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Application Progress of Data-Driven Digital Twin Technology in Smart Mines
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Jian ZHU1, Biao GUO2, Qingfu LIU3, Xin CAO3, Xu QIAO4, Xirong LIANG4
Mining Research and Development | 2026, 46(1) : 217 - 226
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Mining Research and Development | 2026, 46(1): 217-226
Application Progress of Data-Driven Digital Twin Technology in Smart Mines
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Jian ZHU1, Biao GUO2, Qingfu LIU3, Xin CAO3, Xu QIAO4, Xirong LIANG4
Affiliations
  • 1.Beijing Huayu Engineering Co., Ltd., Beijing 100011, China
  • 2.Beijing Hollysys Digital Technology Co., Ltd., Beijing 100176, China
  • 3.Hollysys Kayoubei Technology Co., Ltd., Xi'an, Shaanxi 710075, China
  • 4.China University of Mining and Technology-Beijing, Beijing 100083, China
Published: 2026-01-25
Outline
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With the intensification of China's population aging, the traditional mine operation mode relying on the experience of technicians can no longer meet the needs of the sustainable development of modern mining. There is an urgent need to introduce advanced technologies such as digital twins to promote the transformation of mines towards intelligence and greening. With its data-driven characteristics, digital twin technology can provide accurate simulation for mine construction plans. By integrating multi-source data such as geological and geographical data, a virtual model corresponding to the real mine was constructed to optimize the layout planning and reduce resource waste and risks in actual construction. Data-driven digital twin technology has been increasingly applied in scenarios such as mine construction planning, mine production safety, and mine production optimization. However, restricted by the current cognitive level, technical capacity, and infrastructure conditions, the constructed virtual models cannot fully guarantee authenticity and reliability of data, leading to deviations in prediction results. By constructing a complete standard system, enhancing data fusion and processing capabilities, optimizing intelligent algorithms and models, and strengthening interdisciplinary cooperation and technology integration, the mining industry is driven toward unmanned and intelligent transformation.

Smart mine  /  Digital twin technology  /  Data-driven  /  Real-time monitoring  /  Intelligent decision-making
祝坚, 郭彪, 刘庆富, 曹鑫, 乔旭, 梁熙蓉. 数据驱动的数字孪生技术在智慧矿山中的应用进展. 矿业研究与开发, 2026 , 46 (1) : 217 -226 .
Jian ZHU, Biao GUO, Qingfu LIU, Xin CAO, Xu QIAO, Xirong LIANG. Application Progress of Data-Driven Digital Twin Technology in Smart Mines[J]. Mining Research and Development, 2026 , 46 (1) : 217 -226 .
Year 2026 volume 46 Issue 1
39
9
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Article Info
  • Receive Date:2025-01-15
  • Online Date:2026-10-29
  • Published:2026-01-25
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  • Received:2025-01-15
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Affiliations
    1.Beijing Huayu Engineering Co., Ltd., Beijing 100011, China
    2.Beijing Hollysys Digital Technology Co., Ltd., Beijing 100176, China
    3.Hollysys Kayoubei Technology Co., Ltd., Xi'an, Shaanxi 710075, China
    4.China University of Mining and Technology-Beijing, Beijing 100083, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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