China Safety Science Journal ›› 2024, Vol. 34 ›› Issue (8): 178-185.doi: 10.16265/j.cnki.issn1003-3033.2024.08.1901

• Safety engineering technology • Previous Articles     Next Articles

ChatSOS: large language model-based knowledge Q&A system for safety engineering

TANG Haiyang(), LIU Zhenyi, CHEN Dongping, CHU Qingzhao**()   

  1. School of Mechatronic Engineering, Beijing Institute of Technology, Beijing 100081, China
  • Received:2024-02-20 Revised:2024-05-25 Online:2024-08-28 Published:2025-02-28
  • Contact: CHU Qingzhao

Abstract:

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.

Key words: chat safety oracles (ChatSOS), large language model, safety engineering, knowledge question answering (Q&A) system, accident investigation, vector database

CLC Number: