Objective:Faced with the global mining industry's core contradiction between the depletion of shallow easy-to-mine resources and the growing demand for resources,coupled with the prominent challenge of collaborative management and control of"safety-efficiency-environmental protection"in extreme mining environments(characterized by depth,remoteness,danger,and difficulty),traditional"manual experience+decentralized automation"models can no longer meet the comprehensive requirements of mine operation.Existing intelligent mine systems are mostly limited by the linear logic of analytical AI("data→pattern recognition→prediction")and lack the ability to deeply understand complex causal relationships and generate innovative solutions.In order to address these issues,it aims to propose a generative artificial intelligence(GAI)-driven intelligent mine digital brain(GAI-Mine Brain)technology framework,establish an advanced and comprehensive intelligent central hub for mines,and promote the transformation of mine operation from"manual intervention-oriented"to"AI autonomous decision-making with human-machine collaborative supervision".The ultimate goal is to achieve"autonomous intelligence"in mine operations,effectively reduce accident rates,improve resource recovery rates,and support the efficient,safe,and sustainable development of intelligent mines.Methods:In order to achieve the aforementioned research objectives,a systematic research approach integrating theoretical construction,technical development,and architectural design were adopted.Firstly,an innovative"Creation-Cognition-Execution(CCE)"overall architecture was proposed,which took the physical mine as the foundation and forms a complete intelligent closed loop of"perception-twinning-cognition-decision-execution"through multi-level linkage.Secondly,three core key technologies were focused on for in-depth research and implementation:1)Construction and fine-tuning of the Mine Multimodal Foundation Model(Mine-MFM),which realized mine domain adaptation through four links:multimodal data alignment,incremental pre-training,instruction fine-tuning,and model operation,integrating text,images,point clouds,and time-series data.2)Construction of mine domain knowledge graph(KG)and integration with Retrieval-Augmented Generation(RAG),including ontology design covering 6 core entity categories and 12 relationship types,knowledge extraction from multi-source data,and a three-step RAG mechanism(retrieval-augmentation-generation)to suppress model"hallucinations".3)Design and game collaboration of agents based on large models,constructing a multi-agent system consisting of coordinator agents and executor agents(production scheduling,equipment health,energy management,safety risk control,etc.),with each agent equipped with planning,tool use,and memory modules to achieve global optimal decision-making through collaborative mechanisms.Additionally,a model operation platform(ModelOps)was built to support flexible deployment,continuous monitoring,and iterative optimization of the system.Results:A systematic technical system is successfully established for the GAI-driven intelligent mine digital brain,achieving significant breakthroughs in both theoretical architecture and key technologies.The proposed CCE overall architecture fills the gap in the deep integration of GAI and mine system engineering,clarifying the functional positioning and collaborative logic of each level(perceptual layer,data convergence and digital twin layer,basic model and intelligent hub layer,generative cognition and decision core layer,intelligent application and human-machine collaboration layer).The developed Mine-MFM effectively solves the problem of poor domain adaptation of general large models,enabling cross-modal understanding of mine scenarios.The integration of knowledge graph and RAG reduces the model"hallucination"rate and improves the accuracy and credibility of output results,with generated answers supported by traceable knowledge sources.The multi-agent collaborative system successfully resolves multi-objective conflicts(such as efficiency and safety)in mine operations,realizing autonomous optimization of production processes,intelligent diagnosis of complex equipment faults,immersive training,and global optimal coordination of complex systems.The entire technical framework has been verified to support millisecond-level response requirements for mine disaster decision-making,laying a solid foundation for engineering application.Conclusion:It demonstrates that GAI is a revolutionary breakthrough point for mine intellectualization,which upgrades the mine"digital brain"from a"data analysis center"to a"knowledge creation and intelligent decision-making hub"through its"induction followed by deductive creation"paradigm.The proposed CCE architecture and three core key technologies provide a replicable technical paradigm and engineering support for the deep application of GAI in the mining field,effectively addressing industry pain points such as insufficient domain adaptation of general models,prominent"hallucination"risks,and unmet real-time requirements.The results promote the transformation of mine operation mode,realizing multi-dimensional improvements in cost,efficiency,safety,and environmental protection.Although challenges such as model credibility enhancement,small-sample learning,and reduction of engineering landing costs remain,with continuous technological iteration and ecological improvement,GAI-Mine Brain will eventually promote the mining industry to achieve a leap from"autonomous decision-making"to"autonomous evolution",moving towards a new form of"unmanned,autonomous,and intelligent"mining,and providing core technical support for the safe supply of global mineral resources and the achievement of the"double carbon"goal.