[Python舆情分析] 二.时间间隔分布研究及幂律分布图绘制

最新推荐文章于 2024-06-09 22:16:06 发布
最新推荐文章于 2024-06-09 22:16:06 发布
阅读量9.1k 收藏 26
点赞数 5
CC 4.0 BY-SA版权
分类专栏： 知识图谱、web数据挖掘及NLP 文章标签： 舆情分析 Python 幂律分布 时间间隔分析 power-low
版权声明：本文为博主原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接和本声明。
本文链接：https://blog.youkuaiyun.com/Eastmount/article/details/86631366
本文探讨了幂律分布的概念，如二八原则，并在舆情分析的背景下，介绍了如何使用Python绘制时间间隔分布的幂律特性图。内容涵盖了Python绘制幂律分布图的步骤，以及通过SQL获取时间间隔的方法。文章适合舆情分析、数据科学和人类行为动力学的学习者。

摘要生成于 C知道 ，由 DeepSeek-R1 满血版支持， 前往体验 >

本文主要是作者学习舆情分析、情感分析、人类行为动力学分析的在线笔记，主要包括两方面内容，一是幂律特性，二是讲解时间间隔分布，三是Python绘制基于时间间隔分布的幂律特性图，四提供了另一种方法。基础性文章，希望对您有所帮助。

文章目录

参考前文：
[Python舆情分析] 一.舆情事件的幂律特性分析及时间间隔分布图绘制
[python数据分析] 简述幂率定律及绘制Power-law函数

PS：最近参加优快云2018年博客评选，希望您能投出宝贵的一票。我是59号，Eastmount，杨秀璋。投票地址：https://bss.youkuaiyun.com/m/topic/blog_star2018/index

