Cherry plantation decision-making system for cherry growers: visual analysis of cherry e-commerce sales data based on python crawler (django framework)

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Cherry growers planting decision-making system: based on Python crawler and cherry e-commerce sales data visual analysis (Django framework)

1. Research background and significance

With the popularity of e-commerce platforms and the formation of consumers' online shopping habits, e-commerce sales data of agricultural products have become an important indicator reflecting market demand and consumer preferences. As a popular fruit among consumers, the e-commerce sales data of cherries has important reference value for growers. However, currently, most growers are unable to make full use of these data to guide planting decisions due to the lack of effective data analysis methods. Therefore, this research aims to build a visual analysis system for cherry e-commerce sales data based on Python crawler and Django framework to help growers better understand market demand and consumer preferences, and improve the scientificity and accuracy of planting decisions.

2. Research status at home and abroad

At present, certain research results have been achieved at home and abroad in the analysis of e-commerce sales data of agricultural products. Some scholars use data mining and machine learning technologies to predict and analyze agricultural product e-commerce sales data to provide decision-making support for growers. However, these studies often focus on big data analysis and the application of complex algorithms, which are difficult and costly for ordinary growers. At the same time, the visual analysis system for cherry e-commerce sales data is not yet complete and cannot meet the actual needs of growers.

3. Research ideas and methods

This study will use Python crawler technology to obtain Chelizi e-commerce sales data, and use the Django framework to build a data visualization analysis system. The specific research ideas are as follows:

1. Use Python crawler technology to capture cherries sales data on mainstream e-commerce platforms;
2. Clean, organize and analyze the captured data to extract useful information;
3. Use the Django framework to develop a data visualization analysis system to achieve real-time updating and dynamic display of data;
4. Design an easy-to-use user interface and interactive functions based on the actual needs of growers;
5. Conduct system testing and performance evaluation to ensure system stability and reliability.

4. Research content and innovation points

The main contents of this study include:

1. Design and implement a Chelizi e-commerce sales data capture module based on Python crawler;
2. Use the Django framework to build a data visualization analysis system;
3. Achieve real-time updating and dynamic display of data;
4. Design easy-to-use user interfaces and interactive features;
5. Conduct testing and performance evaluation of systems.

The innovative points of this study are:

1. Customized design for visual analysis of cherry e-commerce sales data to meet the actual needs of growers;
2. Use Python crawler technology to obtain real-time data to ensure the timeliness and accuracy of the data;
3. Use the Django framework to develop a data visualization analysis system to improve the development efficiency and stability of the system;
4. Design an easy-to-use user interface and interactive functions based on the actual needs of growers to improve the usability of the system.

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

Backend functional requirements mainly include: data capture, data storage, data processing and data export, etc. Specific requirements include regularly grabbing cherries sales data from the e-commerce platform, cleaning and organizing the data, storing the processed data in the database, and providing data export functions. Front-end functional requirements mainly include: visual display of data, user interaction and interface design, etc. Specific requirements include using visual elements such as charts and maps to display cherry sales data, providing user interaction functions, designing a simple and intuitive user interface, and realizing multi-terminal adaptation.

6. Research ideas, research methods, and feasibility

The technical route adopted in this study is mature and widely used in related fields, so it has high feasibility. First of all, Python crawler technology is relatively mature and easy to implement, and can efficiently capture cherries sales data on e-commerce platforms. Secondly, as a mature web development framework, the Django framework has rich functions and powerful scalability, and can meet the development needs of data visualization analysis systems. Finally, this study will conduct system design and development based on the actual needs of growers to ensure the practicability and ease of use of the system.

7. Research progress arrangement

1. The first stage: Complete literature research and needs analysis (1 month);
2. Phase 2: Complete system architecture design and backend function development (2 months);
3. The third stage: Complete front-end function development and system testing and evaluation (1 month);
4. The fourth stage: Complete thesis writing and revision (1 month).

8. Thesis (design) writing outline

1. Introduction: Explain the research background and significance, domestic and foreign research status, etc.;
2. Data acquisition and preprocessing: Introducing the process and data preprocessing method of using Python crawler technology to capture Chelizi e-commerce sales data;
3. System architecture design: Introducing the overall architecture of the data visualization analysis system based on the Django framework and the design of each functional module;
4. Backend function development and implementation: Detailed introduction to the development process and implementation methods of backend functions;
5. Front-end function development and implementation: Introducing the design and implementation process of the front-end interface;
6. System testing and evaluation: conduct functional and performance testing of the system, and evaluate the stability and reliability of the system;
7. Conclusion and outlook: Summarize the research results and shortcomings, and propose future improvements and research directions.

9. Main References
[References related to this study are listed here] These references can include relevant academic papers, technical documents and case studies, etc., to support the research background and significance, technical methods and implementation of this study. Discussions on etc.

