基于灰色神经网络的管制员人力资源需求预测  

Human Resource Demand Prediction of Controllers Based on GreyNeural Network

作　　者：申晨 张美薇 章月 任帅里 SHEN Chen;ZHANG Meiwei;ZHANG Yue;REN Shuaili(Jiangsu Sub-bureau,East China Air Traffic Management Bureau CAAC,Nanjing 211113,Jiangsu,China;College of Civil Aviation,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,Jiangsu,China)

机构地区：[1]中国民用航空华东地区空中交通管理局江苏分局,江苏南京211113 [2]南京航空航天大学民航学院,江苏南京210016

出　　处：《民航学报》2023年第6期146-152,共7页Journal of Civil Aviation

摘　　要：近年来,民用航空事业发展迅速,尤其是飞行流量的增加更加重了空中交通管制的负担。因此对于空管人员的需求也在不断提高,所以本文将对空管局内管制员进行人力资源需求预测。本文结合交通需求与相关数学模型提出定量预测方法,进而使空管人力资源得到合理有效的应用。首先,对管制员劳动强度进行了分析;其次,使用灰色神经网络模型,通过每年该区的GDP数据预测未来区域交通流量,进而预测出区域扇区开放时长;在此基础上建立基于劳动强度分析与扇区开放时长的空管人力资源需求预测模型;最后以云南分局为例,得出区域未来人员需求数量。本文的研究成果,可以为国内各空管局提供参考,也可以作为保证安全运行、提高服务品质、促进行业发展的必要条件。In recent years,with the rapid development of civil aviation,the increase of flight flow adds to the burden of air traffic control.Therefore,the demand for air traffic controllers is also increasing,so this paper forecasts the human resource demand for air traffic controllers inside the Air traffic Management Bureau.A quantitative forecasting method is proposed based on the trafic demand and related mathematical models so that the human resources of air traffic control can be applied reasonably and effectively.Firstly,the labor intensity of controllers is analyzed.Secondly,the grey neural network model is used to predict the future regional traffic flow and then the opening time of regional sectors based on the annual GDP data of the region.On this basis,a prediction model of ATC human resource demand based on labor intensity analysis and sector opening time is established.Finally,with the Yunnan Sub-bureau as an example,the future personnel demand of the region is obtained.The research results of this paper can provide reference for domestic air traffic management bureaus,and be used as a necessary condition to ensure safe operation,improve service and promote the development of the industry.

关 键 词：管制员 人力资源需求预测 交通流量 灰色神经网络 扇区开放时长 

分 类 号：V2[航空宇航科学技术]