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Scrapy项目实战之爬取某社区用户详情
脚本专栏 2024/12/24 佚名
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本文介绍了Scrapy项目实战之爬取某社区用户详情，分享给大家，具有如下：

get_cookies.py

from selenium import webdriver
from pymongo import MongoClient
from scrapy.crawler import overridden_settings
# from segmentfault import settings
import time
import settings

class GetCookies(object):
 def __init__(self):
  # 初始化组件
  # 设定webdriver选项
  self.opt = webdriver.ChromeOptions()
  # self.opt.add_argument("--headless")
  # 初始化用户列表
  self.user_list = settings.USER_LIST
  # 初始化MongoDB参数
  self.client = MongoClient(settings.MONGO_URI)
  self.db = self.client[settings.MONGO_DB]
  self.collection = self.db["cookies"]

 def get_cookies(self,username,password):
  """

  :param username:
  :param password:
  :return: cookies
  """
  # 使用webdriver选项创建driver
  driver = webdriver.Chrome(executable_path="/Users/Hank/scrapy/segmentfault/segmentfault/chromedriver",options=self.opt)
  driver.get("https://segmentfault.com/user/login")
  driver.find_element_by_name("username").send_keys(username)
  driver.find_element_by_name("password").send_keys(password)
  driver.find_element_by_xpath("//button[@type='submit']").click()
  time.sleep(2)
  driver.get("https://segmentfault.com/u/luwangmeilun/users/following")
  # 登陆之后获取页面cookies
  cookies = driver.get_cookies()
  driver.quit()

  return cookies

 def format_cookies(self,cookies):
  """

  :param cookies:
  从driver.get_cookies的形式为：
  [{'domain': 'segmentfault.com', 'httpOnly': False, 'name': 'PHPSESSID',
  'path': '/', 'secure': False, 'value': 'web2~5grmfa89j12eksub8hja3bvaq4'},
  {'domain': '.segmentfault.com', 'expiry': 1581602940, 'httpOnly': False,
  'name': 'Hm_lvt_e23800c454aa573c0ccb16b52665ac26', 'path': '/', 'secure': False,
  'value': '1550066940'},
  {'domain': '.segmentfault.com', 'httpOnly': False,
  'name': 'Hm_lpvt_e23800c454aa573c0ccb16b52665ac26',
  'path': '/', 'secure': False, 'value': '1550066940'},
  {'domain': '.segmentfault.com', 'expiry': 1550067000, 'httpOnly': False,
  'name': '_gat', 'path': '/', 'secure': False, 'value': '1'},
  {'domain': '.segmentfault.com', 'expiry': 1550153340, 'httpOnly': False,
  'name': '_gid', 'path': '/', 'secure': False, 'value': 'GA1.2.783265084.1550066940'},
  {'domain': '.segmentfault.com', 'expiry': 1613138940, 'httpOnly': False, 'name': '_ga',
  'path': '/', 'secure': False, 'value': 'GA1.2.1119166665.1550066940'}]
  只需提取每一项的name与value即可

  :return:
  """
  c = dict()
  for item in cookies:
   c[item['name']] = item['value']

  return c

 def save(self):
  print("开始获取Cookies....")
  # 从用户列表中获取用户名与密码，分别登陆获取cookies
  for username,password in self.user_list:
   cookies = self.get_cookies(username,password)
   f_cookies = self.format_cookies(cookies)
   print("insert cookie:{}".format(f_cookies))
   # 将格式整理后的cookies插入MongoDB数据库
   self.collection.insert_one(f_cookies)

  # s = db[self.collection].find()
  # for i in s:
  #  print(i)


if __name__ == '__main__':

 cookies = GetCookies()
 for i in range(20):
  cookies.save()

item.py

# -*- coding: utf-8 -*-

# Define here the models for your scraped items
#
# See documentation in:
# https://doc.scrapy.org/en/latest/topics/items.html

