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Design and implementation of large-screen full-screen system for Chongqing shopping data visualization using Python (Django framework)
1. Research background and significance
With the rapid development of e-commerce, shopping data has shown explosive growth. How to effectively utilize these data and mine their value has become an important research issue in the field of e-commerce. Especially in large cities like Chongqing, the scale of shopping data is huge. It is of great practical significance for merchants and consumers to quickly and intuitively understand the statistical characteristics, trends and patterns of shopping data.
Specifically, the significance of this study is reflected in the following aspects: first, by obtaining and displaying shopping data in Chongqing in real time, it is convenient for merchants to understand sales situations in a timely manner and formulate corresponding marketing strategies; secondly, by analyzing historical shopping data and visualization, which can help merchants better understand consumers' shopping habits and preferences, optimize product layout and promotional activities; finally, through the large-screen full-screen display system, shopping data can be presented in a more intuitive and vivid way, improving the public Attention and awareness of e-commerce.
2. Research status at home and abroad
At present, there are already some related shopping data visualization systems at home and abroad. Abroad, some well-known e-commerce companies have launched relevant systems and applications for real-time monitoring and display of sales data. These systems often use advanced technologies and tools, with rich functionality and a good user experience. In China, with the continuous opening of the e-commerce market and the advancement of technology, more and more cities have begun to establish their own shopping data visualization systems and launched related applications. However, there are still some problems, such as untimely data updates, insufficient intuitive visualization, and poor user experience. Therefore, this research aims to design and implement a large-screen full-screen system for Chongqing shopping data visualization based on Python and Django frameworks to solve the above problems and improve the practicality and user experience of the system.
3. Research ideas and methods
This study adopts the following ideas and methods: first, obtain shopping data in Chongqing through crawler technology, clean and process it; then, carry out system design and development based on the Django framework to achieve real-time display of data and historical data analysis; then, use Python's data visualization library is used for visual design and implementation; finally, the shopping data is presented in a more intuitive and vivid way through a large-screen full-screen display system. Specifically, the research methods of this study include literature research method, system design method, system development method, experimental testing method, etc.
4. Research content and innovation points
The research contents of this study mainly include the following aspects:
- Acquisition and preprocessing of shopping data: Obtain shopping data in Chongqing through crawler technology, and clean and process it to ensure the accuracy and completeness of the data. At the same time, consider real-time data updates and historical data storage issues.
- System design and development: System design and development based on the Django framework to achieve real-time display of data and historical data analysis. Specifically, the backend management system needs to have functions such as user management, data management, and visual analysis; the front-end display interface needs to have functions such as real-time display and interactive experience optimization. At the same time, consider the scalability and maintainability of the system.
- Data visualization design and implementation: Use Python's data visualization library for visual design and implementation, including chart selection, color matching, animation design, etc. At the same time, consider the special needs and technical implementation issues of large-screen full-screen display systems.
- Design and implementation of large-screen full-screen display system: The large-screen full-screen display system presents shopping data in a more intuitive and vivid way to increase public attention and awareness of e-commerce. Also consider system stability and security issues.
The innovations of this study are mainly reflected in the following aspects:
- System design and development based on Python and Django frameworks improves the scalability and maintainability of the system and reduces development difficulty and cost.
- Using Python's data visualization library for visual design and implementation improves the data visualization effect and user experience, making shopping information more intuitive, vivid, and comprehensive.
- The large-screen full-screen display system presents shopping data in a more intuitive and vivid way, which increases the public's attention and awareness of e-commerce and enhances the practicality and social value of the system.
5. Backend functional requirement analysis and front-end functional requirement analysis
Backend functional requirements analysis:
- User management: including user registration, login, rights management and other functions to ensure the security and privacy of the system.
- Data management: including data import, export, query, modification and other functions to facilitate users to maintain and manage data.
- Visual analysis: Provides visual analysis functions including chart generation, data statistics and analysis, etc. to help users better understand shopping data and consumer behavior characteristics.
- System monitoring: including log viewing, performance monitoring and other functions to facilitate users to monitor and manage the system's operation in real time.
Front-end functional requirements analysis:
- Real-time display: It can display Chongqing’s shopping data in real time, including numerical values and change trend charts of indicators such as sales, order volume, product categories, etc.
- Interaction experience optimization: Improve the practicality and user experience of the system through friendly operation interface and interactive experience design to facilitate users' query and operation.
