The invention provides a ship motion prediction and compensation method based on domain adaptive transfer learning, and aims to solve the prediction and compensation problems of rolling motion of a service workboat in offshore wind power construction under complex sea conditions. According to the method, ship rolling angle data are collected in real time through a sensor, and time similarity quantitative division is carried out on the data to divide the data into source domain motion data and target domain motion data. And then, processing the data by using a domain adaptive transfer learning model, and predicting ship rolling motion at a future moment. The prediction result and actual data are subjected to inverse kinematics solution to obtain the expansion and contraction amount of the six electric cylinders of the Stewart platform, the expansion and contraction amount is fed back to a ship control system, the ship attitude is adjusted in real time, and the operation safety and efficiency are improved. According to the method, model parameters can be dynamically adjusted according to feature differences under different sea conditions, the prediction precision is remarkably improved, the method is suitable for rolling motion prediction and compensation under variable sea conditions, and improvement of the safety and stability of SOV operation is facilitated.
本发明提供了一种基于领域自适应迁移学习的船舶运动预测补偿方法,旨在解决海上风电施工中服务作业船横摇运动在复杂海况下的预测与补偿问题。该方法通过传感器实时采集船舶横摇角度数据,并对数据进行时间相似性量化划分,分为源域与目标域运动数据。接着,利用领域自适应迁移学习模型处理这些数据,预测未来时刻的船舶横摇运动。通过将预测结果与实际数据进行运动学反解,得到Stewart平台六个电动缸的伸缩量,反馈至船舶控制系统,实时调整船舶姿态,以提高作业安全性与效率。该方法能够根据不同海况下的特征差异,动态调整模型参数,显著提高了预测精度,适用于多变海况下的横摇运动预测与补偿,有助于提升SOV作业的安全性与稳定性。
Ship motion prediction compensation method based on domain adaptive transfer learning
一种基于领域自适应迁移学习的船舶运动预测补偿方法
2025-05-02
Patent
Electronic Resource
Chinese
IPC: | B63B Schiffe oder sonstige Wasserfahrzeuge , SHIPS OR OTHER WATERBORNE VESSELS |
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