def func(data): print(data) def worker(array): pool = ThreadPoolExecutor(1) for a in array: pool.submit( func,a ) if __name__ == '__main__': processes = [] data = [i for i in range(300)] for i in range(3): p = multiprocessing.Process(target=worker,args=(data[100*i:100*(i+1)],)) processes.append(p) p.start() for p in processes: p.join() Typeerror cannot serializer '_io.BufferedReader' object

这个错误可能是因为`multiprocessing.Process`不支持序列化`_io.BufferedReader`对象。你可以尝试将`data`对象转换为可以序列化的对象，例如使用`pickle`模块进行序列化和反序列化。具体来说，你可以将`data`对象序列化并将其作为参数传递给`worker`函数，然后在`worker`函数中反序列化它。以下是修改后的代码示例：

import multiprocessing
import pickle
from concurrent.futures import ThreadPoolExecutor

def func(data):
    print(data)

def worker(data):
    array = pickle.loads(data)
    with ThreadPoolExecutor(1) as pool:
        for a in array:
            pool.submit(func, a)

if __name__ == '__main__':
    processes = []
    data = [i for i in range(300)]
    serialized_data = pickle.dumps(data)
    for i in range(3):
        p = multiprocessing.Process(target=worker, args=(serialized_data,))
        processes.append(p)
        p.start()

    for p in processes:
        p.join()

在这个修改后的代码中，我们首先使用`pickle`模块将`data`对象序列化为一个字节串`serialized_data`，然后将`serialized_data`作为参数传递给`worker`函数。在`worker`函数中，我们使用`pickle`模块的`loads`函数将字节串反序列化为原始的`array`列表。这样，我们就可以避免将`_io.BufferedReader`对象传递给`multiprocessing.Process`，从而避免了这个错误。