（八六）ArkCompiler 在智能物流系统中的应用：编译优化与效率提升 原创

# 模拟 AGV 路径规划                def agv_path_planning(start, end, warehouse_map):                    open_list = []                    closed_list = []                    open_list.append(start)                    while open_list:                        current = open_list.pop(0)                        if current == end:                            path = []                            while current in closed_list:                                path.append(current)                                current = closed_list[current]                            path.reverse()                            return path                        closed_list.append(current)                        neighbors = get_neighbors(current, warehouse_map)                        for neighbor in neighbors:                            if neighbor in closed_list:                                continue                            open_list.append(neighbor)                    return None                                 def get_neighbors(node, warehouse_map):                    neighbors = []                    rows, cols = len(warehouse_map), len(warehouse_map[0])                    x, y = node                    directions = [(0, 1), (0, -1), (1, 0), (-1, 0)]                    for dx, dy in directions:                        new_x, new_y = x + dx, y + dy                        if 0 <= new_x < rows and 0 <= new_y < cols and warehouse_map[new_x][new_y] == 0:                            neighbors.append((new_x, new_y))                    return neighbors
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#include <stdio.h>                                 // 模拟智能卡车自动驾驶控制                void truck_autopilot(float speed, float steering_angle) {                    // 假设这里有一些复杂的控制逻辑                    printf("Truck is moving at speed: %.2f km/h, steering angle: %.2f degrees\n", speed, steering_angle);                }                                 int main() {                    float speed = 60.0;                    float steering_angle = 5.0;                    truck_autopilot(speed, steering_angle);                    return 0;                }
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class WarehouseManagement:                    def __init__(self):                        self.inventory = {}                                     def add_goods(self, goods_id, quantity):                        if goods_id in self.inventory:                            self.inventory[goods_id] += quantity                        else:                            self.inventory[goods_id] = quantity                                     def remove_goods(self, goods_id, quantity):                        if goods_id in self.inventory:                            if self.inventory[goods_id] >= quantity:                                self.inventory[goods_id] -= quantity                                if self.inventory[goods_id] == 0:                                    del self.inventory[goods_id]                            else:                                print("Insufficient goods in stock.")                        else:                            print("Goods not found in inventory.")                                     def get_inventory(self):                        return self.inventory
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def transportation_scheduling(orders, vehicles):                    schedules = []                    for order in orders:                        best_vehicle = None                        min_cost = float('inf')                        for vehicle in vehicles:                            cost = calculate_cost(order, vehicle)                            if cost < min_cost:                                min_cost = cost                                best_vehicle = vehicle                        schedules.append((order, best_vehicle))                    return schedules                                 def calculate_cost(order, vehicle):                    # 假设这里有一些复杂的成本计算逻辑                    return 10.0
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import itertools                                 def delivery_path_planning(locations):                    min_distance = float('inf')                    best_path = None                    all_permutations = itertools.permutations(locations)                    for path in all_permutations:                        distance = calculate_distance(path)                        if distance < min_distance:                            min_distance = distance                            best_path = path                    return best_path                                 def calculate_distance(path):                    # 假设这里有一些复杂的距离计算逻辑                    return 100.0
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