├── demo ├── __init__.py ├── migrations │ ├── __init__.py │ └── __pycache__ │ │ └── __init__.cpython-37.pyc ├── models.py ├── admin.py ├── tests.py ├── data │ ├── UpdateTime.json │ ├── 高风险.json │ ├── 风险地区.json │ ├── 中风险.json │ └── 实时热点.json ├── apps.py ├── __pycache__ │ ├── ksh.cpython-37.pyc │ ├── urls.cpython-37.pyc │ ├── admin.cpython-37.pyc │ ├── models.cpython-37.pyc │ ├── pachong.cpython-37.pyc │ ├── views.cpython-37.pyc │ ├── __init__.cpython-37.pyc │ └── serializer.cpython-37.pyc ├── serializer.py ├── csv │ ├── 近31省市区现有本土病例.csv │ ├── 近2个月新增情况.csv │ ├── 近2个月累计情况.csv │ ├── 丁香园世界疫情.csv │ ├── 丁香园国内疫情.csv │ └── 丁香园国内每日疫情情况.csv ├── urls.py ├── views.py ├── ksh.py └── pachong.py ├── CeleryTask ├── __init__.py ├── migrations │ └── __init__.py ├── models.py ├── tests.py ├── admin.py ├── views.py ├── apps.py ├── __pycache__ │ ├── task.cpython-37.pyc │ └── __init__.cpython-37.pyc └── task.py ├── static ├── css │ ├── 2.css │ └── ssbb.css ├── video │ └── 1.mp4 ├── font │ └── LcdD.ttf └── js │ ├── chalk.js │ ├── index.js │ └── china.js ├── keshihua ├── __init__.py ├── __pycache__ │ ├── urls.cpython-37.pyc │ ├── wsgi.cpython-37.pyc │ ├── __init__.cpython-37.pyc │ ├── celery.cpython-37.pyc │ └── settings.cpython-37.pyc ├── celery.py ├── wsgi.py ├── urls.py └── settings.py ├── README.md ├── manage.py ├── requirements.txt └── templates └── index.html /demo/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /CeleryTask/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /demo/migrations/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /CeleryTask/migrations/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /demo/models.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/whhsky/COVID-19-KSH/HEAD/demo/models.py -------------------------------------------------------------------------------- /CeleryTask/models.py: -------------------------------------------------------------------------------- 1 | from django.db import models 2 | 3 | # Create your models here. -------------------------------------------------------------------------------- /demo/admin.py: -------------------------------------------------------------------------------- 1 | from django.contrib import admin 2 | 3 | # Register your models here. 4 | -------------------------------------------------------------------------------- /demo/tests.py: -------------------------------------------------------------------------------- 1 | from django.test import TestCase 2 | 3 | # Create your tests here. 4 | -------------------------------------------------------------------------------- /static/css/2.css: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/whhsky/COVID-19-KSH/HEAD/static/css/2.css -------------------------------------------------------------------------------- /CeleryTask/tests.py: -------------------------------------------------------------------------------- 1 | from django.test import TestCase 2 | 3 | # Create your tests here. 4 | -------------------------------------------------------------------------------- /demo/data/UpdateTime.json: -------------------------------------------------------------------------------- 1 | { 2 | "code": 200, 3 | "数据获取时间": "2022-02-04 10:58:42" 4 | } -------------------------------------------------------------------------------- /static/video/1.mp4: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/whhsky/COVID-19-KSH/HEAD/static/video/1.mp4 -------------------------------------------------------------------------------- /CeleryTask/admin.py: -------------------------------------------------------------------------------- 1 | from django.contrib import admin 2 | 3 | # Register your models here. 4 | -------------------------------------------------------------------------------- /CeleryTask/views.py: -------------------------------------------------------------------------------- 1 | from django.shortcuts import render 2 | 3 | # Create your views here. 4 | -------------------------------------------------------------------------------- /static/font/LcdD.ttf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/whhsky/COVID-19-KSH/HEAD/static/font/LcdD.ttf -------------------------------------------------------------------------------- /demo/apps.py: -------------------------------------------------------------------------------- 1 | from django.apps import AppConfig 2 | 3 | 4 | class DemoConfig(AppConfig): 5 | name = 'demo' 6 | -------------------------------------------------------------------------------- /keshihua/__init__.py: -------------------------------------------------------------------------------- 1 | import pymysql 2 | pymysql.version_info = (1, 4, 13, "final", 0) 3 | pymysql.install_as_MySQLdb() -------------------------------------------------------------------------------- /demo/__pycache__/ksh.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/whhsky/COVID-19-KSH/HEAD/demo/__pycache__/ksh.cpython-37.pyc -------------------------------------------------------------------------------- /demo/__pycache__/urls.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/whhsky/COVID-19-KSH/HEAD/demo/__pycache__/urls.cpython-37.pyc -------------------------------------------------------------------------------- /CeleryTask/apps.py: -------------------------------------------------------------------------------- 1 | from django.apps import AppConfig 2 | 3 | 4 | class CelerytaskConfig(AppConfig): 5 | name = 'CeleryTask' 6 | -------------------------------------------------------------------------------- /demo/__pycache__/admin.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/whhsky/COVID-19-KSH/HEAD/demo/__pycache__/admin.cpython-37.pyc -------------------------------------------------------------------------------- /demo/__pycache__/models.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/whhsky/COVID-19-KSH/HEAD/demo/__pycache__/models.cpython-37.pyc -------------------------------------------------------------------------------- /demo/__pycache__/pachong.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/whhsky/COVID-19-KSH/HEAD/demo/__pycache__/pachong.cpython-37.pyc -------------------------------------------------------------------------------- /demo/__pycache__/views.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/whhsky/COVID-19-KSH/HEAD/demo/__pycache__/views.cpython-37.pyc -------------------------------------------------------------------------------- /demo/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/whhsky/COVID-19-KSH/HEAD/demo/__pycache__/__init__.cpython-37.pyc -------------------------------------------------------------------------------- /keshihua/__pycache__/urls.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/whhsky/COVID-19-KSH/HEAD/keshihua/__pycache__/urls.cpython-37.pyc -------------------------------------------------------------------------------- /keshihua/__pycache__/wsgi.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/whhsky/COVID-19-KSH/HEAD/keshihua/__pycache__/wsgi.cpython-37.pyc -------------------------------------------------------------------------------- /CeleryTask/__pycache__/task.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/whhsky/COVID-19-KSH/HEAD/CeleryTask/__pycache__/task.cpython-37.pyc -------------------------------------------------------------------------------- /demo/__pycache__/serializer.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/whhsky/COVID-19-KSH/HEAD/demo/__pycache__/serializer.cpython-37.pyc -------------------------------------------------------------------------------- /keshihua/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/whhsky/COVID-19-KSH/HEAD/keshihua/__pycache__/__init__.cpython-37.pyc -------------------------------------------------------------------------------- /keshihua/__pycache__/celery.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/whhsky/COVID-19-KSH/HEAD/keshihua/__pycache__/celery.cpython-37.pyc -------------------------------------------------------------------------------- /keshihua/__pycache__/settings.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/whhsky/COVID-19-KSH/HEAD/keshihua/__pycache__/settings.cpython-37.pyc -------------------------------------------------------------------------------- /CeleryTask/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/whhsky/COVID-19-KSH/HEAD/CeleryTask/__pycache__/__init__.cpython-37.pyc -------------------------------------------------------------------------------- /demo/migrations/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/whhsky/COVID-19-KSH/HEAD/demo/migrations/__pycache__/__init__.cpython-37.pyc -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # COVID-19-KSH 2 | html+ python +django +爬虫 +pyecharts 实时疫情动态 3 | 4 | 请使用python3.6版本 5 | 6 | 安装requirements.txt依赖：pip install -r requirements.txt 7 | 8 | 生成迁移文件 python manage.py makemigrations 9 | 10 | 执行迁移 python manage.py migrate 11 | 12 | 启动项目 python manage.py runserver 13 | 14 | -------------------------------------------------------------------------------- /keshihua/celery.py: -------------------------------------------------------------------------------- 1 | import os 2 | from celery import Celery 3 | from django.conf import settings 4 | 5 | # 设置celery环境变量和django-celery的工作目录 6 | os.environ.setdefault("DJANGO_SETTINGS_MODULE", "CeleryTask.settings") 7 | 8 | # 实例化celery 9 | app = Celery("our_projeect") 10 | 11 | # 加载celery配置 12 | app.config_from_object("django.conf:settings") 13 | 14 | # 如果项目当中有task.py, 那么celery使用app当中的task来生成任务 15 | app.autodiscover_tasks(lambda : settings.INSTALLED_APPS) -------------------------------------------------------------------------------- /keshihua/wsgi.py: -------------------------------------------------------------------------------- 1 | """ 2 | WSGI config for keshihua project. 3 | 4 | It exposes the WSGI callable as a module-level variable named ``application``. 5 | 6 | For more information on this file, see 7 | https://docs.djangoproject.com/en/2.0/howto/deployment/wsgi/ 8 | """ 9 | 10 | import os 11 | 12 | from django.core.wsgi import get_wsgi_application 13 | 14 | os.environ.setdefault("DJANGO_SETTINGS_MODULE", "keshihua.settings") 15 | 16 | application = get_wsgi_application() 17 | -------------------------------------------------------------------------------- /manage.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | import os 3 | import sys 4 | 5 | if __name__ == "__main__": 6 | os.environ.setdefault("DJANGO_SETTINGS_MODULE", "keshihua.settings") 7 | try: 8 | from django.core.management import execute_from_command_line 9 | except ImportError as exc: 10 | raise ImportError( 11 | "Couldn't import Django. Are you sure it's installed and " 12 | "available on your PYTHONPATH environment variable? Did you " 13 | "forget to activate a virtual environment?" 14 | ) from exc 15 | execute_from_command_line(sys.argv) 16 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | amqp==1.4.9 2 | anyjson==0.3.3 3 | asgiref==3.5.0 4 | beautifulsoup4==4.10.0 5 | billiard==3.3.0.23 6 | bs4==0.0.1 7 | celery==3.1.25 8 | certifi==2021.10.8 9 | charset-normalizer==2.0.11 10 | Django==2.2.6 11 | django-celery==3.2.1 12 | django-crontab==0.7.1 13 | django-rest-framework==0.1.0 14 | djangorestframework==3.13.1 15 | greenlet==1.1.2 16 | idna==3.3 17 | importlib-metadata==4.10.1 18 | Jinja2==3.0.3 19 | kombu==3.0.37 20 | MarkupSafe==2.0.1 21 | numpy==1.21.5 22 | pandas==1.3.5 23 | prettytable==3.0.0 24 | pyecharts==1.9.1 25 | PyMySQL==1.0.2 26 | python-dateutil==2.8.2 27 | pytz==2021.3 28 | redis==2.10.6 29 | requests==2.27.1 30 | simplejson==3.17.6 31 | six==1.16.0 32 | soupsieve==2.3.1 33 | SQLAlchemy==1.4.31 34 | sqlparse==0.4.2 35 | typing_extensions==4.0.1 36 | urllib3==1.26.8 37 | vine==1.3.0 38 | wcwidth==0.2.5 39 | zipp==3.7.0 40 | -------------------------------------------------------------------------------- /demo/serializer.py: -------------------------------------------------------------------------------- 1 | from rest_framework import serializers 2 | # -*- coding:utf-8 -*- 3 | from demo.models import Ssrd 4 | import datetime 5 | # 时间转换 6 | def time_diff(timestamp): 7 | onlineTime = datetime.datetime.fromtimestamp(timestamp) 8 | localTime = datetime.datetime.now() 9 | result = localTime - onlineTime 10 | hours = int(result.seconds / 3600) 11 | minutes = int(result.seconds % 3600 / 60) 12 | seconds = result.seconds % 3600 % 60 13 | if result.days > 0: 14 | x = f'{result.days}天前' 15 | elif hours > 0: 16 | x = f'{hours}小时前' 17 | elif minutes > 0: 18 | x = f'{minutes}分钟前' 19 | else: 20 | x = f'{seconds}秒前' 21 | return x 22 | 23 | # 热门资讯 24 | class ReDianSerializer(serializers.ModelSerializer): 25 | time_ago = serializers.SerializerMethodField() 26 | 27 | 28 | class Meta: 29 | model = Ssrd 30 | fields = "__all__" 31 | 32 | def get_time_ago(self, obj): 33 | time_ago = time_diff(int(obj.eventtime)) 34 | return time_ago -------------------------------------------------------------------------------- /CeleryTask/task.