数据与智能是驱动精准化、高效化和安全化智能采矿可持续发展的核心引擎。提出了基于“数据−算法−装备−生态”四维协同架构的智能采矿数智赋能技术体系，构建了涵盖数据治理、智能决策、装备执行与人机协同的采矿全链条智能化闭环框架。数据层通过标准化存储架构与多模态数据融合，建立全矿井数据资产平台，支撑实时数据流服务与历史数据挖掘；算法层结合工业机理模型与群智能算法，构建基于多目标优化的动态决策体系，实现采矿工序协同优化与安全权重优先控制；装备层依托智能新型煤机装备群，开发装备自适应控制与多机协同联动机制；生态层通过数字孪生、人在回路优化与专家规则嵌入，构建“人−机−智−环”共生体系，驱动系统动态迭代。基于上述框架，提出了智能采矿“数据流−智能流”双向协同机制与分层解耦逻辑，实现毫秒级装备控制、秒级算法决策与分钟级人工干预的动态响应，构建AI与人类双向赋能的新型采矿生产关系。以综采工艺为典型场景，基于“需求牵引−数据驱动−智能决策−装备执行”的闭环赋能路径，构建了综采工艺的智能采矿数智赋能应用范式，建立了“自动化工艺执行→AI策略生成→人工校验→人机协同控制”循环流程，支持人工/分工/批准/否决多模式动态切换，可实现采煤工艺自动化与AI辅助决策的深度协作，推动采矿行业从“机器替代人”向“人智增强机”范式转型。

Data and intelligence are the core engines driving the precision, efficiency, and safety of sustainable intelligent mining development. A system for intelligent mining data intelligence enabling technology based on the "data-algorithm-equipment-ecology" four-dimensional collaborative architecture was proposed, and an intelligent closed-loop framework covering data governance, intelligent decision-making, equipment execution, and human-machine collaboration for the entire mining chain was constructed. The data layer established a comprehensive mine data asset platform through standardized storage architecture and multi-modal data fusion, supporting real-time data flow services and historical data mining. The algorithm layer combined industrial mechanism models and swarm intelligence algorithms to construct a dynamic decision-making system based on multi-objective optimization, achieving collaborative optimization of mining processes and safety-weighted priority control. The equipment layer relied on intelligent new coal machine equipment groups, developing equipment adaptive control and multi-machine collaborative linkage mechanisms. The ecology layer built a "human-machine-intelligence-environment" symbiosis system through digital twins, human-in-the-loop optimization, and expert rule embedding, driving the system's dynamic iteration. Based on the above framework, a bidirectional coordination mechanism of "data flow-intelligence flow" and a layered decoupling logic were proposed, achieving dynamic responses with millisecond-level equipment control, second-level algorithmic decision-making, and minute-level human intervention, establishing a new mining production relationship with bidirectional enabling between AI and humans. Using fully mechanized mining process as a typical scenario, a closed-loop enabling path based on "demand-driven - data-driven - intelligent decision-making - equipment execution" was constructed, establishing an application paradigm of intelligent mining data intelligence enabling for fully mechanized mining technology. A cyclical process of "automated process execution → AI strategy generation → human verification → human-machine collaborative control" was established, supporting dynamic switching between multiple modes, including manual, division of labor, approval, and rejection. The deep collaboration between coal mining automation and AI-assisted decision-making facilitated the transition of the mining industry from the "machine replacing humans" paradigm to the "human intelligence enhancing machines" paradigm.