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

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

1. Research background and significance

With the advent of the big data era, data visualization has become an important means of information display and data analysis. Especially in the recruitment industry, visual analysis of recruitment data can help companies and job seekers better understand market dynamics and industry trends. As an important city in southwest China, Chongqing's data visualization analysis of recruitment information is of great significance in guiding recruitment strategies and optimizing resource allocation.

However, there is currently a lack of an effective data visualization analysis system to integrate and display recruitment information data in Chongqing. Most of the existing data analysis tools can only provide simple data reports and charts, which cannot meet the needs of enterprises for complex data analysis and intuitive display. Therefore, designing and implementing a full-screen large-screen system for visual analysis of Chongqing recruitment information data based on Python has important practical significance and application value.

2. Research status at home and abroad

At home and abroad, there have been many studies on data visualization and full-screen large-screen display technology. For example, D3.js, ECharts, etc. are commonly used data visualization libraries that can realize complex analysis and intuitive display of data. At the same time, web frameworks such as Django are also widely used in back-end development. However, there are still few studies on full-screen large-screen systems for visual analysis of Chongqing recruitment information data.

3. Research ideas and methods

This research will adopt the following ideas and methods:

1. Data collection: Use Python’s Scrapy framework to implement automated data collection. Specifically, we will write a crawler program to visit major recruitment websites and extract the required recruitment information data.
2. Data preprocessing: Perform preprocessing operations such as cleaning, deduplication, and standardization on the collected data to eliminate noise and outliers in the data and ensure the accuracy and consistency of the data.
3. Data storage: Store preprocessed data in the database for subsequent data analysis and visual display.
4. Data visualization: Use data visualization libraries such as D3.js to achieve full-screen large-screen display of data. Specifically, we will design and implement a series of intuitive charts and graphs to display recruitment information data in Chongqing, including the number of positions, salary levels, company size and other indicators.
5. System integration: Integrate functions such as data collection, preprocessing, storage and visualization into a complete system, and use the Django framework to implement front-end and back-end data interaction and user management functions.

4. Research content and innovation points

The main content of this research is to design and implement a full-screen large-screen system for visual analysis of Chongqing recruitment information data based on Python. Specifically, the innovations of this study include:

1. Designed and implemented an effective data collection and preprocessing solution for recruitment information data in Chongqing;
2. Use data visualization libraries such as D3.js to realize full-screen large-screen display of data, so that users can intuitively understand the market dynamics and industry trends of recruitment information;
3. Through in-depth analysis and visualization of data, we can provide enterprises with decision-making support in aspects such as recruitment strategy optimization and resource allocation;
4. Combined with the Django framework, a stable and reliable data visualization analysis system is designed and implemented, which has good practicality and scalability.

5. Detailed introduction of front and back functions

The front and back functions of this system are as follows:

1. Backend functions: Administrators can configure and manage the system through the backend management interface. Specifically, administrators can configure data collection rules, manage user accounts, view system logs, etc. In addition, administrators can clean, analyze, and visualize the collected data to better understand the dynamics and trends of the recruitment market.
2. Front-end function: Users can view and analyze recruitment information data displayed in full screen through the Web interface. Specifically, users can view real-time updated data on the number of various positions, salary levels, company size and other indicators, and can also filter, sort and export the data. In addition, the system also provides a job search function, allowing users to search for jobs by keywords and view detailed information.

6. Research ideas, research methods, feasibility
This research will adopt the following ideas and research methods:

1. Through research and analysis of the page structure and data format of major recruitment websites, design and implement an effective automated data collection program;
2. Use Python's Scrapy framework to realize automated data collection, and use the Django framework to realize back-end management of data;

1. Design and implement a stable and reliable data storage solution, including data cleaning, deduplication, standardization and other pre-processing operations to ensure data accuracy and consistency;
2. Use data visualization libraries such as D3.js to design and implement full-screen large-screen display of data, so that users can intuitively understand the market dynamics and industry trends of recruitment information;
3. Design and implement a complete Web interface, including data display, filtering, sorting, export and other functions, while also realizing front-end and back-end data interaction and user management functions;
4. Carry out system testing and debugging, including testing in data collection, storage, analysis and visualization, to ensure the stability and reliability of the system;
5. Conduct demand analysis and collect user feedback to optimize and improve the system to improve its practicality and scalability;
6. Organize and submit research results, including academic papers, patent applications, software copyright applications, etc.

