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. 2023 Feb 11;9(3):e13608. doi: 10.1016/j.heliyon.2023.e13608

Research on coal mine safety management based on digital twin

Jiaqi Wang a,∗, Yanli Huang a,b,∗∗, Wenrui Zhai a,∗∗∗, Junmeng Li a, Shenyang Ouyang a, Huadong Gao a, Yahui Liu a, Guiyuan Wang a
PMCID: PMC9988487  PMID: 36895337

Abstract

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.

Keywords: Coal mine safety, Gas accident, Digital twin, Quality functional deployment (QFD), House of quality (HOQ)

1. Introduction

The status of coal as the main energy source in China will not change for a long time in the future, and coal mine safety management plays a very important role in coal production, which is the key factor determining the development of mining areas. According to the statistics of China's National Mine Safety Administration and relevant literature [[1], [2], [3]], during the ten years from 2012 to 2021, a total of 3342 coal mine death accidents occurred in China, with a death toll of 5978 people. It can be seen that there is still a lot of room for improvement in the prevention and management of mine accidents. Although the situation of coal mine safety management is improving at the present stage, and the number of deaths caused by coal mining is decreasing every year, coal mine safety is still the top priority in mining activities. Therefore, with the development of science and technology, how to apply modern technology or methods to the safety management of coal mines, reduce the occurrence of safety accidents in the mining areas, and achieve the purpose of improving the safety management of mining areas, is a difficult problem to be solved urgently.

Digital twin technology is an emerging technology. Driven by data and models, it can achieve the purpose of monitoring, simulation, predicting and optimization of research objects [4]. Application of digital twin has been carried out in many areas, manufacturing industry is one of the most widely used field, for example, digital twin technology was used to study the manufacturing plant in the quality and efficiency of the robot production assembly problem [5], Vrabi et al. [6] and Kuts et al. [7] to study the application of mobile robots in the manufacturing and industrial robots respectively, Wang [8] presents the digital-driven by twin ship intelligent manufacturing system application framework, Zhang and Zhu [9] for an aircraft engine blade manufacture as an example, proposes a new digital twin drive products of intelligent manufacturing system application framework, Li et al. [10] take the aerospace manufacturing as the research object, proposes the digital twin intelligent monitoring mode, a digital CAM servo motion system based on digital twin was designed to provide a reference for the design, manufacture and application of electronic cams [11]. Roque et al. [12] proposed a distributed manufacturing system based on digital twin technology.

In addition to the wide range of applications in manufacturing, digital twin is also being researched in other areas. Wei et al. [13] discuss the twin technology in the field of energy use and management, Gary et al. [14] take the Ireland Docklands area as an example, explained the twin city model numbers in the role of urban planning and construction, and studied the added value of digital twin technology to agriculture and proposed a roadmap for agricultural digital twin technology [15]. Lee et al. [16] developed a technical framework that combines digital twin technology with blockchain to realize information sharing among stakeholders in engineering projects. And put forward the first creative digital twin applied to a case study of severe trauma [17]. Alban et al. [18] take Perugia peter's memorial tower as an example, based on the number of twin technology puts forward a new method for earthquake damage identification, proposed a 3d scanning system based on digital technology of twin. The research in the field of cultural relic protection is also explored [19].

In summary, although digital twin technology is an emerging technology, due to its significant advantages and broad application prospects, relevant research has been carried out in many fields. Including intelligent manufacturing, energy utilization, urban planning, agricultural science, smart healthcare, architectural engineering and so on. Exploratory research has even been carried out in seismic identification and historical relic protection. Therefore, we have reason to believe that digital twin technology also has a good application prospect in coal mine safety management, so this paper combines digital twin technology and coal mine safety management to explore a new mode of coal mine safety management driven by digital twin.

