摘要
船舶分布状态数据为一组非线性组合的离散数据,采用大数据分析方法进行船舶调度,提高船舶分配的有效性,提出一种基于关联匹配的船舶分布状态数据聚类及船舶调度方法。对采集的船舶大数据进行模糊C均值聚类处理,根据船舶状态特征属性分布进行大数据环境下的关联规则挖掘,提取反映船舶属性的特征量,以提取的特征量进行关联匹配,实现船舶优化调度。仿真结果表明,采用该方法进行船舶调度能有效反映船舶的类别属性,提高船舶的分类管理和调度能力,从而提高船舶的运输效率。
The distribution of data for discrete data, a set of nonlinear combination, using a large data analysis method for ship scheduling, improve the effectiveness of the ship distribution, put forward a kind of association, the distribution of ship and ship scheduling data clustering method based on data collected on the ship. Fuzzy C means clustering processing,mining according to the association the rules of the big data environment under the condition of the ship distribution feature extraction, reflect the characteristics of ship attributes, in the feature extraction of correlation matching, the realization of the ship scheduling optimization, the simulation results show that using the method of ship scheduling can effectively reflect the class attribute of the ship, improve the classification ability of ship management and scheduling, so as to improve the efficiency of ships.
出处
《舰船科学技术》
北大核心
2018年第1X期34-36,共3页
Ship Science and Technology
关键词
大数据
聚类
船舶调度
特征提取
关联匹配
big data
clustering
ship scheduling
feature extraction
association matching
