基于Openlayers3和Cassandra的船舶碰撞风险识别系统
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摘要
为提高船舶交通管理服务系统(VTS)智能化水平,保障航运安全,利用上海港水域内海量AIS数据,建立船舶领域模型,应用改进的聚类算法,识别船舶碰撞风险。利用开源地图框架Openlayers,实现碰撞风险可视化,利用Cassandra数据库,实现海量AIS数据存储和高效检索。设计实时船舶可视化显示,单船、区域船舶历史轨迹回放,模拟船舶航行等功能。最后使用上海港2015年全年AIS历史数据进行碰撞风险识别实验,结果和历史高危区域较为吻合,可作为辅助分析系统供海事人员决策参考。
The ship field model is established by applying the improved clustering algorithm to improve the intelligent level of vessel traffic service( VTS),ensure the safety of shipping,and identify the ship collision risk on the basis of the massive AIS data in Shanghai harbor. Using the open source map framework Openlayers, the collision risk visualization is realized,and the Cassandra database is used to realize the mass AIS data storage and efficient retrieval.After that,we designed the real-time ship visual display,single-ship,regional ship history track playback,simulation ship navigation and other functions. Finally,we used the AIS historical data of Shanghai in 2015 to carry out collision risk identification experiment. The results indicates the similar areas as the previous high-risk areas,which can be used as an auxiliary analysis system for maritime personnel decision-making reference.
The ship field model is established by applying the improved clustering algorithm to improve the intelligent level of vessel traffic service( VTS),ensure the safety of shipping,and identify the ship collision risk on the basis of the massive AIS data in Shanghai harbor. Using the open source map framework Openlayers, the collision risk visualization is realized,and the Cassandra database is used to realize the mass AIS data storage and efficient retrieval.After that,we designed the real-time ship visual display,single-ship,regional ship history track playback,simulation ship navigation and other functions. Finally,we used the AIS historical data of Shanghai in 2015 to carry out collision risk identification experiment. The results indicates the similar areas as the previous high-risk areas,which can be used as an auxiliary analysis system for maritime personnel decision-making reference.
引文
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[8]Hansen M G,Jensen T K,Lehn-Schiler T,et al.Empirical ship domain based on AIS data[J].Journal of Navigation,2013,66(6):931-940.
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[2]周梦婕,葛泉波,黄习刚,等.海上智能交通系统的新型船舶航行系统风险识别方案[J].中国航海,2013(4):114-118.
[3]李丽娜,王俊玲,陈国权.船舶拟人智能避碰决策理论的集成机器学习策略[J].信息与控制,2011,40(3):359-368.
[4]Liu Z,Hu M B,Jiang R,et al.Method to enhance traffic capacity for scale-free networks[J].Physical Review E,2007,76(3):037101.
[5]杨鹏,邹时林.基于OpenL ayers的WebG IS客户端的研发[J].测绘与空间地理信息,2012,35(3):131-133.
[6]Tang Y,Sun H,Wang X,et al.An efficient and highly available framework of data recency enhancement for eventually consistent data stores[J].Frontiers of Computer Science,2017,11(1):1-17.
[7]王博千,于齐,刘辛,等.面向Cassandra数据库的高效动态数据管理机制[J].计算机科学,2016,43(7):197-202.
[8]Hansen M G,Jensen T K,Lehn-Schiler T,et al.Empirical ship domain based on AIS data[J].Journal of Navigation,2013,66(6):931-940.
[9]Yan Z L,Luo W J,Bu C Y,et al.Clustering spatial data by the neighbors intersection and the density difference[C]//Proceedings of the 3rd IEEE/ACM International Conference on Big Data Computing,Applications and Technologies.ACM,2016:217-226.