MATHEMATICAL MODELING OF AGGREGATION AND PROCESSING OF MEASUREMENT DATA IN AUTOMATED METROLOGICAL MONITORING SYSTEMS

Abstract

The article presents a comprehensive mathematical model for the aggregation and processing of measurement data within automated metrological monitoring systems. It addresses the challenges of synthesizing heterogeneous data streams from various sensors, emphasizing the necessity to maintain key metrological characteristics such as accuracy, repeatability, timeliness, and representativeness. To achieve this, the study introduces a formalized, multi-stage aggregation algorithm that includes preprocessing, normalization, adaptive weighting, and validation stages. This structure ensures the algorithm can dynamically adjust to varying quality and availability of input data, thus improving the robustness and scalability of real-time monitoring applications.

A central component of the model is the introduction of an integral quality indicator, designed to evaluate both the reliability and the practical usability of aggregated data. This indicator supports real-time decision-making by highlighting deviations in input quality and triggering appropriate aggregation responses. A novel feature of the model is the adaptive weighting mechanism, which modulates the contribution of each sensor’s data based on its individual quality profile. This enables the system to prioritize high-quality sources while down-weighting or even excluding unreliable ones.

The model's effectiveness is validated through analytical simulations under several hypothetical but realistic scenarios, such as degradation in precision, delayed transmission, and incomplete data sampling. These case studies illustrate how the integral quality indicator reacts to various disruptions and guides the system's response to maintain optimal data fusion. The proposed approach enhances the trustworthiness and resilience of metrological systems, making it particularly suitable for deployment in Industry 4.0 environments, where the ability to integrate diverse sensor inputs in real time is critical. Furthermore, the model facilitates the early detection of faulty data sources and supports automated reconfiguration of the aggregation logic, thereby increasing operational efficiency and decision support capabilities.