Coal Mine Safety Management Research Articles

Application and Research of Mine Safety Management Strategy Based on the BP Network Model

Coal mine safety production is undoubtedly the cornerstone of the healthy, table, and sustainable development of China’s coal industry, and it also profoundly affects the effective implementation of the national energy strategy. In this context, exploring the application of network models in coal mine safety management strategies not only has significant practical significance, but also has profound development value. By integrating these factors, we can have a more comprehensive understanding of the causes of coal mine safety accidents and provide a scientific basis for formulating effective preventive measures. By collecting and analyzing various data in real-time during the coal mine production process, we can promptly detect abnormal situations, predict potential safety risks, and take corresponding measures for intervention and prevention. This dynamic monitoring and early warning mechanism can not only improve the safety of coal mine production, but also enhance the efficiency and efficiency of coal mine production. In the analysis process, we particularly focused on the advantages of Back Propagation (BP) neural network models in mine safety evaluation. BP neural network models can handle uncertainty and fuzzy information and have strong learning and adaptive capabilities. By constructing a safety evaluation model based on BP neural networks, we can more accurately evaluate the safety risks in the coal mine production process, providing strong support for formulating more scientific and reasonable safety management strategies.

Analysis of the Effect of Coal and Mineral Mining Safety Management System (SMKP Minerba) Implementation on the Mineral Exploration Drilling Company’s Safety Mining Performance at PT.

Background: In Indonesia, mining companies such as PT X, which has an IUJP for mineral exploration drilling, must implement SMKP Minerba. This system, which is aligned with government guidelines, includes seven elements such as Policy, Planning, Organization, Implementation, Evaluation, Documentation, and Review. Research purposes: The objective is to analyse the effect of implementing the Mineral and Coal Mining Safety Management System (SMKP Minerba) on the work safety performance of Mineral Exploration Drilling Company at PT X. Research methods: This study used a descriptive quantitative method and a case study approach, to analyse SMKP Minerba documents, including Mining Safety Performance records. Descriptive statistics, simple linear regression, and Pearson Correlation assessed variable impacts and relationships. From 144 engineers, the Slovin formula required a minimum sample of 98. Research result: The study reported that the Internal Audit of the Mineral and Coal Mining Safety Management System (SMKP Minerba) achieved a score of 84.1%. Mining safety performance was evaluated across four key indicators. Worker Participation, Leadership Responsibility, Incident Analysis and Statistics, Occupational Diseases and Incidents, and Risk Control Efforts, yielding an overall score of 0.78 out of 1, placing it in the "Planned" category. Pearson correlation analysis indicated a positive association between Position, Tenure, and SMKP Audit Scores with Mining Safety Performance. ANOVA demonstrated a significant impact of Position and Tenure on Mining Safety Performance. Conclusion: The study found that employees holding higher positions, possessing longer tenure, and achieving higher SMKP audit scores exhibited superior mining safety performance. The application of SMKP Minerba positively influences the safety performance in mining operations at PT X, contributing to the sustainable enhancement of mining safety practices in Indonesia.

A Coal Mine Tunnel Deformation Detection Method Using Point Cloud Data.

In recent years, the deformation detection technology for underground tunnels has played a crucial role in coal mine safety management. Currently, traditional methods such as the cross method and those employing the roof abscission layer monitoring instrument are primarily used for tunnel deformation detection in coal mines. With the advancement of photogrammetric methods, three-dimensional laser scanners have gradually become the primary method for deformation detection of coal mine tunnels. However, due to the high-risk confined spaces and distant distribution of coal mine tunnels, stationary three-dimensional laser scanning technology requires a significant amount of labor and time, posing certain operational risks. Currently, mobile laser scanning has become a popular method for coal mine tunnel deformation detection. This paper proposes a method for detecting point cloud deformation of underground coal mine tunnels based on a handheld three-dimensional laser scanner. This method utilizes SLAM laser radar to obtain complete point cloud information of the entire tunnel, while projecting the three-dimensional point cloud onto different planes to obtain the coordinates of the tunnel centerline. By using the calculated tunnel centerline, the three-dimensional point cloud data collected at different times are matched to the same coordinate system, and then the tunnel deformation parameters are analyzed separately from the global and cross-sectional perspectives. Through on-site collection of tunnel data, this paper verifies the feasibility of the algorithm and compares it with other centerline fitting and point cloud registration algorithms, demonstrating higher accuracy and meeting practical needs.

