Breeding decision-making system for chicken and duck farmers: visual analysis of chicken and duck e-commerce sales data based on python reptile (django framework)

 Blogger Introduction : Teacher Huang Juhua, author of the books "Getting Started with Vue.js and Mall Development" and "WeChat Mini Program Mall Development", CSDN blog expert, online education expert, CSDN Diamond Lecturer; focuses on graduation project education and guidance for college students.
All projects are equipped with basic knowledge video courses from entry to mastering, free of charge
The projects are equipped with corresponding development documents, proposal reports, task books, PPT, and papers. Templates, etc.

The project has recorded release and functional operation demonstration videos; the interface and functions of the project can be customized, and installation and operation are included! ! !

If you need to contact me, you can check Teacher Huang Juhua on the CSDN website
You can get the contact information at the end of the article

Breeding decision-making system for chicken and duck farmers: Visual analysis of chicken and duck e-commerce sales data based on Python reptile (Django framework)

1. Research background and significance

With the improvement of people's living standards, the demand for food has gradually increased. Poultry products such as chickens and ducks have always been favored by consumers in the market. However, due to fierce market competition, farmers face various difficulties in the breeding process. How to make scientific breeding decisions and improve breeding efficiency has become an urgent problem to be solved. Therefore, this research aims to design a visual analysis system for chicken and duck e-commerce sales data based on Python crawlers to help farmers make more scientific breeding decisions and improve breeding efficiency. Specifically, the significance of this study is mainly reflected in the following aspects:

1. Improve breeding efficiency: Through visual analysis of chicken and duck sales data on e-commerce platforms, farmers can more intuitively understand market demand and price trends, thereby formulating more scientific breeding plans and sales strategies and improving breeding efficiency.
2. Reduce breeding risks: Through the analysis of sales data, farmers can more accurately judge market trends and risks, avoid blindly expanding the scale of breeding or blindly following the trend of breeding, and reduce breeding risks.
3. Promote the development of the poultry industry: This system can provide technical support and solutions for poultry industries such as chickens and ducks, promote the digital transformation and upgrading of the poultry industry, and promote the development of the poultry industry.

2. Research status at home and abroad

At present, there have been many studies and practices on the visual analysis of e-commerce platform sales data at home and abroad. Abroad, some well-known e-commerce platforms such as Amazon and eBay have implemented visual analysis functions of sales data; domestically, e-commerce platforms such as Taobao and JD.com also provide corresponding data analysis tools. However, in the field of visual analysis of sales data of poultry products such as chickens and ducks, although some platforms provide simple data statistics functions, there is still a lack of professional analysis tools for farmers. Therefore, this study is prospective and practical.

3. Research ideas and methods

This study uses Python crawler technology to crawl the sales data of chickens, ducks and other poultry products on the e-commerce platform, cleans and processes the data and stores it in the database. Then, use the Django framework to build a backend server to implement data addition, deletion, modification, and other operations, and design an API interface for front-end calls. The front-end uses HTML, CSS, JavaScript and other technologies to realize the visual display and analysis functions of data. The specific research methods are as follows:

1. Data crawling: Use Python crawler technology to crawl the sales data of chickens, ducks and other poultry products on the e-commerce platform, including product names, prices, sales, reviews and other information.
2. Data cleaning and processing: Clean and process the crawled data, remove duplicate information, filter irrelevant data, etc.
3. Database design: Design the database table structure and store the cleaned data in the database.
4. Backend development: Use the Django framework to build a backend server to implement operations such as adding, deleting, modifying, and checking data, and designing API interfaces for front-end calls.
5. Front-end development: Use HTML, CSS, JavaScript and other technologies to realize the visual display and analysis of data, including charts, reports and other forms.
6. System testing and optimization: Test the system and optimize performance bottlenecks.

4. Research content and innovation points

The main contents of this study include:

1. Crawling and cleaning of chicken and duck e-commerce sales data: Crawling and cleaning of chicken and duck sales data on the e-commerce platform will provide basic data support for subsequent data analysis and visualization.
2. Database design and implementation: Based on the characteristics of chicken and duck sales data, the database table structure is designed to realize data storage and management.
3. Analysis and implementation of back-end functional requirements: Analyze the system's needs for farmers' breeding decisions, implement operations such as adding, deleting, modifying, and checking back-end data, as well as the design and implementation of API interfaces.
4. Analysis and implementation of front-end functional requirements: Design the interface layout and interaction method of the visual analysis system to realize the graphical display and analysis functions of data while taking into account the system's response speed and user experience.
5. System testing and optimization: Comprehensive testing of the system to identify and resolve potential problems and performance bottlenecks.