2. Coal mine safety management and digital twin

2.1. Coal mine safety management

The safety management of coal mines is the basis for coal mine enterprises. At present, with the strengthening of the enterprise safety management concept, the introduction of advanced technology and equipment, and the improvement of staff safety consciousness, the safety management level of the coal mines has been greatly improved. However, most coal mining enterprises still adopt the human-based coal mine safety supervision model [20], although the personnel safety consciousness improved, there are still some shortages in the actual coal mine safety management, leading to frequent coal mine safety accidents. The main reason is the mining work environment is complicated, the cause of the accident hazards is more and more concealed, relying on human-oriented coal mine safety management model has the problem of strong subjective consciousness, which will lead to the frequent occurrence of coal mine accidents. Given the existing problems in the traditional coal mine safety management model, many scholars have also conducted research, such as using game theory [21], rent-seeking theory [22], constraint theory [20], system dynamics [23] and other methods to improve the level of coal mine safety supervision by improving the safety supervision institutions, optimizing the supervision system, and studying the subject of safety supervision. Subsequently, some safety supervision models have been proposed, such as the hybrid self-regulation model of coal mine safety and the multicentre governance model [24]. However, with the development of science and technology, great changes have taken place in the coal mine enterprise production environment, increased number of contributing factors to coal mine safety accidents, and changes to the mine safety management model alone will not achieve a significant improvement in mine safety management, mainly since the predominantly human form of mine safety management has not changed, and mining operations have more common features, with the improvement of mining mechanization level, it is gradually transforming from a single project to several joint operations, and the human-oriented management model is more difficult to solve problems. Therefore, the existing safety supervision model can't meet the needs of coal mine safety development. It is necessary to establish a safety supervision model that can give full play to the synergistic and complementary role of various elements from the perspective of rapid response and joint analysis. Based on the above analysis, combined with the digital twin technology, this study gives full play to the advantages of the digital twin model and overcomes the deficiencies of the existing safety management model, to achieve the purpose of improving the level of coal mine safety management.

2.2. The proposal for digital twin

In 2003, the concept of the digital twin was originally proposed by Michael Grieves in his product lifecycle management course at the University of Michigan [25]. In the beginning, digital twins were employed primarily in the military and aerospace. The National Space Administration applied the digital twin technology in the Apollo project, mainly for the health maintenance and guarantee of space vehicles. According to the real aircraft, NASA built a virtual aircraft exactly like the real aircraft, which is called the twin. When working, the real aircraft in space by sensors and other transport tools from the flight data real-time transmission to the laboratory twin body, guarantee and real aircraft have identical twin body state, then the space agency's control personnel can judge the true state of aircraft accurately through twin flight status, and auxiliary astronaut to make optimal operation.

At present, digital twin mainly refers to the creation of virtual models of physical entities in digital form, simulating the behavior of physical entities in the real environment with the help of data, and adding or expanding new capabilities for physical entities by means of virtual and real interactive feedback, data fusion analysis, and decision iterative optimization [26]. By the definition, the digital twin is similar to a virtual simulation to some degree, but it is different from traditional simulation technology and has the following characteristics, as shown in Fig. 1.

Fig. 1.

Fig. 1

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Advantages of digital twin technology.

In the traditional simulation software, the entity and the simulation object are separated in the simulation process. The twin object can achieve information sharing and real-time synchronization with the physical entity, which has strong interaction. Besides, digital twin is more realistic than traditional simulation software. It can realize the complete consistency between a virtual entity and a physical entity in appearance, content and nature, with a high degree of quasi reality, and can accurately reflect the real state of the physical entity. The digital twin is the use of the most advanced scientific and technological means to achieve twin interaction, can achieve a large number of simulation experiments in a very short time, which the traditional method can not achieve, so it has high efficiency. Interaction ability is the key to the digital twin's unique features, digital twin virtual entity can be realized through a variety of sensors and dynamic interaction of physical entity, through the dynamic interaction can connect the physical entity and virtual entity as a whole, both in its form within the overall interaction and feedback of information, so as to realize real-time interaction. Self-learning ability is an exponential twin that can self-learn according to historical data and realistic situations, and use cognitive mechanisms and rules to deduce all scenarios at a specific moment in the future, and predict the occurrence probability and future development trend of their respective states.

Based on the advantages of digital twin technology analyzed above and the characteristics of coal mine safety management, this paper introduces digital twin into coal mine safety management, constructs a new model of coal mine safety management driven by digital twin technology, and puts forward a new idea of mine safety management.

2.3. Digital twin model architecture

In this paper, the five-dimensional model is selected as the modeling basis [27]. As shown in Fig. 2.

Fig. 2.

Fig. 2

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Digital twin five-dimensional model.

