Abstract: The Practical Byzantine Fault Tolerance (PBFT) algorithm faces challenges such as high communication complexity, incomplete node management, and the lack of dynamic behavior evaluation, which limit its performance and security in large-scale blockchain systems. To address these issues, a reputation-based grouped consensus algorithm is proposed. According to a multi-dimensional reputation assessment, nodes are divided into excellent, good, and observer classes. Only excellent and good nodes participate in consensus, where leaders are primarily selected from the excellent class. Nodes with insufficient reputation but no malicious records act as observers, limited to ledger synchronization. Nodes identified with malicious actions are isolated from the consensus and synchronization process to improve system security and robustness.To reduce communication overhead, the algorithm integrates Boneh–Lynn–Shacham (BLS) aggregate signature technology, which compresses multiple signatures into a single fixed-length signature. This reduces the size of transmitted data and eases intra-group and inter-group message broadcasting. A dynamic node management mechanism is also designed to allow flexible node entry and exit, enhancing adaptability and fault tolerance.Experimental results show that compared with PBFT, DT-PBFT, and NBR-PBFT, the proposed algorithm reduces consensus latency by approximately 45.3%, 29.3%, and 17.4%, and improves throughput by around 17.4%, 10.6%, and 4.5%, respectively. These improvements demonstrate better scalability and communication efficiency in consortium blockchain environments.

摘要： 实用拜占庭容错算法（Practical Byzantine Fault Tolerance，PBFT）算法在实际应用中存在通信复杂度高、节点管理机制不完善及缺乏动态行为评估等问题，限制了其在大规模区块链系统中的性能与安全表现。为解决上述问题，设计了一种基于信誉分组的改进算法。首先，通过设计节点信誉评估机制，节点根据信誉值被划分为优节点、良节点和观察节点，前两类参与共识，领导者优先从优节点中选取。信誉较低但未作恶的节点作为观察节点，仅同步账本，不参与共识；存在作恶行为的节点将被识别并隔离，提升系统的安全性与鲁棒性。其次，引入BLS（Boneh–Lynn–Shacham）多重签名技术，通过聚合多个节点的签名为固定长度的签名，减少了节点数据传输过程中的签名数据量，降低了组内和组间广播过程中的通信负担。最后，设计了一种节点动态管理机制，允许节点根据需求灵活加入或退出系统，从而增强系统的动态适应能力和鲁棒性。实验结果表明，与PBFT、DT-PBFT、NBR-PBFT算法相比，该算法在共识时延上分别降低了约45.3、29.3和17.4个百分点；在吞吐量上分别提高了约17.4、10.6和4.5个百分点。