Gas concentration prediction by LSTM network combined with wavelet thresholding denoising and phase space reconstruction

The Long Short-Term Memory neural network is a specialized architecture designed for handling time series data, extensively applied in the field of predicting gas concentrations. In the harsh conditions prevalent in coal mines, the time series data of gas concentrations collected by sensors are susceptible to noise interference. Directly inputting such noisy data into a neural network for training would significantly reduce predictive accuracy and lead to deviations from the actual values. The Empirical Mode Decomposition method, commonly employed in gas concentration prediction, faces challenges in practical engineering applications due to the substantial influence of newly acquired data on the initial decomposition subsequence values. Consequently, it is difficult to use this method as intended. Conversely, the Wavelet Threshold Denoising method does not encounter this issue. Furthermore, gas concentration sequences exhibit chaotic characteristics. Performing phase space reconstruction allows for the extraction of additional valuable hidden information. In light of these factors, a prediction model is proposed, integrating WTD, Phase Space Reconstruction, and LSTM neural networks. Initially, the gas concentration sequence itself is subjected to wavelet threshold denoising. Subsequently, phase space reconstruction is performed, and the resulting reconstructed phase space matrix serves as the input for the LSTM neural network. The outcomes from the final LSTM neural network reveal that the PS method indeed extracts more valuable information. The Mean Absolute Error and Root Mean Square Error are reduced by 35.1% and 25%, respectively. Additionally, when compared to the PS-LSTM model without utilizing the WTD method, the WTD-PS-LSTM predictive model showcases reductions of 77.1% and 80% in MAE and RMSE, respectively. Compared with the LSTM model, the MAE and RMSE of the WTD-PS-LSTM prediction model were reduced by 81.4% and 82.6%, respectively. This greatly improves the credibility of whether or not a response related to coal mine safety management is implemented.

The impact of identity conflicts among Chinese knowledge-based miners on unsafe behavior: A moderated chain mediation model.

In the context of smart mine construction, coal mine safety management is imposing stringent requirements on the safety competence of miners. To meet these demands, coal mine enterprises have initiated proactive measures to recruit a new generation of knowledge-based miners who possess high qualifications and specialized backgrounds. This study aims to explore the underlying mechanisms of identity conflict (IC) and its influence on unsafe behavior (USB) among KBMs within the context of China's smart mining initiatives, as well as to identify the role of potential mediating and moderating variables in this relationship. An empirical study was conducted on a sample of 304 KBMs with a college degree or above, selected from multiple coal mines in Shanxi Province, China. Hierarchical regression analysis and the Bootstrap method were employed for data analysis. This model incorporated IC as the antecedent variable and meticulously investigated the mediating effects of role breadth self-efficacy (RBSE) and psychosocial resources (PSR), along with the moderating effect of positive explanatory bias (PEB). The findings indicate a significant positive correlation between IC and USB among KBMs. RBSE and PSR were found to mediate the relationship between IC and USB. Moreover, this mediating effect further influenced the relationship between IC and USB through a chain mediating effect. Additionally, PEB strengthened the positive impact of KBMs' RBSE on PSR. These results provide a broader perspective on the antecedent variables associated with KBMs' USB and provide valuable insights and practical management strategies enhancing the safety management practices within coal mining enterprise.