一.幂律分布

在我们的日常生活中Power Law（幂次分布，Power-law Distributions）是常见的一

了解本专栏 订阅专栏 解锁全文
利用 Python 爬虫抓取新闻网站数据并进行舆情分析
专注于Python爬虫开发，分享爬虫技巧、项目实战与反爬经验，使用Scrapy、BeautifulSoup等工具，解决数据抓取难题。
06-30 1308
抓取新闻网站数据：从新闻网站（例如新浪新闻、搜狐新闻等）抓取新闻内容，包括标题、正文、发布时间、来源等。数据清洗与预处理：对抓取到的数据进行清洗，去除冗余信息。舆情分析：通过对新闻内容进行情感分析、关键词提取等，分析公众的情感倾向和社会热点话题。通过本项目，我们展示了如何使用 Python 爬虫抓取新闻网站的数据，并基于这些数据进行舆情分析。数据抓取：使用requests和从新闻网站抓取新闻标题、内容等信息。数据清洗与处理：对抓取到的内容进行清洗，去除广告和无关信息，并进行中文分词。舆情分析。
[Python舆情分析] 一.舆情事件的幂律特性分析及时间间隔分布图绘制
杨秀璋的专栏
01-22 1万+
本文主要是作者学习舆情分析、情感分析、人类行为动力学分析的在线笔记，主要包括两方面内容，一是结合参考文献介绍人类行为动力学分析，二是讲解Python绘制幂律特性及时间间隔分布图的方法。基础性文章，希望对您有所帮助。
绘制社交网络图的幂律分布python代码
05-10
这是一个用于进行大规模社交网络图绘制幂律分布曲线的python代码
赠书五本《Python神经网络入门与实战》
weixin_38753422的博客
12-08 533
神经网络能够解决许多复杂的问题，而其中最典型的两类问题就是回归问题和分类问题。本文就来简单介绍一下究竟什么是回归问题。所谓回归问题，就是一种需要来拟合的问题，例如我们有一个待拟合的散点图...
利用 Logarithmic Binning （Log-Binning）方法绘制幂律分布（Power-law Distributions）曲线
qq_32515081的博客
03-09 2259
绘制度分布是分析网络属性的一个组成部分。该过程从获得NkN_{k}Nk​开始，即度数为kkk的节点数。这可以通过直接测量或模型来提供。从NkN_{k}Nk​我们计算出pk=Nk/Np_{k}=N_{k}/Npk​=Nk​/N。问题是，如何绘制pkp_{k}pk​以最好地提取其属性。 ​​ 使用log-log图 在无标度网络中，具有一或两条链路的众多节点与少数节点共存，其中少数节点为具有数千甚至数百万链路的节点。使用线性 k 轴压缩无数小k区域中的节点，使它们不可见。类似地，由于k=1k=1k=1和大kkk
基于python的幂律分布中帕累托分布拟合
weixin_44032965的博客
09-25 592
【代码】基于python的幂律分布中帕累托分布拟合。
Python数据可视化:幂律分布实例详解
01-20
1、公式推导 对幂律分布公式： 对公式两边同时取以10为底的对数： 所以对于幂律公式，对X,Y取对数后，在坐标轴上为线性方程。 2、可视化 从图形上来说，幂律分布及其拟合效果： 对X轴与Y轴取以10为底的对数。效果上就是X轴上1与10，与10与100的距离是一样的。 对XY取双对数后，坐标轴上点可以很好用直线拟合。所以，判定数据是否符合幂律分布，只需要对XY取双对数，判断能否用一个直线很好拟合就行。常见的直线拟合效果评估标准有拟合误差平方和、R平方。 3、代码实现 #!/usr/bin/env python # -*-coding:utf-8 -*- import matplo
[Pyhon大数据分析] 二.PyEcharts绘制全国各地区、某省各城市地图及可视化分析
热门推荐
杨秀璋的专栏
05-21 2万+
本系列文章是Python大数据分析系列博客，包括网络爬虫、可视化分析、GIS地图显示、情感分析、舆情分析、主题挖掘、威胁情报溯源、知识图谱、预测预警及AI和NLP应用等。前文分享了数据抓取，并将数据存储至本地，最后调用Maplotlib和Seaborn绘制。本文将结合PyEcharts绘制地图、折线图、柱状图，希望这篇可视化分析文章对您有所帮助，也非常感谢参考文献中老师的分享，一起加油！如果您有想学习的知识或建议，可以给作者留言~
[Python从零到壹] 十六.文本挖掘之词云热点与LDA主题分布分析万字详解
杨秀璋的专栏
08-10 1万+
前一篇文章讲述了数据预处理、Jieba分词和文本聚类知识，这篇文章可以说是文本挖掘和自然语言处理的入门文章。本文将详细讲解文本挖掘领域的词云热点分析和LDA主题分布分析。两万字基础文章，希望对您有所帮助。欢迎大家来到“Python从零到壹”，在这里我将分享约200篇Python系列文章，带大家一起去学习和玩耍，看看Python这个有趣的世界。所有文章都将结合案例、代码和作者的经验讲解，真心想把自己近十年的编程经验分享给大家。
[数智人文实战] 03.舆情分析之基于SnowNLP的公共事件情感分析
杨秀璋的专栏
06-09 1147
《数智人文实战》专栏将以实战为主，分享数智人文相关的案例100个，旨在帮助初学者和探索数智人文发展。前文分享了中文分词处理及文本聚类、LDA主题模型分析。这篇文章将抓取微博话题及评论信息，采用SnowNLP进行简单的情感分析及文本挖掘，包括随时间的情感分布。希望这篇基础性文章对您有所帮助，也非常感谢参考文献中老师的分享！
幂律分布拟合曲线
12-16
plot (on log-log axes) the empirical distribution along with the fitted power-law distribution
Python建立时间事件引擎原理剖析
weixin_30621711的博客
06-08 417
作为python小白，学习量化交易的曲线是非常陡峭的，唯一好的办法就是一点点啃代码。以下代码案例来自vnpy的引擎代码。 # encoding: UTF-8 #定义时间事件 EVENT_TIMER = ‘eTimer’ # 系统模块 from Queue import Queue, Empty from threading import Thread from time imp...
python 拟合幂律分布_几分钟搞懂统计概率中的几个经典分布
weixin_39882589的博客
12-19 1373
概念理解在介绍经典分布之前，先来认识几个名词。什么是‘统计’，即对过去数据的汇总，分析，总结。什么是‘概率’，是一个数值，表示某件事发生的可能性大小。（一般概率是在过去经验的总结基础上，对未发生事件的预测）什么是‘随机事件’，在一定条件下，可能发生，也可能不发生的事件。什么是‘随机变量’，将随机事件结果映射到一个数字化的函数上，通常用大写字母X 表示。包括两种类型：离散型随机变量连续性随机变量什么...
数据分析------统计学知识点（二）
zfyzfw的博客
06-04 1401
①收入分布：个人收入分布往往呈现右偏特征，少数人占据了大部分财富，了解收入分布的偏态性，有助于制定合理的税收政策和社会保障制度。例如：一个企业80%的利润可能来自于20%的顾客，80%的财富被20%的人口所拥有，20%的员工可能完成80%的工作。①误差分析：测量和实验中，误差分布有时服从拉普拉斯分布，用拉普拉斯分布刻画误差，可提高参数估计和假设检验的效率。指数据的大部分集中在左侧，而右侧的尾部更长，均值通常大于中位数，分布的峰值点位于中位数左侧。偏度系数的绝对值越大，分布的偏态程度越严重。
