Research on abnormal data detection of gas boiler supply based on deep learning network

Abstract

On the basis of YOLO deep network detection method, a new abnormal data detection method is proposed to meet the needs of gas boiler abnormal data detection. In the feature extraction layer, the SENet structure is embedded between DBL and Pooling. Through compression, excitation and recalibration, the feature extraction of data information is more accurate. After feature extraction layer, multi-scale pooling processing mechanism is introduced to improve the learning efficiency of YOLO network. The first group and the second group of experiments respectively proved that the introduction of SENet structure and multi-scale pooling mechanism improved the feature extraction accuracy of YOLO network and the convergence speed of iteration process. The third group of experimental results show that the detection accuracy of the detection method proposed in this paper is significantly higher than CNN method, RNN method and YOLO method, and it is more suitable for the detection of abnormal data of gas boilers.