一种船舶作业中原始数据质量的实用评估方法

查看全文 作　　者：Gang [1]Chen;Jie [2]Cai;Niels Gorm Maly [2]Rytter;Marie [3]Lützen 机构地区：[1]World Maritime University,Malmö,Sweden;[2]Department of Technology and Innovation,University of Southern Denmark,Campusvej 55,DK-5230 Odense M,Denmark;[3]Department of Mechanical and Electrical Engineering,University of Southern Denmark,Campusvej 55,DK-5230 Odense M,Denmark 出　　处：《哈尔滨工程大学学报(英文版)》索引2023年第22卷第2期,共11页 基　　金：Danish Blue Innoship project which is partly funded by the Innovation Fund Denmark under File No.155-2014-10;the Danish Maritime Fund;the Orient’s Fund;Shipping Lab project“Digital vessel operation”;the Innovation Fund Denmark(IFD)under File No.8090-00063B;the Lauritzen Fonden;the Orient’s Fond。 摘　　要：With the current revolution in Shipping 4.0, a tremendous amount of data is accumulated during vessel operations. Dataquality (DQ) is becoming more and more important for the further digitalization and effective decision-making in shippingindustry. In this study, a practical DQ assessment method for raw data in vessel operations is proposed. In this method,specific data categories and data dimensions are developed based on engineering practice and existing literature. Concretevalidation rules are then formed, which can be used to properly divide raw datasets. Afterwards, a scoring method is usedfor the assessment of the data quality. Three levels, namely good, warning and alarm, are adopted to reflect the final dataquality. The root causes of bad data quality could be revealed once the internal dependency among rules has been built,which will facilitate the further improvement of DQ in practice. A case study based on the datasets from a Danish shippingcompany is conducted, where the DQ variation is monitored, assessed and compared. The results indicate that theproposed method is effective to help shipping industry improve the quality of raw data in practice. This innovationresearch can facilitate shipping industry to set a solid foundation at the early stage of their digitalization journeys. 关 键 词：Data quality Vessel operations SHIPPING Validation rules Noon reports