To solve various issues faced by Electronic Medical Record （EMR） sharing， such as centralized data provider， passive patient data management， low interoperability efficiency and malicious dissemination， a blockchain-based EMR secure sharing method was proposed. Firstly， a more secure and efficient Universal Designated Verifier Signature Proof （UDVSP） scheme based on the commercial cryptography SM2 digital signature algorithm was proposed. Then， a smart contract with functionalities of uploading， verification， retrieval and revocation was designed， and a blockchain-based EMR secure sharing system was constructed. Finally， the feasibilities of UDVSP scheme and sharing system were demonstrated through security analysis and performance analysis. The security analysis shows that the proposed UDVSP is probably secure. The performance analysis shows that compared with existing UDVSP/UDVS schemes， the proposed UDVSP scheme saves the computation cost at least 87.42% and communication overhead at least 93.75%. The prototype of blockchain smart contract further demonstrates the security and efficiency of the sharing system.

In the few-shot object detection task based on transfer learning， due to the lack of attention mechanism to focus on the object to be detected in the image， the ability of the existing models to suppress the surrounding background area of the object is not strong， and in the process of transfer learning， it is usually necessary to fine-tune the meta-features to achieve cross-domain sharing， which will cause meta-feature shift， and lead to the decline of the model’s ability to detect large-sample images. To solve the above problems， an improved meta-feature transfer model Up-YOLOv3 based on the attention mechanism and the meta-feature secondary reweighting mechanism was proposed. Firstly， the Convolution Block Attention Module （CBAM）-based attention mechanism was introduced in the original meta-feature transfer model Base-YOLOv2， so that the feature extraction network was able to focus on the object area in the image and pay attention to the detailed features of the image object class， thereby improving the model’s detection performance for few-shot image objects. Then， the Squeeze and Excitation-Secondary Meta-Feature Reweighting （SE-SMFR） module was introduced to reweight the meta-features of the large-sample image for the second time in order to obtain the secondary reweighted meta-features， so that the model was not only able to improve the performance of few-shot object detection， but also able to reduce the weight shift of the meta-feature information of the large-sample image. Experimental results on PASCAL VOC2007/2012 dataset show that， compared with Base-YOLOv2， Up-YOLOv3 has the detection mean Average Precision （mAP） for few-shot object images increased by 2.3 to 9.1 percentage points； compared with the original meta-feature transfer model based on YOLOv3 Base-YOLOv3， mAP for large-sample object images increased by 1.8 to 2.4 percentage points. It can be seen that the improved model has good generalization ability and robustness for both large-sample images and few-shot images of different classes.

In order to solve the problem of lack of co-rated users caused by data sparseness and similarity calculation method, the authors, by making use of the advantage of cloud model transformation between qualitative concept and quantitative numerical value, proposed an improved personal recommendation algorithm based on cloud model and users clustering. The users’ preference on the evaluation of item attribute was transformed to preference on digital characteristics represented by integrated cloud model. By using the improved clustering algorithm, the authors clustered the rating data and the standardized original user attribute information, and at the same time, by taking into account the changes of the users’ interests, recommended the neighbor users’ union generated by similarity based on integrated cloud model of items attributes evaluation between users, clustering of users for item rating, and clustering of user attributes these three methods. The theoretical analysis and experimental results show that the proposed improved algorithm can not only solve the problem of lack of co-rated users caused by data sparseness, but also obtain satisfactory mean absolute error and root-mean-square error even when the users are new. Theoretical analysis and experimental results show that the proposed improved algorithm can not only solve the problem of lack of co-rated users caused by sparseness data, but also obtain satisfactory mean absolute error and root-mean-square error even when the users are new.