Pathway and Empirical Study of Fire Accident Intelligence Support Based on Case Reasoning and Event Logic Graph
Kexiang Xu · 2024
This study aims to address the common and destructive disaster of fires by proposing an innovative intelligence support pathway to enhance the efficiency and quality of emergency decision-making. Traditional intelligence support faces challenges in handling fire accidents, such as insufficient construction of expertise databases, overlooking the dynamic nature of events, and a lack of quantitative research. Therefore, this research combines case-based reasoning and matter-element graph methods to improve decision quality by analyzing historical fire cases and establishing logical connections between events. A database including 128 fire cases was constructed, employing a combination of case-based and rule-based reasoning to generate decision-making support schemes. The study also utilized matter-element graphs to gain a deeper understanding of complex disaster situations. Empirical research was conducted using the fire incident at Beijing Changfeng Hospital as a case study. The study successfully matched the most similar case, optimized rules, and analyzed key links in the disaster chain and their interactions through the constructed matter-element graph. The research demonstrates the practical feasibility and effectiveness of the intelligence support pathway that combines case-based reasoning and matter-element graphs in emergency management of fires, providing theoretical and practical guidance for emergency decision-making in other fields. Moreover, this study's proposed approach, integrating qualitative and quantitative intelligence support, offers conceptual innovation and provides new theoretical perspectives for the construction of knowledge bases in specific fields.