import scrapy


class SegmentfaultItem(scrapy.Item):
 # define the fields for your item here like:
 # 个人属性
 # 姓名
 name = scrapy.Field()
 # 声望
 rank = scrapy.Field()
 # 学校
 school = scrapy.Field()
 # 专业
 majors = scrapy.Field()
 # 公司
 company = scrapy.Field()
 # 工作
 job = scrapy.Field()
 # blog
 blog = scrapy.Field()
 # 社交活动数据
 # 关注人数
 following = scrapy.Field()
 # 粉丝数
 fans = scrapy.Field()
 # 回答数
 answers = scrapy.Field()
 # 提问数
 questions = scrapy.Field()
 # 文章数
 articles = scrapy.Field()
 # 讲座数
 lives = scrapy.Field()
 # 徽章数
 badges = scrapy.Field()
 # 技能属性
 # 点赞数
 like = scrapy.Field()
 # 技能
 skills = scrapy.Field()
 # 注册日期
 register_date = scrapy.Field()
 # 问答统计
 # 回答最高得票数
 answers_top_score = scrapy.Field()
 # 得票数最高的回答对应的问题的标题
 answers_top_title = scrapy.Field()
 # 得票数最高的回答对应的问题的标签
 answers_top_tags = scrapy.Field()
 # 得票数最高的回答对应的问题的内容
 answers_top_question = scrapy.Field()
 # 得票数最高的回答对应的问题的内容
 answers_top_content = scrapy.Field()

pipeline.py

# -*- coding: utf-8 -*-

# Define your item pipelines here
#
# Don't forget to add your pipeline to the ITEM_PIPELINES setting
# See: https://doc.scrapy.org/en/latest/topics/item-pipeline.html
import pymongo

class SegmentfaultPipeline(object):
 # 设定MongoDB集合名称
 collection_name = 'userinfo'

 def __init__(self,mongo_uri,mongo_db):
  self.mongo_uri = mongo_uri
  self.mongo_db = mongo_db

 # 通过crawler获取settings.py中设定的MongoDB连接信息
 @classmethod
 def from_crawler(cls,crawler):
  return cls(
   mongo_uri = crawler.settings.get('MONGO_URI'),
   mongo_db = crawler.settings.get('MONGO_DB','segmentfault')
  )

 # 当爬虫启动时连接MongoDB
 def open_spider(self,spider):
  self.client = pymongo.MongoClient(self.mongo_uri)
  self.db = self.client[self.mongo_db]

 # 当爬虫关闭时断开MongoDB连接
 def close_spider(self,spider):
  self.client.close()

 # 将Item插入数据库保存
 def process_item(self, item, spider):
  self.db[self.collection_name].insert_one(dict(item))
  return item

settings.py

# -*- coding: utf-8 -*-

# Scrapy settings for segmentfault project
#
# For simplicity, this file contains only settings considered important or
# commonly used. You can find more settings consulting the documentation:
#
#  https://doc.scrapy.org/en/latest/topics/settings.html
#  https://doc.scrapy.org/en/latest/topics/downloader-middleware.html
#  https://doc.scrapy.org/en/latest/topics/spider-middleware.html

BOT_NAME = 'segmentfault'

SPIDER_MODULES = ['segmentfault.spiders']
NEWSPIDER_MODULE = 'segmentfault.spiders'


# Crawl responsibly by identifying yourself (and your website) on the user-agent
USER_AGENT = 'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36'

# Obey robots.txt rules
ROBOTSTXT_OBEY = False

# Configure maximum concurrent requests performed by Scrapy (default: 16)
CONCURRENT_REQUESTS = 100

# Configure a delay for requests for the same website (default: 0)
# See https://doc.scrapy.org/en/latest/topics/settings.html#download-delay
# See also autothrottle settings and docs
# DOWNLOAD_DELAY = 2
# The download delay setting will honor only one of:
# CONCURRENT_REQUESTS_PER_DOMAIN = 32
# CONCURRENT_REQUESTS_PER_IP = 32

# Disable cookies (enabled by default)
# COOKIES_ENABLED = False

# Disable Telnet Console (enabled by default)
#TELNETCONSOLE_ENABLED = False

RETRY_ENABLED = False

REDIRECT_ENABLED = False

DOWNLOAD_TIMEOUT = 5

# HTTPALLOW

# Override the default request headers:
#DEFAULT_REQUEST_HEADERS = {
# 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
# 'Accept-Language': 'en',
#}


# Enable or disable spider middlewares
# See https://doc.scrapy.org/en/latest/topics/spider-middleware.html
SPIDER_MIDDLEWARES = {
 'segmentfault.middlewares.SegmentfaultSpiderMiddleware': 543,
}

