面向安全关键工业过程异常状况管理的大型推理模型
Large reasoning models for abnormal situation management in safety-critical industrial processes
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中文总结 AI 辅助
该研究提出通用大型推理模型，通过有界经程序验证的动作接口管理安全关键工业过程的异常状况，在39种异常测试中表现优于基础调节控制，可实现无需人类参与的运行时异常状况管理。
中文摘要 AI 辅助
自动化系统在设计范围内运行安全关键过程，而将异常状况留给人类操作员处理。这类状况的管理失误是引发过程安全事故的主要原因，也是实现自主性的阻碍。本文展示了一种通用大型推理模型，该模型未经过特定任务训练，仅使用操作员可获取的信息，通过有界、经程序验证的动作接口在运行时管理异常状况。在全厂工业基准过程的39种异常状况及工况点变化测试中，该推理模型在全部39种状况下均将工厂维持在所有硬约束范围内，而基础调节控制在15种状况下失效；其表现与工厂专家设计的高级控制相当，且在15种安全关键状况中成功诊断出全部15种根本原因故障。三款独立开发、成本相差30倍的模型均超出基线水平。在完全可审计的评估中，这些结果证明了无需人类参与的运行时异常状况管理是可行的。
英文摘要
Automation operates safety-critical processes inside their design envelope and leaves abnormal situations to human operators. Mismanagement of these situations is a leading contributor to process-safety incidents and a hindrance to achieving autonomy. Here we show that a general-purpose large reasoning model, with no task-specific training and only the information available to an operator, manages abnormal situations at run time through a bounded, programmatically verified action interface. Across 39 abnormal situations and operating-point changes on a plant-wide industrial benchmark process, the reasoning model maintained the plant within all hard constraints in all 39, while basic regulatory control failed in 15. It matched the plant's expert-engineered advanced control and diagnosed the root-cause fault in 15 of 15 safety-critical situations. Three independently developed models spanning a thirty-fold cost range exceeded the baseline. In a fully auditable evaluation, these results demonstrate run-time abnormal situation management without a human in the loop.
