5G Energy Internet Network Security Load Forecasting System Based on Quantum Reinforcement Learning and Dynamic Topology Perception
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Abstract
In 5G energy internet environments, load forecasting is challenged by frequent topology reconstruction and diversified security threats, which affect real-time power stability and operational reliability. To overcome the limitations of traditional methods, this paper proposes a collaborative optimization model integrating Quantum Proximal Policy Optimization and dynamic topology perception. The closed-loop system integrates power-load, communication, and security information into a unified state tensor. For dynamic topology modeling, a GraphSAGE-TGAT graph neural network with event-driven updates enables millisecond-level reconstruction and adapts to highly dynamic 5G network structures. At the policy optimization level, a quantum-classical hybrid framework is constructed using multimodal features, compound action spaces, and a multi-objective reward system to improve search efficiency and convergence. Real-time response is further enhanced through offline pre-training, online fine-tuning, and adaptive learning-rate adjustment. For security defense, a risk-scoring module is combined with quantum-resistant encryption switching and network path reconstruction to support dynamic threat response. Experiments on 10 typical nodes show that the system achieves MAPE of 2.2%, MSE of 0.84, average inference delay of 28.5 ms, and MAPE fluctuation controlled within 2.5%–3.2% in robustness tests.
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