The present disclosure discloses a four-dimensional assimilation method for wind farm data based on physical constraints and generative AI, belonging to the technical field of numerical weather prediction and wind energy prediction. The method includes: collecting various types of wind-detecting observation data from a wind farm and performing quality control; performing multi-scale preprocessing and uncertainty encoding on raw data of background field; encoding the wind-detecting observation data into conditional features; using the raw data of background field and the conditional features as joint conditional inputs to drive a generative model, and performing a reverse denoising sampling to generate a preliminary 3D analysis field; applying soft constraints and a projection correction based on physical constraints such as MO similarity theory and wake model; and performing multi-sample generation and statistical integration to output a mean and a uncertainty of analysis field and products for key height layers of wind power.