Python幂分布、帕累托分布、Zipf分布
微小冷的学习笔记
12-20 2311
帕累托在1906年提出了有关意大利社会财富分配的分配规律，即20%的人口掌握了80%的财富，这个规律后来被发现十分普遍，以至于约瑟夫·朱兰后来将其称为帕累托法则，也被成为八二法则。
复杂网络度分布（幂律分布）图Python
Morty的博客
03-12 1万+
复杂网络度分布（幂律分布）图Python 1. 无向图 直接使用networkx所提供的相关函数。 代码 import matplotlib.pyplot as plt # 导入科学绘图包 import networkx as nx import numpy as np adj = np.loadtxt('adj.txt', dtype=np.int) # 邻接矩阵 G = nx.from_numpy_matrix(adj) # 网络图 print("某个节点的度:", G.degree(0)
[python数据分析] 简述幂率定律及绘制Power-law函数
杨秀璋的专栏
03-23 2万+
这篇文章主要是最近研究人类行为应用的内容，主要简单叙述下复杂网络的幂率分布以及绘制Power-law函数一些知识，同时是一篇在线笔记。希望对您有所帮助，如果文章中存在不足或错误的地方，还请海涵~ 1.幂率分布 2.Zipf定律 3.Scale free 4.Python绘制幂率分布 这篇文章基础知识转载了别人的内容，这里强烈推荐大家阅读原文。此篇文章主要是讲述代码部分，定律我也还在学习中，和大家共勉。
Python数据可视化：幂律分布
墨竹
09-27 2万+
1、公式推导 对幂律分布公式： 对公式两边同时取以10为底的对数： 令，且为常数，所以公式变为： 所以对于幂律公式，对X,Y取对数后，在坐标轴上为线性方程。2、可视化 从图形上来说，幂律分布及其拟合效果： 对X轴与Y轴取以10为底的对数。效果上就是X轴上1与10，与10与100的距离是一样的。 对XY取双对数后，坐标轴上点可以很好用直线拟合。所以，判定数据是否符合幂律分
python爬虫微博舆情分析系统需求分析
最新发布
04-02
### Python 爬虫微博舆情分析系统的需求分析与开发教程 #### 一、需求分析 在构建基于Python的微博舆情分析系统时，需明确其核心目标和功能模块。该系统旨在通过爬取微博平台上的公开数据，利用自然语言处理（NLP）、机器学习等技术手段完成数据分析，并最终以可视化的形式呈现结果。 1. **数据采集** 数据采集是整个系统的起点，主要依赖于网络爬虫技术从微博平台上抓取用户发布的内容及相关元数据。此过程可能涉及反爬虫策略应对措施[^2]。 2. **情感分析** 利用NLP技术和预训练模型对采集到的数据进行情感分类，判断每条微博的情绪倾向为正面、负面或中性。这一步骤通常会借助第三方库如`jieba`分词器、`SnowNLP`或其他深度学习框架来实现[^1]。 3. **数据存储** 将经过初步清洗后的原始数据存入数据库以便进一步查询和管理。常用的关系型数据库有MySQL, PostgreSQL；对于大规模非结构化数据则可考虑MongoDB等NoSQL解决方案。 4. **可视化展示** 提供直观易懂的结果展现方式给终端使用者查看整体舆论走向及热点话题分布情况。可以采用Matplotlib、Seaborn绘制图表或者更高级别的前端框架Dash、Flask配合HTML/CSS制作交互式网页应用界面[^4]。 5. **系统性能优化** - 定期更新算法模型以适应新出现的语言现象和社会事件变化带来的影响； - 对高并发访问场景下的服务稳定性做出保障计划； - 设计合理的缓存机制减少重复计算开销提高响应速度。 --- #### 二、开发流程概述 以下是关于如何着手搭建这样一个完整的微博舆情监控体系的一些指导方针： ##### （一）环境准备阶段 安装必要的软件包版本控制工具Git以及编程IDE PyCharm/Sublime Text/VsCode等等。同时确认服务器端口开放状态允许外部连接操作数据库实例运行正常无误。 ##### （二）编写爬虫脚本部分 选取合适的HTTP请求发送类库requests/selenium模拟真实浏览器行为规避检测风险。解析返回页面html内容提取所需字段信息保存至本地文件夹待后续加工处理前先做基本去重校验工作确保质量可靠稳定持续执行周期任务调度安排合理时间间隔防止被封禁账号权限受限等问题发生。 ```python import requests from bs4 import BeautifulSoup def fetch_weibo_data(url): headers = {'User-Agent': 'Mozilla/5.0'} response = requests.get(url, headers=headers) soup = BeautifulSoup(response.text, 'lxml') posts = [] for post in soup.find_all('div', class_='weibo-post'): text = post.find('p').get_text() date = post.find('span', class_='date').get_text() posts.append({'text': text, 'date': date}) return posts ``` ##### （三）实施文本挖掘环节 加载中文停用词表过滤掉无关紧要词汇降低维度提升效率效果更好些。调用开源项目gensim word embedding向量表示方法捕捉语义关联关系特征表达更加精准细腻深入理解材料内涵实质所在之处何方也！ ##### （四）建立持久层架构布局规划蓝图图纸草稿方案构思创意灵感火花碰撞交流分享共同进步成长历程记录点滴回忆珍藏美好瞬间永恒定格画面留念纪念册子收藏盒子里装满幸福快乐时光岁月静好现世安稳最好不过如此这般模样罢了而已啦呵呵哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒哒
评论 1
添加红包

请填写红包祝福语或标题

个

红包个数最小为10个

元

红包金额最低5元

当前余额3.43元 前往充值 >
需支付：10.00元
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包

打赏作者

Eastmount

你的鼓励将是我创作的最大动力

¥1 ¥2 ¥4 ¥6 ¥10 ¥20
扫码支付：¥1
获取中
扫码支付

您的余额不足，请更换扫码支付或充值

打赏作者

实付元
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明：

1.余额是钱包充值的虚拟货币，按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载，可以购买VIP、付费专栏及课程。

余额充值