Architecture and methods for building an intelligent monitoring system for operator safety centers

V.R. Iksanov, S.V. Daragan

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Abstract. Currently, information technologies are fundamental to process automation, particularly within scientific and research centers. Developing architectures and methods for automated systems allows for increasing the efficiency of data processing, modeling, and management in informatics and telecommunications.
Aim. To develop the architecture and methods for automated systems centered on real-time information processing, considering the specific nature of scientific research.
Materials and methods. The study employs system analysis, computer modeling in Python, and a comprehensive literature review aligned with specialty 2.3.8, utilizing MathType for formula representation. The research materials consist of open-source data and experimental modeling results derived from over 100 test datasets.
Results. A new system architecture with specialized data processing modules is proposed, featuring optimization algorithms that reduce processing time by 30%. Methods of automation have been developed, incorporating analytical expressions for efficiency calculation. Experimental testing showed a 25 % increase in system accuracy.
Conclusions. The proposed architecture and methods are applicable to scientific centers to optimize system performance. Future empirical testing under real-world conditions is recommended.

Keywords: monitoring center, security operator, intelligent monitoring system, microservices architecture, event-driven architecture, streaming processing, event correlation

For citation. Iksanov V.R., Daragan S.V. Architecture and methods for building an intelligent monitoring system for operator safety centers. News of the Kabardino-Balkarian Scientific Center of RAS. 2026. Vol. 28. No. 3. Pp. 122–131. DOI: 10.35330/1991-6639-2026-28-3-122-131

©   Iksanov V.R., Daragan S.V., 2026

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Information about the authors

Vladislav R. Iksanov, Senior Lecturer, Department of Informatics, Plekhanov Russian University of Economics;
36, Stremyannyy lane, Moscow, 115054, Russia;
vlad-iksanov@mail.ru, ORCID: https://orcid.org/0009-0003-7810-3720, SPIN-code: 6750-3298
Svetlana V. Daragan, Senior Lecturer, Department of Informatics, Plekhanov Russian University of Economics;
36, Stremyannyy lane, Moscow, 115054, Russia;
daragan.sv@rea.ru, SPIN-code: 6731-1884

Funding

The study was performed without external funding.