# Enable or disable downloader middlewares
# See https://doc.scrapy.org/en/latest/topics/downloader-middleware.html
DOWNLOADER_MIDDLEWARES = {
 # 'segmentfault.middlewares.SegmentfaultHttpProxyMiddleware': 543,
 'segmentfault.middlewares.SegmentfaultUserAgentMiddleware':643,
 'segmentfault.middlewares.SegmentfaultCookiesMiddleware':743,
 'scrapy.downloadermiddlewares.httpproxy.HttpProxyMiddleware': None,
 'scrapy.downloadermiddlewares.useragent.UserAgentMiddleware': None,
 # 'scrapy.downloadermiddlewares.cookies.CookiesMiddleware':None,

}

# Enable or disable extensions
# See https://doc.scrapy.org/en/latest/topics/extensions.html
#EXTENSIONS = {
# 'scrapy.extensions.telnet.TelnetConsole': None,
#}

# Configure item pipelines
# See https://doc.scrapy.org/en/latest/topics/item-pipeline.html
ITEM_PIPELINES = {
 'segmentfault.pipelines.SegmentfaultPipeline': 300,
}

# Enable and configure the AutoThrottle extension (disabled by default)
# See https://doc.scrapy.org/en/latest/topics/autothrottle.html
# AUTOTHROTTLE_ENABLED = True
# # The initial download delay
# AUTOTHROTTLE_START_DELAY = 5
# # The maximum download delay to be set in case of high latencies
# AUTOTHROTTLE_MAX_DELAY = 60
# # The average number of requests Scrapy should be sending in parallel to
# # each remote server
# AUTOTHROTTLE_TARGET_CONCURRENCY = 1.0
# # Enable showing throttling stats for every response received:
# AUTOTHROTTLE_DEBUG = False

# Enable and configure HTTP caching (disabled by default)
# See https://doc.scrapy.org/en/latest/topics/downloader-middleware.html#httpcache-middleware-settings
#HTTPCACHE_ENABLED = True
#HTTPCACHE_EXPIRATION_SECS = 0
#HTTPCACHE_DIR = 'httpcache'
#HTTPCACHE_IGNORE_HTTP_CODES = []
#HTTPCACHE_STORAGE = 'scrapy.extensions.httpcache.FilesystemCacheStorage'

# 配置MONGODB
MONGO_URI = 'localhost:27017'
MONGO_DB = 'segmentfault'

# 用户列表
USER_LIST = [
 ("798549150@qq.com","guoqing1010"),
 ("learnscrapy@163.com","guoqing1010"),
]

# 配置代理列表
PROXY_LIST = [
 'http://115.182.212.169:8080',
 'http://121.61.25.149:9999',
 'http://180.118.247.189:9000',
 'http://115.151.3.12:9999',
 'http://183.154.213.160:9000',
 'http://113.128.9.106:9999',
 'http://124.42.68.152:90',
 'http://49.70.48.50:9999',
 'http://113.128.11.172:9999',
 'http://111.177.177.40:9999',
 'http://59.62.83.253:9999',
 'http://39.107.84.185:8123',
 'http://124.94.195.107:9999',
 'http://111.177.160.132:9999',
 'http://120.25.203.182:7777'
]

USER_AGENT_LIST = [
 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36 OPR/26.0.1656.60',
 'Opera/8.0 (Windows NT 5.1; U; en)',
 'Mozilla/5.0 (Windows NT 5.1; U; en; rv:1.8.1) Gecko/20061208 Firefox/2.0.0 Opera 9.50',
 'Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; en) Opera 9.50',
 'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:34.0) Gecko/20100101 Firefox/34.0',
 'Mozilla/5.0 (X11; U; Linux x86_64; zh-CN; rv:1.9.2.10) Gecko/20100922 Ubuntu/10.10 (maverick) Firefox/3.6.10',
 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.71 Safari/537.36',
 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.11 (KHTML, like Gecko) Chrome/23.0.1271.64 Safari/537.11',
 'Mozilla/5.0 (Windows; U; Windows NT 6.1; en-US) AppleWebKit/534.16 (KHTML, like Gecko) Chrome/10.0.648.133 Safari/534.16',
 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.1 (KHTML, like Gecko) Chrome/21.0.1180.71 Safari/537.1 LBBROWSER',
 'Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; QQDownload 732; .NET4.0C; .NET4.0E; LBBROWSER)',
 'Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; QQDownload 732; .NET4.0C; .NET4.0E)',
 'Mozilla/5.0 (Windows NT 5.1) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.84 Safari/535.11 SE 2.X MetaSr 1.0',
 'Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; Trident/4.0; SV1; QQDownload 732; .NET4.0C; .NET4.0E; SE 2.X MetaSr 1.0)',
 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Maxthon/4.4.3.4000 Chrome/30.0.1599.101 Safari/537.36',
 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/38.0.2125.122 UBrowser/4.0.3214.0 Safari/537.36'
]

