diff --git a/zhihucrawling/simsun.ttf b/zhihucrawling/simsun.ttf
new file mode 100644
index 0000000..e0115ab
Binary files /dev/null and b/zhihucrawling/simsun.ttf differ
diff --git a/zhihucrawling/zhihucrawlling.ipynb b/zhihucrawling/zhihucrawlling.ipynb
new file mode 100644
index 0000000..a027057
--- /dev/null
+++ b/zhihucrawling/zhihucrawlling.ipynb
@@ -0,0 +1,410 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "█\r"
+     ]
+    }
+   ],
+   "source": [
+    "import itchat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m\u001b[36m$RECYCLE.BIN\u001b[m\u001b[m\r\n",
+      "2020-6-18-Python脚本爬虫.md\r\n",
+      "\u001b[1m\u001b[36mDesktop\u001b[m\u001b[m\r\n",
+      "E Book Research Design Cressweell 2014.pdf\r\n",
+      "GROUP 1 CLASS ACTIVITY 2.docx\r\n",
+      "GROUP 1 CLASS ACTIVITY1 (1).docx\r\n",
+      "GROUP 1 CLASS ACTIVITY2.docx\r\n",
+      "LIU,HONGYANG.mp4\r\n",
+      "Lab Test (HBase).pdf\r\n",
+      "\u001b[1m\u001b[36mLiterature Review\u001b[m\u001b[m\r\n",
+      "Proposal rubric.docx\r\n",
+      "\u001b[35mRelocated Items\u001b[m\u001b[m\r\n",
+      "\u001b[1m\u001b[36mResearch Methogologies\u001b[m\u001b[m\r\n",
+      "Screenshot 2020-02-29 at 14.07.04.png\r\n",
+      "Screenshot 2020-04-27 at 18.43.15.png\r\n",
+      "Screenshot 2020-05-20 at 18.46.10.png\r\n",
+      "Screenshot 2020-05-20 at 18.52.42.png\r\n",
+      "Screenshot 2020-05-23 at 21.22.07.png\r\n",
+      "Screenshot 2020-05-31 at 10.42.07.png\r\n",
+      "Screenshot 2020-06-03 at 19.20.25.png\r\n",
+      "Screenshot 2020-06-10 at 18.43.44.png\r\n",
+      "Screenshot 2020-06-10 at 18.43.50.png\r\n",
+      "\u001b[1m\u001b[36mShData-master\u001b[m\u001b[m\r\n",
+      "Thumbs.db\r\n",
+      "Untitled.ipynb\r\n",
+      "\u001b[1m\u001b[36mbig data management assignment\u001b[m\u001b[m\r\n",
+      "\u001b[1m\u001b[36mdash-oil-and-gas\u001b[m\u001b[m\r\n",
+      "\u001b[1m\u001b[36mdash-oil-gas-ternary\u001b[m\u001b[m\r\n",
+      "\u001b[1m\u001b[36mdash-sample-apps\u001b[m\u001b[m\r\n",
+      "\u001b[1m\u001b[36mdata mining\u001b[m\u001b[m\r\n",
+      "\u001b[1m\u001b[36mdata mining期末考试\u001b[m\u001b[m\r\n",
+      "\u001b[1m\u001b[36mdataMining\u001b[m\u001b[m\r\n",
+      "\u001b[1m\u001b[36mdbm\u001b[m\u001b[m\r\n",
+      "\u001b[1m\u001b[36mlatex\u001b[m\u001b[m\r\n",
+      "\u001b[1m\u001b[36mmachine learning test\u001b[m\u001b[m\r\n",
+      "\u001b[1m\u001b[36mml project\u001b[m\u001b[m\r\n",
+      "\u001b[1m\u001b[36mresearch\u001b[m\u001b[m\r\n",
+      "\u001b[1m\u001b[36mresearch methodology\u001b[m\u001b[m\r\n",
+      "\u001b[1m\u001b[36mresearch methodology2\u001b[m\u001b[m\r\n",
+      "\u001b[1m\u001b[36mterminal.app\u001b[m\u001b[m\r\n",
+      "test_composition.csv\r\n",
+      "\u001b[1m\u001b[36mtution visa\u001b[m\u001b[m\r\n",
+      "\u001b[1m\u001b[36mubuntu18\u001b[m\u001b[m\r\n",
+      "\u001b[1m\u001b[36mubuntuFile\u001b[m\u001b[m\r\n",
+      "\u001b[1m\u001b[36muniversity malaysia\u001b[m\u001b[m\r\n",
+      "v2-97942ea6f9fb33661fbbec825a550bb9_720w.jpg\r\n",
+      "\u001b[1m\u001b[36mweka\u001b[m\u001b[m\r\n",
+      "~$OUP 1 CLASS ACTIVITY 2.docx\r\n",
+      "~$OUP 1 CLASS ACTIVITY2.docx\r\n",
+      "~$U,HONGYANG.docx\r\n",
+      "~$ticle 1.docx\r\n",
+      "\u001b[1m\u001b[36m作业\u001b[m\u001b[m\r\n",
+      "\u001b[1m\u001b[36m数据研发\u001b[m\u001b[m\r\n",
+      "\u001b[1m\u001b[36m毕业论文\u001b[m\u001b[m\r\n",
+      "\u001b[1m\u001b[36m续签材料\u001b[m\u001b[m\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from DecryptLogin import login"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lg = login.Login()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'lg' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-1-f2ed3192fd13>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0minfos_return\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msession\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzhihu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"xxx\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"xxx\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'pc'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'lg' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "infos_return, session = lg.zhihu(\"xxx\",\"xxx\", 'pc')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import re, json, random, requests, urllib3\n",
+    "from bs4 import BeautifulSoup\n",
+    "url = \"https://www.zhihu.com/question/34098079\"\n",
+    "\n",
