Blogger introduction : 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; Focus 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! ! !
Contact information can be found at the end of the article
Graduation project proposal report on agricultural product price information detection and analysis visualization system based on Python
1. Research background and significance
Agricultural product prices are an important indicator of the agricultural market and are of great significance to farmers, consumers and governments. However, the prices of agricultural products are affected by many factors, such as climate, season, supply and demand, etc. The prices fluctuate greatly, making prediction and regulation difficult. Therefore, establishing a Python-based agricultural product price information detection, analysis and visualization system is of great significance for realizing real-time monitoring, analysis and prediction of agricultural product prices and improving market transparency and decision-making efficiency.
This research aims to use Python's powerful data processing and analysis capabilities, combined with visualization technology, to develop a set of agricultural product price information detection and analysis visualization systems. The system will be able to realize functions such as real-time monitoring of agricultural product prices, data cleaning, feature extraction, modeling analysis, prediction and visualization, and provide decision support and market information services to the government, enterprises and farmers.
2. Research status at home and abroad
In the field of agricultural product price analysis, there is a certain research foundation at home and abroad. At present, domestic and foreign research mainly focuses on the analysis of influencing factors of agricultural product prices, the construction of price prediction models and the study of price fluctuation patterns. In terms of data processing and analysis methods, statistical methods, time series analysis, machine learning and other methods are mainly used. However, existing research still has certain shortcomings in real-time monitoring of agricultural product price information, multi-dimensional data analysis and visualization.
3. Research ideas and methods
This study will adopt the following research ideas and methods:
-
Data collection: Obtain agricultural product price data from major agricultural product trading platforms, government public data and other channels through crawler technology.
-
Data preprocessing: Clean, integrate and standardize raw data to ensure data quality and availability.
-
Real-time monitoring: Use Python to realize the real-time monitoring function of agricultural product prices and obtain price fluctuations in a timely manner.
-
Data analysis: Comprehensive use of statistical methods, time series analysis, machine learning and other technologies to conduct in-depth mining and analysis of agricultural product price data to reveal price fluctuation patterns and influencing factors.
-
Visual display: Use the Python visualization library to visually display the analysis results in the form of charts, maps, etc., to provide users with intuitive data interpretation.
4. Research content and innovation points
The research contents of this study include the collection and preprocessing of agricultural product price data, the development of real-time monitoring functions, the research and implementation of multi-dimensional data analysis algorithms, data visualization and system implementation, etc. The innovation points are mainly reflected in the following aspects:
-
Build a complete agricultural product price information detection and analysis visualization system based on Python to achieve all-round monitoring and analysis of agricultural product prices.
-
Introduce advanced crawler technology and machine learning algorithms to achieve real-time acquisition and intelligent analysis of agricultural product price data.
-
Design and implement multi-dimensional data analysis functions, including price fluctuation pattern mining, influencing factor analysis, etc., to provide comprehensive data support for agricultural product market participants.
-
Use visualization technology to intuitively display agricultural product price analysis results, reduce the difficulty for users to understand the data, and improve decision-making efficiency.
5. Backend functional requirement analysis and front-end functional requirement analysis
Backend functional requirements mainly include data collection and cleaning, real-time monitoring module development, data analysis and modeling and other functions. At the same time, in order to ensure system stability and security, the backend also needs to have functions such as user rights management, data backup and recovery.
Front-end functional requirements mainly include user interaction interface design, real-time data display, historical data query and visualization and other functions. The front-end design needs to be concise and clear, provide a friendly user experience, and meet the needs of different users.
6. Research ideas and feasibility of research methods
The Python language used in this study has extensive applications and mature technical support in data processing, crawler technology, machine learning, and visualization. At the same time, team members have a solid programming foundation and research experience in related fields, and are capable of completing the goals and tasks of this research. Therefore, this research idea and research method are feasible.
7. Research progress arrangement
- The first stage (1-2 months): Conduct literature review and background research, clarify research objectives and research content; complete data collection and pre-processing.
- The second stage (3-4 months): Develop real-time monitoring functions to achieve real-time acquisition and display of agricultural product prices.
- The third stage (5-6 months): Research and implement multi-dimensional data analysis algorithms, including mining price fluctuation patterns and analyzing influencing factors.