Fire sensing system optimization method based on deep learning surrogate model
The reasonable arrangement of sensors significantly affects the actual effectiveness of the fire monitoring system.A fire sensor system optimization approach based on a deep learning surrogate model is developed to ensure the effectiveness of early warning and characteristic identification of fires.The multi-scene simulation data is obtained using the fire simulation model,and the deduction law of the fire field is learned using deep learning technology.After studying and quantifying the model identification accuracy,precision,recall and F1 score in various data settings,the monitoring system is optimized in terms of spatial layout,acquisition frequency,and detection time length.The results show that the optimized sensor system can significantly reduce both the number of sensors and the data acquisition cost while maintaining fire detection's effectiveness.This solution provides decision support for designing a fire monitoring system and can be used in different building scenarios.The study results are helpful for the advancement of intelligent buildings and fire protection.
NETL
NSTL
万方数据