+    "headers = {\n",
+    "        \"User-Agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3497.100 Safari/537.36\",\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "\n",
+    "resp = requests.get(url,headers=headers)\n",
+    "\n",
+    "# soup = BeautifulSoup(resp.text,'lxml')\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "soup = BeautifulSoup(resp.text,'lxml')\n",
+    "\n",
+    "text = soup.find('div',{'id':'QuestionAnswers-answers'}).find_all('div',{'class':'List-item'})\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in text:\n",
+    "    content=i.find_all('p')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pattern =re.compile(u\"[\\u4e00-\\u9fa5]+\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "还能一键扣图，让开淘宝店的设计师下岗，在一旁痛哭流涕……\n",
+      "\n",
+      "\n",
+      "再也不用担心工作忙，没法经常和她/他/它聊天了。\n",
+      "可以实时知道女友的情感情绪指数，再也不用担心女友莫名其妙生气了。\n",
+      "还能顺道教女朋友学英语（女朋友一定很惊喜）\n",
+      "为了方便快速开发，我们使用python中的wxpy模块完成微信的基本操作。\n",
+      "首先，我们设置一个config.ini配置文件，并从这个配置文件开始读取信息。这些参数一看就懂，所以无需多言。\n",
+      "设置完相关参数以后，我们再来学习一下，如何每天教女友学英语：\n",
+      "如果你愿意，可以在上面对时间的判断中，加入一些其他你想要的，这样你女友就更开心啦！后期如果有时间，我将会加上以上节日问候功能。\n",
+      "接着，开启微信机器人，为了程序的健壮性，自动判断一下操作系统，根据不同操作系统执行不同指令：\n",
+      "只有每天的问候和节日问候是仅仅不够的，我们必须时刻知道她的情绪指数，这里可以使用snowNlp或者jieba来做分析，但是为了能够在打包成exe可执行文件时使得程序尽可能小，我们采取直接调用接口的方式来做。代码如下：\n",
+      "教完女友学英语后，开始把我们的关心语发给她。这里涉及到wxpy模块的相关操作，很简单，看我的例子就会了：\n",
+      "最后，就是如何每天定时发关心语给女友的问题了。\n",
+      "首先来个while循环，365天无限关心\n",
+      "最后，输入以下代码开始守护女友模式吧~\n",
+      "pip安装下列包：\n",
+      "设置以下内容：\n",
+      "至于没有女朋友的……\n",
+      "要不考虑一下它\n",
+      "这张逼死淘宝专业抠图店家的照片，用3行Python代码，花5秒就能超高精度抠图。\n",
+      "这里的 API 接口来源于 Remove.bg 网站，一个邮箱账号可以申请一个免费接口，可处理 50 张照片，如果想处理更多或者生成高清照片，需要买套餐，算下来价格大概是 1 元一张。\n",
+      "上淘宝搜索「证件照换底色」的店铺，发现多数店铺收费是 5 元，觉得利用好价格差空间，应该有商机。\n",
+      "接着比较感兴趣淘宝店家是怎么抠图的，抠图的质量如何，于是选择了排名前两位的店家来做测试，跟掌柜开始了一段「套路」聊天。\n",
+      "先找了第一家店主，店主上来就说先发照片，抠图满意再付款，于是就发了文章开头那张比较难抠的一张，想看看他们水平怎么样：\n",
+      "没想到店主这一抠就是二十分钟。。。\n",
+      "满心期待地打开图片一看，头发丝抠的不好，照片色彩也变了：\n",
+      "跟第二家店掌柜聊了后，也是花了 16 分钟弄好，比第一家稍好一点：\n",
+      "把三幅图一对比，从头发丝抠的效果和照片的色彩还原度就可以看出还是 AI 效果最好，而且只需要 5 秒钟。\n",
+      "于是，大致可以总结这款 AI 工具从效果和效率上基本碾压手动 PS 的淘宝店家。\n",
+      "心疼掌柜，花了 20 分钟还没有拿下我这一单……\n",
+      "这么难抠的图 AI 工具效果都好，那简单的证件照应该更没问题，基本确定有商机。\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "接下来用 Python 把上面的代码进行完善打包成 exe 文件执行。\n",
+      "轻松实现这样的功能：只需要简单敲几下键盘，就可以随意批量更换照片的背景色（常见的白、蓝、红三种颜色），然后秒换背景出图。\n",
+      "效果如下：\n",
+      "具体实现很简单，第一步输入 API，第二步输入图片所在文件夹，接着程序就会先抠图，生成带透明背景的 PNG 格式图形。  \n",
+      "接下来第三步利用 PIL 库来设置图片的背景颜色，键入一个字母就可以秒生成对应的背景色证件照。\n",
+      "这样就做成了一个简单的证件照更换工具，拿去开个淘宝店和抠图的设计师抢饭吃没有压力……\n",
+      "估计打死店主也都想不到让自己下岗的是几行代码……效率还是自己的N倍……\n",
+      "此工具可关注公众号“七月在线实验室”后，发“证件照”获取。\n",
+      "\n",
+      "看完这篇内容后，相信以下三件事，也会对你的个人提升有所帮助：\n",
+      "1、点赞，让更多人能看到这篇内容，同时你的认可也会鼓励我创作更多优质内容。\n",
+      "2、让自己变的更强：七月在线推出【Python基础特训4】，高中生都能学会的Python基础课程，限时优惠！并赠送150页python基础文档还等什么点击下方开始学习吧！\n",
+      "关注微信公众号 【七月在线实验室】，让自己变得更强！在公众号内发“ZH”。即可获得BAT面试题100道和课程代金劵！\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in content:\n",
+    "    print(i.text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text=\"\"\n",
+    "for i in content:\n",
+    "    text=text+i.text\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(-0.5, 399.5, 199.5, -0.5)"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from wordcloud import WordCloud\n",
+    "wordcloud = WordCloud(font_path=\"simsun.ttf\").generate(mytext)\n",
+    "%pylab inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.imshow(wordcloud, interpolation='bilinear')\n",
+    "plt.axis(\"off\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_answers_by_page(topic_id, page_no):\n",
+    "    global db, answer_ids, maxnum\n",
+    "    limit = 10\n",
+    "    offset = page_no * limit\n",
+    "    url = \"https://www.zhihu.com/api/v4/topics/\" + str(\n",
