Challenges of Data Consistency in High-Concurrency Environments: Algorithms and Implementation for the Electric Power Industrial Internet Platform
Zhongran Zhou, Lili Wang, Chunhe Song, Yaowei Shen, Mafeng Li, Sai Liu · 2024
With the rapid development of the Electric Power Industrial Internet, challenges in high-concurrency data processing have become increasingly prominent, particularly issues related to data consistency, which directly affect the stability and reliability of the system. This study addresses the distributed data consistency problems in the Electric Power Industrial Internet platform, proposing an optimized algorithmic framework designed specifically for high concurrency and dynamic environments to enhance the response speed and data processing capabilities of the power system. This paper first analyzes common data consistency challenges in high-concurrency environments within the power system, including data synchronization delays, complexity in fault recovery, and resource allocation issues. Based on these challenges, we introduce and optimize distributed data consistency algorithms, combined with load balancing and resource optimization techniques, significantly reducing communication costs and enhancing processing efficiency. Additionally, the paper designs a robust fault tolerance mechanism to ensure data integrity and consistency in the event of node failures. Through simulation experiments, we have confirmed the effectiveness of the proposed algorithms in terms of processing speed, system response time, and fault recovery, significantly optimizing the performance of the Electric Power Industrial Internet platform. This research not only enhances the robustness of the power system in facing high-concurrency data challenges but also provides important technical support and a theoretical basis for future research and applications in the Electric Power Industrial Internet.