Mango growers planting decision-making system: visual analysis of apple e-commerce sales data based on python crawler (django framework)

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Mango growers planting decision-making system: Visual analysis of apple e-commerce sales data based on Python crawler (Django framework) Proposal report

1. Research background and significance

With the rapid development of e-commerce, online sales have become an important channel for the circulation of agricultural products. However, the planting and sales of agricultural products are affected by many factors, such as market demand, price fluctuations, weather changes, etc., which pose great challenges to growers. In order to better grasp market dynamics, optimize planting strategies, and increase profits, growers need a tool that can obtain and analyze sales data in real time. This research aims to design a mango planting decision-making system based on Python crawler and Django framework. By crawling the sales data of the Apple e-commerce platform, visual analysis is performed to provide decision-making support for mango farmers.

2. Research status at home and abroad

In terms of data crawling, Python crawler technology is quite mature and is widely used in the field of Internet data acquisition. In the field of agriculture, some studies have used crawler technology to obtain agricultural product sales data, analyze market demand and price trends, and provide decision-making basis for growers. In terms of data visualization, various visualization tools and libraries emerge in endlessly, providing rich display methods for data analysis. However, there are relatively few studies on combining crawler technology with data visualization and applying it to mango planting decision-making systems.

3. Research ideas and methods

This research adopts the following ideas and methods:

1. Requirements analysis: By communicating with mango farmers, we understand their actual needs and expectations and clarify the functional and non-functional requirements of the system.
2. Technical research: In-depth study of Python crawler technology, Django framework, data visualization technology, etc., and master relevant principles and implementation methods.
3. System design: Based on the requirements analysis results and technical research results, design the overall architecture and module division of the system, and clarify the functions and implementation methods of each module.
4. System implementation: Carry out system development and implementation work according to the system design results, including the realization of back-end functions and the realization of front-end 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 ensure the stability and usability of the system.

4. Research content and innovation points

The research contents of this study mainly include:

1. Design and implementation of a decision-making system for mango growers: Use Python crawler technology to crawl the sales data of the Apple e-commerce platform, and build a Web application system through the Django framework to achieve real-time acquisition, storage and analysis of sales data.
2. Data visualization analysis: Use data visualization technology to display sales data in the form of charts, maps, etc., allowing users to intuitively understand sales, price trends, market demand and other information.
3. Planting decision support: Based on the analysis results of sales data, provide planting decision support to mango farmers, including suggestions on planting variety selection, planting area adjustment, sales strategy formulation, etc.

The innovation points are mainly reflected in the following aspects:

1. Combining Python crawler technology with data visualization, it is applied to the mango planting decision-making system to achieve real-time acquisition and analysis of sales data.
2. Using the Django framework to build a web application system improves the stability and usability of the system.
3. Through visual analysis of sales data, it provides mango farmers with intuitive decision-making basis, reducing the difficulty and risk of decision-making.
4. Based on the actual needs of mango farmers, a targeted planting decision support system was designed to improve planting efficiency and market competitiveness.

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

Backend function requirement analysis: The backend functions of this system mainly include user management, data management, data analysis and security management. Specifically, it needs to support user registration, login, rights management and other operations; support the import, export, query and modification of crawled sales data; conduct in-depth analysis and mining of sales data through algorithms and models, and provide Users provide valuable information; ensure system data security and user privacy, including data encryption, preventing SQL injection, etc.

Front-end functional requirements analysis: The front-end functions of this system mainly include data visualization display and interaction design. Specifically, it is necessary to support a variety of charts and map display methods so that users can intuitively understand sales, price trends, market demand and other information; provide a friendly user interface and interactive design so that users can easily view and operate sales Data; supports responsive layout, allowing the system to adapt to different screen sizes and devices.

6. Feasibility analysis

1. Technical feasibility: This study uses Python and Django frameworks for Web development, combined with data visualization technology, which are currently relatively mature and widespread applications. Through in-depth research and mastery of relevant technologies, the technical feasibility of the system can be guaranteed.
2. Economic feasibility: The implementation of this system can provide better planting decision support tools for mango farmers, reduce planting risks and increase profits. Therefore, the implementation of this system is feasible from an economic point of view.
3. Operational feasibility: This system will provide a friendly user interface and interactive design to enable users to easily view and operate sales data. At the same time, the system will also provide detailed user manuals and operation guides to help users better use the system. Therefore, the implementation of this system is feasible from an operational perspective.

7. Research progress arrangement

This research plan is divided into the following stages:
The first stage (1-2 months): Conduct demand analysis and technical research to clarify the functional requirements and non-functional requirements of the system Master relevant technologies and tools.
The second stage (2-4 months): Carry out system design to design the overall architecture and module division of the system to clarify the functions and implementation methods of each module. At the same time, complete the database design and construction work.
The third stage (4-6 months): Carry out system implementation. Carry out system development and implementation work based on the system design results, including the realization of back-end functions and front-end functions. At the same time, the data crawling and cleaning work is completed to provide basic data for data analysis.
The fourth stage (6-8 months): Carry out system testing and evaluation. Use unit testing, integration testing and user testing to conduct comprehensive testing and performance evaluation of the system to ensure the stability of the system. performance and availability. At the same time, complete data analysis work to provide users with valuable information and suggestions.
The fifth stage (8-10 months): System optimization and improvement. Based on test results and user feedback, the system optimization and improvement work includes performance optimization, interface optimization, function improvement, etc. At the same time, complete the writing of user manuals and operation guides to help users better use the system.
The sixth stage (10-12 months): Carry out system online and maintenance. Put the system online and perform maintenance and management work, including data update, system upgrade, user feedback processing, etc. to ensure the normality of the system. operation and continued development. At the same time, the writing and organizing work of the paper was completed to summarize and summarize the research results.