The five-dimensional model consists of a physical entity (PE), virtual entity (VE), service system (SS), twin data (DD), and connection (CN). PE refers to the real entity in reality, which is the object of the research, and it is also the basis of the construction of the digital twin model. VE is an ultra-high fidelity, fully digital simulation model of PE. It realizes real-time simulation and trend prediction of the whole process of production through real-time data transmission, provides an optimization strategy for the service system, and monitors and regulates the production process in real time. SS is a set of service systems, which is composed of several manufacturing systems in the production process and can provide support and service for the manufacturing of products. DD refers to all the data related to PE, VE and SS, as well as the data crossing and fusion of the above three parts, which is the driving source of the digital twin. CN is a necessary way to realize the interconnection of entities, services and data. It realizes the interconnection of all parts through sensors, data acquisition cards and other ways.

3. Digital twin model construction

3.1. Coal mine gas accident

Coal mine safety accidents can be mainly classified into roof, gas, mechanical or electrical, water disaster, transport, blasting and others. By querying the data of the China State Administration of Mine Safety and China Coal Mine Statistical Yearbook, the basic data of the occurrence and death toll of various coal mine safety accidents from 2013 to 2020 were obtained (Appendix). Plot the death toll of different types of coal mine accidents from 2013 to 2020 (Fig. 3).

Fig. 3.

Fig. 3

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Deaths due to various coal mine accidents and deaths in China (2013–2020).

According to Fig. 3, among all types of coal mine accidents, roof accidents and gas accidents cause the largest number of deaths. Although the number of deaths in roof and gas accidents has decreased significantly in recent years, they still account for the highest proportion of safety accidents in all types of coal mine accidents. In order to analyze various types of coal mine accidents more clearly, the proportion chart of the starting number of various types of accidents (Fig. 4a) and the number of deaths (Fig. 4b) is drawn. As can be seen from Fig. 4, in terms of the number of accidents, roof accident accounted for the largest proportion of 38%, and in terms of the number of accident deaths, gas accident accounted for 28.5%, accounting for the highest proportion. It can be seen that roof and gas accidents were the accident types with the largest number of accidents and deaths, respectively. In addition, analysis by the number of fatalities per accident, roof accident is 1.34 people deaths per accident, and gas accident is 5.00 deaths per accident, far greater than roof accident, this indicates that gas accident is significantly more serious than roof accident, the gas accident once happened, its damage to the mine safety ability than other types of security incidents. What's more, according to Fig. 3, it can be seen that the number of deaths in gas accidents is prone to a repeated phenomenon, with great volatility, and it is difficult to control.

Fig. 4.

Fig. 4

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(a) Percentage of different types of accidents. (b) Percentage of deaths from different types of accidents.

In addition, an analysis of the literature related to coal mine accidents revealed that, in recent years, underground coal mining has long been a dangerous occupation, and coal gas outbursts are severe accidents that can occur in coal mines [28]. There have been frequent gas explosion accidents in China [29], and coal mine gas accidents account for half of all coal mine accidents [30]. Duan et al. [31] also find that gas explosions can easily cause a large number of casualties and huge property losses in coal mines. You et al. [32] point out that gas accident is the main accident type that affects coal mine safety production. Therefore, this paper selects coal mine gas accidents as the research object and builds a new safety management model for coal mine gas accidents based on digital twin technology.

3.2. Twin model construction of gas accidents

Gas is a general term for all harmful gases in the process of coal mining. Generally speaking, underground coal mine gas accidents can be divided into three categories: gas explosion, coal and gas outburst, and poisoning and asphyxia [33]. Gas explosion refers to the phenomenon that when gas is in an environment of high temperature and open fire, chemical reactions are generated to generate carbon dioxide and water, and a large amount of heat is released, and then the generated carbon dioxide and water are rapidly expanded and vaporized and impact outward at a very high speed. Coal and gas outburst refers to the phenomenon that under the action of a certain pressure, the broken coal and gas are suddenly ejected from the coal body to the mining space in large quantities. Poisoning and asphyxia refer to the phenomenon that when the toxic gas in the coal mine reaches a certain concentration, the mine workers are poisoned or asphyxiated. From the above analysis, it can be seen that coal mine gas accidents have the characteristics of suddenness and contingency, but the occurrence of gas accidents generally needs to meet specific conditions to occur, so coal mine gas accidents also have the characteristics of predictability.