userinfo.py

# -*- coding: utf-8 -*-
import scrapy
import time
from scrapy import Request
from pymongo import MongoClient
from scrapy.linkextractors import LinkExtractor
from scrapy.spiders import CrawlSpider,Rule
from scrapy.http import FormRequest
from segmentfault.items import SegmentfaultItem


class UserinfoSpider(CrawlSpider):
 name = 'userinfo'
 allowed_domains = ['segmentfault.com']
 start_urls = ['https://segmentfault.com/u/mybigbigcat/users/following']

 rules = (
  # 用户主页地址，跟进并进行解析
  Rule(LinkExtractor(allow=r'/u/\w+$'),callback='parse_item',follow=True),
  # 用户关注列表，跟进列表页面，抓取用户主页地址进行后续操作
  # Rule(LinkExtractor(allow=r'/users/followed$'),follow=True),
  # 用户粉丝列表，跟进列表页面，抓取用户主页地址进行后续操作
  Rule(LinkExtractor(allow=r'/users/following$'),follow=True),
  # 跟进其他页面地址
  # Rule(LinkExtractor(allow=r'/users/[followed|following]"https://segmentfault.com",
      cookies=cookies,
      meta={'cookiejar':1},
      callback=self.after_login)]

 # 登录之后从start_url中开始抓取数据
 def after_login(self,response):
  for url in self.start_urls:
   return self.make_requests_from_url(url)
 # def after_login(self,response):
 #  yield Request(self.start_urls[0],
 #     meta={'cookiejar':response.meta['cookiejar']},
 #     callback=self.parse_item)

 def parse_item(self, response):
  """
  :param response:
  :return:
  """
  item = SegmentfaultItem()
  # 个人属性模块
  profile_head = response.css('.profile__heading')
  # 姓名
  item['name'] = profile_head.css('h2[class*=name]::text').re_first(r'\w+')
  # 声望
  item['rank'] = profile_head.css('.profile__rank-btn > span::text').extract_first()
  # 学校专业信息
  school_info = profile_head.css('.profile__school::text').extract()
  if school_info:
   # 学校
   item['school'] = school_info[0]
   # 专业
   item['majors'] = school_info[1].strip()
  else:
   item['school'] = ''
   item['majors'] = ''
  # 公司职位信息
  company_info = profile_head.css('.profile__company::text').extract()
  if company_info:
   # 公司
   item['company'] = company_info[0]
   # 职位
   item['job'] = company_info[1].strip()
  else:
   item['company'] = ''
   item['job'] = ''
  # 个人博客
  item['blog'] = profile_head.css('a[class*=other-item-link]::attr(href)').extract_first()

  # 统计面板模块
  profile_active = response.xpath("//div[@class='col-md-2']")
  # 关注人数
  item['following'] = profile_active.css('div[class*=info] a > .h5::text').re(r'\d+')[0]
  # 粉丝人数
  item['fans'] = profile_active.css('div[class*=info] a > .h5::text').re(r'\d+')[1]
  # 回答问题数
  item['answers'] = profile_active.css('a[href*=answer] .count::text').re_first(r'\d+')
  # 提问数
  item['questions'] = profile_active.css('a[href*=questions] .count::text').re_first(r'\d+')
  # 文章数
  item['articles'] = profile_active.css('a[href*=articles] .count::text').re_first(r'\d+')
  # 讲座数
  item['lives'] = profile_active.css('a[href*=lives] .count::text').re_first(r'\d+')
  # 徽章数
  item['badges'] = profile_active.css('a[href*=badges] .count::text').re_first(r'\d+')
  # 徽章详细页面地址
  badge_url = profile_active.css('a[href*=badges]::attr(href)').extract_first()

