_words: totalWords
}
};
}
async getTextEmbedding(text) {
const useModel = this.models.get('universal-sentence-encoder');
if (!useModel) {
throw new Error('Universal Sentence Encoder not loaded');
}
try {
const embedding = await useModel.model.embed([text]);
const values = await embedding.data();
return {
embedding: Array.from(values),
dimensions: embedding.shape[1]
};
} catch (error) {
console.error('Text embedding error:', error);
throw error;
}
}
async detectIntent(text) {
// 意图检测的简单实现
const intents = {
search: /\b(search|find|look for|where is|what is)\b/i,
purchase: /\b(buy|purchase|order|get|want to buy)\b/i,
support: /\b(help|support|problem|issue|bug)\b/i,
information: /\b(tell me|explain|how to|tutorial)\b/i,
booking: /\b(book|reserve|schedule|appointment)\b/i,
comparison: /\b(compare|vs|versus|better|best)\b/i
};
for (const [intent, pattern] of Object.entries(intents)) {
if (pattern.test(text)) {
return {
intent,
confidence: 0.8,
matched_pattern: pattern.source
};
}
}
return {
intent: 'unknown',
confidence: 0.1,
matched_pattern: null
};
}
async analyzeImage(imageElement) {
const mobilenetModel = this.models.get('mobilenet');
if (!mobilenetModel) {
throw new Error('MobileNet model not loaded');
}
try {
// 预处理图像
const tensor = this.tfjs.browser.fromPixels(imageElement)
.resizeNearestNeighbor([224, 224])
.expandDims()
.div(127.5)
.sub(1);
// 运行推理
const predictions = await mobilenetModel.model.predict(tensor);
// 获取前5个预测结果
const topPredictions = await this.getTopPredictions(predictions, 5);
// 清理tensor
tensor.dispose();
predictions.dispose();
return {
predictions: topPredictions,
model: 'mobilenet',
timestamp: Date.now()
};
} catch (error) {
console.error('Image analysis error:', error);
throw error;
}
}
async getTopPredictions(predictions, topK = 5) {
const values = await predictions.data();
const indices = Array.from(values)
.map((prob, index) => ({ probability: prob, index }))
.sort((a, b) => b.probability - a.probability)
.slice(0, topK);
// 这里需要ImageNet类别标签
const labels = await this.getImageNetLabels();
return indices.map(({ probability, index }) => ({
className: labels[index],
probability: probability,
index: index
}));
}
async getImageNetLabels() {
// 返回ImageNet类别标签（简化版本）
return [
'Egyptian cat', 'tabby cat', 'tiger cat', 'Persian cat', 'Siamese cat',
'African elephant', 'Indian elephant', 'goldfish', 'great white shark',
// ... 更多标签
];
}
async generateRecommendations(userBehavior, context) {
// 基于用户行为和上下文生成推荐
const recommendations = [];
// 分析用户行为模式
const patterns = this.analyzeBehaviorPatterns(userBehavior);
// 基于当前上下文生成推荐
if (context.currentPage) {
const pageAnalysis = await this.analyzePageContent(context.currentPage);
recommendations.push(...this.generateContextualRecommendations(pageAnalysis));
}
// 基于历史行为生成推荐
recommendations.push(...this.generateBehavioralRecommendations(patterns));
// 对推荐进行排序和过滤
return this.rankRecommendations(recommendations);
}
analyzeBehaviorPatterns(userBehavior) {
const patterns = {
mostVisitedDomains: {},
timePatterns: {},
categoryPreferences: {},
searchQueries: []
};
userBehavior.forEach(action => {
// 分析域名偏好
if (action.url) {
const domain = new URL(action.url).hostname;
patterns.mostVisitedDomains[domain] =
(patterns.mostVisitedDomains[domain] || 0) + 1;
}
// 分析时间模式
const hour = new Date(action.timestamp).getHours();
patterns.timePatterns[hour] = (patterns.timePatterns[hour] || 0) + 1;
// 分析搜索查询
if (action.type === 'search') {
patterns.searchQueries.push(action.query);
}
});
return patterns;
}
async analyzePageContent(pageUrl) {
try {
// 获取页面内容
const response = await fetch(pageUrl);
const html = await response.text();
// 提取文本内容
const parser = new DOMParser();
const doc = parser.parseFromString(html, 'text/html');
const textContent = doc.body.textContent || '';
// 分析内容
const sentiment = await this.analyzeSentiment(textContent);
const intent = await this.detectIntent(textContent);
return {
url: pageUrl,
textLength: textContent.length,
sentiment: sentiment.sentiment,
