摘要
随着信息时代的到来,空管领域数字化、网络化特征逐渐呈现,塔台作为空管体系运行的重要组成部分,影响其运行效能的因素均呈网络化相互作用。根据影响因素及客观数据挖掘塔台运行基础指标,使用最大信息系数法对塔台运行指标进行相关性分析确定底层网络化关联关系,再结合复杂网络FN算法对底层关联网划分功能社团,产生了一种根据数据自发涌现的网络化指标体系(指标网),为后期科学评价塔台运行效能奠定了基础。
With the advent of the information age,the digital and networked features of the air traffic control field have gradually emerged.As an important part of the operation of the air traffic control system,the towers have a networked interaction with the factors that affect their operational efficiency.According to the influencing factors and objective data mining tower operation basic indicators,the maximum information coefficient method was used to perform correlation analysis on the tower operation indicators to determine the underlying network association relationship,and then combined with the complex network FN algorithm to divide the underlying association network into functional communities,resulting in a spontaneous emergence of data based on the networked index system,referred to as the"index network",laid the foundation for the later scientific evaluation of the toweroperational efficiency.
作者
徐萌
刘鸿潮
李印凤
李铮
张旭
XU Meng;LIU Hong-chao;LI Yin-feng;LI Zheng;ZHANG Xu(College of Civil and Architectural Engineering,North China University of Science and Technology,Tangshan 063210,China;State Key Laboratory of Air Traffic Management System and Technology,Nanjing 210007,China;China Civil Aviation Air Traffic Management Bureau in North China,Beijing 100621,China)
出处
《航空计算技术》
2020年第5期80-84,共5页
Aeronautical Computing Technique
基金
江苏省自然科学基金青年基金项目资助(BK20170157)
国家重点实验室开放基金项目资助(SKLATM201802)
2020年民航安全能力建设资金项目资助。
关键词
指标网
最大信息系数法
关联关系
FN算法
功能社团
index network
maximum information coefficient method
association relationship
FN algorithm
functional community