The production organization of open-pit coal mines represents the final stage in executing production plans, di-rectly influencing whether mining operations can be completed efficiently, rationally, and with the required qual-ity and quantity. The current production organization and management model, which primarily relies on static CAD files combined with production task plans, fails to dynamically and accurately describe the production pro-cess, thus falling short of the requirements for precision mining and refined production management. To address this limitation, this study proposes a multi-factor spatiotemporal data representation method for mining models based on a detailed analysis of the application structure and spatiotemporal evolution characteristics of geological models in open-pit mining production organization. First, to enhance the applicability of geological models in engineering practice, this study introduces the concept and modeling principles of parametric mining models by integrating mining parameters with equipment production capacity. Utilizing Boolean logic-based sequential op-erations, the regular mining model is automatically discretized and decoupled. Second, a hierarchical five-level operational workflow is designed with the mining model as the core data carrier. The spatiotemporal data of the mining model is systematically described across five dimensions: attribute markers, temporal markers, change markers, spatial position sets, and operational equipment sets. Third, leveraging the principles of baseline correc-tion and object-oriented methodologies, a hybrid spatiotemporal data model is proposed for the storage and or-ganization of spatiotemporal data. This model employs different storage strategies for various multi-factor data of the mining model at different time points. Finally, based on the multi-factor spatiotemporal database, a dynamic production plan inversion algorithm is developed. The algorithm enables production plan inversion at specific temporal granularities by dynamically extracting the corresponding multi-factor spatiotemporal information snapshots of the mining model over time. The proposed method provides an effective means of describing the multi-factor spatiotemporal evolution of mining models at different temporal granularities throughout the pro-duction organization process. It provides a theoretical reference for integrating geological foundations, produc-tion planning, and engineering execution to achieve comprehensive and refined control from macro-level pro-duction planning to precise mining design in open-pit mines.