Real-time fire temperature field reconstruction using multi-source acoustic wave data and a hybrid deep learning approach

Accurate reconstruction of temperature distributions in complex fire scenarios is essential for effective fire monitoring, early warning, and firefighting decision-making. Traditional methods often face challenges in both accuracy and computational efficiency due to the highly nonlinear and dynamic nature of fire environments. To address this issue, a novel reconstruction framework driven by multi-source acoustic wave data is presented. This approach integrates an Adaptive Weighted Hybrid Convolution–Dynamic Residual Attention-Aware Fusion Network (AWHC-DRAAFN) with an Integrated K-Nearest Neighbor (IKNN) model. The AWHC-DRAAFN facilitates efficient extraction and fusion of multi-scale features by combining various convolution operations with adaptive weighting mechanisms, thereby enhancing the network’s capacity to capture complex nonlinear relationships between acoustic wave propagation and temperature distribution. Meanwhile, the IKNN model transforms discrete temperature data into a continuous field through a locally weighted K-nearest neighbor interpolation strategy. Experimental results demonstrate that the proposed method achieves high prediction accuracy (MAE $<$ 5.3%, MSE $<$ 0.7%, RMSE $<$ 8.3%) and high computational efficiency (reconstruction time $<$ 0.54s), highlighting its potential as a reliable solution for real-time reconstruction of fire temperature fields.