Research on the Standardization of AI-driven Data Security Communication Protocols for Power Trading Networks

Abstract

This paper addresses the core security issues faced by power trading networks, including threats from quantum computing, rigid static protocol configurations, and poor cross-domain heterogeneous communication compatibility. It also presents research on AI-driven standardized data security communication protocols. Unlike existing studies that mainly focus on single technological applications, this paper innovatively proposes an intelligent secure communication protocol framework that integrates deep reinforcement learning, post-quantum cryptography, knowledge graphs, and blockchain, achieving multi-technology collaborative optimization and standardized design across the protocol’s lifecycle. Through a deep reinforcement learning agent, the framework senses network status in real-time and dynamically optimizes encryption algorithms and transmission parameters. It integrates MLWE-1024-based post-quantum cryptographic mechanisms and quantum key distribution technology to build forward-secure channels, uses graph neural networks to construct power entity knowledge graphs for high-precision anomaly detection, and incorporates a blockchain-driven trusted settlement mechanism to ensure transaction data integrity. In practical validation on a provincial power trading platform, this protocol outperformed traditional solutions in key metrics such as quantum security strength, protocol conversion delay, consensus convergence efficiency, and anomaly detection accuracy, demonstrating superior dynamic adaptability, attack resistance, and system compatibility. Furthermore, it proposes a phased standardization pathway covering architectural specifications, technical implementation, and evaluation certification, providing critical technical support and standardization foundations for building high-security, low-latency, and strongly interoperable power trading communication infrastructure.