Ocean buoy life prediction method based on multi-class machine learning method

Abstract

The invention discloses an ocean buoy life prediction method based on a multi-class machine learning method, and the method comprises the specific steps: S1, building different buoy life prediction models based on the machine learning method, carrying out the feature selection of each hardware feature of a buoy, obtaining the static attribute of the buoy, and enabling the survival time of the buoyto serve as a dynamic attribute, jointly forming a data set for training a buoy life prediction model, and further evaluating the prediction accuracy of the buoy life prediction model; wherein the buoy life prediction model comprises a regression decision tree, a gradient boosting regression tree, a random forest and a support vector regression machine; and S2, respectively inputting the to-be-predicted data set into the trained buoy life prediction model to obtain four prediction results, and obtaining a final prediction result according to the four prediction results. According to the method, prediction results of various models are comprehensively considered to make an optimal decision, and the accuracy of the prediction method is effectively improved.

Description

technical field [0001] The present invention relates to the technical field of marine monitoring equipment, and more specifically relates to a marine buoy life prediction method based on multi-class machine learning methods. Background technique [0002] The buoy survival time prediction problem is essentially the remaining useful life prediction problem (Remaining Useful Life, RUL), that is, to predict the time interval between the current moment and the death moment of the buoy. Existing RUL prediction methods can be mainly divided into two categories, that is, prediction methods based on physical models, and the other is based on data-driven prediction methods. Traditional RUL prediction methods based on physical models assume that the degradation model is known in advance, and use monitoring data to estimate the parameters of the model online or offline, which has the advantages of accurate prediction results and strong interpretability. However, the degradation model i...

AI Technical Summary

Problems solved by technology

Among them, traditional mathematical statistical models such as AutoRegressive Moving Average model (ARMA) and Markov Model (Markov Model) are easily disturbed by noise in the data and are not suitable for long-term forecasting problems.

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