A low-light image enhancement method for personnel safety monitoring in underground coal mines

Intelligent monitoring technology plays an important role in promoting the development of coal mine safety management. Low illumination in the coal mine underground leads to difficult recognition of monitoring images and poor personnel detection accuracy. To alleviate this problem, a low illuminance image enhancement method proposed for personnel safety monitoring in underground coal mines. Specifically, the local enhancement module maps low illumination to normal illumination at pixel level preserving image details as much as possible. The transformer-based global adjustment module is applied to the locally enhanced images to avoid over-enhancement of bright areas and under-illumination of dark areas, and to prevent possible color deviations in the enhancement process. In addition, a feature similarity loss is proposed to constrain the similarity of target features to avoid the possible detrimental effect of enhancement on detection. Experimental results show that the proposed method improves the detection accuracy by 7.1% on the coal mine underground personal dataset, obtaining the highest accuracy compared to several other methods. The proposed method effectively improves the visualization and detection performance of low-light images, which contributes to the personnel safety monitoring in underground coal mines.

Implementation of safety management system in nickel mining: A case of PT Putra Perkasa Abadi Job Site MLP in 2023

Background: The audit of the Mineral and Coal Mining Safety Management System aims to obtain an overview of the level of implementation of the mineral and coal mining management system, assess the capacity of the mining system to ensure compliance with the legislative requirements, evaluate the effectiveness of mining systems in meeting the set targets and identify the potential application of the MSMS to be enhanced at the P.T. Putra Perkasa Eternal Site of PT Makmur Lestari Primatama from the period January 1, 2023 until December 2023. The purpose of this study was to find out the implementation of the safety management system in nickel mining of PT. Putra Perkasa Abadi Job Site MLP in 2023. Method: This type of research is quantitative descriptive. The research conducted in December 2023. Compliance measurements are carried out on 7 elements consisting of Policy, Planning, Organization and Personnel, Implementation, Monitoring, Evaluation and Follow-up Action, Documentation, and Management Review and Performance Improvement. Data collection methods are carried out with interviews, observations, and document explanations. Results: The policy element achieved 74% compliance from the maximum achievable value. Although the maximum value has not been reached, it still shows a good level of compliance. The planning element achieves 59% compliance from the maximum achievable value. Further evaluation is needed to understand the factors that lead to this achievement. Organizational and staff elements reach 51% compliance from the maximum value. Implementation elements reach 38% compliance. There is great potential for improvement in the implementation of this element. The monitoring, evaluation, and follow-up elements reached 46% compliance. More effort is needed to improve performance in this regard. Despite low maximum values, documentation elements reach 58% compliance, indicating a good focus on document management but still requiring optimization. Management review elements and performance improvements reached 46% compliance. This aspect can be a focus of improvement to improve overall performance. Overall, the management system achieved 49% compliance from the maximum value. Conclusion: In general, PT Putra Perkasa Abadi Job Site MLP has procedures and mechanisms that support the creation of a mining safety system. However, improvements are needed that can be directed to elements with lower compliance scores. It requires optimization of each element consisting of Policy, Planning, Organization and Personnel, Implementation, Monitoring, Evaluation and Follow-up, Documentation, as well as management review and performance improvement. Data collection methods were carried out using interviews, observation and document review.

Knowledge graph construction and visualization analysis of coal mine accident safety management research based on CiteSpace and VOSviewer

Although the coal mining industry has implemented mechanization and automation, frequent coal mine accidents still occur, making coal mine safety a primary concern. To gain a comprehensive understanding of global progress in preventing coal mine accidents and managing safety, we utilized the Web of Science database (WOS) as our sample source. With the support of CiteSpace and VOSviewer software, we constructed a knowledge graph depicting publication numbers, research institutions, and keyword clustering to visually analyze coal mine accident prevention and safety management. Our research reveals that this field has undergone three stages of development: initial growth, steady progress, and rapid advancement. Particularly noteworthy is the current golden age of rapid development from 2018 to 2023 which has garnered significant attention from researchers. In terms of author collaboration networks, notable contributors with high publication rates and citation counts include Wu Qiang, Liu Quanlong, et al. Regarding institutional cooperation networks, major research institutions such as China University of Mining & Technology, Shandong University of Science & Technology, and Anhui University are actively engaged in important scientific research related to coal mining. Through keyword clustering analysis based on relevant literature review, it is evident that research on coal mine safety management primarily focuses on high-frequency keywords such as "coal and gas outburst","risk assessment" and "coal miners" By extracting the top nine emergent keywords with high-intensity levels and arranging them chronologically according to their emergence year, the evolution pattern over time was obtained for emergent words. Furthermore, it was observed that emerging keywords like "China", "subsidence" and "workers" have also gained increasing attention during this period.

