Research on Layered Consensus Algorithms for Performance Evaluation of Human Resource Systems Oriented to Blockchain

This study addresses the issues of low throughput, high latency, and difficulties in verifying employee reliability in human resource performance evaluation within a blockchain environment. It proposes a layered consensus algorithm for a blockchain-based human resource system, starting from the selection of consensus algorithms and hierarchical structure design. To solve these problems, it employs a credit mechanism, hierarchical consensus structure design, and reputation-based node election technology, effectively analyzing employees’ historical performance and real-time performance data. Simulation tests were conducted using the ns3 platform, yielding experimental data on throughput and latency at different node scales. The results indicate that the proposed DLPBFT algorithm maintains low latency and high throughput even with an increasing number of nodes, significantly improving the efficiency of human resource performance evaluation and demonstrating the feasibility of this technical solution.