This application relates to a method,
system, equipment, and medium for collaborative optimization of cross-process parameters in intelligent manufacturing of
traditional Chinese medicine (TCM) formulas. The method includes: acquiring a multimodal dataset across processes during TCM formula production; inputting the dataset into a mechanistic skeleton model to obtain mechanistic prediction values; iteratively optimizing a digital twin
generative adversarial network environment based on the mechanistic prediction values; calculating a distribution difference metric between historical
simulation features and historical real features; solving for the feature
space mapping relationship and correcting the dataset; and generating cross-
process optimization parameters through multi-agent collaborative decision-making based on the corrected dataset. This method can construct a low-cost, high-fidelity collaborative optimization environment, overcoming the bottlenecks of high trial-and-error costs and insufficient
simulation fidelity in TCM production, improving the reliability of optimization strategy transfer, and achieving a synergistic improvement in product quality and production efficiency.