intent: intent.intent,
keywords: this.extractKeywords(textContent)
};
} catch (error) {
console.error('Page content analysis error:', error);
return { url: pageUrl, error: error.message };
}
}
extractKeywords(text) {
// 简单的关键词提取
const words = text.toLowerCase()
.match(/\b\w{4,}\b/g) || [];
const wordFreq = {};
words.forEach(word => {
wordFreq[word] = (wordFreq[word] || 0) + 1;
});
return Object.entries(wordFreq)
.sort(([,a], [,b]) => b - a)
.slice(0, 10)
.map(([word]) => word);
}
generateContextualRecommendations(pageAnalysis) {
const recommendations = [];
if (pageAnalysis.intent === 'search') {
recommendations.push({
type: 'tool',
title: 'Enhanced Search',
description: 'Try our advanced search features',
priority: 0.8
});
}
if (pageAnalysis.sentiment === 'negative') {
recommendations.push({
type: 'support',
title: 'Need Help?',
description: 'Our support team is here to help',
priority: 0.9
});
}
return recommendations;
}
generateBehavioralRecommendations(patterns) {
const recommendations = [];
// 基于最常访问的域名推荐
const topDomains = Object.entries(patterns.mostVisitedDomains)
.sort(([,a], [,b]) => b - a)
.slice(0, 3);
topDomains.forEach(([domain, count]) => {
recommendations.push({
type: 'bookmark',
title: `Quick access to ${domain}`,
description: `You visit this site frequently (${count} times)`,
priority: 0.7
});
});
return recommendations;
}
rankRecommendations(recommendations) {
return recommendations
.sort((a, b) => b.priority - a.priority)
.slice(0, 10); // 限制推荐数量
}
// 模型性能监控
getModelPerformance() {
const performance = {};
for (const [name, modelInfo] of this.models) {
if (modelInfo.loaded) {
performance[name] = {
loaded: true,
memoryUsage: this.tfjs.memory().numBytes,
numTensors: this.tfjs.memory().numTensors
};
} else {
performance[name] = { loaded: false };
}
}
return performance;
}
dispose() {
// 清理所有模型和tensor
for (const [name, modelInfo] of this.models) {
if (modelInfo.model && typeof modelInfo.model.dispose === 'function') {
modelInfo.model.dispose();
}
}
this.models.clear();
this.initialized = false;
}
}
// 智能推荐系统
class SmartRecommendationEngine {
constructor(aiManager) {
this.aiManager = aiManager;
this.userProfile = {};
this.contextHistory = [];
}
async updateUserProfile(userAction) {
// 更新用户画像
const actionAnalysis = await this.aiManager.analyzeText(
userAction.description || '',
'intent'
);
if (!this.userProfile.interests) {
this.userProfile.interests = {};
}
const intent = actionAnalysis.intent;
this.userProfile.interests[intent] =
(this.userProfile.interests[intent] || 0) + 1;
// 记录上下文历史
this.contextHistory.push({
timestamp: Date.now(),
action: userAction,
intent: intent
});
// 保持最近100个上下文
if (this.contextHistory.length > 100) {
this.contextHistory.shift();
}
}
async getPersonalizedRecommendations(currentContext) {
const recommendations = await this.aiManager.generateRecommendations(
this.contextHistory,
currentContext
);
// 基于用户画像个性化推荐
return recommendations.map(rec => ({
...rec,
personalizedScore: this.calculatePersonalizationScore(rec)
}));
}
calculatePersonalizationScore(recommendation) {
const userInterests = this.userProfile.interests || {};
const totalInteractions = Object.values(userInterests)
.reduce((sum, count) => sum + count, 0);
if (totalInteractions === 0) return recommendation.priority;
const relevantInterest = userInterests[recommendation.type] || 0;
const interestWeight = relevantInterest / totalInteractions;
return recommendation.priority * (1 + interestWeight);
}
}
// 初始化AI管理器
const aiManager = new AIManager();
const recommendationEngine = new SmartRecommendationEngine(aiManager);高级开发技巧
- 使用WebAssembly处理计算密集型任务
- 集成机器学习模型进行智能分析
- 实现跨浏览器兼容性适配
- 使用现代Web API增强功能
- 优化性能和内存使用
性能考虑
- WebAssembly模块加载可能需要时间
- AI模型会占用较多内存
- 在移动设备上要注意性能限制
- 考虑模型的延迟加载和按需加载
学习小结
本章学习了Chrome扩展的高级特性：
- Manifest V3高级特性：Service Worker、Tab Groups、Declarative Net Request
- WebAssembly集成：高性能计算、图像处理、数据压缩
- AI和机器学习：文本分析、图像识别、智能推荐
- 跨浏览器兼容性：Firefox、Edge、Safari适配
- 现代Web技术：PWA特性、高级安全、性能优化
掌握这些高级特性将帮助开发者构建功能强大、性能优秀的现代化Chrome扩展。