In this paper, we propose a novel framework for predicting and planning resilience in industrial systems, called “Industrial Brain.” The Industrial Brain is a human-like cognitive decision-making and planning framework that autonomously plans resilience directly from observation data by integrating high-order activity-based neural networks and CT-OODA (Observe-Orient-Decide-Act) symbolic reasoning. To address the problem of poor generalization performance in various chaotic data environments of existing deep learning-based methods, the Industrial Brain understands and models the structure of node activity dynamics and network coevolution topology without simplifying assumptions, and reveals the fundamental laws hidden behind complex networks, enabling accurate resilience prediction, inference, and planning. Experimental results show that the Industrial Brain improves the accuracy by up to 10.8% and 11.03% over existing GoT, OlaGPT frameworks, and spectral dimensionality reduction methods, and generalizes to unknown topologies and dynamics, and maintains robust performance against observational disturbances.
