融合LSTM、Transformer与时间序列混洗器，精准预测钻进速率

Rate of Penetration (ROP) prediction is critical for drilling optimization yet remains challenging due to the nonlinear, dynamic, and heterogeneous characteristics of drilling data. Conventional empirical, physics-based, and standard machine learning models rely on oversimplified assumptions or intensive feature engineering, constraining their capacity to model long-term dependencies and intricate feature interactions. To address these issues, this study presents a new deep learning Hybrid LSTM-Trans-Mixer-Att framework that first processes input data through a customized Long Short-Term Memory (LSTM) network to capture multi-scale temporal dependencies aligned with drilling cycles. Subsequently, an Enhanced Transformer encoder with drilling-specific positional encodings and real-time optimization refines the features. Concurrently, a parallel Time-Series Mixer (TS-Mixer) block introduced facilitates efficient cross-feature interaction modeling of static and categorical parameters, including lithological indices and mud properties. The feature representations extracted from the Enhanced Transformer and TS-Mixer modules are integrated through a dedicated fusion layer. Finally, an adaptive attention mechanism then dynamically assigns contextual weights to salient features, enhancing discriminative representation learning and enabling high-fidelity ROP prediction. The proposed framework combines sequential memory, static feature interactions, global context learning, and dynamic feature weighting, providing a comprehensive solution for the heterogeneous and event-driven nature of drilling dynamics. Experimental validation on real-world drilling datasets demonstrates superior performance, achieving an Rsquare of 0.9991 and a MAPE of 1.447%, significantly outperforming existing baseline and hybrid models.