Currently， collaborative filtering-based Graph Neural Network （GNN） recommendation systems face data sparsity and cold start issues. Many related algorithms introduce external knowledge of items for supplementary expansion to alleviate these issues， but these algorithms ignore the severe information utilization imbalance caused by direct fusion of sparse collaborative signals and redundant supplementary parts， as well as the problems of information sharing and propagation among different data. Therefore， a Multi-view Knowledge-aware and interactive Distillation Recommendation algorithm （MKDRec） was proposed. Firstly， to tackle data sparsity， the formed collaborative view was enhanced through random dropout， and then neighborhood contrastive learning was applied to node representations in this view. Secondly， regarding to knowledge redundancy problem， each relation type of edge in the knowledge view was encoded， and an items’ knowledge view was reconstructed on the basis of the head and tail entities as well as connecting relations to fully utilize the information. Finally， an associated view with remote connections was constructed on the basis of the equivalence relations between items and entities. With all the above， graph node representations were learned by different convolutional aggregation methods on the three views to extract multiple types of information for users and items， and embedded representations of multiple users and items were obtained. Besides， knowledge distillation and fusion of node feature vectors in pairwise views were performed to realize information sharing and propagation. Experimental results on Book-Crossing， MovieLens-1M， and Last.FM datasets show that compared to the best results among the baseline methods， MKDRec’s AUC （Area Under Curve） are improved by 2.13%， 1.07%， and 3.44%， respectively， and MKDRec’s F1-scores are improved by 3.56%， 1.14%， and 4.46%， respectively.

In financial transactions, some safety and reliability problems exist in the client authentication system with single keyboard password mode. To solve these problems, the password multimodality method was proposed. Modal sensors accessed the password codeword information and transmitted its normalized result. The formatted code information was pre-processed in property and then classified by its attributes. After that, the multimode passwords were fused through sharing public units. For a certain M bits password, if each bit had N kinds of possible modals, the password theft rate was 1/(10MCN×MM). In this multimode input system, the keyboard password and black box handwriting are the two default modals, and the application results demonstrate that the proposed method realizes the disordered blending input of multimodal password codeword. At M=6, the password theft rate is 1/(106C2×66), and the method types and difficulty of cracking those passwords increase with the number of modals. The password input system's safety, reliability and other performances are significantly better than those with only one modal.

Image quality detection technology can automatically detection the image abnormality in order to replace manual inspection methods for the monitoring system. It can accurately analyze abnormalities of the video, and alarm the system in order to ensure normal running of the expanding network video surveillance system. Noise detection technology based on the spatial domain use the image information of the field characteristics, profile and orientation distributions of various kinds of abnormal noise in spatial domain, and take advantage of OpenCV-based image processing technology achieving detection of noise points, snowflakes and stripes. The noise detection algorithm in spatial domain proposed, is consistent with human visual perception and can be used to monitoring video for real-time detection.

As for the large differences between the visual and infrared images in gray value caused by different imaging mechanism, inconsistent contour, the low matching probability of traditional matching methods based on gray or feature, the gray information of visual and infrared images was introduced after researching a variety of Hausdorfff Distance (HD) algorithms. Image matching method based on the neighbor grayscale information Hausdorfff distance was proposed. Based on the calculation of the similarity of edge feature points, the calculation of image normalized grayscale variance was added into this method, which effectively solved the low probability problem caused by different edge of visual/infrared image in Hausdorff distance matching algorithms. The simulation results of visual and infrared images matching show that under various conditions, compared with the conventional Hausdorff distance method, the proposed algorithm effectively improves matching effect under different light conditions and anti-jamming of noise.