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QAR data and Bayesian network-based flight risk analysis method

A technology of Bayesian network and risk analysis, applied in data processing applications, instruments, calculations, etc.

Pending Publication Date: 2018-10-26
CHONGQING UNIV
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the occurrence of unsafe incidents is often related to many risk factors
For the relationship between specific unsafe incidents and risk factors, how to evaluate and research, determine the degree of influence of risk factors on unsafe incidents, and increase the emphasis on important risks have not been involved in existing research

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  • QAR data and Bayesian network-based flight risk analysis method
  • QAR data and Bayesian network-based flight risk analysis method
  • QAR data and Bayesian network-based flight risk analysis method

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Embodiment Construction

[0028] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, but the examples are not intended to limit the present invention.

[0029] The flight risk analysis method based on QAR data and Bayesian network of the present embodiment comprises the following steps:

[0030] 1) Extracting key parameters of flight risk; due to different risk factors and different unsafe events during different stages of aircraft flight; in this embodiment, the landing stage is selected to extract key parameters for risk analysis. In the landing stage, the 60S before and 30S after the switching time of the air-to-ground switch are taken as the analysis intervals.

[0031] Ts = Time-60s;

[0032] When Air / Gnd SW=On Ground(0);

[0033] Te = Time + 30s;

[0034] Among them, Ts represents the start time of the landing phase, Te represents the end time of the landing phase, and Time is a parameter representing the time in t...

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Abstract

The invention relates to the technical field of aviation safety, and discloses a QAR data and Bayesian network-based flight risk analysis method. The method comprises the following steps of: 1) extracting a flight risk key parameter; 2) determining Bayesian network nodes and defining a calculation caliber of each node; 3) constructing a Bayesian network; 4) determining a prior probability of a root node in the Bayesian network and a conditional probability of an intermediate node in the Bayesian network; and 5) reading a section of QAR data, and inputting a risk factor occurrence state of theQAR data into the Bayesian network to obtain a target event occurrence probability. According to the method, the Bayesian network is imported, and quantified key risk points are taken as the nodes ofthe Bayesian network so as to establish a risk analysis model. The prior probability of the network is determined by using mass history QAR data, so that forward rational analysis and backward diagnosis analysis can be carried out by utilizing the established network structure, and an association relationship between specific unsecure events and risk factors can be assessed more comprehensively.

Description

technical field [0001] The invention relates to the technical field of aviation safety, in particular to a flight risk analysis method. Background technique [0002] In recent years, the air transport industry has developed rapidly, and the total volume of flight traffic has continued to increase. Aviation safety is one of the most important contents of civil aviation, and ensuring aviation safety is the eternal theme of the development of the aviation industry. Flight risk refers to the fact that one or more factors are triggered under certain conditions during the entire flight of the aircraft, leading to the occurrence of flight over-limit events, resulting in consequences of different severity levels. There is a strong correlation between aviation flight risk factors and target unsafe events. Therefore, the analysis of aviation flight risk factors has become the focus of the aviation industry. [0003] There are a variety of data acquisition equipment installed on the ...

Claims

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Application Information

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IPC IPC(8): G06Q10/06G06Q50/30G06K9/62
CPCG06Q10/0635G06F18/24155G06Q50/40
Inventor 郑林江刘柳
Owner CHONGQING UNIV
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