+    "        topic_id) + \"/feeds/essence?include=data%5B%3F(target.type%3Dtopic_sticky_module)%5D.target.data%5B%3F(target.type%3Danswer)%5D.target.content%2Crelationship.is_authorized%2Cis_author%2Cvoting%2Cis_thanked%2Cis_nothelp%3Bdata%5B%3F(target.type%3Dtopic_sticky_module)%5D.target.data%5B%3F(target.type%3Danswer)%5D.target.is_normal%2Ccomment_count%2Cvoteup_count%2Ccontent%2Crelevant_info%2Cexcerpt.author.badge%5B%3F(type%3Dbest_answerer)%5D.topics%3Bdata%5B%3F(target.type%3Dtopic_sticky_module)%5D.target.data%5B%3F(target.type%3Darticle)%5D.target.content%2Cvoteup_count%2Ccomment_count%2Cvoting%2Cauthor.badge%5B%3F(type%3Dbest_answerer)%5D.topics%3Bdata%5B%3F(target.type%3Dtopic_sticky_module)%5D.target.data%5B%3F(target.type%3Dpeople)%5D.target.answer_count%2Carticles_count%2Cgender%2Cfollower_count%2Cis_followed%2Cis_following%2Cbadge%5B%3F(type%3Dbest_answerer)%5D.topics%3Bdata%5B%3F(target.type%3Danswer)%5D.target.annotation_detail%2Ccontent%2Crelationship.is_authorized%2Cis_author%2Cvoting%2Cis_thanked%2Cis_nothelp%3Bdata%5B%3F(target.type%3Danswer)%5D.target.author.badge%5B%3F(type%3Dbest_answerer)%5D.topics%3Bdata%5B%3F(target.type%3Darticle)%5D.target.annotation_detail%2Ccontent%2Cauthor.badge%5B%3F(type%3Dbest_answerer)%5D.topics%3Bdata%5B%3F(target.type%3Dquestion)%5D.target.annotation_detail%2Ccomment_count&limit=\" + str(\n",
+    "        limit) + \"&offset=\" + str(offset)\n",
+    "    headers = {\n",
+    "        \"User-Agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3497.100 Safari/537.36\",\n",
+    "    }\n",
+    "    try:\n",
+    "        r = requests.get(url, verify=False, headers=headers)\n",
+    "    except requests.exceptions.ConnectionError:\n",
+    "        return False\n",
+    "    content = r.content.decode(\"utf-8\")\n",
+    "    data = json.loads(content)\n",
+    "    is_end = data[\"paging\"][\"is_end\"]\n",
+    "    items = data[\"data\"]\n",
+    "    if len(items) <= 0:\n",
+    "        return True\n",
+    "    pre = re.compile(\">(.*?)<\")\n",
+    "    for item in items:\n",
+    "        if maxnum <= 0:\n",
+    "            return True\n",
+    "        answer_id = item[\"target\"][\"id\"]\n",
+    "        if answer_id in answer_ids:\n",
+    "            continue\n",
+    "        if item[\"target\"][\"type\"] != \"answer\":\n",
+    "            continue\n",
+    "        if int(item[\"target\"][\"voteup_count\"]) < 10000:\n",
+    "            continue\n",
+    "        answer = ''.join(pre.findall(item[\"target\"][\"content\"].replace(\"\\n\", \"\").replace( \" \" , \"\")))\n",
+    "        if len(answer) == 0:\n",
+    "            continue\n",
+    "        if len(answer) > 200:\n",
+    "            continue\n",
+    "        answer_ids.append(answer_id)\n",
+    "        question = item[\"target\"][\"question\"][\"title\"].replace(\"\\n\", \"\")\n",
+    "        vote_num = item[\"target\"][\"voteup_count\"]\n",
+    "        if answer.find(\"<\") > -1 and answer.find(\">\") > -1:\n",
+    "            pass\n",
+    "        sline = \"=\" * 50\n",
+    "        content = sline + \"\\nQ: {}\\nA: {}\\nvote: {}\\n\".format(question, answer, vote_num)\n",
+    "        print(content)\n",
+    "        save2file(content)\n",
+    "        maxnum -= 1\n",
+    "    return is_end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def save2file(content):\n",
+    "    with open('result', 'a', encoding='utf-8') as file:\n",
+    "        file.write(content)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git "a/\345\244\247\344\274\227\347\202\271\350\257\204/02-\350\256\241\347\247\2211606-\346\235\250\350\212\263\350\212\263-\345\274\200\351\242\230\346\212\245\345\221\212-\346\217\220\344\272\244\347\211\210.pdf" "b/\345\244\247\344\274\227\347\202\271\350\257\204/02-\350\256\241\347\247\2211606-\346\235\250\350\212\263\350\212\263-\345\274\200\351\242\230\346\212\245\345\221\212-\346\217\220\344\272\244\347\211\210.pdf"
new file mode 100755
index 0000000..32adec6
Binary files /dev/null and "b/\345\244\247\344\274\227\347\202\271\350\257\204/02-\350\256\241\347\247\2211606-\346\235\250\350\212\263\350\212\263-\345\274\200\351\242\230\346\212\245\345\221\212-\346\217\220\344\272\244\347\211\210.pdf" differ
diff --git "a/\345\244\247\344\274\227\347\202\271\350\257\204/Dz_shopdetails.py" "b/\345\244\247\344\274\227\347\202\271\350\257\204/Dz_shopdetails.py"
new file mode 100755
index 0000000..4439ffa
--- /dev/null
+++ "b/\345\244\247\344\274\227\347\202\271\350\257\204/Dz_shopdetails.py"
@@ -0,0 +1,424 @@
+import os
+import urllib
+from bs4 import BeautifulSoup
+from fontTools.ttLib import TTFont
+import requests
+import time
+import numpy as pd
+import pandas as pd
+from pandas import DataFrame
+
+
+
+'''
+df = pd.DataFrame(pd.read_excel( './data/beijing/beijing.xlsx',sheet_name ='大众点评餐厅信息'))
+shopURL = df['shopURL']
+#df['Tele']= None
+#df.insert(3,'Tele',None)
+for i in range(0,5):
+    df.iloc[i,3] = 1
+DataFrame(df).to_excel('./data/beijing/beijing.xlsx',sheet_name ='大众点评餐厅信息',index=False, header=True)
+'''
+
+def get_Shopdetails(font_name_tel,font_name_time,font_name_dish):
