Optimization of Real-time Input System of Power Data Driven by Intelligent Agent

As the construction of new power systems advances, power data is experiencing explosive growth, posing significant challenges to traditional data entry systems. Intelligent agent-driven optimization strategies can significantly enhance the real-time performance, accuracy, and security of power data entry systems, providing crucial support for the digital transformation of power systems. This paper starts by analyzing the spatiotemporal characteristics of power data and the existing processing architecture, highlighting issues such as poor compatibility among heterogeneous systems, inadequate data quality control, low resource scheduling efficiency, lagging security measures, and weak business collaboration capabilities. It then proposes solutions including an intelligent agent-driven collaborative architecture, an intelligent data governance chain, a dynamic resource scheduling mechanism, an active security protection system, and an intelligent business collaboration platform. The results show that through key technologies like cloud-edge-terminal collaborative deployment, adaptive protocol conversion, full-chain data quality tracking, distributed computing coordination, and federated learning security detection, data processing latency can be effectively reduced, the accuracy of abnormal data identification can be improved, and resource utilization can be enhanced.