  # 技能面板模块
  profile_skill = response.xpath("//div[@class='col-md-3']")
  # 技能标签列表
  item['skills'] = profile_skill.css('.tag::text').re(r'\w+')
  # 获得的点赞数
  item['like'] = profile_skill.css('.authlist').re_first(r'获得 (\d+) 次点赞')
  # 注册日期
  item['register_date'] = profile_skill.css('.profile__skill--other p::text').extract_first()
  # if register_time:
  #  item['register_date'] = ''.join(re.findall(r'\d+',register_time))
  # else:
  #  item['register_date'] = ''

  # 产出数据模块
  profile_work = response.xpath("//div[@class='col-md-7']")
  # 回答获得的最高分
  item['answers_top_score'] = profile_work.css('#navAnswer .label::text').re_first(r'\d+')
  # 最高分回答对应的问题的标题
  item['answers_top_title'] = profile_work.css('#navAnswer div[class*=title-warp] > a::text').extract_first()
  # 最高分回答对应的问题的url
  answer_url = profile_work.css('#navAnswer div[class*=title-warp] > a::attr(href)').extract_first()

  # 将需要继续跟进抓取数据的url与item作为参数传递给相应方法继续抓取数据
  request = scrapy.Request(
   # 问题详细页url
   url=response.urljoin(answer_url),
   meta={
   # item需要传递
   'item':item,
   # 徽章的url
   'badge_url':response.urljoin(badge_url)},
   # 调用parse_ansser继续处理
   callback=self.parse_answer)
  yield request

 def parse_answer(self,response):
  # 取出传递的item
  item = response.meta['item']
  # 取出传递的徽章详细页url
  badge_url = response.meta['badge_url']
  # 问题标签列表
  item['answers_top_tags'] = response.css('.question__title--tag .tag::text').re(r'\w+')
  # 先获取组成问题内容的字符串列表
  question_content = response.css('.widget-question__item p').re(r'>(.*"htmlcode">



# -*- coding: utf-8 -*-

# Define here the models for your spider middleware
#
# See documentation in:
# https://doc.scrapy.org/en/latest/topics/spider-middleware.html
import random
import re
import datetime
import scrapy
import logging
import time
from scrapy.conf import settings
from pymongo import MongoClient
from scrapy.downloadermiddlewares.httpproxy import HttpProxyMiddleware
import pymongo
logger = logging.getLogger(__name__)


class SegmentfaultSpiderMiddleware(object):
 """
 处理Item中保存的三种类型注册日期数据：
 1. 注册于 2015年12月12日
 2. 注册于 3 天前
 3. 注册于 5 小时前
 """

 def process_spider_output(self,response,result,spider):

  """
  输出response时调用此方法处理item中register_date
  :param response:
  :param result: 包含item
  :param spider:
  :return:处理过注册日期的item
  """
  for item in result:
   # 判断获取的数据是否是scrapy.item类型
   if isinstance(item,scrapy.Item):
    # 获取当前时间
    now = datetime.datetime.now()
    register_date = item['register_date']
    logger.info("获取注册日志格式为{}".format(register_date))
    # 提取注册日期字符串，如'注册于2015年12月12日' => '20151212'
    day = ''.join(re.findall(r'\d+',register_date))
    # 如果提取数字字符串长度大于4位，则为'注册于2015年12月12日'形式
    if len(day) > 4:
     date = day
    # 如果‘时'在提取的字符串中，则为'注册于8小时前'形式
    elif '时' in register_date:
     d = now - datetime.timedelta(hours=int(day))
     date = d.strftime("%Y%m%d")
    # 最后一种情况就是'注册于3天前'形式
    else:
     d = now - datetime.timedelta(days=int(day))
     date = d.strftime("%Y%m%d")

    # 更新register_date值
    item['register_date'] = date
   yield item


class SegmentfaultHttpProxyMiddleware(object):
 # Not all methods need to be defined. If a method is not defined,
 # scrapy acts as if the downloader middleware does not modify the
 # passed objects.
 def __init__(self):
  self.proxy_list = settings['PROXY_LIST']

 def process_request(self, request, spider):
  proxy = random.choice(self.proxy_list)
  logger.info('使用代理:{}'.format(proxy))
  request.meta['proxy'] = proxy


class SegmentfaultUserAgentMiddleware(object):
 def __init__(self):
  self.useragent_list = settings['USER_AGENT_LIST']

 def process_request(self,request,spider):
  user_agent = random.choice(self.useragent_list)

