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In order to solve the complexity and uncertainty problems in the berthing process of large ships, this paper develops a decision support model based on Case-Based Reasoning (CBR). This model integrates CBR technology with a cloud model and BP neural networks. It comprehensively considers multi-dimensional attributes such as vessel characteristics, meteorological and hydrological conditions, and port factors to establish a case framework comprising a basic information domain, a characteristic attribute domain, and a decision support domain. By integrating expert scoring with the cloud model, the model processes the randomness and fuzziness in expert evaluations to optimize the case attribute weights. Furthermore, it utilizes BP neural network to achieve case reuse and decision prediction, thereby reducing subjective errors introduced by manual intervention. In this paper, we collect the berthing cases of Chiwan and Shekou container terminals at Shenzhen Port for model validation. The preliminary verification model can provide relevant decision support for pilots, expand new scenarios of artificial intelligence technology in maritime applications, and provide new ideas for intelligent berthing planning of unmanned ships.

, correspAuthors=Ranxuan KE, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Ranxuan KE, Jiarun LIU, Hao FANG), CN=ArticleExt(id=1263557431167168757, articleId=1263557425588744372, tenantId=1146029695717560320, journalId=1240618002186551303, language=CN, title=基于案例推理的大型船舶靠泊辅助决策研究, columnId=1240655018068595518, journalTitle=中国航海, columnName=海上交通安全, runingTitle=null, highlight=null, articleAbstract=

文章针对大型船舶靠泊过程中的复杂性与不确定性问题，构建一种基于案例推理（CBR）的辅助决策模型。该模型基于案例推理技术结合云模型和BP神经网络，综合考虑船舶特性、气象水文条件、港口因素等多维属性，建立包含基本信息域、特征属性域和辅助决策域的案例框架。通过专家评分与云模型相结合的方式，对专家评价结果的随机性和模糊性进行处理，优化案例属性权重的分配，并利用BP神经网络实现案例重用与决策预测，从而减小人工干预的主观性误差。通过收集深圳港的赤湾和蛇口集装箱码头的靠泊案例进行实例验证，初步验证模型可以为引航员提供相关辅助的决策支持，拓展人工智能技术在海事应用的新场景，也为无人船智能靠泊规划提供新思路。

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柯冉绚（1977—），女，博士，教授，研究方向为交通运输工程、创新工程。

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