+    #获取已爬取的shopurl
+    df = pd.DataFrame(pd.read_excel('./data/beijing/beijing.xlsx', sheet_name='大众点评餐厅信息'))
+    shopURL = df['shopURL']
+    shopid_list = df['shop_id']
+    #修改插入值
+    df.insert(3, 'Tele', None)
+    df.insert(4, 'Time', None)
+    for i in range(len(shopURL)):
+        time.sleep(40)
+        url = shopURL[i]
+        shopid = shopid_list[i]
+        headers = {
+            'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9',
+            'Accept-Encoding': 'gzip, deflate',
+            'Accept-Language': 'zh-CN,zh;q=0.9',
+            'Connection': 'keep-alive',
+            "Cookie": 's_ViewType=10; _lxsdk_cuid=16fa87fc490c8-02426df1c4ac71-6701b35-144000-16fa87fc491c8; _lxsdk=16fa87fc490c8-02426df1c4ac71-6701b35-144000-16fa87fc491c8; _hc.v=e341e466-9834-a0f5-107b-9fdab1b34191.1579079944; ua=Daisy._620; ctu=d465b1dcc3f659856cf30445bbdb5b7325f916625944c0eda4a4e09ab5c3dc95; cityid=341; switchcityflashtoast=1; default_ab=index%3AA%3A3%7CshopList%3AC%3A5; dplet=63b9a755291eec6e054067fdf1d6842b; dper=7c5d01df3117bf3d570e5b81af787981fa361e78dc792a941622ce17921ba5378670a472fa2c5b2fac712fff4394b541a9c5ef1545188b7744a47cd0139069c27d09701ca681e46e40e04145b54eb0c46981b154f3182aaadacc9c5c52e20048; _lx_utm=utm_source%3DBaidu%26utm_medium%3Dorganic; cy=2; cye=beijing; ll=7fd06e815b796be3df069dec7836c3df; _lxsdk_s=171073f9ee5-65f-7a7-038%7C%7C282',
+            'Host': 'www.dianping.com',
+            'If-Modified-Since': 'Mon, 23 Mar 2020 05:11:12 GMT',
+            'If-None-Match': "6d7f33e3ecead2ca33bd5e1d99b0f990",
+            'Referer': 'http://www.dianping.com/beijing/ch10',
+            'Upgrade-Insecure-Requests': '1',
+            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.149 Safari/537.36'
+        }
+        html = requests.get(url, headers = headers).text
+
+        #对加密文本进行解码
+        for code, value in font_name_tel.items():
+            code_str = code
+            text = value
+            # 再在html文本中进行对比，如果文本中的编码字符与字典中的解码字符一致，就进行替换
+            if code_str in html:
+                html = html.replace(code_str, text)
+
+        for code, value in font_name_hour.items():
+            code_str = code
+            text = value
+            # 再在html文本中进行对比，如果文本中的编码字符与字典中的解码字符一致，就进行替换
+            if code_str in html:
+                html = html.replace(code_str, text)
+
+        for code, value in font_name_dish.items():
+            code_str = code
+            text = value
+            # 再在html文本中进行对比，如果文本中的编码字符与字典中的解码字符一致，就进行替换
+            if code_str in html:
+                html = html.replace(code_str, text)
+            '''
+        #创建餐厅文件，存储推荐菜名称、图片
+        shop_path = "./data/beijing/" + str(shopid) + '/'
+        if not os.path.exists(shop_path):
+            os.makedirs(shop_path)
+        dish_img = shop_path + '/' + 'Re-dish' + '/'
+        if not os.path.exists(dish_img):
+            os.makedirs(dish_img)
+
+        #创建CSV文件，存储推荐菜名称
+        re_dish_File = open(shop_path +'/dishname.csv','w+')
+        '''
+        try:
+            #对网页进行解析，提取需要的数据
+            bs = BeautifulSoup(html, 'lxml')
+            basic_info = bs.find('div', class_='main')
+            tel = basic_info.find('p', class_= 'expand-info tel').get_text()
+            print(tel)
+            btime = basic_info.find('p', class_='info info-indent').find('span',class_='item').get_text()
+            print(btime)
+            df.iloc[i, 3] = tel
+            df.iloc[i,4] = btime
+
+            #推荐菜
+
+            Re_img = basic_info.find('ul', class_='recommend-photo clearfix').find('img').get('src')
+
+
+            '''
+            for f in range(len(Re_dish_name)):
+                urllib.request.urlretrieve(Re_img_url[f], r'./' +  shopid +'%s.jpd' % Re_dish_name[f])
+                print(Re_dish_name,Re_img_url)
+            
+            writer = csv.writer(re_dish_File)
+            for f in range(len(Re_dish_name)):
+                writer.writerow(f)
+                urllib.request.urlretrieve(Re_img_url[f], r'./' + dish_img + '%s.jpd' % Re_dish_name[f])
+
+            re_dish_File.close()
+        
+            '''
+            #营业时间
+        except:
+            continue
+
+    DataFrame(df).to_excel('./data/beijing/beijing.xlsx',sheet_name ='大众点评餐厅信息',index=False, header=True)
+
+
+def get_fonttel():
+    font_t = TTFont('./woff/b24aae10.woff')
+    font_names_t = font_t.getGlyphOrder()
+    text_t = ['', '', '1', '2', '3', '4', '5', '6', '7', '8',
