Marine environment prediction method based on principal component analysis-converter fusion model

Through the combination of principal component analysis dimensionality reduction and converter model, the problem of insufficient selection of forecast factors in marine environmental factor prediction and the difficulty of traditional models to capture long-distance spatial dependence is solved, and efficient and accurate prediction of marine environmental factor is achieved.

CN119940123AInactive Publication Date: 2025-05-06TIANJIN UNIV
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Patent Information

Application Number
CN202510033346.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-01-09
Publication Date
2025-05-06
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

The existing marine environmental factor prediction methods have problems such as insufficient selection of forecast factors, and excessive forecast factors lead to low efficiency, and traditional models are difficult to capture the long-distance spatial dependence between marine environmental factors.

Method used

Key forecast factors are extracted using principal component analysis and dimensionality reduction technology, and input them into the converter model for prediction, and the long-term dependence relationship between marine environmental factors is captured through self-attention mechanism.

Benefits of technology

It significantly improves the accuracy and efficiency of marine environmental factors prediction, reduces the average relative error, and realizes long-term stable prediction of marine environmental factors.

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Abstract

The invention discloses a marine environment element prediction method based on a principal component analysis-converter fusion model. The method comprises the following steps: performing quality control and data cleaning on a marine environment element data set to realize time-space dimension unification of heterogeneous data; building a prediction model on the basis of a converter network, taking day-by-day data after quality control and data cleaning as a target variable of the model, performing dynamics mechanism analysis based on an observation time sequence of the target variable, and selecting a dynamics prediction factor; dimensionality reduction processing is carried out on the high-dimensional forecasting factors through principal component analysis, and key feature information is effectively extracted; and inputting the optimized features into a converter network for modeling prediction. Compared with a traditional converter model, the average prediction relative error of the fusion model provided by the invention is reduced by 30%, and the prediction precision is remarkably improved. According to the invention, a new thought is provided for improving the marine environment element prediction precision through deep fusion, and more reliable decision support can be provided for the fields of marine navigation safety and the like.
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