INTELLIGENT PLATFORM FOR OPTIMIZING MANAGEMENT OF AGRO-INDUSTRIAL ECONOMY BASED ON CALS TECHNOLOGIES

Abstract

Research objective. Development of an intelligent platform for optimizing the management of the agroindustrial economy, which is based on CALS technologies and integrates modern approaches to the automation of data collection, processing and analysis. The research is aimed at creating a system that provides operational forecasting of production processes, optimization of management decisions and adaptation to rapidly changing market conditions, which is a relevant task in the context of the digital transformation of the agricultural sector. Methodology. The research used a comprehensive approach, including mathematical modeling of system dynamics using differential equations, development of an expert forecasting module based on artificial neural networks, and development of algorithms for optimizing management decisions. The platform architecture includes integrated data management, a configuration module for specific farm conditions, and a decision support unit that ensures cyclic adaptation of the system to external and internal influences. Scientific novelty. A new approach to managing the agro-industrial economy is proposed, based on the synergy of CALS technologies with modern information systems. The developed mathematical model allows not only to predict changes in key indicators (production, product quality, market demand), but also to adjust management decisions in real time. The innovation of the platform lies in the use of hybrid methods of data analysis and the integration of machine learning algorithms to support optimal decision-making. Conclusions. The results obtained demonstrate the effectiveness of the developed platform, which allows adaptively responding to changes in market conditions and increasing the competitiveness of agro-industrial enterprises. Numerical modeling confirmed the possibility of optimizing production processes through cyclical adjustment of investments, which positively affects the volume of production, product quality and the level of market demand. The proposed system has high potential for practical implementation in the agricultural sector, and also opens up prospects for further research in the field of digitalization of economic process management.