## 5 Discussion  
This study employed mathematical tools for intelligent knowledge analysis using both one-dimensional and multi-dimensional membership functions. Regression approaches were also applied to process the data to obtain a comprehensive trend relationship. The proper combination of these contemporary mathematical approaches makes it possible to adequately determine the mental health levels of flight instructors and their psychophysiological workload during the performance of official duties. Furthermore, the quality of well-grounded decisions is improved by using fuzzy set theory. This work used both one-dimensional and multidimensional membership functions; the advantages of each approach depended on the nature of the input data. One-dimensional functions provide better interpretability when working with individual parameters, while multidimensional ones allow the relationships between several factors to be considered. The choice of functions for the model was made considering the balance between accuracy and clarity. The application of fuzzy set theory made it possible to consider expert uncertainty and smooth transitions between levels of load and mental state, which is impossible or significantly complicated in classical models. The accuracy of the results was ensured by verifying the intelligent analytical platform with real data. These results have significant practical implications for enhancing flight safety and the effectiveness of the training process. Timely measures to support the health of flight instructors and improve the quality of training new pilots contribute to creating a safer and more conducive working environment for flight instructors, which ultimately increases the overall level of aviation safety. The value of the developed platform lies in the fact that it takes into account quantitative dynamic data obtained from the device for measuring the individual psychophysiological workload of the flight instructor in the working environment, incorporates expert assessments of the mental health of the flight instructor, and predicts quantitative dynamic data, which results in a comprehensive trend relationship for the determined indicator of the working environment. The indicators of the working environment and the set of criteria for assessing mental health are open sets for knowledge acquisition, which can be modified or supplemented if necessary. The output provides knowledge about the level of psychophysiological workload of the flight instructor. A limitation of our study was the use of different types of membership functions, both single-variable and multi-variable, which may have led to slight ambiguities in the results. With real data, the distinction between linguistic assessment of the flight instructor's mental health and psychophysiological workload is adjusted. False positives are also possible, due to the individual characteristics of instructors, differences in the design and acoustic characteristics of the Zlin Z43 and Diamond DA-40 TDI aircraft, and the specifics of different flight phases. Analysis of the results showed that these factors can cause data variability. Normalization and adaptive model tuning were used to mitigate this; however, to increase the reliability of the platform, further expansion of samples and deeper modeling of the system's sensitivity to these factors are necessary. It is worth noting that the use of one- and multidimensional membership functions within the framework of fuzzy set theory, despite its flexibility, is accompanied by increased sensitivity to the choice of parameters of these functions. In this study, no separate testing for reliability or quantitative assessment of the level of ambiguities associated with the variability of membership functions was carried out. This creates a potential risk of unclear interpretations of results in the border zones between load levels.