In terms of feasibility, this research will make full use of existing big data technology and Web development framework, and design and implement the system based on actual conditions. At the same time, this research will fully test and debug the performance and stability of the system to ensure the reliability and practicality of the system.

7. Research schedule
This research will be divided into the following stages:

1. The first stage (1-2 months): Conduct project requirements analysis and system design to determine the overall architecture and functional modules of the system. At the same time, the design and construction of the database, as well as the required technology selection and environment configuration are carried out.
2. The second stage (3-4 months): Write a crawler program to realize automatic collection of data. At the same time, we design and implement the backend management system, including user management, data management, log management and other functions.
3. The third stage (5-6 months): Carry out data cleaning, analysis and visualization, design and implement the front-end web interface, including data display, filtering, sorting, export and other functions. At the same time, system testing and debugging are carried out to ensure the stability and reliability of the system.
4. The fourth stage (7-8 months): Carry out trial operation of the system and collect user feedback to optimize and improve the system. At the same time, write and organize related documents, including user manuals, administrator manuals, development documents, etc.
5. The fifth stage (9-10 months): Summary and results report of the project, including research results, technological innovation points, application prospects, etc. At the same time, organize and submit research results, including academic papers, patent applications, software copyright applications, etc.

8. Thesis (design) writing outline
This thesis (design) will be written in the following parts:

1. Introduction: Introduce the research background and significance of this topic, and explain the purpose and content of the research.
2. Literature review: Review the current status and development trends of relevant research at home and abroad, and analyze the shortcomings of existing research.
3. Research methods and technologies: Introduce the research ideas and methods of this study, including technical details such as data crawling, data storage, data analysis, and visual display.
4. System design and implementation: Detailed introduction to the design process and implementation methods of the system, including the design and implementation of front-end and back-end functions, the design and construction of databases, etc.
5. Experiment and analysis: Display the experimental results and analysis process, including detailed elaboration of data cleaning, analysis and visualization.
6. Conclusion and outlook: Summarize the research results and innovations of this topic, and propose future research directions and application prospects.
7. References: List the relevant literature and materials cited in this paper (design).
8. Appendix: Provide materials or proofs that need to be supplemented in this paper (design), such as program code, data samples, etc.

9. Main References
[Main references are listed here]

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research background and meaning

With the advent of the digital and intelligent era, more and more data are being generated, especially data such as recruitment information. In-depth analysis of the recruitment market plays an important role in talent recruitment and corporate employment. As a new type of data visualization display method, the large-screen system can intuitively present complex data and information, allowing people to better understand and grasp the key information in the data, and is widely used in various occasions. Therefore, the design and implementation of a full-screen large-screen system based on python Chongqing recruitment information data visualization analysis has great practical application value and research significance.

Research status at home and abroad

At present, research on large-screen systems at home and abroad has made certain progress. Foreign research mainly uses large data centers, exhibition centers, traffic control centers, financial institutions, etc. as application scenarios. Large-area data display and control have become the main applications of large-screen systems. Domestic research focuses more on smart cities, transportation, security and other fields, and large-screen systems are widely used.

There have been many research results in data visualization. Among them, data visualization tools such as matplotlib, seaborn, and Bokeh based on Python technology have been widely used in the field of data visualization, while the Django framework, as an excellent web framework for Python, is recognized and used by more and more people.

