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| PythonSecond-hand housing crawler data in Guangzhou, Guangdong Visual analysis large-screen full-screen system Opening report |
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PythonGuangdong Guangzhou second-hand housing crawler data visual analysis large-screen full-screen systemDesign and implementation |
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| Basis for topic selection: 1. Research background and significance; 2. Current research (application and development) situation at home and abroad. 1: Research background and significance The research background and significance of the large-screen full-screen system for visual analysis of second-hand housing crawler data in Guangdong and Guangzhou using Python are as follows: Research Background: Prosperous real estate market: As one of the important cities in China, Guangzhou, Guangdong's real estate market continues to prosper, attracting a large number of home buyers and investors. Importance of the second-hand housing market: The second-hand housing market occupies an important position in the Guangzhou real estate market and is of great significance for understanding market dynamics and seizing investment opportunities. Needs in the big data era: With the advent of the big data era, traditional data processing and analysis methods can no longer meet the demand for rapid and accurate processing of massive data. Popularization of Python technology: Python, as a powerful and easy-to-learn programming language, is widely used in fields such as data crawling, processing and analysis, providing new technical means for second-hand housing market research. Significance: Improve data processing efficiency: Through Python crawler technology, data on second-hand housing listings in Guangzhou can be automatically obtained, greatly improving the efficiency of data acquisition and processing. Reveal market rules: Through data visualization analysis, the supply and demand relationship, price trends and other rules of the second-hand housing market in Guangzhou can be intuitively displayed to help investors make more informed decisions. Assist policy formulation: Government departments can monitor and analyze the dynamics of the second-hand housing market in real time through this system, provide data support for policy formulation, and promote the healthy development of the market. Innovative data display methods: The large-screen full-screen display system can present data analysis results in a more intuitive and eye-catching way, improving users' data understanding and usage experience. Promote technological innovation: As an application case of Python and big data technology in actual business scenarios, this system can promote technological innovation and application expansion in related fields. In addition, this research can also promote the application and development of Python and visualization technology in the real estate field, and promote the digital transformation and upgrading of related industries. At the same time, this research can also provide reference for second-hand housing market research in other cities and regions, and has broad application prospects and promotion value. In short, the Python Guangdong Guangzhou second-hand housing crawler data visual analysis large-screen full-screen system has important research background and significance. It can not only improve data processing efficiency, reveal market rules, and assist policy formulation, but also innovate data display methods, promote technological innovation and Industrial upgrading. 2: CountryResearch status at home and abroad The domestic and foreign research status of large-screen full-screen system for visual analysis of second-hand housing crawler data in Guangdong and Guangzhou using Python is as follows: Research state in China: In China, with the rapid development of big data and artificial intelligence technology, more and more researchers and companies have begun to pay attention to data analysis and visualization of the second-hand housing market. As an important city in China, Guangzhou, Guangdong has attracted much attention for its second-hand housing market. At present, some research teams and companies have developed a Python-based Guangzhou second-hand housing crawler system, which is used to crawl second-hand housing information on the Internet and perform simple data processing and analysis. These systems mainly focus on data acquisition, cleaning and organization, and have achieved certain results. In terms of data visualization, domestic research mainly focuses on traditional chart displays, such as bar charts, line charts, and pie charts. Although these charts can display some basic statistical information, they still have certain limitations for a comprehensive and in-depth understanding of the second-hand housing market. In addition, there is relatively little domestic research on large-screen display of second-hand housing data, and there are still some technical challenges that need to be overcome, such as real-time data updating and interactivity. Current status of foreign research: In contrast, foreign research on large-screen full-screen systems for visual analysis of Python second-hand housing crawler data is more mature. They not only possess advanced crawler technology and data processing methods, but also focus on combining data analysis with business practices to develop application systems with more practical and commercial value. In terms of data visualization, foreign research pays more attention to innovation and interactivity, trying to use various novel visualization technologies and tools to display second-hand housing market data. For example, some foreign research teams use large-screen full-screen systems to