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基于QAR数据的飞行员个体超限风险精细化评价模型

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  • NETL
  • NSTL
  • 万方数据
为定量评价飞行员个体超限风险,提出一种基于快速存取记录器(QAR)数据和飞行品质监控(FOQA)的飞行员个体超限风险精细化评价模型.首先,根据事故统计结果、国际民航组织(ICAO)及FOQA基站划分的核心风险类别,选取其中3 类风险的FOQA监控项目作为评价指标,计算飞行员个体单项核心风险值;然后,运用熵权逼近理想解排序法(TOPSIS)测算各项核心风险值所占权重,提出飞行员个体超限风险精细化评价模型;最后,将模型运用于实际飞行风险量化评价,通过采集中国民航(CAAC)FOQA基站中共9 317 条多源融合数据,量化飞行员个体超限风险,得到飞行员个体超限风险量化值排序,结合K-means聚类算法划分飞行员个体超限风险等级.结果表明:该模型可量化排序1 693 名飞行员个体超限风险,并将飞行员超限风险等级划分为高风险、中风险和低风险3 类.
Refined evaluation model for pilot's individual exceedance risk based on QAR data
In order to achieve a quantitative evaluation of individual pilot's exceedance risk,a refined evaluation model for pilot's individual exceedance risk was established based on QAR data and the flight operations quality assurance(FOQA)monitoring items.Firstly,according to accident statistics,International Civil Aviation Organization(ICAO)and the core risks divided by the FOQA station,the FOQA monitoring items associated with the three types of core risks were selected as the evaluation indexes,and the risk value for each core risk of the individual pilots was calculated.In the next step,the weight of each core risk value was calculated by entropy weighted TOPSIS.Then,the refined evaluation model for the pilot's individual exceedance risk was established.Finally,the model was applied to the quantitative evaluation of actual flight risk.By collecting 9 317 pieces of multi-source fusion data from the FOQA station of Civil Aviation Administration of China(CAAC),the individual exceedance risk of the pilots were quantified,the ranking of individual pilots'exceedance risk was obtained,and the pilot's individual exceedance risk levels were also divided with the use of K-means clustering algorithm.The results show that the model can quantify and rank 1 693 individual pilots'exceedance risk,and divide the pilot's exceedance risk into three types,including high risk,medium risk and low risk.

quick access recorder(QAR)dataindividual exceedance riskrefined evaluationflight operations quality assurance(FOQA)entropy-weighted technique for order preference by similarity to an ideal solution(TOPSIS)K-means clustering

汪磊、安佳宁、赵新斌、俞力玲

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中国民航大学 安全科学与工程学院,天津 300300

中国民航科学技术研究院 航空安全研究所,北京 100028

快速存取记录器(QAR)数据 个体超限风险 精细化评价 飞行品质监控(FOQA) 熵权逼近理想解排序(TOPSIS)法 K-means聚类

国家自然科学基金资助

32071063

2024

10.16265/j.cnki.issn1003-3033.2024.08.1824

中国安全科学学报
中国职业安全健康协会

中国安全科学学报

CSTPCD北大核心
影响因子：1.548
ISSN：1003-3033
年,卷(期)：2024.34(8)