Abstract: In community emergency management during scenarios such as pandemic disasters, traditional methods can not quickly and accurately capture community dynamics. Therefore, an intelligent perception system combining MLLMs(Multimodal Large Language Models) and YOLOv8(You Only Look Once version 8) is proposed. The system comprehensively analyzes textual data from social media and community video surveillance streams to identify changes in community public service needs in real-time. Experimental results demonstrate high accuracy and responsiveness in demand recognition and anomaly detection. This enhances the responsiveness of public services in emergency management and provides strong technical support for smart city development.

Key words: emergency management, multimodal large language models, object detection, smart city