display real-time data and analysis results on the second-hand housing market, and present market dynamics and trends through dynamic charts, maps, heat maps, etc. These visualization methods not only provide a more intuitive and comprehensive display of information, but also enhance the interactive experience between users and data. In summary, there is a certain research foundation and practical experience at home and abroad in the field of Python Guangdong Guangzhou second-hand housing crawler data visualization analysis large-screen full-screen system. Domestic research mainly focuses on data acquisition, cleaning and organization, and has achieved certain results, but there are still some shortcomings and challenges in data visualization and large-screen display. Foreign research is relatively more mature and extensive, and can provide useful reference for domestic research. Through further research and technological innovation, it is expected to promote the healthy development of the second-hand housing market and data-driven decision support in Guangzhou, Guangdong. 3: Research ideas and methods 3.1Research path Solve technical problems by borrowing development-related books from the library or searching for relevant topic videos on the Internet, searching the Internet, and seeking help from instructors. The specific steps are: (1) Conduct a demand analysis on the system to clarify administrator functions, front-end development functions, development framework models, etc.; (2) Conduct an outline design of the system, build the development process, and establish the system's architecture diagram, functional module diagram, etc.; (3) Design all functional modules for the system management background; (4) For the user front-end, design all functional modules; (5) Carry out software coding to realize various functions of the system; (6) Conduct various tests on the system; (7) Submit the system and write the paper. After selecting the project development model and back-end development framework, we set up the development environment and installed the corresponding development tools; then we designed the database, developed the back-end and interfaces, developed the complete project back-end and front-end, and completed the final work, testing, use. 3.2Research method In order to better improve the system, the following research methods are used: (1) Literature reading method Search and refer to thesis materials related to the topic through various literature search websites, school libraries and Baidu Encyclopedia, and then save the appropriate materials locally for use during development. (2) Comparative method: Through comparative analysis of the functions, related technologies, contents, etc. of relevant domestic and foreign subject systems, we can propose problems existing in the system and propose corresponding solutions. (3) Simulation method The simulation method is a description method that first creates a similar model based on the main characteristics of the prototype, and then indirectly studies the prototype through the model. We achieve the final effect of development by simulating the local computer as a server for local operations. 3.3Possibility 1. Technical feasibility It uses Windows 7 or 10 as the operating system, based on python3.8 version, uses PyCharm software as the development tool, and uses mysql for database storage; the background management system hardware environment is a PC. Users can use any computer with Internet access to set up and use a browser. Access the news management system. 2.经济可行性 一方面，只要有能上网的电脑，系统的管理员在任何地方任何时候都可以管理，工作效率进一步提高从而节省人力、物力，只要会打字即可，不需要很高的学历；另一方面，系统的制作成本低，在现有的PC机上即可使用PyCharm开发者工具进行开发。 3.操作可行性 从管理来说，只要有一台普通的电脑就可以进行网站信息的设置、录入、修改，操作非常方便而且可行度很高。 4.数据来源可行性 来源知名房产网站数据，数据已经很普及了，使用也很广，有代表性 4：系统初步设计方案 4.1主要设计技术 开发环境：python3.8+ 开发语言：Python 开发框架：Django框架 数据采集：requests + parsel + Xpath 可视化模块：Echarts 开发工具：Pycharm 数据库：mysql8 数据库管理工具：navicat 其他开发语言：html + css +javascript 4.2研究内容 我们这里以我们打算实现的系统内容，分析如下，数据来源淘宝 大屏全屏可视化展示：
后台内容：
5：进度安排 2023.09.10—2023.10.15 查看大量的文献，收集课题有关资料，确定论文选题； 2023.10.16—2023.10.30 在老师的指导下，填写毕业论文任务书； 2023.10.31—2023.11.15 大量收集论文资料，理清论文思路，对论文思路进行完善。 2023.11.16—2023.12.22 完成开题报告答辩； 2023.12.23—2023.12.27 根据指导老师提出的建议再进行修改，完善系统功能设计 2023.12.28—2024.04.10 在查阅大量文献之后，运用多种研究方案，完成系统开发并基本完成论文初稿。 2024.04.01—2024.04.15 将初稿完善交由导师审阅，提出修改建议。 2024.04.16—2024.05.14 在导师指导下，对论文进行反复修改形成终稿，装订成册上交学院，同时为毕业论文答辩做准备工作 2024.05.15 进行毕业论文答辩 6：论文（设计）写作提纲 摘要 第1章 绪论 1.1 项目研究背景和意义 1.2 论文研究目的 1.3 系统主要功能 第2章 系统相关技术 2.1 开发概要 2.2 开发技术 2.2.1 Python介绍 2.2.2 Django框架 2.3 MYSQL 数据库 2.4 其他网页技术 2.5.1 什么是HTML 2.5.2 什么是 CSS 2.5.3 JavaScript 2.6 本章小结 第3章 系统分析 3.1 系统概要 3.2 数据库和图形 3.2.1 数据ER原型图 3.1.2 实体图 3.1.3 数据库表 3.3 前端需求分析 3.4 后台需求分析 3.5 本章小结 第4章 系统设计与实现 4.1 前端实现 4.2 后台实现 4.3 本章小结 第5章 总结与展望 5.1 总结 5.2 展望 参考文献 致谢 7：参考文献 [1]麻清应，马权. Web前端框架开发技术[M].重庆大学电子音像出版社，2020. 08. [2]李云.基于网站制作的Web前端开发技术与优化[J].电子技术与软件工程,2021(22): 50-52. [3]黑马程序员.HTMLHSS+JavaScript网页制作案例教程(第2版)[M].北京:人民邮电出版社，2021. [4]王千林.基于B/S架构固定资产管理系统设计与实现[J].电脑知识与技术.2020(07) [5]代飞，艾迪. Web前端开发项目案例教程[M],北京理工大学出版社，2020. 08. [6]郑智方. MySQL的重要性以及步入云的应用实例[J].计算机产品与流通，2020(01):151. [7]陈漫红.数据库原理与应用教程SQL Server 2012[M],北京理工大学出版社，2021. 01. [8]李曼. MySQL数据库系统中文乱码问题及解决方案[J].电子技术与软件程,2021(12):176-177. [9]王征，李晓波 著. Python从入门到精通[M], 中国铁道出版社，2020-01-01 [10]胡阳. Django企业开发实战[M], 人民邮电出版社，2021. 06. [11]李宁，python从菜鸟到高手[M]. 北京：清华大学出版社，2018. 219~315 [12]关东升，看漫画学python[M]. 北京：电子工业出版社，2020. 36~78 [13]王英英，MySQ 8 快速入门[M]. 北京：清华大学出版社，2020. 200~256 [14]慕课教育研发中心，HTML+CSS3+JavaScript从入门到项目实践[M]. 北京：清华大学出版社，2019. 11~40 [15]黄永祥，精通Django 3 web开发[M]. 北京：清华大学出版社，2020. 50~148 [16]胡阳，Django 企业开发实战[M]. 北京：人民邮电出版社，2019. 108~210 |
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| 指导教师意见： 意见从以下几个方面展开：
3、对研究思路、方法的评价。4、是否同意开题。（指导意见打印，签名指导教师务必手写） 指导教师签名： 年 月 日 |
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