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The main cause of coal mine safety accidents is the unsafe behavior of miners who are affected by their emotional state. Therefore, the implementation of effective emotional supervision is important for achieving the sustainable development of coal mining enterprises in China. Assuming rational players, a signaling game between miners (emotion-driv...
Citations
... Yang, X. et al. studied the influence of emotions on miners' behavior and analyzed the participants' strategy choices and factors affecting the equilibrium state. The results of the analysis showed that the security of safety risk payments and the cost of masking miners' negative emotions influenced the equilibrium 3 [19]. Gunduz, M. et al. used a questionnaire to explore the extent and frequency of 37 labor productivity factors affecting construction projects and pointed out that poor labor supervision, delayed payment, poor working conditions, low-skilled labor, and bad weather are the most significant factors affecting production [20]. ...
This paper builds a power operation target detection model based on the YOLOv4 algorithm in intelligent image recognition, and optimizes the YOLOv4 algorithm by combining with the loss function to improve the accuracy of power target operation detection. The kmeans++ algorithm was used to cluster the electric power operation behaviors to obtain a more accurate electric power operation behavior dataset. Three sets of tests were conducted after the model was constructed, targeting the behavioral set of electric power workers in a certain place and the behavior in VOC format, followed by the multi-target tracking effect test. The analysis based on the obtained data showed that the helmet placement detection confidence, fatigue detection confidence, smoking detection confidence, and fall detection confidence reached 0.97, 0.93, 0.89, and 0.93, respectively. The transmission speed got 53.58 fps, and the recall and precision of the multi-target tracking were also above 93%. The YOLOv4 detection model based on keans++ clustering algorithm can effectively detect and identify the variable power operation behavior images.
... It is difficult to prevent and control production safety accidents, mainly because employees' safety literacy is not high, machinery and equipment are prone to accidents, personnel behavior is difficult to control, the natural environment is difficult to improve and safety technology is not advanced. In order to prevent production safety accidents, coal mining enterprises must improve the safety management level and reduce the incidence of accidents and the degree of accident loss by improving safety management and considering the five aspects of "human-machine-management-environment-technology" [26,27]. This paper therefore constructs as safety evaluation index system for filling mining mines by considering five aspects: personnel literacy, filling mining equipment and facilities, safety management of filling mining mine, filling mining environment and filling mining technical ability. ...
With the demand for green mining in coal mines, filling mining is becoming more and more popular, resulting in more complex production systems and more potential safety hazards. Therefore, it is very important to evaluate the safety of filling mining mines and propose improvement measures. Aiming at the safety evaluation method of filling mining mines, this paper innovatively proposes a safety evaluation method based on entropy weight–attribute mathematical theory, which enriches the theoretical research related to the safety evaluation model of filling mining mines. Five secondary indexes and twenty-two tertiary indexes were selected. The weights were determined via the entropy weight method, and then the attribute mathematical theory was used for safety evaluation. The evaluation results show that the safety level of Jisuo Coal Mine is “relatively safe”, and the evaluation results are in good agreement with the actual situation of Jisuo Coal Mine, which verifies the applicability of the attribute mathematical theory. Finally, from the perspective of safety input, the simulation study is carried out by using system dynamics, and the dynamic change rule is analyzed. Additionally, improvement measures for filling mining mine safety are proposed so as to realize the reasonable optimization of resource allocation.
... Emotion is the manifestation of the human state. Yang et al. believed that one of the main reasons for coal-mine-safety accidents was that the emotional state affected the unsafe behaviors of miners [29]. Anger is a common negative emotion. ...