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | import json 3 | from keshihua.celery import app 4 | from datetime import datetime 5 | from demo.pachong import lishishuju, yqday, yqveryday, ssrd, parse, fxdq, Moon_Tow_Near 6 | 7 | # 数据更新 8 | @app.task 9 | def start_get_data(): 10 | print('正在获取历史数据...') 11 | lishishuju() # 历史数据 12 | print('正在获取中国今日疫情情况...') 13 | yqday() # 中国今日疫情情况 14 | print('正在获取中国每日疫情...') 15 | yqveryday() # 中国每日疫情 16 | print('正在获取实时热点...') 17 | ssrd() # 实时热点 18 | print('正在获取国内各省目前疫情...') 19 | parse() # 国内各省目前疫情 20 | print('正在获取国内风险地区...') 21 | fxdq() # 国内风险地区 22 | print('正在获取近两月份的疫情趋势...') 23 | Moon_Tow_Near() 24 | 25 | nowtime = datetime.now().strftime("%Y-%m-%d %H:%M:%S") 26 | with open('demo/data/UpdateTime.json', 'w', encoding='utf-8') as f: 27 | f.write(json.dumps({ 28 | 'code': 200, 29 | '数据获取时间': nowtime 30 | }, ensure_ascii=False, indent=4)) 31 | print('获取完毕数据已更新!') 32 | print('更新时间:' + nowtime) -------------------------------------------------------------------------------- /keshihua/urls.py: -------------------------------------------------------------------------------- 1 | """keshihua URL Configuration 2 | 3 | The `urlpatterns` list routes URLs to views. For more information please see: 4 | https://docs.djangoproject.com/en/2.0/topics/http/urls/ 5 | Examples: 6 | Function views 7 | 1. Add an import: from my_app import views 8 | 2. Add a URL to urlpatterns: path('', views.home, name='home') 9 | Class-based views 10 | 1. Add an import: from other_app.views import Home 11 | 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') 12 | Including another URLconf 13 | 1. Import the include() function: from django.urls import include, path 14 | 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) 15 | """ 16 | from django.contrib import admin 17 | from django.urls import path, include 18 | 19 | from demo import views 20 | from demo.views import IndexView 21 | from django.views.generic import RedirectView 22 | 23 | urlpatterns = [ 24 | path('admin/', admin.site.urls), 25 | path('app/', include('demo.urls')), 26 | path('COVID-19/', views.IndexView.as_view(), name='COVID-19'), 27 | path('', RedirectView.as_view(url='COVID-19/')), 28 | ] 29 | -------------------------------------------------------------------------------- /demo/csv/近31省市区现有本土病例.csv: -------------------------------------------------------------------------------- 1 | address,addqz,xyqz,fxqy 2 | 广东深圳,4,28,部分中风险 3 | 浙江杭州,2,143,部分中高风险 4 | 北京大兴,0,10,部分中风险 5 | 北京丰台,0,72,部分中高风险 6 | 天津河北区,0,34,部分中高风险 7 | 天津滨海新区,0,9,全部低风险 8 | 广东云浮,0,5,全部低风险 9 | 河北衡水,0,5,全部低风险 10 | 天津红桥区,0,5,全部低风险 11 | 北京朝阳,0,13,部分中高风险 12 | 黑龙江牡丹江,0,29,部分中风险 13 | 河南安阳,0,281,部分中高风险 14 | 天津津南区,0,38,部分中风险 15 | 新疆伊犁州,0,8,部分中风险 16 | 北京西城,0,3,全部低风险 17 | 山西大同,0,2,全部低风险 18 | 河北保定,0,2,全部低风险 19 | 河北廊坊,0,4,全部低风险 20 | 上海奉贤,0,2,部分中风险 21 | 辽宁沈阳,0,1,全部低风险 22 | 云南西双版纳州,0,5,部分中风险 23 | 河北雄安新区,0,5,部分中风险 24 | 广东珠海,0,35,全部低风险 25 | 北京房山,0,11,部分中风险 26 | 山东济南,0,2,全部低风险 27 | 北京海淀,0,14,全部低风险 28 | 陕西西安,0,11,全部低风险 29 | 河南郑州,0,154,全部低风险 30 | 河南许昌,0,346,全部低风险 31 | 天津西青区,0,12,全部低风险 32 | 广东中山,0,3,部分中风险 33 | 天津河西区,0,6,全部低风险 34 | 河南商丘,0,2,全部低风险 35 | 浙江宁波,0,110,全部低风险 36 | 河南洛阳,0,10,部分中风险 37 | 河南信阳,0,3,全部低风险 38 | 浙江金华,0,2,部分中风险 39 | 河南周口,0,24,全部低风险 40 | 陕西延安,0,13,全部低风险 41 | 陕西咸阳,0,5,全部低风险 42 | 广西防城港,0,2,全部低风险 43 | 浙江绍兴,0,10,全部低风险 44 | 云南昆明,0,5,全部低风险 45 | 广东东莞,0,26,全部低风险 46 | 四川成都,0,14,全部低风险 47 | 广东广州,0,3,全部低风险 48 | 云南德宏州,0,6,全部低风险 49 | 北京昌平,0,37,全部低风险 50 | 陕西渭南,0,1,全部低风险 51 | 北京通州,0,1,全部低风险 52 | 河南平顶山,0,1,全部低风险 53 | 山西运城,0,1,全部低风险 54 | 广西崇左,0,1,全部低风险 55 | 河南三门峡,0,1,全部低风险 56 | -------------------------------------------------------------------------------- /demo/data/高风险.json: -------------------------------------------------------------------------------- 1 | { 2 | "name": "高风险", 3 | "children": [ 4 | { 5 | "name": "北京丰台区(3)", 6 | "children": [ 7 | { 8 | "name": "新城街道怡海花园社区" 9 | }, 10 | { 11 | "name": "玉泉街营街道黄土岗村" 12 | }, 13 | { 14 | "name": "玉泉营街道万柳社区" 15 | } 16 | ] 17 | }, 18 | { 19 | "name": "北京朝阳区(1)", 20 | "children": [ 21 | { 22 | "name": "安贞街道安贞里三区4号楼" 23 | } 24 | ] 25 | }, 26 | { 27 | "name": "天津河北区(2)", 28 | "children": [ 29 | { 30 | "name": "宁园街舒园里5号楼" 31 | }, 32 | { 33 | "name": "望海楼街万福园7号楼" 34 | } 35 | ] 36 | }, 37 | { 38 | "name": "浙江杭州(1)", 39 | "children": [ 40 | { 41 | "name": "富阳区咕甜全球母婴店（咕甜全球进口母婴生活馆）" 42 | } 43 | ] 44 | } 45 | ] 46 | } -------------------------------------------------------------------------------- /demo/urls.py: -------------------------------------------------------------------------------- 1 | """keshihua URL Configuration 2 | 3 | The `urlpatterns` list routes URLs to views. For more information please see: 4 | https://docs.djangoproject.com/en/2.0/topics/http/urls/ 5 | Examples: 6 | Function views 7 | 1. Add an import: from my_app import views 8 | 2. Add a URL to urlpatterns: path('', views.home, name='home') 9 | Class-based views 10 | 1. Add an import: from other_app.views import Home 11 | 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') 12 | Including another URLconf 13 | 1. Import the include() function: from django.urls import include, path 14 | 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) 15 | """ 16 | from django.contrib import admin 17 | from django.urls import path 18 | from . import views 19 | 20 | urlpatterns = [ 21 | path('index/', views.IndexView.as_view(), name='index'), 22 | path('xyqz/', views.qgyq.as_view(), name='xyqz'), # 中国累计新增/确诊折线图 23 | path('zgdt/', views.Zgdt.as_view(), name='Zgdt'), # 中国地图 24 | path('yqbt/', views.Yqbt.as_view(), name='yqbt'), # 饼图 25 | path('bg/', views.Bg.as_view(), name='bg'), # 近期31省区市本土现有病例 26 | path('gfx/', views.gfx1.as_view(), name='gfx'), # 高风险 27 | path('zfx/', views.zfx1.as_view(), name='zfx'), # 中风险 28 | path('fxdq/', views.fxdq1.as_view(), name='fxdq'), # 中风险 29 | path('chinayq/', views.chinayq1.as_view(), name='chinayq'), # 更新时间 30 | path('ssrd/', views.ssrd1.as_view(), name='ssrd'), # 实时热点 31 | ] 32 | -------------------------------------------------------------------------------- /demo/data/风险地区.json: -------------------------------------------------------------------------------- 1 | { 2 | "code": 200, 3 | "data": [ 4 | { 5 | "风险等级": "高风险", 6 | "数量": 7, 7 | "地区": [ 8 | "北京丰台区新城街道怡海花园社区", 9 | "北京丰台区玉泉街营街道黄土岗村", 10 | "北京丰台区玉泉营街道万柳社区", 11 | "北京朝阳区安贞街道安贞里三区4号楼", 12 | "天津河北区宁园街舒园里5号楼", 13 | "天津河北区望海楼街万福园7号楼", 14 | "浙江杭州富阳区咕甜全球母婴店（咕甜全球进口母婴生活馆）" 15 | ] 16 | }, 17 | { 18 | "风险等级": "中风险", 19 | "数量": 54, 20 | "地区": [ 21 | "北京丰台区南苑街道南庭新苑北区", 22 | "北京丰台区王佐镇翡翠山社区", 23 | "北京丰台区玉泉营街道万柳西园", 24 | "北京丰台区玉泉营街道亿朋苑一区", 25 | "北京房山区长阳镇北广阳城大街8号", 26 | "北京大兴区西红门镇宏福园小区", 27 | "天津河北区建昌道街红波西里21号楼", 28 | "天津河北区建昌街道中山北里20门", 29 | "天津河北区王串场街汇仁云居7号楼", 30 | "天津滨海新区大沽街安阳里13号楼", 31 | "天津河东区春华街道月光园2号楼", 32 | "河北雄安新区安新县端村镇西堤东堤片区", 33 | "河北衡水故城县建国镇叶庄村", 34 | "黑龙江牡丹江东宁市春城小区", 35 | "黑龙江牡丹江东宁市邮政小区", 36 | "黑龙江牡丹江绥芬河春龙农贸市场", 37 | "黑龙江牡丹江绥芬河阜宁雅居17号楼", 38 | "黑龙江牡丹江绥芬河海融富华苑13号楼", 39 | "黑龙江牡丹江绥芬河花园小区3号楼", 40 | "黑龙江牡丹江绥芬河集资1号楼4单元", 41 | "黑龙江牡丹江绥芬河老妇幼院里3单元", 42 | "黑龙江牡丹江绥芬河市二中集资楼", 43 | "黑龙江牡丹江绥芬河市环保局家属楼", 44 | "黑龙江牡丹江绥芬河市鸿福小区", 45 | "黑龙江牡丹江绥芬河市龙泉文苑8号楼", 46 | "黑龙江牡丹江绥芬河市良运公司", 47 | "黑龙江牡丹江绥芬河市旗苑嘉园C区", 48 | "黑龙江牡丹江绥芬河市千园小区", 49 | "黑龙江牡丹江绥芬河市兴建大厦", 50 | "黑龙江牡丹江绥芬河市馨怡纯粮油公司", 51 | "黑龙江牡丹江绥芬河五彩楼7单元", 52 | "黑龙江牡丹江绥芬河鑫城小区8号楼", 53 | "黑龙江牡丹江绥芬河阳光小区7号楼", 54 | "黑龙江牡丹江绥芬河迎泽丽都三期9号楼", 55 | "上海奉贤区奉城镇幸福村8组", 56 | "浙江杭州滨江区白马湖和院", 57 | "浙江杭州滨江区春波小区", 58 | "浙江杭州滨江区冠新佳苑小区", 59 | "浙江杭州滨江区华城和瑞科技园", 60 | "浙江杭州滨江区漫寓公寓", 61 | "浙江杭州滨江区新浦苑", 62 | "浙江杭州滨江区长河街道建业路151号（慧而特（中国）餐饮设备有限公司）", 63 | "浙江杭州滨江区长江小区", 64 | "浙江杭州滨江区长江西苑小区", 65 | "浙江杭州富阳区洞桥镇贤德村何村坞168号", 66 | "浙江杭州富阳区富春街道桂花西路262号", 67 | "浙江杭州富阳区润泽院", 68 | "广东深圳宝安区石岩街道罗租社区艾美特科技园B栋", 69 | "云南西双版纳勐腊县勐满镇集镇片区", 70 | "新疆伊犁州北京路公租房2号楼", 71 | "新疆伊犁州丁香湾小区", 72 | "新疆伊犁州霍尔果斯雅馨小区", 73 | "新疆伊犁州时代嘉苑小区", 74 | "新疆伊犁州雅居小区" 75 | ] 76 | } 77 | ] 78 | } -------------------------------------------------------------------------------- /demo/views.py: -------------------------------------------------------------------------------- 1 | import json 2 | from django.http import HttpResponse 3 | from rest_framework.views import APIView 4 | 5 | # Create your views here. 6 | from .ksh import xyqz, zgdt, yqbt, bg, gfx 7 | from .models import Ssrd 8 | from .serializer import ReDianSerializer 9 | 10 | 11 | def response_as_json(data): 12 | json_str = json.dumps(data) 13 | response = HttpResponse( 14 | json_str, 15 | content_type="application/json", 16 | ) 17 | response["Access-Control-Allow-Origin"] = "*" 18 | return response 19 | 20 | 21 | def json_response(data, code=200): 22 | data = { 23 | "code": code, 24 | "msg": "success", 25 | "data": data, 26 | } 27 | return response_as_json(data) 28 | 29 | 30 | def json_error(error_string="error", code=500, **kwargs): 31 | data = { 32 | "code": code, 33 | "msg": error_string, 34 | "data": {} 35 | } 36 | data.update(kwargs) 37 | return response_as_json(data) 38 | 39 | 40 | JsonResponse = json_response 41 | JsonError = json_error 42 | 43 | 44 | 45 | # 全国疫情 46 | class qgyq(APIView): 47 | def get(self, request, *args, **kwargs): 48 | return JsonResponse(json.loads(xyqz())) 49 | 50 | # 中国疫情地图 51 | class Zgdt(APIView): 52 | def get(self, request, *args, **kwargs): 53 | return JsonResponse(json.loads(zgdt())) 54 | 55 | # 中国疫情地图 56 | class Yqbt(APIView): 57 | def get(self, request, *args, **kwargs): 58 | return JsonResponse(json.loads(yqbt())) 59 | 60 | # 近期31省区市本土现有病例 61 | class Bg(APIView): 62 | def get(self, request, *args, **kwargs): 63 | return JsonResponse(json.loads(bg())) 64 | 65 | 66 | # 高风险地区 67 | class gfx1(APIView): 68 | def get(self, request, *args, **kwargs): 69 | return JsonResponse(json.loads(gfx('高风险'))) 70 | # 中风险地区 71 | class zfx1(APIView): 72 | def get(self, request, *args, **kwargs): 73 | return JsonResponse(json.loads(gfx('中风险'))) 74 | # 风险地区 75 | class fxdq1(APIView): 76 | def get(self, request, *args, **kwargs): 77 | with open('demo/data/风险地区.json', 'r', encoding='utf-8') as f: 78 | data = f.read() 79 | return JsonResponse(json.loads(data)) 80 | # 中国疫情 81 | class chinayq1(APIView): 82 | def get(self, request, *args, **kwargs): 83 | with open('demo/data/中国疫情.json', 'r', encoding='utf-8') as f: 84 | data = f.read() 85 | return JsonResponse(json.loads(data)) 86 | # 实时热点 87 | class ssrd1(APIView): 88 | def get(self, request, *args, **kwargs): 89 | ssrd = Ssrd.objects.all() 90 | serializer = ReDianSerializer(ssrd, many=True) 91 | return JsonResponse(serializer.data) 92 | # 主页 93 | class IndexView(APIView): 94 | def get(self, request, *args, **kwargs): 95 | return HttpResponse(content=open("./templates/index.html", 'rb').read()) -------------------------------------------------------------------------------- /demo/csv/近2个月新增情况.csv: -------------------------------------------------------------------------------- 1 | infect,localConfirmadd,date,heal,importedCase,dead,localinfectionadd,deadRate,healRate,y,confirm,suspect,dateId 2 | 14,89,12.06,49,34,0,4,0.0,45.0,2021,109,2,2021-12-06 3 | 23,77,12.07,50,30,0,5,0.0,50.0,2021,100,1,2021-12-07 4 | 33,79,12.08,43,23,0,13,0.0,48.9,2021,88,0,2021-12-08 