Nondimensional analysis and application of gas desorption and diffusion driven by density gradient in coal particles.

An in-depth understanding of gas diffusion characteristics in coal is of great value to coalbed methane (CBM) production planning and coal mine safety management. However, the mechanism and model of gas diffusion is still unclear, and some methods for determining diffusion coefficients are not accurate enough. Accordingly, a free gas density gradient (FGDG)-driven coal particle gas desorption and diffusion model was established in this work, and numerical solutions were performed via finite difference method (FDM) and dimensionless method. The variation rules of dimensionless gas pressure, gas content, desorption capacity, and desorption rate were obtained. Finally, the application of the dimensionless method in diffusion modeling and diffusion coefficient inversion was discussed. The results show that the dimensionless method can simplify mathematical equation processing and analyze the common phenomena of desorption and diffusion under different parameters. The gas desorption diffusion in coal particles is from the surface to the inside, and there is obvious desorption hysteresis effect. The larger the dimensionless radius or dimensionless time, the smaller the dimensionless gas pressure, gas content, and dimensionless desorption rate. The dimensionless cumulative gas desorption amount increased rapidly first and then tended to flat with dimensionless time. The simulated curve can be easily converted into the variation curves of several different measured parameters, and the diffusion coefficient can be calculated accurately. The prediction curve of the FGDG diffusion model is in good agreement with the experimentally measured data, which verifies its reasonableness. The research content aims to provide some ideas for modeling gas desorption and diffusion behavior.

Gas explosion early warning method in coal mines by intelligent mining system and multivariate data analysis.

In order to predict gas explosion disasters rapidly and accurately, this study utilizes real-time data collected from the intelligent mining system, including mine safety monitoring, personnel positioning, and video surveillance. Firstly, the coal mine disaster system is decomposed into sub-systems of disaster-causing factors, disaster-prone environments, and vulnerable bodies, establishing an early warning index system for gas explosion disasters. Then, a training set is randomly selected from known coal mine samples, and the training sample set is processed and analyzed using Matlab software. Subsequently, a training model based on the random forest classification algorithm is constructed, and the model is optimized using two parameters, Mtry and Ntree. Finally, the constructed random forest-based gas explosion early warning model is compared with a classification model based on the support vector machine (SVM) algorithm. Specific coal mine case studies are conducted to verify the applicability of the optimized random forest algorithm. The experimental results demonstrate that: The optimized random forest model has achieved 100% accuracy in predicting gas explosion disaster of coal mines, while the accuracy of SVM model is only 75%. The optimized model also shows lower model error and relative error, which proves its high performance in early warning of coal mine gas explosion. This study innovatively combines intelligent mining system with multidimensional data analysis, which provides a new method for coal mine safety management.

A study on the simulation of psychological and behavioral dynamics in the unsafe state of coal mine workers

The coal mine production safety hazards are large, and the situation is difficult, and coal mine workers are affected psychologically, producing unsafe psychological states and triggering unsafe behavior. This paper firstly studies the interactive influence process of the unsafe psychological state of coal mine workers and reveals the emergence process of unsafe psychology and behavior of coal mine workers. According to the characteristics of qualitative simulation of safety behavior, safety psychology and unsafe state of coal mine personnel, 23 simulation schemes are designed, and simulation analysis is carried out using QSIM simulation software platform. According to the simulation results of 23 schemes, the dynamic emergence process of safety behavior, safety psychology, and unsafe state of coal mine workers was sorted out from the aspects of different states and the intensity of different management measures, and the law of the effect of coal mine workers' safety psychology on safety behavior and safety state was obtained , revealing the interactive influence of coal mine workers' psychology on the unsafe state and the internal evolution mechanism of the emergent process. It is of great theoretical and practical significance for coal mine safety management to study the interaction of hazardous psychological states of coal mine workers and the dynamic generation mechanism of the emergence process of unsafe behaviors.

