（九一）ArkCompiler 在智能环保中的应用：编译优化与性能提升 原创

import random                                 # 模拟传感器数据采集                def collect_air_quality_data():                    return {                        "pm2_5": random.uniform(0, 100),                        "pm10": random.uniform(0, 200),                        "so2": random.uniform(0, 50),                        "no2": random.uniform(0, 50)                    }                                 # 数据处理函数，计算空气质量指数（AQI）                def calculate_aqi(data):                    # 简单示例，实际 AQI 计算更复杂                    pm2_5 = data["pm2_5"]                    pm10 = data["pm10"]                    aqi = (pm2_5 + pm10) / 2                    return aqi                                 # 模拟数据采集和处理                air_data = collect_air_quality_data()                aqi = calculate_aqi(air_data)                print(f"当前空气质量指数（AQI）: {aqi}")
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# 定义污水处理系统类                class SewageTreatmentSystem:                    def __init__(self, max_water_level):                        self.max_water_level = max_water_level                        self.current_water_level = 0                                     def update_water_level(self, inflow):                        self.current_water_level += inflow                        if self.current_water_level > self.max_water_level:                            self.drain_water()                                     def drain_water(self):                        self.current_water_level = self.max_water_level                        print("排水操作已执行，水位已恢复正常。")                                 # 创建污水处理系统实例                treatment_system = SewageTreatmentSystem(100)                                 # 模拟污水流入                treatment_system.update_water_level(80)                treatment_system.update_water_level(30)
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import cv2                import numpy as np                                 # 模拟图像识别函数                def classify_waste(image):                    # 简单示例，实际图像识别更复杂                    height, width, _ = image.shape                    if height > width:                        return "可回收物"                    else:                        return "有害垃圾"                                 # 模拟读取图像                image = np.random.randint(0, 256, (100, 200, 3), dtype=np.uint8)                waste_type = classify_waste(image)                print(f"识别结果: {waste_type}")
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# 定义无人机类                class Drone:                    def __init__(self, battery_capacity):                        self.battery_capacity = battery_capacity                        self.current_battery = battery_capacity                                     def fly(self, distance):                        power_consumption = distance * 0.1                        if self.current_battery >= power_consumption:                            self.current_battery -= power_consumption                            print(f"无人机飞行 {distance} 公里，剩余电量: {self.current_battery}")                        else:                            print("电量不足，无法飞行。")                                 # 创建无人机实例                drone = Drone(100)                                 # 模拟飞行                drone.fly(20)                drone.fly(90)
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# 模拟工业废气处理设备状态数据                device_status = [1, 1, 0, 1, 1]  # 0 表示故障                                 # 故障监测函数                def monitor_device(status):                    for i, state in enumerate(status):                        if state == 0:                            print(f"检测到设备故障，位置: {i}")                            return i                    return None                                 fault_location = monitor_device(device_status)                if fault_location is not None:                    print(f"故障已定位，正在进行处理...")
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