Python实现爬取腾讯招聘网岗位信息

介绍

开发环境

Windows 10

python3.6

开发工具

pycharm

numpy、matplotlib、time、xlutils.copy、os、xlwt, xlrd, random

效果展示

代码运行展示

实现思路

1.打开腾讯招聘的网址右击检查进行抓包,进入网址的时候发现有异步渲染,我们要的数据为异步加载

2.构造起始地址:

start_url = ‘https://careers.tencent.com/tencentcareer/api/post/Query’

参数在headers的最下面

timestamp: 1625641250509

countryId:

cityId:

bgIds:

productId:

categoryId:

parentCategoryId:

attrId:

keyword:

pageIndex: 1

pageSize: 10

language: zh-cn

area: cn

3.发送请求,获取响应

self.start_url = 'https://careers.tencent.com/tencentcareer/api/post/Query'

# 构造请求参数

params = {

# 捕捉当前时间戳

'timestamp': str(int(time.time() * 1000)),

'countryId': '',

'cityId': '',

'bgIds': '',

'productId': '',

'categoryId': '',

'parentCategoryId': '',

'attrId': '',

'keyword': '',

'pageIndex': str(self.start_page),

'pageSize': '10',

'language': 'zh-cn',

'area': 'cn'

}

headers = {

'user-agent': random.choice(USER_AGENT_LIST)

}

response = session.get(url=self.start_url, headers=headers, params=params).json()

4.提取数据,获取岗位信息大列表,提取相应的数据

# 获取岗位信息大列表

json_data = response['Data']['Posts']

# 判断结果是否有数据

if json_data is None:

# 没有数据,设置循环条件为False

self.is_running = False

# 反之,开始提取数据

else:

# 循环遍历,取出列表中的每一个岗位字典

# 通过key取value值的方法进行采集数据

for data in json_data:

# 工作地点

LocationName = data['LocationName']

# 往地址大列表中添加数据

self.addr_list.append(LocationName)

# 工作属性

CategoryName = data['CategoryName']

# 往工作属性大列表中添加数据

self.category_list.append(CategoryName)

# 岗位名称

RecruitPostName = data['RecruitPostName']

# 岗位职责

Responsibility = data['Responsibility']

# 发布时间

LastUpdateTime = data['LastUpdateTime']

# 岗位地址

PostURL = data['PostURL']

5.数据生成折线图、饼图、散点图、柱状图

# 第一张图:根据岗位地址和岗位属性二者数量生成折线图

# 146,147两行代码解决图中中文显示问题

plt.rcParams['font.sans-serif'] = ['SimHei']

plt.rcParams['axes.unicode_minus'] = False

# 由于二者数据数量不统一,在此进行切片操作

x_axis_data = [i for i in addr_dict.values()][:5]

y_axis_data = [i for i in cate_dict.values()][:5]

# print(x_axis_data, y_axis_data)

# plot中参数的含义分别是横轴值,纵轴值,线的形状,颜色,透明度,线的宽度和标签

plt.plot(y_axis_data, x_axis_data, 'ro-', color='#4169E1', alpha=0.8, linewidth=1, label='数量')

# 显示标签,如果不加这句,即使在plot中加了label='一些数字'的参数,最终还是不会显示标签

plt.legend(loc="upper right")

plt.xlabel('地点数量')

plt.ylabel('工作属性数量')

plt.savefig('根据岗位地址和岗位属性二者数量生成折线图.png')

plt.show()

# 第二张图:根据岗位地址数量生成饼图

"""工作地址饼图"""

addr_dict_key = [k for k in addr_dict.keys()]

addr_dict_value = [v for v in addr_dict.values()]

plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']

plt.rcParams['axes.unicode_minus'] = False

plt.pie(addr_dict_value, labels=addr_dict_key, autopct='%1.1f%%')

plt.title(f'岗位地址和岗位属性百分比分布')

plt.savefig(f'岗位地址和岗位属性百分比分布-饼图')

plt.show()