6. Technical implementation and feasibility analysis
In terms of technical implementation, this research will use Python as the main programming language and use its rich data processing and visualization libraries for data processing, analysis, and visualization. The Django framework will be used to build web applications, and its powerful functionality and flexibility can meet various system needs. At the same time, large-screen full-screen display systems need to take into account hardware factors such as resolution and screen size to ensure the best display effect.
In terms of feasibility analysis, the technical route of this study is feasible. First of all, Python and Django are mature and widely used technologies with rich resources and community support, which can greatly reduce development difficulty and risks. Secondly, in terms of data acquisition, shopping data in Chongqing can be obtained through crawler technology, and data acquisition and update can be guaranteed. Finally, large-screen display technology is also quite mature and can be implemented with appropriate hardware and software solutions. At the same time, this research will also take into account issues such as system security, stability, and scalability to ensure the normal operation and continued development of the system.
7. Research progress arrangement
This research plan is divided into the following stages:
- The first stage (1-2 months): Conduct demand analysis and system design, clarify the functions and requirements of the system, and design the overall architecture and database structure of the system.
- The second stage (2-3 months): Carry out system development and implementation, including the development of the back-end management system and the development of the front-end display interface.
- The third stage (3-4 months): Design and implement data visualization, including chart selection, color matching, animation design, etc. At the same time, we will design and implement a large-screen full-screen display system.
- The fourth stage (4-5 months): Carry out system testing and evaluation, including functional testing, performance testing, user experience testing, etc., to optimize and improve the system.
- The fifth stage (5-6 months): Systematic summary and paper writing, including systematic summary and evaluation, paper writing and revision, etc.
8. Thesis (design) writing outline
The writing outline of this paper is as follows:
- Introduction: Introduce the research background and significance, domestic and foreign research status, research ideas and methods, etc.
- System requirements analysis: Detailed analysis and description of the system's back-end functional requirements and front-end functional requirements.
- System design and implementation: Introduce the overall design of the system, database design, back-end management system design, front-end display interface design, etc., and describe the system implementation process in detail.
- Data visualization design and implementation: Introducing the design ideas, implementation methods, effect display, etc. of data visualization.
- Design and implementation of large-screen full-screen display system: Introducing the design ideas, implementation methods, effect display, etc. of large-screen full-screen display system.
- System testing and evaluation: Introduce system testing methods, test results, evaluation indicators, etc., and optimize and improve the system.
- Conclusion and outlook: Summarize the results and contributions of this study, look forward to future research directions and application prospects, etc.
- References: List relevant literature and materials cited in this study.
9. Main references
[Please insert reference here]
10. Conclusion and outlook
This study designed and implemented a large-screen full-screen system for Chongqing shopping data visualization based on Python and Django frameworks, which has important practical significance and practical value. By displaying shopping data in Chongqing in real time, it is convenient for merchants to understand sales in a timely manner and formulate corresponding marketing strategies; through the analysis and visualization of historical shopping data, it can help merchants better understand consumers' shopping habits and preferences, and optimize product layout. and promotional activities; through the large-screen full-screen display system, shopping data can be presented in a more intuitive and vivid way, increasing the public's attention and awareness of e-commerce. In the future, this system can be further optimized and improved to improve system performance and user experience, expand the system's application scenarios and functions, and bring more convenience and value to the city's e-commerce development and public life.
Proposal report: Design and implementation of large-screen full-screen system for Chongqing shopping data visualization using Python (Django framework)
1. Research background and significance
Data visualization is to display data in visual ways such as charts and graphs to help users understand the trends, relationships and patterns of data more intuitively and understandably. With the development of information technology and the explosive growth of data, data visualization has become an important tool in the big data era. In the field of shopping, data visualization can be used to analyze user purchasing preferences, product sales trends, etc., helping merchants make more accurate decisions and increase sales.
This research aims to design and implement a Python Chongqing shopping data visualization large-screen full-screen system, which collects and analyzes shopping data and displays it visually on the large screen to provide decision support and improve the shopping experience. The system has a good user interface, data visualization effects and background management functions, can update data in real time and provide a variety of visual display methods.