Research ideas and methods

The main idea of ​​this study is to crawl the data of Chongqing recruitment information website, clean and process the data, and establish a database. Then, use the Django framework for data display and visualization, and combine it with front-end technology to implement a full-screen large-screen system. Specific methods include the following points:

1. Web crawler technology: Use Python’s Scrapy framework to crawl website data.

2. Data analysis and cleaning: Analyze and process the acquired data to remove duplicate data, abnormal data, etc.

3. Database design and construction: Use MySQL as the database and design corresponding data tables and fields according to requirements.

4. Django framework development: Use Python's Django framework for background development to achieve data display and visualization.

5. Front-end development: Use HTML, CSS, JavaScript and other technologies to design and develop front-end pages.

6. Realization of full-screen large-screen system: Through the combination of front-end and back-end technologies, the display of full-screen large-screen system is realized.

Research internal customers and innovation points

The core of this study is to use Python's Scrapy framework for data crawling and combine it with the Django framework for data display and visualization to realize a full-screen large-screen system based on Chongqing recruitment information, which provides important information for talent recruitment, corporate employment and other fields. Data analysis and referencing. The innovation is that this study uses Python technology to crawl and process data, and uses the Django framework to realize data visualization and display. At the same time, it combines front-end technology to implement a full-screen large-screen system, which improves the readability of data and user experience.

Detailed introduction of front and back functions

The front-end page of this system adopts a responsive layout design and can adapt to screens of different resolutions. Pages include: homepage, corporate recruitment information display page, talent recruitment information display page, analysis report display page, etc. The background mainly implements the following functions:

1. Database design and construction: Use MySQL as the database and design corresponding data tables and fields according to requirements.

2. Web crawler: Use Python's Scrapy framework to crawl data and store the data in a MySQL database.

3. Backend management: enables administrators to view, modify, delete and other operations on data to ensure the accuracy and completeness of the data.

4. Data display and visualization: Use Python's Django framework for data display and visualization, supporting chart display of data and generation of analysis reports.

5. Front-end display: Use HTML, CSS, JavaScript and other technologies to develop front-end pages to achieve a complete experience of data display and user interaction.

Research ideas, research methods, feasibility

This study uses Python's Scrapy framework for data crawling and processing, uses the Django framework for data display and visualization, and combines front-end technology to realize the display of a full-screen large-screen system. This research idea and method has certain feasibility, because Python technology has been widely used in the fields of data processing and visualization, and the Django framework is also one of the mainstream frameworks for Python back-end development. At the same time, it is also somewhat innovative because it uses Python technology for data processing and visualization, and its application in full-screen large-screen systems is also quite groundbreaking.

Research schedule

The schedule of this study is as follows:

1. The first phase (August 2021-September 2021): Research and analysis of the research background and significance. Complete the literature review and identify research ideas.

2. Phase 2 (October 2021-November 2021): Data crawling and processing. Use Python's Scrapy framework to crawl and clean data and build a database.

3. The third phase (December 2021-January 2022): Backend development and front-end design. Back-end development is based on the Django framework, and technologies such as HTML, CSS, and JavaScript are used for the design and development of front-end pages.

4. The fourth phase (February 2022-March 2022): system testing and performance optimization. Conduct system testing and performance optimization to ensure system stability and performance.

5. The fifth stage (April 2022-May 2022): Writing and revising the paper (design). Complete the first draft and revision of the thesis (design).

Thesis (design) writing outline

The paper (design) of this research will include the following parts:

Chapter One Introduction. Introduce the background and significance of the research, analyze the current research status at home and abroad, explain the research ideas and methods, and provide an overall overview of the main content of this research.

Chapter 2: Related technologies and tools. Introduces the related technologies and tools involved in this research, including Python technology, Scrapy framework, Django framework, HTML, CSS, JavaScript, etc.

Chapter 3: Data crawling and processing. Describe in detail the process of data crawling and processing, including website selection, preparation of relevant crawlers, data cleaning and organization, etc.

Chapter 4: Backend development and front-end design. Detailed description of the back-end development and front-end design process, including the use of the Django framework, data display and visualization, front-end page design, etc.

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