+               '9', '0', '店', '中', '美', '家', '馆', '小', '车', '大',
+               '市', '公', '酒', '行', '国', '品', '发', '电', '金', '心',
+               '业', '商', '司', '超', '生', '装', '园', '场', '食', '有',
+               '新', '限', '天', '面', '工', '服', '海', '华', '水', '房',
+               '饰', '城', '乐', '汽', '香', '部', '利', '子', '老', '艺',
+               '花', '专', '东', '肉', '菜', '学', '福', '饭', '人', '百',
+               '餐', '茶', '务', '通', '味', '所', '山', '区', '门', '药',
+               '银', '农', '龙', '停', '尚', '安', '广', '鑫', '一', '容',
+               '动', '南', '具', '源', '兴', '鲜', '记', '时', '机', '烤',
+               '文', '康', '信', '果', '阳', '理', '锅', '宝', '达', '地',
+               '儿', '衣', '特', '产', '西', '批', '坊', '州', '牛', '佳',
+               '化', '五', '米', '修', '爱', '北', '养', '卖', '建', '材',
+               '三', '会', '鸡', '室', '红', '站', '德', '王', '光', '名',
+               '丽', '油', '院', '堂', '烧', '江', '社', '合', '星', '货',
+               '型', '村', '自', '科', '快', '便', '日', '民', '营', '和',
+               '活', '童', '明', '器', '烟', '育', '宾', '精', '屋', '经',
+               '居', '庄', '石', '顺', '林', '尔', '县', '手', '厅', '销',
+               '用', '好', '客', '火', '雅', '盛', '体', '旅', '之', '鞋',
+               '辣', '作', '粉', '包', '楼', '校', '鱼', '平', '彩', '上',
+               '吧', '保', '永', '万', '物', '教', '吃', '设', '医', '正',
+               '造', '丰', '健', '点', '汤', '网', '庆', '技', '斯', '洗',
+               '料', '配', '汇', '木', '缘', '加', '麻', '联', '卫', '川',
+               '泰', '色', '世', '方', '寓', '风', '幼', '羊', '烫', '来',
+               '高', '厂', '兰', '阿', '贝', '皮', '全', '女', '拉', '成',
+               '云', '维', '贸', '道', '术', '运', '都', '口', '博', '河',
+               '瑞', '宏', '京', '际', '路', '祥', '青', '镇', '厨', '培',
+               '力', '惠', '连', '马', '鸿', '钢', '训', '影', '甲', '助',
+               '窗', '布', '富', '牌', '头', '四', '多', '妆', '吉', '苑',
+               '沙', '恒', '隆', '春', '干', '饼', '氏', '里', '二', '管',
+               '诚', '制', '售', '嘉', '长', '轩', '杂', '副', '清', '计',
+               '黄', '讯', '太', '鸭', '号', '街', '交', '与', '叉', '附',
+               '近', '层', '旁', '对', '巷', '栋', '环', '省', '桥', '湖',
+               '段', '乡', '厦', '府', '铺', '内', '侧', '元', '购', '前',
+               '幢', '滨', '处', '向', '座', '下', '県', '凤', '港', '开',
+               '关', '景', '泉', '塘', '放', '昌', '线', '湾', '政', '步',
+               '宁', '解', '白', '田', '町', '溪', '十', '八', '古', '双',
+               '胜', '本', '单', '同', '九', '迎', '第', '台', '玉', '锦',
+               '底', '后', '七', '斜', '期', '武', '岭', '松', '角', '纪',
+               '朝', '峰', '六', '振', '珠', '局', '岗', '洲', '横', '边',
+               '济', '井', '办', '汉', '代', '临', '弄', '团', '外', '塔',
+               '杨', '铁', '浦', '字', '年', '岛', '陵', '原', '梅', '进',
+               '荣', '友', '虹', '央', '桂', '沿', '事', '津', '凯', '莲',
+               '丁', '秀', '柳', '集', '紫', '旗', '张', '谷', '的', '是',
+               '不', '了', '很', '还', '个', '也', '这', '我', '就', '在',
+               '以', '可', '到', '错', '没', '去', '过', '感', '次', '要',
+               '比', '觉', '看', '得', '说', '常', '真', '们', '但', '最',
+               '喜', '哈', '么', '别', '位', '能', '较', '境', '非', '为',
+               '欢', '然', '他', '挺', '着', '价', '那', '意', '种', '想',
+               '出', '员', '两', '推', '做', '排', '实', '分', '间', '甜',
+               '度', '起', '满', '给', '热', '完', '格', '荐', '喝', '等',
+               '其', '再', '几', '只', '现', '朋', '候', '样', '直', '而',
+               '买', '于', '般', '豆', '量', '选', '奶', '打', '每', '评',
+               '少', '算', '又', '因', '情', '找', '些', '份', '置', '适',
+               '什', '蛋', '师', '气', '你', '姐', '棒', '试', '总', '定',
+               '啊', '足', '级', '整', '带', '虾', '如', '态', '且', '尝',
+               '主', '话', '强', '当', '更', '板', '知', '己', '无', '酸',
+               '让', '入', '啦', '式', '笑', '赞', '片', '酱', '差', '像',
+               '提', '队', '走', '嫩', '才', '刚', '午', '接', '重', '串',
+               '回', '晚', '微', '周', '值', '费', '性', '桌', '拍', '跟',
+               '块', '调', '糕'
+               ]
+    # 然后我们创建一个font_name的字典，用来装字体编码和 --> 所对应的数字、汉字
+    font_name_tel = {}
+    for index,value in enumerate(text_t):
+        # 这就是大众点评乱码的结构：由前面的三个字符‘&#x’+后面是小写字母和数字+分号‘；’
+        t = font_names_t[index].replace('uni', '&#x').lower() + ';'
+        # 下面生成的这个就是乱码，他就对应刚刚字体文件的对应数字，可以运行看看
+        font_name_tel[t] = value
+
+    return font_name_tel
+
+def get_fonttime():
+    font_d = TTFont('./woff/45ab0ca2.woff')
+    font_names_d = font_d.getGlyphOrder()
+    text_d = ['', '', '1', '2', '3', '4', '5', '6', '7', '8',
