区间化AIS数据驱动的抵泊过程多维控制模型

doi:10.16265/j.cnki.issn1003-3033.2021.03.007

中国安全科学学报 ›› 2021, Vol. 31 ›› Issue (3): 47-53.doi: 10.16265/j.cnki.issn1003-3033.2021.03.007

区间化AIS数据驱动的抵泊过程多维控制模型

秦庭荣¹ 讲师, 李东阳¹, 李鹏昊¹, 马国梁¹, 陈伟炯² 教授

1 上海海事大学商船学院,上海 201306;
2 上海海事大学海洋科学与工程学院,上海 201306

收稿日期:2020-12-06 修回日期:2021-02-05 出版日期:2021-03-28 发布日期:2021-12-20
作者简介:秦庭荣 (1976—),男,安徽和县人,博士,讲师,主要从事船舶安全管理与评估技术、航运大数据挖掘技术等方面的研究。E-mail: trqin@shmtu.edu.cn。
基金资助:
国家自然科学基金资助(51709168);上海市科技创新行动计划项目(18DZ1206104)。

Multi-dimensional control model of interval AIS data-driven berthing process

QIN Tingrong¹, LI Dongyang¹, LI Penghao¹, MA Guoliang¹, CHEN Weijiong²

1 Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China;
2 College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai 201306, China

Received:2020-12-06 Revised:2021-02-05 Online:2021-03-28 Published:2021-12-20

摘要/Abstract

摘要： 为提高船舶靠泊安全,对接智能靠泊,实现船舶抵泊过程中的姿态控制,运用切比雪夫拟合法、95%置信度区间法、最小二乘法及上述组合方法的2次拟合等优化策略,拟合区间化船舶自动识别系统(AIS)数据中速度、航向、艏向和船位等靠泊控制参数,构建区间化AIS数据驱动的多维抵泊姿态控制模型,并采用某轮靠泊南京龙潭港一年的AIS数据测试模型可靠性。研究结果表明:模型拟合方法优于现有拟合方法,且输出结果符合相关规则规定,所提出的模型满足抵泊过程船舶姿态安全控制要求。

关键词: 区间化, 自动识别系统(AIS)数据, 抵泊过程, 多维控制, 切比雪夫拟合法

Abstract: In order to improve berthing safety and docking intelligent berthing, attitude control in the process of ship docking was realized, through using some optimization strategies such as Chebyshev fitting method, 95% confidence interval method, least 2 method,two times fitting of above combination method and so on. Berthing control parameters, such as speed, heading, heading and ship position were fitted in the data of interval vessel AIS. A multi-dimensional attitude control model driven by interval AIS data was constructed, and reliability of model was tested by AIS data of a certain wheel berthing in Longtan Port, Nanjing for one year. The results show that model fitting method is superior to existing fitting method, and output results are in line with relevant rules, and proposed model meets requirements of ship attitude safety control during docking process.

Key words: interval, automatic identification system (AIS) data, berthing process, multi-dimensional control, Chebyshev fitting method

中图分类号:

X913.3

秦庭荣, 李东阳, 李鹏昊, 马国梁, 陈伟炯. 区间化AIS数据驱动的抵泊过程多维控制模型[J]. 中国安全科学学报, 2021, 31(3): 47-53.

QIN Tingrong, LI Dongyang, LI Penghao, MA Guoliang, CHEN Weijiong. Multi-dimensional control model of interval AIS data-driven berthing process[J]. China Safety Science Journal, 2021, 31(3): 47-53.

