Connotation and application paradigm of intelligent mining data intelligence enabling technology
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摘要:
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。
Abstract: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.
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【编者按】智能采矿数智赋能是通过工业互联网、人工智能、数字孪生等技术手段，深度融合采矿工业机理与数字化能力，实现矿山全流程的感知透明化、决策自主化、执行精准化、系统自进化的综合性技术体系。当前，智能采矿技术正处于加速落地阶段，数据和智能的融合创新，为煤矿数智化转型注入了新动能。为进一步总结我国智能采矿领域的前沿成果，深化数智赋能技术在煤矿场景的应用实践，《工矿自动化》编辑部特邀太原理工大学王开教授担任专题客座主编，太原理工大学王然风副教授、付翔副教授担任专题客座副主编，于2025年第3期组织出版“智能采矿·数智赋能”专题。在专题刊出之际，衷心感谢各位专家学者的大力支持！
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图 1 智能采矿“数据−算法−装备−生态”四维协同架构
Figure 1. Four-dimensional collaborative architecture of "data-algorithm-equipment-ecology" for intelligent mining
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图 2 智能采矿数智赋能技术原理
Figure 2. Principle of data intelligence enabling technology in intelligent mining
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图 3 综采工艺的智能采矿数智赋能技术架构
Figure 3. Intelligent mining data intelligence enabling technology architecture for fully mechanized mining technology
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图 4 综采工艺的智能采矿数智赋能技术应用范式
Figure 4. Application paradigm of intelligent mining data intelligence enabling technology for fully mechanized mining technology
表 1 数智技术对四维协同架构赋能路径
Table 1 Enabling paths of data intelligence technology for four-dimensional collaborative architecture
赋能维度 数字技术支撑 智能技术实现 数据层 全矿井数据湖构建、
时序数据流服务数据特征提取、
异常模式挖掘算法层 多源数据融合分析、
知识图谱构建混合智能建模、
多目标优化决策装备层 设备状态实时监控、
执行数据反馈自适应控制算法、
多机协同策略生态层 人工标注数据回流、
跨系统数据交互人在回路优化、
专家规则嵌入
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表 2 智能煤矿装备−算法−生态的分层解耦逻辑
Table 2 Hierarchical decoupling logic of intelligent coal mine equipment-algorithm-ecology
层级 功能定位 技术特性 应用举例 装备层 高频率控制与
物理执行毫秒级响应，微控
制器实时控制液压支架自动
跟机控制算法层 复杂计算与多
目标优化秒级−分钟级决策，
模型计算推理液压支架直线
度调控生态层 不确定性任务与
战略决策分钟级−小时级响
应，人类专家介入顶板来压采煤
工艺调整
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表 3 智能采矿过程AI与人工优势领域
Table 3 AI and human advantage fields in intelligent mining process
能力维度 AI优势领域 人工优势领域 任务类型 重复性、高精度计算
（如设备毫秒级控制）创造性、高不确定性推理
（如地质突变处置）数据处理 海量数据模式挖掘
（如TB级日志分析）小样本专家经验提炼
（如特殊工况数据分析）决策边界 规则明确的优化问题
（如设备协同调度）价值观权衡的复杂问题
（如安全与效率平衡）
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