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Graduation project proposal report on weather data prediction analysis and visualization system based on Python
1. Research background and significance
Weather phenomena have a profound impact on human life and production activities. Accurately predicting weather changes is of great significance to various fields such as agriculture, transportation, energy, and environmental protection. However, weather change is a complex nonlinear system, and traditional prediction methods often have low accuracy and poor timeliness. Therefore, this research is based on Python language, using big data technology and machine learning algorithms to design and develop a weather data prediction analysis and visualization system. This will help improve the accuracy and timeliness of weather forecasts and provide a more scientific and reliable basis for decision-making in various fields.
2. Research status at home and abroad
In recent years, with the rapid development of big data and artificial intelligence technology, research in the field of weather prediction has also made significant progress. Foreign countries have always been in a leading position in weather prediction technology. Institutions such as NOAA in the United States and ECMWF in Europe have advanced weather prediction systems and models. China has also made great progress in this regard. For example, units such as the China Meteorological Administration have made important contributions to the research and application of weather prediction technology.
However, most of the current weather prediction systems at home and abroad are based on professional meteorological observation data and complex mathematical models. For ordinary users, the threshold for use is high and they lack intuitive visual display. Therefore, this research aims to develop a weather data prediction analysis and visualization system based on Python that is easy to use and understand to meet the needs of a wide range of users.
3. Research ideas and methods
This study will adopt the following research ideas and methods:
- Data collection and processing: Collect historical weather data from public meteorological observation data sets, and perform preprocessing and feature extraction.
- Model construction and training: Use machine learning algorithms (such as LSTM, GRU, etc.) to build weather prediction models and use historical data for training.
- System design and development: Design and develop a weather data prediction analysis and visualization system based on Python language and related data analysis and visualization libraries (such as Pandas, Matplotlib, etc.). The system will include core functional modules such as data input, data processing, model prediction, and result visualization.
- System testing and optimization: Test the system and perform model optimization and system improvement based on the test results.
4. Research content and innovation points
The contents of this research include weather data collection and processing, weather prediction model construction and training, system design and development, etc. The innovation points are mainly reflected in the following aspects:
- Weather prediction analysis and visualization based on Python language and related libraries lowers the user threshold and improves the ease of use and understandability of the system.
- The use of big data technology and machine learning algorithms for weather prediction improves the accuracy and timeliness of predictions.
- An intuitive visual display interface is designed to facilitate users to understand and apply weather prediction results.
5. Backend functional requirement analysis and front-end functional requirement analysis
Backend functional requirements analysis: including data collection, processing, storage and other functions, which require the ability to achieve automated and efficient data processing and model training. At the same time, the backend should also provide an API interface for the frontend to call and display prediction results.
Front-end functional requirements analysis: It is required to provide a user-friendly interface and support functions such as real-time query of weather data, historical data display, and visualization of forecast results. The front end should also support multi-platform (such as PC, mobile phone, etc.) access to meet the needs of different users.
6. Research ideas and feasibility of research methods
The research ideas and methods used in this study are technically feasible. The Python language has rich libraries and tool support for data processing, machine learning, and visualization. At the same time, the application of big data technology and machine learning algorithms in the field of weather prediction has also been widely verified and recognized. Therefore, the technical route of this study is feasible and expected to achieve expected research results.
7. Research progress arrangement
- The first stage (1-2 months): Complete the literature review and determine the research goals and plan.
- The second stage (3-4 months): Complete data collection, processing and analysis.
- The third stage (5-6 months): Build and train a weather prediction model and optimize model performance.
- The fourth stage (7-8 months): Complete the design and development of the system, including the implementation of front-end and back-end functions.
- The fifth stage (9-10 months): Carry out system testing and optimization work to improve the user interface and interactive experience.
- Stage six (11-12 months): Write and complete the thesis and prepare for defense.