Novel power supply train multi-target optimization-oriented intelligent operation control prediction method

Abstract

The invention discloses a novel power supply train multi-target optimization-oriented intelligent operation control prediction method. According to the method, original data is obtained via static anddynamic big data of a novel power supply train, and parameterization / regularization and normalization are carried out to obtain train information sequence data; train operation state data under an ideal condition is obtained by utilizing local data in the train information sequence data; the train operation state data is input into a long and short-term memory network for training so as to obtaina basic model, the train information sequence data is divided into multiple parts which are input into the basic model in sequence and then retrained to obtain a basic model with knowledge; square loss training is established; and a practically acquired speed is input into the basic model with the knowledge for prediction output so as to obtain traction at the next moment. The method is independent of mass data, so that the training data size is reduced, the model according with the train operation data distribution is obtained, the model correctness is improved and the model can be conveniently and directly used for practical application.

Description

technical field [0001] The invention relates to an industrial control operation prediction regression method in the field of computer deep learning, and in particular to a multi-objective optimization intelligent operation control prediction method for new-type power supply trains. Background technique [0002] The train operation control model oriented to multi-objective optimization is novel in technology, involves many parameters and complex correlations, and the traditional modeling method based on mechanism and expert knowledge is difficult to deal with. To do this, a data-driven machine learning approach is needed to build relevant models. [0003] The train operation control directly affects the energy consumption during the train operation. In order to ensure the normal operation of the train, in the process of solving the multi-objective optimization-oriented train operation control strategy, it is necessary to use the parameters of the train power supply system as ...

AI Technical Summary

Problems solved by technology

[0005] Therefore, the operation control of the new power supply train has many parameters, complex correlations, and little available data, making it difficult to directly build a model

The traditional modeling method based on mechanism and expert knowledge has long modeling time and high cost, and the accuracy of the model does not support direct use in actual scenarios

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