A multidimensional digital twin framework for mine water-sand mixture inrush prevention and control: collaborative multi-physical entities and multi-source data-driven intelligent governance

Water-sand mixture inrush (WSMI) is a severe geological hazard in deep mining, challenging conventional control methods due to its complexity and concealment. Digital Twin (DT) technology, with advanced data fusion and interactive functions, offers an innovative approach for mine hazard governance. Based on the Cuihongshan iron-polymetallic mine, this study integrated the IoT -enabled sensor networks, finite element analysis, and deep learning algorithms to construct a multidimensional DT framework and apply it in practice. First, the connotation and key features of the DT model were defined, clarifying the interaction between physical entities and their digital counterparts within a comprehensive architecture. Second, a perception layer with sensor networks and dynamic updating was established, while the modelling layer adopted multiple approaches to construct three categories of geometric models and a semantic model. Finite-element simulations enabled multiphysics coupling of grouting, accurately revealing grout diffusion. A novel multi-activation adaptive multilayer perceptron (MMLP) was proposed for high-precision grouting prediction. Furthermore, in the application layer, an operational platform established closed-loop management, comprising perception, cognition, decision, and application. Results show that the DT framework greatly improves governance efficiency and accuracy, offering new theoretical foundations and technological pathways for mine hazard prevention.