Analyses of Critical Success Factors and Barriers to the Implementation of Indonesian Mining Safety Management System: Case Study of a Nickel Mine & Processing Company

A very high level of work risk makes the implementation of an occupational health and safety management system imperative, particularly for mining businesses. Because it has an impact on the business's performance, sustainability, and reputation, mining safety is an issue of critical importance. The Republic of Indonesia's government has enacted regulations requiring the Mineral and Coal Mining Safety Management System (SMKP MINERBA) to be applied, as detailed in MEMR Regulation No. 26 of 2018 and its derivatives, through the Ministry of Energy and Mineral Resources (MEMR). The implementation of SMKP MINERBA, however, is still insufficient as shown by the rise in mining accidents that result in harm, lost productivity, and even fatalities. The study came to the conclusion that the existing CSFs could be categorized using the seven factors: SMKP good governance; strong organizational safety culture; organizing and resource availability; Specific - Measurable - Attainable - Relevant - Time Bound (SMART) planning and periodic monitoring; management commitment and strategy; effective Occupational Health & Industrial Hygiene (OH-IH) management; and compliance and safety leadership. In the meantime, the barriers may be classified as follows: low SMKP management commitment, inadequate safety leadership, and poor OHS strategy and execution. Rankings of CSFs and Barriers were also established using the Relative Importance Index (RII), and the ratings might be used by Management to enhance the organization further. As a consequence, the findings of this study and the solutions offered are beneficial to the industry since they may increase practitioners' knowledge and support them in expanding their acceptance in organizations

Where risk, where capability? Building the emergency management capability structure of coal mining enterprises based on risk matching perspective

The emergency management capability of coal mine enterprises is a crucial element of coal mine safety management. Exploring the structure of the emergency management capability of coal mine enterprises under the perspective of risk matching is conducive to clarifying the core nodes of emergency management capability construction. A qualitative analysis of Chinese underground coal mine accident reports, related laws and regulations, and relevant academic papers was conducted using grounded theory. Currently, the risks in underground coal mines in China can be divided into source-generated risks, derivative risks, and residual risks. The elements of emergency management capabilities that match them are absorption capability, detection and control capability, and response capability. At the same time, some capability elements across different risks are summarized as internal enhancement and external enhancement, respectively. The study provides a reference for coal mining enterprises to improve emergency management capability and reduce risks.

Safety culture system construction in coal mine enterprises from the perspective of hazards: A case study on the Yimei Coal Group.

The construction of a safety culture in coal mine enterprises is an essential component of coal mine safety management. Current studies do not address the mechanism for forming and constructing systems for coal mine enterprise safety culture, and many studies are based on imperfect theoretical frameworks and unsystematic empirical research, their policy recommendations are not systematic or operable, and they offer no feasible safety culture construction system. This study is devoted to analysing the theoretical basis of safety culture construction in coal mine enterprises from the perspective of hazards and proposing the content, stages and targets of constructing a safety culture and develops pragmatic approaches for coal mining enterprises to improve safety culture. A theoretical basis for safety culture construction in coal mine enterprises is proposed from the perspective of hazards by considering accident-causing mechanisms based on hazards. Furthermore, this study applied the case analysis and application to conduct empirical research on the proposed theoretical basis for safety culture construction from the perspective of hazards. Four aspects are proposed to capture the content and objectives of safety culture construction from the perspective of hazards: safety concept, behaviour safety, material state safety, and safety institutions. Furthermore, this paper provides a case study of safety culture construction by the Yimei coal group from the perspective of hazards, identifies the hazards based on the above four aspects, and then identifies preventative measures and controls for the identified hazards. Constructing a safety culture in coal mine enterprises from the perspective of hazards is operable and practical, and thus this study provides essential theoretical and practical value for improving coal mine safety.