In the traditional way of managing coal mine gas accidents, gas, humidity and temperature data can be obtained through optical gas checkers, oxygen meters and thermometers, etc. However, due to the complexity of the working environment in coal mines, it is very difficult to take action in a short time when warning signs of danger occur. In addition, as the detection equipment can only achieve real-time status detection, it is difficult to make reasonable predictions and cannot achieve the purpose of timely response and prior prevention of gas accidents. Thus, based on the above analysis, a digital twin-driven gas accident twin model is constructed in conjunction with the digital twin five-dimensional model (Fig. 5).

Fig. 5.

Fig. 5

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Gas accident safety management twin model.

In practice, the physical entity and the virtual entity exchange data in real-time, so that the virtual entity and the physical entity have a high degree of consistency, ensuring that the mine safety manager can monitor the virtual entity to understand the condition of the actual mine. In addition, the physical and virtual entities transmit various data to the twin data, the twin data center processes and analyses the transmitted data, and predicts the possible future data conditions in that condition based on the trend characteristics of the actual data, combined with historical data, while transmitting the possible condition data to the virtual entity, driving the virtual entity to carry out the simulation, so that predicted the possible future trend state of the physical entity accurately. After predicting the possible situations, the virtual entity will feed its prediction results to the service system, which will generate some solutions for the possible dangerous situations and synchronize the solutions to the virtual entity, simulate and verify each solution again in the virtual entity to determine the optimal solution, and then feed the optimal solution to the service system, which will guide the physical entity to operate reasonably according to the optimal solution, it can achieve the purpose of rapid response and prevention of gas accidents. In addition, when an emergency occurs in a physical entity, twin data can make the best decision scenario based on history and transfer its data to the virtual entity for simulation to ensure that the scenario is reasonable and then feedback to the physical entity through the service system to achieve minimal damage from the incident. All these operations are carried out within the twin model, which allows for a significant reduction in time and rapid response, with an efficiency that is unmatched by traditional methods. Connectivity is an essential part of the model for information interoperability and rapid response and is one of the essential elements of this twin model. In addition, the twin data plays a very important role in the process, it can obtain a large amount of data from the interaction with physical and virtual entities, store and self-learn this data, and make scientific decisions about the actual situation based on a large amount of data available, and transfer the data to virtual entities and service systems for simulation and validation, it is the driving part of this gas accident twin model.

Compared with the traditional coal mine management model, the twin model of gas accident safety management has the characteristics of rapid response and prior prevention. For example, an underground gas explosion should have the following three conditions, gas concentration, oxygen concentration, and the open flame source. Therefore, in the construction of the gas accident twin model, set the concentration value lower than the gas explosion, generally speaking, the gas concentration is 5%–16%, the oxygen concentration is not less than 10%, and encountering a spark may explode [34]. Therefore, when constructing the gas accident twin model, the gas concentration can be set to 4% and the oxygen concentration to 8%, and the twin model can respond when the concentration value of the underground environment reaches the set threshold, thus ensuring sufficient response time before a gas accident occurs. In addition, the threshold value when a gas accident occurs in different coal mines may not be the same, so the twin data in the gas accident twin model can be used to determine the threshold value when a gas accident occurs in different mines through data analysis and processing, thus achieving the purpose of prior prevention.

4. Realization of the gas accident twin model

In order to accelerate the application of digital twin technology in coal mine safety management, this paper, based on the construction of the gas accident twin model, combined with the House of Quality in quality function deployment, transformed the actual requirements of the twin model into technical characteristics in the implementation process.

4.1. Determination of product requirements and design characteristics

The gas accident twin model is an early warning system. On the basis of the existing gas accident causation mechanism and management model [35,36], the product requirements of the gas accident twin model are proposed as follows: Ease of operation (B1), precise positioning (B2), multi-directional real-time monitoring (B3), rapid response (B4), strong portability (B5); degree of intelligence (E1), complete auxiliary functions (E2), data security (E3), the search of convenience (E4), data comprehensiveness (E5); human-machine interface friendly (A1), data compatibility (A2), model reliability (A3), model self-learning capability (A4), low cost of the model (A5). Combined with the transformation method between user requirements and technical characteristics [37], the user requirements of the gas accident twin model are transformed into corresponding technical characteristics, as shown in Table 1.