5 | 43,55,12.09,37,26,0,26,0.0,43.0,2021,86,0,2021-12-09 6 | 30,57,12.10,40,36,0,14,0.0,37.0,2021,108,0,2021-12-10 7 | 20,122,12.11,30,26,0,10,0.0,34.9,2021,86,1,2021-12-11 8 | 17,126,12.12,48,21,0,0,0.0,42.9,2021,112,0,2021-12-12 9 | 15,79,12.13,31,25,0,4,0.0,37.3,2021,83,0,2021-12-13 10 | 24,105,12.14,45,17,0,2,0.0,54.9,2021,82,0,2021-12-14 11 | 20,104,12.15,33,8,1,1,1.2,38.4,2021,86,0,2021-12-15 12 | 23,130,12.16,26,20,0,2,0.0,26.5,2021,98,3,2021-12-16 13 | 19,115,12.17,33,36,0,2,0.0,22.4,2021,147,3,2021-12-17 14 | 41,51,12.18,48,39,0,4,0.0,47.5,2021,101,2,2021-12-18 15 | 33,38,12.19,53,65,1,2,0.9,45.7,2021,116,1,2021-12-19 16 | 19,59,12.20,49,24,0,1,0.0,49.0,2021,100,0,2021-12-20 17 | 19,63,12.21,80,20,0,0,0.0,84.2,2021,95,2,2021-12-21 18 | 19,102,12.22,42,29,0,2,0.0,34.7,2021,121,0,2021-12-22 19 | 26,63,12.23,71,34,0,1,0.0,63.4,2021,112,6,2021-12-23 20 | 24,89,12.24,113,53,0,4,0.0,68.5,2021,165,6,2021-12-24 21 | 29,161,12.25,76,48,0,1,0.0,30.5,2021,249,0,2021-12-25 22 | 27,162,12.26,53,38,0,0,0.0,22.7,2021,233,4,2021-12-26 23 | 21,182,12.27,92,27,0,3,0.0,39.1,2021,235,0,2021-12-27 24 | 15,152,12.28,57,45,0,1,0.0,25.7,2021,222,2,2021-12-28 25 | 27,156,12.29,61,51,0,1,0.0,26.0,2021,235,2,2021-12-29 26 | 29,166,12.30,47,29,0,0,0.0,20.3,2021,232,1,2021-12-30 27 | 38,182,12.31,69,56,0,5,0.0,23.7,2021,291,1,2021-12-31 28 | 52,134,01.01,67,60,0,4,0.0,29.1,2022,230,1,2022-01-01 29 | 35,108,01.02,52,60,0,4,0.0,29.2,2022,178,0,2022-01-02 30 | 54,112,01.03,51,67,0,21,0.0,23.7,2022,215,3,2022-01-03 31 | 71,57,01.04,67,54,0,22,0.0,45.3,2022,148,0,2022-01-04 32 | 45,133,01.05,200,57,0,10,0.0,85.8,2022,233,2,2022-01-05 33 | 45,127,01.06,102,58,0,3,0.0,42.3,2022,241,6,2022-01-06 34 | 52,99,01.07,151,64,0,6,0.0,64.0,2022,236,1,2022-01-07 35 | 46,104,01.08,148,73,0,0,0.0,64.3,2022,230,2,2022-01-08 36 | 42,152,01.09,186,60,0,2,0.0,77.2,2022,241,3,2022-01-09 37 | 50,176,01.10,150,82,0,11,0.0,52.3,2022,287,0,2022-01-10 38 | 32,214,01.11,211,55,0,4,0.0,68.7,2022,307,1,2022-01-11 39 | 31,155,01.12,218,66,1,9,0.3,73.2,2022,298,9,2022-01-12 40 | 42,146,01.13,241,58,1,4,0.3,81.1,2022,297,9,2022-01-13 41 | 25,106,01.14,145,61,0,2,0.0,87.9,2022,165,6,2022-01-14 42 | 52,65,01.15,136,54,0,5,0.0,68.3,2022,199,9,2022-01-15 43 | 28,165,01.16,217,60,0,0,0.0,77.2,2022,281,4,2022-01-16 44 | 33,132,01.17,158,44,0,1,0.0,66.1,2022,239,3,2022-01-17 45 | 37,56,01.18,193,32,0,2,0.0,121.4,2022,159,5,2022-01-18 46 | 28,49,01.19,251,23,0,2,0.0,197.6,2022,127,3,2022-01-19 47 | 31,32,01.20,218,50,0,2,0.0,160.3,2022,136,1,2022-01-20 48 | 43,23,01.21,196,40,0,6,0.0,138.0,2022,142,0,2022-01-21 49 | 34,27,01.22,224,37,0,7,0.0,112.6,2022,199,3,2022-01-22 50 | 27,28,01.23,254,39,0,5,0.0,102.8,2022,247,1,2022-01-23 51 | 43,40,01.24,213,27,0,18,0.0,128.3,2022,166,0,2022-01-24 52 | 64,24,01.25,194,20,0,20,0.0,124.4,2022,156,0,2022-01-25 53 | 55,25,01.26,220,38,0,17,0.0,93.2,2022,236,0,2022-01-26 54 | 54,39,01.27,189,25,0,12,0.0,60.0,2022,315,3,2022-01-27 55 | 32,37,01.28,223,22,0,3,0.0,106.2,2022,210,0,2022-01-28 56 | 65,54,01.29,202,27,0,16,0.0,97.1,2022,208,1,2022-01-29 57 | 52,40,01.30,170,18,0,4,0.0,95.5,2022,178,3,2022-01-30 58 | 32,36,01.31,146,39,0,1,0.0,78.1,2022,187,0,2022-01-31 59 | 28,43,02.01,171,27,0,4,0.0,82.6,2022,207,0,2022-02-01 60 | 96,21,02.02,120,18,0,1,0.0,68.6,2022,175,0,2022-02-02 61 | 52,12,02.03,151,17,0,1,0.0,81.6,2022,185,4,2022-02-03 62 | -------------------------------------------------------------------------------- /static/css/ssbb.css: -------------------------------------------------------------------------------- 1 | * { 2 | padding: 0; 3 | margin: 0; 4 | user-select: none; 5 | } 6 | 7 | body, 8 | html { 9 | width: 99.9%; 10 | height: 99.9%; 11 | } 12 | 13 | header { 14 | width: 100%; 15 | display: flex; 16 | justify-content: space-evenly; 17 | } 18 | 19 | section { 20 | width: 100%; 21 | height: 97%; 22 | } 23 | 24 | .heade { 25 | width: 100%; 26 | text-align: center; 27 | line-height: 30px; 28 | } 29 | 30 | .box { 31 | width: 100%; 32 | text-align: center; 33 | display: flex; 34 | justify-content: space-around; 35 | } 36 | 37 | .boxA { 38 | height: 44%; 39 | } 40 | 41 | .boxB { 42 | 43 | height: 55.5%; 44 | } 45 | 46 | .boxA>div { 47 | width: 32.6%; 48 | } 49 | 50 | .boxB>div { 51 | margin-top: 10px; 52 | width: 32.6%; 53 | } 54 | 55 | .sjly { 56 | color: royalblue; 57 | 58 | } 59 | 60 | #bjsj { 61 | text-align: center; 62 | font-size: 24px; 63 | color: chocolate; 64 | position: relative; 65 | } 66 | 67 | .ly { 68 | position: absolute; 69 | right: 200px; 70 | color: slateblue; 71 | z-index: 1; 72 | } 73 | 74 | .ly>a { 75 | text-decoration: none; 76 | color: salmon; 77 | } 78 | 79 | 80 | .boxchild { 81 | width: 100%; 82 | position: relative; 83 | z-index: 3; 84 | margin: 0 auto; 85 | } 86 | 87 | .boxchild::before { 88 | content: ''; 89 | position: absolute; 90 | left: 0; 91 | top: 0; 92 | bottom: 0; 93 | right: 0; 94 | border: 2px solid slateblue; 95 | margin: 0px; 96 | z-index: -1; 97 | -webkit-animation: line11 8s linear infinite; 98 | -o-animation: line11 8s linear infinite; 99 | animation: line11 8s linear infinite; 100 | } 101 | 102 | .boxchild::after { 103 | content: ''; 104 | position: absolute; 105 | left: 0; 106 | top: 0; 107 | bottom: 0; 108 | right: 0; 109 | border: 2px solid darkmagenta; 110 | margin: 0px; 111 | z-index: -1; 112 | -webkit-animation: line22 8s linear infinite; 113 | -o-animation: line22 8s linear infinite; 114 | animation: line22 8s linear infinite; 115 | } 116 | 117 | #box1 { 118 | width: 89%; 119 | height: 40%; 120 | position: absolute; 121 | overflow: hidden; 122 | top: 58%; 123 | left: 0; 124 | right: 0; 125 | bottom: 0; 126 | margin: 0 auto; 127 | background-color: rgba(101, 132, 226, 0.1); 128 | border-radius: 10px; 129 | } 130 | 131 | .aa { 132 | width: 35rem; 133 | } 134 | 135 | .aa>div { 136 | width: 80px; 137 | height: 21px; 138 | text-align: center; 139 | float: left; 140 | color: salmon; 141 | margin-top: 10px; 142 | margin-left: 14px; 143 | border-bottom: 1px solid salmon; 144 | border-radius: 10px; 145 | } 146 | 147 | .timez { 148 | width: 80px; 149 | height: 21px; 150 | text-align: center; 151 | float: right; 152 | color: salmon; 153 | margin-top: 10px; 154 | border-bottom: 1px solid salmon; 155 | border-radius: 10px; 156 | } 157 | 158 | .aa>li { 159 | float: left; 160 | width: 350px; 161 | height: 20px; 162 | border-bottom: 1px solid slateblue; 163 | border-radius: 10px; 164 | margin-top: 10px; 165 | margin-left: 10px; 166 | list-style: none; 167 | overflow: hidden; 168 | text-overflow: ellipsis; 169 | white-space: nowrap; 170 | text-align: center; 171 | color: indianred; 172 | } 173 | 174 | .aa>li>a { 175 | text-decoration: none; 176 | color: hotpink; 177 | } 178 | 179 | 180 | 181 | @keyframes line11 { 182 | 183 | 0%, 184 | 100% { 185 | clip-path: inset(0 0 99% 0) 186 | } 187 | 188 | 25% { 189 | clip-path: inset(0 99% 0 0) 190 | } 191 | 192 | 50% { 193 | clip-path: inset(99% 0 0 0) 194 | } 195 | 196 | 75% { 197 | clip-path: inset(0 0 0 99%) 198 | } 199 | } 200 | 201 | @keyframes line22 { 202 | 203 | 0%, 204 | 100% { 205 | clip-path: inset(99% 0 0 0) 206 | } 207 | 208 | 25% { 209 | clip-path: inset(0 0 0 99%) 210 | } 211 | 212 | 50% { 213 | clip-path: inset(0 0 99% 0) 214 | } 215 | 216 | 75% { 217 | clip-path: inset(0 99% 0 0) 218 | } 219 | } 220 | -------------------------------------------------------------------------------- /static/js/chalk.js: -------------------------------------------------------------------------------- 1 | !function(o,r){"function"==typeof define&&define.amd?define(["exports","echarts"],r):"object"==typeof exports&&"string"!=typeof exports.nodeName?r(exports,require("echarts")):r({},o.echarts)}(this,function(o,r){var e;if(!r)return e="ECharts is not Loaded",void("undefined"!=typeof console&&console&&console.error&&console.error(e));r.registerTheme("chalk",{color:["#fc97af","#87f7cf","#f7f494","#72ccff","#f7c5a0","#d4a4eb","#d2f5a6","#76f2f2"],backgroundColor:"rgba(0,0,0,0.01)",textStyle:{},title:{textStyle:{color:"#ffffff"},subtextStyle:{color:"#dddddd"}},line:{itemStyle:{normal:{borderWidth:"4"}},lineStyle:{normal:{width:"3"}},symbolSize:"0",symbol:"circle",smooth:!0},radar:{itemStyle:{normal:{borderWidth:"4"}},lineStyle:{normal:{width:"3"}},symbolSize:"0",symbol:"circle",smooth:!0},bar:{itemStyle:{normal:{barBorderWidth:0,barBorderColor:"#ccc"},emphasis:{barBorderWidth:0,barBorderColor:"#ccc"}}},pie:{itemStyle:{normal:{borderWidth:0,borderColor:"#ccc"},emphasis:{borderWidth:0,borderColor:"#ccc"}}},scatter:{itemStyle:{normal:{borderWidth:0,borderColor:"#ccc"},emphasis:{borderWidth:0,borderColor:"#ccc"}}},boxplot:{itemStyle:{normal:{borderWidth:0,borderColor:"#ccc"},emphasis:{borderWidth:0,borderColor:"#ccc"}}},parallel:{itemStyle:{normal:{borderWidth:0,borderColor:"#ccc"},emphasis:{borderWidth:0,borderColor:"#ccc"}}},sankey:{itemStyle:{normal:{borderWidth:0,borderColor:"#ccc"},emphasis:{borderWidth:0,borderColor:"#ccc"}}},funnel:{itemStyle:{normal:{borderWidth:0,borderColor:"#ccc"},emphasis:{borderWidth:0,borderColor:"#ccc"}}},gauge:{itemStyle:{normal:{borderWidth:0,borderColor:"#ccc"},emphasis:{borderWidth:0,borderColor:"#ccc"}}},candlestick:{itemStyle:{normal:{color:"#fc97af",color0:"transparent",borderColor:"#fc97af",borderColor0:"#87f7cf",borderWidth:"2"}}},graph:{itemStyle:{normal:{borderWidth:0,borderColor:"#ccc"}},lineStyle:{normal:{width:"1",color:"#ffffff"}},symbolSize:"0",symbol:"circle",smooth:!0,color:["#fc97af","#87f7cf","#f7f494","#72ccff","#f7c5a0","#d4a4eb","#d2f5a6","#76f2f2"],label:{normal:{textStyle:{color:"#293441"}}}},map:{itemStyle:{normal:{areaColor:"#f3f3f3",borderColor:"#999999",borderWidth:.5},emphasis:{areaColor:"rgba(255,178,72,1)",borderColor:"#eb8146",borderWidth:1}},label:{normal:{textStyle:{color:"#893448"}},emphasis:{textStyle:{color:"rgb(137,52,72)"}}}},geo:{itemStyle:{normal:{areaColor:"#f3f3f3",borderColor:"#999999",borderWidth:.5},emphasis:{areaColor:"rgba(255,178,72,1)",borderColor:"#eb8146",borderWidth:1}},label:{normal:{textStyle:{color:"#893448"}},emphasis:{textStyle:{color:"rgb(137,52,72)"}}}},categoryAxis:{axisLine:{show:!0,lineStyle:{color:"#666666"}},axisTick:{show:!1,lineStyle:{color:"#333"}},axisLabel:{show:!0,textStyle:{color:"#aaaaaa"}},splitLine:{show:!1,lineStyle:{color:["#e6e6e6"]}},splitArea:{show:!1,areaStyle:{color:["rgba(250,250,250,0.05)","rgba(200,200,200,0.02)"]}}},valueAxis:{axisLine:{show:!0,lineStyle:{color:"#666666"}},axisTick:{show:!1,lineStyle:{color:"#333"}},axisLabel:{show:!0,textStyle:{color:"#aaaaaa"}},splitLine:{show:!1,lineStyle:{color:["#e6e6e6"]}},splitArea:{show:!1,areaStyle:{color:["rgba(250,250,250,0.05)","rgba(200,200,200,0.02)"]}}},logAxis:{axisLine:{show:!0,lineStyle:{color:"#666666"}},axisTick:{show:!1,lineStyle:{color:"#333"}},axisLabel:{show:!0,textStyle:{color:"#aaaaaa"}},splitLine:{show:!1,lineStyle:{color:["#e6e6e6"]}},splitArea:{show:!1,areaStyle:{color:["rgba(250,250,250,0.05)","rgba(200,200,200,0.02)"]}}},timeAxis:{axisLine:{show:!0,lineStyle:{color:"#666666"}},axisTick:{show:!1,lineStyle:{color:"#333"}},axisLabel:{show:!0,textStyle:{color:"#aaaaaa"}},splitLine:{show:!1,lineStyle:{color:["#e6e6e6"]}},splitArea:{show:!1,areaStyle:{color:["rgba(250,250,250,0.05)","rgba(200,200,200,0.02)"]}}},toolbox:{iconStyle:{normal:{borderColor:"#999999"},emphasis:{borderColor:"#666666"}}},legend:{textStyle:{color:"#999999"}},tooltip:{axisPointer:{lineStyle:{color:"#cccccc",width:1},crossStyle:{color:"#cccccc",width:1}}},timeline:{lineStyle:{color:"#87f7cf",width:1},itemStyle:{normal:{color:"#87f7cf",borderWidth:1},emphasis:{color:"#f7f494"}},controlStyle:{normal:{color:"#87f7cf",borderColor:"#87f7cf",borderWidth:.5},emphasis:{color:"#87f7cf",borderColor:"#87f7cf",borderWidth:.5}},checkpointStyle:{color:"#fc97af",borderColor:"rgba(252,151,175,0.3)"},label:{normal:{textStyle:{color:"#87f7cf"}},emphasis:{textStyle:{color:"#87f7cf"}}}},visualMap:{color:["#fc97af","#87f7cf"]},dataZoom:{backgroundColor:"rgba(255,255,255,0)",dataBackgroundColor:"rgba(114,204,255,1)",fillerColor:"rgba(114,204,255,0.2)",handleColor:"#72ccff",handleSize:"100%",textStyle:{color:"#333333"}},markPoint:{label:{normal:{textStyle:{color:"#293441"}},emphasis:{textStyle:{color:"#293441"}}}}})}); 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数据来源：丁香园， 腾讯， 百度
