Enabling Smart Emergency Management with Large Language Models: A Multi-Dimensional Framework Integrating 4R Model

With the frequent occurrence of emergencies such as natural disasters, accidents, public health events, and social security incidents, Large Language Models (LLMs) enabled intelligent emergency response has become a frontier and hot topic in the field of emergency management disciplines. With its powerful capabilities in information collection, natural language understanding, generation, and reasoning, large language models can provide scientific support for emergency management departments across the full emergency lifecycle (prevention, preparation, response, and recovery). This study proposes a dual-dimensional LLM-enabled framework structured around event typology and emergency management activities. Guided by the framework, we systematically investigate LLM-driven methodologies for diverse emergency scenarios, including the identification of potential risks in the prevention stage, the automated contingency plan generation and real-time event monitoring in preparedness, the decision-making of emergency command in the response stage, and post-event analysis during recovery. The framework demonstrates how LLM technologies can be strategically integrated to advance intelligent emergency management systems.