Table 1.

Relationship Table between product demand and technical characteristics of the gas accident twin model.

The user requirements Technical characteristics
Basic requirement BR Ease of operation B1 Menu design clear I1
The comprehensiveness of the tutorial I2
Precise positioning B2 High precision navigation system I3
Omnidirectional positioning system I4
Multi-directional real-time monitoring B3 Monitoring point layout comprehensive I5
Various kinds of sensors I6
Rapid response B4 High sensitivity sensor I7
Data fast processing I8
Alarm concentration quick I9
The virtual model runs at high speed I10
Strong portability B5 Working principle clear I11
Expected requirement ER Degree of intelligence E1 Accurate voice control I12
Information processing automation I13
Complete auxiliary functions E2 Various types of functional modules I14
Data security E3 Automatic backup of data I15
Unlock permission Settings I16
Search for convenience E4 Database build I17
Data comprehensiveness E5 Blockchain browser settings I18
Attractive requirement AR Human-machine interface friendly A1 High recognition of icons I19
The boot interface is standardized I20
Data compatibility A2 Modular data setup I21
Model reliability A3 Setting for the alternate model I22
Fault tolerance of the sensor I23
Model self-learning capability A4 Introduction of algorithm I24
Low cost of model A5 Introduction of advanced technology I25
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4.2. Determination of the importance of user requirements

Based on the Kano model, this paper divides the above 15 user requirements into three categories [38], basic requirement BR {B1, B2, B3, B4, B5}, expected requirement ER {E1, E2, E3, E4, E5}, attractive requirement AR {A1, A2, A3, A4, A5}.

The entropy weight method (EWM) is used to rank the importance of user requirements [39]. The steps to calculate the weight based on EWM are as follows.

Firstly, constructed the judgment matrix R = (rij)m × n (i = 1, …, m; j = 1, …, n), where m is the number of evaluation objects, and n is the evaluation index. And then normalize the R matrix to get the Z matrix, Z= (zij)m × n (i = 1, …, m; j = 1, …, n).

Secondly, according to the entropy principle, the entropy value ej of an index is calculated by Eq. (1).

ej=−1lnm∑i=1mpijlnpij (1)

where pij=zij/∑i=1mzij.

Thirdly, the difference coefficient of each index is calculated by Eq. (2). The smaller of the value, the greater of the contribution rate of the index.

dj=1−ej (2)

Finally, the entropy weight of each index is calculated by Eq. (3).

wj=dj/∑j=1ndj (3)

The weight value of each index can be obtained by EWM, and the calculation results are shown in Table 2.

Table 2.

User requirements weighting.

User requirement weights Combined weights
BR (0.6580) B1 (0.2694) 0.1773
B2 (0.2168) 0.1427
B3 (0.0898) 0.0590
B4 (0.3438) 0.2262
B5 (0.0802) 0.0528
ER (0.1926) E1 (0.1521) 0.0293
E2 (0.0612) 0.0118
E3 (0.4234) 0.0815
E4 (0.2599) 0.0501
E5 (0.1034) 0.0199
AR (0.1494) A1 (0.1268) 0.0189
A2 (0.0978) 0.0146
A3 (0.3426) 0.0512
A4 (0.3005) 0.0449
A5 (0.1323) 0.0198
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4.3. Establishment of house of quality

4.3.1. The concept of house of quality

The concept of house of quality (HOQ) was introduced by Hauser and Elausing in 1988 [40]. They argued that the quality function could not be developed without the cooperation of quality houses, which calculate customer needs with the help of a matrix and then design the service attributes of the product according to the customer needs. HOQ was first applied in the manufacturing industry, and Toyota was the first company to introduce and apply HOQ. Until the 1980s, it was also introduced in western countries. As a bridge between the “Voice of customer” and the “Voice of enterprise”, HOQ is the process of converting customer needs into enterprise quality requirements. The structure diagram of HOQ is shown in Fig. 6.

  • (1)

    Left wall

Fig. 6.

Fig. 6

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Structure diagram HOQ.

Indicates customer needs and importance. Methods to obtain the quality of customer needs include network research, talks, interviews, etc. The information collected is summarized, categorized, and simplified to extract key elements as indicators of customer needs. The weighting method can generally be used to determine the importance of indicators.