+               '9', '0', '店', '中', '美', '家', '馆', '小', '车', '大',
+               '市', '公', '酒', '行', '国', '品', '发', '电', '金', '心',
+               '业', '商', '司', '超', '生', '装', '园', '场', '食', '有',
+               '新', '限', '天', '面', '工', '服', '海', '华', '水', '房',
+               '饰', '城', '乐', '汽', '香', '部', '利', '子', '老', '艺',
+               '花', '专', '东', '肉', '菜', '学', '福', '饭', '人', '百',
+               '餐', '茶', '务', '通', '味', '所', '山', '区', '门', '药',
+               '银', '农', '龙', '停', '尚', '安', '广', '鑫', '一', '容',
+               '动', '南', '具', '源', '兴', '鲜', '记', '时', '机', '烤',
+               '文', '康', '信', '果', '阳', '理', '锅', '宝', '达', '地',
+               '儿', '衣', '特', '产', '西', '批', '坊', '州', '牛', '佳',
+               '化', '五', '米', '修', '爱', '北', '养', '卖', '建', '材',
+               '三', '会', '鸡', '室', '红', '站', '德', '王', '光', '名',
+               '丽', '油', '院', '堂', '烧', '江', '社', '合', '星', '货',
+               '型', '村', '自', '科', '快', '便', '日', '民', '营', '和',
+               '活', '童', '明', '器', '烟', '育', '宾', '精', '屋', '经',
+               '居', '庄', '石', '顺', '林', '尔', '县', '手', '厅', '销',
+               '用', '好', '客', '火', '雅', '盛', '体', '旅', '之', '鞋',
+               '辣', '作', '粉', '包', '楼', '校', '鱼', '平', '彩', '上',
+               '吧', '保', '永', '万', '物', '教', '吃', '设', '医', '正',
+               '造', '丰', '健', '点', '汤', '网', '庆', '技', '斯', '洗',
+               '料', '配', '汇', '木', '缘', '加', '麻', '联', '卫', '川',
+               '泰', '色', '世', '方', '寓', '风', '幼', '羊', '烫', '来',
+               '高', '厂', '兰', '阿', '贝', '皮', '全', '女', '拉', '成',
+               '云', '维', '贸', '道', '术', '运', '都', '口', '博', '河',
+               '瑞', '宏', '京', '际', '路', '祥', '青', '镇', '厨', '培',
+               '力', '惠', '连', '马', '鸿', '钢', '训', '影', '甲', '助',
+               '窗', '布', '富', '牌', '头', '四', '多', '妆', '吉', '苑',
+               '沙', '恒', '隆', '春', '干', '饼', '氏', '里', '二', '管',
+               '诚', '制', '售', '嘉', '长', '轩', '杂', '副', '清', '计',
+               '黄', '讯', '太', '鸭', '号', '街', '交', '与', '叉', '附',
+               '近', '层', '旁', '对', '巷', '栋', '环', '省', '桥', '湖',
+               '段', '乡', '厦', '府', '铺', '内', '侧', '元', '购', '前',
+               '幢', '滨', '处', '向', '座', '下', '県', '凤', '港', '开',
+               '关', '景', '泉', '塘', '放', '昌', '线', '湾', '政', '步',
+               '宁', '解', '白', '田', '町', '溪', '十', '八', '古', '双',
+               '胜', '本', '单', '同', '九', '迎', '第', '台', '玉', '锦',
+               '底', '后', '七', '斜', '期', '武', '岭', '松', '角', '纪',
+               '朝', '峰', '六', '振', '珠', '局', '岗', '洲', '横', '边',
+               '济', '井', '办', '汉', '代', '临', '弄', '团', '外', '塔',
+               '杨', '铁', '浦', '字', '年', '岛', '陵', '原', '梅', '进',
+               '荣', '友', '虹', '央', '桂', '沿', '事', '津', '凯', '莲',
+               '丁', '秀', '柳', '集', '紫', '旗', '张', '谷', '的', '是',
+               '不', '了', '很', '还', '个', '也', '这', '我', '就', '在',
+               '以', '可', '到', '错', '没', '去', '过', '感', '次', '要',
+               '比', '觉', '看', '得', '说', '常', '真', '们', '但', '最',
+               '喜', '哈', '么', '别', '位', '能', '较', '境', '非', '为',
+               '欢', '然', '他', '挺', '着', '价', '那', '意', '种', '想',
+               '出', '员', '两', '推', '做', '排', '实', '分', '间', '甜',
+               '度', '起', '满', '给', '热', '完', '格', '荐', '喝', '等',
+               '其', '再', '几', '只', '现', '朋', '候', '样', '直', '而',
+               '买', '于', '般', '豆', '量', '选', '奶', '打', '每', '评',
+               '少', '算', '又', '因', '情', '找', '些', '份', '置', '适',
+               '什', '蛋', '师', '气', '你', '姐', '棒', '试', '总', '定',
+               '啊', '足', '级', '整', '带', '虾', '如', '态', '且', '尝',
+               '主', '话', '强', '当', '更', '板', '知', '己', '无', '酸',
+               '让', '入', '啦', '式', '笑', '赞', '片', '酱', '差', '像',
+               '提', '队', '走', '嫩', '才', '刚', '午', '接', '重', '串',
+               '回', '晚', '微', '周', '值', '费', '性', '桌', '拍', '跟',
+               '块', '调', '糕'
+               ]
+    # 然后我们创建一个font_name的字典，用来装字体编码和 --> 所对应的数字、汉字
+    font_name_time = {}
+    for index,value in enumerate(text_d):
+        # 这就是大众点评乱码的结构：由前面的三个字符‘&#x’+后面是小写字母和数字+分号‘；’
+        t = font_names_d[index].replace('uni', '&#x').lower() + ';'
+        # 下面生成的这个就是乱码，他就对应刚刚字体文件的对应数字，可以运行看看
+        font_name_time[t] = value
+
+    return font_name_time
+
+def get_fonthour():
+    font_h = TTFont('./woff/45ab0ca2.woff')
+    font_names_h = font_h.getGlyphOrder()
+    text_h = ['', '', '1', '2', '3', '4', '5', '6', '7', '8',
+               '9', '0', '店', '中', '美', '家', '馆', '小', '车', '大',
