Design And Implementation Of An Emergency Intelligent Decision System Based On IoT Perception

With the acceleration of urbanization,the frequency of urban fires has increased,posing a serious threat to the safety of people’s lives and property.Against this backdrop,there is an urgent need for an efficient fire rescue system capable of real-time acquisition of fire scene information,accurate prediction of fire development,efficient indoor path planning,and scientific rescue decision-making.The emergency intelligent decision-making system developed in this study,based on IoT perception,is designed to quickly respond to emergencies,and reduce personnel casualties and property loss.The main features and achievements of the system include:(1)The system provides a detailed indoor and outdoor dualmap display platform that visualizes real-time perceived data on the map,offering interactive operations such as information reporting and dispatch of rescue forces for the rescue personnel.In addition,the system offers a plug-and-play fire evolution model to predict the development of the fire scene,and encapsulates a rule engine that provides decision support for commanders in vehicle dispatch,task priority confirmation,and rescue force allocation.(2)An improved multi-layer connected cellular automaton model has been designed and implemented as the default fire evolution model within the system.Compared to traditional threedimensional cellular automata,it clearly records the vertical connectivity points within buildings,reducing the computation of cells where vertical fire spread does not exist,which is more in line with real situations and also improves computational efficiency.(3)An environment-aware line-of-sight A*path planning algorithm has been designed and implemented.Compared to traditional A*path planning,it not only considers the length of the path but also the cost of the fire scene to avoid routes through potential danger zones.Considering the non-uniform fire scene costs and the excessive turns and redundancies caused by the grid map,a line-of-sight algorithm has been added to smooth and optimize the path under safe conditions,resulting in a rescue path that is safe,efficient,and compliant with firefighting operation standards.The system has been subjected to functional point testing and performance testing after deployment.The test results show that the system meets the requirements of emergency intelligent decisionmaking and has a high concurrent processing capacity.While supporting various rules,it offers powerful interactivity and can significantly enhance the efficiency and accuracy of emergency response in complex dynamic fire scenes.