Freight train future speed prediction method and device based on brain-like acceleration and medium

CN121859966APending Publication Date: 2026-04-14CASCO SIGNAL LTD
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Patent Information

Application Number
CN202512039049.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-31
Publication Date
2026-04-14

AI Technical Summary

Technical Problem

Existing technologies for predicting freight train speed suffer from high modeling complexity, weak generalization ability, and poor real-time performance. In particular, they consume a lot of power in edge computing scenarios, making it difficult to meet the needs of real-time decision-making.

Method used

It employs a multi-head self-attention mechanism based on the Transformer architecture to capture long-term temporal features in parallel. Combined with neuromorphic computing chips and near-memory computing technology, it achieves low-power and high-efficiency computing through a three-level network structure of MLP encoder-Transformer encoder-MLP decoder.

Benefits of technology

It achieves high-precision, real-time, and generalization-oriented freight train speed prediction, maintaining high accuracy under complex and variable track conditions, reducing maintenance costs and improving computational efficiency.

✦ Generated by Eureka AI based on patent content.

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Abstract

The invention relates to a freight train future speed prediction method and device based on brain-like acceleration and a medium, the method inputs obtained freight train related data into a speed prediction model, a freight train future speed prediction result is output, the speed prediction model adopts a three-level network structure of an MLP encoder-Transformer encoder-MLP decoder, and the speed prediction model is used for predicting the freight train future speed. After the MLP encoder encodes the two paths of input respectively, the two paths of input are input into the Transform encoder for spatio-temporal feature extraction, decoding is carried out through the MLP decoder to obtain a prediction result, the forward propagation process of the Transform is converted into a brain-like instruction sequence, and the brain-like chip is controlled based on the brain-like instruction sequence to carry out operation so as to realize accelerated prediction. Compared with the prior art, the method has the advantages that the static characteristics can be dynamically processed in a time sequence mode, and the prediction efficiency can be improved by fully utilizing the brain-like chip.
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Citation Information

Patent Citations

  • Freight train optimal speed curve dynamic planning successive approximation method based on deep learning

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