关键词: 大语言模型, 贝叶斯推理, 电力操作票, 不确定性量化, 智能执行, 置信度评估

Abstract:

With the rapid development of artificial intelligence, Large Language Models (LLMs) show great potential in power system operation and maintenance. However, challenges such as high output uncertainty, difficulty in quantifying reliability, and insufficient domain adaptability severely restrict their application in safety-critical scenarios like power operation ticket processing. To address these challenges and achieve precise understanding and reliable execution of operation tickets by power robots, this paper proposes an intelligent system that fuses the semantic understanding capabilities of LLMs with the uncertainty quantification advantages of Bayesian inference. An intelligent understanding and execution system based on an LLM-Bayesian cascade architecture is constructed. First, a Understanding-Action-Reflection (UAR) three-module collaborative architecture is designed to enhance domain adaptability through a closed-loop information flow. Second, a multi-dimensional confidence evaluation algorithm is established to verify LLM outputs from three dimensions: consistency checking, attention pattern analysis, and historical accuracy statistics. Finally, a three-level Bayesian uncertainty quantification model covering decomposition, semantic, and strategic levels is built to support confidence-driven dynamic execution strategies (autonomous, supervised, step-by-step confirmation, and expert intervention). Experiments were conducted on a dataset of 800 real power operation tickets (covering 220kV and 500kV substations). The results demonstrate that the system achieves 95.8% understanding accuracy, 92.4% execution success rate, and 94.5% risk identification rate. Compared with existing pure LLM methods (such as Qwen2.5-72B, DeepSeek-R1) and traditional expert systems, the understanding accuracy and execution success rate are improved by over 6.6% and 10.7%, respectively. The LLM-Bayesian cascade architecture effectively compensates for the "black box" defects of LLMs through probabilistic modeling, significantly improving the safety and reliability of power operation ticket processing, and providing technical support for intelligent operation and maintenance of power systems.

Key words: large language models, Bayesian inference, power operation tickets, uncertainty quantification, intelligent execution, confidence assessment