METHODS AND MEANS OF DATA PROCESSING IN MODERN AUTOMATED SYSTEMS

Introduction. In modern automated systems, data processing is crucial for ensuring efficient and accurate processes. The use of various methods and tools, such as artificial intelligence algorithms, cloud technologies and big data, enables system performance to be optimised and decision-making to be accelerated. These approaches increase productivity, reduce costs, and improve adaptability to changes in the digital environment.Goal. The aim is to analyse the methods and means of data processing in modern automated systems in order to determine their efficiency, application features and impact on process optimisation.Research objectives: The aim is to investigate and evaluate the methods and means of data processing in modern automated systems, with a view to improving their effectiveness. This will involve developing a conceptual model for managing information flows in the context of digital transformation.Research methods. The research employed literature analysis methods to study the theoretical foundations of data processing in modern automated systems. It also used comparison and generalisation methods to determine the efficiency and application features of different approaches. A systematic approach was also employed to evaluate the relationship between data processing methods and their effect on process optimisation.The results. A lack of comprehensive studies covering the synergy of machine learning methods, big data processing, and quantum computing in the context of optimising analytical processes was revealed in the studied subject area, modern standards, and the current state of scientific research in the field of automated data processing systems.This requires the establishment of a conceptual framework and the development of an integrated data processing model that takes into account the volatility of the digital environment and the evolving nature of information technology. The formation of the data processing concept in automated systems is based on a multifactor approach that includes five vector directions of information flow management: analysis of the structure and sources of big data, application of machine learning methods for classification and forecasting, optimization of distributed processing processes, implementation of streaming analytics technologies, and the use of quantum algorithms to improve analytical computations. The developed concept involves creating an adaptive system that enables real-time decision-making based on large volumes of information, considering environmental variability factors and data security. The study of data processing revealed the benefits of an integrated approach combining traditional statistical methods, deep learning algorithms, quantum optimisation models and streaming analytics technologies. A conceptual data management model for automated systems has been developed. Consisting of five interconnected blocks, it provides comprehensive data analysis and processing at various levels of digital infrastructure. These include systemic big data analysis, the application of neural network technologies for classification and forecasting, the implementation of streaming data processing algorithms, the integration of distributed computing systems and the implementation of quantum algorithms for accelerating computation. The proposed model improves the accuracy of analytical forecasts, reduces data processing time and ensures continuous information updates.Conclusions. This study examines modern data processing methods in automated systems, including statistical approaches, machine learning, distributed computing and quantum technologies. The advantages and limitations of each approach are identified, and the importance of integrating them to improve the efficiency and adaptability of such systems is emphasised. Particular attention is paid to the role of automated systems in different sectors, ranging from medicine to industry, and their effect on data-driven decision-making processes. The paper also emphasises the importance of the ethical and security aspects of data processing within the context of digital transformation. The development of a conceptual data management model is feasible and aims to support the evolution of a resilient and secure digital infrastructure.