To improve intelligent construction standard systems in coal mines, we must promote the high-quality development of the coal mining industry. The current intelligent construction of coal mines is inefficient. Considering the complexity and diversity of coal mine intelligent construction index factors, this paper proposes an intelligent coal mine construction evaluation model that integrates the fuzzy decision-making trial and evaluation laboratory (FDEMATEL) and the analytical network process (ANP). Firstly, the evaluation index system is established based on the intelligent construction of coal mines. Secondly, the FDEMATEL is applied to deal with the fuzziness in the evaluation process and determine the influence relationship between the evaluation indexes of coal mine intelligent construction to draw the ANP network structure diagram. Finally, super decision software is used to calculate the weight of coal mine intelligent construction evaluation indexes, and then obtain the combination weight and correlation degree of each evaluation index. By applying the evaluation model to conduct a comprehensive evaluation of coal mine intelligent construction, the results show that there is a significant correlation between the indexes affecting the intelligent construction of coal mines. Basic platform intelligence and safety monitoring intelligence are the two most important aspects of intelligent coal mine construction. Database construction, mobile internet construction, big data support, and model algorithm support are the key indexes affecting the intelligent construction of coal mines.

/pdf/evaluation-model-research-of-coal-mine-intelligent-2qariy8k.pdf

Evaluation Model Research of Coal Mine Intelligent Construction Based on FDEMATEL-ANP

Online intelligent recognition of coal and gangue is an important aspect of coal mine intelligent development. Current research focuses more on improving recognition accuracy through optimizing image processing algorithms, but there is limited research on the characteristics of coal gangue materials themselves, particularly the comparative analysis of their appearance in images under different scenarios. Therefore, this paper proposes a multi-dimensional feature-based analysis method for coal-gangue images, which can be used to evaluate the characteristics and distinguishability of coal and gangue images. Firstly, the original feature space of coal and gangue images is constructed based on 55 global image feature operators. Then, the characteristics of coal and gangue images in both the original space and the two-dimensional space after dimensionality reduction are analyzed using the similarity between the left and right sides of the split violin plot and the distance of probability distributions. At the same time, different types of features are compared and analyzed in terms of their impact on classification performance when used as inputs with various machine learning classifiers. Furthermore, by leveraging the interpretability of tree models, the contribution of different feature types to coal gangue classification tasks is analyzed. The proposed method is applied to analyze a large number of raw coal images collected from three different conditions. The experimental results demonstrate that the distinguishability of coal gangue images varies under different scenarios, and the distance between the two distributions in the reduced space effectively measures this distinguishability. In coal gangue images, the contribution of color features and texture features to the classification task is related to the type of coal, with texture features playing a more significant role in classifying coking coal compared to thermal coal. However, overall, color features dominate the classification task. These findings are further validated in experiments using machine learning classifiers, which are crucial for designing more refined classification algorithms. 

Investigating comparisons on the coal and gangue in various scenarios using multidimensional image features

Pose measurement of coal mine excavation equipment is an important part of roadway excavation. However, in the underground mining roadway of coal mine, there are some influencing factors such as low illumination, high dust and interference from multiple equipment, which lead to the difficulty in the position and pose measurement of roadheader with low measurement accuracy and poor stability. A combination positioning method based on machine vision and optical fiber inertial navigation is proposed to realize the position and pose measurement of roadheader and improve the accuracy and stability of the position and pose measurement. The visual measurement model of arm roadheader is established, and the optical fiber inertial navigation technology and the spatial coordinate transformation method are used. Finally, the Kalman filter fusion algorithm is used to fuse the two kinds of data to get the accurate roadheader pose data, and the inertia is compensated and corrected. Underground coal mine experiments are designed to verify the performance of the proposed method. The results show that the positioning error of the roadheader body using this method is within 40 mm, which meets the positioning accuracy requirements of roadway construction. This method compensates for the shortcomings of low accuracy and poor reliability of single vision measurement, single inertial navigation measurement and single odometer measurement.

/pdf/vision-and-inertial-navigation-combined-based-pose-1ie8jcid.pdf

Vision and Inertial Navigation Combined-Based Pose Measurement Method of Cantilever Roadheader

The mining environment of thin coal seam working faces is generally harsh, the labor intensity is high, and the production efficiency is low. Previous studies have shown that thin coal seam mining finds it difficult to follow machines, does not have complete sets of equipment, has a low degree of automation, and has difficult system co-control, which easily causes production safety accidents. In order to effectively solve the problems existing in thin coal seam mining, Binhu Coal Mine has established intelligent fully mechanized mining and actively explored automatic coal cutting, automatic support following, and intelligent control. The combination of an SAC electro-hydraulic control system and SAP pumping station control system has been applied in 16,108 intelligent fully mechanized coal mining faces, which realizes the automatic following of underground support and the control of adjacent support, partition support, and group operation; the automatic coal cutting of the shearer is realized by editing the automatic coal-cutting state of the shearer and adjusting the automatic parameters. A centralized control center is set up, which realizes the remote control and one-button start–stop of working face equipment. Through a comparative analysis of 16,108 intelligent fully mechanized mining faces and traditional fully mechanized mining faces, it is found that intelligent fully mechanized mining faces have obvious advantages in terms of equipment maintenance, equipment operation mode, and working face efficiency, which improve the equipment and technical mining level of thin coal seam. The application of intelligent mining in Binhu Coal Mine has a great and far-reaching impact on the development of thin coal seam mining technology in China.

Research on an Intelligent Mining Complete System of a Fully Mechanized Mining Face in Thin Coal Seam

Under the new wave of scientific and technological revolution, the construction industry finds itself with a critical need to alter the traditional and outdated production mode through technological innovation in order to realize industry transformation and move towards a new era characterized by digitalization, informatization, and intelligence. As intelligent construction is the indispensable pathway for the transformation and upgrading of the construction industry, it is of great significance to conduct in-depth research on its evaluation indicators and causality. This paper adopts the system dynamics method, based on the overall structure of intelligent construction, extracts the causality chain and causal feedback loop of intelligent construction, and presents a causality diagram and system dynamics diagram to build a robust system dynamics model for intelligent construction. On this basis, an evaluation index system for intelligent construction is constructed from the five dimensions—investment, design, construction, operation, and environment—for a holistic assessment of the current state of intelligent construction. The research aims to provide a valuable reference for professionals focusing on intelligent construction and the broader development of the industry.

Development of an Evaluation System for Intelligent Construction Using System Dynamics Modeling

http://www.cell.com/article/S2405844022022216/pdf

Evolution dynamic of intelligent construction strategy of coal mine enterprises in China

Intelligent Well Path Planning in Hydrocarbon Recovery: Pave the Way for Unmanned Drilling Practice

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Evolution dynamic of intelligent construction strategy of coal mine enterprises in China

Intelligent Well Path Planning in Hydrocarbon Recovery: Pave the Way for Unmanned Drilling Practice