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

1. For the first time, a data visualization analysis system based on the Django framework has been designed for the sales data of poultry products such as chickens and ducks, which is forward-looking and practical.
2. Rich data visualization methods are used to display and analyze chicken and duck sales data, which improves the readability and ease of use of the data.
3. The real-time update of chicken and duck sales data on the e-commerce platform allows farmers to understand market dynamics and price trends in a more timely manner and make more scientific breeding decisions.

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

(1) Analysis of background functional requirements

1. User management: System administrators can add, delete, modify and query user information, including user names, passwords, contact information, etc.
2. Data management: Administrators can add, delete, modify and check the crawled chicken and duck sales data to ensure the accuracy and completeness of the data.
3. Report generation: The system can generate various sales reports according to user needs, such as sales reports, sales volume reports, etc., to facilitate users to conduct data analysis and comparison.
4. System settings: Administrators can perform basic settings on the system, such as changing login passwords, setting data update frequency, etc.

(2) Front-end functional requirements analysis

1. Data visualization: The system can display background data in the form of charts, such as line charts, bar charts, pie charts, etc., to facilitate users to intuitively understand the changing trends and distribution of sales data.
2. Data query: Users can query sales data based on time, product name and other conditions. The system supports fuzzy query and precise query.
3. Report export: Users can export the generated reports to Excel, PDF and other formats to facilitate offline analysis and sharing.
4. Real-time update: The system can update chicken and duck sales data on the e-commerce platform in real time to ensure that the information obtained by users is the latest.

7. Research ideas, research methods, and feasibility

This study uses Python crawler technology to obtain chicken and duck sales data on the e-commerce platform, and uses the Django framework to build a backend server to achieve data storage and management. The front-end uses HTML, CSS, JavaScript and other technologies to realize the visual display and analysis functions of data. Through the design and implementation of the system, it aims to help farmers make more scientific breeding decisions and improve breeding efficiency.

In terms of feasibility, the technologies used in this study are currently relatively mature and popular, such as Python crawler technology, Django framework, etc., which have been widely used and verified. At the same time, this research has also received support and cooperation from relevant companies and institutions, which provides guarantee for the smooth progress of the research.

8. Research progress arrangement

This research plan is divided into the following stages:

1. The first stage (1-2 months): Conduct demand analysis and technical research to determine the functional requirements and technical solutions of the system.
2. The second stage (2-3 months): Carry out system design and database construction, including the design and implementation of back-end and front-end.
3. The third stage (3-4 months): Carry out system development and testing, including the implementation of back-end functions, the development of front-end interfaces, and system testing and debugging.
4. The fourth stage (4-5 months): Carry out online operation and maintenance of the system, collect user feedback, and continuously improve and optimize the system.
5. The fifth stage (5-6 months): Conduct summary and evaluation, write the thesis and prepare for defense.

9. Thesis (design) writing outline

1. Introduction: Introduce the background and significance of the research, the current research status at home and abroad, and the goals and content of this research.
2. System requirements analysis: Elaborate the functional requirements of the backend and front-end to provide a basis for subsequent system design and implementation.
3. System design: Introduce the overall architecture of the system and the design ideas of each module, including database design, back-end module design, front-end module design, etc.
4. System implementation: Detailed description of the system implementation process including implementation methods and code implementation of key technologies.
5. System testing and optimization: Introduce system testing methods and results and optimize performance bottlenecks to improve system stability and ease of use.
6. Application examples and effect evaluation: Show the effect of the system in actual applications and evaluate it.
7. Conclusion and outlook: Summarize the main results and contributions of this study and propose follow-up research directions and improvement measures.
8. References: List relevant literature and materials cited in this study.

10. Main references

[Please insert reference here]

The above is the content of the project report on the breeding decision-making system for chicken and duck farmers: visual analysis of chicken and duck e-commerce sales data based on Python reptiles (Django framework). I hope it can meet your needs.

---

Proposal report: Research and development of breeding decision-making system for chicken and duck farmers

1. Research background and significance Chicken and duck breeding industry is an important part of agriculture and is of great significance to the development of rural economy and the increase of farmers' income. However, currently, chicken and duck farmers often face problems such as poor sales channels, large sales price fluctuations, and lack of analysis of sales data during the breeding process, resulting in unstable income for farmers. Therefore, visual analysis of chicken and duck e-commerce sales data to provide scientific decision support is of great practical significance for solving these problems.

