基于机器学习技术的耙吸挖泥船施工行为识别  

Construction behavior recognition of trailing suction hopper dredger based on machine learning technology

作　　者：徐婷 戴文伯 张晴波 周雨淼 XU Ting;DAI Wen-bo;ZHANG Qing-bo;ZHOU Yu-miao(CCCC National Engineering Research Center of Dredging Technology and Equipment Co.,Ltd.,Shanghai 201208,China)

机构地区：[1]中交疏浚技术装备国家工程研究中心有限公司,上海201208

出　　处：《水运工程》2022年第12期221-224,共4页Port & Waterway Engineering

摘　　要：耙吸挖泥船施工区域轨迹密集,有明确的抛泥区和挖泥区,但轨迹密度不同,传统的轨迹识别技术对于其施工行为模式识别困难,难以有效应用。针对该问题,提出一种无监督的耙吸挖泥船施工行为识别框架。首先,基于卡尔曼滤波算法解决轨迹跳变问题,提升轨迹数据的质量;然后,基于HDBSCAN算法同时识别出密度不同的挖泥和抛泥轨迹,解决了传统DBSCAN算法在类间密度不均衡的情况下参数设置困难的问题;最后,基于航向因素建立高斯混合模型GMM可进一步识别出运泥轨迹和返回轨迹。结果表明,上述方法能够快速、有效地实现耙吸船施工轨迹的精准识别。The construction area of a trailing suction hopper dredger has dense trajectories and separate mud dumping and dredging areas.However,these trajectories have different densities,and traditional trajectory recognition technology fails to effectively recognize the construction behavior and mode of the dredger and thus cannot be successfully applied.In view of these problems,this paper proposes an unsupervised framework for recognizing the construction behavior of the dredger.Firstly,the paper solves the problem of trajectory jump based on the Kalman filter algorithm and improves the quality of trajectory data.Then,the paper uses the HDBSCAN algorithm to identify mud dredging and dumping trajectories with different densities simultaneously and solves the problem of difficult parameter setting by the traditional DBSCAN algorithm in the case of uneven density between classes.Finally,the paper establishes a GMM model based on directional factors,so as to further identify the mud transportation and return trajectories.The results show that the above method can quickly and accurately identify the construction trajectory of a trailing suction hopper dredger.

关 键 词：耙吸挖泥船 HDBSCAN算法 轨迹聚类 行为识别 

分 类 号：U616.2[交通运输工程—船舶及航道工程]