基于数据融合的棉花仓库环境监测OA

A high-precision fusion strategy for layered wireless sensor networks is proposed to address the problems of large errors in collected data and poor monitoring real-time and accuracy of wireless sensor networks in cotton warehouses.In the bottom layer,the collected data were denoised by CEEMDAN joint wavelet thresholding.In the middle layer,the FASTDTW algorithm was used to optimize the fuzzy support function for the weighted fusion of similar sensor data.In the top layer,the improved pelican optimization algorithm was used to optimize the deep confidence neural network for the feature fusion of dissimilar sensor data.The experimental results show that the fusion strategy has strong anti-interference ability,short fusion time,and enriches the fusible feature categories while ensuring the fusion ac-curacy.