Methods of aggregation of fuzzy estimations and analysis by multi-agent systems of large volumes of information

In this work, issues related to the development of methodological foundations for building monitoring and decision support systems for managing applied scientific research and space experiments are considered. Various approaches to the integration of mathematical models into information systems aimed at analyzing complex multi-criteria tasks are presented. Particular attention is given to the processing of qualitative, quantitative, and fuzzy expert evaluation data in different formats. The possibility of minimizing information loss during the analysis of hierarchically organized structures is examined. New methods for aggregating fuzzy evaluations and analyzing large data volumes using multi-agent systems have been developed. At the initial stage of the study, source data were verified and computational experiments were conducted, demonstrating the effectiveness of the proposed approaches. As a result, a model for planning applied scientific research was developed. Its application has shown the potential to improve the accuracy and speed of analysis when forming space experiment programs. The obtained results can be used in the development of intelligent project management systems in high-tech industries. A promising direction for application is the creation of tools for efficiency assessment, state monitoring, and resource management in experimental planning within the framework of research and development projects.