QR code payment plays an indispensable role in the mobile payment market, and the security of scanning codes has always been a problem in the field of information security. Static QR codes are easily copied and replaced, and there are huge security loopholes. The QR code payment in a closed system still faces security challenges. In order to solve the security problem of QR code payment, we have studied dynamic QR code payment system that supports SM2, SM3, and SM4 cryptographic algorithms, which can realize QR code scanning and scanned transactions, UnionPay cloud QuickPass transactions, etc., and generate dynamic QR code information in real time during the transaction process, one order and one code. Through dynamic algorithm distribution, the randomness and uniqueness of QR code generation are guaranteed, and it is suitable for multi-scene application transactions. The algorithm correctness test result shows that the system has achieved the expected effect. The performance test results show that the hardware of the security module implements the algorithm flow and improves the payment performance. Compared with some other algorithms, the processing time is shorter, the running speed is faster, and the system is more secure.

Real-time detection and identification of orchard pests is related to the economy of the orchard industry. Using lab picture collections and pictures from web crawling, a dataset of common pests in orchards has been created. It contains 24,748 color images and covers seven types of orchard pests. Based on this dataset, this paper combines YOLOv5 and GhostNet and explains the benefits of this method using feature maps, heatmaps and loss curve. The results show that the mAP of the proposed method increases by 1.5% compared to the original YOLOv5, with 2× or 3× fewer parameters, less GFLOPs and the same or less detection time. Considering the fewer parameters of the Ghost convolution, our new method can reach a higher mAP with the same epochs. Smaller neural networks are more feasible to deploy on FPGAs and other embedding devices which have limited memory. This research provides a method to deploy the algorithm on embedding devices.

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