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Design and implementation of large-screen full-screen system for visualizing air quality data in Guizhou and Guiyang based on Django framework Project proposal report
Summary
With the acceleration of industrialization, air quality issues have attracted increasing attention. In order to better monitor, analyze and display air quality data in Guiyang, Guizhou, this research aims to design a full-screen visualization large-screen system based on the Django framework. This report will elaborate on the research background, significance, current situation, ideas, methods and expected results of this system.
Keywords: Django framework, air quality, data visualization, large screen system
1. Research background and significance
In recent years, Guiyang's economy has continued to develop, but environmental problems have gradually become more prominent, especially air quality issues. In order to better understand the air quality status of Guiyang City and provide decision-making support to relevant departments, it is necessary to build a system that can monitor, analyze and display air quality data in real time.
2. Current research status at home and abroad
At present, there have been many studies on air quality monitoring and data visualization at home and abroad. Some developed countries abroad have established complete air quality monitoring systems and adopted advanced data visualization technology. In China, although some large cities have established similar systems, there are relatively few studies in this area in small and medium-sized cities, especially Guiyang.
3. Research ideas and methods
This research will adopt the following ideas and methods:
- Use the Django framework to build web applications to achieve real-time collection, storage and display of data.
- Use Python related libraries for data cleaning and processing.
- Use visualization tools such as Echarts or Highcharts for data visualization.
- Design a full-screen large-screen system to facilitate users to view and analyze data intuitively.
4. Research content and innovation points
4.1 Research content
- Data collection: Obtain real-time air quality data from Guiyang Environmental Protection Bureau or other relevant websites.
- Data processing: Clean and process the collected data to ensure the accuracy and completeness of the data.
- Data storage: Store the processed data in the database to provide support for subsequent queries and analysis.
- Data visualization: Use tools such as Echarts or Highcharts to display data in the form of charts.
- Large-screen system design: Design a full-screen large-screen system that integrates visual charts to facilitate user viewing and analysis.
4.2 Innovation points
- Use the Django framework to achieve rapid development of web applications.
- Use Python's powerful data processing capabilities to clean and process data.
- Use advanced data visualization tools to display data in an intuitive and easy-to-understand way.
- Design a full-screen large-screen system to improve user experience and data viewing efficiency.
5. Backend functional requirement analysis and front-end functional requirement analysis
5.1 Analysis of background functional requirements
- User management: realize user registration, login and permission management.
- Data collection: Collect air quality data from relevant websites regularly.
- Data processing: Clean and process the collected data.
- Data storage: Store the processed data in the database.
5.2 Analysis of front-end functional requirements
- Data display: Display air quality data in the form of charts.
- Large-screen system: Design a full-screen large-screen system that integrates all charts to facilitate user viewing and analysis.
- Interactive functions: realize functions such as zooming, dragging and data querying of charts.
6. Research feasibility analysis
The technical route of this research is feasible, mainly based on the following points: the maturity and stability of the Django framework; Python’s powerful data processing capabilities; rich data Visualization tools and public data interface provided by Guiyang Environmental Protection Bureau. In addition, the results of this study will provide strong support for air quality monitoring and management in Guiyang City, and have important practical significance and value. Therefore, this study has high feasibility.
7. Research schedule
This research plan is divided into the following stages: demand research (1 month), system design (2 months), system development (3 month), system testing (1 month) and results summary (1 month). In total, it is estimated that it will take 8 months to complete the research and implementation of the entire project. The specific time node will be adjusted according to the actual situation.
8. Thesis (design) writing outline
The thesis writing outline of this study is as follows: introduction, research background and significance, domestic and foreign research status, research ideas and methods, research content and It consists of innovation points, back-end functional requirement analysis and front-end functional requirement analysis, system implementation and testing, conclusions and prospects. Among them, each part will elaborate on the corresponding content and methodology selection basis, as well as a description of the specific implementation process. Finally, we summarize and evaluate the entire project and look forward to possible future improvement directions or application values and other prospect predictions are presented as the concluding part for readers' reference.
