AI人工智能领域的智能物流智能仓储系统

    import numpy as np
    import pandas as pd
    from statsmodels.tsa.arima.model import ARIMA
    from sklearn.ensemble import RandomForestRegressor
    from tensorflow.keras.models import Sequential
    from tensorflow.keras.layers import LSTM, Dense
    
    class InventoryOptimizer:
    def __init__(self, historical_data):
        self.data = historical_data
        
    def arima_predict(self, steps=7):
        model = ARIMA(self.data, order=(5,1,0))
        model_fit = model.fit()
        return model_fit.forecast(steps=steps)
    
    def lstm_predict(self, steps=7):
        # 数据预处理
        scaler = MinMaxScaler()
        scaled_data = scaler.fit_transform(self.data.values.reshape(-1,1))
        
        # 创建训练数据集
        X, y = [], []
        for i in range(60, len(scaled_data)):
            X.append(scaled_data[i-60:i, 0])
            y.append(scaled_data[i, 0])
        
        # 构建LSTM模型
        model = Sequential()
        model.add(LSTM(50, return_sequences=True, input_shape=(X.shape[1], 1)))
        model.add(LSTM(50))
        model.add(Dense(1))
        model.compile(optimizer='adam', loss='mean_squared_error')
        
        # 训练模型
        model.fit(X, y, epochs=20, batch_size=32)
        
        # 预测
        inputs = scaled_data[-60:]
        predictions = []
        for _ in range(steps):
            pred = model.predict(inputs.reshape(1, -1, 1))
            predictions.append(pred[0,0])
            inputs = np.append(inputs[1:], pred)
            
        return scaler.inverse_transform(np.array(predictions).reshape(-1,1))
    
    def ensemble_predict(self):
        arima_pred = self.arima_predict()
        lstm_pred = self.lstm_predict()
        return (arima_pred + lstm_pred) / 2

    import heapq
    
    class AGVPathPlanner:
    def __init__(self, warehouse_map):
        self.map = warehouse_map
        self.width = len(warehouse_map[0])
        self.height = len(warehouse_map)
        
    def heuristic(self, a, b):
        return abs(a[0] - b[0]) + abs(a[1] - b[1])
    
    def a_star_search(self, start, goal):
        neighbors = [(0,1), (1,0), (0,-1), (-1,0)]
        close_set = set()
        came_from = {}
        gscore = {start:0}
        fscore = {start:self.heuristic(start, goal)}
        oheap = []
        heapq.heappush(oheap, (fscore[start], start))
        
        while oheap:
            current = heapq.heappop(oheap)[1]
            
            if current == goal:
                path = []
                while current in came_from:
                    path.append(current)
                    current = came_from[current]
                return path[::-1]
                
            close_set.add(current)
            for i, j in neighbors:
                neighbor = current[0] + i, current[1] + j                
                if 0 <= neighbor[0] < self.height and 0 <= neighbor[1] < self.width:
                    if self.map[neighbor[0]][neighbor[1]] == 1:
                        continue
                        
                    tentative_g_score = gscore[current] + self.heuristic(current, neighbor)
                    
                    if neighbor in close_set and tentative_g_score >= gscore.get(neighbor, float('inf')):
                        continue
                        
                    if tentative_g_score < gscore.get(neighbor, float('inf')) or neighbor not in [i[1] for i in oheap]:
                        came_from[neighbor] = current
                        gscore[neighbor] = tentative_g_score
                        fscore[neighbor] = tentative_g_score + self.heuristic(neighbor, goal)
                        heapq.heappush(oheap, (fscore[neighbor], neighbor))
        
        return False
    
    def dynamic_obstacle_avoidance(self, start, goal, dynamic_obstacles):
        # 动态障碍物处理
        path = self.a_star_search(start, goal)
        if not path:
            return False
            
        for obstacle in dynamic_obstacles:
            if obstacle in path:
                # 重新规划避开障碍物的路径
                self.map[obstacle[0]][obstacle[1]] = 1
                new_path = self.a_star_search(start, goal)
                self.map[obstacle[0]][obstacle[1]] = 0
                return new_path
                
        return path

    # 创建conda环境
    conda create -n smart_warehouse python=3.8
    conda activate smart_warehouse
    
    # 安装核心包
    pip install tensorflow-gpu==2.5.0
    pip install scikit-learn pandas numpy matplotlib
    pip install pymysql sqlalchemy
    pip install paho-mqtt
    pip install opencv-python

    import cv2
    import numpy as np
    from tensorflow.keras.models import load_model
    
    class SmartSorter:
    def __init__(self, model_path='models/object_classifier.h5'):
        self.model = load_model(model_path)
        self.classes = ['box', 'envelope', 'plastic_bag', 'other']
        self.camera = cv2.VideoCapture(0)
        
    def preprocess_image(self, img):
        img = cv2.resize(img, (224, 224))
        img = img / 255.0
        return np.expand_dims(img, axis=0)
    
    def get_object_class(self):
        ret, frame = self.camera.read()
        if not ret:
            return None
            
        processed = self.preprocess_image(frame)
        pred = self.model.predict(processed)
        class_id = np.argmax(pred)
        return self.classes[class_id]
    
    def direct_to_chute(self, class_name):
        # 根据分类结果控制分拣滑槽
        if class_name == 'box':
            self.activate_chute(1)
        elif class_name == 'envelope':
            self.activate_chute(2)
        elif class_name == 'plastic_bag':
            self.activate_chute(3)
        else:
            self.activate_chute(4)
    
    def activate_chute(self, chute_num):
        # 通过GPIO或MQTT控制物理分拣装置
        print(f"Activating chute {chute_num}")
        # 实际实现中这里会包含硬件控制代码
        
    def run(self):
        try:
            while True:
                obj_class = self.get_object_class()
                if obj_class:
                    self.direct_to_chute(obj_class)
        except KeyboardInterrupt:
            self.camera.release()
            print("Sorter stopped")