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115 | 116 | 117 | 118 | 119 | 120 | 121 | 122 | -------------------------------------------------------------------------------- /keshihua/settings.py: -------------------------------------------------------------------------------- 1 | """ 2 | Django settings for keshihua project. 3 | 4 | Generated by 'django-admin startproject' using Django 2.0.5. 5 | 6 | For more information on this file, see 7 | https://docs.djangoproject.com/en/2.0/topics/settings/ 8 | 9 | For the full list of settings and their values, see 10 | https://docs.djangoproject.com/en/2.0/ref/settings/ 11 | """ 12 | 13 | import os 14 | 15 | # Build paths inside the project like this: os.path.join(BASE_DIR, ...) 16 | BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) 17 | 18 | 19 | # Quick-start development settings - unsuitable for production 20 | # See https://docs.djangoproject.com/en/2.0/howto/deployment/checklist/ 21 | 22 | # SECURITY WARNING: keep the secret key used in production secret! 23 | SECRET_KEY = 'u%g9-8u6ow^xn1eys6#=cb0&&0m#)97z__qzj&cc%c$aow&p4r' 24 | 25 | # SECURITY WARNING: don't run with debug turned on in production! 26 | DEBUG = True 27 | 28 | ALLOWED_HOSTS = [] 29 | 30 | 31 | # Application definition 32 | 33 | INSTALLED_APPS = [ 34 | 'django.contrib.admin', 35 | 'django.contrib.auth', 36 | 'django.contrib.contenttypes', 37 | 'django.contrib.sessions', 38 | 'django.contrib.messages', 39 | 'django.contrib.staticfiles', 40 | 'demo', 41 | 'rest_framework', 42 | 'django_crontab', 43 | 'djcelery' 44 | ] 45 | 46 | MIDDLEWARE = [ 47 | 'django.middleware.security.SecurityMiddleware', 48 | 'django.contrib.sessions.middleware.SessionMiddleware', 49 | 'django.middleware.common.CommonMiddleware', 50 | 'django.middleware.csrf.CsrfViewMiddleware', 51 | 'django.contrib.auth.middleware.AuthenticationMiddleware', 52 | 'django.contrib.messages.middleware.MessageMiddleware', 53 | 'django.middleware.clickjacking.XFrameOptionsMiddleware', 54 | ] 55 | 56 | ROOT_URLCONF = 'keshihua.urls' 57 | 58 | TEMPLATES = [ 59 | { 60 | 'BACKEND': 'django.template.backends.django.DjangoTemplates', 61 | 'DIRS': [os.path.join(BASE_DIR, 'templates')], 62 | 'APP_DIRS': True, 63 | 'OPTIONS': { 64 | 'context_processors': [ 65 | 'django.template.context_processors.debug', 66 | 'django.template.context_processors.request', 67 | 'django.contrib.auth.context_processors.auth', 68 | 'django.contrib.messages.context_processors.messages', 69 | ], 70 | }, 71 | }, 72 | ] 73 | 74 | WSGI_APPLICATION = 'keshihua.wsgi.application' 75 | 76 | 77 | # Database 78 | # https://docs.djangoproject.com/en/2.0/ref/settings/#databases 79 | 80 | # DATABASES = { 81 | # 'default': { 82 | # 'ENGINE': 'django.db.backends.sqlite3', 83 | # 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), 84 | # } 85 | # } 86 | 87 | DATABASES = { 88 | 'default': { 89 | 'ENGINE': 'django.db.backends.mysql', 90 | 'NAME': 'ksh', 91 | 'USER': 'root', 92 | 'PASSWORD': '147123', 93 | 'PORT': '3306', 94 | 'POST': '127.0.0.1' 95 | } 96 | } 97 | 98 | 99 | # Password validation 100 | # https://docs.djangoproject.com/en/2.0/ref/settings/#auth-password-validators 101 | 102 | AUTH_PASSWORD_VALIDATORS = [ 103 | { 104 | 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', 105 | }, 106 | { 107 | 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', 108 | }, 109 | { 110 | 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', 111 | }, 112 | { 113 | 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', 114 | }, 115 | ] 116 | 117 | 118 | # Internationalization 119 | # https://docs.djangoproject.com/en/2.0/topics/i18n/ 120 | 121 | LANGUAGE_CODE = 'zh-hans' 122 | 123 | TIME_ZONE = 'Asia/Shanghai' 124 | 125 | USE_I18N = True 126 | 127 | USE_L10N = True 128 | 129 | USE_TZ = True 130 | 131 | 132 | # Static files (CSS, JavaScript, Images) 133 | # https://docs.djangoproject.com/en/2.0/howto/static-files/ 134 | 135 | STATIC_URL = '/static/' 136 | STATICFILES_DIRS = [ 137 | os.path.join(BASE_DIR, "static") 138 | ] 139 | 140 | # CRONTAB_COMMAND_PREFIX = 'LANG_ALL=zh_cn.UTF-8' 141 | # CRONJOBS = [ 142 | # # 每1分钟执行一次生成主页静态文件 143 | # ('*/1 * * * *', 'demo.pachong.lishishuju', '>> /Users/12829/Desktop/logs/crontab.log') 144 | # ] 145 | 146 | 147 | # celery 148 | import djcelery 149 | djcelery.setup_loader() # 重载 150 | 151 | BROKER_URL = "redis://127.0.0.1:6379/1" # 任务存放的容器 152 | CELERY_IMPORTS = ("CeleryTask.task") # 任务 153 | CELERY_TIMEZONE = "Asia/Shanghai" # 时区 154 | CELERYBEAT_SCHEDULER = "djcelery.schedulers.DatabaseScheduler" 155 | 156 | 157 | # 定时任务 158 | from celery.schedules import timedelta, crontab 159 | 160 | # python manage.py celery worker --loglevel 启动workder 161 | # python manage.py celery beat --loglevel 启动定时任务 162 | 163 | CELERYBEAT_SCHEDULE = { 164 | u'疫情数据更新': { 165 | 'task': "CeleryTask.task.start_get_data", 166 | "schedule": timedelta(seconds=30) 167 | } 168 | } -------------------------------------------------------------------------------- /demo/csv/近2个月累计情况.csv: -------------------------------------------------------------------------------- 1 | nowSevere,importedCase,noInfectH5,nowConfirm,date,localConfirm,deadRate,healRate,localConfirmH5,local_acc_confirm,suspect,dead,heal,confirm,y,noInfect,dateId 2 | 12,10316,67,3281,12.06,694,4.4,93.0,694,99326,3,5697,119524,128502,2021,471,2021-12-06 3 | 22,10346,66,3331,12.07,716,4.4,93.0,716,99404,4,5697,119574,128602,2021,465,2021-12-07 4 | 24,10369,78,3391,12.08,740,4.4,92.9,740,99454,2,5697,119602,128690,2021,484,2021-12-08 5 | 29,10395,98,3445,12.09,758,4.4,92.9,758,99535,1,5697,119638,128776,2021,508,2021-12-09 6 | 28,10431,86,3513,12.10,785,4.4,92.9,785,99610,1,5697,119674,128884,2021,489,2021-12-10 7 | 28,10457,73,3569,12.11,892,4.4,92.8,892,99752,2,5697,119704,128970,2021,474,2021-12-11 8 | 27,10478,46,3635,12.12,900,4.4,92.8,900,99824,2,5697,119750,129082,2021,444,2021-12-12 9 | 22,10503,46,3687,12.13,935,4.4,92.7,935,99878,1,5697,119781,129165,2021,445,2021-12-13 10 | 17,10520,48,3724,12.14,985,4.4,92.7,985,99966,1,5697,119826,129247,2021,452,2021-12-14 11 | 14,10528,45,3777,12.15,1027,4.4,92.7,1027,100033,1,5698,119859,129332,2021,459,2021-12-15 12 | 10,10548,45,3849,12.16,2106,4.4,92.6,2106,100150,4,5698,119883,129430,2021,471,2021-12-16 13 | 6,10584,46,3963,12.17,1130,4.4,92.5,1130,100227,6,5698,119916,129577,2021,471,2021-12-17 14 | 6,10623,48,4016,12.18,1118,4.4,92.5,1118,100291,5,5698,119964,129678,2021,496,2021-12-18 15 | 5,10688,48,4078,12.19,1079,4.4,92.5,1079,100387,4,5699,120017,129794,2021,511,2021-12-19 16 | 5,10712,40,4128,12.20,1069,4.4,92.4,1069,100467,1,5699,120069,129893,2021,505,2021-12-20 17 | 7,10732,38,4143,12.21,1126,4.4,92.4,1126,100550,3,5699,120146,129988,2021,501,2021-12-21 18 | 10,10761,38,4252,12.22,1173,4.4,92.4,1173,100622,3,5699,120188,130139,2021,496,2021-12-22 19 | 9,10793,36,4263,12.23,1180,4.4,92.3,1180,100739,8,5699,120249,130211,2021,497,2021-12-23 20 | 9,10846,40,4315,12.24,1187,4.4,92.3,1187,100873,10,5699,120362,130376,2021,492,2021-12-24 21 | 9,10894,38,4488,12.25,1293,4.4,92.2,1293,101080,3,5699,120438,130625,2021,504,2021-12-25 22 | 13,10932,27,4668,12.26,1385,4.4,92.1,1385,101277,6,5699,120526,130858,2021,495,2021-12-26 23 | 12,10959,29,4811,12.27,1540,4.3,92.0,1540,101486,3,5699,120583,131093,2021,496,2021-12-27 24 | 14,11004,30,4976,12.28,1664,4.3,91.9,1664,101683,3,5699,120640,131315,2021,496,2021-12-28 25 | 15,11055,30,5150,12.29,1793,4.3,91.8,1793,101890,2,5699,120701,131550,2021,507,2021-12-29 26 | 15,11083,28,5333,12.30,1933,4.3,91.6,1933,102083,2,5699,120748,131780,2021,512,2021-12-30 27 | 15,11139,29,5555,12.31,2091,4.3,91.5,2091,102321,1,5699,120817,132071,2021,514,2021-12-31 28 | 16,11199,31,5718,01.01,2194,4.3,91.4,2194,102508,1,5699,120884,132301,2022,550,2022-01-01 29 | 22,11259,33,5844,01.02,2275,4.3,91.3,2275,102673,0,5699,120936,132486,2022,562,2022-01-02 30 | 30,11326,48,6006,01.03,2366,4.3,91.2,2366,102845,3,5699,120987,132692,2022,588,2022-01-03 31 | 31,11380,67,6079,01.04,2387,4.3,91.1,2387,102947,1,5699,121052,132830,2022,632,2022-01-04 32 | 30,11433,46,6112,01.05,2343,4.3,91.1,2343,103121,2,5699,121252,133063,2022,629,2022-01-05 33 | 30,11491,28,6251,01.06,2392,4.3,91.0,2392,103306,7,5699,121354,133304,2022,632,2022-01-06 34 | 27,11555,33,6336,01.07,2366,4.3,91.0,2366,103458,1,5699,121505,133540,2022,661,2022-01-07 35 | 26,11628,30,6418,01.08,2356,4.3,90.9,2356,103631,2,5699,121653,133770,2022,675,2022-01-08 36 | 27,11688,31,6513,01.09,2375,4.3,90.9,2375,103817,3,5699,121839,134003,2022,699,2022-01-09 37 | 21,11770,40,6545,01.10,2337,4.2,90.9,2337,103977,1,5699,121989,134233,2022,721,2022-01-10 38 | 14,11825,43,6641,01.11,2376,4.2,90.8,2376,104237,1,5699,122200,134540,2022,724,2022-01-11 39 | 10,11891,52,6721,01.12,2314,4.2,90.8,2314,104410,9,5670,122418,134840,2022,726,2022-01-12 40 | 11,11949,55,6778,01.13,2234,4.2,90.8,2234,104583,13,5700,122659,135135,2022,748,2022-01-13 41 | 11,12010,54,6854,01.14,2213,4.2,90.7,2213,104747,9,5700,122816,135370,2022,743,2022-01-14 42 | 13,12064,50,6917,01.15,2208,4.2,90.7,2208,104869,14,5700,122952,135569,2022,753,2022-01-15 43 | 15,12124,52,6981,01.16,2199,4.2,90.7,2199,105089,8,5700,123169,135850,2022,742,2022-01-16 44 | 14,12168,51,7062,01.17,2250,4.2,90.6,2250,105263,8,5700,123327,136089,2022,760,2022-01-17 45 | 15,12200,50,7028,01.18,2180,4.2,90.7,2180,105346,11,5700,123520,136248,2022,767,2022-01-18 46 | 16,12223,45,6904,01.19,2053,4.2,90.8,2053,105411,10,5700,123771,136375,2022,755,2022-01-19 47 | 12,12273,45,6822,01.20,1961,4.2,90.8,1961,105484,5,5700,123989,136511,2022,765,2022-01-20 48 | 12,12313,51,6768,01.21,1884,4.2,90.9,1884,105547,4,5700,124185,136653,2022,778,2022-01-21 49 | 10,12350,57,6743,01.22,1791,4.2,90.9,1791,105611,7,5700,124409,136852,2022,751,2022-01-22 50 | 11,12389,60,6736,01.23,1660,4.2,90.9,1660,105670,7,5700,124663,137099,2022,736,2022-01-23 51 | 9,12416,76,6688,01.24,1560,4.2,91.0,1560,105727,6,5700,124876,137264,2022,747,2022-01-24 52 | 8,12436,94,6650,01.25,1438,4.1,91.0,1438,105749,3,5700,125070,137420,2022,778,2022-01-25 53 | 7,12473,104,6665,01.26,1330,4.1,91.0,1330,105811,0,5700,125290,137655,2022,784,2022-01-26 54 | 9,12498,112,6791,01.27,1274,4.1,90.9,1274,105875,0,5700,125479,137970,2022,808,2022-01-27 55 | 8,12520,111,6778,01.28,1169,4.1,91.0,1169,105934,1,5700,125702,138180,2022,777,2022-01-28 56 | 10,12547,122,6784,01.29,1110,4.1,91.0,1110,106015,1,5700,125904,138388,2022,800,2022-01-29 57 | 10,12565,125,6792,01.30,1069,4.1,91.0,1069,106073,4,5700,126074,138566,2022,812,2022-01-30 58 | 10,12604,121,6833,01.31,1037,4.1,91.0,1037,106148,0,5700,126220,138753,2022,814,2022-01-31 59 | 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| "name": "建昌道街红波西里21号楼" 42 | }, 43 | { 44 | "name": "建昌街道中山北里20门" 45 | }, 46 | { 47 | "name": "王串场街汇仁云居7号楼" 48 | } 49 | ] 50 | }, 51 | { 52 | "name": "天津滨海新区(1)", 53 | "children": [ 54 | { 55 | "name": "大沽街安阳里13号楼" 56 | } 57 | ] 58 | }, 59 | { 60 | "name": "天津河东区(1)", 61 | "children": [ 62 | { 63 | "name": "春华街道月光园2号楼" 64 | } 65 | ] 66 | }, 67 | { 68 | "name": "河北雄安新区(1)", 69 | "children": [ 70 | { 71 | "name": "安新县端村镇西堤东堤片区" 72 | } 73 | ] 74 | }, 75 | { 76 | "name": "河北衡水(1)", 77 | "children": [ 78 | { 79 | "name": "故城县建国镇叶庄村" 80 | } 81 | ] 82 | }, 83 | { 84 | "name": "黑龙江牡丹江(21)", 85 | "children": [ 86 | { 87 | "name": "东宁市春城小区" 88 | }, 89 | { 90 | "name": "东宁市邮政小区" 91 | }, 92 | { 93 | "name": "绥芬河春龙农贸市场" 94 | }, 95 | { 96 | "name": "绥芬河阜宁雅居17号楼" 97 | }, 98 | { 99 | "name": "绥芬河海融富华苑13号楼" 100 | }, 101 | { 102 | "name": "绥芬河花园小区3号楼" 103 | }, 104 | { 105 | "name": "绥芬河集资1号楼4单元" 106 | }, 107 | { 108 | "name": "绥芬河老妇幼院里3单元" 109 | }, 110 | { 111 | "name": "绥芬河市二中集资楼" 