A virtual test and evaluation method for fully mechanized mining production system with different smart levels

Abstract

A smart fully mechanized coal mining working face is comprised of various heterogeneous equipment that work together in unknown coal seam environments. The goal is to form a smart operational system with comprehensive perception, decision-making, and control. This involves many work points and complex coupling relationships, indicating it needs to be performed in stages and coordinated to address key problems in all directions and along multiple points. However, there are no existing unified test or analysis tools. Therefore, this study proposed a virtual test and evaluation method for a fully mechanized mining production system with different smart levels. This is based on the concept of “real data processing–virtual scene construction–setting key information points–virtual operation and evaluation.” The actual operational data for a specific working face geology and equipment were reasonably transformed into a visual virtual scene through a movement relationship model. The virtual operations and mining conditions of the working face were accurately reproduced. Based on the sensor and execution error analyses for different smart levels, the input interface for sensing, decision-making, and control was established for each piece of equipment, and an operation evaluation system was constructed. The system comprehensively simulates and tests the key points of sensing decision-making and control with various smart levels. The experimental results showed that the virtual scene constructed based on actual operational data has a high simulation degree. Users can simulate, analyze, and evaluate the overall operations of the smart mining 2.0–4.0 working face by inputting key information. The future direction for the smart development of fully mechanized mining is highlighted.

System of Mining and Analysis of Business Administration Data Based on POS Intelligent Algorithm

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A VR-based interactive teaching and practice environment for supporting the whole process of mining engineering education

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Predicting Sandstone Brittleness under Varying Water Conditions Using Infrared Radiation and Computational Techniques

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