A brain–edge co-evolution framework for zero-trust real-time hot patching in power equipment

Under current zero-trust security architectures, real-time hot patching of power field equipment remains constrained by three critical technological limitations: excessive authentication latency that violates millisecond-level control requirements, the lack of quantitative mechanisms to prevent runtime structural disorder during patch injection, and the absence of effective integration between human operational expertise and automated decision systems. These limitations make existing zero-trust and fully automated hot patching approaches unsuitable for safety-critical power equipment operating under strict real-time and fault-intolerant conditions. To address these gaps, this paper proposes a brain–computer co-evolution–driven negative entropy zero-trust hot patching framework. Compared with conventional zero-trust implementations and automated reinforcement learning–based patching strategies, the proposed method introduces human EEG-derived risk intuition into the security decision loop and establishes a multidimensional negative entropy model to explicitly quantify and constrain system structural order during runtime updates. By combining these mechanisms with millisecond-level eBPF-based atomic code replacement, the framework aligns strong security verification with real-time operational constraints. Experimental results on an RTDS simulation platform and a real IED cluster (1,200 hot patching operations) demonstrate that the proposed framework reduces high-risk security decision latency to 12.3 ms—significantly lower than current zero-trust baselines—while limiting entropy increase risk to 3.5% and maintaining 99.99% service availability. These results indicate that the proposed approach bridges a critical gap between current zero-trust standards and the practical requirements of real-time, safety-critical power equipment updates.