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Flight emergency prediction method and device based on LSTM neural network

A neural network, emergency event technology, applied in biological neural network models, aircraft indicating devices, neural architectures, etc., can solve problems such as personnel and property losses, and achieve the effect of ensuring safe execution and strong modeling capabilities

Pending Publication Date: 2021-04-09
AIR FORCE EARLY WARNING ACADEMY
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AI Technical Summary

Problems solved by technology

[0002] The occurrence of an aircraft release accident is a small probability event, but once it occurs, it may cause significant loss of life and property

Method used

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  • Flight emergency prediction method and device based on LSTM neural network
  • Flight emergency prediction method and device based on LSTM neural network
  • Flight emergency prediction method and device based on LSTM neural network

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Embodiment Construction

[0022] The principles and features of the present invention are described below in conjunction with the accompanying drawings, and the examples given are only used to explain the present invention, and are not intended to limit the scope of the present invention.

[0023] refer to Figure 1-2 In the first aspect of the present invention, a method for predicting flight emergencies based on LSTM neural network is provided, comprising the steps of: S101. Acquiring sample data of historical accident flight records, said sample data including flight equipment data, pilot state data , Meteorological data; S102. Preprocess the sample data of a single flight record in the sample data, and then use MDDM (Maximum Dependency Dimension Reduction Method Algorithm) to reduce its dimension, and map it to a multidimensional vector; S103. The multidimensional vector is used as a sample, and the corresponding flight emergency is used as a label to construct a prediction data set; S104. Divide t...

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Abstract

The invention relates to a flight emergency prediction method and apparatus based on an LSTM neural network. The method comprises the steps of obtaining sample data of historical accident flight records; preprocessing the sample data, then performing dimension reduction on the sample data by utilizing an MDDM algorithm, and mapping the sample data into a multi-dimensional vector; constructing a prediction data set by taking the multi-dimensional vector as a sample; dividing the prediction data set into a training set, a verification set and a test set, and training an LSTM neural network until the error of the LSTM neural network is lower than a threshold value and tends to be stable; and inputting the current flight data into the trained LSTM neural network to obtain the flight emergency probability. According to the invention, related data of historical flight emergencies are subjected to preprocessing, dimensionality reduction and feature extraction and are used as samples to train the LSTM neural network, so that various data in the flight process are automatically and comprehensively monitored, early warning is given out for the flight emergencies in time, and safe execution of flight tasks is guaranteed.

Description

technical field [0001] The invention belongs to the field of flight safety prediction, and in particular relates to a flight emergency prediction method and device based on an LSTM neural network. Background technique [0002] The occurrence of an aircraft release accident is a small probability event, but once it occurs, it may cause significant loss of life and property. The main causes of flight accidents are bad weather conditions, mechanical failure of the aircraft, pilot operation errors, ground command and service support errors, birds hitting the aircraft, violent hijacking of the aircraft and so on. Most modern aircraft accidents are due to encountering emergency situations in flight, such as dangerous weather, mechanical failure, etc., and the driver's improper handling or the commander's command error. Finding out the reasons of flight accidents is very important in preventing flight accidents, because only by finding out the reasons can we prevent the recurrence...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/04B64D45/00
CPCG06N3/049B64D45/0051G06N3/044G06F18/213
Inventor 何松彭晓明郭乐江胡俊
Owner AIR FORCE EARLY WARNING ACADEMY
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