This invention belongs to the field of soft measurement modeling technology and discloses a soft measurement modeling method based on mode-related spatiotemporal
diffusion generation, which includes the following steps: (1) acquiring data of multi-mode dynamic processes; (2)
data partitioning and preprocessing operations; (3) establishing a mode-related spatiotemporal
diffusion model and generating virtual samples; (4) predicting pressure variables of three-phase flow processes and evaluating model performance. This invention proposes a soft measurement modeling method based on mode-related spatiotemporal
diffusion generation. By using the DSTN-Net
noise prediction network to capture the dependence of
dynamic data in the time and space dimensions, the mode learner learns the multi-mode distribution characteristics and distinguishes the data distribution of different
modes, which can generate virtual samples with high similarity to the original samples, thereby improving the prediction performance of the soft measurement model in the case of small samples.