基于流推理的定线制水域船舶航行安全评估方法
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摘要
为了更有效地利用船舶航行过程中人员、船舶、环境、管理等数据,实时评估船舶航行安全状态,提出基于流推理(SR)的定线制水域船舶航行安全评估方法。首先,利用粗糙集条件信息熵(RSCIE)约简评估要素,结合专家知识,设计评估本体;然后,通过语义Web技术融合实时采集的相关流数据,用SR引擎推理船舶航行安全状态;最后,运用该方法评估南京下游定线制水域的船舶航行安全状态,并与基于云理论(CT)的安全评估方法等对比分析。结果表明:本文提出的方法具有更好的时效性,而且检测率高、误报率低。
In order to use the data of personnel,ship,environment and management during ship navigation more effectively and conduct real-time assessment of navigation safety,this paper proposed a method for evaluating ship navigation safety based on SR in routing waters. Firstly,the safety assessment elements of ship navigation were reduced based on RSCIE and the assessment ontology was constructed by combining expert knowledge. Then the ship navigation-related streaming data collected in real time was integrated by semantic Web technology,and the SR engine was used to infer the navigation safety. Finally,the ship navigation safety in a certain routing water system in Nanjing was evaluated and the method presented in this paper was compared with the other two methods based on cloud theory. The results show that the proposed method has better timeliness,higher detection rate and lower false alarm rate.
In order to use the data of personnel,ship,environment and management during ship navigation more effectively and conduct real-time assessment of navigation safety,this paper proposed a method for evaluating ship navigation safety based on SR in routing waters. Firstly,the safety assessment elements of ship navigation were reduced based on RSCIE and the assessment ontology was constructed by combining expert knowledge. Then the ship navigation-related streaming data collected in real time was integrated by semantic Web technology,and the SR engine was used to infer the navigation safety. Finally,the ship navigation safety in a certain routing water system in Nanjing was evaluated and the method presented in this paper was compared with the other two methods based on cloud theory. The results show that the proposed method has better timeliness,higher detection rate and lower false alarm rate.
引文
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[2]李磊.基于云理论的船舶航行安全评价[D].大连:大连海事大学,2015.LI Lei. Ship navigation safety evaluation based on cloud theory[D].Dalian:Dalian Maritime University,2015.
[3]黄常海,肖英杰,高德毅,等.定线制水域航行危险度评价模型及其应用[J].中国安全科学学报,2014,24(2):93-99.HUANG Changhai,XIAO Yingjie,GAO Deyi,et al. Evaluation model and application of navigation danger degree in the alignment system[J]. China Safety Science Journal,2014,24(2):93-99.
[4] PARK Youngsoo,PARK Jinsoo,SHIN Daewoon,et al. Application of potential assessment of risk(PARK)model in Korea waterways[J]. Journal of International Maritime Safety,Environmental Affairs,and Shipping,2017,1(1):1-10.
[5] DANIEL De Leng,HEINTZ Fredrik. Dy Know:a dynamically reconfigurable stream reasoning framework as an extension to the robot operating system[C]. IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots,2017:55-60.
[6] CHENG Bo,ZHONG Shengda,CHEN Junliang. Context ontology-based reasoning service for multimedia conferencing process intelligence[J]. IEEE Transactions on Systems Man&Cybernetics Systems,2017,47(12):3 219-3 232.
[7] FARIAS Tarcisio Mendes De,ROXIN Ana,NICOLLE Christophe. SWRL rule-selection methodology for ontology interoperability[J]. Data&Knowledge Engineering,2016,105:53-72.
[8] ABADI Asmae, BEN-AZZA Hussain, SEKKAT Souhail. Improving integrated product design using SWRL rules expression and ontology-based reasoning[J]. Procedia Computer Science,2018,127:416-425.
[9] MEDITSKO Georgios,BASSILIADES Nick. DLEJena:a practical forward-chaining OWL 2 RL reasoner combining Jena and Pellet[J]. Web Semantics Science Services&Agents on the World Wide Web,2010,8(1):89-94.
[10] HAZRINA Sofian,SHAREF Nurfdhlina Mohd,IBRAHIM Hamidah,et al. Linguistic-based SPARQL translation model for semantic question answering system[J]. Advanced Science Letters,2018,24(2):1 375-1 381.