Python Anhui Hefei shopping store data visualization large-screen full-screen system design and implementation (django framework)

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Design and implementation of large-screen full-screen system for data visualization of shopping stores in Hefei, Anhui using Python for college students (Django framework) proposal report

1. Research background and significance

With the rapid development of the Internet and e-commerce, shopping store data visualization has become more and more widely used in the fields of retail industry, market analysis and business decision-making. Through data visualization technology, the operation status and market dynamics of shopping stores can be intuitively displayed, helping merchants better understand consumer needs and market trends, and providing strong support for business decisions. This research aims to design and implement a large-screen full-screen system for data visualization of shopping stores in Hefei, Anhui based on the Django framework, which has important practical significance and practical value.

2. Research status at home and abroad

In terms of data visualization, there have been many research and practical results at home and abroad. For example, tools such as Tableau and Power BI have high visibility and market share in the field of data visualization. In the field of business intelligence, there are also many successful cases and application practices. However, shopping store data visualization systems for specific regions and specific industries still have large research space and application prospects.

3. Research ideas and methods

This research will adopt the following ideas and methods:

1. Requirements analysis: Conduct an in-depth analysis of the data visualization needs of shopping stores in Hefei, Anhui, and clarify system functions and performance requirements.
2. Technology selection: Choose Django as the development framework, and use its MVC architecture and rich plug-in resources to quickly build the system framework.
3. Data processing: Obtain shopping store data in Hefei, Anhui, and clean, organize and format the data.
4. Visual design: Use Django's template system and front-end technology to design an intuitive and beautiful data visualization interface to achieve a large-screen full-screen display effect.
5. System implementation and testing: Complete the coding implementation of the system, and conduct functional testing and performance testing to ensure system stability and reliability.

4. Research content and innovation points

The main contents of this study include:

1. Shopping store data acquisition and processing: Obtain shopping store data in Hefei, Anhui through appropriate data sources, and perform necessary data processing.
2. Data visualization design: Design an intuitive and beautiful data visualization interface to display various indicators and changing trends of shopping store data.
3. System function implementation: Realize core functions such as user login, data query, and data visualization display.
4. System testing and optimization: Comprehensive testing and optimization of the system to ensure system performance and stability.

Innovation points include:

1. Design a special data visualization solution based on the specific needs of Hefei, Anhui.
2. Take advantage of the Django framework to achieve rapid development and deployment of the system.
3. Provide an immersive user experience through large-screen full-screen display.
4. Combined with geographical location information, it displays the distribution and competition of shopping stores.

5. Backend functional requirement analysis and front-end functional requirement analysis

Backend functional requirements mainly include: user management, data acquisition and processing, data storage and query, etc. Front-end functional requirements mainly include: user login, data visualization display, interactive operations, etc. Ensure that the system meets user needs and provides a good user experience through detailed analysis of front-end and back-end functional requirements.

6. Research ideas, research methods, and feasibility

This research will follow the research idea of ​​"demand analysis-technology selection-system design-system implementation-testing and optimization". In terms of methodology, literature research, case analysis, experiments and other methods will be used for research. In terms of feasibility, the maturity of the Django framework, rich plug-in resources, and open source features provide a strong guarantee for the implementation of the project. At the same time, team members have relevant technical background and project experience to ensure the smooth progress of the project.

7. Research progress arrangement

1. The first stage (1-2 months): Complete demand analysis and technology selection, and clarify research goals and methods.
2. The second stage (3-4 months): Complete the system design and data processing work, and build the basic framework.
3. The third stage (5-6 months): Implement the main functions of the system and data visualization interface, and complete preliminary testing.
4. The fourth stage (7-8 months): Carry out system optimization and performance improvement, complete the final test and go online.
5. The fifth stage (9 months): Summarize research results, write a thesis and prepare for defense.

8. Thesis (design) writing outline

1. Introduction: Explain the background and significance of the research, and propose research questions and methods.
2. Review of related work: Summarizes the research progress and application status of shopping store data visualization at home and abroad.
3. Demand analysis and technology selection: Analyze the data visualization needs and technical requirements of shopping stores in Hefei, Anhui, and select the appropriate development framework and technical route.
4. System design: Elaborate on the overall architecture, functional module design and database design of the system.
5. System implementation and testing: Describe the development environment of the system, the implementation process of the main functions, and the testing methods and results of the system.
6. Results display and analysis: display the system's operating effects and data visualization results, and analyze system performance and user experience and other indicators.
7. Summary and Outlook: Summarize the main work and contributions of the paper, point out the shortcomings of the research and future improvement directions.
8. References: List the main documents and related materials cited in the paper.

9. Main references (this part will be supplemented based on the specific research content and literature)
10. Expected results
Through this research, we expect to achieve the following results:

1. Developed a fully functional large-screen full-screen system for visualizing shopping store data to meet the needs of users in Hefei, Anhui;
2. Verify the effectiveness and feasibility of the Django framework in the design of large-screen data visualization systems;
3. Provide a set of practical solutions and technical routes to provide reference for research and applications in related fields;
4. Through the actual application and testing of the system, we will continuously optimize and improve system functions and improve user experience;
5. Publish relevant academic papers or technical reports to promote research and development in related fields.
   11. Research Team and Division of Labor
   This research will be completed by a team with rich experience and professional skills. Team members include project leaders, back-end developers, front-end developers, data analysts, testers, etc. The specific division of labor is as follows:
6. Project leader: Responsible for the overall planning and progress management of the project, and coordinating the work of team members;
7. Back-end developer: Responsible for the back-end development of the system, including data processing, database design and interface development, etc.;
8. Front-end developer: Responsible for the front-end development of the system, including interface design, interaction implementation, data visualization, etc.;
9. Data Analyst: Responsible for cleaning, organizing and analyzing shopping store data, and providing data support for data visualization and system functions;
10. Tester: Responsible for system testing, including functional testing, performance testing and security testing.

