With the rapid development of artificial intelligence, the purpose of image classification is not only to identify the major categories of objects, but also to classify the images of the same category into more detailed subcategories. In order to effectively discriminate small differences between categories, a fine-grained classification algorithm was proposed based on Attention-Attention Bilinear Pooling (AABP). Firstly, the Inception V3 pre-training model was applied to extract the global image features, and the local attention region on the feature mapping was forecasted with the deep separable convolution. Then, the Weakly Supervised Data Augmentation Network (WS-DAN) was applied to feed the augmented image back into the network, so as to enhance the generalization ability of the network to prevent overfitting. Finally, the linear fusion of the further extracted attention features was performed in AABP network to improve the accuracy of the classification. Experimental results show that this method achieves accuracy of 88.51% and top5 accuracy of 97.65% on CUB-200-2011 dataset, accuracy of 89.77% and top5 accuracy of 99.27% on Stanford Cars dataset, and accuracy of 93.5% and top5 accuracy of 97.96% on FGVC-Aircraft dataset.

Aiming at the problem of ensuring terminal communication in the scenarios of strong interference and high failure risk in the power wireless private network, a security-risk-oriented energy-efficient distributed resource allocation method was proposed. Firstly, the energy consumption compositions of the base stations were analyzed, and the resource allocation model of system energy efficiency maximization was established. Then, K-means++ algorithm was adopted to cluster the base stations in the network, so as to divide the whole network into several independent areas, and the high-risk base stations were separately processed in each cluster. Then, in each cluster, the high-risk base stations were turned into the sleep mode based on the risk values of the base stations, and the users under the high-risk base stations were transferred to other base stations in the same cluster. Finally, the transmission powers of normal base stations in clusters were optimized. Theoretical analysis and simulation experimental results show that, the clustering of base stations greatly reduces the complexity of base station sleeping as well as power optimization and allocation, and the overall network energy efficiency is increased from 0.158 9 Mb/J to 0.195 4 Mb/J after turning off the high-risk base stations. The proposed distributed resource allocation method can effectively improve the energy efficiency of system.