Background: To effectively avoid accidents caused by practitioners’ unsafe state in the process of coal mine production processes, it is necessary to clarify the factors influencing the practitioners’ unsafe state, and take corresponding control measures accordingly. Methods: With the help of literature research and on-site interviews, grounded theory was used to construct the influencing factor index system of the coal mine practitioners’ unsafe state. The index system primary includes indices of four core categories, physiology, psychology, organization, and technology, and secondary indices of fourteen main categories. An AHP-DEMATEL model was constructed to calculate the comprehensive degree of influence of each influencing factor and rank it. Results: The results show that the main factors affecting the coal mine practitioners’ unsafe state are physical quality, degree of fatigue, safety attitude, safety awareness, safety culture, and vigilance. Physical quality and degree of fatigue are the key factors that affect the coal mine practitioners’ unsafe state, which is more consistent with the actual situation of coal mine practitioners. With the findings of this study, coal mine managers can take relevant countermeasures to intervene in coal mine practitioners’ unsafe state and reduce the occurrence of accidents.
... (1) This study enriches the content of coal mine safety management and fills the research gap regarding the "unsafe behavior of miners" in deep coal mine safety management. Previous studies have often discussed the unsafe behavior of shallow coal miners [68]. Compared to shallow mines, deep mines' inner and outer environments have changed dramatically. ...
The unsafe behavior of miners seriously affects the safety of deep mining. A comprehensive evaluation of miners’ unsafe behavior in deep coal mines can prevent coal mine accidents. This study combines HFACS-CM, SEM, and SD models to evaluate miners’ unsafe behaviors in deep coal mining. First, the HFACS-CM model identifies the risk factors affecting miners’ unsafe behavior in deep coal mines. Second, SEM was used to analyze the interaction between risk factors and miners’ unsafe behavior. Finally, the SD model was used to simulate the sensitivity of each risk factor to miners’ unsafe behavior to explore the best prevention and control strategies for unsafe behavior. The results showed that (1) environmental factors, organizational influence, unsafe supervision, and unsafe state of miners are the four main risk factors affecting the unsafe behavior of miners in deep coal mines. Among them, the unsafe state of miners is the most critical risk factor. (2) Environmental factors, organizational influence, unsafe supervision, and the unsafe state of miners have both direct and indirect impacts on unsafe behaviors, and their immediate effects are far more significant than their indirect influence. (3) Environmental factors, organizational influence, and unsafe supervision positively impact miners’ unsafe behavior through the mediating effect of miners’ unsafe states. (4) Mental state, physiological state, business abilities, resource management, and organizational climate were the top five risk factors affecting miners’ unsafe behaviors. Taking measures to improve the adverse environmental factors, strengthening the organization’s supervision and management, and improving the unsafe state of miners can effectively reduce the risk of miners’ unsafe behavior in deep coal mines. This study provides a new idea and method for preventing and controlling the unsafe behavior of miners in deep coal mines.
... RDEU theory is a utility theory that considers the psychological preferences and emotions of decision makers and can measure the influence of emotions on decision making. Regarding this theoretical approach, Guo et al. estimated the influences of heterogeneous emotions of government and individuals on the equilibrium strategy of an individual carbon trading scheme implementation model by constructing an RDEU evolutionary game model [49]; Ni et al. combined game theory and RDEU theory to construct an RDEU game model of insiders and the nuclear security sector to study the existence of two-way strategic equilibrium solutions under different emotional states, and evaluated (from a dynamic perspective) the influence and change process of emotions on participants' decision-making behavior [50]; Liu et al. used a static game, RDEU game, and sequential game to study the equilibrium strategies of emitters and stakeholders under different situations [51]; and Yang et al. established a signaling game between miners (emotion-driven and judgment-driven) and managers from the perspective of emotional event theory to examine the effect of managers' emotions on miners' behavior [52]. It is evident that participant emotions have a significant impact on the behavioral decisions of decision makers. ...
