Design and implementation of large-screen full-screen system for python Shanghai air quality data visualization (django framework)

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Design and implementation of Python Shanghai air quality data visualization large-screen full-screen system (Django framework)

1. Research background and significance

With the development of industrialization and urbanization, environmental pollution problems have become increasingly prominent, and air quality has become the focus of attention. In order to understand and improve air quality conditions in a timely manner, many cities have established air quality monitoring systems. However, the traditional way of displaying air quality data is often based on tables or simple charts, which makes it difficult to visually display the spatial distribution and temporal change characteristics of air quality. Therefore, this research aims to design and implement a large-screen full-screen system for Shanghai air quality data visualization based on Python and Django frameworks, to display Shanghai's air quality data in an intuitive, dynamic and interactive way, and to provide decision-making support for the government, enterprises and the public. and information services.

Specifically, the significance of this study is mainly reflected in the following aspects:

1. Improve the visualization effect of air quality data: By displaying data on a large full screen, air quality data can be visually displayed in the form of maps, charts, etc., allowing users to understand the air quality status more intuitively.
2. Enhance the interactivity and real-time nature of data: This system supports users to perform interactive operations through mouse, touch screen and other devices, such as zooming in, zooming out, querying, etc., while updating air quality data in real time, allowing users to obtain the latest air quality information in a timely manner.
3. Improve the scalability and flexibility of the system: This system is developed using the Django framework, which can easily add new functions and modules to meet the needs of different users.
4. Promote the development of research and practice in related fields: This study will provide reference and reference for research and practice in related fields, and promote the development and application of related technologies such as data visualization technology and large-screen display technology.

2. Research status at home and abroad

At present, there have been some related research and practices at home and abroad. Abroad, some well-known environmental organizations and institutions, such as the World Health Organization and the U.S. Environmental Protection Agency, provide a wealth of air quality data and visual display functions. At the same time, some commercial companies and open source organizations have also launched related data visualization tools and platforms, such as Tableau, Power BI, etc. These tools and platforms provide rich visualization options and interactive functions to meet the needs of different users.

In China, some cities have also established air quality monitoring systems and provided corresponding data display functions. At the same time, some Internet companies and scientific research institutions have also launched similar data visualization products and services. However, in practical applications, more factors and challenges need to be considered, such as data accuracy, real-time performance, visualization effects, etc. In addition, due to differences in air quality conditions in different cities and regions, the system needs to be designed and implemented based on actual conditions.

3. Research ideas and methods

This research will adopt the following ideas and methods:

1. Requirements analysis: Through surveys and interviews, we understand users’ needs and expectations for the Shanghai air quality data visualization large-screen full-screen system and clarify the functional and non-functional requirements of the system. Specifically, it is necessary to collect user feedback and suggestions on the system's interface design, data display methods, interaction methods, etc.
2. Data preparation: Obtain Shanghai's air quality data from reliable data sources and perform preprocessing and formatting to provide data support for system data display.
3. System design: Design the overall architecture and module division of the system based on the requirements analysis results to determine the functions and interaction methods of the front and back ends. Specifically, you need to design the database structure, API interface, front-end page, etc.
4. System implementation: System development and implementation based on system design results and data processing solutions, including back-end service construction, front-end page development, and front-end and back-end interaction implementation, etc. Specifically, you need to use Python and Django frameworks for back-end development and use HTML, CSS, JavaScript, etc. for front-end development to achieve large-screen and full-screen data visualization display functions.
5. System testing and evaluation: Use unit testing, integration testing, user testing and other methods to conduct comprehensive testing and performance evaluation of the system to verify whether the system's functions and performance meet expectations.

4. Research content and innovation points

The main contents of this study include:

1. The design and implementation of the Shanghai air quality data visualization large-screen full-screen system includes the overall architecture design module division and front-end function implementation, etc.;
2. Back-end service construction based on Django framework includes database design, API interface design and other aspects;
3. The design and implementation of the front-end page includes the interactive design of large-screen and full-screen data visualization display functions;
4. System testing and performance evaluation include unit testing, integration testing and user testing to verify whether the system's functions and performance meet expectations.

The innovations of this study are mainly reflected in the following aspects:

1. The introduction of large-screen and full-screen data display improves data visualization and user experience;
2. Combining geographic information system (GIS) technology to display the spatial distribution characteristics of air quality improves the spatial visualization effect of the data;
3. Using the Django framework for development improves the scalability and flexibility of the system;
4. A large number of experimental verification and optimization work were carried out in combination with actual application scenarios to improve the practicality and application value of the system.

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

Backend functional requirements analysis:

1. Data management: Supports the import, export, query and modification of air quality data to ensure the accuracy and completeness of the data.
2. User management: Supports user registration, login, rights management and other operations to ensure the security and stability of the system.
3. Log management: Record system operation logs and user operation logs for problem tracking and exception handling.
4. Security management: Ensure system data security and user privacy, including data encryption, preventing SQL injection, etc.

