ChatSOS:基于大语言模型的安全工程知识问答系统

To address the limitations of large language models in safety engineering, such as the corpus size, input processing capabilities and privacy concerns, ChatSOS, a Q&A system based on large language models, was developed. Based on 117 explosion incident reports from 2013 to 2023, a vector database to enhance the system's capability was constructed. ChatSOS integrated prompt engineering and external knowledge base to retrieve and analyze relevant data from the database. Compared to ChatGPT, ChatSOS integrated the external knowledge base, so that the big language model could retrieve the relevant corpus from the database according to the user's input information and make in-depth analysis. The results show that ChatSOS excels in in-depth professional problem analysis, autonomous task allocation, and providing detailed summaries and recommendations based on incident reports. By combining with the external knowledge database, the limitations of the large language model's professional corpus in safety engineering are overcome, which prevents performance degradation associated with fine-tuning on new datasets, broadens the application of large language models in this field, and paves the way for future advancements in automation and intelligent systems.