增加线程数量可能并不会提高程序的效率，因为程序的瓶颈可能在于网络请求的速度和响应时间，而不是线程的数量。如果要提高程序效率，可以考虑使用多进程或异步编程来处理网络请求，或者优化代码逻辑和算法。另外，可以通过监控程序的资源占用情况来确定是否需要增加线程数量或调整其他参数。

import requests import time import os import urllib3 import sys import random from bs4 import BeautifulSoup from concurrent.futures import ThreadPoolExecutor from threading import Lock from colorama import Fore, init

now_time = time.strftime('%Y-%m-%d %H-%M')

读取Dorks

def work(dorks): with open(dorks, mode='r', encoding='utf-8') as file: read_content = file.readlines() # 将内容加入列表 content = [result.strip() for result in read_content] # 返回数量丢给任务池 return len(read_content), content

Google搜索

def google_serach(query, locks, filename): try: # 关闭HTTPS报错信息 urllib3.disable_warnings() filename = os.path.join(os.getcwd(), f'{filename}.txt') domains = ['fr','it','ca','co.uk','ru','co,jp','co.kr','com.au','co.in','com.br','com.ar','co.za','co.nz','es','se','nl','ch','at','dk','be','pl','fi','ie','pt','gr', 'tw', 'com', 'uk', 'de', 'br', 'ca', 'kr', 'mx', 'au', 'za'] random_domain = random.choice(domains) url = f'https://www.google.{random_domain}/search?q={query}&num=100' # 请求头 headers = { 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36', 'accept-language': 'zh-CN,zh;q=0.9', 'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,/;q=0.8,application/signed-exchange;v=b3;q=0.7', 'referer': 'https://www.google.com/', 'origin': 'https://www.google.com', 'Sec-Fetch-Site': 'same-origin', 'Sec-Fetch-Mode': 'navigate', 'Sec-Fetch-User': '?1', 'Sec-Fetch-Dest': 'document' } # 代理 proxies = {'http': 'http://127.0.0.1:7890', 'https': 'http://127.0.0.1:7890'} response = requests.get(url=url, headers=headers, proxies=proxies, verify=False, timeout=5) soup = BeautifulSoup(response.content, 'html.parser') # 查找全部div标签 find_div = soup.find_all('div', {'class': 'yuRUbf'}) # 开启线程锁 locks.acquire() # 加入列表 get_url = [url.findNext('a')['href'] + '\n' for url in find_div if 'google.com.tw' not in url.findNext('a')['href']] global url_num, dork_finish_num url_num += len(get_url) dork_finish_num += 1 print(Fore.GREEN + f'\r{now_time}[INFO]{ '-' * 10}>已获取Url数量：{url_num} Dorsk数量：{dork_finish_num} / {dork_total_num}', end='' + Fore.RESET) # 写入文件 write_info(filename, get_url) # 释放线程锁 locks.release()

except TimeoutError:
    pass

写入文件函数

def write_info(filename, get_url):

with open(filename, mode='a+', encoding='utf-8') as file:

    file.writelines(get_url)

if name == 'main': while True: try: init() # 初始化颜色模块 dorks_file = input(Fore.YELLOW + f'\n{now_time}[INFO]{ '-' * 10}>input file:' + Fore.RESET) print('') filename = input(Fore.YELLOW + f'\n{now_time}[INFO]{ '-' * 10}>output file:' + Fore.RESET) # 接受work函数返回的元组 dork_total_num, query_list = work(dorks_file) # 定义全局变量完成数量/URL数量 dork_finish_num = url_num = 0

        # 定义线程池数量
        threads = ThreadPoolExecutor(max_workers=20)

        # 定义全局锁
        threads_lock = Lock()

        # 分配进程池任务
        for dokr_list in query_list:

            threads.submit(google_serach, dokr_list, threads_lock, filename)

        threads.shutdown()
        if len(sys.argv) == 1:
            pass
        input(Fore.YELLOW + f'\n\n{now_time}[INFO]{'-' * 10}>程序运行完毕，按回车退出' + Fore.RESET)
        break
    # 文件为空
    except FileNotFoundError:
        print(Fore.RED + f'{now_time}[Error]{'-' * 10}>文件不存在' + Fore.RESET)
    # 中断异常
    except KeyboardInterrupt:
        sys.exit(1