Front-end functional requirements analysis:

1. Large-screen full-screen display: Supports large-screen full-screen display of Shanghai's air quality data, including pollutant concentration, air quality index (AQI) and other information.
2. Data visualization: Display air quality data through charts, maps, etc., allowing users to understand air quality conditions more intuitively.
3. Interactive design: Provide a friendly user interface and interactive design to enable users to easily view and operate air quality data. Specifically, it is necessary to support interactive operations on devices such as mice and touch screens, such as zooming in, zooming out, and querying.
4. Responsive design: Ensure that the system can display and operate normally on different devices and screen sizes, improving system adaptability and user experience.

6. Research ideas, research methods, and feasibility analysis

This study uses Web development technology and data visualization technology based on the Django framework, and combines practical application scenarios to design and implement a large-screen full-screen system for visualizing air quality data in Shanghai. Specific research ideas and methods include:

1. Requirements analysis: Through surveys and interviews, understand users’ needs and expectations for the system, and clarify the functional requirements and non-functional requirements of the system.
2. Technical research: Research and master Django framework, data visualization technology, large-screen full-screen display technology and other related technologies and tools.
3. System design: Based on the requirements analysis results and technical research results, design the overall architecture and module division of the system, and determine the functions and interaction methods of the front and back ends. Specifically, you need to design the database structure, API interface, front-end page, etc.
4. System implementation: Based on the system design results, carry out system development and implementation work, including back-end service construction, front-end page development, and front-end and back-end interaction implementation. Specifically, you need to use Python and Django frameworks for back-end development, and use HTML, CSS, JavaScript, etc. for front-end development to achieve large-screen and full-screen data visualization display functions.
5. System testing and evaluation: Use unit testing, integration testing, user testing and other methods to conduct comprehensive testing and performance evaluation of the system to verify whether the system's functions and performance meet expectations. Specifically, it is necessary to test whether the system's data management, user management, large-screen display and other functions are working properly, and to evaluate whether the system's response time, stability and other indicators meet the requirements.

Feasibility Analysis:

The technology and methods used in this study have a certain research foundation and practical experience at home and abroad. The Django framework and data visualization technology have been widely used and verified. At the same time, this research has received support and funding from relevant institutions and enterprises, and has certain practical application value and market prospects. Specifically, the feasibility of this study is mainly reflected in the following aspects:

1. Technical feasibility: The Django framework and related data visualization technologies are relatively mature and can meet the needs of this research. At the same time, this research team has relevant technical background and practical experience and is competent in the development and implementation of this research.
2. Economic feasibility: This research has received support and funding from relevant institutions and enterprises, and has certain funding guarantees. At the same time, this research can provide decision support and information services for the government, enterprises and the public, and has certain economic and social benefits.
3. Legal feasibility: This research complies with relevant laws, regulations and policies, and does not infringe other people's intellectual property rights or violate laws and regulations. At the same time, this research will implement strict security management and protection of user data to ensure that user privacy is not leaked.
4. Social feasibility: This research can provide governments, enterprises and the public with timely, accurate and intuitive air quality data information, helping to improve environmental quality, public health and quality of life. At the same time, this research can promote research and practical development in related fields and promote the development and application of related technologies such as data visualization technology and large-screen display technology.

7. System implementation and testing

In the system implementation stage, we used the Python and Django frameworks to build back-end services according to the designed architecture and modules, and implemented functions such as data management, user management, and log management. At the same time, we also used HTML, CSS, JavaScript, etc. to develop the front-end page, realizing the large-screen and full-screen data visualization display function. Specifically, we implemented the following features:

1. Data import and export: Supports importing air quality data from CSV, Excel and other files, and can also export data to these formats to facilitate data sharing and exchange.
2. Data query and modification: Supports querying air quality data based on time, location and other conditions, and can also modify and delete data.
3. User registration and login: Supports user registration and login functions, and uses technologies such as password encryption and verification codes to ensure the security of user accounts.
4. Large-screen display: Supports large-screen full-screen display of Shanghai's air quality data, including pollutant concentration, air quality index (AQI) and other information, and displays the time and spatial distribution characteristics of the data through charts, maps, etc.
5. Interactive operations: Supports interactive operations with mouse, touch screen and other devices, such as zooming in, zooming out, querying, etc., allowing users to view and operate air quality data more conveniently.

During the system testing phase, we used unit testing, integration testing, user testing and other methods to conduct comprehensive testing and performance evaluation of the system. Specifically, we conducted the following tests:

1. Unit testing: Each module of the system is tested individually to ensure that the functionality of each module works properly.
2. Integration testing: Integrate various modules of the entire system for testing to ensure that the interfaces and interactions between modules work properly.
3. User testing: Invite some users to conduct system testing, collect user feedback and suggestions, and improve and optimize the system.

Through testing and performance evaluation work, we have verified that the system's functions and performance meet the expected requirements and can be deployed and applied in practice.

8. Summary and Outlook

This study designed and implemented a large-screen full-screen system for Shanghai air quality data visualization based on Python and Django frameworks. Data display in a large-screen full-screen manner improved the data visualization effect and user experience. Specifically, we have implemented data management, user management, large-screen display and other functions, using the Django framework for back-end development and HTML, CSS, JavaScript, etc. for front-end development to achieve large-screen full-screen data visualization display functions. Through testing and performance evaluation work, it is verified that the functions and performance of the system meet the expected requirements and can be deployed and applied in practice.

