为了给不同海况下的船舶安全航行提供保障，设计基于数据驱动的船舶航线实时优化方法。利用数据驱动方法采集船舶历史航线、海域风速、风向、波高等海况数据，选取K-means聚类算法聚类海况数据，构建海况知识库。依据海况知识库内的船舶航线信息与航线转向点信息，划分船舶航线为不同航段。依据船舶航线的航段划分结果，以航行总时间最短以及总油耗最低为目标函数，设置船舶航速约束与转向点位置约束作为约束条件，构建航线实时优化模型。选取蚁群算法求解所构建的优化模型，输出航线实时优化结果。结果表明，该方法可以实时优化航线，降低船舶的航行时间与主机油耗，适用于不同海况的船舶航行。
The real-time optimization method of ship route based on data is studied to provide the basis for ship safe navigation under different sea conditions. The data driven method is used to collect the historical ship route, sea speed, wind direction and wave level sea state data, and the K-means clustering algorithm is selected to cluster the sea state data and build the sea state knowledge base. According to the information of ship route and turn point in sea state knowledge database, ship route is divided into different segments. According to the results of segment division of ship route, taking the shortest total sailing time and the lowest total fuel consumption as the objective function, the ship speed constraint and the position constraint of turning point as the constraint conditions, the ship route real-time optimization model is constructed. Ant colony algorithm is selected to solve the optimization model, and the real-time optimization results of ship routes are output. The experimental results show that the proposed method can optimize ship route in real time, reduce ship sailing time and fuel consumption of main engine, and is suitable for ship sailing applications in different sea conditions.
2023,45(18): 154-157 收稿日期：2023-05-26
DOI：10.3404/j.issn.1672-7649.2023.18.027
分类号：U675
基金项目：江苏省职业教育“双师型”名师工作室培养项目（2022-09）
作者简介：臧继明(1981-),男,硕士,讲师,研究方向为航海技术
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