Research on coal mine safety management based on digital twin

Coal mine safety management is the foundation and decisive factor of coal mining. The manual detection model is the main way for traditional coal mine safety management, which has problems such as inefficient identification of safety risks in coal mines, poor control accuracy and slow response measures and so on. Therefore, to make up for the shortcomings in the traditional coal mine safety management model, this paper introduces digital twin technology into coal mine safety management to achieve intelligent and efficient management of coal mine safety accidents. Firstly, we introduce the digital twin technology, select the five-dimensional model as the modeling basis, based on the existing twin model architecture, analyze the types of coal mine accidents and disasters, select the most destructive gas accidents as the research object, construct a twin safety management model for coal mine gas accidents using the digital twin five-dimensional model. Secondly, analyses of the actual operation mechanism of the digital twin model, and the advantages of the twin model in achieving prior prevention, rapid response and accurate control of gas incidents are pointed out. Finally, the house of quality of the gas accident digital twin model is established through the quality functional deployment tool, and key technical requirements for the construction of the twin model are given to accelerate the application of the gas accident twin model in the field.This study innovatively introduces digital twin technology into the field of coal mine safety management, proposes the application scenarios of emerging technologies such as digital twins in the coal mine field, and provides the possibility of multi-scene application of smart mine construction and technologies such as digital twins.

Research on coal mine hidden danger analysis and risk early warning technology based on data mining in China

The development of intelligence and informatization is the inevitable trend of safety production in coal enterprises. Data mining technology plays an important role in promoting the development of coal mine safety management. This paper uses natural language processing technology and the Bi-LSTM model to automatically classify the hidden danger information and extract the theme of the coal mine. Then, taking the risk of a coal mine gas explosion as the starting point, the gas safety situation prediction model is constructed. Finally, an accurate management and control information system based on data mining technology and intelligent technology is developed, which realizes the information analysis of a large visual screen and the intelligent information processing of the APP. The system has been applied in the Luxi coal mine, and the application effect is good, which significantly improves the safety management ability and efficiency of the coal mine. This study can provide new ideas and methods for the dual pre-control construction of coal mines, strengthen the information processing capacity of the existing pre-control system, and promote the healthy development of the coal industry.

Coal mine electrical safety management and accident prevention based on neural network and signal processing

Based on the upgrading of electronic components, communication and computer technology have made rapid progress. Neural network technology which mainly relies on the simulation of animal nerves, realize large-scale node information processing functions. Signal processing can complete the processing of various types of signals to obtain key data. With the further shortage of global energy, countries have begun to pay attention to the development of energy. As one of the traditional energy supply methods, coal mines have received a lot of financial support from the government. However, coal mine safety accidents have always been the focus of many enterprises and governments. In the process of coal mine development, there are many electrical equipment, and there are many changes in the underground environment. The problems of flooding, subsidence, and rodents biting off the line cause the paralysis of the electrical system and eventually lead to safety accidents in coal mine development. The neural network technology, can add all the electrical equipment in the mine operation, and to the network for analysis, and the signal processing technology can centrally process the signals of a variety of electrical equipment, and analyze the abnormal signals sent by the electrical equipment, and finally realize the prediction of electrical accidents. This paper firstly introduces the neural network and signal processing technology used in the text. Using the examples of hidden safety hazards in mining areas discovered during the team’s inspection as a basis, and combining with the actual situation in our country, these cases are classified. Next, using literature analysis and comparative research methods to analyze the problems existing in the current coal mine electrical safety management and accident prevention. A programme of safety management and accident prevention. The O-Net monitoring system constructed by the institute has its own unique advantage that it has more inputs than other networks, and its network structure is more complex and diverse, which of course also shows that it has more functions than other networks. The results of the scheme test show that the scheme can effectively reduce the probability of electrical safety accidents in coal mines, and at the same time effectively improve the efficiency of electrical safety management in coal mines. After multiple cycles of verification, the algorithm designed in this study has a very good effect in improving the safety of electrical equipment in coal mines, and the risk factors of each work have been effectively controlled, which is worthy of promotion in coal mines with similar environments.