2. Current research status at home and abroad
Currently, there are many studies on data visualization at home and abroad, most of which focus on algorithms and technologies, as well as case studies applied in specific fields. There are many domestic and foreign data visualization tools and frameworks, such as Tableau, PowerBI, D3.js, etc. These tools help users quickly build a variety of charts and visualizations.
However, there is relatively little research in the field of shopping data visualization. Some domestic and foreign research mainly focuses on data analysis of e-commerce platforms, while the visual display of shopping data often relies on hand-made static charts. Lack of a complete system to update and display visualizations of shopping data in real time.
3. Research ideas and methods
The idea of this research is to design and implement a Python Chongqing shopping data visualization large-screen full-screen system based on the Django framework. The main research methods include data collection, data analysis, visual display and system design and implementation.
The research process is as follows:
- Collect shopping data in Chongqing: Collect shopping data in Chongqing from different shopping platforms and merchants, including product information, user behavior, etc.
- Data cleaning and preprocessing: Clean and preprocess the collected data, including removing duplicate data, filling in missing values, etc.
- Data analysis and feature extraction: Obtain relevant information and features of shopping data through data analysis and feature extraction.
- Visual display design: According to the characteristics and needs of shopping data, design appropriate visual display methods, including bar charts, line charts, heat maps, etc.
- System design and implementation: Design and implement a shopping data visualization system based on the Django framework, including back-end management functions and front-end display functions.
4. Research content and innovation points
The main contents and innovations of this study are as follows:
- Design and implement a visual display system for shopping data: By collecting and analyzing shopping data, design and implement a visual display system that provides a variety of visual ways to display the trends and relationships of shopping data.
- Backend management function: Design and implement a backend management function, including data import, data cleaning and preprocessing, data analysis and other functions, to facilitate administrators to manage and analyze shopping data.
- Front-end functional requirements analysis and design: Based on user needs and characteristics of shopping data, design appropriate front-end functions, including data filtering, data comparison, data export, etc., to provide a good user experience.
5. Backend functional requirement analysis and front-end functional requirement analysis
Backend functional requirements analysis:
- Data import function: Supports importing shopping data from files or databases.
- Data cleaning and preprocessing functions: Supports preprocessing operations such as cleaning, deduplication, and filling in missing values for shopping data.
- Data analysis function: Supports statistical analysis, feature extraction and data mining of shopping data.
Front-end functional requirements analysis:
- Data visualization display function: supports various charts and visualization effects for displaying shopping data according to user needs.
- Data filtering function: Supports filtering and searching of shopping data based on different conditions and dimensions.
- Data comparison function: Supports comparative analysis of shopping data in different time periods or different regions.
- Data export function: Supports exporting shopping data to Excel, CSV and other formats to facilitate users for secondary analysis and processing.
6. Research ideas, research methods, and feasibility
The idea of this research is to collect Chongqing shopping data, conduct data analysis and feature extraction, and design and implement a large-screen full-screen shopping data visualization system. Research methods mainly include data collection, data cleaning and preprocessing, data analysis and visual display.
The feasibility of the research is mainly reflected in the following aspects:
- Data collection: Shopping data in Chongqing is relatively abundant, and a large amount of shopping data can be collected through various shopping platforms and merchants.
- Data analysis and preprocessing: There are many mature algorithms and tools for data analysis and feature extraction, which can be used for the analysis and preprocessing of shopping data.
- Visual display tools and frameworks: The Django framework provides a wealth of visual display tools and plug-ins, which can help us realize the visual display needs of shopping data.
7. Research schedule
The schedule of this study is as follows:
- Collect shopping data in Chongqing (1 month): Collect shopping data in Chongqing, including product information, user behavior, etc.
- Data cleaning and preprocessing (1 month): Clean and preprocess the collected data, remove duplicate data, fill in missing values, etc.
- Data analysis and feature extraction (2 months): Obtain relevant information and features of shopping data through data analysis and feature extraction.
- System design and implementation (3 months): System design and implementation based on the Django framework, including back-end management functions and front-end display functions.
- System testing and optimization (1 month): Test the system, optimize and improve the system's functions and performance.
- Thesis writing and defense preparation (2 months): Write the thesis and prepare materials related to the defense.
8. Paper (design) writing outline
The writing outline of this paper is as follows:
- introduction
- Research Background
- The goal and purpose of research
- Research status at home and abroad
- Research content and innovation points
- Research methods and feasibility analysis
- Related technologies and tools
- data collection
- Data clear