112 | }, 113 | { 114 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224 | "siteName": "中国游戏公会联盟" 225 | } 226 | ] -------------------------------------------------------------------------------- /static/js/index.js: -------------------------------------------------------------------------------- 1 | var localhost = "http://127.0.0.1:8000/" // API路由地址 2 | var gnyq = echarts.init(document.getElementById('gnmrxz'), 'white', {renderer: 'canvas'}); // 国内疫情 3 | var zgdt = echarts.init(document.getElementById('zgdt'), 'white', {renderer: 'canvas'}); // 中国地图 4 | var yqbt = echarts.init(document.getElementById('yqbt'), 'white', {renderer: 'canvas'}); // 饼图 5 | var gfx = echarts.init(document.getElementById('gfx'), 'white', {renderer: 'canvas'}); // 高风险地区 6 | var zfx = echarts.init(document.getElementById('zfx'), 'white', {renderer: 'canvas'}); // 中风险地区 7 | var bj = echarts.init(document.getElementById('bj'), 'chalk', {renderer: 'canvas'}); // 近期31省区市本土现有病例 8 | 9 | var q = document.getElementsByClassName('today') 10 | var gd = document.getElementsByClassName('aa') 11 | 12 | var ly = document.getElementById('ly') //当前时间 13 | 14 | var fxp = document.getElementsByClassName('pp') 15 | var fxsl = document.getElementsByClassName('fxsl') 16 | 17 | var total = new Array() 18 | var add = new Array() 19 | 20 | var ul1 = document.getElementById("aa"); 21 | var ul2 = document.getElementById("aa1"); 22 | var ulbox = document.getElementById("box1"); 23 | 24 | var rand = Math.floor(Math.random() * 888); 25 | 26 | window.onload = function (){ 27 | getdata(); 28 | getssbb(); 29 | } 30 | 31 | function roll(t) { 32 | ul2.innerHTML = ul1.innerHTML; 33 | ulbox.scrollTop = 0; // 开始无滚动时设为0 34 | var timer = setInterval(rollStart, t); // 设置定时器，参数t用在这为间隔时间（单位毫秒），参数t越小，滚动速度越快 35 | // 鼠标移入div时暂停滚动 36 | ulbox.onmouseover = function() { 37 | clearInterval(timer); 38 | } 39 | // 鼠标移出div后继续滚动 40 | ulbox.onmouseout = function() { 41 | timer = setInterval(rollStart, t); 42 | } 43 | } 44 | // 开始滚动函数 45 | function rollStart() { 46 | // 正常滚动不断给scrollTop的值+1,当滚动高度大于列表内容高度时恢复为0 47 | if(ulbox.scrollTop >= ul1.scrollHeight) { 48 | ulbox.scrollTop = 0; 49 | } else { 50 | ulbox.scrollTop++; 51 | } 52 | } 53 | 54 | 55 | 56 | function getTime() { 57 | var myDate = new Date(); 58 | var myYear = myDate.getFullYear(); //获取完整的年份(4位,1970-????) 59 | var myMonth = myDate.getMonth() + 1; //获取当前月份(0-11,0代表1月) 60 | var myToday = myDate.getDate(); //获取当前日(1-31) 61 | var myDay = myDate.getDay(); //获取当前星期X(0-6,0代表星期天) 62 | var myHour = myDate.getHours(); //获取当前小时数(0-23) 63 | var myMinute = myDate.getMinutes(); //获取当前分钟数(0-59) 64 | var mySecond = myDate.getSeconds(); //获取当前秒数(0-59) 65 | var week = ['星期日', '星期一', '星期二', '星期三', '星期四', '星期五', '星期六']; 66 | var nowTime; 67 | 68 | nowTime = '北京时间：' + myYear + '年' + fillZero(myMonth) + '月' + fillZero(myToday) + '日' + ' ' + fillZero(myHour) + ':' + 69 | fillZero(myMinute) + ':' + fillZero(mySecond) + ' ' + week[myDay] + ' '; 70 | $('#bjsj').html(nowTime); 71 | }; 72 | 73 | function fillZero(str) { 74 | var realNum; 75 | if (str < 10) { 76 | realNum = '0' + str; 77 | } else { 78 | realNum = str; 79 | } 80 | return realNum; 81 | } 82 | setInterval(getTime, 1000); 83 | 84 | 85 | var old_data = []; 86 | var bjtimer; 87 | 88 | function bjfc() { 89 | bjtimer = setInterval(function () { 90 | bj1(bj); 91 | }, 1500) 92 | } 93 | 94 | $( 95 | function () { 96 | gryq(gnyq); 97 | Zgdt(zgdt); 98 | Yqbt(yqbt); 99 | bj1(bj); 100 | gfx1(gfx); 101 | zfx1(zfx); 102 | fxdqlb1(fxp); 103 | gxsj(ly); 104 | bj1(bj); 105 | } 106 | ); 107 | 108 | 109 | function getdata(){ 110 | $.ajax({ 111 | type: "GET", 112 | url: localhost + "app/chinayq/?p=" + rand, 113 | dataType: "json", 114 | success: function (result) { 115 | total.push(result.data.diseaseh5Shelf.chinaTotal.localConfirm); 116 | total.push(result.data.diseaseh5Shelf.chinaTotal.nowConfirm); 117 | total.push(result.data.diseaseh5Shelf.chinaTotal.confirm); 118 | total.push(result.data.diseaseh5Shelf.chinaTotal.noInfect); 119 | total.push(result.data.diseaseh5Shelf.chinaTotal.importedCase); 120 | total.push(result.data.diseaseh5Shelf.chinaTotal.dead); 121 | 122 | add.push(result.data.diseaseh5Shelf.chinaAdd.localConfirmH5); 123 | add.push(result.data.diseaseh5Shelf.chinaAdd.nowConfirm); 124 | add.push(result.data.diseaseh5Shelf.chinaAdd.confirm); 125 | add.push(result.data.diseaseh5Shelf.chinaAdd.noInfect); 126 | add.push(result.data.diseaseh5Shelf.chinaAdd.importedCase); 127 | add.push(result.data.diseaseh5Shelf.chinaAdd.dead); 128 | for(var i=0; i

' + total[i] + '

较昨日+' + add[i] + '

'; 130 | } 131 | } 132 | }); 133 | } 134 | 135 | function gryq() { 136 | $.ajax({ 137 | type: "GET", 138 | url: localhost + "app/xyqz/", 139 | dataType: "json", 140 | success: function (result) { 141 | var options = result.data; 142 | gnyq.setOption(options); 143 | old_data = gnyq.getOption().series[0].data; 144 | } 145 | }); 146 | } 147 | 148 | function Zgdt() { 149 | $.ajax({ 150 | type: "GET", 151 | url: localhost + "app/zgdt/?p=" + rand, 152 | dataType: "json", 153 | success: function (result) { 154 | // console.log(result) 155 | var options = result.data; 156 | zgdt.setOption(options); 157 | old_data = zgdt.getOption().series[0].data; 158 | } 159 | }); 160 | } 161 | 162 | function Yqbt() { 163 | $.ajax({ 164 | type: "GET", 165 | url: localhost + "app/yqbt/", 166 | dataType: "json", 167 | success: function (result) { 168 | // console.log(result) 169 | var options = result.data; 170 | yqbt.setOption(options); 171 | old_data = yqbt.getOption().series[0].data; 172 | } 173 | }); 174 | } 175 | 176 | function bj1() { 177 | $.ajax({ 178 | type: "GET", 179 | url: localhost + "app/bg/", 180 | dataType: "json", 181 | success: function (result) { 182 | // console.log(result) 183 | var options = result.data; 184 | bj.setOption(options); 185 | old_data = bj.getOption().series[0].data; 186 | } 187 | }); 188 | } 189 | 190 | function gfx1() { 191 | $.ajax({ 192 | type: "GET", 193 | url: localhost + "app/gfx/?p="+rand, 194 | dataType: "json", 195 | success: function (result) { 196 | var options = result.data; 197 | gfx.setOption(options); 198 | old_data = gfx.getOption().series[0].data; 199 | } 200 | }); 201 | } 202 | 203 | function zfx1() { 204 | $.ajax({ 205 | type: "GET", 206 | url: localhost + "app/zfx/?p="+rand, 207 | dataType: "json", 208 | success: function (result) { 209 | var options = result.data; 210 | zfx.setOption(options); 211 | old_data = zfx.getOption().series[0].data; 212 | } 213 | }); 214 | } 215 | 216 | var str = '' 217 | function getssbb() { 218 | $.ajax({ 219 | type: "GET", 220 | url: localhost + "app/ssrd/", 221 | dataType: "json", 222 | success: function (result) { 223 | result = result.data 224 | for(var j=0; j' 226 | + result[j].eventdescription + '
'+ result[j].time_ago + '
' 227 | } 228 | gd[0].innerHTML = str; 229 | roll(50); 230 | } 231 | }); 232 | } 233 | 234 | 235 | // 风险地区 236 | function fxdqlb1() { 237 | $.ajax({ 238 | type: "GET", 239 | url: localhost + "app/fxdq/?p=" + rand, 240 | dataType: "json", 241 | success: function (result) { 242 | // console.log(result) 243 | var gaddress = '' 244 | var zaddress = '' 245 | for(var i=0; i< result.data.data[0].地区.length; i++){ 246 | gaddress += '
  • ' + result.data.data[0].地区[i] + '
    • '; 247 | } 248 | for(var i=0; i< result.data.data[1].地区.length; i++){ 249 | zaddress += '
  • ' + result.data.data[1].地区[i] + '
    • '; 250 | } 251 | fxsl[0].innerHTML = '高风险(' + result.data.data[0].数量 + ')' 252 | fxsl[1].innerHTML = '中风险(' + result.data.data[1].数量 + ')' 253 | fxp[0].innerHTML = gaddress; 254 | fxp[1].innerHTML = zaddress; 255 | 256 | $('#J_scroll1').width($('#J_scroll1').width() - $('#J_scroll1 li:first-child').innerWidth()); 257 | $('#J_scroll1').addClass('theanimation'); 258 | 259 | $('#J_scroll2').width($('#J_scroll2').width() - $('#J_scroll2 li:first-child').innerWidth()); 260 | $('#J_scroll2').addClass('theanimation1'); 261 | } 262 | }); 263 | } 264 | 265 | // 更新时间 266 | function gxsj() { 267 | $.ajax({ 268 | type: "GET", 269 | url: localhost + "app/chinayq/?p=" + rand, 270 | dataType: "json", 271 | success: function (result) { 272 | ly.innerText = '更新时间：'+ result.data.diseaseh5Shelf.lastUpdateTime 273 | } 274 | }) 275 | } -------------------------------------------------------------------------------- /demo/ksh.py: -------------------------------------------------------------------------------- 1 | import copy 2 | import datetime 3 | import json 4 | import pandas as pd 5 | from pyecharts.charts import Line, Map, Timeline, Pie, Bar 6 | from pyecharts import options as opts 7 | from pyecharts.globals import ThemeType 8 | 9 | from demo.models import Bentuxianyou31, J2Yxz, J2Ylj, Xyyq, Gnlssj 10 | 11 | 12 | def xyqz(): # 新增确诊/现有确诊 13 | y = list(J2Yxz.objects.values_list('confirm', flat=True)) # 新增 14 | y1 = list(J2Ylj.objects.values_list('nowconfirm', flat=True)) # 现有 15 | x = list(J2Yxz.objects.values_list('dateid', flat=True)) # 日期 16 | 17 | tl = Timeline() 18 | xxx = ['新增确诊', '现有确诊'] 19 | y = [y, y1] 20 | for i in range(len(xxx)): 21 | bar = ( 22 | Line() 23 | .add_xaxis(x) 24 | .add_yaxis(xxx[i], y[i], symbol='circle', 25 | is_smooth=True, 26 | is_symbol_show=True, 27 | symbol_size=6, 28 | linestyle_opts=opts.LineStyleOpts(width=2), 29 | label_opts=opts.LabelOpts(is_show=True, position="top", color="white"), 30 | itemstyle_opts=opts.ItemStyleOpts( 31 | color="skyblue", border_color="#fff", border_width=2 32 | )) 33 | .set_series_opts(markline_opts=opts.MarkLineOpts( 34 | data=[opts.MarkLineItem(type_="max", name="max"), opts.MarkLineItem(type_="min", name="min")]), 35 | markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max", name="max"), 36 | opts.MarkPointItem(type_="min", name="min")])) 37 | .set_global_opts(title_opts=opts.TitleOpts(title='全国' + xxx[i] + '人数趋势', 38 | title_textstyle_opts=opts.TextStyleOpts(color='#ffffff')), 39 | visualmap_opts=opts.VisualMapOpts(textstyle_opts=opts.TextStyleOpts(color='#ffffff'), 40 | max_=int(max(y[i])), is_piecewise=True, 41 | pos_right='5%', pos_top='60%'), 42 | xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=45, color='#ffffff')), 43 | tooltip_opts=opts.TooltipOpts(trigger='axis', axis_pointer_type='cross'), 44 | 45 | yaxis_opts=opts.AxisOpts(name="Y", splitline_opts=opts.SplitLineOpts(is_show=True), 46 | axislabel_opts=opts.LabelOpts(rotate=90, color='#ffffff')), 47 | 48 | legend_opts=opts.LegendOpts(pos_left='50%', 49 | textstyle_opts=opts.TextStyleOpts(color='#ffffff')), 50 | ) 51 | ) 52 | tl.add(bar, xxx[i]) 53 | tl.add_schema(is_auto_play=True, pos_bottom='88%', pos_left='25%', 54 | label_opts=opts.LabelOpts(color='#ffffff', font_size=12)) 55 | return tl.dump_options_with_quotes() 56 | 57 | def zgdt(): # 中国现有确诊地图 58 | df = pd.read_csv('demo/csv/国内疫情数据.csv') 59 | time = df.drop_duplicates(subset=['dateId'], keep='last') 60 | 61 | tl = Timeline() 62 | for i in time['dateId']: 63 | df1 = copy.copy(df[df['dateId'] == i]) 64 | df1.drop_duplicates(subset=['province'], keep='last', inplace=True) 65 | 66 | c = ( 67 | Map() 68 | .add('累计确诊', [list(i) for i in zip(df1['province'], df1['confirm'])], 'china') 69 | .set_global_opts(title_opts=opts.TitleOpts(title='中国累计确诊时间线', pos_left='38%', title_textstyle_opts=opts.TextStyleOpts(color='#ffffff')), 70 | visualmap_opts=opts.VisualMapOpts(pos_right='0%', textstyle_opts=opts.TextStyleOpts(color='#ffffff'), 71 | is_piecewise=True, max_=int(df1['confirm'].max()), min_=int(df1['confirm'].min()), 72 | pieces=[ 73 | {"min": 5001}, 74 | {"min": 2001, 'max': 5000}, 75 | {"min": 1801, "max": 2000}, 76 | {"min": 1401, "max": 1800}, 77 | {"min": 1001, "max": 1400}, 78 | {"min": 801, "max": 1000}, 79 | {"min": 601, "max": 800}, 80 | {"min": 401, "max": 600}, 81 | {"min": 201, "max": 400}, 82 | {"min": 5, "max": 200, }, 83 | {"max": 5} 84 | ] 85 | ), toolbox_opts=opts.ToolboxOpts(), legend_opts=opts.LegendOpts(textstyle_opts=opts.TextStyleOpts(color='#ffffff'), pos_right='85%')) 86 | ) 87 | 88 | tl.add(c, i) 89 | tl.add_schema(is_auto_play=True, play_interval=1500, label_opts=opts.LabelOpts(color='#ffffff', font_size=12), pos_bottom='1%') 90 | return tl.dump_options_with_quotes() 91 | 92 | def yqbt(): # 疫情饼图 93 | df = pd.DataFrame(Xyyq.objects.all().values()) 94 | df = df.astype({'currentconfirmedcount': 'int64', 'confirmedcount': 'int64', 'curedcount': 'int64', 'deadcount':'int64'}) 95 | df = df.sort_values('confirmedcount', ascending=False) # 根据累计确诊去排序-降序 96 | df = df.drop_duplicates('provincename', keep='first') # 根据省份去重,取第一次出现的数据 97 | df['provincename'] = df['provincename'].str.strip('省').str.strip('市').str.strip('壮族自治区').str.strip('自治区').str.strip('回族自治区').str.strip( 98 | '维吾尔自治区') # pyecharts去除这些参数 99 | 100 | tl = Timeline() 101 | x = ['累计确诊', '累计死亡', '累计治愈', '现有确诊'][::-1] 102 | y = ['confirmedcount', 'deadcount', 'curedcount', 'currentconfirmedcount'][::-1] 103 | for i in range(len(x)): 104 | c = ( 105 | Pie() 106 | .add(x[i], data_pair=[list(i) for i in zip(df['provincename'], df[y[i]])], 107 | radius=['15%', '35%'], center=["55%", "60%"]) 108 | .set_global_opts(title_opts=opts.TitleOpts(title='中国疫情情况', pos_left='75%', pos_top='5%', title_textstyle_opts=opts.TextStyleOpts(color='#ffffff')), 109 | legend_opts=opts.LegendOpts(is_show=False)) 110 | .