A Physical Twin Modeling Method for Industrial Control Systems Across Operating Conditions

By constructing a data-physical fusion physical twin model, the problem of anomaly detection in industrial control systems under multiple operating conditions was solved, achieving high-precision and interpretable anomaly detection that can adapt to complex processes and equipment aging scenarios.

CN121901993BActive Publication Date: 2026-05-26GUANGZHOU UNIVERSITY +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
GUANGZHOU UNIVERSITY
Filing Date
2026-03-24
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

Existing methods for detecting anomalies in industrial control systems cannot simultaneously meet the requirements of high reliability detection, physical consistency assurance, and dynamic environmental adaptability. In particular, they cannot effectively identify progressive covert attacks when faced with multiple operating conditions and complex coupled processes.

Method used

A data-physical fusion physical twin model is constructed, which combines a system of differential equations and a multi-task PINN model with a shared temporal feature network, a derivative numerical prediction network, and cross-domain transfer learning to achieve cross-condition anomaly detection in industrial control systems.

Benefits of technology

It improves the accuracy and stability of anomaly detection, enables real-time consistency analysis and rapid adaptation to new operating conditions, reduces noise interference and false alarm rate, and achieves interpretable, locatable and classifiable anomalies.

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Abstract

This invention discloses a physical twin modeling method for cross-condition industrial control systems. It constructs a data-physical fusion physical twin model applied to an anomaly detection framework for industrial control systems. This framework uses the physical twin model as its core, expressing industrial physical processes through a system of differential equations and introducing physical constraints using PINN. Simultaneously, it continuously calibrates the model using real-time operational data to achieve dynamic updates. Furthermore, it achieves adaptive capabilities across conditions, stages, and scenarios through parameter sharing and transfer learning. The trained physical twin model is compared with real-time data, and the existence of anomalies is determined by analyzing the deviation between predicted and observed values. This framework organically combines the high fitting properties of data-driven approaches with the interpretability of physical-driven approaches, providing industrial control systems with high-precision, interpretable, and transferable anomaly detection capabilities.
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