Simulation training method for special situation fault of unmanned aerial vehicle and system thereof

Abstract

The invention provides a simulation training method for a special situation fault of an unmanned aerial vehicle and a system thereof, and belongs to the field of data processing of the unmanned aerial vehicle. A fault tree theory-based unmanned combat aerial vehicle system fault traversing analysis method is provided; a fault which affects the safety of flight is used as a top event of a fault tree to decompose the fault step by step to obtain all fault modes which affect the safety of the flight; meanwhile, a simulation snapshot technology-based unmanned combat aerial vehicle training process decoupling design technology is provided, current training state data is extracted in real time to establish a snapshot database, snapshot data of corresponding stages is only required to be called aiming at different training stages, and simulation initialization is performed, so the problem of extraction of initial training parameters of different task stages is solved; the method and the system have a random selection function at the specific training stage of the special situation fault of the unmanned aerial vehicle and a random fault selection function.

Description

technical field [0001] The invention relates to the field of data processing of unmanned aerial vehicles, in particular to a simulation training method and system for special failures of unmanned aerial vehicles. Background technique [0002] Special circumstances refer to special circumstances encountered during flight. When special situations occur, flight safety is often endangered. In order to improve the combat effectiveness of the aircraft system, special situation training in advance can effectively relieve the tension of pilots when facing special situations. For UAVs, through high-fidelity special situation simulation training The training of human-machine ground operators has gradually become the main means of training drone operators. [0003] In the prior art, flight simulators for combat UAV simulation training cannot accurately determine the types of faults and special situations that pilots need to deal with. Usually, only some common faults can be set. On th...

AI Technical Summary

Problems solved by technology

[0003] In the prior art, flight simulators for combat UAV simulation training cannot accurately determine the types of faults and special situations that pilots need to deal with. Usually, only some common faults can be set. On the other hand, only the whole process simulation can be performed. Training, when training for specific flight phases (such as landing), it takes a lot of time to wait, such as image 3 As shown, there is a strong coupling between the training stages. After a stage of special situation training, the UAV may have left the normal task envelope, and it must be manually corrected by the trainer for a long time to return to the normal state. During the flight stage, the next stage of training is carried out. Due to the particularity of the special situation training, the controllability of the UAV in the special situation state is greatly reduced. Once the training of a certain stage fails and the UAV crashes, it needs to be repeated. The entire task envelope is at the same time, and at the same time, the development of special situation simulation training lags behind the development of aircraft, and the development progress is slow

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