Construction of intelligent geoinformation system for a mine using forecasting analytics techniques

Construction of an intelligent geoinformation system (GIS) for management of open pit mining site using robotic and self-contained mining and transportation equipment is discussed. The duties and capabilities of modern mining-and-geological, dispatching and production systems for monitoring and management of the transport process are described. The approaches to integration of various-type information towards the intelligent surface mining management using robotic and unmanned machines are reviewed. The standard structure of the onboard control systems of unmanned dump trucks, which continuously provide factual data on operating conditions, is presented. The conceptual flow chart of the intelligent GIS platform architecture as well as the flow chart of information interaction between different industrial agents—mining-and-geological, dispatching and production systems and objects of a mining and transportation system—are illustrated. The principles and application of the forecasting analytics techniques in intelligent GIS are formulated and justified for the multi-agent production system management. The usability of the telemetry and mining-and-geological data in solution of wide-range critical engineering problems connected with interpretation of information, identification of objects, diagnostics of parameters and conditions and robotic interaction management is demonstrated. It is shown which new maintenance tasks are solvable by the agency of computer learning. The approaches creating a universal tool of automated assumption and check of hypotheses through prediction of dump truck tyre life are considered.

Keywords: intelligent geoinformation system, open-pit mining, forecasting analytics, robotic dump truck, tyre life.
For citation:

Temkin I. O., Klebanov D. A., Deryabin S. A., Konov I. S. Construction of intelligent geoinformation system for a mine using forecasting analytics techniques. MIAB. Mining Inf. Anal. Bull. 2020;(3):114-125. [In Russ]. 10.25018/0236-1493-2020-3-0-114-125.

Acknowledgements:

The study was supported by the Russian Science Foundation, Grant No. 19-17-00184.

Issue number: 3
Year: 2020
Page number: 114-125
ISBN: 0236-1493
UDK: 622:681.518
DOI: 10.25018/0236-1493-2020-3-0-114-125
Article receipt date: 22.11.2019
Date of review receipt: 30.12.2019
Date of the editorial board′s decision on the article′s publishing: 20.02.2020
About authors:

I.O. Temkin1, Dr. Sci. (Eng.), Professor, Head of Chair, e-mail: igortemkin@yandex.ru,
D.A. Klebanov, Cand. Sci. (Eng.), Development Director, VIST Group, Moscow, Russia,
S.A. Deryabin1, Head of Laboratory,
I.S. Konov1, Senior Lecturer,
1 National University of Science and Technology «MISiS», 119049, Moscow, Russia.

 

For contacts:

I.O. Temkin, e-mail: igortemkin@yandex.ru.

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