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}")) 111 | ) 112 | tl.add(c, x[i]) 113 | tl.add_schema(is_auto_play=True, pos_bottom='85%', orient='vertical', width='80px', height='400px', pos_right='5%', pos_top='16%', 114 | label_opts=opts.LabelOpts(color='#ffffff', font_size=12), is_inverse=True) 115 | 116 | return tl.dump_options_with_quotes() 117 | 118 | 119 | # 近31省市区现有病例 120 | def bg(): 121 | address = list(Bentuxianyou31.objects.values_list('address', flat = True)) 122 | xyqz = list(Bentuxianyou31.objects.values_list('xyqz', flat = True)) 123 | 124 | c = ( 125 | Bar(init_opts=opts.InitOpts(theme=ThemeType.CHALK)) 126 | .add_xaxis(address) 127 | .add_yaxis('', xyqz) 128 | .set_series_opts(markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="max",name="max"),opts.MarkLineItem(type_="min",name="min")]), 129 | markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max",name="max"),opts.MarkPointItem(type_="min",name="min")])) 130 | .set_global_opts(title_opts=opts.TitleOpts(title='近期31省区市本土现有病例', title_textstyle_opts=opts.TextStyleOpts(color='#ffffff'), pos_left='35%', pos_top='5%'), 131 | visualmap_opts=opts.VisualMapOpts(textstyle_opts=opts.TextStyleOpts(color='#ffffff'), 132 | max_=int(max(xyqz)), min_=int(min(xyqz)), is_piecewise=True, pos_right='5%', pos_top='40%'), 133 | xaxis_opts=opts.AxisOpts(name="省市区", axislabel_opts=opts.LabelOpts(rotate=90, color='#ffffff'), 134 | axispointer_opts=opts.AxisPointerOpts(is_show=True, type_="shadow")), 135 | tooltip_opts=opts.TooltipOpts(trigger='axis', axis_pointer_type='cross'), 136 | datazoom_opts=opts.DataZoomOpts(is_show=False, type_='inside', range_start=30), 137 | legend_opts=opts.LegendOpts(pos_right='5%', pos_top='5%', textstyle_opts=opts.TextStyleOpts(color='#ffffff')), 138 | yaxis_opts=opts.AxisOpts(name="数量", axislabel_opts=opts.LabelOpts(rotate=90, color='#ffffff'), splitline_opts=opts.SplitLineOpts(is_show=True))) 139 | ) 140 | return c.dump_options_with_quotes() 141 | 142 | # 高风险树形图 143 | def gfx(t): 144 | import json 145 | from pyecharts import options as opts 146 | from pyecharts.charts import Tree 147 | 148 | with open(f"demo/data/{t}.json", "r", encoding="utf-8") as f: 149 | j = json.load(f) 150 | if t == '高风险': 151 | x = 'orangered' 152 | y = 1.5 153 | elif t == '中风险': 154 | x = 'darkorange' 155 | y = 1 156 | c = ( 157 | Tree() 158 | .add("", [j], collapse_interval=20, is_roam=True, layout="radial", initial_tree_depth=y, label_opts=opts.LabelOpts(color='#ffffff', font_size=9)) 159 | .set_global_opts(title_opts=opts.TitleOpts(title=f"{t}地区", title_textstyle_opts=opts.TextStyleOpts(color=x, font_size=16),)) 160 | ) 161 | return c.dump_options_with_quotes() -------------------------------------------------------------------------------- /demo/csv/丁香园世界疫情.csv: -------------------------------------------------------------------------------- 1 | 洲系,国家,国家英文名,现有确诊,累计确诊,累计治愈,累计死亡 2 | 北美洲,美国,United States of America,19553791,63203443,42805090,844562 3 | 欧洲,法国,France,12125933,12620080,368023,126124 4 | 欧洲,英国,The United Kingdom,8125486,14767284,6491069,150729 5 | 欧洲,荷兰,Netherlands,3373902,3406969,11886,21181 6 | 亚洲,土耳其,Turkey,3284237,10119884,6751667,83980 7 | 欧洲,德国,Germany,3247493,7690632,4328400,114739 8 | 欧洲,意大利,Italy,2684524,7774863,4950780,139559 9 | 欧洲,西班牙,Spain,2667335,7592242,4834524,90383 10 | 欧洲,比利时,Belgium,2260065,2307843,19239,28539 11 | 亚洲,印度,India,2166356,35875790,33225221,484213 12 | 欧洲,瑞典,Sweden,1466889,1487291,4971,15431 13 | 欧洲,塞尔维亚,Serbia,1352696,1522813,154190,15927 14 | 南美洲,阿根廷,Argentina,1289955,6533635,5126085,117595 15 | 欧洲,瑞士,Switzerland,1241472,1571481,317600,12409 16 | 欧洲,希腊,Greece,1182741,1568215,363915,21559 17 | 欧洲,斯洛伐克,Slovakia,1129620,1401959,255300,17039 18 | 欧洲,爱尔兰,Ireland,970639,1002013,25422,5952 19 | 亚洲,哈萨克斯坦,Kazakhstan,944355,1083988,121347,18286 20 | 亚洲,伊朗,Iran (Islamic Republic of),942471,6210298,5135887,131940 21 | 大洋洲,澳大利亚,Australia,793782,1223505,427258,2465 22 | 北美洲,墨西哥,Mexico,736225,4170066,3133267,300574 23 | 欧洲,奥地利,Austria,636025,1361741,711844,13872 24 | 南美洲,巴西,Brazil,629581,22636359,21386271,620507 25 | 欧洲,俄罗斯,Russian Federation,582169,10684204,9784348,317687 26 | 欧洲,斯洛文尼亚,Slovenia,490339,498506,2027,6140 27 | 欧洲,丹麦,Denmark,483390,1006295,519497,3408 28 | 欧洲,葡萄牙,Portugal,466526,1693398,1207711,19161 29 | 欧洲,波兰,Poland,462066,4232386,3670066,100254 30 | 北美洲,加拿大,Canada,405132,2627864,2191715,31017 31 | 欧洲,挪威,Norway,370228,460530,88952,1350 32 | 亚洲,格鲁吉亚,Georgia,359380,968313,594669,14264 33 | 北美洲,波多黎各,Puerto Rico,358770,362143,0,3373 34 | 亚洲,韩国,Republic of Korea,332154,674868,336548,6166 35 | 欧洲,芬兰,Finland,305949,338615,31000,1666 36 | 亚洲,越南,Vietnam,305551,1930428,1590090,34787 37 | 欧洲,乌克兰,Ukraine,303670,3890974,3483354,103950 38 | 亚洲,约旦,Jordan,267807,1085500,804784,12909 39 | 北美洲,洪都拉斯,Honduras,247820,382440,124171,10449 40 | 亚洲,以色列,Israel,244712,1589099,1336113,8274 41 | 南美洲,玻利维亚,Bolivia (Plurinational State of),217176,697236,460004,20056 42 | 亚洲,黎巴嫩,Lebanon,185051,787498,593136,9311 43 | 亚洲,菲律宾,Philippines,171676,3026473,2802286,52511 44 | 欧洲,克罗地亚,Croatia,167254,775139,594929,12956 45 | 亚洲,伊拉克,Iraq,165390,2100518,1910908,24220 46 | 亚洲,阿塞拜疆,Azerbaijan,153027,622236,460738,8471 47 | 北美洲,哥斯达黎加,Costa Rica,151081,595795,437328,7386 48 | 南美洲,哥伦比亚,Colombia,149230,5380841,5101151,130460 49 | 欧洲,保加利亚,Bulgaria,140224,794240,622255,31761 50 | 北美洲,古巴,Cuba,132991,982614,841128,8495 51 | 非洲,南非,South Africa,131747,3534131,3309735,92649 52 | 非洲,利比亚,Libya,127780,394470,260885,5805 53 | 亚洲,塞浦路斯,Cyprus,120654,212074,90755,665 54 | 亚洲,斯里兰卡,Sri Lanka,119838,593072,458085,15149 55 | 北美洲,多米尼加,Dominican Republic,115042,469065,349768,4255 56 | 亚洲,老挝,Laos,111376,120520,8687,457 57 | 欧洲,匈牙利,Hungary,105473,1300994,1155505,40016 58 | 北美洲,危地马拉,Guatemala,102586,641164,522451,16127 59 | 欧洲,波黑,Bosnia and Herzegovina,98965,304856,192218,13673 60 | 亚洲,亚美尼亚,Armenia,96377,346224,241841,8006 61 | 欧洲,捷克,Czechia,90853,2538298,2410762,36683 62 | 亚洲,巴勒斯坦,occupied Palestinian territory,90516,473302,377793,4993 63 | 南美洲,厄瓜多尔,Ecuador,86649,564238,443883,33706 64 | 非洲,留尼旺,Réunion,85541,85960,0,419 65 | 南美洲,委内瑞拉,Venezuela,85165,447865,357339,5361 66 | 非洲,埃塞俄比亚,Ethiopia,84665,448728,356997,7066 67 | 非洲,阿尔及利亚,Algeria,81875,223196,134958,6363 68 | 非洲,埃及,Egypt,73292,394740,299434,22014 69 | 非洲,摩洛哥,Morocco,66879,1012042,930236,14927 70 | 非洲,毛里求斯,Mauritius,66683,69323,1854,786 71 | 亚洲,马来西亚,Malaysia,64561,2792035,2695751,31723 72 | 非洲,莫桑比克,Mozambique,63479,212859,147281,2099 73 | 非洲,斯威士兰,Swaziland,63096,67373,2936,1341 74 | 南美洲,法属圭亚那,French Guiana,60146,60490,0,344 75 | 北美洲,瓜德罗普岛,Guadeloupe,59000,59835,0,835 76 | 非洲,博茨瓦纳,Botswana,58501,232432,171434,2497 77 | 北美洲,巴拿马,Panama,57242,533559,468838,7479 78 | 非洲,乌干达,Uganda,55985,155443,96097,3361 79 | 亚洲,泰国,Thailand,54779,2284609,2207980,21850 80 | 欧洲,罗马尼亚,Romania,53420,1857502,1745012,59070 81 | 亚洲,孟加拉国,Bangladesh,53381,1598389,1516901,28107 82 | 南美洲,乌拉圭,Uruguay,52023,463357,405132,6202 83 | 非洲,肯尼亚,Kenya,51664,313677,256551,5462 84 | 北美洲,马提尼克,Martinique,51052,51840,0,788 85 | 欧洲,立陶宛,Lithuania,50373,547020,489082,7565 86 | 非洲,突尼斯,Tunisia,50135,749832,673990,25707 87 | 非洲,刚果（金）,Democratic Republic of the Congo,49636,81719,30858,1225 88 | 南美洲,智利,Chile,48267,1837390,1749834,39289 89 | 亚洲,日本,Japan,45820,1776780,1712555,18405 90 | 北美洲,牙买加,Jamaica,43417,102505,56589,2499 91 | 亚洲,沙特阿拉伯,Saudi Arabia,42839,588183,536447,8897 92 | 北美洲,特立尼达和多巴哥,Trinidad & Tobago,40510,97897,54285,3102 93 | 亚洲,阿联酋,United Arab Emirates,39267,790698,749254,2177 94 | 非洲,尼日利亚,Nigeria,38814,249154,207254,3086 95 | 欧洲,黑山,Montenegro,36961,194695,155281,2453 96 | 非洲,布隆迪共和国,The Republic of Burundi,35713,35755,4,38 97 | 亚洲,巴基斯坦,Pakistan,35106,1309248,1245155,28987 98 | 亚洲,卡塔尔,Qatar,32001,278698,246076,621 99 | 南美洲,苏里南,Suriname,31668,59776,26909,1199 100 | 非洲,马约特,Mayotte,31294,31480,0,186 101 | 亚洲,阿曼,Oman,29771,737830,703940,4119 102 | 非洲,坦桑尼亚,Tanzania,29657,30564,167,740 103 | 非洲,卢旺达,Rwanda,29526,122403,91490,1387 104 | 亚洲,科威特,Kuwait,28951,441999,410575,2473 105 | 非洲,加纳,Ghana,28455,152243,122456,1332 106 | 非洲,喀麦隆,Cameroon,27380,109666,80433,1853 107 | 非洲,纳米比亚,Namibia,26459,152982,122788,3735 108 | 亚洲,阿富汗,Afghanistan,26364,158471,124733,7374 109 | 非洲,安哥拉,Angola,26013,91148,63295,1840 110 | 北美洲,巴巴多斯,Barbados,25710,32795,6819,266 111 | 非洲,赞比亚共和国,The Republic of Zambia,24652,287110,258633,3825 112 | 北美洲,库拉索岛,Curaçao,24032,28663,4438,193 113 | 亚洲,叙利亚,Syrian ArabRepublic,24020,50552,23599,2933 114 | 非洲,莱索托,Lesotho,23856,31372,6830,686 115 | 欧洲,爱沙尼亚,Estonia,21354,255755,232442,1959 116 | 北美洲,萨尔瓦多,The Republic of El Salvador,20551,122469,98091,3827 117 | 非洲,马拉维,Malawi,19626,80867,58814,2427 118 | 北美洲,伯利兹,Belize,19509,38072,17956,607 119 | 非洲,科特迪瓦,Cote d Ivoire,18641,77857,58474,742 120 | 欧洲,卢森堡,Luxembourg,18556,117001,97515,930 121 | 欧洲,阿尔巴尼亚,Albania,18135,222664,201282,3247 122 | 非洲,加蓬,Gabon,17066,44409,27048,295 123 | 欧洲,拉脱维亚,Latvia,16570,290956,269718,4668 124 | 非洲,津巴布韦,Zimbabwe,16171,223000,201649,5180 125 | 非洲,毛里塔尼亚,Mauritania,15224,50702,34587,891 126 | 亚洲,柬埔寨,Cambodia,14828,120670,102827,3015 127 | 欧洲,泽西岛,Jersey,14443,24209,9676,90 128 | 非洲,佛得角,Cabo Verde,13978,50959,36615,366 129 | 亚洲,缅甸,Myanmar,13843,532402,499264,19295 130 | 非洲,刚果（布）,Congo,13840,22418,8208,370 131 | 非洲,索马里,Somalia,13676,24261,9250,1335 132 | 非洲,苏丹,Sudan,13412,48813,32054,3347 133 | 北美洲,阿鲁巴,Aruba,13283,28601,15065,253 134 | 亚洲,巴林,Bahrain,12832,296253,282024,1397 135 | 欧洲,马耳他,Malta,12696,61255,48067,492 136 | 亚洲,马尔代夫,Maldives,12667,99099,86168,264 137 | 北美洲,尼加拉瓜,Nicaragua,12651,17563,4694,218 138 | 大洋洲,法属波利尼西亚,French Polynesia,12577,46713,33500,636 139 | 欧洲,北马其顿,North Macedonia,12333,236241,215853,8055 140 | 南美洲,巴拉圭,Paraguay,11832,476892,448387,16673 141 | 非洲,马达加斯加,Madagascar,11662,54101,41322,1117 142 | 非洲,马里,Mali,11553,26513,14285,675 143 | 非洲,多哥,Togo,11492,34658,22908,258 144 | 亚洲,文莱,Brunei Darussalam,10858,15678,4722,98 145 | 亚洲,尼泊尔,Nepal,10611,835927,813709,11607 146 | 北美洲,巴哈马,Bahamas,10451,29489,18321,717 147 | 北美洲,开曼群岛,Cayman Islands,10433,11226,781,12 148 | 亚洲,吉尔吉斯斯坦,Kyrgyzstan,10259,186878,173796,2823 149 | 南美洲,圭亚那,Guyana,8939,46005,35993,1073 150 | 欧洲,摩尔多瓦,Republic of Moldova,8859,382124,362868,10397 151 | 欧洲,根西岛,Guernsey,8412,9945,1505,28 152 | 非洲,塞舌尔,Seychelles,7993,29030,20903,134 153 | 亚洲,也门共和国,The Republic of Yemen,7871,10208,350,1987 154 | 欧洲,冰岛,Iceland,7541,40037,32455,41 155 | 欧洲,安道尔,Andorra,7183,28542,21218,141 156 | 亚洲,中国,China,6786,134903,122418,5699 157 | 北美洲,海地,The Republic of Haiti,6564,26807,19463,780 158 | 非洲,塞内加尔,Senegal,6501,80154,71752,1901 159 | 北美洲,圣文森特和格林纳丁斯,Saint Vincent and the Grenadines,6016,6247,147,84 160 | 欧洲,马恩岛,Isle of Man,6003,18847,12776,68 161 | 北美洲,美属维尔京群岛,United States Virgin Islands,5980,12541,6472,89 162 | 亚洲,印度尼西亚,Indonesia,5960,4267451,4117347,144144 