# 第三张图:根据岗位地址和岗位属性二者数量生成散点图

# 这两行代码解决 plt 中文显示的问题

plt.rcParams['font.sans-serif'] = ['SimHei']

plt.rcParams['axes.unicode_minus'] = False

# 输入岗位地址和岗位属性数据

production = [i for i in data.keys()]

tem = [i for i in data.values()]

colors = np.random.rand(len(tem)) # 颜色数组

plt.scatter(tem, production, s=200, c=colors) # 画散点图,大小为 200

plt.xlabel('数量') # 横坐标轴标题

plt.ylabel('名称') # 纵坐标轴标题

plt.savefig(f'岗位地址和岗位属性散点图')

plt.show()

# 第四张图:根据岗位地址和岗位属性二者数量生成柱状图

import matplotlib;matplotlib.use('TkAgg')

plt.rcParams['font.sans-serif'] = ['SimHei']

plt.rcParams['axes.unicode_minus'] = False

zhfont1 = matplotlib.font_manager.FontProperties(fname='C:\Windows\Fonts\simsun.ttc')

name_list = [name for name in data.keys()]

num_list = [value for value in data.values()]

width = 0.5 # 柱子的宽度

index = np.arange(len(name_list))

plt.bar(index, num_list, width, color='steelblue', tick_label=name_list, label='岗位数量')

plt.legend(['分解能耗', '真实能耗'], prop=zhfont1, labelspacing=1)

for a, b in zip(index, num_list): # 柱子上的数字显示

plt.text(a, b, '%.2f' % b, ha='center', va='bottom', fontsize=7)

plt.xticks(rotation=270)

plt.title('岗位数量和岗位属性数量柱状图')

plt.ylabel('次')

plt.legend()

plt.savefig(f'岗位数量和岗位属性数量柱状图-柱状图', bbox_inches='tight')

plt.show()

源码展示

"""ua大列表"""

USER_AGENT_LIST = [

'Mozilla/5.0 (Windows NT 6.2; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/60.0.3112.90 Safari/537.36',

'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3451.0 Safari/537.36',

'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.9; rv:57.0) Gecko/20100101 Firefox/57.0',

'Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/28.0.1500.71 Safari/537.36',

'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.2999.0 Safari/537.36',

'Mozilla/5.0 (Windows NT 6.3; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.70 Safari/537.36',

'Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10.4; en-US; rv:1.9.2.2) Gecko/20100316 Firefox/3.6.2',

'Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/44.0.2403.155 Safari/537.36 OPR/31.0.1889.174',

'Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 6.1; Trident/4.0; SLCC2; .NET CLR 2.0.50727; .NET CLR 3.5.30729; .NET CLR 3.0.30729; .NET CLR 1.1.4322; MS-RTC LM 8; InfoPath.2; Tablet PC 2.0)',

'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.100 Safari/537.36',

'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/68.0.3440.106 Safari/537.36 OPR/55.0.2994.61',

'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/535.1 (KHTML, like Gecko) Chrome/14.0.814.0 Safari/535.1',

'Mozilla/5.0 (Macintosh; U; PPC Mac OS X; ja-jp) AppleWebKit/418.9.1 (KHTML, like Gecko) Safari/419.3',

'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/43.0.2357.134 Safari/537.36',

'Mozilla/5.0 (compatible; MSIE 10.0; Windows NT 6.1; Trident/6.0; Touch; MASMJS)',

'Mozilla/5.0 (X11; Linux i686) AppleWebKit/535.21 (KHTML, like Gecko) Chrome/19.0.1041.0 Safari/535.21',

'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3497.100 Safari/537.36',

'Mozilla/5.0 (Windows NT 6.2; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/60.0.3112.90 Safari/537.36',

'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3451.0 Safari/537.36',

'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.9; rv:57.0) Gecko/20100101 Firefox/57.0',

'Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/28.0.1500.71 Safari/537.36',

'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.2999.0 Safari/537.36',

'Mozilla/5.0 (Windows NT 6.3; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.70 Safari/537.36',

'Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10.4; en-US; rv:1.9.2.2) Gecko/20100316 Firefox/3.6.2',

'Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/44.0.2403.155 Safari/537.36 OPR/31.0.1889.174',

'Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 6.1; Trident/4.0; SLCC2; .NET CLR 2.0.50727; .NET CLR 3.5.30729; .NET CLR 3.0.30729; .NET CLR 1.1.4322; MS-RTC LM 8; InfoPath.2; Tablet PC 2.0)',

'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.100 Safari/537.36',

'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/68.0.3440.106 Safari/537.36 OPR/55.0.2994.61',

'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/535.1 (KHTML, like Gecko) Chrome/14.0.814.0 Safari/535.1',

'Mozilla/5.0 (Macintosh; U; PPC Mac OS X; ja-jp) AppleWebKit/418.9.1 (KHTML, like Gecko) Safari/419.3',

'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/43.0.2357.134 Safari/537.36',

'Mozilla/5.0 (compatible; MSIE 10.0; Windows NT 6.1; Trident/6.0; Touch; MASMJS)',

'Mozilla/5.0 (X11; Linux i686) AppleWebKit/535.21 (KHTML, like Gecko) Chrome/19.0.1041.0 Safari/535.21',

'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3497.100 Safari/537.36',

'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4093.3 Safari/537.36',

'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_5) AppleWebKit/537.36 (KHTML, like Gecko; compatible; Swurl) Chrome/77.0.3865.120 Safari/537.36',

'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36',

'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36',

'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.131 Safari/537.36',

'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4086.0 Safari/537.36',

'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:75.0) Gecko/20100101 Firefox/75.0',

'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) coc_coc_browser/91.0.146 Chrome/85.0.4183.146 Safari/537.36',

'Mozilla/5.0 (Windows; U; Windows NT 5.2; en-US) AppleWebKit/537.36 (KHTML, like Gecko) Safari/537.36 VivoBrowser/8.4.72.0 Chrome/62.0.3202.84',

'Mozilla/5.0 (Windows NT 6.3; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.101 Safari/537.36',

'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36 Edg/87.0.664.60',

'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.16; rv:83.0) Gecko/20100101 Firefox/83.0',

'Mozilla/5.0 (X11; CrOS x86_64 13505.63.0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36',

'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.9; rv:68.0) Gecko/20100101 Firefox/68.0',

'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.101 Safari/537.36',

'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36',

'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/86.0.4240.198 Safari/537.36 OPR/72.0.3815.400',

'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.101 Safari/537.36',

]

from requests_html import HTMLSession

import os, xlwt, xlrd, random

from xlutils.copy import copy

import numpy as np

from matplotlib import pyplot as plt

from matplotlib.font_manager import FontProperties # 字体库

import time

session = HTMLSession()

class TXSpider(object):

def __init__(self):

# 起始的请求地址

self.start_url = 'https://careers.tencent.com/tencentcareer/api/post/Query'

# 起始的翻页页码

self.start_page = 1

# 翻页条件

self.is_running = True

# 准备工作地点大列表

self.addr_list = []

# 准备岗位种类大列表

self.category_list = []

def parse_start_url(self):

"""

解析起始的url地址

:return:

"""

# 条件循环模拟翻页

while self.is_running:

# 构造请求参数

params = {

# 捕捉当前时间戳

'timestamp': str(int(time.time() * 1000)),

'countryId': '',

'cityId': '',

'bgIds': '',

'productId': '',

'categoryId': '',

'parentCategoryId': '',

'attrId': '',

'keyword': '',

'pageIndex': str(self.start_page),

'pageSize': '10',

'language': 'zh-cn',

'area': 'cn'

}

headers = {

'user-agent': random.choice(USER_AGENT_LIST)

}

response = session.get(url=self.start_url, headers=headers, params=params).json()

"""调用解析响应方法"""

self.parse_response_json(response)

"""翻页递增"""

self.start_page += 1

"""翻页终止条件"""

if self.start_page == 20:

self.is_running = False

"""翻页完成,开始生成分析图"""

self.crate_img_four_func()

def crate_img_four_func(self):

