摘要
以港口水域船舶的节能减排为目标,研究港口水域船舶异常能耗云数据挖掘方法。采集港口水域船舶的AIS云数据,删除与船舶能耗无关以及异常数据,利用K-means聚类算法对船舶能耗相关船舶主机转速以及船舶主机功率等数据进行聚类,输出船舶不同运行工况的能耗。利用贝叶斯分类器依据聚类结果识别港口水域船舶能耗云数据是否为异常数据,完成港口水域船舶异常能耗云数据挖掘。实验结果表明,该方法的船舶异常能耗数据挖掘精度高,为船舶的节能减排提供依据。
Aiming at the energy conservation and emission reduction of ships in port waters,the cloud data mining method of abnormal energy consumption of ships in port waters is studied.Collect AIS cloud data of ships in port waters,delete irrelevant and abnormal data related to ship energy consumption,use K-means clustering algorithm to cluster the data related to ship energy consumption,such as ship main engine speed and ship main engine power,output the energy consumption of ships under different operating conditions,and use Bayesian classifier to identify whether the ship energy consumption cloud data in port waters is abnormal data according to the clustering results.Complete cloud data mining of abnormal energy consumption of ships in port waters.The experimental results show that the data mining accuracy of ship abnormal energy consumption is high,which provides a basis for ship energy conservation and emission reduction.
作者
李莹
LI Ying(Jiangsu University,Nantong 226007,China;Nantong College of Science and Technology,Nantong 226007,China)
出处
《舰船科学技术》
北大核心
2022年第3期143-146,共4页
Ship Science and Technology
基金
江苏高校研究项目(2021SJB0878)。
关键词
港口水域船舶
异常能耗
云数据挖掘
聚类算法
贝叶斯分类器
ships in port waters
abnormal energy consumption
cloud data mining
clustering algorithm
bayesian classifier
