通过对船舶航线数据趋势性预测实现航线智能调度，提出基于深度学习的船舶航线数据趋势性预测方法。构建集中式访问和分布式传感结合的方式，实现对船舶航线数据采样和融合处理。采用负载均衡调度和数据块分组特征距离方法进行船舶航线数据趋势性相关特征检测，采用深度学习和时间序列标签特征检测方法实现对船舶航线数据的多元时间序列的分布重构，根据重构结果和数据聚类趋势性实现对船舶航线数据趋势性预测。由仿真结果可知该方法对航线数据趋势性预测的聚类性较好，预测精度较高。
By predicting the trend of ship route data to achieve intelligent route scheduling, a deep learning based method for predicting the trend of ship route data is proposed. Constructing a combination of centralized access and distributed sensing to achieve sampling and fusion processing of ship route data, using load balancing scheduling and data block grouping feature distance methods for trend related feature detection of ship route data, and using deep learning and time series label feature detection methods to achieve distribution reconstruction of multiple time series of ship route data, According to the reconstruction results and cluster analysis trend, the trend prediction of ship route data is realized. The simulation results show that this method has good clustering performance and high prediction accuracy in predicting the trend of route data.
2023,45(10): 164-167 收稿日期：2022-11-22
DOI：10.3404/j.issn.1672-7649.2023.10.033
分类号：TP391.1
基金项目：广西高校中青年教师科研基础能力提升项目（2023KY1867）
作者简介：韦丽兰(1982-),女,硕士,讲师,研究方向为优化控制及数学教育
