基于脑电信号数据分类的飞行状态监测装置及方法
By integrating the spatial and temporal features of EEG signals and utilizing graph convolutional networks and long short-term memory networks, the real-time and accuracy problems of flight status monitoring in existing technologies have been solved, enabling real-time, accurate monitoring and dynamic adjustment of flight status, thereby improving driving safety.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA AERO POLYTECH ESTAB
- Filing Date
- 2025-04-29
- Publication Date
- 2026-07-17
AI Technical Summary
Existing flight status monitoring methods rely on indirect and subjective assessment methods, which make it difficult to provide real-time, high-precision flight status monitoring, cannot adapt to dynamic changes, and ignore the spatial topological and temporal information characteristics of EEG signals.
By combining graph convolutional networks, long short-term memory networks, and attention mechanisms, spatial and temporal features of EEG signals are integrated. Composite features are extracted through graph sequential attention network modules to construct a flight status monitoring model and dynamically adjust the driving weights of the shared control system.
It improves the accuracy of flight status identification and driving safety, enables real-time, accurate monitoring and dynamic adjustment of flight status, and enhances driving safety and system intelligence.
Smart Images
Figure CN120541591B_ABST