语义驱动的作战资源服务聚类方法  

Semantic-Driven Clustering Method of Combat Resource Service

作　　者：何宜超 孙鹏[1] 焦志强[1] 张杰勇[1] 王衡 HE Yichao;SUN Peng;JIAO Zhiqiang;ZHANG Jieyong;WANG Heng(Information and Navigation College,Air Force Engineering University,Xi’an 710077,China;Hefei First Military Generation Room,Hefei,230000,China)

机构地区：[1]空军工程大学信息与导航学院,西安710077 [2]空装合肥第一军代室,合肥230000

出　　处：《空军工程大学学报（自然科学版）》2020年第4期101-107,共7页Journal of Air Force Engineering University(Natural Science Edition)

基　　金：国家自然科学基金(61573017,61703425)。

摘　　要：针对SOA架构下指挥信息系统中作战资源服务池规模大、服务组织效率不高的特点,进行了服务组织聚类的研究。首先对作战资源进行属性分析,基于OWL-S描述规范对其进行服务化建模,形成作战资源原子级服务本体模型,实现云服务化;然后构建了作战资源服务聚类模型,并在遗传算法中将模拟退火方法和K-means方法相结合,提出了一种作战资源服务聚类方法;最后通过仿真与GS和GK算法进行了对比。实验结果表明,所提算法能够在可接受的时间内找到适应度值更高的聚类方案,且方案结果适应度值的标准差较低。该方法相比于GS与GK算法具有更好的寻优性与稳定性。Aiming at the characteristics of large-scale combat resource service pools and low service organization efficiency in the command information system under the SOA architecture,a clustering study of service organizations is conducted.Firstly,the attributes of combat resources are analyzed,based on the OWL-S description specification,service-oriented modeling of combat resources is formed,and an atomic-level service ontology model of combat resources is formed to realize the cloud service of combat resources.Then a clustering model of combat resource service is constructed,and the simulated annealing method and the K-means method are combined in the genetic algorithm,and a clustering method of combat resource service is proposed.Finally,the simulation is compared with the GS and GK algorithms.The experimental results show that the proposed algorithm can find a clustering scheme with a higher fitness value within an acceptable time,and the standard deviation of the fitness value of the solution results is lower.Compared with GS and GK algorithms,this method has better optimization and stability.

关 键 词：作战资源服务化 服务聚类 OWL-S K-MEANS 遗传算法 

分 类 号：TP391.3[自动化与计算机技术—计算机应用技术]