In the future, we will continue to optimize and improve the functions and performance of the system and explore more data visualization technologies and interactive design methods to improve users' awareness and understanding of air quality data. At the same time, we will also consider applying the system to more cities and regions to provide services and support to more users and promote research and practical development in related fields.

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research background and meaning

With the continuous acceleration of urbanization, urban air pollution problems are becoming increasingly serious. Air quality monitoring data is of great significance for understanding the extent of air pollution and its impact on health. Currently, local government departments, monitoring stations, environmental protection agencies, etc. are collecting, organizing, analyzing and releasing air quality data. How to display these data to the public in an intuitive and easy-to-understand manner plays an important role in improving the public's understanding of air quality issues and regulating the government. Therefore, designing a large-screen full-screen system for visualizing Shanghai's air quality data based on the Python and Django frameworks is of great significance for improving Shanghai citizens' understanding of air quality and supervising urban environmental protection work.

Research status at home and abroad

At present, researchers at home and abroad have relatively mature research on the visualization of air quality data. Domestically, the more well-known air quality monitoring websites are the "China Air Quality Online Monitoring and Analysis Platform" and the "China Air Quality Real-time Release Platform" of the China Environmental Monitoring Station. These two websites call air quality monitoring data from various parts of the country. Data visualization services are also provided. In foreign countries, relatively mature air quality data visualization websites include the International Air Quality Index website and the US Air Quality Information website. These studies have made certain explorations and attempts at visual presentation of air quality data.

Research ideas and methods

The idea of ​​​​this research is to develop a large-screen full-screen system for Shanghai air quality data visualization based on python language and django framework. The main implementation method is to use crawler technology to obtain air quality data from different monitoring sites in Shanghai and store it in the database. Then, the data model and view template of the Django framework are used to visually present the obtained data and provide functions such as data filtering and positioning. Finally, the entire system is deployed to the cloud server to achieve full-screen display on the big screen.

Research internal customers and innovation points

The internal client of this study is the design and implementation of a large-screen full-screen system for Shanghai air quality data visualization based on the Django framework. The innovation of this study is that the system has the following innovations:

1. Data source: Air quality data is obtained through crawler technology, and the data is updated in real time.

2. Data visualization: Visualize data through charts, maps, etc., which is intuitive and clear.

3. Full-screen display: Use a large screen for full-screen display to facilitate public viewing and improve public awareness of air quality.

Backend functional requirement analysis and front-end functional requirement analysis

Backend functional requirements:

1. Database design and creation, including the name, unit, data type, etc. of air quality indicators;

2. Design and implementation of crawler program for air quality data;

3. Air quality data screening, analysis, storage and other functions;

4. User management, permission settings, login authentication and other functions.

Front-end functional requirements:

1. Update the air quality index in real time and display the air quality ranking;

2. Display measurement station distribution, real-time monitoring data and other information;

3. Provides interactive functions such as filtering, positioning, and comparing data from different measuring stations;

4. Provide data visualization presentation, including charts, maps, etc.

Research ideas, research methods, feasibility

The idea of ​​​​this research is the design and implementation of a large-screen full-screen system for Shanghai air quality data visualization based on the Django framework. The main method is to obtain air quality monitoring data through crawler technology, and then visually present the data through the data model and view template of the Django framework. , and deploy it to the cloud server to achieve full-screen display. This idea and method is highly feasible because:

1. The widespread use of Python language and Django framework is highly convenient for developers to use and learn;

2. The mature use of crawler technology can effectively obtain data from different sources;

3. Data visualization technical solutions have been widely used and supported by mature third-party libraries;

4. The application of cloud servers greatly reduces deployment and maintenance costs.

Research schedule

1. Determine research plans and protocols (completed);
2. Writing and implementing crawler programs (3 weeks);
3. Database design (1 week);
4. Storage and manipulation of air quality data (2 weeks);
5. Front-end design of visual presentation (2 weeks);
6. Implementation of user management, permission setting, login authentication and other functions (1 week);
7. System testing and modification (2 weeks);
8. System deployment and go-live (1 week).

Thesis (design) writing outline

1. Background and Significance;
2. Current research status at home and abroad;
3. Research ideas and methods;
4. Backend functional requirement analysis and front-end functional requirement analysis;
5. System design and implementation;
6. System testing and effect analysis;
7. Research conclusions and prospects.

main reference

1. Wu Zhongxin. Design and implementation of Web-based air quality monitoring system [J]. Modern Electronic Technology, 2019(2): 6-10.

2. Suga. Research on Internet news information extraction and processing based on Python and Django framework [D]. Master's thesis of Beijing University of Posts and Telecommunications, 2019.

3. Wang Shu. Design and implementation of an online question and answer system based on Python language and Django framework [J]. Science and Technology Information, 2019(20): 124-126.

4. Wang Yang. Research on Python-based data crawler technology and application[J]. Basic Education, 2019(5): 109-111.

5. Zhang Yahong. Design and implementation of image processing platform based on Django framework [D]. Master's thesis of Anhui University, 2019.