2. Research status at home and abroad There have been some studies on the visualization of agricultural product sales data at home and abroad. For example, researchers use Python crawler technology to obtain agricultural product sales data and perform visual analysis on the data to help farmers understand market demand and adjust breeding plans. However, in the field of chicken and duck breeding, there is currently a lack of corresponding research and development, so this study has the significance to fill this gap.

3. Research ideas and methods The idea of ​​​​this research is based on Python crawler technology, collecting chicken and duck e-commerce sales data, and using the Django framework to conduct data visualization analysis. The specific method includes the following steps:

1. Data collection: Use Python crawler technology to automatically obtain sales data on the Chicken and Duck e-commerce platform, including sales quantity, price, region and other information.

2. Data cleaning: Clean and organize the collected data, remove duplicate data, deal with missing values, etc.

3. Data storage: Store the cleaned data in the database for subsequent analysis and visual display.

4. Data analysis: Use the Django framework to conduct data visualization analysis, including sales volume trend analysis, price fluctuation analysis, regional sales, etc.

5. Decision support: Based on the analysis results, provide scientific decision support for chicken and duck farmers, including rationally arranging breeding plans, selecting appropriate sales channels, etc.

4. Research on internal customers and innovation points The internal customers of this study are chicken and duck farmers. It aims to provide farmers with scientific decision-making support and help them increase sales income through visual analysis of chicken and duck e-commerce sales data. The innovations of this study are mainly reflected in the following aspects:

1. Data collection: By using Python crawler technology, sales data on the chicken and duck e-commerce platform are automatically collected, avoiding the tedious process of manual data entry.

2. Data visualization: Using the Django framework for data visualization analysis, farmers can intuitively understand the changing trends and key influencing factors of sales data.

3. Decision support: Based on data analysis results, provide farmers with scientific decision-making support to help them adjust their breeding plans, choose appropriate sales channels, and increase sales revenue.

5. Backend functional requirement analysis and front-end functional requirement analysis Backend functional requirement analysis mainly includes functions such as data collection, data cleaning, data storage and data analysis. Front-end functional requirements analysis mainly includes data visualization display and decision support functions.

Backend functional requirements include:

1. Data collection function: realize the automatic collection of sales data on the chicken and duck e-commerce platform, and save the data into the database.

2. Data cleaning function: Clean and organize the collected data, such as removing duplicate data, processing missing values, etc.

3. Data storage function: Store the cleaned data in the database to facilitate subsequent data analysis and visual display.

4. Data analysis function: Use the Django framework to conduct visual analysis of sales data, including sales volume trend analysis, price fluctuation analysis, regional sales, etc.

Front-end functional requirements include:

1. Data visualization display function: Display sales data in the form of charts to intuitively reflect sales trends and key influencing factors.

2. Decision support function: Based on the results of data analysis, provide farmers with scientific decision-making support, including adjusting breeding plans, selecting appropriate sales channels, etc.

6. Research ideas, research methods, and feasibility The idea of ​​this research is based on Python crawler technology and Django framework to collect chicken and duck e-commerce sales data and conduct visual analysis to provide scientific decision-making support. This idea is feasible because Python crawler technology is mature and widely used, and the Django framework provides a wealth of tools and libraries to achieve visual analysis of data.

7. Research Progress Arrangement This research plan is divided into the following stages:

1. Research preparation stage (1 week): Conduct research on the chicken and duck breeding industry to understand the characteristics and needs of chicken and duck e-commerce sales data.

2. Data collection and cleaning stage (2 weeks): Use Python crawler technology to collect chicken and duck e-commerce sales data, and clean and organize the data.

3. Data storage and analysis stage (2 weeks): Store the cleaned data in the database, and use the Django framework for data visualization analysis.

4. Decision support and function development stage (2 weeks): Based on the analysis results, develop decision support functions and realize data visualization display.

5. Testing and improvement phase (1 week): Test and optimize the system to improve system functions and user experience.

8. Thesis (design) writing outline

1. Introduction 1.1 Research background and significance 1.2 Research status at home and abroad 1.3 Research ideas and methods

2. System design and implementation 2.1 Analysis and implementation of back-end functional requirements 2.2 Analysis and implementation of front-end functional requirements

3. Data analysis and visualization 3.1 Data collection and cleaning 3.2 Data storage and analysis 3.3 Data visualization display

4. Decision support and result analysis 4.1 Implementation of decision support function 4.2 Analysis of data analysis results

5. Test and Optimize

6. Conclusion and Outlook