Deep Convolutional Neural Network （CNN） has impressive performance in image super-resolution reconstruction. However， many current related methods have a lot of model parameters， making them unsuitable for devices with limited computational resources. To address the above problem， a lightweight Asymmetric Information Distillation Network （AIDN） was proposed. Firstly， effective feature information was extracted from the input original images and edge images. Secondly， an asymmetric information distillation module was designed for non-linear mapping learning on these features. Thirdly， multiple residual images were reconstructed by an upsampling module and fused into one residual image through attention mechanism. Finally， the fused residual image was added to the interpolation of the input image to generate the super-resolution image. Experimental results on Set14， Urban100， and Manga109 datasets show that the 4× super-resolution Peak Signal-to-Noise Ratio （PSNR） values of AIDN model are improved by 0.03 dB， 0.14 dB， and 0.06 dB， respectively， compared to those of Spatial Adaptive Feature Modulation Network （SAFMN）. This demonstrates that AIDN model achieves a superior balance between model parameters and performance.

Aiming at the problem that in the dynamic evolution of information diffusion， it is difficult to effectively capture structural features， temporal features， and the interactive expression between them， an information diffusion prediction model based on Transformer and Relational Graph Convolutional Network （TRGCN） was proposed. Firstly， a dynamic heterogeneous graph composed of the social network graph and the diffusion cascade graph was constructed. The structural features of each node in this graph were then extracted using Relational Graph Convolutional Network （RGCN）. Secondly， the time embedding of each node was re-encoded using Bi-directional Long Short-Term Memory （Bi-LSTM） network. Then a time decay term was introduced to give different weights to the nodes at different time positions， so as to obtain the temporal features of nodes. Finally， structural features and temporal features were input into Transformer and then merged. Finally， the spatial-temporal features were obtained for information diffusion prediction. The experimental results on three real datasets of Twitter， Douban and Memetracker show that compared with the optimal model in the comparison experiment， the Hits@100 of TRGCN increase by 3.18%， 5.96% and 3.34% respectively， the Map@100 of TRGCN increase by 11.60%， 19.72% and 8.47% respectively， proving its validity and rationality.

In order to solve the problems that the iterative efficiencies of the existing privacy protection k-means clustering schemes are low， the server in the centralized differential privacy preserving k-means clustering scheme may be attacked， and the server in the localized differential privacy protection k-means clustering scheme may return wrong clustering results， a Multi-party Privacy Protection k-means Clustering Scheme based on Blockchain （M-PPkCS/B） was proposed. Taking advantages of localized differential privacy technology and the characteristics of the blockchain such as being open， transparent， and non-tamperable， firstly， a Multi-party k-means Clustering Center Initialization Algorithm （M-kCCIA） was designed to improve the iterative efficiency of clustering while protecting user privacy， and ensure the correctness of initial clustering centers jointly generated by the users. Then， a Blockchain-based Privacy Protection k-means Clustering Algorithm （Bc-PPkCA） was designed， and a smart contract of clustering center updating algorithm was constructed. The clustering center was updated iteratively by the above smart contract on the blockchain to ensure that each user was able to obtain the correct clustering results. Through experiments on the datasets HTRU2 and Abalone， the results show that while ensuring that each user obtains the correct clustering results， the accuracy can reach 97.53% and 96.19% respectively， the average iteration times of M-kCCIA is 5.68 times and 2.75 times less than that of the algorithm of randomly generating initial cluster center called Random Selection （RS）.

For cloud manufacturing environment, many manufacturing cloud services have the same or similar function, so it is difficult to get the most suitable cloud services. This study designed a selection method of the manufacturing cloud services based on trust evaluation. How to select cloud services was described by abstraction; the reliability, usability, timeliness, price and honesty were used as the trust characteristics together; and the evaluation time and effect of estimators' honesty on the service's credibility were also taken into account; and then the overall credibility was calculated from all above data by weighted average method. Furthermore, with all factors such as the cloud services' function, workload, current state and physical distance considered in package, the method was built to guide the cloud service selection by matching the services' function, workload and price and combining the trust evaluation. The results of simulation experiments show that the service selection method is able to recognize entities of manufacturing cloud services, and it improves the rate of the cloud service trades and meets users' functional and non-functional requests better.