"""

生成四张图方法

:return:

"""

# 统计数量

data = {} # 大字典

addr_dict = {} # 工作地址字典

cate_dict = {} # 工作属性字典

for k_addr, v_cate in zip(self.addr_list, self.category_list):

if k_addr in data:

# 大字典统计工作地址数据

data[k_addr] = data[k_addr] + 1

# 地址字典统计数据

addr_dict[k_addr] = addr_dict[k_addr] + 1

else:

data[k_addr] = 1

addr_dict[k_addr] = 1

if v_cate in data:

# 大字典统计工作属性数据

data[v_cate] = data[v_cate] + 1

# 工作属性字典统计数据

cate_dict[v_cate] = data[v_cate] + 1

else:

data[v_cate] = 1

cate_dict[v_cate] = 1

# 第一张图:根据岗位地址和岗位属性二者数量生成折线图

# 146,147两行代码解决图中中文显示问题

plt.rcParams['font.sans-serif'] = ['SimHei']

plt.rcParams['axes.unicode_minus'] = False

# 由于二者数据数量不统一,在此进行切片操作

x_axis_data = [i for i in addr_dict.values()][:5]

y_axis_data = [i for i in cate_dict.values()][:5]

# print(x_axis_data, y_axis_data)

# plot中参数的含义分别是横轴值,纵轴值,线的形状,颜色,透明度,线的宽度和标签

plt.plot(y_axis_data, x_axis_data, 'ro-', color='#4169E1', alpha=0.8, linewidth=1, label='数量')

# 显示标签,如果不加这句,即使在plot中加了label='一些数字'的参数,最终还是不会显示标签

plt.legend(loc="upper right")

plt.xlabel('地点数量')

plt.ylabel('工作属性数量')

plt.savefig('根据岗位地址和岗位属性二者数量生成折线图.png')

plt.show()

# 第二张图:根据岗位地址数量生成饼图

"""工作地址饼图"""

addr_dict_key = [k for k in addr_dict.keys()]

addr_dict_value = [v for v in addr_dict.values()]

plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']

plt.rcParams['axes.unicode_minus'] = False

plt.pie(addr_dict_value, labels=addr_dict_key, autopct='%1.1f%%')

plt.title(f'岗位地址和岗位属性百分比分布')

plt.savefig(f'岗位地址和岗位属性百分比分布-饼图')

plt.show()

# 第三张图:根据岗位地址和岗位属性二者数量生成散点图

# 这两行代码解决 plt 中文显示的问题

plt.rcParams['font.sans-serif'] = ['SimHei']

plt.rcParams['axes.unicode_minus'] = False

# 输入岗位地址和岗位属性数据

production = [i for i in data.keys()]

tem = [i for i in data.values()]

colors = np.random.rand(len(tem)) # 颜色数组

plt.scatter(tem, production, s=200, c=colors) # 画散点图,大小为 200

plt.xlabel('数量') # 横坐标轴标题

plt.ylabel('名称') # 纵坐标轴标题

plt.savefig(f'岗位地址和岗位属性散点图')

plt.show()

# 第四张图:根据岗位地址和岗位属性二者数量生成柱状图

import matplotlib;matplotlib.use('TkAgg')

plt.rcParams['font.sans-serif'] = ['SimHei']

plt.rcParams['axes.unicode_minus'] = False

zhfont1 = matplotlib.font_manager.FontProperties(fname='C:\Windows\Fonts\simsun.ttc')

name_list = [name for name in data.keys()]

num_list = [value for value in data.values()]

width = 0.5 # 柱子的宽度

index = np.arange(len(name_list))

plt.bar(index, num_list, width, color='steelblue', tick_label=name_list, label='岗位数量')

plt.legend(['分解能耗', '真实能耗'], prop=zhfont1, labelspacing=1)

for a, b in zip(index, num_list): # 柱子上的数字显示

plt.text(a, b, '%.2f' % b, ha='center', va='bottom', fontsize=7)

plt.xticks(rotation=270)

plt.title('岗位数量和岗位属性数量柱状图')

plt.ylabel('次')

plt.legend()

plt.savefig(f'岗位数量和岗位属性数量柱状图-柱状图', bbox_inches='tight')

plt.show()

def parse_response_json(self, response):

