The Design and Implementation of an Intelligent Q&A System for Electric Power Safety Regulations Based on Large Language Model Technology
B.-L. Li, Yan Jiang, Jie Xu, Zheng Liu, Zhiqiang Sheng, Xueying Song, Fu-qiang Chen, Zibing Meng
Abstract
In response to the challenges of the vast and scattered content, as well as the low efficiency in learning and searching within the safety regulations in the field of electric power, an intelligent question-answering system has been designed based on large model technology. This system aims to organize knowledge related to safety regulations and improve search efficiency. The system employs natural language processing techniques to categorize entries in safety regulations. It utilizes a vector database for entry vectorization and storage. The integration of large models with safety regulation entries is achieved through a large model development framework, forming a question-answering system. A file management module is constructed using a frontend framework to provide interactive functionality to the question-answering system. The performance experiment of the system on the question answering system shows that the overall accuracy of the system's answer to the safety rules and regulations in the field of electric power is more than 60%, which is professional than the general large model.