Parallel Mining Operating Systems: From Digital Twins to Mining Intelligence

dc.contributor.authorChen, Long
dc.contributor.authorLong, Xiaoming
dc.contributor.authorWang, Ge
dc.contributor.authorCao, Dongpu
dc.contributor.authorLi, Lingxi
dc.contributor.authorWang, Fei-Yue
dc.contributor.departmentElectrical and Computer Engineering, School of Engineering and Technologyen_US
dc.date.accessioned2023-03-03T17:58:01Z
dc.date.available2023-03-03T17:58:01Z
dc.date.issued2021-07
dc.description.abstractWith the rapid development and modernization requirement of global coal industry, there is an emerging need for intelligent and unmanned mining systems. In this paper, the Intelligent Mining Operating System (IMOS) is proposed and developed, based on the parallel management and control of mining operating infrastructure that integrates the intelligent mining theory, the ACP-based (Artificial societies, Computational experiments, Parallel execution) parallel intelligence approaches, and the new generation of artificial intelligence (AI) technologies. To satisfy the intelligent and unmanned demand of open-pit mines, the IMOS architecture is developed by integrating the theory of digital quadruplets. The main subsystems and functions of IMOS are elaborated in detail, including a single-vehicle operating subsystem, multi-vehicle collaboration subsystem, vehicle-road collaboration subsystem, unmanned intelligent subsystem, dispatch management subsystem, parallel management and control subsystem, supervisory subsystem, remote takeover subsystem, and communication subsystem. The IMOS presented in this paper is the first integrated solution for intelligent and unmanned mines in China, and has been implemented over ten main open pits in the past few years. Its deployment and utilization will effectively improve the production efficiency and safety level of open-pit mines, promote the construction of ecological mines, and bring great significance to the realization of sustainable mining development.en_US
dc.eprint.versionAuthor's manuscripten_US
dc.identifier.citationChen, L., Long, X., Wang, G., Cao, D., Li, L., & Wang, F.-Y. (2021). Parallel Mining Operating Systems: From Digital Twins to Mining Intelligence. 2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence (DTPI), 469–473. https://doi.org/10.1109/DTPI52967.2021.9540195en_US
dc.identifier.issn978-1-66543-337-2en_US
dc.identifier.urihttps://hdl.handle.net/1805/31599
dc.language.isoen_USen_US
dc.publisherIEEE Xploreen_US
dc.relation.isversionof10.1109/DTPI52967.2021.9540195en_US
dc.relation.journal2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence (DTPI)en_US
dc.rightsPublisher Policyen_US
dc.sourceAuthoren_US
dc.subjectCoal industryen_US
dc.subjectDigital twinen_US
dc.subjectIntelligent mining operating systemen_US
dc.titleParallel Mining Operating Systems: From Digital Twins to Mining Intelligenceen_US
dc.typeConference proceedingsen_US
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