9. Main references
[Insert references here] During the actual writing process, we will consult a large number of literature and official documents to support our research work and ensure Its scientificity and accuracy.
Proposal report: Design and implementation of large-screen full-screen system for visualizing air quality data in Guizhou and Guiyang using python (django framework)
research background and meaning:
As people pay more and more attention to environmental quality, air quality has become an important indicator. As an inland city in China, the air quality of Guiyang, Guizhou has also attracted much attention. In order to better understand and monitor the air quality in Guiyang, it is of great application significance to develop a system that can display air quality data in real time.
Research status at home and abroad:
There have been many studies on air quality data visualization at home and abroad. These studies mainly focus on data visualization technologies and methods, including traditional chart display, map display, heat map display, etc. However, there is currently a lack of a full-screen system to display air quality data, especially for Guiyang City.
Research ideas and methods:
This research will use python language and django framework to design and implement a large-screen full-screen system for air quality data visualization. Specific research ideas and methods include the following aspects:
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Data collection: Real-time air quality data is obtained through the air quality monitoring station in Guiyang, Guizhou, and stored in the database.
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Design and implementation of backend functions: Use the Django framework to build a backend management system, including user management, rights management, data management and other functions.
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Front-end function design and implementation: Use front-end technologies such as HTML, CSS, and JavaScript to design and implement a large-screen full-screen system for data visualization.
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Data visualization design and implementation: Use various chart display technologies, such as line charts, bar charts, pie charts, etc., to display air quality data and customize the display according to user needs.
Research internal customers and innovation points:
The internal client of this study is to design and implement a python Guizhou Guiyang air quality data visualization large-screen full-screen system. The innovation points mainly include the following aspects:
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Use the Django framework to build a backend management system to implement user management and permission management functions.
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Use full-screen display to provide better user experience and data display effects.
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Customized display of air quality data according to user needs makes the data more intuitive and easy to understand.
Backend functional requirement analysis and front-end functional requirement analysis:
Backend functional requirement analysis mainly includes user management, rights management, data management, etc. Front-end functional requirements analysis mainly includes data visualization display, interactive operations, etc.
Research ideas, research methods, and feasibility:
The idea of this research is to build a back-end management system through the Django framework and use front-end technology to achieve data visualization display. Research methods mainly include data collection, system design and implementation, system testing, etc. Through the comprehensive application of existing technologies and methods, the feasibility of this research is relatively high.
Research schedule:
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Data collection and storage: Completion time is two weeks.
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Backend function design and implementation: completion time is three weeks.
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Front-end function design and implementation: completion time is four weeks.
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Data visualization design and implementation: completion time is four weeks.
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System testing and optimization: Completion time is two weeks.
Total completion time is fifteen weeks.
Thesis (design) writing outline:
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Introduction: Introduce the background, purpose and significance of the research.
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Current research status at home and abroad: A review of existing air quality data visualization research.
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System design and implementation: Detailed introduction to the design and implementation of the system's backend functions and the design and implementation of its front-end functions.
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Data visualization design and implementation: Detailed introduction to the design and implementation process of data visualization.
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System testing and optimization: Test and optimize the system to improve its performance and functionality.
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Conclusion and outlook: Summarize the work of this study and make outlook on future research directions.
main reference:
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R. S. Cardoso, A. A. Carvalho, A. R. Costa, and V. S. Costa, "Visualization of air quality data," in 2016 20th International Conference Information Visualisation, 2016.
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B. Li, H. Wu, and G. Liu, "Air quality data visualization system based on Python," in 2018 International Conference on Intelligent Transportation, Big Data & Smart City, 2018.
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M. J. Hussain, "Visualization of air pollution data for smart cities," in 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 2019.
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J. C. Liu, H. Q. Ren, and T. Zhu, "Design and Implementation of Air Quality Data Visualization System," in 2018 7th International Conference on Computer Science and Network Technology (ICCSNT), 2018.
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K. Wu, Z. Gao, and L. Wang, "A Visualized Air Quality Index Based on WebGIS," in 2016 3rd International Conference on Energy, Environment, Ecology, Ocean and Geotechnical Engineering (ICEEOT), 2016.