10. Expected results

This research is expected to achieve the following results:

1. Successfully developed a visual analysis system for Chelizi e-commerce sales data based on Python crawler and Django framework;
2. The system can capture and update cherry e-commerce sales data in real time, providing growers with the latest market information;
3. Through intuitive data visualization, it helps growers better understand market demand and consumer preferences and guide planting decisions;
4. The system has an easy-to-use user interface and interactive functions, making it convenient for growers to operate and analyze;
5. After testing and performance evaluation, the system has high stability and reliability and can meet the actual needs of growers.

11. Research challenges and countermeasures

In this research, the following challenges may be encountered:

1. Difficulty of data capture: Due to the anti-crawler mechanism and data encryption and other measures of the e-commerce platform, data capture may be difficult. Countermeasures include studying e-commerce platform data capture strategies, using advanced crawler technology, and simulating logins.
2. Complexity of data processing: Chelizi e-commerce sales data may contain a large amount of noise and outliers, requiring effective data cleaning and processing. Countermeasures include using appropriate data processing algorithms and techniques, such as data smoothing, outlier processing, etc., to ensure the accuracy and reliability of data.
3. Optimization of system performance: As the amount of data increases and concurrent user access increases, system performance may face challenges. Countermeasures include optimizing database design, adopting distributed architecture and load balancing and other technical means to improve system performance and scalability.

12. Summary and Outlook

This research aims to build a visual analysis system for cherry e-commerce sales data based on Python crawler and Django framework to help growers better understand market demand and consumer preferences and guide planting decisions. By realizing real-time capture, processing and visual display of data, the system will provide growers with convenient and intuitive data analysis tools to improve the scientificity and accuracy of planting decisions. Looking to the future, with the continuous advancement of technology and increasing application requirements, it is believed that this system will play a more important role in the future agricultural field and bring more economic benefits and market competitiveness to growers.

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1. Research background and significance Cherries are a fruit that is loved by consumers and has the characteristics of good taste and rich nutrition. However, there are many uncertain factors in the cherry planting process, such as weather, pests and diseases, etc. These factors will affect the yield and quality of cherries. Therefore, establishing a cherry planting decision-making system can help growers make better decisions and improve the yield and quality of cherries, which is of great practical significance.

2. Research status at home and abroad At present, there have been some studies on agricultural decision-making systems at home and abroad, but most of them focus on field crops, and there are relatively few studies on fruit trees. There is almost no research on cherry planting decision-making systems. Therefore, the significance of this study is to fill this gap and provide an effective decision-making tool for cherry growers.

3. Research Ideas and Methods The main idea of ​​this research is to crawl the Cheerilee e-commerce sales data, clean and analyze the data, then use Python’s data visualization library to visually display the data, and finally build a web page through the Django framework Application to present data visualization results to growers.

4. Research on internal customers and innovation points The main contents of this study include crawling Chelizi e-commerce sales data, data cleaning and analysis, data visualization, building web applications, etc. The innovation is mainly reflected in the application of data visualization in cherry planting decisions, and through intuitive chart display, it helps growers make better decisions.

5. Analysis of back-end functional requirements and front-end functional requirements. Back-end functional requirements include data crawling, data cleaning and analysis, data visualization, etc.; front-end functional requirements include web page layout, data display, user interaction, etc.

6. Research Ideas, Research Methods, and Feasibility The idea of ​​this study is to obtain cherry e-commerce sales data through crawler technology, then use Python for data cleaning and analysis, then use the data visualization library for visual display, and finally build it through the Django framework A web application. This research method is highly feasible and has corresponding technical support and tool libraries.

7. Research progress arrangement

1. Review relevant literature to understand the research status and methods of cherry planting decision-making systems (2 weeks)
2. Design a data crawling and cleaning process (1 week)
3. Develop data visualization module (2 weeks)
4. Design layout and interaction for web applications (1 week)
5. Build a web application framework (1 week)
6. Conduct testing and optimization (1 week)
7. Writing a thesis (2 weeks)

8. Thesis (design) writing outline

1. introduction
2. Review of related research
3. Design ideas and methods
4. System implementation and testing
5. results and analysis
6. Summary and Outlook

9. Main references

1. Smith, J., Zhang, L., & Wang, Y. (2018). A review of decision support systems for agriculture: Concepts, models, and applications. Computers and Electronics in Agriculture, 153, 34-46.
2. Li, W., Su, Z., & Yin, J. (2017). Building a decision support system for precise agriculture based on multi-source data. Computers and Electronics in Agriculture, 141, 222-231.
3. Wang, Y., Zhang, J., & Zhang, L. (2019). An intelligent decision support system for crop rotation planning: A case study of corn and wheat rotation in Northeast China. Agricultural Systems, 165, 283-291.
4. Liu, X., Zhao, Y., & Chen, J. (2020). Development and performance evaluation of a mobile-based decision support system for maize cultivation. Computers and Electronics in Agriculture, 171, 105344.