+               '市', '公', '酒', '行', '国', '品', '发', '电', '金', '心',
+               '业', '商', '司', '超', '生', '装', '园', '场', '食', '有',
+               '新', '限', '天', '面', '工', '服', '海', '华', '水', '房',
+               '饰', '城', '乐', '汽', '香', '部', '利', '子', '老', '艺',
+               '花', '专', '东', '肉', '菜', '学', '福', '饭', '人', '百',
+               '餐', '茶', '务', '通', '味', '所', '山', '区', '门', '药',
+               '银', '农', '龙', '停', '尚', '安', '广', '鑫', '一', '容',
+               '动', '南', '具', '源', '兴', '鲜', '记', '时', '机', '烤',
+               '文', '康', '信', '果', '阳', '理', '锅', '宝', '达', '地',
+               '儿', '衣', '特', '产', '西', '批', '坊', '州', '牛', '佳',
+               '化', '五', '米', '修', '爱', '北', '养', '卖', '建', '材',
+               '三', '会', '鸡', '室', '红', '站', '德', '王', '光', '名',
+               '丽', '油', '院', '堂', '烧', '江', '社', '合', '星', '货',
+               '型', '村', '自', '科', '快', '便', '日', '民', '营', '和',
+               '活', '童', '明', '器', '烟', '育', '宾', '精', '屋', '经',
+               '居', '庄', '石', '顺', '林', '尔', '县', '手', '厅', '销',
+               '用', '好', '客', '火', '雅', '盛', '体', '旅', '之', '鞋',
+               '辣', '作', '粉', '包', '楼', '校', '鱼', '平', '彩', '上',
+               '吧', '保', '永', '万', '物', '教', '吃', '设', '医', '正',
+               '造', '丰', '健', '点', '汤', '网', '庆', '技', '斯', '洗',
+               '料', '配', '汇', '木', '缘', '加', '麻', '联', '卫', '川',
+               '泰', '色', '世', '方', '寓', '风', '幼', '羊', '烫', '来',
+               '高', '厂', '兰', '阿', '贝', '皮', '全', '女', '拉', '成',
+               '云', '维', '贸', '道', '术', '运', '都', '口', '博', '河',
+               '瑞', '宏', '京', '际', '路', '祥', '青', '镇', '厨', '培',
+               '力', '惠', '连', '马', '鸿', '钢', '训', '影', '甲', '助',
+               '窗', '布', '富', '牌', '头', '四', '多', '妆', '吉', '苑',
+               '沙', '恒', '隆', '春', '干', '饼', '氏', '里', '二', '管',
+               '诚', '制', '售', '嘉', '长', '轩', '杂', '副', '清', '计',
+               '黄', '讯', '太', '鸭', '号', '街', '交', '与', '叉', '附',
+               '近', '层', '旁', '对', '巷', '栋', '环', '省', '桥', '湖',
+               '段', '乡', '厦', '府', '铺', '内', '侧', '元', '购', '前',
+               '幢', '滨', '处', '向', '座', '下', '県', '凤', '港', '开',
+               '关', '景', '泉', '塘', '放', '昌', '线', '湾', '政', '步',
+               '宁', '解', '白', '田', '町', '溪', '十', '八', '古', '双',
+               '胜', '本', '单', '同', '九', '迎', '第', '台', '玉', '锦',
+               '底', '后', '七', '斜', '期', '武', '岭', '松', '角', '纪',
+               '朝', '峰', '六', '振', '珠', '局', '岗', '洲', '横', '边',
+               '济', '井', '办', '汉', '代', '临', '弄', '团', '外', '塔',
+               '杨', '铁', '浦', '字', '年', '岛', '陵', '原', '梅', '进',
+               '荣', '友', '虹', '央', '桂', '沿', '事', '津', '凯', '莲',
+               '丁', '秀', '柳', '集', '紫', '旗', '张', '谷', '的', '是',
+               '不', '了', '很', '还', '个', '也', '这', '我', '就', '在',
+               '以', '可', '到', '错', '没', '去', '过', '感', '次', '要',
+               '比', '觉', '看', '得', '说', '常', '真', '们', '但', '最',
+               '喜', '哈', '么', '别', '位', '能', '较', '境', '非', '为',
+               '欢', '然', '他', '挺', '着', '价', '那', '意', '种', '想',
+               '出', '员', '两', '推', '做', '排', '实', '分', '间', '甜',
+               '度', '起', '满', '给', '热', '完', '格', '荐', '喝', '等',
+               '其', '再', '几', '只', '现', '朋', '候', '样', '直', '而',
+               '买', '于', '般', '豆', '量', '选', '奶', '打', '每', '评',
+               '少', '算', '又', '因', '情', '找', '些', '份', '置', '适',
+               '什', '蛋', '师', '气', '你', '姐', '棒', '试', '总', '定',
+               '啊', '足', '级', '整', '带', '虾', '如', '态', '且', '尝',
+               '主', '话', '强', '当', '更', '板', '知', '己', '无', '酸',
+               '让', '入', '啦', '式', '笑', '赞', '片', '酱', '差', '像',
+               '提', '队', '走', '嫩', '才', '刚', '午', '接', '重', '串',
+               '回', '晚', '微', '周', '值', '费', '性', '桌', '拍', '跟',
+               '块', '调', '糕'
+               ]
+    # 然后我们创建一个font_name的字典，用来装字体编码和 --> 所对应的数字、汉字
+    font_name_hour = {}
+    for index,value in enumerate(text_h):
+        # 这就是大众点评乱码的结构：由前面的三个字符‘&#x’+后面是小写字母和数字+分号‘；’
+        h = font_names_h[index].replace('uni', '&#x').lower() + ';'
+        # 下面生成的这个就是乱码，他就对应刚刚字体文件的对应数字，可以运行看看
+        font_name_hour[h] = value
+
+    return font_name_hour
+
+def get_fontdish():
+    font_d = TTFont('./woff/9ff532af.woff')
+    font_names_d = font_d.getGlyphOrder()
+    text_d = ['', '', '1', '2', '3', '4', '5', '6', '7', '8',
+               '9', '0', '店', '中', '美', '家', '馆', '小', '车', '大',
