Information system for assessing reliability a decoder remote piloting station

Under conditions of armed aggression by Russia against Ukraine, which escalated into a full-scale military invasion on February 24, 2022, national security is facing the greatest threat, as it is about destroying Ukrainian statehood. As experience in preparing and conducting intelligence operations by the Armed Forces of Ukraine during the large-scale invasion by the armed forces of the Russian Federation into your country has shown, using class I unmanned aerial vehicles has proven their effectiveness, which has raised issues of improving intelligence gathering with their help. A key role in the operations of a first-class unmanned aerial vehicle is played by a remote pilot station decoder. Quality operation of airborne reconnaissance assets depends on its efficiency. The article considers main reliability indicators of a human-machine system operator. The analysis results of the existing methods for controlling the humanmachine system operator, which shows that indicators of his performance are not measured, but are determined by monitoring his functional state with further evaluation of performance indicators. Based on results of the analysis, it was found that using fuzzy set theory and convolution according to a nonlinear trade-off scheme in tasks of evaluating efficiency of human-machine systems makes it possible to identify reliability status of a remote piloting station decoder in real time, taking into account human performance and equipment efficiency. The article develops an information system for assessing the reliability of a remote piloting station decoder based on a generalized mathematical model in the form of a multilevel hierarchical tree of logical inference that reflects classification of indicators and intermediate estimates. The roots of the trees correspond to evaluation results, and tops correspond to reliability indicators of a remote piloting point. This process is based on mathematical apparatus of fuzzy logic and is carried out using available expert information in form of logical rules "IF - THEN" that link fuzzy terms of remote piloting point reliability indicators and the assessment result. Reliability of the obtained data is achieved by forming a fuzzy knowledge base using fuzzy terms that take into account specifics of process obtaining intelligence information at remote piloting points.