We propose a triple-frequency Beidou navigation satellite system (BDS) cycle-slip detection and repair method that balances accuracy and interpretability. It addresses the limited sensitivity of traditional approaches under diverse cycle-slip scenarios. We construct three detection observables from two geometry-free phase combinations (GFCs) and one pseudorange-phase combination (PPC), and augment them with sliding-window statistics as input features. We train a Kolmogorov-Arnold network (KAN) with Huber loss to learn the slip-free trend. We then apply robust residual-based thresholding to detect and repair slips. To align model behavior with physical mechanisms and enhance interpretability, we develop a SHAP-KAN framework that quantifies feature contributions and visualizes KAN's internal spline mappings. With the same number of hidden units, our method outperforms a backpropagation (BP) baseline: MAE/RMSE decrease by ~32%/~31%, R² increases by ~0.21, and runtime decreases by ~22%. Across satellites, KAN outputs exhibit marked noise reduction and enable tighter decision thresholds. All slip types are detected with no missed detections, and post-repair biases are < 0.1 cycles, consistent with the injected values. Overall, the method achieves accurate cycle-slip detection and repair for triple-frequency BDS and provides a physically consistent interpretation of the model outputs.

Satellite clock bias (SCB) prediction is essential for enhancing the accuracy and reliability of real-time precise point positioning (RT-PPP) in Global Navigation Satellite Systems (GNSS). To address the nonlinearity, non-stationarity, and short-term interruptions of SCB data under complex environments, this paper proposes an enhanced SCB prediction model combining Temporal Convolutional Networks (TCN) and Transformers. Experimental results indicate that, in a 24-h prediction task, the proposed model reduces root mean square error (RMSE) and range error (RE) by 95.6%, 86.0%, and 61.3%, and 93.7%, 86.3%, and 58.8%, respectively, compared with LSTM, Transformer, and CNN-BiGRU-Attention models, while improving computational efficiency by 48.6% over the Transformer. Moreover, although the clock bias products generated by the proposed method result in slightly higher static PPP positioning errors than the International GNSS Service (IGS) rapid clock products, the error differences are generally at the millimeter level, demonstrating the feasibility of using predicted clock bias products to replace rapid clock products in the short term. This method addresses the PPP positioning issue during short-term network service interruptions from the perspective of time series prediction and provides potential solutions for engineering applications such as landslide, earthquake, and subsidence monitoring.