Modern neural network-based speech processing systems usually need to have reverberation resistance, so the training of such systems requires a large amount of reverberation data. In the process of system training, it is now more inclined to use sampling static systems to simulate dynamic systems, or to supplement data through actually recorded data. However, this cannot fundamentally solve the problem of simulating motion data that conforms to physical laws. Aiming at the core issue of insufficient training data for speech enhancement models in moving scenarios, this paper proposes Yang's motion spatio-temporal sampling reconstruction theory to realize efficient simulation of motion continuous time-varying reverberation. This theory breaks through the limitations of the traditional static Image-Source Method (ISM) in time-varying systems. By decomposing the impulse response of the moving image source into two parts: linear time-invariant modulation and discrete time-varying fractional delay, a moving sound field model conforming to physical laws is established. Based on the band-limited characteristics of motion displacement, a hierarchical sampling strategy is proposed: high sampling rate is used for low-order images to retain details, and low sampling rate is used for high-order images to reduce computational complexity. A fast synthesis architecture is designed to realize real-time simulation. Experiments show that compared with the open-source models, the proposed theory can more accurately restore the amplitude and phase changes in moving scenarios, solving the industry problem of motion sound source data simulation, and providing high-quality dynamic training data for speech enhancement models.