Academician Melnikov Institute of Comprehensive Exploitation of Mineral Resources—IPKON, Russian Academy of Sciences, Moscow, Russia:

V. N. Zakharov, Director, Academician of the Russian Academy of Sciences
D. R. Kaplunov, Chief Researcher, Doctor of Engineering Sciences, Corresponding Member of the Russian Academy of Sciences
D. A. Klebanov, Head of Laboratory, Candidate of Engineering Sciences, Klebanov_d@ipkonran.ru
D. N. Radchenko, Head of Laboratory, Candidate of Engineering Sciences

The authors review the existing data logging sources in management of geotechnical systems in different functioning periods and point at the relevance of new tools for safe and efficient solid mineral mining based on the methods of predictive analytics. In modern conditions, the introduction of data acquisition and processing facilities toward advancement of the predictive analytics techniques is meant for the on-going commercial operations after approval of their economic efficiency. As a consequence, companies operate for many years without collecting and storing valuable data on geotechnical systems. Moreover, the versatility of data acquisition/processing program suppliers makes integration of the programs impossible. Considering the existing instructional approaches to the behavior assessment of a geotechnical system, prediction of its stable functioning scenarios, analysis of its malfunction causes and to the introduction of corrective effects, it is required to enhance accuracy of predictive modeling by means of improving quality of data on the geotechnical system and allied facilities. The article offers the review of international data acquisition standards and open interfaces for introduction and control of information accumulation and storage systems in the mining industry. The classification of geotechnical system information sources is proposed and used as the framework for the methods of aggregation, processing and conversion of information in data banks of a mining company, as well as for the quantitative assessment of influence exerted by every physical object of a geotechnical system, personnel and environmental parameters on the process flow efficiency. The introduction of the proposed classification enables foreseeing sequences of introduction of the equipment and tools to acquire and process the required volume of data at the mine project stage, and allows machine learningbased study into causes of incidents, accidents and induced disasters at the stage of mine operation, alongside with development of preventive measures at hazardous production facilities in mineral mining. The routes of developing standards for the science-based and adjusted approach to engineering information systems for the mining industry are identified.
The study was supported by the Russian Science Foundation, Grant No. 22-17-00142.

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