+               '市', '公', '酒', '行', '国', '品', '发', '电', '金', '心',
+               '业', '商', '司', '超', '生', '装', '园', '场', '食', '有',
+               '新', '限', '天', '面', '工', '服', '海', '华', '水', '房',
+               '饰', '城', '乐', '汽', '香', '部', '利', '子', '老', '艺',
+               '花', '专', '东', '肉', '菜', '学', '福', '饭', '人', '百',
+               '餐', '茶', '务', '通', '味', '所', '山', '区', '门', '药',
+               '银', '农', '龙', '停', '尚', '安', '广', '鑫', '一', '容',
+               '动', '南', '具', '源', '兴', '鲜', '记', '时', '机', '烤',
+               '文', '康', '信', '果', '阳', '理', '锅', '宝', '达', '地',
+               '儿', '衣', '特', '产', '西', '批', '坊', '州', '牛', '佳',
+               '化', '五', '米', '修', '爱', '北', '养', '卖', '建', '材',
+               '三', '会', '鸡', '室', '红', '站', '德', '王', '光', '名',
+               '丽', '油', '院', '堂', '烧', '江', '社', '合', '星', '货',
+               '型', '村', '自', '科', '快', '便', '日', '民', '营', '和',
+               '活', '童', '明', '器', '烟', '育', '宾', '精', '屋', '经',
+               '居', '庄', '石', '顺', '林', '尔', '县', '手', '厅', '销',
+               '用', '好', '客', '火', '雅', '盛', '体', '旅', '之', '鞋',
+               '辣', '作', '粉', '包', '楼', '校', '鱼', '平', '彩', '上',
+               '吧', '保', '永', '万', '物', '教', '吃', '设', '医', '正',
+               '造', '丰', '健', '点', '汤', '网', '庆', '技', '斯', '洗',
+               '料', '配', '汇', '木', '缘', '加', '麻', '联', '卫', '川',
+               '泰', '色', '世', '方', '寓', '风', '幼', '羊', '烫', '来',
+               '高', '厂', '兰', '阿', '贝', '皮', '全', '女', '拉', '成',
+               '云', '维', '贸', '道', '术', '运', '都', '口', '博', '河',
+               '瑞', '宏', '京', '际', '路', '祥', '青', '镇', '厨', '培',
+               '力', '惠', '连', '马', '鸿', '钢', '训', '影', '甲', '助',
+               '窗', '布', '富', '牌', '头', '四', '多', '妆', '吉', '苑',
+               '沙', '恒', '隆', '春', '干', '饼', '氏', '里', '二', '管',
+               '诚', '制', '售', '嘉', '长', '轩', '杂', '副', '清', '计',
+               '黄', '讯', '太', '鸭', '号', '街', '交', '与', '叉', '附',
+               '近', '层', '旁', '对', '巷', '栋', '环', '省', '桥', '湖',
+               '段', '乡', '厦', '府', '铺', '内', '侧', '元', '购', '前',
+               '幢', '滨', '处', '向', '座', '下', '県', '凤', '港', '开',
+               '关', '景', '泉', '塘', '放', '昌', '线', '湾', '政', '步',
+               '宁', '解', '白', '田', '町', '溪', '十', '八', '古', '双',
+               '胜', '本', '单', '同', '九', '迎', '第', '台', '玉', '锦',
+               '底', '后', '七', '斜', '期', '武', '岭', '松', '角', '纪',
+               '朝', '峰', '六', '振', '珠', '局', '岗', '洲', '横', '边',
+               '济', '井', '办', '汉', '代', '临', '弄', '团', '外', '塔',
+               '杨', '铁', '浦', '字', '年', '岛', '陵', '原', '梅', '进',
+               '荣', '友', '虹', '央', '桂', '沿', '事', '津', '凯', '莲',
+               '丁', '秀', '柳', '集', '紫', '旗', '张', '谷', '的', '是',
+               '不', '了', '很', '还', '个', '也', '这', '我', '就', '在',
+               '以', '可', '到', '错', '没', '去', '过', '感', '次', '要',
+               '比', '觉', '看', '得', '说', '常', '真', '们', '但', '最',
+               '喜', '哈', '么', '别', '位', '能', '较', '境', '非', '为',
+               '欢', '然', '他', '挺', '着', '价', '那', '意', '种', '想',
+               '出', '员', '两', '推', '做', '排', '实', '分', '间', '甜',
+               '度', '起', '满', '给', '热', '完', '格', '荐', '喝', '等',
+               '其', '再', '几', '只', '现', '朋', '候', '样', '直', '而',
+               '买', '于', '般', '豆', '量', '选', '奶', '打', '每', '评',
+               '少', '算', '又', '因', '情', '找', '些', '份', '置', '适',
+               '什', '蛋', '师', '气', '你', '姐', '棒', '试', '总', '定',
+               '啊', '足', '级', '整', '带', '虾', '如', '态', '且', '尝',
+               '主', '话', '强', '当', '更', '板', '知', '己', '无', '酸',
+               '让', '入', '啦', '式', '笑', '赞', '片', '酱', '差', '像',
+               '提', '队', '走', '嫩', '才', '刚', '午', '接', '重', '串',
+               '回', '晚', '微', '周', '值', '费', '性', '桌', '拍', '跟',
+               '块', '调', '糕'
+               ]
+    # 然后我们创建一个font_name的字典，用来装字体编码和 --> 所对应的数字、汉字
+    font_name_dish = {}
+    for index,value in enumerate(text_d):
+        # 这就是大众点评乱码的结构：由前面的三个字符‘&#x’+后面是小写字母和数字+分号‘；’
+        h = font_names_d[index].replace('uni', '&#x').lower() + ';'
+        # 下面生成的这个就是乱码，他就对应刚刚字体文件的对应数字，可以运行看看
+        font_name_dish[h] = value
+
+    return font_name_dish
+
+if __name__ == '__main__':
+    font_name_tel = get_fonttel()
+    font_name_time = get_fonttime()
+    font_name_hour = get_fonthour()
+    font_name_dish = get_fontdish()
+    get_Shopdetails(font_name_tel,font_name_time,font_name_dish)
\ No newline at end of file