Evolving fault-tolerant control models for optimized big data transmission in power systems using crossover-based chimp optimization

Abstract The current parallel data transmission method faces challenges such as low data transfer rates and compromised data integrity, leading to node congestion, instability, prolonged processing times, and inefficient data switching during big data scheduling processes. To address these issues, this paper proposes a Cluster Scheduling Fault-Tolerant Control method tailored for transmitting big data within new power systems. The method integrates a crossover-based Chimp optimization to enhance fault control performance in power system operations. The approach analyzes the integrity control principles of Parallel Data Transmission and employs a hybrid queuing model combining a single service window and raster analysis method to regulate Parallel Data Transmission rates. Additionally, a 3 + 1 integration framework adjusts the signal intensity fluctuations to optimize data clustering computations and stabilize nonlinear switched systems within large data clusters. In a closed-loop system configuration, data switching operations are orchestrated to achieve Fault-Tolerant Control in the scheduling of big data clusters. Experimental findings demonstrate the proposed model significantly improves data transmission rates, integrity, and network resource utilization. The developed method enhances efficiency as evidenced by improvements in precision, accuracy, recall, and throughput metrics, validating its effectiveness in real-world applications.