Since Practical Byzantine Fault Tolerance （PBFT） consensus algorithm applied to consortium blockchain has the problems of insufficient scalability and high communication overhead， an improved practical Byzantine fault tolerance consensus algorithm based on Raft algorithm named K-RPBFT （K-medoids Raft based Practical Byzantine Fault Tolerance） was proposed. Firstly， blockchain was sharded based on K-medoids clustering algorithm， all nodes were divided into multiple node clusters and each node cluster constituted to a single shard， so that global consensus was improved to hierarchical multi-center consensus. Secondly， the consus between the cluster central nodes of each shard was performed by adopting PBFT algorithm， and the improved Raft algorithm based on supervision nodes was used for intra-shard consensus. The supervision mechanism in each shard gave a certain ability of Byzantine fault tolerance to Raft algorithm and improved the security of the algorithm. Experimental analysis shows that compared with PBFT algorithm， K-RPBFT algorithm greatly reduces the communication overhead and consensus latency， improves the consensus efficiency and throughput while having Byzantine fault tolerance ability， and has good scalability and dynamics， so that the consortium blockchain can be applied to a wider range of fields.

Traditional satellite scheduling models are generally simple, when the problem is large and the tasks are concentrated, the disadvantages of mutual exclusion between tasks and low task revenue often occur. To solve this problem, an intensive task imaging satellite scheduling method based on Improved FireWorks Algorithm (IFWA) was proposed. On the basis of analyzing the characteristics of intensive task processing and imaging satellite observation, synthetic constraint analysis on the tasks was firstly carried out, and then a multi-satellite intensive task scheduling Constraint Satisfaction Problem (CSP) model based on task synthesis was established by comprehensively considering the constraints such as the observable time of the imaging satellite, the attitude adjustment time between tasks, the energy and capacity of the imaging satellite method. Finally, an improved fireworks algorithm was used to solve the model, elitist selection strategy was used to ensure the diversity of population and accelerate the convergence of the algorithm, thus a better satellite scheduling scheme was obtained. The simulation results show that the proposed model increases the average revenue by 30% to 35% and improves the time efficiency by 32% to 45% compared with the scheduling model without consideration of task synthesis factor, which validates its feasibility and effectiveness.

With regard to the characteristics of randomness and fuzziness in service trust under computing environment, and lack of consideration in timeliness and recommend trust, a service trust evaluation method based on weighted multiple attribute cloud was proposed. Firstly, each service evaluation was given weight by introducing time decay factor, the evaluation granularity was refined by trust evaluation from multiple attribute of service, and direct trust cloud could be generated using the weighted attribute trust cloud backward generator. Then, the weight of recommender could be confirmed by similarity of evaluation, and recommended trust cloud was obtained by recommend information. Finally, the comprehensive trust cloud was obtained by merging direct and recommended trust cloud, and the trust rating could be confirmed by cloud similarity calculation. The simulation results show that the proposed method can improve the success rate of services interaction obviously, restrain malicious recommendation effectively, and reflect the situation of service trust under computing environment more truly.

Since most current attributes reduction algorithm can be only used for discrete decision tables, the correlation degree between condition attributes and decision attributes was defined as the importance degree of attributes, and meanwhile the overlap degree was defined based on the correlation degree and importance degree among attributes. The condition attributes' importance was renewed according to the overlap degree. To achieve the reduction of continuous-valued attributes set, an attributes reduction algorithm based on gray correlation analysis was proposed. The feasibility and effectiveness of the algorithm were verified in the simulation.