163 | 大洋洲,斐济,The Republic of Fiji,5868,57849,51267,714 164 | 亚洲,新加坡,Singapore,5846,287243,280559,838 165 | 非洲,中非共和国,Central African Republic,5685,12650,6859,106 166 | 大洋洲,巴布亚新几内亚,Papua New Guinea,5551,36415,30269,595 167 | 非洲,几内亚,Guinea,5459,34852,28990,403 168 | 非洲,布基纳法索,Burkina Faso,5411,19636,13892,333 169 | 非洲,南苏丹,South Sudan,5006,16337,11195,136 170 | 欧洲,直布罗陀,Gibraltar,4619,10096,5377,100 171 | 大洋洲,关岛,Guam,4573,16018,11171,274 172 | 北美洲,多米尼克,Dominica,4509,7562,3006,47 173 | 非洲,贝宁,Benin,4431,25522,20930,161 174 | 欧洲,圣马力诺,San Marino,4105,9518,5310,103 175 | 北美洲,百慕大,Bermuda,4051,7880,3719,110 176 | 亚洲,乌兹别克斯坦,Uzbekstan,3814,201155,195840,1501 177 | 北美洲,圣马丁岛,Saint Martin,3712,5351,1598,41 178 | 大洋洲,北马里亚纳群岛联邦,Northern Mariana Islands (Commonwealth of the),3613,3665,32,20 179 | 非洲,科摩罗,Union des Comores,3533,7655,3963,159 180 | 非洲,赤道几内亚,Eq.Guinea,3398,14871,11296,177 181 | 欧洲,列支敦士登,Liechtenstein,3390,6824,3363,71 182 | 北美洲,圣其茨和尼维斯,Saint Kitts and Nevis,3311,4339,1000,28 183 | 北美洲,格陵兰,Greenland,3241,5996,2753,2 184 | 非洲,塞拉利昂,Sierra Leone,2899,7454,4381,174 185 | 欧洲,摩纳哥,Monaco,2867,6165,3254,44 186 | 北美洲,安提瓜和巴布达,Antigua & Barbuda,2804,5058,2134,120 187 | 北美洲,格林那达,Grenada,2778,8601,5622,201 188 | 北美洲,荷属圣马丁,Sint Maarten,2580,6692,4037,75 189 | 北美洲,圣卢西亚,Saint Lucia,2477,15372,12585,310 190 | 北美洲,圣巴泰勒米岛,Saint Barthelemy,2386,2390,0,4 191 | 非洲,圣多美和普林西比,São Tomé and Príncipe,2084,4892,2745,63 192 | 北美洲,英属维尔京群岛,"VirginIslands,British",2075,4764,2649,40 193 | 非洲,吉布提,The Republic of Djibouti,2064,14415,12162,189 194 | 亚洲,蒙古,Mongolia,2026,398155,394053,2076 195 | 非洲,尼日尔,Niger,2026,8076,5769,281 196 | 非洲,厄立特里亚,Eritrea,1812,8559,6663,84 197 | 欧洲,法罗群岛,Faroe Islands,1563,7930,6352,15 198 | 北美洲,安圭拉,Anguilla,1562,1942,374,6 199 | 非洲,乍得,Chad,1516,6558,4857,185 200 | 亚洲,东帝汶,Tinor-Leste,1463,19860,18275,122 201 | 非洲,几内亚比绍,Guinea-Bissau,1408,6794,5237,149 202 | 北美洲,特克斯和凯科斯群岛,Turks & Caicos Islands,1287,4094,2780,27 203 | 大洋洲,新喀里多尼亚,New Caledonia,1280,13265,11704,281 204 | 非洲,利比里亚,Liberia,1279,7054,5488,287 205 | 非洲,冈比亚,Gambia,1190,11122,9588,344 206 | 大洋洲,新西兰,New Zealand,996,14848,13800,52 207 | 欧洲,白俄罗斯,Belarus,667,709622,703216,5739 208 | 北美洲,圣皮埃尔和密克隆群岛,Saint Pierre and Miquelon,237,268,31,0 209 | 亚洲,不丹,Bhutan,234,2830,2593,3 210 | 南美洲,荷兰加勒比地区,"Bonaire, Sint Eustatius and Saba",174,177,0,3 211 | 其他,钻石公主号邮轮,International conveyance (Diamond Princess),154,741,574,13 212 | 北美洲,蒙特塞拉特,Montserrat,83,114,30,1 213 | 南美洲,福克兰群岛,Falkland Islands,2,85,83,0 214 | 欧洲,梵蒂冈,Holy See,2,29,27,0 215 | 亚洲,塔吉克斯坦,Tajikistan,-368,17493,16960,901 216 | 南美洲,秘鲁,Peru,-119937,2304674,2221793,202818 217 | -------------------------------------------------------------------------------- /demo/pachong.py: -------------------------------------------------------------------------------- 1 | import datetime 2 | import json 3 | import re 4 | import pandas as pd 5 | import requests 6 | from bs4 import BeautifulSoup 7 | from sqlalchemy import create_engine 8 | 9 | 10 | headers = { 11 | 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.81 Safari/537.36' 12 | } 13 | con = create_engine("mysql+mysqlconnector://root:147123@localhost:3306/ksh") 14 | # 历史数据 15 | def lishishuju(): 16 | df = pd.read_csv('demo/csv/丁香园国内疫情.csv', engine='c') # 读取数据 17 | 18 | df = df.sort_values('累计确诊', ascending=False) # 根据累计确诊去排序-降序 19 | 20 | df = df.drop_duplicates('省份', keep='first') # 根据省份去重,取第一次出现的数据 21 | 22 | df['省份'] = df['省份'].str.strip('省').str.strip('市').str.strip('壮族自治区').str.strip('自治区').str.strip('回族自治区').str.strip('维吾尔自治区') 23 | 24 | url = 'https://api.inews.qq.com/newsqa/v1/query/pubished/daily/list?province=湖北' 25 | data = json.loads(requests.get(url, headers=headers).content.decode())['data'] 26 | 27 | for i in df['省份']: 28 | if i != '湖北': 29 | url = 'https://api.inews.qq.com/newsqa/v1/query/pubished/daily/list?province=' + i 30 | x = json.loads(requests.get(url).content.decode())['data'] 31 | data = data + x 32 | 33 | def funx(x): 34 | if len(x) == 3: 35 | x = x + '0' 36 | return x 37 | 38 | df = pd.DataFrame(data) 39 | x = df['year'].astype('str') + '.' 40 | y = (df['date'].astype('str')) 41 | y = y.apply(lambda x: funx(x)) 42 | 43 | df['dateId'] = x + y 44 | df['dateId'] = pd.DatetimeIndex(df['dateId']).astype('str').str[:7] 45 | 46 | df.to_csv('demo/csv/国内疫情数据.csv', index=False, encoding='utf-8-sig') 47 | df.to_sql('gnlssj', if_exists='replace', con=con, index=False) 48 | con.execute('ALTER TABLE gnlssj ADD id INT(16) NOT NULL PRIMARY KEY AUTO_INCREMENT FIRST;') # 添加自增字段id 49 | 50 | # 中国今日疫情情况 51 | def yqday(): 52 | url = 'https://api.inews.qq.com/newsqa/v1/query/inner/publish/modules/list?modules=statisGradeCityDetail,diseaseh5Shelf' 53 | data = json.loads(requests.post(url=url, headers=headers).content.decode())['data'] 54 | x = data['statisGradeCityDetail'] 55 | y = [] 56 | for i in x: 57 | j = [i['province'] + i['city'], i['confirmAdd'], i['nowConfirm'], i['grade']] 58 | y.append(j) 59 | 60 | x = ['address', 'addqz', 'xyqz', 'fxqy'] 61 | 62 | # 使用create_engine + pandas 快捷保存数据库 63 | df = pd.DataFrame(y, columns=x) 64 | df.to_sql('bentuxianyou31', if_exists='replace', con=con, index=False) 65 | con.execute('ALTER TABLE bentuxianyou31 ADD id INT(16) NOT NULL PRIMARY KEY AUTO_INCREMENT FIRST;') # 添加自增字段id 66 | with open('demo/data/中国疫情.json', 'w', encoding='utf-8') as f: 67 | f.write(json.dumps(data, ensure_ascii=False, indent=4)) 68 | 69 | pd.DataFrame(y).to_csv('demo/csv/近31省市区现有本土病例.csv', index=False, encoding='utf-8', header=x) 70 | # 使用create_engine + pandas 快捷保存数据库 71 | 72 | 73 | # 中国每日疫情 74 | def yqveryday(): 75 | url = 'https://file1.dxycdn.com/2021/1228/171/2851867762198723253-135.json?t=27344362' # url 76 | head_data = requests.get(url=url, headers=headers).content # 获取数据 77 | data = json.loads(head_data)['data'] # 把取到的数据返回 78 | # 使用pandas快捷保存csv 79 | pd.DataFrame(data).to_csv('demo/csv/丁香园国内每日疫情情况.csv', encoding='utf-8', index=False) 80 | pd.DataFrame(data).to_sql('mrsj', if_exists='replace', con=con, index=False) 81 | con.execute('ALTER TABLE mrsj ADD id INT(16) NOT NULL PRIMARY KEY AUTO_INCREMENT FIRST;') # 添加自增字段id 82 | 83 | # 实时热点 84 | def ssrd(): 85 | url = 'https://opendata.baidu.com/data/inner?tn=reserved_all_res_tn&dspName=iphone&from_sf=1&dsp=iphone&resource_id=28565&alr=1&query=%E5%9B%BD%E5%86%85%E6%96%B0%E5%9E%8B%E8%82%BA%E7%82%8E%E6%9C%80%E6%96%B0%E5%8A%A8%E6%80%81&cb=jsonp_1642854207390_27502' 86 | data = json.loads(requests.get(url=url, headers=headers).content.decode().split('(')[1][:-1])['Result'][0]['DisplayData']['result'][ 87 | 'items'] 88 | with open('demo/data/实时热点.json', 'w', encoding='utf-8') as f: 89 | f.write(json.dumps(data, ensure_ascii=False, indent=4)) 90 | pd.DataFrame(data).to_sql('ssrd', if_exists='replace', con=con, index=False) 91 | con.execute('ALTER TABLE ssrd ADD id INT(16) NOT NULL PRIMARY KEY AUTO_INCREMENT FIRST;') # 添加自增字段id 92 | 93 | # 国内各省目前疫情 94 | def parse(): 95 | data = [] # 定义全局列表t 96 | data1 = [] 97 | url = 'https://ncov.dxy.cn/ncovh5/view/pneumonia' # url 98 | 99 | head_data = requests.get(url=url, headers=headers).content # 获取数据 100 | res = BeautifulSoup(head_data, 'html.parser') # 利用bs4解析数据 101 | res = res.find('script', {'id': 'getAreaStat'}).text # 利用bs4获取国内的数据 102 | res = re.findall('try \{ window.getAreaStat = (.*)}catch', res, re.S)[0] # 利用正则表达式先取得里面的所有数据 103 | res = re.findall('\{(.*?)]}', res) # 利用正则表达式再去取每个省的数据 104 | for i in res: 105 | provinceName = re.findall('"provinceName":"(.*?)"', i) # 取省份名 106 | cityName = re.findall('"cityName":"(.*?)"', i) # 取城市名 107 | if len(cityName) == 0: # 判断城市的长度是否为0 108 | cityName = provinceName # 为零则把城市 = 省份 方便后面的保存 109 | else: 110 | cityName.insert(0, provinceName[0]) # 在城市列表最开始的位置插入省份名 111 | currentConfirmedCount = re.findall('"currentConfirmedCount":(.*?),', i) # 取现有确诊 112 | confirmedCount = re.findall('"confirmedCount":(.*?),', i) # 去取累计确诊 113 | curedCount = re.findall('"curedCount":(.*?),', i) # 取累计治愈 114 | deadCount = re.findall('"deadCount":(.*?),', i) # 取累计死亡 115 | 116 | for i in range(0, len(currentConfirmedCount)): # 遍历存到列表t 117 | data.append({ 118 | 'provinceName': cityName[0], 119 | 'cityName': cityName[i], 120 | 'currentConfirmedCount': currentConfirmedCount[i], 121 | 'confirmedCount': confirmedCount[i], 122 | 'curedCount': curedCount[i], 123 | 'deadCount': deadCount[i], 124 | }) 125 | for i in range(0, len(currentConfirmedCount)): # 遍历存到列表t 126 | data1.append({ 127 | '省份': cityName[0], 128 | '城市': cityName[i], 129 | '现有确诊': currentConfirmedCount[i], 130 | '累计确诊': confirmedCount[i], 131 | '累计治愈': curedCount[i], 132 | '累计死亡': deadCount[i], 133 | }) 134 | pd.DataFrame(data1).to_csv('demo/csv/丁香园国内疫情.csv', encoding='utf-8', index=False) 135 | pd.DataFrame(data).to_sql('xyyq', if_exists='replace', con=con, index=False) 136 | con.execute('ALTER TABLE xyyq ADD id INT(16) NOT NULL PRIMARY KEY AUTO_INCREMENT FIRST;') # 添加自增字段id 137 | 138 | # 风险地区 139 | def fxdq(): 140 | url = 'https://file1.dxycdn.com/2021/0202/196/1680100273140422643-135.json' 141 | resdata = json.loads(requests.get(url, headers=headers).content.decode())['data'] 142 | fxlevel = ['高风险', '中风险'] 143 | data = { 144 | 'code': 200, 145 | 'data': [] 146 | } 147 | for i in range(len(resdata)): 148 | tt = i 149 | res = resdata[i]['dangerPros'] 150 | x = [] 151 | for i in range(len(res)): 152 | for j in res[i]["dangerAreas"]: 153 | x.append({ 154 | "provinceName": res[i]["provinceShortName"], 155 | "cityName": j["cityName"], 156 | "areaName": j["areaName"], 157 | }) 158 | 159 | df = pd.DataFrame(x) 160 | df['全称'] = df['provinceName'] + df['cityName'] + df['areaName'] 161 | df['address'] = df['provinceName'] + df['cityName'] 162 | data['data'].append({'风险等级': fxlevel[tt], '数量': len(df), '地区': []}) 163 | 164 | for i in range(len(df)): 165 | data['data'][tt]['地区'].append(df.iloc[i]['全称']) 166 | 167 | 168 | q = { 169 | "name": fxlevel[tt], 170 | "children": [] 171 | } 172 | 173 | provinceName = df.drop_duplicates(subset=['address'], keep='first') 174 | 175 | for i in provinceName['address']: 176 | t = df[df['address'] == i] 177 | cd = len(t) 178 | q['children'].append({ 179 | "name": f'{i}({cd})', 180 | "children": [] 181 | }) 182 | 183 | for kk in t['areaName']: 184 | for k in range(len(q['children'])): 185 | if(q['children'][k]['name'] == f'{i}({cd})'): 186 | q['children'][k]['children'].append({ 187 | 'name': kk 188 | }) 189 | 190 | with open(f'demo/data/{fxlevel[tt]}.json', 'w', encoding='utf-8') as f: 191 | f.write(json.dumps(q, ensure_ascii=False, indent=4)) 192 | with open(f'demo/data/风险地区.json', 'w', encoding='utf-8') as f: 193 | f.write(json.dumps(data, ensure_ascii=False, indent=4)) 194 | 195 | # 近2个月中国疫情情况 196 | def Moon_Tow_Near(): 197 | url = 'https://api.inews.qq.com/newsqa/v1/query/inner/publish/modules/list?modules=chinaDayList,chinaDayAddList,nowConfirmStatis,provinceCompare' 198 | data = json.loads(requests.post(url, headers=headers).content.decode())['data'] 199 | res = data['chinaDayAddList'] 200 | res1 = data['chinaDayList'] 201 | 202 | df = pd.DataFrame(res) 203 | df1 = pd.DataFrame(res1) 204 | 205 | def funx(x): 206 | if len(x) == 3 or (len(x.split('.')[0]) == 2 and len(x) == 4): 207 | x = x + '0' 208 | return x 209 | t = [df, df1] 210 | for i in t: 211 | x = i['y'].astype('str') + '.' 212 | y = (i['date'].astype('str')) 213 | y = y.apply(lambda x: funx(x)) 214 | i['dateId'] = x + y 215 | i['dateId'] = pd.DatetimeIndex(i['dateId']).astype('str') 216 | 217 | pd.DataFrame(df).to_csv('demo/csv/近2个月新增情况.csv', encoding='utf-8', index=False) 218 | pd.DataFrame(df1).to_csv('demo/csv/近2个月累计情况.csv', encoding='utf-8', index=False) 219 | pd.DataFrame(df).to_sql('j2yxz', if_exists='replace', con=con, index=False) 220 | con.execute('ALTER TABLE j2yxz ADD id INT(16) NOT NULL PRIMARY KEY AUTO_INCREMENT FIRST;') # 添加自增字段id 221 | pd.DataFrame(df1).to_sql('j2ylj', if_exists='replace', con=con, index=False) 222 | con.execute('ALTER TABLE j2ylj ADD id INT(16) NOT NULL PRIMARY KEY AUTO_INCREMENT FIRST;') # 添加自增字段id 223 | 224 | if __name__ == '__main__': 225 | pass 226 | # print('正在获取历史数据...') 