海洋运输业的迅速发展对大型航运公司的船舶管理水平提出了更高要求，随着船舶数量的迅速增加，针对船舶航行异常轨迹的识别和监控技术成为了研究热点。本文对如何提高船舶航行异常轨迹的识别效果进行了研究，引入一种基于AIS和雷达的航行轨迹识别系统，基于云分段最优熵算法，实现了船舶航行异常轨迹数据的快速识别和剔除，有助于提高海上航运公司对船舶管理水平，及早发现船舶的异常航行状态并进行航线调整，防止船舶事故的发生。
The rapid development of marine transportation industry puts forward higher requirements for the ship management level of large shipping companies. With the rapid increase of the number of ships, the identification and monitoring technology of ship navigation abnormal trajectory has become a research hotspot. This paper studies how to improve the recognition effect of ship navigation abnormal trajectory, introduces a navigation trajectory recognition system based on AIS and radar, and realizes the rapid recognition and elimination of ship navigation abnormal trajectory data based on cloud segmented optimal entropy algorithm, which is helpful to improve the ship management level of maritime shipping companies. Find the abnormal navigation status of the ship as soon as possible and adjust the route to prevent the occurrence of ship accidents.
2022,44(7): 150-153 收稿日期：2021-08-24
DOI：10.3404/j.issn.1672-7649.2022.07.030
分类号：U612.22
作者简介：陈小海(1985-),男,硕士,讲师,主要从事计算机软件开发及算法研究
参考文献：
[1] 宋鑫, 朱宗良, 高银萍, 等. 动态阈值结合全局优化的船舶AIS轨迹在线压缩算法[J]. 计算机科学, 2019, 46(7): 6
[2] 郑义成, 莫钦华, 王海鸿. 基于ElasticSearch的海量AIS数据存储方法[J]. 指挥信息系统与技术, 2016, 7(3): 6
[3] 李名, 胡勤友, 孟良. 基于AIS的船舶运动轨迹压缩技术研究[J]. 航海技术, 2010(1): 3
[4] 马瑞鑫, 杨凯, 尚东方. 基于船舶自动识别系统和高频地波雷达的船舶轨迹数据融合[J]. 2022(5).
[5] 姚高乐, 邓斌, 林泽强, 等. 船舶AIS数据处理系统的研究与实现[J]. 商品与质量, 2017(43): 10