  # logger.info('使用的USE USER-AGENT:{}'.format(user_agent))
  request.headers['User-Agent'] = user_agent



class SegmentfaultCookiesMiddleware(object):
 client = MongoClient(settings['MONGO_URI'])
 db = client[settings['MONGO_DB']]
 collection = db['cookies']

 def get_cookies(self):
  """
  随机获取cookies
  :return:
  """
  cookies = random.choice([cookie for cookie in self.collection.find()])
  # 将不需要的"_id"与"_gat"参数删除
  cookies.pop('_id')
  cookies.pop('_gat')
  # 将"Hm_lpvt_e23800c454aa573c0ccb16b52665ac26"填充当前时间
  cookies['Hm_lpvt_e23800c454aa573c0ccb16b52665ac26'] = str(int(time.time()))
  return cookies

 def remove_cookies(self,cookies):
  """
  删除已失效的cookies
  :param cookies:
  :return:
  """
  # 随机获取cookies中的一对键值,返回结果是一个元祖
  i = cookies.popitem()
  # 删除cookies
  try:
   logger.info("删除cookies{}".format(cookies))
   self.collection.remove({i[0]:i[1]})
  except Exception as e:
   logger.info("No this cookies:{}".format(cookies))

 def process_request(self,request,spider):
  """
  为每一个request添加一个cookie
  :param request:
  :param spider:
  :return:
  """
  cookies = self.get_cookies()
  request.cookies = cookies

 def process_response(self,request,response,spider):
  """
  对于登录失效的情况，可能会重定向到登录页面，这时添加新的cookies继续，将请求放回调度器
  :param request:
  :param response:
  :param spider:
  :return:
  """
  if response.status in [301,302]:
   logger.info("Redirect response:{}".format(response))
   redirect_url = response.headers['location']
   if b'/user/login' in redirect_url:
    logger.info("Cookies失效")

    # 请求失败，重新获取一个cookie，添加到request，并停止后续中间件处理此request，将此request放入调度器
    new_cookie = self.get_cookies()
    logger.info("获取新cookie:{}".format(new_cookie))
    # 删除旧cookies
    self.remove_cookies(request.cookies)
    request.cookies = new_cookie
   return request
  #
  return response






run.py







from scrapy import cmdline
# from segmentfault.get_cookies import GetCookies
from get_cookies import GetCookies

if __name__ == '__main__':
 cookies = GetCookies()
 cookies.save()
 name = 'userinfo'
 ""
 cmd = 'scrapy crawl {}'.format(name)
 cmdline.execute(cmd.split())
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一口气升级7个大模型SaaS应用，百度智能云：突出一个“开箱即用” - 2024/12/24

这一波大模型产业落地浪潮里，不少企业其实处在 “干瞪眼“的状态。

一种情况是，很多大模型产品看得见却摸不着，在台上一个个遥遥领先——今天Sora技精四座，明天英伟达的机器人又赢得满堂彩，可是到了台下一问：啥时候能用上啊？答曰：遥遥无期。

另一种情况是，企业想用上大模型，却又难免瞻前顾后——既要考虑场景融合，又得兼顾安全性，还要考虑打通现有系统，再加上各种部署成本和繁琐的采购流程……最后只能拂袖：罢了，再等等吧。

稳了！魔兽国服回归的3条重磅消息！官宣时间再确认！

昨天有一位朋友在大神群里分享，自己亚服账号被封号之后居然弹出了国服的封号信息对话框。

这里面让他访问的是一个国服的战网网址，com.cn和后面的zh都非常明白地表明这就是国服战网。

而他在复制这个网址并且进行登录之后，确实是网易的网址，也就是我们熟悉的停服之后国服发布的暴雪游戏产品运营到期开放退款的说明。这是一件比较奇怪的事情，因为以前都没有出现这样的情况，现在突然提示跳转到国服战网的网址，是不是说明了简体中文客户端已经开始进行更新了呢？

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