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| # 端云协同推理
class EdgeCloudCollaborativeInference:
"""端云协同推理"""
def __init__(self, edge_model, cloud_api):
self.edge_model = edge_model # 轻量级边缘模型
self.cloud_api = cloud_api # 云端API
# 阈值配置
self.confidence_threshold = 0.8
self.latency_threshold = 100 # ms
self.bandwidth_threshold = 100 # KB/s
async def predict(self, input_data):
"""智能推理决策"""
# 尝试边缘推理
edge_result = await self._edge_inference(input_data)
# 评估边缘结果
if self._should_use_edge_result(edge_result):
return edge_result
else:
# 回退到云端
return await self._cloud_inference(input_data)
async def _edge_inference(self, input_data):
"""边缘推理"""
start_time = time.time()
# 本地推理
result = self.edge_model.predict(input_data)
latency = (time.time() - start_time) * 1000 # ms
return {
'result': result,
'confidence': result.get('confidence', 0.5),
'latency': latency,
'source': 'edge'
}
async def _cloud_inference(self, input_data):
"""云端推理"""
start_time = time.time()
# 调用云端API
cloud_result = await self.cloud_api.predict(input_data)
latency = (time.time() - start_time) * 1000 # ms
return {
'result': cloud_result,
'confidence': cloud_result.get('confidence', 0.9),
'latency': latency,
'source': 'cloud'
}
def _should_use_edge_result(self, edge_result):
"""判断是否使用边缘结果"""
# 高置信度：使用边缘结果
if edge_result['confidence'] >= self.confidence_threshold:
return True
# 低延迟要求：使用边缘结果
if edge_result['latency'] <= self.latency_threshold:
return True
# 低带宽环境：使用边缘结果
if self._get_bandwidth() < self.bandwidth_threshold:
return True
return False
def _get_bandwidth(self):
"""获取当前带宽"""
# 简化实现
return 1000 # KB/s
# 2. 分层推理模型
class HierarchicalInference:
"""分层推理：边缘预筛选+云端精细处理"""
def __init__(self, edge_filter, cloud_processor):
self.edge_filter = edge_filter # 轻量级过滤器
self.cloud_processor = cloud_processor # 重量级处理器
async def process(self, input_data):
"""分层处理"""
# 第一层：边缘快速过滤
filter_result = await self._filter_on_edge(input_data)
if filter_result['is_simple']:
# 简单案例：直接返回
return filter_result['result']
else:
# 复杂案例：云端深度处理
return await self._process_on_cloud(input_data, filter_result)
async def _filter_on_edge(self, input_data):
"""边缘过滤"""
# 快速分类
category = self.edge_filter.predict(input_data)
return {
'is_simple': category == 'simple',
'result': category,
'confidence': 0.9
}
async def _process_on_cloud(self, input_data, filter_result):
"""云端深度处理"""
# 将边缘过滤结果传递给云端
cloud_result = await self.cloud_processor.process(
input_data,
context=filter_result
)
return cloud_result
# 3. 增量学习架构
class IncrementalLearningEdge:
"""边缘增量学习"""
def __init__(self, base_model):
self.base_model = base_model
self.local_adaptations = {}
async def predict_with_adaptation(self, input_data, user_id):
"""带本地适配的预测"""
# 检查是否有用户特定适配
if user_id in self.local_adaptations:
adapted_model = self.local_adaptations[user_id]
result = adapted_model.predict(input_data)
else:
result = self.base_model.predict(input_data)
return result
async def update_local_model(self, user_id, new_data):
"""更新本地模型"""
# 基于新数据微调模型
adapted_model = self._fine_tune_model(
self.base_model,
new_data
)
self.local_adaptations[user_id] = adapted_model
# 定期同步到云端
await self._sync_to_cloud(user_id, adapted_model)
def _fine_tune_model(self, base_model, data):
"""微调模型"""
# 简化实现：迁移学习
# 实际应使用更复杂的微调策略
return base_model # 返回适配后的模型
|