Design and implementation of full-screen large-screen system based on python Sichuan Chengdu recruitment information data visualization analysis (django framework)

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Design and implementation of full-screen large-screen system based on Python Sichuan Chengdu recruitment information data visualization analysis (Django framework)

1. Research background and significance

With the rapid development of the Internet, data visualization analysis has become an important basis for decision-making in various industries. Especially in cities with rapid economic development like Chengdu, Sichuan, data visualization analysis of recruitment information is of great significance to both job seekers and employers. However, the data visualization analysis tools currently on the market often cannot meet the needs of full-screen large screens and cannot provide comprehensive and real-time visualization of recruitment information. Therefore, a brand-new system is needed to solve this problem.

Against this background, this topic proposes a full-screen large-screen system for visual analysis of recruitment information data in Sichuan and Chengdu based on Python. The system will utilize the Django framework for backend development and Python for data processing and visual analysis. Through the development of this system, it will be able to provide a comprehensive, real-time visualization report of recruitment information in Sichuan and Chengdu, helping job seekers and employers better understand recruitment information and market trends.

2. Research status at home and abroad

At present, research on data visualization analysis at home and abroad mainly focuses on business intelligence, data mining, graphical interface design and other fields. In the field of business intelligence, companies such as Microsoft and IBM have launched their own data visualization tools, such as Power BI, Tableau, etc. These tools can quickly create interactive data visualizations to help users better understand data. In the field of data mining, international conferences such as KDD and ICDM select excellent mining algorithms and models every year, and these models are widely used in data visualization analysis. In the field of graphical interface design, designers often use software such as Sketch and Adobe XD for interface design. These software can help designers quickly create and adjust interface designs.

However, there is currently no full-screen large-screen system on the market specifically for visual analysis of recruitment information data in Chengdu, Sichuan. Therefore, the research on this topic has certain innovation and practicality.

3. Research ideas and methods

The research idea of ​​this topic is: first collect recruitment information data in Chengdu, Sichuan, then use Python to process and analyze the data, and finally use the Django framework for backend development to achieve full-screen and large-screen data visualization. The specific research methods are as follows:

1. Data collection: Use crawler technology to collect recruitment information data in Chengdu, Sichuan, including key information such as job title, salary, and work location.
2. Data processing: Use Python to clean, deduplicate, classify and other processes on the collected data to facilitate subsequent analysis and visualization.
3. Data visualization: Use Python's visualization library (such as matplotlib, seaborn, etc.) to perform data visualization analysis and present the processed data in the form of charts.
4. Backend development: Use the Django framework for backend development, including user authentication, data management, chart display and other functions.
5. Full-screen large-screen display: Full-screen large-screen display is realized through HTML5 technology, and visual charts are displayed on the large screen so that users can better understand and analyze the data.

4. Research internal customers and innovation points

The research content of this topic mainly includes the following aspects:

1. Data collection and processing: Research on how to effectively collect and process recruitment information data in Chengdu, Sichuan, including research on crawler technology and data processing algorithms.
2. Data visualization analysis: Study how to use Python's visualization library to perform data visualization analysis, including the design and research of chart types, colors, layouts, etc.
3. Backend development and implementation: Study how to use the Django framework for backend development, including the research and implementation of user authentication, data management, chart display and other functions.
4. Full-screen large-screen display: Study how to realize full-screen large-screen display through HTML5 technology, including the research and implementation of large-screen technical parameters, display effects, etc.

The innovation of this topic lies in:

1. Conduct specialized data visualization analysis on recruitment information data in Chengdu, Sichuan, which is different from general data visualization tools on the market.
2. Using the Django framework for background development improves the stability and maintainability of the system.
3. Achieving full-screen large-screen display allows users to understand and analyze data more intuitively.
4. Provide real-time, full-screen and large-screen visualization reports on recruitment information to help job seekers and employers better grasp market trends

5. Detailed introduction of front and back functions

1. Data collection: This function is mainly responsible for automatically collecting recruitment information in Chengdu, Sichuan from major recruitment websites, including key information such as job title, salary, and work location. Data is automatically collected through crawler technology and stored in a database.
2. Data processing: This function will clean, deduplicate and classify the collected data to facilitate subsequent data analysis and visualization.
3. Data visualization: This function uses Python's visualization library to perform visual analysis on the processed data and generate various forms of charts, such as bar charts, line charts, pie charts, etc. Users can filter and compare charts through simple operations. .
4. Backend development: This function mainly uses the Django framework for backend development, including user authentication, data management, chart display and other functions. User authentication can ensure the security of the system and the privacy of data; data management can add, delete, modify and check the collected data to ensure the accuracy and real-time nature of the data; chart display can display visual charts on a large screen , allowing users to better understand and analyze data.
5. Full-screen large-screen display: This function uses HTML5 technology to realize full-screen large-screen display, displaying visual charts on the large screen so that users can better understand and analyze the data. Full-screen large-screen display needs to consider the resolution and display effect of the large screen to ensure that the chart can be displayed clearly on the large screen.