参考文献

[1] 王德龙. 船舶操纵避碰智能评估模型[D]. 大连:大连海事大学. 2018.
WANG Delong. The intelligent assessment model for ship maneuvering and collision-avoidance[D]. Dalian: Dalian Maritime University, 2018.
[2] 黄泽洋, 邵哲平, 潘家财,等. 基于AIS的大型船舶靠泊航速分布规律[J]. 中国航海, 2016,39(2):55-58,130.
HUANG Zeyang, SHAO Zheping, PAN Jiacai, et al. Distribution law of large vessel berthing speed based on AIS data[J]. Navigation of China, 2016,39(2):55-58,130.
[3] 司东森, 张英俊, 郎坤. 基于改进BN的集装箱船舶碰撞事故致因分析[J]. 中国安全科学学报, 2019, 29(10): 31-37.
SI Dongsen, ZHANG Yingjun, LANG Kun. Causation analysis of container ship collision accidents based on improved BN [J]. China Safety Science Journal, 2019, 29(10): 31-37.
[4] 张欣欣, 胡甚平, 陈渊,等. 船舶港口引航风险致因人-组织因素影响分析[J]. 中国安全科学学报, 2019, 29(12): 78-84.
ZHANG Xinxin, HU Shenping, CHEN Yuan, et al. Impact analysis of human-organizational factors on pilotage risks at harbor [J]. China Safety Science Journal, 2019, 29(12): 78-84.
[5] Container ship crashing into shore crane [EB/OL].(2020-04-07).[2020-05-05]. https://xindemarinenews.com/en/market/2020/0407/19578.html.
[6] PIAO Zaiji, GUO Chen, SUN Shuang. Research into the automatic berthing of underactuated unmanned ships under wind loads based on experiment and numerical analysis [J]. Journal of Marine Science and Engineering, 2019, DOI:10.3390/jmse7090300.
[7] LIAO Yulei, JIA Zhihao, ZHANG Weibin, et al. Layered berthing method and experiment of unmanned surface vehicle based on multiple constraints analysis[J]. Applied Ocean Research, 2019, 86: 47-60.
[8] ATSUO M, NAOKI S, YOUHEI A,et al. Application of optimal control theory based on the evolution strategy (CMA-ES) to automatic berthing[J]. Journal of Marine Science & Technology, 2020,25(1): 221-233.
[9] PARK J, KIM N. Design of an adaptive backstepping controller for auto-berthing a cruise ship under wind loads[J]. International Journal of Naval Architecture and Ocean Engineering, 2014,6(2): 347-360.
[10] MIZUNO K, KUBOSHIMA R. Implementation and evaluation of non-linear optimal feedback control for ship's automatic berthing by recurrent neural network [J]. IFAC Papers Online, 2019,52(21): 91-96.
[11] ZHANG Qiang, ZHU Guibing, HU Xin.et al. Adaptive neural network auto-berthing control of marine ships[J]. Ocean Engineering, 2019, 177: 40-48.
[12] 尤庆华, 陈杰, 胡甚平,等. 集装箱船舶顺流靠泊风险评估与操作要领[J]. 中国航海, 2013,36(4): 143-146.
YOU Qinghua, CHEN Jie, HU Shenping, et al. Risk assessment of berthing container ships in fair current condition and hints on operation[J]. Navigation of China, 2013,36(4): 143-146.
[13] 鲍文明. 港作拖轮在大型船舶港内操纵中的使用[J]. 中国航海, 2006,29(3): 23-26.
BAO Wenming. Application of harbor operational tugs in maneuvering large vessels in port[J]. Navigation of China, 2006, 29(3): 23-26.
[14] 张威, 张庆年, 杨杰,等. 内河无人驾驶船舶航行安全状况指标体系研究[J]. 中国安全科学学报, 2019, 29(5): 105-110.
ZHANG Wei, ZHANG Qingnian, YANG Jie, et al. Research on index system for inland unmanned ships navigation safety status[J]. China Safety Science Journal, 2019, 29(5): 105-110.
[15] 彭国均, 刘洋, 张杏谷,等. 大型船舶辅助靠泊仪设计[J]. 交通运输工程学报, 2012,12(6) 48-54.
PENG Guojun, LIU Yang, ZHANG Xinggu, et al. Design of auxiliary berthing instrument of large ship [J]. Journal of Traffic and Transportation Engineering, 2012,12(6) 48-54.
[16] FANG X, HAN L, COENVAN G, et al. Comparison study on AIS data of ship traffic behavior[J]. Ocean Engineering, 2015, 95: 84-93.
[17] HUANG Juanchen, NIEH C Y, KUO H C. Risk assessment of ships maneuvering in an approaching channel based on AIS data[J]. Ocean Engineering, 2019, 173: 399-414.
[18] 肖潇, 邵哲平, 纪贤标,等. 基于AIS信息的船舶进出港航速控制模型[J].上海海事大学学报, 2014,35(4): 11-14.
XIAO Xiao, SHAO Zheping, JI Xianbiao, et al. Speed control model of ships entering and leaving ports based on AIS data[J]. Journal of Shanghai Maritime University, 2014,35(4): 11-14.
[19] PALLOTTA G, VESPE M. Vessel pattern knowledge discovery from AIS data: a framework for anomaly detection and route prediction [J]. Entropy, 2013, 15: 2 218-2 245.
[20] XU Haitong, RONG Hao, GUEDES C. Use of AIS data for guidance and control of path-following autonomous vessels [J]. Ocean Engineering, 2019,194: 1-19.
[21] 江苏VTS服务指南[EB/OL].(2019-07-17). https://www.js.msa.gov.cn/art/2019/7/17/art_65_1124052.html.
[22] 中华人民共和国江苏海事局船舶交通服务系统安全监督实施办法[EB/OL].(2018-11-12).https://www.js.msa.gov.cn/art/2018/11/12/art_65_462838.html.
[23] 中华人民共和国海事局. 长江江苏段船舶定线制规定(2013)[L]. 2014-01-20.

区间化AIS数据驱动的抵泊过程多维控制模型

Multi-dimensional control model of interval AIS data-driven berthing process

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