摘要
为协助船舶运营商有侧重点地做好船舶维护工作及通过PSC检查,本文基于数据关联理念,针对PSC数据库信息特征,创建了辨识度指数公式,设计并编制了船舶滞留规律挖掘与表达算法.用该算法对台湾十年船检数据进行处理,获得了船舶滞留的深层信息,揭示了港口受检船只被滞留的风险程度.通过编制可视化软件,研究双项及多项缺陷代码与船舶滞留的分层关联,使缺陷代码与滞留情况的内在关系以图示的形式展示,清晰地呈现出随着缺陷代码组合的逐渐复杂化而体现出的各种滞留规律,为船舶的安全运营及顺利通过PSC检查提供了科学、客观的量化指导.
To assist ship＇s operators focusing on ship maintenance and PSC inspection,the identification index formula is designed based on the data association concepts,and taking PSC database information features into account.This paper also designs a set of ship detention mining and expressing algorithm.It is applied to analyze the PSC data from Taiwan 10 year＇ s.It obtains depth information on ship detention,and reveals the risk degree of ship detention.Via developing of visualization software,and multi-layered association of double-code/multi-code,it shows the inherent relationship between codes and detention by figures,and gives a clear reflection of various detention rules in pace with defect code group＇s increasingly complex.It provides a scientific,objective and quantitative guidance for ship＇s safe navigation and passing PSC inspection.
出处
《交通运输系统工程与信息》
EI
CSCD
北大核心
2014年第1期102-108,共7页
Journal of Transportation Systems Engineering and Information Technology
基金
国家自然科学基金(51279099)
上海市科学技术委员会基金资助项目(12ZR1412500)
上海市教委科研创新基金资助重点项目(13ZZ124)
上海海事大学优秀博士学位论文培育项目(2013bxlp004)
关键词
水路运输
数据关联
辨识度
船舶滞留
可视化
waterway transportation
data association
identification index
ship detention
visualization
