Algorithmic methods of event-based predictive quality control of complex data processing systems: integration of system analysis and computational modeling

The purpose of this research is to develop an algorithmic framework for event-forecast quality management in complex data processing systems (CDPS), through the integration of systemic analysis methods and computational modeling. Contemporary approaches to quality assessment, based on static metrics defined by GOST R 59797–2021, fail to account for dynamic emergent properties and predictive operational scenarios of CDPS. The study proposes a hybrid model that combines multi-level system analysis with L-stable numerical simulation techniques, enabling formalization of the “event-forecast quality level” as a function of temporal system parameters. The developed algorithmic framework includes a three-tier data aggregation architecture with adaptive weighting coefficients, a dynamic quality management system integrated into the CDPS lifecycle, a neural network module for preventive optimization based on reinforcement learning. Experimental validation on 15 industrial CDPS demonstrated improved critical event prediction accuracy up to 89.7% and reduced system response time from 15.3 to 2.7 seconds. Implementation within the control loop of a petroleum refinery reduced energy consumption per operation by 33% and increased service intervals by 27%. The originality of the work lies in the synthesis of relational analysis methods with deep learning neural architectures, ISO 25010 quality management principles with predictive analytics of rigid systems, real-time dynamic parameter adaptation using a modified (2,1)-method. Practical significance is confirmed by the integration of the algorithm into design, testing, and operation phases of CDPS, meeting the requirements of GOST R 59797–2021. Research outcomes are applicable to the development of fault-tolerant control systems for mission-critical infrastructure in energy, telecommunications, and finance. Future perspectives include adapting the algorithm for quantum computing systems and distributed IoT architectures.