ANALYSIS AND OPTIMIZATION OF PROCESSES IN TECHNICAL SYSTEMS USING TEXT DATA PROCESSING

Tereshenko Andrey Alekseevich

Modern technical systems combine hardware, software, and information resources to form complex structures that critical functions depend on. A significant portion of the data in such systems is presented in text form, including technical documentation, reports, and protocols. However, existing methods for processing text information are not sufficiently adapted to work with technical vocabulary and specialized contexts, which limits their use in control, diagnostics, and forecasting tasks. As part of the study, methods for processing text data were developed aimed at optimizing processes in technical systems. The proposed approach is based on the integration of methods of system analysis, machine learning, and text processing. The main focus is on creating specialized models that take into account the specifics of technical information, structure and classify data, and predict their impact on key processes. The results of the work contribute to increasing the reliability, performance, and adaptability of technical systems. The study has practical and scientific significance, providing tool solutions for analyzing text data in engineering and laying the foundation for further developments in this area.

Keywords:technical systems, text data processing, machine learning, system analysis, technical documentation, text information, process optimization, forecasting, diagnostics, engineering