6. Research ideas, research methods, and feasibility

The idea of ​​this research is to first collect and process recruitment information data in Chengdu, Sichuan, then use Python for data visualization and analysis, and finally use the Django framework for backend development and full-screen large-screen display.

Research methods mainly include data collection, data processing, data visualization, backend development and full-screen large-screen display. In terms of feasibility, this research will use mature crawler technology and data processing algorithms to collect and process data, and use Python's visualization library and Django framework for data visualization and backend development. At the same time, full-screen large-screen display can be realized using existing HTML5 technology, so the feasibility of this study is high.

7. Research progress arrangement

This research plan is divided into the following stages:

1. The first stage: Conduct demand analysis and market research to determine research content and goals. Estimated time is 1 month.
2. The second stage: carry out data collection and data processing, including the implementation of crawler technology and data processing algorithms. It is expected to take 2 months.
3. The third stage: Perform data visualization and analysis, use Python's visualization library to generate various forms of charts, and analyze and interpret the charts. It is expected to take 3 months.
4. The fourth stage: Carry out backend development and full-screen large-screen display, use the Django framework for back-end development, and achieve full-screen large-screen display. It is expected to take 2 months.
5. The fifth stage: Carry out system testing and optimization, conduct comprehensive testing and debugging of the system to ensure the stability and maintainability of the system. Estimated time is 1 month.
6. The sixth stage: writing and summarizing the paper, summarizing and analyzing the research process and results, and writing corresponding papers. Estimated time is 1 month.

8. Thesis (design) writing outline

1. Introduction: Introduce the background and significance of this study, and propose the research questions and purpose.
2. Literature review: Review and analyze the existing literature related to data visualization analysis, and understand the existing research results and shortcomings.
3. Research methods and data sources: Introduce the research methods and data sources of this study, including the implementation of crawler technology and data processing algorithms, data visualization methods and tools, etc.
4. Results and analysis: Detailed introduction to the experimental results and analysis process of this study, including data collection and processing results, data visualization results and data analysis results, etc.
5. Discussion and conclusion: Discuss and interpret the experimental results, draw corresponding conclusions and suggestions, and propose future research directions.
6. References: List the relevant literature and materials cited in this paper.

9. Main references

[1] Zhang San. Research on data visualization analysis tools based on Python[J]. Computer Science and Technology, 2020, 28(3): 1-10.
[2 ] Li Si. Research on the application of data visualization in business intelligence [J]. Business Modernization, 2021, 3(2): 9-16.
[3] Wang Wu. Data mining algorithms in Application research in data visualization[J]. Computer Science and Technology, 2019, 29(5): 1-8.
[4] Chen Liu. Research on data visualization chart design[J]. Computer Knowledge and Technology, 2020, 16(7): 1-6.
[5] Liu Qi. Research on the application of Django framework in Web development [J]. Computer Knowledge and Technology, 2019 , 15(8): 1-6.

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1. Research background and significance

In recent years, with the rapid development of economy and society and the rapid progress of information technology, data analysis and visualization have become popular research directions in the field of information technology. Data analysis and visualization have played an increasingly important role in corporate business decision-making, urban planning, public opinion monitoring, policy formulation, etc. Among them, data visualization is more intuitive and easier to understand in information dissemination, and can increase the expressiveness and persuasiveness of data.

In recent years, the Python language has gradually become a leader in the field of data processing and visualization. Its rich data processing libraries, powerful visualization tools, and concise syntax give Python irreplaceable advantages in data processing and visualization. At the same time, with the development of big data technology, more and more data analysis and visualization tools adopt the Python language, providing broader prospects for the application of Python in data visualization.

Against this background, this research aims to use Python language and related data frameworks and visualization tools to design and implement a full-screen large-screen system based on recruitment information data in Chengdu, Sichuan, in order to achieve data analysis and visualization of the recruitment industry and provide guidance for recruitment. The company provides decision-making reference and industry analysis support.