  • (2)

    Ceiling

Here refers to the quality attributes of services and products. Then combined with the importance of customer needs, these needs are the elements that must be fully considered when companies develop their own products or services.

  • (3)

    Room

Specifically referred to here is the relationship matrix. This matrix links the quality attributes of a product or service to the customer's needs. The core of the QFD method is the relationship matrix, with the help of which the quality attributes of a product or service can be perceived by the customer.

  • (4)

    Roof

Here it refers to the autocorrelation matrix. It brings out the full relationship between product technology and quality, i.e., the influence between different quality or technology elements. The main expressions are “positive correlation”, “negative correlation” and “no correlation”.

  • (5)

    Right wall

Market competition assessment, the core of this section is the analysis of customer satisfaction. It also includes the satisfaction of customers when competitors provide the same products or services. And compare the two results to find out the shortcomings of the company itself, and implement improvements for the shortcomings.

  • (6)

    Basement

The weight of this part depends on two more elements, including the initial importance of customer needs, and the relationship matrix. If the weight is a larger score, it means that this element is more important. According to the weight score, sorting the elements, it is easier to find out the important elements.

4.3.2. Construction the HOQ of twin model

In this paper, the construction of HOQ for the gas accident twin model is realized based on the product requirements, the technical characteristics relationship table and the importance of the user requirements [41]. In the relationship matrix between user requirements and technical characteristics, the values “1”, “3” and “5” indicate weakly, and strong correlations between user requirements and technical characteristics respectively, while the numbers “2” and “4” indicate the relationship between weak and moderate, moderate and strong correlation. While in the autocorrelation matrix of technical characteristics, the symbol " + " indicates a positive correlation between technical characteristics, and the symbol "−" indicates a negative correlation between technical characteristics [42].

In addition, at the stage of market competition analysis, this paper is selected for the gas monitoring and surveillance system. Coal mine gas monitoring and surveillance system mainly consist of four parts: sensors, underground substations, information transmission system and surface central station, which is an important part of coal mine safety production at this stage and is an important measure to prevent major accidents such as mine ventilation and gas and to guarantee safe production [43]. However, there are shortcomings in the current gas monitoring and control system, such as different technical standards produced by different manufacturers, resulting in poor compatibility between gas monitoring and control systems, and there are also problems such as non-standard communication protocols and physical interface protocols for underground information transmission equipment. Therefore, based on the above analysis, this paper constructs a HOQ of the gas accident twin model, as shown in Fig. 7.

Fig. 7.

Fig. 7

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HOQ of gas accident twin model.

The HOQ enables the transformation from theoretical user requirements to actual product characteristics, and it has accelerates the application of the gas accident twin model. Based on the HOQ constructed for the gas incident twin model, the correlation between the importance and the values of the user requirements and technical characteristics can be used to derive the importance of each technical characteristic in the system and as a key technical measure in the construction of the actual model. By calculating the importance of each technical characteristic (Fig. 7), it was found that the relatively high importance of the indicators I17, I15, I8, I21, I13 and I24 indicates that more emphasis should be placed on the construction of the database, automatic data backup, rapid data processing, modular data setup, automation of information processing and the introduction of algorithms in the later stages of the construction, which can be seen that the main technical measures for the actual construction of the gas accident twin model are the acquisition, processing, storage and application of data, the above problems can be solved in order to better meet the purpose of the gas accident twin model construction. In addition, based on the market analysis of HOQ of the gas accident twin model, it can be seen that compared to the gas monitoring and surveillance system, the gas accident twin model proposed in this paper is more efficient in terms of rapid response, data compatibility, model reliability and self-learning habit, and can also make up for the shortcomings of traditional coal mine safety management, which has a broader application prospect.

5. Discussion and conclusion

5.1. Discussion

Firstly, this paper analyzes the advantages of the gas accident twin model compared with the traditional coal mine safety management mode, which can make up for the shortcomings of the traditional management mode and reduce the incidence of coal mine accidents. In addition, the construction of the gas accident twin model HOQ gives the technical requirements of coal mining enterprises in the actual construction of the model and solves the problem from functional requirements to the technical implementation of the twin model, which has guiding significance for coal mining enterprises to build the gas twin model.

