Contrastive-learning-based wireless communication link quality assessment model for grids

The rapid advancement of smart grids necessitates robust dynamic assessment of wireless communication link quality, which faces dual challenges: complex electromagnetic interference (EMI) and the need for effective multi-source temporal data correlation modeling. Traditional methods relying on manual expertise and existing data-driven models often inadequately capture intricate multi-source temporal features. To address these limitations, this paper proposes a novel contrastive learning-based model for wireless link quality assessment in smart grids. Our framework employs Link Quality Indicator (LQI), Received Signal Strength Indicator (RSSI), and Signal-to-Noise Ratio (SNR) as multi-view inputs. A cross-view semantic alignment strategy is introduced to extract noise-robust shared features across these heterogeneous indicators. Furthermore, we design a hybrid attention temporal encoder integrating Long Short-Term Memory (LSTM) networks, adaptive channel attention, and global temporal attention modules. This cascaded architecture achieves deep fusion of local dynamic feature enhancement and global long-range dependency modeling. Experimental validation on 48 hours of continuously collected real-world communication link data demonstrates that the proposed model outperforms baseline methods, achieving accuracy improvements of 2.5% to 7.7% with validated statistical significance. Specifically, for abnormal link states, the model maintains a high recall rate of over 92.1%, ensuring reliable fault detection. While maintaining high overall stability, we observe minor performance degradation under conditions of extreme burst noise or high rates of missing data. Crucially, it exhibits substantially enhanced robustness and generalization capability, particularly in identifying abnormal link states under challenging EMI conditions.