video_rewriting/job_wxk.py

import os
import concurrent.futures

import schedule
import time
import threading
from common import Material
# 控制读写速度的参数
from video_rewriting.video_prep import getVideo
from video_rewriting.video_processor import VideoProcessor

MAX_BPS = 120 * 1024 * 1024  # 120MB/s
MAX_WORKERS = os.cpu_count() * 2  # 线程池最大工作线程数量
READ_WRITE_CHUNK_SIZE = 1024 * 1024  # 每次读写的块大小 (1MB)
SLEEP_INTERVAL = READ_WRITE_CHUNK_SIZE / MAX_BPS  # 控制每次读写的延迟时间
# 全局锁，用于同步读写操作
lock = threading.Lock()

def video_task_start(data):
    mark = VideoProcessor.main(data)
    print(f"返回用户名{mark}")


# data = Material.feishu_list()
# video_task_start(data[0])

def controlled_io_operation(data):
    with lock:
        start_time = time.time()
        time.sleep(SLEEP_INTERVAL)
        end_time = time.time()
        elapsed_time = end_time - start_time
        if elapsed_time < SLEEP_INTERVAL:
            time.sleep(SLEEP_INTERVAL - elapsed_time)
    video_task_start(data)

def video_start():
    print("开始执行生成视频脚本.")
    data = Material.feishu_list()
    data = data[0]
    with concurrent.futures.ThreadPoolExecutor(max_workers=MAX_WORKERS) as executor:
        futures = {executor.submit(controlled_io_operation, data)}
        for future in concurrent.futures.as_completed(futures):
            try:
                future.result()
                print("处理结果: 成功")
            except concurrent.futures.TimeoutError:
                print("任务超时，已取消.")
            except Exception as e:
                print("处理任务时出现异常:", e)
    print("执行生成视频脚本结束.")



video_start()
schedule.every(6).hours.do(video_start)



while True:
    schedule.run_pending()
    time.sleep(1)