Probability statistics method-based controller fatigue detection method and system

Abstract

The invention provides a probability statistics method-based controller fatigue detection method. The method comprises the steps of obtaining brain waves of a controller, wherein the brain waves include slow alpha waves, alpha waves, beta waves and theta waves; performing calculation to obtain a power percentage of the slow alpha waves, a power ratio of the alpha waves to the beta waves, and a power ratio of the theta waves to the slow alpha waves; obtaining a probability statistics method-based fatigue detection model, and inputting the power percentage of the slow alpha waves, the power ratio of the alpha waves to the beta waves, and the power ratio of the theta waves to the slow alpha waves to the fatigue detection model, thereby obtaining a PERCLOS value simulation result; and performing controller fatigue detection according to the PERCLOS value simulation result. According to the probability statistics method-based controller fatigue detection method and system, brain wave parameters are input to the fatigue detection model built based on a probability statistics method to obtain the PERCLOS value simulation result, and the fatigue detection is performed; and the brain waves are used for indirectly reflecting fatigue levels of people, and a brain wave obtaining method is simple, easy to realize and low in cost.

Description

technical field [0001] The invention relates to the aerospace field, in particular to fatigue detection. Background technique [0002] With the increasing air traffic flow, the workload of air traffic controllers is increasing, and their fatigue level has an important impact on the safety level of air traffic system. ICAO has developed Doc9966 manual of rules and regulations for fatigue risk management. Developed countries in Europe and the United States have successively extended fatigue detection systems or methods for pilots to controller fatigue detection applications. The Civil Aviation Administration of China is guided by ICAO Doc9966, and also clarified the rules of fatigue risk management in the CCAR-121 document. [0003] Existing technology: So far, researchers at home and abroad have proposed a variety of fatigue detection and management methods and systems. The first method is to collect questionnaires from a large number of subjects for fatigue judgment and p...

AI Technical Summary

Problems solved by technology

In this way, the current physical condition of the controller is directly ignored, which may have a certain impact on the detection results; the third method: most of the currently existing methods suitable for real-time fatigue detection use the method of collecting and recognizing facial features. The method requires high-precision video detection equipment to shoot controllers at any time, and it has no advantages in terms of cost analysis

[0004] Insufficiency of the first method: subject to the subjective influence of the researcher, making the judgment and prediction of the fatigue of the subject inaccurate; shortcoming of the second method: real-time detection cannot be carried out, and a certain period of time can be speculated based on the performance of the subject for a period of time. Whether the controller is fatigued for a period of time, ignoring the current physical condition of the controller, making the fatigue determination and prediction inaccurate; the deficiency of the third method: Although the fatigue status of the controller has been monitored in real time, the realization of this method requires High-precision video detection equipment, high cost, not applicable

[0005] Therefore, the defective in the prior art is: commonly used method adopts the parameter that directly reflects people's fatigue degree to carry out fatigue detection, as: people's facial feature (blink eye closure degree), needs the video detection equipment of high precision, and cost is high, Poor applicability, unable to provide effective fatigue detection for controllers

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