Research And Application Of Blockchain Consensus Algorithm Based On Reputation Reward And Punishment Mechanism

As the core of blockchain,the performance of consensus algorithm directly affects the efficiency of blockchain system.Proof of Activity(Po A)consensus algorithm is a hybrid algorithm combining Proof of Work(Po W)and Proof of Stake(Po S).It has the advantages of easily extended network structure and faster transaction processing speed,but also has problems such as lack of trust between nodes and block discarding caused by offline representative nodes.To solve the above problems,two improved consensus algorithms were proposed,and designed for the consensus framework and overall architecture of the traceable academic information supervision system.The details of the research are as follows:(1)In view of the lack of trust and waste of resources caused by block discarding in Po A algorithm,the Proof of Activity Consensus Algorithm Based on Credit Reward Mechanism(CPo A)was proposed.First of all,the credit mechanism was introduced into the algorithm to improve the creation method of blocks according to the credit value,and adjust the cost and difficulty of nodes creating blocks.Secondly,the block creation method that represents the node to generate multiple blocks of data from a single block header was set,to reduce the waste of resources caused by block discarding.Finally,the credit reward and punishment scheme was set up to realize the timely processing of malicious nodes and improve the enthusiasm of nodes.Through the test of the experiment,the generation rate of data blocks in CPo A was increased by about 1.75 times of Po A.The proposed reward and punishment scheme can actualize the rapidly processing malicious nodes.When the proportion of malicious nodes increased from 30% to 70%,the average decline rate of the overall reputation value of malicious nodes increased by about 1.7 times,which could increase the cost of subsequent blocks by malicious nodes and enhance the stability of the system.(2)In view of the lack of evaluation of historical data on the credit reward and punishment scheme in CPo A,and the insufficient rationality of representative node selection under the large number of nodes,the Proof of Activity Consensus Algorithm Based on K-Medoids Clustering(KPo A)was proposed.First of all,participant and representative nodes were selected successively by the follow-the-satoshi mechanism and K-medoids algorithm.Secondly,the hierarchical consensus of blocks was realized according to the grouping of nodes after clustering.Finally,the credit reward and punishment scheme was set up according to the historical and current behavior data of nodes,which was used to judge the advantages and disadvantages of nodes more comprehensively and realize the timely treatment of malicious nodes.Simulation experiments show that KPo A improved the consensus efficiency through node grouping,and the output rate of data blocks was increased by about 2.5 times that of Po A.The set credit reward and punishment scheme could effectively deal with malicious nodes and maintain the stability of the system.(3)Based on the improved consensus algorithm,the consensus framework and overall architecture of the traceable academic information regulatory system were designed.According to the requirement analysis,the consensus framework of the system was designed as an on-campus and off-campus two-tier structure,and the two improved consensus algorithms were applied to different levels,thus effectively improving the efficiency of academic information storage and traceability.Meanwhile,the overall architecture and functional modules of the system were constructed based on the application requirements,and the operation mechanism of the system was designed and implemented.