Research on Pilot's Workload Based on Multisource Data.

This study examines the mechanisms by which different working conditions impact pilot’s workload. The study is conducted to explore the impact of clear weather and low visibility on pilot’s workload using the human-machine environment synchronous cloud platform and EEG big data analysis technology. First, subjective and EEG signal data from pilots were collected. Subsequently, the power spectral density and sample entropy of the EEG signal data were extracted using frequency domain analysis and linear dynamic analysis. Finally, paired T-tests were conducted to analyze workload differences among participants under varying weather conditions in flight experiments. The results demonstrate significant effects of both clear weather and low visibility on various EEG indicators and subjective ratings of participants. Pilot’s workload is significantly lower under clear weather conditions compared to low visibility conditions, thus confirming the effectiveness of EEG big data analysis and subjective ratings in evaluating workload.