Unmanned aerial vehicle fault detection method, device and equipment and storage medium

Abstract

The embodiment of the invention discloses an unmanned aerial vehicle fault detection method, device and equipment and storage medium The method comprises the steps that obtaining a frame of flight data from all frames of flight data in a flight log of the unmanned aerial vehicle, and setting the flight data as target flight data; extracting an emergency response parameter, a sensor parameter, a motor parameter, an attitude parameter, a speed parameter and a position parameter in the target flight data; determining at least one parameter abnormal result corresponding to the target flight data according to the emergency response parameter, the sensor parameter, the motor parameter, the attitude parameter, the speed parameter and the position parameter; and determining a fault reason of the unmanned aerial vehicle according to the at least one parameter abnormal result. According to the embodiment of the invention, the fault reason of the unmanned aerial vehicle can be automatically determined in real time according to the data generated during flight of the unmanned aerial vehicle, the fault detection effect is ensured, and the fault detection efficiency is improved.

Description

technical field [0001] Embodiments of the present invention relate to unmanned aerial vehicle technology, and in particular to a fault detection method, device, equipment and storage medium of an unmanned aerial vehicle. Background technique [0002] At present, the fault detection method of UAVs is mainly to manually analyze the flight control log data through software and judge it in combination with the photos and descriptions of the bombing environment. A device that communicates with the drone) or warning lights and bells on the drone for warning. [0003] Traditional fault detection methods have high requirements on the knowledge and skills of analysts, and the timeliness of analysis is not high. In the busy season, there are often 70 to 80 or nearly hundreds of flight accident data a day that require manual analysis and processing. On the one hand, this method of fault detection requires a large amount of tasks for the analysts. Some faults can only be discovered by...

AI Technical Summary

Problems solved by technology

On the one hand, this method of fault detection requires a large amount of tasks for the analysts. Some faults can only be discovered by carefully looking at the data, and the recognition accuracy is low. Moreover, different analysts have different fault analysis habits, and the output faults are not uniform, making it difficult to count. On the one hand, customers cannot analyze the cause of drone accidents in a timely manner, which will greatly affect the customer's operating efficiency

In addition, even if some abnormalities can be prompted through warning means such as the client, these warnings are all discrete abnormal points, and further analysis and judgment of these abnormal information are required to obtain specific fault judgment results

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