This application provides a
dynamic control method for mines based on
system instability potential energy and collaborative autonomy, relating to the field of
intelligent control and
automation technology for mine production. The method includes: collecting multi-source heterogeneous data from the mine production
system, including equipment status data, environmental parameters, and production task flow data, and constructing a dynamic relationship graph, where nodes represent mine equipment, production areas, or
environmental monitoring points, and edges represent the interaction relationships between nodes; based on the dynamic relationship graph, using a graph neural network to calculate the state and local
instability potential energy of each node; based on the local
instability potential energy of all nodes, generating a
system-level instability potential energy field and predicting the future potential energy field to identify high-risk areas; based on the system-level instability potential energy field and gradient information, controlling an
intelligent agent to perform
autonomous control through a
distributed decision-making approach to reduce the risk of system instability; after executing the control, collecting new data, updating the dynamic relationship graph and potential energy field, and fine-tuning the model by comparing predictions with actual results.