Shared manufacturing provides a new path for the transformation and development of the manufacturing industry, but challenges such as low quality and poor positivity for quality improvement limit the positive role of shared manufacturing. Considering the influences of heterogeneous emotions of subjects on quality decision making, the theory of rank-dependent expected utility (RDEU) and evolutionary game theory were integrated to establish an evolutionary game model of shared manufacturing quality innovation synergy with multi-agent participation and analyze how sentiment affects motivation for quality improvement. The study showed that: (1) emotions, an irrational factor, can significantly change the stable state of the evolution of the shared manufacturing quality innovation synergetic system by influencing the decision-making behavior of decision makers; (2) in terms of the specific microscopic influence mechanism, rationality is the key to ensuring that the behavioral decisions of decision makers do not enshrine large systemic deviations. (3) In terms of the mechanism of heterogeneous emotions, when one party is optimistic, the deepening of the other party’s pessimism tends to bring positive effects; when one party is pessimistic, the deepening of the other party’s optimism tends to bring negative effects. The main management insights are as follows: (1) correctly recognizing and treating heterogeneous emotions of decision makers and regulating the formation and role of heterogeneous emotions of decision makers; (2) appropriately creating an atmosphere of pessimistic emotions, and guiding shared manufacturing to pay attention to manufacturing quality innovation synergy; (3) appropriately releasing favorable information about quality innovation synergy, and continuously promoting high-quality development of shared manufacturing. This study broadens the path of quality improvement in shared manufacturing and the scope of application of emotion theory in a certain sense.
... An individual's safety behavior is affected by emotional state; Affective Event Theory (AET) suggests that employees' behavior and performance at work are largely determined by the changes in their emotions at each moment rather than their attitudes or personalities (Weiss and Cropanzano, 1996). The AET has been demonstrated effectively in the areas of mine worker safety behavior (Yang et al., 2020), driver driving safety (Muller et al., 2014) and among other areas. Kajiwara verified that emotions can influence the productivity and accuracy of workers in a logistics picking system (Kajiwara et al., 2019). ...
The construction industry is one of the most dangerous industries with grave situation owing to high accident rate and mortality rate, which accompanied with a series of security management issues that need to be tackled urgently. The unsafe behavior of construction workers is a critical reason for the high incidence of safety accidents. Affective Events Theory suggests that individual emotional states interfere with individual decisions and behaviors, which means the individual emotional states can significantly influence construction workers’ unsafe behaviors. As the complexity of the construction site environment and the lack of attention to construction workers’ emotions by managers, serious potential emotional problems were planted, resulting in the inability of construction workers to effectively recognize safety hazards, thus leading to safety accidents. Consequently, the study designs a behavioral experiment with E-prime software based on social cognitive neuroscience theories. Forty construction workers’ galvanic skin response signals were collected by a wearable device (HKR-11C+), and the galvanic skin response data were classified into different emotional states with support vector machine (SVM) algorithm. Variance analysis, correlation analysis and regression analysis were used to analyze the influence of emotional states on construction workers’ recognition ability of safety hazards. The research findings indicate that the SVM algorithm could effectively classify galvanic skin response data. The construct ion workers’ the reaction time to safety hazards and emotional valence were negatively correlated, while the accuracy of safety hazards recognition and the perception level of safety hazard separately had an inverted “U” type relationship with emotional valence. For construction workers with more than 20 years of working experience, work experience could effectively reduce the influence of emotional fluctuations on the accuracy of safety hazards identification. This study contributes to the application of physiological measurement techniques in construction safety management and shed a light on improving the theoretical system of safety management.
... The mental status of individual workers can also influence their unsafe behaviors, which mainly include safety attitude, safety awareness, work pressure and risk perception. Yang et al. (37,38) reported that the main cause of coal mine safety accidents was the unsafe behavior of miners affected by emotions. When workers are in a bad mental state, they are prone to unsafe behaviors. ...
Coal mine accidents are mainly caused by the unsafe behavior of workers. Studying workers' unsafe behaviors can help in regulating such behaviors and reducing the incidence of accidents. However, there is a dearth of systematic literature review in this area, which has hindered mine managers from fully understanding the unsafe behavior of workers. This study aims to address this research gap based on the literature retrieved from the Web of Science. First, a descriptive statistical analysis is conducted on the year, quantity, publications, and keywords of the literature. Second, the influencing factors, formation mechanism, and pre-control methods of coal miners' unsafe behavior are determined and discussed, and the research framework and future research directions of this study are proposed. The study results will help mine safety managers fully understand the influencing factors, formation mechanism, and pre-control methods of workers' unsafe behavior, and lay a theoretical foundation for the future research direction in this field.