Data Mining in Coal-Mine Gas Explosion Accidents Based on Evidence-Based Safety: A Case Study in China

From an informatics perspective, decision-making failures in accident prevention are due to insufficient necessary safety evidence. Analyzing accident data can help in obtaining safety evidence. Currently, such a practice mostly relies on experts’ judgement and experience, which are subjective and inefficient. Furthermore, due to the inadequate safety-related theoretical support, the sustainable safety of a system can hardly be achieved purposefully. To automatically explore and obtain latent safety evidence in coal-mine data, and improve the reliability and sustainability of coal-mine safety management, a novel framework of combining data mining technology and evidence-based safety (EBS) theory is proposed, and was applied to a coal gas explosion accident. First, the term frequency-inverse document (TF-IDF) and TextRank algorithms were fused to extract keywords, and keyword evolution word cloud maps from the time dimension were drawn to obtain keyword safety evidence. Then, on the basis of the latent Dirichlet allocation (LDA) model, the best safety evidence, such as accident causation topics and causation factors, were mined, and safety decisions were given. The results show that accident data mining, based on evidence-based safety, can effectively and purposefully mine the best safety evidence, and guide safety decision making to optimize safety management models and achieve sustainable safety.

Evaluation Cloud Model of Spontaneous Combustion Fire Risk in Coal Mines by Fusing Interval Gray Number and DEMATEL

Coal still occupies a key position in China’s energy consumption structure, and ensuring safe production in coal mines is a key focus for ensuring energy security. Spontaneous combustion fires in coal mines are a serious threat to the sustainability of safe production in coal mines. In order to prevent coal mine fire risk scientifically and effectively and to assess the level of disaster risk effectively and rationally, a study was conducted on the risk of spontaneous combustion fires in underground coal mines. An evaluation cloud model of spontaneous combustion fire risk in coal mines integrating the interval gray number with the Decision-Making Trial and Evaluation Laboratory (DEMATEL) was established. Seventeen representative risk evaluation indicators were selected, and a coal mine spontaneous combustion fire risk evaluation index system was constructed based on four aspects: personnel, machinery, environment, and management. The interval gray number theory was introduced to improve the classical DEMATEL analysis method, which fully expresses the expert empirical knowledge and solves the problem of ambiguity and randomness in the semantic expression of expert evaluation. The relative importance of each indicator was determined by analyzing the influence relationships between risk evaluation indicators through the improved DEMATEL. A cloud model capable of transforming quantitative descriptions and qualitative concepts was used for comprehensive evaluation of risk, and based on the results of DEMATEL analysis, a comprehensive evaluation cloud model of coal mine spontaneous combustion fire risk was formed. Finally, the validity and practicality of the model were verified by using a mine in Shenmu City, Shaanxi Province, China as an example. This study provides a powerful tool to prevent spontaneous combustion fires in coal mines and makes a positive contribution to the sustainable development of coal mine safety management.

Research on a Risk Early Warning Mathematical Model Based on Data Mining in China’s Coal Mine Management

The degree of informatization of coal mine safety management is becoming higher and higher, and a large amount of information is generated in this process. How to convert the existing information into useful data for risk control has become a challenge. To solve this challenge, this paper studies the mathematical model of coal mine risk early warning in China based on data mining. Firstly, the coal mine risk data was comprehensively analyzed to provide basic data for the risk prediction model of data mining. Then, the adaptive neuro-fuzzy inference system (ANFIS) was optimized twice to build the coal mine risk prediction model. By optimizing the calculation method of the control chart, the coal mine risk early warning system was proposed. Finally, based on the coal mine risk early warning model, the software platform was developed and applied to coal mines in China to control the risks at all levels. The results show that the error of the optimized ANFIS was reduced by 66%, and the early warning error was reduced by 57%. This study aimed to provide implementation methods and tools for coal mine risk management and control, and data collected has reference significance for other enterprises.