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Proposal report title: Design and implementation of large-screen full-screen system for data visualization of shopping stores in Hefei, Anhui using Python for college students (Django framework)

1. Research background and significance: With the development of the Internet and the popularity of smartphones, people's consumption habits have undergone tremendous changes. Shopping behavior has become an indispensable part of people's daily lives, and the attractiveness and influence of shopping stores on consumers are becoming increasingly important. Therefore, visual analysis of shopping store data is of great significance for understanding consumer preferences, optimizing store management strategies, and improving consumer experience. This research aims to design and implement a large-screen full-screen system for data visualization of college students' shopping stores based on Python to provide data analysis and decision-making support for shopping stores for college students in Hefei.

2. Current research status at home and abroad: At present, research on data visualization at home and abroad has achieved remarkable results. For example, various types of charts and visualizations can be achieved by using Python's data visualization libraries such as Matplotlib, Seaborn, and Plotly. At the same time, combined with the Django framework, a powerful data visualization system can be built. However, there are relatively few studies on visual analysis of shopping store data in specific regions, especially for college students. Therefore, based on the current research status at home and abroad, this study will focus on the visual analysis of college students' shopping store data.

3. Research ideas and methods: This research will adopt the following ideas and methods: (1) Data collection: Use web crawler technology to obtain relevant data about college student shopping stores in Hefei. (2) Data cleaning and processing: Clean and process the collected data, including data formatting, deduplication, missing value processing, etc. (3) Data visualization design: Based on the research on college students’ shopping behavior, design appropriate data visualization charts and images to display information such as store operations, product sales, and user preferences. (4) Front-end and back-end system design and implementation: Use the Django framework to develop back-end functions, including data management, user rights management, etc.; use HTML, CSS, and JavaScript to design and implement front-end pages. (5) System testing and optimization: Functional testing and performance optimization of the system to ensure system stability and user experience.

4. Research internal customers and innovation points: The internal customer of this study is to analyze the data of college students’ shopping stores through data visualization, so as to provide specific and practical shopping decision support for college students in Hefei area. The innovation is to use the Django framework and Python's data visualization library to implement a fully functional large-screen full-screen system to achieve dynamic updating and interactive display of data.

5. Backend functional requirements analysis and front-end functional requirements analysis: Backend functional requirements include:

• Database management: Realize the addition, deletion, modification, and import and export functions of data.
• User rights management: Authentication and rights control for users.
• Data analysis and visualization configuration: Provides flexible data analysis and visualization configuration options.
• Data update and scheduled tasks: realize the function of automatically updating data and executing tasks on a scheduled basis.

Front-end functional requirements include:

• Data visualization display: Visually display data on a large-screen full-screen system in the form of charts, images, etc.
• User interactive interface: Provides user interactive operations, such as selecting different time periods and stores, and displaying different data analysis results.
• Responsive design: Ensure that the system displays well on screens of different sizes.

1. Research ideas, research methods, and feasibility: The idea of ​​​​this research is based on the process of data collection, data processing, data visualization design, and system design and implementation. It is developed by using Python's data visualization library and Django framework to realize a complete college student. Shopping store data visualization large-screen full-screen system. Development using Python and Django has broad application prospects and feasibility.

2. Research schedule: The schedule of this research is as follows:

• The first stage (1-2 weeks): Conduct literature research and research to clarify the research direction and goals.
• The second stage (3-4 weeks): collect and process data and complete the development of backend functions.
• The third stage (2-3 weeks): Carry out data visualization design and implementation of front-end functions.
• The fourth stage (1-2 weeks): Carry out system testing and optimization, and write a paper (design).
• The fifth stage (1 week): revision and improvement of the paper (design).

1. Thesis (design) writing outline:

• introduction
  • Background and Significance
  • Research status at home and abroad
  • Research objectives and main contributions
• Introduction to related technologies and methods
  • Python data visualization library
  • Django framework
  • Data crawler technology
• System design and implementation
  • Backend functional requirement analysis and implementation
  • Front-end functional requirements analysis and implementation
  • System testing and optimization
• results and analysis
  • Data visualization results display and analysis
  • System performance evaluation and optimization
• Summary and Outlook
  • Summary of thesis work
  • Problems and deficiencies
  • Follow-up research directions and expansion

1. main reference:

• Healy, K. (2018). Data visualization: A practical introduction. Princeton University Press.
• McKinney, W. (2017). Python for data analysis: Data wrangling with Pandas, NumPy, and IPython. O'Reilly Media.
• Vlachopoulos, P. (2011). Investigating the use of Facebook in Greek education: The case of the University of Thessaly. Computers & Education, 56(2), 478-488.

The above is the complete content of the proposal report, which covers the research background and significance, domestic and foreign research status, research ideas and methods, research internal customers and innovation points, back-end functional demand analysis and front-end functional demand analysis, research ideas and research methods, research Progress arrangement, paper (design) writing outline, main references, etc.