Ship weather risk and congestion probability prediction method based on multi-source data

By combining the model structure of CNN and LSTM, introducing attention mechanisms and dynamically analyzing multi-source data, the problems of poor data timeliness and low prediction accuracy in traditional prediction methods are solved, and high-precision weather risk and port congestion prediction are achieved, and reliable decision support is provided.

CN120277393APending Publication Date: 2025-07-08GUILIN UNIVERSITY OF TECHNOLOGY
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Patent Information

Application Number
CN202510438414.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-04-09
Publication Date
2025-07-08

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Abstract

The invention discloses a prediction method, and particularly relates to a ship weather risk and congestion probability prediction method based on multi-source data, which comprises a data preprocessing module, a data rebalancing module, a model training module and a weather and congestion risk prediction module, and is characterized in that the data preprocessing module is connected with the data rebalancing module; the data rebalancing module is connected with the model training module, and the model training module is connected with the weather and congestion risk prediction module. Compared with a traditional data processing method, the multi-source data processing method has the advantages that the multi-source data is comprehensively preprocessed, including data cleaning, normalization and denoising, and the quality and consistency of the data are effectively improved. Therefore, the model is more stable in performance under different weather and congestion conditions, so that the accuracy and reliability of risk prediction are improved.
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