Connotation and application paradigm of intelligent mining data intelligence enabling technology

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.