基于大数据的船舶会遇热点区域挖掘技术开发  

Development of hot spot area mining technology for ship encounters based on big data

作　　者：陈麒龙 黄鹏飞[2] CHEN Qilong;HUANG Pengfei(School of Science,Jimei University,Xiamen 361021,China;Navigation College,Jimei University,Xiamen 361021,China)

机构地区：[1]集美大学理学院,福建厦门361021 [2]集美大学航海学院,福建厦门361021

出　　处：《舰船科学技术》2025年第4期168-172,共5页Ship Science and Technology

摘　　要：船舶交通密度的增大会直接影响船舶航行的安全性,本文提出一种基于大数据挖掘技术的船舶会遇热点区域分析方法,首先将AIS数据、雷达数据以及图像数据作为船舶会遇热点区域分析的数据来源,并提出一种多源数据的预处理和融合方法。定义了3种船舶会遇基本特征,探讨了船舶属性、环境属性和船舶会遇之间的关联规则,在此基础上使用K-means聚类算法对船舶会遇热点区域进行分析,结果表明本文提出的方法可以有效对船舶会遇热点区域进行分析和标定,有效降低会遇热点区域的船舶碰撞概率。The increase in ship traffic density will directly affect the safety of ship navigation.This article proposes a method for analyzing the hot spot areas of ship encounters based on big data mining technology.Firstly,AIS data,radar data,and image data are used as the data sources for analyzing the hot spot areas of ship encounters,and a preprocessing and fu-sion method for multi-source data is proposed.Three basic characteristics of ship encounters are defined,the association rules among ship attributes,environmental attributes and ship encounters are discussed.On this basis,the K-means cluster-ing algorithm is used to analyze the hot spot areas of ship encounters.The results show that the method proposed in this art-icle can effectively analyze and calibrate the hot spot areas of ship encounters and effectively reduce the probability of ship collisions in the encounter hot spot areas.

关 键 词：会遇 大数据挖掘 K-MEANS 聚类分析 

分 类 号：U675.7[交通运输工程—船舶及航道工程]