Design and Research of Intelligent Safety Monitoring Robot for Coal Mine Shaft Construction

Fu, Wenjun; Xu, Ying; Liu, Liangping; Zhang, Liang

doi:10.1155/2021/6897767

Cited by 4 publications

(2 citation statements)

References 32 publications

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“…This study establishes the safety evaluation index system of each element from the four elements of man, machine, environment, and management [ 29 , 30 ]. The construction of a coal mine safety evaluation model needs to organically combine the four elements of man, machine, environment, and management.…”

Section: Comprehensive Evaluation Model Of Coal Mine Safetymentioning

confidence: 99%

Coal Mine Safety Evaluation Based on Machine Learning: A BP Neural Network Model

Bai

2022

Computational Intelligence and Neuroscience

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As the core of artificial intelligence, machine learning has strong application advantages in multi-criteria intelligent evaluation and decision-making. The level of sustainable development is of great significance to the safety evaluation of coal mining enterprises. BP neural network is a classical algorithm model in machine learning. In this paper, the BP neural network is applied to the sustainable development level decision-making and safety evaluation of coal mining enterprises. Based on the analysis of the evaluation method for sustainable development of coal enterprises, the evaluation index system of sustainable development of coal enterprises is established, and a multi-layer forward neural network model based on error backpropagation algorithm is constructed. Based on the system theory of man, machine, environment, and management, and taking the four single elements and the whole system in a coal mine as the research object, this paper systematically analyzes and studies the evaluation and continuous improvement of coal mine intrinsic safety. The BP neural network evaluation model is used to analyze and study the intrinsic safety of coal mines, the shortcomings of the intrinsic safety construction of coal mines are found, and then improvement measures are put forward to effectively promote the safe production of coal mines and finally realize the intrinsic safety goal of the coal mine.

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Section: Comprehensive Evaluation Model Of Coal Mine Safetymentioning

confidence: 99%

Coal Mine Safety Evaluation Based on Machine Learning: A BP Neural Network Model

Bai

2022

Computational Intelligence and Neuroscience

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“…It mostly adopts the methods of structural equation model, case study and empirical analysis, which focuses on the research on the influencing factors ( Goh, 2006 ), action mechanism ( Bolek and Romanová, 2020 ; Wang et al., 2020 ) and realization path ( Mao and Huang, 2021 ; Lacheheub and Maamri, 2016 ; Chowdary and Kuppili, 2021 ) of intelligent construction. The research on intelligent construction of coal mine is mainly concentrated in the field of engineering technology, focusing on intelligent technology development of coal mine ( Fu et al., 2021 ), system construction ( Agrawal et al., 2017 ), intelligent optimization ( Xing et al., 2021 ; Laub, 1999 ), etc., which lacks the research on intelligent construction of coal mine enterprises from the perspective of management. The problem of promoting the intelligent construction of enterprises is actually manifested in the game process of strategy interaction of relevant stakeholders.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Yang

et al. 2022

Heliyon

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Design and Implementation of Coal Mine Safety Monitoring System Based on GIS

Zhang¹

2022

Wireless Communications and Mobile Computing

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In order to further improve the safety production management capacity of coal enterprises and minimize the rate of coal mine safety accidents, based on the purpose of improving safety production efficiency, this paper constructs a coal mine safety detection system based on GIS technology. At the same time, on the basis of fixed-point positioning and remote monitoring at the PC end, this paper implements the whole process detection and monitoring of coal mine safety production with the help of mobile terminal intelligent system. The system software uses Java language to code it. In the first round of test, one function omission, three serious system errors, 14 general errors, and 21 minor errors are found. The accident passing rate of the second test is greater than 99.88%, and all performance indexes of the system meet the performance requirements, which proves the feasibility of the system.

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Design and Research of Intelligent Safety Monitoring Robot for Coal Mine Shaft Construction

Cited by 4 publications

References 32 publications

Coal Mine Safety Evaluation Based on Machine Learning: A BP Neural Network Model

Coal Mine Safety Evaluation Based on Machine Learning: A BP Neural Network Model

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

Design and Implementation of Coal Mine Safety Monitoring System Based on GIS

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