227 | # lishishuju() # 历史数据 228 | # print('正在获取中国今日疫情情况...') 229 | # yqday() # 中国今日疫情情况 230 | # print('正在获取中国每日疫情...') 231 | # yqveryday() # 中国每日疫情 232 | # print('正在获取实时热点...') 233 | # ssrd() # 实时热点 234 | # print('正在获取国内各省目前疫情...') 235 | # parse() # 国内各省目前疫情 236 | # print('正在获取国内风险地区...') 237 | # fxdq() # 国内风险地区 238 | # print('正在获取进两月份的疫情趋势...') 239 | # Moon_Tow_Near() 240 | # print('获取完毕数据已更新!') 241 | -------------------------------------------------------------------------------- /demo/csv/丁香园国内疫情.csv: -------------------------------------------------------------------------------- 1 | 省份,城市,现有确诊,累计确诊,累计治愈,累计死亡 2 | 台湾,台湾,4365,18958,13742,851 3 | 香港,香港,920,14013,12880,213 4 | 河南省,河南省,508,2657,2127,22 5 | 河南省,安阳,467,522,55,0 6 | 河南省,许昌,366,405,38,1 7 | 河南省,郑州,161,488,322,5 8 | 河南省,周口,25,101,75,1 9 | 河南省,境外输入,15,149,134,0 10 | 河南省,商丘,12,103,88,3 11 | 河南省,洛阳,10,41,30,1 12 | 河南省,开封,7,33,26,0 13 | 河南省,驻马店,4,143,139,0 14 | 河南省,信阳,3,277,272,2 15 | 河南省,平顶山,1,59,57,1 16 | 河南省,三门峡,1,8,6,1 17 | 河南省,南阳,0,156,153,3 18 | 河南省,新乡,0,57,54,3 19 | 河南省,漯河,0,36,36,0 20 | 河南省,焦作,0,32,31,1 21 | 河南省,鹤壁,0,19,19,0 22 | 河南省,濮阳,0,17,17,0 23 | 河南省,济源,0,5,5,0 24 | 河南省,待明确地区,-564,6,570,0 25 | 浙江省,浙江省,369,2236,1866,1 26 | 浙江省,杭州,143,328,185,0 27 | 浙江省,宁波,110,269,159,0 28 | 浙江省,境外输入,101,369,268,0 29 | 浙江省,绍兴,37,429,392,0 30 | 浙江省,金华,2,57,55,0 31 | 浙江省,温州,0,504,503,1 32 | 浙江省,台州,0,147,147,0 33 | 浙江省,嘉兴,0,46,46,0 34 | 浙江省,省十里丰监狱,0,36,36,0 35 | 浙江省,丽水,0,17,17,0 36 | 浙江省,衢州,0,14,14,0 37 | 浙江省,湖州,0,10,10,0 38 | 浙江省,舟山,0,10,10,0 39 | 浙江省,待明确地区,-24,0,24,0 40 | 上海市,上海市,260,3799,3532,7 41 | 上海市,境外输入,258,3407,3149,0 42 | 上海市,奉贤区,2,11,9,0 43 | 上海市,外地来沪,0,113,112,1 44 | 上海市,浦东新区,0,82,81,1 45 | 上海市,宝山区,0,27,26,1 46 | 上海市,黄浦区,0,22,22,0 47 | 上海市,闵行区,0,19,19,0 48 | 上海市,徐汇区,0,18,17,1 49 | 上海市,静安区,0,17,16,1 50 | 上海市,松江区,0,16,16,0 51 | 上海市,长宁区,0,14,14,0 52 | 上海市,普陀区,0,12,12,0 53 | 上海市,杨浦区,0,9,9,0 54 | 上海市,嘉定区,0,9,7,2 55 | 上海市,虹口区,0,7,7,0 56 | 上海市,青浦区,0,7,7,0 57 | 上海市,金山区,0,4,4,0 58 | 上海市,崇明区,0,4,4,0 59 | 上海市,境外来沪,0,1,1,0 60 | 广东省,广东省,165,3798,3625,8 61 | 广东省,广州,82,2080,1997,1 62 | 广东省,深圳,34,663,626,3 63 | 广东省,珠海,26,159,132,1 64 | 广东省,佛山,23,294,271,0 65 | 广东省,东莞,8,172,163,1 66 | 广东省,云浮,5,5,0,0 67 | 广东省,中山,3,76,73,0 68 | 广东省,惠州,3,66,63,0 69 | 广东省,湛江,2,43,41,0 70 | 广东省,梅州,2,19,17,0 71 | 广东省,河源,1,6,5,0 72 | 广东省,肇庆,0,47,46,1 73 | 广东省,江门,0,40,40,0 74 | 广东省,阳江,0,28,28,0 75 | 广东省,汕头,0,26,26,0 76 | 广东省,清远,0,23,23,0 77 | 广东省,茂名,0,17,17,0 78 | 广东省,揭阳,0,11,11,0 79 | 广东省,韶关,0,10,9,1 80 | 广东省,潮州,0,7,7,0 81 | 广东省,汕尾,0,6,6,0 82 | 广东省,待明确地区,-24,0,24,0 83 | 广西壮族自治区,广西壮族自治区,140,716,574,2 84 | 广西壮族自治区,境外输入,143,442,299,0 85 | 广西壮族自治区,防城港,11,39,28,0 86 | 广西壮族自治区,崇左,1,1,0,0 87 | 广西壮族自治区,南宁,0,56,56,0 88 | 广西壮族自治区,北海,0,44,43,1 89 | 广西壮族自治区,桂林,0,32,32,0 90 | 广西壮族自治区,河池,0,28,27,1 91 | 广西壮族自治区,柳州,0,24,24,0 92 | 广西壮族自治区,玉林,0,11,11,0 93 | 广西壮族自治区,来宾,0,11,11,0 94 | 广西壮族自治区,钦州,0,8,8,0 95 | 广西壮族自治区,贵港,0,8,8,0 96 | 广西壮族自治区,梧州,0,5,5,0 97 | 广西壮族自治区,贺州,0,4,4,0 98 | 广西壮族自治区,百色,0,3,3,0 99 | 广西壮族自治区,待明确地区,-15,0,15,0 100 | 北京市,北京市,107,1357,1241,9 101 | 北京市,境外输入,127,368,241,0 102 | 北京市,丰台区,72,345,273,0 103 | 北京市,昌平区,36,70,34,0 104 | 北京市,海淀区,14,96,82,0 105 | 北京市,朝阳区,14,94,80,0 106 | 北京市,房山区,11,31,20,0 107 | 北京市,大兴区,9,143,134,0 108 | 北京市,西城区,3,62,59,0 109 | 北京市,通州区,1,21,20,0 110 | 北京市,经济开发区,1,1,0,0 111 | 北京市,顺义区,0,45,45,0 112 | 北京市,外地来京,0,25,25,0 113 | 北京市,东城区,0,19,19,0 114 | 北京市,石景山区,0,15,15,0 115 | 北京市,怀柔区,0,8,8,0 116 | 北京市,密云区,0,7,7,0 117 | 北京市,门头沟区,0,5,5,0 118 | 北京市,延庆区,0,1,1,0 119 | 北京市,待明确地区,-181,1,173,9 120 | 天津市,天津市,72,1076,1001,3 121 | 天津市,津南区,342,344,2,0 122 | 天津市,河北区,38,50,12,0 123 | 天津市,西青区,12,17,5,0 124 | 天津市,滨海新区,9,22,13,0 125 | 天津市,境外输入,8,510,502,0 126 | 天津市,河西区,6,10,4,0 127 | 天津市,红桥区,5,7,2,0 128 | 天津市,河东区,4,19,14,1 129 | 天津市,南开区,1,7,6,0 130 | 天津市,东丽区,1,5,4,0 131 | 天津市,宝坻区,0,60,58,2 132 | 天津市,北辰区,0,7,7,0 133 | 天津市,和平区,0,6,6,0 134 | 天津市,外地来津,0,6,6,0 135 | 天津市,宁河区,0,4,4,0 136 | 天津市,武清区,0,2,2,0 137 | 天津市,待明确地区,-354,0,354,0 138 | 云南省,云南省,49,1882,1831,2 139 | 云南省,境外输入,41,1409,1368,0 140 | 云南省,德宏州,35,294,259,0 141 | 云南省,昆明,5,58,53,0 142 | 云南省,西双版纳,5,20,14,1 143 | 云南省,昭通,0,25,25,0 144 | 云南省,玉溪,0,14,13,1 145 | 云南省,曲靖,0,13,13,0 146 | 云南省,大理州,0,13,13,0 147 | 云南省,红河州,0,9,9,0 148 | 云南省,保山,0,9,9,0 149 | 云南省,丽江,0,7,7,0 150 | 云南省,普洱,0,4,4,0 151 | 云南省,楚雄州,0,4,4,0 152 | 云南省,文山州,0,2,2,0 153 | 云南省,临沧,0,1,1,0 154 | 云南省,待明确地区,-37,0,37,0 155 | 福建省,福建省,44,1456,1411,1 156 | 福建省,境外输入人员,44,687,643,0 157 | 福建省,厦门,0,276,276,0 158 | 福建省,莆田,0,261,261,0 159 | 福建省,福州,0,72,71,1 160 | 福建省,泉州,0,71,71,0 161 | 福建省,宁德,0,26,26,0 162 | 福建省,漳州,0,23,23,0 163 | 福建省,南平,0,20,20,0 164 | 福建省,三明,0,14,14,0 165 | 福建省,龙岩,0,6,6,0 166 | 黑龙江省,黑龙江省,29,2064,2022,13 167 | 黑龙江省,牡丹江,29,65,36,0 168 | 黑龙江省,哈尔滨,0,546,542,4 169 | 黑龙江省,绥化,0,537,533,4 170 | 黑龙江省,境外输入,0,406,406,0 171 | 黑龙江省,黑河,0,295,295,0 172 | 黑龙江省,双鸭山,0,52,49,3 173 | 黑龙江省,鸡西,0,46,46,0 174 | 黑龙江省,齐齐哈尔,0,45,44,1 175 | 黑龙江省,大庆,0,29,28,1 176 | 黑龙江省,七台河,0,17,17,0 177 | 黑龙江省,佳木斯,0,15,15,0 178 | 黑龙江省,鹤岗,0,5,5,0 179 | 黑龙江省,大兴安岭,0,3,3,0 180 | 黑龙江省,待明确,0,2,2,0 181 | 黑龙江省,伊春,0,1,1,0 182 | 四川省,四川省,28,1374,1343,3 183 | 四川省,成都,28,967,936,3 184 | 四川省,甘孜州,0,78,78,0 185 | 四川省,达州,0,42,42,0 186 | 四川省,南充,0,40,40,0 187 | 四川省,广安,0,30,30,0 188 | 四川省,泸州,0,25,25,0 189 | 四川省,巴中,0,24,24,0 190 | 四川省,绵阳,0,23,23,0 191 | 四川省,内江,0,22,22,0 192 | 四川省,德阳,0,18,18,0 193 | 四川省,遂宁,0,17,17,0 194 | 四川省,攀枝花,0,16,16,0 195 | 四川省,凉山州,0,14,14,0 196 | 四川省,宜宾,0,13,13,0 197 | 四川省,自贡,0,10,10,0 198 | 四川省,广元,0,10,10,0 199 | 四川省,眉山,0,8,8,0 200 | 四川省,雅安,0,8,8,0 201 | 四川省,资阳,0,5,5,0 202 | 四川省,乐山,0,3,3,0 203 | 四川省,阿坝州,0,1,1,0 204 | 山东省,山东省,25,1075,1043,7 205 | 山东省,境外输入,23,270,247,0 206 | 山东省,济南,2,49,47,0 207 | 山东省,济宁,0,260,260,0 208 | 山东省,青岛,0,80,79,1 209 | 山东省,烟台,0,58,58,0 210 | 山东省,临沂,0,49,49,0 211 | 山东省,潍坊,0,44,44,0 212 | 山东省,威海,0,38,37,1 213 | 山东省,聊城,0,38,38,0 214 | 山东省,德州,0,37,35,2 215 | 山东省,泰安,0,35,33,2 216 | 山东省,淄博,0,30,29,1 217 | 山东省,日照,0,30,30,0 218 | 山东省,枣庄,0,24,24,0 219 | 山东省,菏泽,0,18,18,0 220 | 山东省,滨州,0,15,15,0 221 | 河北省,河北省,16,1474,1451,7 222 | 河北省,雄安新区,5,18,13,0 223 | 河北省,衡水,5,13,8,0 224 | 河北省,廊坊,4,37,33,0 225 | 河北省,保定,2,40,38,0 226 | 河北省,石家庄,0,1034,1033,1 227 | 河北省,邢台,0,96,95,1 228 | 河北省,唐山,0,58,57,1 229 | 河北省,沧州,0,49,46,3 230 | 河北省,张家口,0,43,43,0 231 | 河北省,境外输入,0,37,37,0 232 | 河北省,邯郸,0,32,32,0 233 | 河北省,秦皇岛,0,10,9,1 234 | 河北省,承德,0,7,7,0 235 | 湖南省,湖南省,12,1230,1214,4 236 | 湖南省,境外输入,12,98,86,0 237 | 湖南省,长沙,0,247,245,2 238 | 湖南省,岳阳,0,156,155,1 239 | 湖南省,株洲,0,110,110,0 240 | 湖南省,邵阳,0,102,101,1 241 | 湖南省,常德,0,82,82,0 242 | 湖南省,张家界,0,77,77,0 243 | 湖南省,娄底,0,76,76,0 244 | 湖南省,益阳,0,63,63,0 245 | 湖南省,衡阳,0,48,48,0 246 | 湖南省,永州,0,44,44,0 247 | 湖南省,怀化,0,40,40,0 248 | 湖南省,郴州,0,39,39,0 249 | 湖南省,湘潭,0,38,38,0 250 | 湖南省,湘西自治州,0,10,10,0 251 | 陕西省,陕西省,11,2826,2812,3 252 | 陕西省,西安,1062,2187,1122,3 253 | 陕西省,咸阳,15,32,17,0 254 | 陕西省,延安,13,21,8,0 255 | 陕西省,渭南,1,18,17,0 256 | 陕西省,境外输入,0,483,483,0 257 | 陕西省,安康,0,26,26,0 258 | 陕西省,汉中,0,26,26,0 259 | 陕西省,宝鸡,0,13,13,0 260 | 陕西省,铜川,0,8,8,0 261 | 陕西省,商洛,0,7,7,0 262 | 陕西省,榆林,0,3,3,0 263 | 陕西省,韩城,0,1,1,0 264 | 陕西省,杨凌,0,1,1,0 265 | 陕西省,待明确地区,-1080,0,1080,0 266 | 新疆维吾尔自治区,新疆维吾尔自治区,11,996,982,3 267 | 新疆维吾尔自治区,伊犁州,8,31,23,0 268 | 新疆维吾尔自治区,兵团第四师,3,13,9,1 269 | 新疆维吾尔自治区,乌鲁木齐,0,845,845,0 270 | 新疆维吾尔自治区,喀什地区,0,80,80,0 271 | 新疆维吾尔自治区,昌吉州,0,5,5,0 272 | 新疆维吾尔自治区,兵团第八师石河子市,0,4,3,1 273 | 新疆维吾尔自治区,兵团第九师,0,4,3,1 274 | 新疆维吾尔自治区,吐鲁番,0,3,3,0 275 | 新疆维吾尔自治区,巴州,0,3,3,0 276 | 新疆维吾尔自治区,兵团第十二师,0,3,3,0 277 | 新疆维吾尔自治区,兵团第六师五家渠市,0,2,2,0 278 | 新疆维吾尔自治区,兵团第七师,0,1,1,0 279 | 新疆维吾尔自治区,阿克苏地区,0,1,1,0 280 | 新疆维吾尔自治区,待明确地区,0,1,1,0 281 | 辽宁省,辽宁省,5,812,805,2 282 | 辽宁省,境外输入,4,173,169,0 283 | 辽宁省,沈阳,1,79,78,0 284 | 辽宁省,大连,0,470,470,0 285 | 辽宁省,锦州,0,12,12,0 286 | 辽宁省,葫芦岛,0,12,11,1 287 | 辽宁省,丹东,0,11,11,0 288 | 辽宁省,盘锦,0,11,11,0 289 | 辽宁省,营口,0,11,11,0 290 | 辽宁省,阜新,0,8,8,0 291 | 辽宁省,铁岭,0,8,8,0 292 | 辽宁省,朝阳,0,6,5,1 293 | 辽宁省,鞍山,0,4,4,0 294 | 辽宁省,本溪,0,3,3,0 295 | 辽宁省,辽阳,0,3,3,0 296 | 辽宁省,抚顺,0,1,1,0 297 | 吉林省,吉林省,3,592,586,3 298 | 吉林省,长春,3,170,167,0 299 | 吉林省,通化,0,313,312,1 300 | 吉林省,吉林市,0,62,61,1 301 | 吉林省,四平,0,17,16,1 302 | 吉林省,延边,0,8,8,0 303 | 吉林省,辽源,0,7,7,0 304 | 吉林省,公主岭,0,6,6,0 305 | 吉林省,松原,0,6,6,0 306 | 吉林省,梅河口,0,2,2,0 307 | 吉林省,白城,0,1,1,0 308 | 山西省,山西省,3,268,265,0 309 | 山西省,大同,2,14,12,0 310 | 山西省,运城,1,20,19,0 311 | 山西省,境外输入,0,127,127,0 312 | 山西省,晋中,0,41,41,0 313 | 山西省,太原,0,21,21,0 314 | 山西省,晋城,0,10,10,0 315 | 山西省,长治,0,8,8,0 316 | 山西省,朔州,0,8,8,0 317 | 山西省,忻州,0,7,7,0 318 | 山西省,吕梁,0,6,6,0 319 | 山西省,阳泉,0,4,4,0 320 | 山西省,临汾,0,2,2,0 321 | 重庆市,重庆市,2,613,605,6 322 | 重庆市,境外输入,2,29,27,0 323 | 重庆市,万州区,0,118,114,4 324 | 重庆市,江北区,0,28,28,0 325 | 重庆市,云阳县,0,25,25,0 326 | 重庆市,长寿区,0,24,24,0 327 | 重庆市,合川区,0,23,23,0 328 | 重庆市,綦江区,0,23,23,0 329 | 重庆市,奉节县,0,22,22,0 330 | 重庆市,九龙坡区,0,22,21,1 331 | 重庆市,开州区,0,21,20,1 332 | 重庆市,忠县,0,21,21,0 333 | 重庆市,渝中区,0,20,20,0 334 | 重庆市,垫江县,0,20,20,0 335 | 重庆市,潼南区,0,18,18,0 336 | 重庆市,两江新区,0,18,18,0 337 | 重庆市,渝北区,0,17,17,0 338 | 重庆市,南岸区,0,15,15,0 339 | 重庆市,石柱县,0,15,15,0 340 | 重庆市,大足区,0,14,14,0 341 | 重庆市,巫溪县,0,14,14,0 342 | 重庆市,铜梁区,0,10,10,0 343 | 重庆市,丰都县,0,10,10,0 344 | 重庆市,巫山县,0,10,10,0 345 | 重庆市,沙坪坝区,0,10,10,0 346 | 重庆市,璧山区,0,9,9,0 347 | 重庆市,荣昌区,0,9,9,0 348 | 重庆市,大渡口区,0,7,7,0 349 | 重庆市,巴南区,0,7,7,0 350 | 重庆市,江津区,0,6,6,0 351 | 重庆市,涪陵区,0,5,5,0 352 | 重庆市,永川区,0,5,5,0 353 | 重庆市,梁平区,0,4,4,0 354 | 重庆市,高新区,0,4,4,0 355 | 重庆市,黔江区,0,2,2,0 356 | 重庆市,城口县,0,2,2,0 357 | 重庆市,彭水县,0,2,2,0 358 | 重庆市,武隆区,0,1,1,0 359 | 重庆市,秀山县,0,1,1,0 360 | 重庆市,酉阳县,0,1,1,0 361 | 重庆市,万盛经开区,0,1,1,0 362 | 湖北省,湖北省,1,68320,63807,4512 363 | 湖北省,境外输入,1,92,91,0 364 | 湖北省,武汉,0,50380,46511,3869 365 | 湖北省,孝感,0,3518,3389,129 366 | 湖北省,黄冈,0,2912,2787,125 367 | 湖北省,荆州,0,1582,1530,52 368 | 湖北省,鄂州,0,1395,1336,59 369 | 湖北省,随州,0,1307,1262,45 370 | 湖北省,襄阳,0,1175,1135,40 371 | 湖北省,黄石,0,1015,976,39 372 | 湖北省,荆门,0,971,930,41 373 | 湖北省,宜昌,0,931,894,37 374 | 湖北省,咸宁,0,836,821,15 375 | 湖北省,十堰,0,672,664,8 376 | 湖北省,仙桃,0,575,553,22 377 | 湖北省,天门,0,498,483,15 378 | 湖北省,恩施州,0,252,245,7 379 | 湖北省,潜江,0,198,189,9 380 | 湖北省,神农架林区,0,11,11,0 381 | 江苏省,江苏省,1,1630,1629,0 382 | 江苏省,境外输入,1,173,172,0 383 | 江苏省,扬州,0,593,593,0 384 | 江苏省,南京,0,331,331,0 385 | 江苏省,苏州,0,87,87,0 386 | 江苏省,徐州,0,79,79,0 387 | 江苏省,淮安,0,78,78,0 388 | 江苏省,无锡,0,55,55,0 389 | 江苏省,常州,0,54,54,0 390 | 江苏省,连云港,0,48,48,0 391 | 江苏省,南通,0,40,40,0 392 | 江苏省,泰州,0,37,37,0 393 | 江苏省,盐城,0,27,27,0 394 | 江苏省,宿迁,0,16,16,0 395 | 江苏省,镇江,0,12,12,0 396 | 贵州省,贵州省,1,161,158,2 397 | 贵州省,安顺,1,5,4,0 398 | 贵州省,遵义,0,44,44,0 399 | 贵州省,贵阳,0,36,35,1 400 | 贵州省,毕节,0,23,23,0 401 | 贵州省,黔南州,0,17,17,0 402 | 贵州省,铜仁,0,11,11,0 403 | 贵州省,六盘水,0,10,9,1 404 | 贵州省,黔东南州,0,10,10,0 405 | 贵州省,黔西南州,0,4,4,0 406 | 贵州省,境外输入,0,1,1,0 407 | 内蒙古自治区,内蒙古自治区,0,1191,1190,1 408 | 内蒙古自治区,呼伦贝尔,0,596,596,0 409 | 内蒙古自治区,境外输入人员,0,336,336,0 410 | 内蒙古自治区,阿拉善盟,0,165,165,0 411 | 内蒙古自治区,锡林郭勒盟,0,29,29,0 412 | 内蒙古自治区,鄂尔多斯,0,12,12,0 413 | 内蒙古自治区,包头,0,11,11,0 414 | 内蒙古自治区,呼和浩特,0,10,10,0 415 | 内蒙古自治区,赤峰,0,9,9,0 416 | 内蒙古自治区,通辽,0,9,9,0 417 | 内蒙古自治区,巴彦淖尔,0,8,7,1 418 | 内蒙古自治区,乌兰察布,0,3,3,0 419 | 内蒙古自治区,乌海市,0,2,2,0 420 | 内蒙古自治区,兴安盟,0,1,1,0 421 | 安徽省,安徽省,0,1009,1003,6 422 | 安徽省,合肥,0,176,175,1 423 | 安徽省,蚌埠,0,160,155,5 424 | 安徽省,阜阳,0,156,156,0 425 | 安徽省,亳州,0,108,108,0 426 | 安徽省,安庆,0,83,83,0 427 | 安徽省,六安,0,77,77,0 428 | 安徽省,宿州,0,42,42,0 429 | 安徽省,马鞍山,0,38,38,0 430 | 安徽省,芜湖,0,34,34,0 431 | 安徽省,铜陵,0,29,29,0 432 | 安徽省,淮北,0,27,27,0 433 | 安徽省,淮南,0,27,27,0 434 | 安徽省,池州,0,17,17,0 435 | 安徽省,滁州,0,13,13,0 436 | 安徽省,黄山,0,9,9,0 437 | 安徽省,境外输入,0,7,7,0 438 | 安徽省,宣城,0,6,6,0 439 | 江西省,江西省,0,959,958,1 440 | 江西省,南昌,0,230,230,0 441 | 江西省,上饶,0,144,144,0 442 | 江西省,新余,0,129,129,0 443 | 江西省,九江,0,117,117,0 444 | 江西省,宜春,0,106,106,0 445 | 江西省,赣州,0,74,73,1 446 | 江西省,抚州,0,72,72,0 447 | 江西省,萍乡,0,33,33,0 448 | 江西省,吉安,0,22,22,0 449 | 江西省,鹰潭,0,18,18,0 450 | 江西省,境外输入,0,7,7,0 451 | 江西省,景德镇,0,6,6,0 452 | 江西省,赣江新区,0,1,1,0 453 | 甘肃省,甘肃省,0,356,354,2 454 | 甘肃省,境外输入,0,120,120,0 455 | 甘肃省,兰州,0,119,117,2 456 | 甘肃省,天水,0,51,51,0 457 | 甘肃省,张掖,0,17,17,0 458 | 甘肃省,平凉,0,9,9,0 459 | 甘肃省,定西,0,9,9,0 460 | 甘肃省,甘南,0,8,8,0 461 | 甘肃省,陇南,0,6,6,0 462 | 甘肃省,嘉峪关,0,5,5,0 463 | 甘肃省,白银,0,4,4,0 464 | 甘肃省,庆阳,0,3,3,0 465 | 甘肃省,临夏,0,3,3,0 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