The rapid development of artificial intelligence has significantly enhanced the potential for target detection and tracking, but the lack of high-quality dynamic infrared datasets has limited the target perception capabilities of space-based staring detection systems. This paper proposes a construction scheme that integrates remote sensing data with physical modeling to generate a multi-frame, three-band infrared dataset containing dynamic clouds, aircraft, and ships. The cloud motion vector fields are retrieved from high-temporal-resolution geostationary observations and mapped onto the high-resolution background of the SDGSAT-1 thermal infrared spectrometer (TIS) to generate dynamic cloud fields. Simultaneously, the motion trajectories and radiation characteristics of aircraft and ships are generated based on historical TIS observations and simulation models. This dataset can be used to train and evaluate target detection and tracking models and provides data support for infrared system performance evaluation.