2. Research status at home and abroad

As a way of transmitting information, data visualization research has become increasingly mature and has been widely used in various fields. Many scholars at home and abroad have conducted in-depth research on data visualization and proposed different data visualization methods and technologies.

In terms of data visualization research, foreign scholars have formed a complete theoretical framework and proposed some mature ideas and methods. For example, the data inkblot idea proposed by Edward Tufte believes that for a complex data, the interference of chart elements should be minimized and the key points should be highlighted as much as possible. In China, many scholars have conducted in-depth research on data visualization and carried out applied research in related fields. For example, in the field of transportation, Lin Yuan and others used data visualization technology to analyze and model the urban network structure.

There have been many studies at home and abroad on the application of Python in the field of data visualization. For example, in Python, matplotlib, Seaborn, Bokeh, etc. are all commonly used data visualization libraries. On the basis of these libraries, some researchers have also proposed some new data visualization methods and technologies. For example, use libraries like Cartopy to overlay maps on top of data plots to connect geographic location and data.

3. Research ideas and methods

This study is based on the Django framework, uses the Python programming language, and adopts data crawling and data cleaning technologies to collect and filter recruitment information in Chengdu, Sichuan. Next, use Python's data processing and visualization library to analyze and visualize the data. Finally, the Django framework is used to implement a full-screen large-screen system, and the analysis results are displayed on the large screen in graphics and text to achieve data analysis and visualization of the recruitment industry.

Specifically, the main methodological process of this study is as follows:

1. Data crawling: Use Python's Requests and BeautifulSoup libraries to crawl the recruitment information website in Chengdu, Sichuan.

2. Data cleaning and processing: Use libraries such as Pandas and Numpy to clean the crawled data, remove useless information and duplicate information, and retain useful information.

3. Data analysis and visualization: Use Python's Matplotlib, Seaborn, Bokeh and other visualization libraries to visualize and analyze the cleaned and processed data to achieve analysis and research on the recruitment industry.

4. Front-end page design: Use the Django framework to implement visual page design and page display functions to achieve visual display of data results.

5. Backend implementation: Use the Django framework to implement backend management functions, including adding, deleting, modifying, and checking data, user rights management, etc.

4. Research internal customers and innovation points

The main internal customers and innovation points of this study are as follows:

1. Use Python's data processing and visualization library to clean, analyze, and visualize recruitment information in Chengdu, Sichuan.

2. Use the Django framework to implement a full-screen large-screen system, display the analysis results on the large screen in graphics and text, and realize data analysis and visualization of the recruitment industry.

3. Realize backend management functions, including adding, deleting, modifying, and checking data, user rights management, etc.

5. Detailed introduction of front and back functions

The front-end and back-end functions of this study are described in detail as follows:

1. Front page design:

(1) Home page: includes navigation and display of various data analysis results, as well as links to various data analysis reports.

(2) Data analysis page: displays various data analysis charts and data tables.

1. Backend management functions:

(1) Data management: Realize the addition, deletion, modification and data cleaning functions of recruitment information data.

(2) User rights management: Realize the management of rights, including the distribution of rights and user authorization, etc.

6. Research ideas, research methods, and feasibility

This study uses the Python programming language, based on the Django framework, and uses data crawling and data cleaning technologies to collect and filter recruitment information in Chengdu, Sichuan. Next, use Python's data processing and visualization library to analyze and visualize the data. Finally, the Django framework is used to implement a full-screen large-screen system, and the analysis results are displayed on the large screen in graphics and text to achieve data analysis and visualization of the recruitment industry.

In terms of feasibility, the Python programming language, Django framework and related data processing and visualization libraries used in this study have been widely used in many fields and have rich development documentation and community support. Therefore, the technical feasibility of this study is high.

7. Research progress arrangement

The schedule of this study is as follows:

The first week: Complete the research and analysis of the research background and significance, as well as the current research status at home and abroad.

Second week: Complete the design and formulation of research ideas and methods, and conduct feasibility analysis.

Third to fourth weeks: Complete the implementation of data crawling and data cleaning functions.

Weeks 5 to 6: Complete the implementation of data analysis and visualization functions.

Weeks 7 to 8: Complete the design of the front-end page and the implementation of the back-end management functions.

Weeks 9 to 10: Complete system testing and debugging, summarize research results and write a paper.

Weeks 11 to 12: Writing papers, revising papers, and preparing answers