ONTOLOGICAL MODELING OF THE KNOWLEDGE BASE FOR A CLOSED AQUATIC ENVIRONMENT CONDITION MANAGEMENT SYSTEM

Abstract

The relevance of managing parameters of closed water environments is determined by the increasing complexity of modern monitoring systems, such as aquaculture installations, swimming pools, and laboratory setups, where it is necessary to maintain stable physicochemical conditions in real time. The large volume of heterogeneous data obtained from sensors and auxiliary sources significantly complicates data processing and decision-making, which highlights the need for intelligent approaches to knowledge organization and system design. In such conditions, traditional data processing methods often demonstrate limited efficiency due to the lack of semantic context and weak integration capabilities, which further emphasizes the importance of advanced knowledge representation models. The paper presents the results of research in which an ontological model describing the process of managing a closed water environment is proposed. The developed approach is based on domain formalization using ontological modeling, including the identification of key entities, their properties, and relationships. Special attention is given to the integration of heterogeneous data obtained from sensors and image analysis modules into a unified semantic structure, ensuring consistency and completeness of knowledge representation. The model also enables explicit representation of dependencies between environmental parameters, which is essential for comprehensive system analysis. The proposed model enhances the efficiency of monitoring and control processes by enabling semantic data processing, structured knowledge representation, and intelligent analysis. The use of ontological modeling allows for reducing the number of queries required to obtain relevant information, improving decision-making accuracy, and ensuring system flexibility and scalability. Additionally, the approach simplifies system modification and extension by allowing the inclusion of new parameters and data sources without significant restructuring. As a result, the approach provides a solid foundation for the development of intelligent information systems for managing complex dynamic environments, including closed water systems.