您没有指定您正在运行的平台，但我必须假设它是使用fork创建新进程（例如 Linux）的平台，否则我不相信您的代码会正常工作，因为在spawn池中的每个进程都会正在创建自己的全局副本，CPU_QUEUE因此每个人都将获得队列中的第一项并相信它是 CPU id 1。

因此，我对代码进行了两项更改：

1. CPU_QUEUE通过使用池初始化程序使用单个队列实例为池中的每个进程初始化全局变量，使代码在平台之间更具可移植性。
2. time.sleep在函数开始时引入了一个调用，process_pool以使池中的每个进程都有机会处理其中一个提交的任务。如果没有这个，理论上池中的一个进程可以处理所有提交的任务，这只会降低这种可能性。

当我在 Linux 下运行代码时，我基本上看到了你所看到的。但是，当我在 Windows 下运行它时，由于上述更改，我现在可以这样做，我看到：

data rearranged for pool: [[0, 1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11]]
Pool CPU_NO: 1, data: [6, 7, 8, 9, 10, 11]
Pool CPU_NO: 2, data: [0, 1, 2, 3, 4, 5]
Threadpool id: 16924 CPU_NO: 1 / 2, data: 8
Threadpool id: 15260 CPU_NO: 1 / 2, data: 6
Threadpool id: 19800 CPU_NO: 2 / 2, data: 1
Threadpool id: 7580 CPU_NO: 2 / 2, data: 2
Threadpool id: 20368 CPU_NO: 1 / 2, data: 7
Threadpool id: 18736 CPU_NO: 2 / 2, data: 0
Threadpool id: 19800 CPU_NO: 2 / 2, data: 3
Threadpool id: 16924 CPU_NO: 1 / 2, data: 9
Threadpool id: 7580 CPU_NO: 2 / 2, data: 4
Threadpool id: 15260 CPU_NO: 1 / 2, data: 10
Threadpool id: 18736 CPU_NO: 2 / 2, data: 5
Threadpool id: 20368 CPU_NO: 1 / 2, data: 11
Program Ended!

这是您期望看到的。我只能得出结论，在 Linux 下threading.get_ident只返回一个进程内的唯一值。但是，如果您使用_thread.get_native_id()我已将其合并到下面的源代码中的相反，那似乎确实给出了 6 个唯一值（如所希望的那样）：

data rearranged for pool: [[0, 1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11]]
Pool CPU_NO: 1, data: [0, 1, 2, 3, 4, 5]
Pool CPU_NO: 2, data: [6, 7, 8, 9, 10, 11]
Threadpool id: 81 CPU_NO: 2 / 2, data: 7
Threadpool id: 83 CPU_NO: 2 / 2, data: 8
Threadpool id: 78 CPU_NO: 1 / 2, data: 0
Threadpool id: 79 CPU_NO: 2 / 2, data: 6
Threadpool id: 80 CPU_NO: 1 / 2, data: 1
Threadpool id: 82 CPU_NO: 1 / 2, data: 2
Threadpool id: 78 CPU_NO: 1 / 2, data: 3
Threadpool id: 83 CPU_NO: 2 / 2, data: 10
Threadpool id: 81 CPU_NO: 2 / 2, data: 9
Threadpool id: 79 CPU_NO: 2 / 2, data: 11
Threadpool id: 80 CPU_NO: 1 / 2, data: 4
Threadpool id: 82 CPU_NO: 1 / 2, data: 5
Program Ended!

修改后的来源

from multiprocessing.pool import Pool, ThreadPool
from multiprocessing import Queue
#from threading import get_ident
from threading import get_native_id
import random
from time import sleep
from functools import partial

# Params
N = 12
N_CPU = 2
N_THREAD = 3

def init_pool_processes(the_queue):
    global CPU_QUEUE
    CPU_QUEUE = the_queue

def split_list_to_pools(ls_data, n_pools):
    """Split data into pools as lists of approx. equal lengths."""
    n_each = int((len(ls_data) - 1) / n_pools) + 1
    return [ls_data[n_each * i:n_each * (i + 1)] for i in range(n_pools)]


def process_threadpool(data, CPU_NO=-1):
    """Process incoming data one-by-one"""
    sleep(3 + random.random())
    print(f"Threadpool id: {get_native_id()} CPU_NO: {CPU_NO} / {N_CPU}, data: {data}")


def process_pool(ls_data):
    """Process a list of data."""
    # Get initial pool status
    sleep(.2)
    CPU_NO = CPU_QUEUE.get()
    print(f"Pool CPU_NO: {CPU_NO}, data: {ls_data}")

    with ThreadPool(N_THREAD) as threadpool:
        for _ in threadpool.imap_unordered(partial(process_threadpool, CPU_NO=CPU_NO), ls_data):
            pass


if __name__ == '__main__':
    # Just for CPU numbering
    CPU_QUEUE = Queue(N_CPU)
    for i in range(1, 1 + N_CPU):
        CPU_QUEUE.put(i)

    # given data
    ls_data = list(range(N))

    # split data to pools
    ls_ls_data = split_list_to_pools(ls_data, N_CPU)
    print(f"data rearranged for pool: {ls_ls_data}")

    # process in parallel
    with Pool(N_CPU, initializer=init_pool_processes, initargs=(CPU_QUEUE,)) as pool:
        for _ in pool.imap_unordered(process_pool, ls_ls_data):
            pass

    print("Program Ended!")