"""

解析响应

:param response:

:return:

"""

# 获取岗位信息大列表

json_data = response['Data']['Posts']

# 判断结果是否有数据

if json_data is None:

# 没有数据,设置循环条件为False

self.is_running = False

# 反之,开始提取数据

else:

# 循环遍历,取出列表中的每一个岗位字典

# 通过key取value值的方法进行采集数据

for data in json_data:

# 工作地点

LocationName = data['LocationName']

# 往地址大列表中添加数据

self.addr_list.append(LocationName)

# 工作属性

CategoryName = data['CategoryName']

# 往工作属性大列表中添加数据

self.category_list.append(CategoryName)

# 岗位名称

RecruitPostName = data['RecruitPostName']

# 岗位职责

Responsibility = data['Responsibility']

# 发布时间

LastUpdateTime = data['LastUpdateTime']

# 岗位地址

PostURL = data['PostURL']

# 构造保存excel所需要的格式字典

data_dict = {

# 该字典的key值与创建工作簿的sheet表的名称所关联

'岗位详情': [RecruitPostName, LocationName, CategoryName, Responsibility, LastUpdateTime, PostURL]

}

"""调用保存excel表格方法,数据字典作为参数"""

self.save_excel(data_dict)

# 提示输出

print(f"第{self.start_page}页--岗位{RecruitPostName}----采集完成----logging!!!")

def save_excel(self, data_dict):

"""

保存excel

:param data_dict: 数据字典

:return:

"""

# 判断保存到当我文件目录的路径是否存在

os_path_1 = os.getcwd() + '/数据/'

if not os.path.exists(os_path_1):

# 不存在,即创建这个目录,即创建”数据“这个文件夹

os.mkdir(os_path_1)

# 判断将数据保存到表格的这个表格是否存在,不存在,创建表格,写入表头

os_path = os_path_1 + '腾讯招聘数据.xls'

if not os.path.exists(os_path):

# 创建新的workbook(其实就是创建新的excel)

workbook = xlwt.Workbook(encoding='utf-8')

# 创建新的sheet表

worksheet1 = workbook.add_sheet("岗位详情", cell_overwrite_ok=True)

excel_data_1 = ('岗位名称', '工作地点', '工作属性', '岗位职责', '发布时间', '岗位地址')

for i in range(0, len(excel_data_1)):

worksheet1.col(i).width = 2560 * 3

# 行,列, 内容, 样式

worksheet1.write(0, i, excel_data_1[i])

workbook.save(os_path)

# 判断工作表是否存在

# 存在,开始往表格中添加数据(写入数据)

if os.path.exists(os_path):

# 打开工作薄

workbook = xlrd.open_workbook(os_path)

# 获取工作薄中所有表的个数

sheets = workbook.sheet_names()

for i in range(len(sheets)):

for name in data_dict.keys():

worksheet = workbook.sheet_by_name(sheets[i])

# 获取工作薄中所有表中的表名与数据名对比

if worksheet.name == name:

# 获取表中已存在的行数

rows_old = worksheet.nrows

# 将xlrd对象拷贝转化为xlwt对象

new_workbook = copy(workbook)

# 获取转化后的工作薄中的第i张表

new_worksheet = new_workbook.get_sheet(i)

for num in range(0, len(data_dict[name])):

new_worksheet.write(rows_old, num, data_dict[name][num])

new_workbook.save(os_path)

def run(self):

"""

启动运行

:return:

"""

self.parse_start_url()

if __name__ == '__main__':

# 创建该类的对象

t = TXSpider()

# 通过实例方法,进行调用

t.run()

以上就是Python实现爬取腾讯招聘网岗位信息的详细内容,更多关于Python爬取招聘网岗位信息的资料请关注其它相关文章!

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