Objective To address the strong coupling, long time delays, and nonlinear characteristics inherent in the traditional Chinese medicine (TCM) extraction process, as well as the issues of prediction lag and extreme-value smoothing commonly exhibited by traditional deep time-series models during operating condition transitions, a decoupled digital twin modeling method of key process parameters for the TCM extraction process, integrating Standard Operating Procedure (SOP) priors, named SOP-guided channel-independent patch time series Transformer (SGC-PatchTST), is proposed. Methods Based on the PatchTST architecture, the proposed model introduces three major enhancements: 1. An orthogonal decoupling late-fusion mechanism is employed to transform discrete SOP instructions into process-intention features, utilizing a soft-gating mechanism for fusion to suppress the lag caused by operating condition switches; 2. A decoupled structure comprising primary and auxiliary branches is constructed to independently model the reflux temperature, thereby mitigating the interference caused by multivariable coupling; 3. An extrema-sensitive hybrid loss function is designed, combining local statistics with extreme-value penalties to enhance the model's extrema-fitting and noise-resistance capabilities under the low-pass filtering conditions of Patch-based tokenization. All baseline models uniformly adopt the OneCycleLR learning rate schedule and an early-stopping strategy. Experimental data are independently partitioned into training, validation, and test sets on a batch-by-batch basis to strictly eliminate cross-batch data leakage. Actual production data from a TCM extraction workshop are utilized to construct the predictive model and evaluate it via autoregressive multi-step inference. Results Under a 50-batch stratified split, the single-evaluation Mean Absolute Errors (MAEs) of the upper and lower temperatures for SGC-PatchTST (optimized hyperparameters τ＝1.5σ，λ＝2.0) decrease to 0.36 ℃ and 0.35 ℃, respectively (which are 40.0% and 50.2% lower than the standard PatchTST model, respectively). Its capability to resist extreme deviations is significantly enhanced, with the 95% percentile errors (P95 errors) for the upper and lower temperatures controlled at 1.31 ℃ and 1.33 ℃, respectively. At the same time, benefiting from the decoupled primary and secondary architectures, the MAE of the reflux temperature, which is susceptible to external disturbances, is reduced to a lower value of 0.04 ℃. The prediction of minor fluctuations in the core safety parameter (in-tank pressure) is also maintained at a relatively high precision of 0.000 6 MPa. Furthermore, under three independent stratified splits, the average upper and lower temperature MAEs remain at 0.33 ℃ (coefficient of variation, CV = 6.8%) and 0.32 ℃ (CV = 6.4%), respectively, confirming its good statistical robustness. A six-step combination ablation study and a 25-group hyperparameter grid search validate the contribution of each module and the hyperparameter robustness, respectively. Conclusion The proposed SGC-PatchTST method achieves an ideal level of prediction accuracy and timeliness while demonstrating strong physical plausibility, providing reliable support for subsequent model-based reinforcement learning control.

traditional Chinese medicine extraction  /  digital twin  /  PatchTST  /  prior injection  /  extrema-sensitive loss  /  multivariate time series forecasting