基于QAR数据的飞行操作模式及其风险分析
发布时间：2018-10-09 16:53
【摘要】：QAR海量数据分析是航空安全管理的一个重要课题,准确而高效地挖掘其中蕴含的飞行操作模式、评估其风险水平,是切实提高飞行操作品质的关键。然而,飞行过程中飞行员需要根据动态变化的环境条件、飞机状态,对油门、杆、盘、舵等设备进行操作,是一个需要不断调整、相互配合的过程,这增加了对飞行操作模式及其风险进行分析的难度。本文从飞行参数多元时间序列数据的特征提取及相似性度量入手,通过聚类挖掘蕴含的飞行操作模式,进而,通过分析飞行操作模式与QAR监控指标值之间的关联关系,量化不同飞行操作模式的风险水平。最后,本文以首都机场特定机型着陆阶段QAR实际数据为例,验证了模型的有效性。
[Abstract]:QAR magnanimity data analysis is an important subject in aviation safety management. Accurately and efficiently mining the flight operation mode and evaluating its risk level is the key to improve flight operation quality. However, during the flight, the pilot needs to operate the accelerator, rod, plate, rudder and other equipment according to the dynamic changing environmental conditions, the status of the aircraft, and so on. It is a process that needs to be adjusted and coordinated with each other. This makes it more difficult to analyze the flight operation mode and its risks. This paper starts with feature extraction and similarity measurement of flight parameter multivariate time series data, mining flight operation mode by clustering, and then analyzes the relationship between flight operation mode and QAR monitoring index. Quantify the risk levels of different flight modes. Finally, this paper takes the actual data of QAR in the landing stage of specific aircraft at Capital Airport as an example to verify the validity of the model.
【作者单位】： 中国科学院科技战略咨询研究院;中国科学院大学;
【分类号】：V328.1
本文编号：2260069
[Abstract]:QAR magnanimity data analysis is an important subject in aviation safety management. Accurately and efficiently mining the flight operation mode and evaluating its risk level is the key to improve flight operation quality. However, during the flight, the pilot needs to operate the accelerator, rod, plate, rudder and other equipment according to the dynamic changing environmental conditions, the status of the aircraft, and so on. It is a process that needs to be adjusted and coordinated with each other. This makes it more difficult to analyze the flight operation mode and its risks. This paper starts with feature extraction and similarity measurement of flight parameter multivariate time series data, mining flight operation mode by clustering, and then analyzes the relationship between flight operation mode and QAR monitoring index. Quantify the risk levels of different flight modes. Finally, this paper takes the actual data of QAR in the landing stage of specific aircraft at Capital Airport as an example to verify the validity of the model.
【作者单位】： 中国科学院科技战略咨询研究院;中国科学院大学;
【分类号】：V328.1
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1 杨绎煊;基于QAR数据的航班运行安全风险研究[D];中国民航大学;2016年
，本文编号：2260069
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