research background and meaning

Mango is one of the characteristic fruits in southern my country. It has high nutritional value and medicinal value and is widely used in food, beverages, cosmetics and other fields. The mango cultivation area is increasing year by year, but due to the lack of scientific planting decision-making support system, mango farmers face various problems in production and operation, such as production cannot meet market demand and sales prices fluctuate greatly. Therefore, establishing a mango planting decision support system based on data analysis has become an urgent problem that needs to be solved.

In recent years, the development of data crawlers and visualization technology has provided new ideas and methods for agricultural data analysis. Through the analysis of mango e-commerce sales data, we can effectively understand market demand and price trends, and provide scientific basis for mango planting decisions. At the same time, visualizing data into charts and other forms can make planting decisions more intuitive and easy to understand.

This research aims to build a mango planting decision support system based on python crawler and django framework. By collecting and analyzing mango e-commerce sales data, it can provide scientific planting decision support for mango farmers and provide support for the sustainable development of the mango industry. Strong technical support.

Research status at home and abroad

At present, agricultural decision support systems based on data analysis have been widely used. Domestic and foreign scholars have proposed a series of effective decision support methods and technologies by collecting, analyzing and using large amounts of agricultural data.

In terms of foreign research, scholars from the United States, Japan, the Netherlands and other countries have achieved certain research results in the field of agricultural data analysis. For example, the agricultural decision support system in the United States is mainly used in crop variety selection, fertilizer application, disaster management, etc., and has become a typical representative of agricultural decision support systems. Dutch scholars have established an agricultural production model based on remote sensing technology, which can monitor the growth status, yield and quality of crops in real time. Japanese scholars use data mining technology to analyze soil environment, meteorological conditions and other factors to predict and optimize rice growth.

In terms of domestic research, research on agricultural decision support systems has also made certain progress. For example, Professor Zou Caineng's team at Huazhong Agricultural University used meteorological data and agricultural management technology to establish a high-quality rice planting regional decision support system based on WebGIS. Professor Zhang Yibing’s team from Nanjing Agricultural University established a decision support system for rice quantitative nitrogen fertilizer application based on farmland microclimate monitoring data.

At present, although the research results at home and abroad are rich and diverse, their application in the field of mango cultivation is not extensive, and there is a lack of corresponding research results and practical experience. Therefore, this research has certain innovative and practical significance.

Research ideas and methods

The idea of ​​​​this research is to use a method that combines data crawlers and django framework to build a mango planting decision support system. The main methods of research include:

1. Data collection: Use Python to write a crawler program to collect mango sales data from multiple e-commerce platforms, and clean and process the data.

2. Data visualization: Use Python's visualization library to convert the collected data into charts and other forms so that mango farmers can understand market demand and price changes more intuitively and clearly.

3. Model analysis: Combined with the collected data, use statistics and machine learning methods to analyze the changing patterns of mango price trends, production cycles and profits, and provide scientific decision-making support for mango farmers.

4. System design: Use the Django framework to build the backend and frontend of the mango planting decision support system, including multiple functional modules such as user management, data maintenance, data query, data analysis and data visualization.

Research internal customers and innovation points

The internal client of this study is to establish a mango planting decision support system based on data analysis. Through the analysis of mango e-commerce sales data, it can provide scientific planting decision support for mango farmers and provide strong support for the sustainable development of the mango industry. technical support.

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

1. A visual mango planting decision support system was constructed by comprehensively applying python crawler and django framework technologies.

2. Data analysis technology is used to conduct in-depth analysis of mango e-commerce sales data to provide more scientific planting decision support for mango farmers.

3. Functional modules covering multiple aspects, including user management, data maintenance, data query, data analysis and data visualization, make the system more practical and valuable.

Backend functional requirement analysis and front-end functional requirement analysis

Backend functional requirements

1. User management: implement functions such as user registration, login, modification and deletion.

2. Data maintenance: Enter, modify and delete the collected mango sales data.

3. Data query: Provides a variety of query methods (such as date, price, production cycle, etc.) to facilitate mango farmers to find the data they need.

4. Data analysis: Analyze the collected data and provide a variety of analysis methods (such as price trends, production cycles, income, etc.) to provide scientific decision-making support for mango farmers.

5. Data visualization: Visualize analysis results into charts, heat maps, etc., making the data more intuitive and easy to understand.

Front-end functional requirements

1. User registration and login: Implement user registration and login functions to provide users with personalized services.

2. Data query and analysis: Mango farmers can query and analyze mango sales data to provide them with scientific decision-making support.

3. Data visualization: Visualize analysis results into charts, heat maps, etc., making the data more intuitive and easy to understand.

4. Interactive functions: Implement interactive functions such as data export and sharing to provide users with more convenient services.