A Personalized Information Push System for Urban Rescue Scenarios

With the gradual implementation of urban agglomeration development plans in China, the emergence of megacities and super-large cities has posed further challenges to the rescue capabilities of firefighting teams in urban rescue scenarios. The complex urban environment demands higher rapid decision-making abilities from the command personnel of firefighting teams. By comprehensively utilizing artificial intelligence services, a personalized information push system for urban rescue scenes is proposed. This system makes comprehensive judgments based on multiple factors such as the situation at the rescue scene and the conditions of the personnel present, proactively providing relevant decision-making suggestions to command personnel to improve rescue efficiency. Current research on information recommendation technologies for urban rescue scenes remains predominantly theoretical, with low technical maturity, which fails to meet actual emergency needs. Additionally, practical issues such as the difficulty in assessing related data (e.g., burnt area, fire scene dynamics, and fire location) persist. This paper conducts a business analysis and capability requirements analysis for an active information push system aimed at urban rescue scenes. It decomposes key technologies including information feature extraction, active information push and expression technologies, and emergency rescue scene matching technologies.