Python使用线程池传递多个参数的几种方法

更新时间：2026年04月14日 09:34:14 作者：独隅

本文介绍了在Python中使用线程池传递多个参数的四种方法,并推荐使用stmap方式,这种方法代码清晰易读,参数传递灵活,易于维护和扩展,适用于为每个设备指定不同参数的场景,需要的朋友可以参考下

方法一：使用zip和*解包

from concurrent.futures import ThreadPoolExecutor

def worker(phone, x, y, duration):
    print(f"设备: {phone}, 位置: ({x}, {y}), 时长: {duration}")
    # 这里执行实际的投屏操作

def thread_pool_with_zip():
    phones = ["172.26.101.164", "172.26.101.112", "172.26.101.113"]
    x_positions = [100, 500, 900]  # X坐标
    y_positions = [100, 100, 100]  # Y坐标
    durations = [20, 25, 30]  # 投屏时长
    
    with ThreadPoolExecutor(max_workers=3) as executor:
        # 使用zip打包参数，然后解包传递给worker函数
        executor.map(worker, phones, x_positions, y_positions, durations)

方法二：使用partial函数固定部分参数

from concurrent.futures import ThreadPoolExecutor
from functools import partial

def worker(phone, x, y, duration):
    print(f"设备: {phone}, 位置: ({x}, {y}), 时长: {duration}")

def thread_pool_with_partial():
    phones = ["172.26.101.164", "172.26.101.112", "172.26.101.113"]
    
    # 固定x, y, duration参数
    fixed_worker = partial(worker, x=100, y=100, duration=20)
    
    with ThreadPoolExecutor(max_workers=3) as executor:
        executor.map(fixed_worker, phones)

方法三：使用字典或元组包装参数

from concurrent.futures import ThreadPoolExecutor

def worker(params):
    phone = params['phone']
    x = params['x']
    y = params['y']
    duration = params['duration']
    print(f"设备: {phone}, 位置: ({x}, {y}), 时长: {duration}")

def thread_pool_with_dict():
    # 参数列表
    params_list = [
        {'phone': "172.26.101.164", 'x': 100, 'y': 100, 'duration': 20},
        {'phone': "172.26.101.112", 'x': 500, 'y': 100, 'duration': 25},
        {'phone': "172.26.101.113", 'x': 900, 'y': 100, 'duration': 30}
    ]
    
    with ThreadPoolExecutor(max_workers=3) as executor:
        executor.map(worker, params_list)

方法四：使用starmap（推荐）

from concurrent.futures import ThreadPoolExecutor

def worker(phone, x, y, duration):
    print(f"设备: {phone}, 位置: ({x}, {y}), 时长: {duration}")

def thread_pool_with_starmap():
    # 参数元组列表
    args_list = [
        ("172.26.101.164", 100, 100, 20),
        ("172.26.101.112", 500, 100, 25),
        ("172.26.101.113", 900, 100, 30)
    ]
    
    with ThreadPoolExecutor(max_workers=3) as executor:
        # 使用starmap解包元组参数
        executor.map(lambda args: worker(*args), args_list)

在您的代码中应用

修改您的代码以支持多参数传递：

def Scrcpy(params):
    """接收参数字典的投屏函数"""
    phone = params['phone']
    x = params.get('x', 100)  # 默认值
    y = params.get('y', 100)  # 默认值
    duration = params.get('duration', 20)  # 默认值
    
    RestartScrcpy(phone)
    time.sleep(5)
    phone_num = Phone_number(phone)
    
    try:
        _IP_CONNEXT = f"{phone}:12324"
        run_cmd(f"adb connect {_IP_CONNEXT}")  
        # 添加窗口位置参数
        run_cmd(f'scrcpy -s {_IP_CONNEXT} --time-limit {duration} --max-size=1280 --window-title={phone_num} --window-x={x} --window-y={y}') 
        run_cmd(f"adb disconnect {_IP_CONNEXT}")
    except Exception as e:
        log(logging.ERROR, f"Scrcpy: {e}")

def thread_pool_multiple_params():
    # 定义设备及其参数
    device_configs = [
        {'phone': "172.26.101.164", 'x': 100, 'y': 100, 'duration': 20},
        {'phone': "172.26.101.112", 'x': 800, 'y': 100, 'duration': 25},
        # 添加更多设备...
    ]
    
    try:
        with ThreadPoolExecutor(max_workers=len(device_configs)) as executor:
            executor.map(Scrcpy, device_configs)
    except Exception as e:
        log(logging.ERROR, f"线程池异常: {e}")

# 在主函数中使用
if __name__ == '__main__':
    while True:
        if len(PHONES) >= 2:
            selected = random.sample(PHONES, 2)
            log(logging.DEBUG, f"--> 随机选取的投屏设备：{selected}")
            
            # 为每个设备创建参数配置
            configs = []
            for i, phone in enumerate(selected):
                configs.append({
                    'phone': phone,
                    'x': 100 + i * 700,  # 水平排列窗口
                    'y': 100,
                    'duration': 20
                })
            
            thread_pool_multiple_params()
            time.sleep(5)
        # ... 其他条件判断

使用进程池（multiprocessing.Pool）的多参数传递

import multiprocessing

def worker(args):
    phone, x, y, duration = args
    print(f"设备: {phone}, 位置: ({x}, {y}), 时长: {duration}")

def multiprocessing_pool_multiple_params():
    args_list = [
        ("172.26.101.164", 100, 100, 20),
        ("172.26.101.112", 500, 100, 25),
        ("172.26.101.113", 900, 100, 30)
    ]
    
    with multiprocessing.Pool(processes=3) as pool:
        pool.map(worker, args_list)