AI 创作日记 | 重构人货场，DeepSeek边缘计算如何破解夜间零售困局

# 设备端持续学习
if new_data.label_conflict > threshold:  
    trigger_model_patch_update()  
    upload_encrypted_feature_vector()

门店A损失函数梯度 → 加密传输 → 中央聚合 → 更新全局模型

边缘服务器检测到暴雨 → 周边3公里客流量下降40% → 激活生鲜商品折扣策略

import time
import random

# 模拟边缘设备（如摄像头）采集数据
def collect_data():
    """
    模拟采集数据，返回一个随机的数值表示顾客流量
    """
    return random.randint(0, 100)

# 边缘设备数据初步处理
def preprocess_data(data):
    """
    对采集到的数据进行初步处理，这里简单地将数据乘以2
    """
    return data * 2

while True:
    data = collect_data()
    processed_data = preprocess_data(data)
    print(f"采集到的数据: {data}, 处理后的数据: {processed_data}")
    time.sleep(1)

def __init__(self, device_id):
    # 设备类型自动检测
    self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    
    # 动态选择计算后端
    providers = ['CUDAExecutionProvider'] if 'cuda' in str(self.device) else ['CPUExecutionProvider']
    
    # 异构模型加载
    self.model = ort.InferenceSession(
        f'models/{device_id}_opt.onnx',
        providers=providers,
        sess_options=self._get_session_options()
    )
    
def _get_session_options(self):
    """动态优化配置"""
    options = ort.SessionOptions()
    options.enable_mem_pattern = False  # 禁用内存复用模式
    options.execution_mode = ort.ExecutionMode.ORT_PARALLEL
    options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
    options.intra_op_num_threads = 4  # 根据CPU核心数动态调整
    return options

class LRUCache:
    def __init__(self, maxsize=1000):
        self.cache = OrderedDict()
        self.maxsize = maxsize
        self.hit_count = 0
        self.miss_count = 0

    def has(self, key):
        """视频帧特征匹配"""
        return self._frame_similarity(key) in self.cache

    def _frame_similarity(self, frame):
        """基于感知哈希的相似度计算"""
        phash = imagehash.phash(Image.fromarray(frame))
        return str(phash)

    def get(self, key):
        self.hit_count += 1
        return self.cache.pop(self._frame_similarity(key))

    def put(self, key, value):
        if len(self.cache) >= self.maxsize:
            self.cache.popitem(last=False)
        self.cache[self._frame_similarity(key)] = value

    def hit_rate(self):
        return self.hit_count / (self.hit_count + self.miss_count + 1e-5)

async def process_frame(self, frame: np.ndarray) -> dict:
    # 帧指纹生成
    frame_hash = hashlib.sha256(frame.tobytes()).hexdigest()
    
    # 分布式锁机制
    async with self.processing_lock(frame_hash):
        if self.cache.has(frame):
            return self.cache.get(frame)
            
        # 硬件加速预处理
        input_tensor = await self._async_preprocess(frame)
        
        # 批量推理优化
        outputs = await self.model.run_async(
            {'input': input_tensor},
            self._get_callback()
        )
        
        # 异构后处理
        return await self._postprocess(outputs)

async def _async_preprocess(self, frame):
    """GPU加速的预处理流水线"""
    loop = asyncio.get_event_loop()
    return await loop.run_in_executor(
        None, 
        cv2.dnn.blobFromImage, 
        frame, 1/255.0, (640,640), swapRB=True
    )

class SocialPropagator(nn.Module):
    def __init__(self, hidden_dim=512):
        super().__init__()
        self.text_encoder = BertForSequenceClassification.from_pretrained('bert-base-chinese')
        self.graph_net = GraphConv(
            in_feats=hidden_dim,
            out_feats=hidden_dim//2,
            aggregator_type='mean'
        )
        self.temporal_net = TemporalConv(
            input_size=hidden_dim,
            hidden_size=hidden_dim//2,
            num_levels=3
        )
        
    def forward(self, text, graph, time_delta):
        # 文本特征提取
        text_feat = self.text_encoder(**text).logits
        
        # 图传播
        graph_feat = self.graph_net(graph, text_feat)
        
        # 时间卷积
        time_feat = self.temporal_net(graph_feat, time_delta)
        
        # 引力波融合
        return torch.sigmoid(
            (graph_feat * time_feat).sum(dim=-1)
        )

class ProductEvolution:
    def __init__(self):
        self.social_radar = SocialRadar()
        self.edge_eye = EdgeInference()
        self.gravity_model = GravityModel()
        
    def evolve(self, product_id):
        # 获取社交引力波
        social_energy = self.social_radar.capture_energy(product_id)
        
        # 解析店内交互数据
        store_data = self.edge_eye.get_latest()
        
        # 计算进化方向
        evolution_vector = self.gravity_model.calculate(
            social_energy, 
            store_data['attention_score'],
            store_data['interaction_count']
        )
        
        # 生成进化策略
        return {
            'price': self._adjust_price(evolution_vector),
            'position': self._optimize_position(evolution_vector),
            'promotion': self._generate_promotion(evolution_vector)
        }

    def _adjust_price(self, vector):
        # 基于引力波的动态定价
        base_price = get_base_price()
        return base_price * (1 + 0.2 * vector['price_sensitivity'])

    def _optimize_position(self, vector):
        # 热力图谱匹配
        heat_peak = find_heat_peak()
        return calculate_optimal_position(heat_peak)

    def _generate_promotion(self, vector):
        # 语义生成促销方案
        return gpt3.generate(
            f"根据社交热度{vector['social']}和店内关注度{vector['attention']}，生成促销文案："
        )

# 带宽优化调度器（保障关键数据传输）
class BandwidthManager:
    def __init__(self, total_bandwidth=100):
        self.bw_pool = {
            'critical': 0.4,  # 异常数据
            'model_update': 0.3,
            'routine': 0.2,
            'other': 0.1
        }
        self.current_usage = defaultdict(float)
        
    def allocate(self, data_type, size):
        """智能带宽分配策略"""
        max_bw = self.bw_pool[data_type] * self.total_bandwidth
        allocated = min(size, max_bw - self.current_usage[data_type])
        self.current_usage[data_type] += allocated
        return allocated

# 使用示例
bw_mgr = BandwidthManager(total_bandwidth=500)  # 500Kbps
critical_data = get_anomaly_data()
allocated = bw_mgr.allocate('critical', len(critical_data))
send_data(critical_data[:allocated])