摘要
为挖掘船舶现有习惯航路并对航行过程中的风险进行预警,结合水域船舶历史AIS数据、水域水文信息等开展航路提取和航行风险预警相关研究。采用Alpha Shape算法建立不同水位下的航路边界提取模型;利用动态时间规整(DTW)算法提取不同水位下的航路中心线;采用速度障碍法量化表征航行风险,实现复杂航行环境下的风险主动预警与可视化。以黄河兰州段作为应用场景,实验结果表明,所提的算法能有效提取不同水位下的航路边界和航路中心线,有助于提高船舶航行安全;建立的风险预警方法与传统的CPA风险模型相比,在风险计算结果上基本一致,在多目标风险计算和可视化方法表现更优。
To identify existing habitual route of vessels and provide risk warnings during navigation,the route extraction and navigation risk warning were investigated by integrating historical AIS data of vessels and hydrological information of the waterway.The Alpha Shape algorithm was employed to establish a route boundary extraction model under different water levels.The dynamic time warping(DTW)algorithm was utilized to extract route centerlines under varying water levels.It quantifies and represents navigation risks by using the velocity obstacle framework,enabling risk warning and visualization in complex navigation environments.Applying this approach to the Lanzhou of the Yellow River as the application scenario for route extraction and navigation risk warning,the experimental results demonstrate that the proposed algorithm effectively extracts route boundaries and centerlines under different water levels,contributing to enhanced vessel navigation safety;the established risk warning methodology is basically the same in terms of risk calculation results and performs better in terms of multi-objective risk calculation and visualization methods compared to the traditional CPA risk model.
作者
董晞中
牟军敏
何林甫
吴宝祺
DONG Xi-zhong;MOU Jun-min;HE Lin-fu;WU Bao-qi(Gansu Provincial Water Transport Development Center,Lanzhou 730000,China;School of Navigation,Wuhan University of Technology,Wuhan 430063,China;Wuhan Institute of Shipbuilding Technology,Wuhan 430050,China)
出处
《船海工程》
北大核心
2025年第4期173-180,共8页
Ship & Ocean Engineering
基金
国家自然科学基金(52271367)。
关键词
数据驱动
航路提取
航行预警
智能航运
data driven
route extraction
risk warning
intelligent shipping
