## 3 Materials and Methods
### Formal Formulation of the Evaluation Problem
These measures may include the development of recommendations or limitations on the flight activities of the evaluated instructor. An illustration of the processes of the intellectual-analytical platform is shown in Figure 1.  
Figure 1 shows the structural diagram of the intellectual-analytical platform, where the evaluated flight instructor \(C\) is defined at the input. The indicators of the working environment \(WE\) are measured for the instructor using the developed apparatus for measuring individual psychophysiological load. The input data are measured by the hardware from flight instructor \(C\) from the research database. Then, these data are processed in the information model--\(I_{\text{\emph{ave}}}\). The normalized input data are sent for further processing to the forecasting model--\(M_{\text{\emph{ave}}}\). Here, as a result, an aggregated predicted assessment of the individual psychophysiological load of the flight instructor in the working environment is obtained--\(we(C)\). In contrast to external monitoring systems, the DM assesses the level of mental health of the flight instructor using a set of assessment criteria \(K\). The obtained expert data are processed in the expert model for assessing the mental health of the flight instructor--\(M_{\text{\emph{mh}}}\). As a result, the quantitative level of mental health of the flight instructor \(\mu_{\text{\emph{mh}}}(C)\) is obtained. Using this, a linguistic assessment ofv the mental health of the flight instructor \(E\) is determined. If there is a situation where the DM can set the linguistic assessment \(E\) based on his own experience, then, the calculation according to the expert model is skipped. The knowledge determining the linguistic assessment \(E\) forms the knowledge base of the current study. Then, the linguistic conclusion \(E\) and the quantitative predicted estimate \(we(C)\) are calculated in the hybrid model--\(H_{\text{\emph{psf}}i}\). As a result, \(\tau(\text{C})\) is derived--the integrated quantitative level of the psychophysiological load on the flight instructor during the performance of official duties. Based on this estimate, \(\Lambda\)--the linguistic assessment of the flight instructor's psychophysiological load is determined. Knowledge regarding the differentiation between levels of linguistic assessment is stored in the knowledge base. Ultimately, a decision is made by the DM. The assessment may be revised by either changing the reasoning of the DM or adjusting the knowledge base settings. The following outlines the structure of the developed intellectual-analytical platform for assessing the psychophysiological workload of flight instructors. The platform consists of interrelated models that support data collection, workload prediction, and mental health assessment. It begins with an information model (Section 3.2) for structuring input data, emphasizing noise level as a key factor. A prediction model (Section 3.3) follows, facilitating workload forecasting using normalized parameters. An expert model (Section 3.4) then evaluates mental health based on expert assessments. The section concludes with the integration of all models into a unified decision-making framework. Each model is described by its structure, input data, processing algorithms, and role in the platform, offering a comprehensive view of real-time workload and mental health monitoring. Information Model for Assessing the Flight Instructor's Workload in the Working Environment Based on the Noise Level--I\({}_{\text{ave}}\)  
Let us assume the evaluation is being conducted for flight instructor C. In this model, input data are obtained through a device developed for measuring the individual psychophysiological workload of the flight instructor [24]. This device includes a stationary input-output block with an integrated data storage system, allowing the creation of an individual set of parameters for each instructor. The device is equipped with sensors and interfaces that allow precise measurement of key parameters.