Comprehensive Evaluation of the Intelligent Degree of the Mining Face Based on Grey Relational Analysis
(1. Intelligent Unmanned Mining Institute, North China Institute of Science and Technology, Beijing 101601; 2. Hebei Key Laboratory of Intelligent Mining Technology, Beijing 101601; 3. State Key Laboratory for Geomechanics & Deep Under-ground Engineering, China University of Mining and Technology (Beijing), Beijing 100083; 4. China Coal Research In-stitute, Beijing 100013; 5. College of Safety Engineering, North China Institute of Science & Technology, Beijing 101601; 6. State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology (Beijing), Beijing 100083)
Abstract：
In order to further evaluate the intelligent degree of the coal mining face scientifically and reasonably, this paper constructs a gray relational analysis based comprehensive evaluation model for the intelligent degree of mining face by deeply analyzing the influencing factors on the intelligent degree of mining face, such as surrounding rock detection, mining equipment, production system, supporting production system, organization and management,etc. By quantifying and scoring factors affecting the intelligence degree of mining face, using the hierarchical analysis method to obtain the weights and determine the correlations, the intelligence degrees of mining face are classified into four levels: excellent, good, medium and poor. Using the comprehensive evaluation model to empirically verify the 802 working face of Shaanxi Huangling Mining Co., Ltd., the calculated correlation is 0.765 8, which indicates that the intelligent degree of 802 working face is excellent according to the rating criteria, and it is consistent with the actual situation. Therefore, the comprehensive evaluation model based on gray relational analysis can accurately and objectively evaluate the intelligent degree of the mining face.
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.Comprehensive Evaluation of the Intelligent Degree of the Mining Face Based on Grey Relational Analysis[J] MODERN TUNNELLING TECHNOLOGY, 2022,V59(1): 69-79
2022, Vol. 59 