Secondly, the digital twin gas accident safety management model proposed in this paper will help to use digital technology to drive the research on coal mine safety management. Due to the increased complexity of coal mining and the increase in mechanical equipment in coal mines, a large amount of digital information is generated during the mining process, and traditional methods will not be able to deal with the surge of information in coal mine safety. Therefore, the future of coal mine safety management is bound to be digitally driven coal mine safety management research, and the twin model of gas accidents constructed in this paper is based on digital information and relies on a large amount of information to make a judgment, so the twin model constructed in this paper is advanced in terms of data-driven coal mine safety management.

6. Conclusion

This paper analyzes the advantages of digital twin technology, introduces digital twin into coal mine safety management, proposes a new model of coal mine gas accident safety management, constructs a quality house of gas accident twin model, provides new ideas for digitally driven coal mine safety management research, and provides theoretical support for the application of emerging technologies such as digital twin in coal mines. The main contributions are as follows.

  • (1)

    By analyzing the types of coal mine accidents, it is found that gas accidents are the main types of accidents affecting coal mine safety production and that gas accidents, once they occur, may lead to the consequences of a large number of casualties and huge property losses.

  • (2)

    Based on the advantages and application scenarios of digital twin technology, the digital twin is introduced into coal mine safety management, and a coal mine gas accident twin model is constructed, and the advantages of the gas accident twin model are analyzed to provide new ideas for coal mine accident safety management research.

  • (3)

    The quality house of the gas accident twin model is constructed, which realizes the fitting of user requirements to product technical characteristics, provides a basis for coal mining enterprises to build a twin model for gas accident safety management, and also proposes technical support to accelerate the application of digital twin technology in coal mine safety management.

  • (4)

    This study introduces the digital twin to coal mine safety management, innovatively proposes a new model of coal mine safety management, provides research directions for coal mine safety management in a big data-driven context and research basis for the construction of smart mines and the application of digital twin in multiple scenarios.

Author contribution statement

Jiaqi Wang: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Yanli Huang: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data.

Wenrui Zhai: Performed the experiments; Analyzed and interpreted the data; Wrote the paper.

Junmeng Li: Performed the experiments; Contributed reagents, materials, analysis tools or data.

Shenyang Ouyang: Analyzed and interpreted the data.

Huadong Gao: Contributed reagents, materials, analysis tools or data.

Yahui Liu: Analysis tools or data.

Guiyuan Wang: Performed the experiments.

Funding statement

This work was supported by the National Natural Science Foundation of China (Nos. 52104103, 52022107, 52174128) and the Natural Science Foundation of Jiangsu Province (Nos. BK20210499, BK20190031).

Data availability statement

Data will be made available on request.

Declaration of interest's statement

The authors declare no conflict of interest.

Contributor Information

Jiaqi Wang, Email: very0712@163.com.

Yanli Huang, Email: 5306@cumt.edu.cn.

Wenrui Zhai, Email: 764290990@qq.com.

Appendix.

Number and deaths of different types of accidents in 2013–2020.

Accident type Year
2013 2014 2015 2016 2017 2018 2019 2020 Number of totals
Roof Number of accidents 274 196 134 83 73 61 49 40 910
Number of deaths 325 292 171 126 86 93 74 55 1222
Gas Number of accidents 59 47 45 36 31 13 26 5 262
Number of deaths 348 266 171 221 132 53 97 22 1310
Mechanical or electrical Number of accidents 59 36 31 22 20 19 15 17 219
Number of deaths 348 37 31 38 22 19 15 18 528
Water disaster Number of accidents 21 19 12 15 12 5 4 7 95
Number of deaths 89 79 64 27 18 14 10 25 326
Transport Number of accidents 109 83 62 54 32 50 47 23 460
Number of deaths 124 103 68 72 42 63 50 26 548
Blasting Number of accidents 16 13 7 8 8 2 3 3 60
Number of deaths 18 19 7 14 8 3 3 3 75
Other Number of accidents 81 114 59 31 43 25 26 28 407
Number of deaths 104 131 63 40 67 30 67 79 581
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Data source: China State Administration of Mine Safety, National Coal Mine Safety Administration.

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Associated Data

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Data Availability Statement

Data will be made available on request.

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