... However, the players of the game are coal miners and safety regulators. e effective supervision of coal miners' emotions is an important means to achieve coal mine safety [40]. When certain conditions are met, all parties in the game can reach the ideal stable state [41]. ...
In recent years, although coal mine accidents in China have decreased, they still occur frequently. Most previous studies on the evolutionary game of safety mining are limited to a focus on system dynamics and two-party game problems and lack a spatial graphic analysis of strategy evolution. The parameters adopted are too few, and the influencing factors considered are too simple. The purpose of the paper is to introduce more parameters to study which will have an important impact on the strategy choices of participants and the evolution path of the strategy over time. We construct a tripartite evolutionary game model of coal mining enterprises, local governments, and central governments. As our method, a payment matrix of participants and replicated dynamic equations is established, and we also implement parameter simulation in MATLAB. In summary, we found that the reward and punishment mechanism plays an important role in safe coal mining. Specifically, (1) intensifying rewards and penalties for coal mining enterprises and local governments will help encourage coal mining enterprises to implement safe production measures and local governments to implement central government safety supervision policies. However, increased rewards will reduce central government’s willingness to adopt incentive strategies. (2) The central government’s reward for coal mining enterprises’ safe production must be greater than the increased cost of safe production to encourage enterprises to implement such production. Economic incentives for local governments must be greater than the benefits of rent-seeking; only then will local governments choose to strictly implement supervision policies. (3) Increasing sales revenue and rent-seeking costs of coal mining enterprises can also encourage them to implement safe production. Therefore, a well-designed reward and punishment mechanism will change the behaviour of coal enterprises and improve the probability of safe production. The research presented in this paper further works on improving safe coal mining production and designing reasonable reward and punishment mechanisms.
1. Introduction
In recent years, with the rapid development of China’s economy, the demand for energy resources such as coal has become increasingly intense. China’s coal production accounts for 46.4% of the world’s production, ranking first in the world for many years [1]. However, the death toll of Chinese coal mine workers accounts for approximately 70% of the total death toll worldwide [2]. In terms of coal mine safety, Australia far exceeds China, and Chinese miners are powerless in terms of safety supervision [3]. The limitations of China’s coal mine safety supervision system are a reason for the frequent occurrence of safety accidents [4]. China’s new coal mine safety management regulations will have a significant impact on coal mine safety [5]. The operation mode of mine management and the employed system of reward and punishment help promote safe mine production [6]. The vertical supervision system of safe coal production in China plays an important role in improving coal mine safety, and an increase in per capita supervision frequency can promote the supervision performance of the National Mine Safety Administration [7]. Relying on unlimited increases in rewards and penalties alone cannot encourage enterprises to invest in mine safety.
Scholars have gradually used game theory to analyse the stakeholders of safe coal mine production, including the central government, local governments, coal mining enterprises, and miners [8, 9]. However, their works largely assume that the subjects participating in the game are completely rational. The evolutionary game, which originated in the field of population biology in the 1980s, considers the irrationality of players and the importance of time factors and provides a new analytical paradigm for safe coal mine production [10–12]. Therefore, although some papers have explored this problem, deficiencies in model design and specific simulation analysis methods remain, so it is necessary to design a more practical model.
The purpose of this paper is to design a more practical model to identify the factors that affect the safe production of coal enterprises, conditions under which reward and publishment mechanisms can work and strategy changes over time, and provide a reference for the reasonable design of safe production reward and punishment systems. This paper makes the following contributions. (1) We establish a replicated dynamic equation and draw a diagram of the corresponding strategy to prove the economic rationality of game participants. (2) We use a spatial three-dimensional diagram to show the impact of the change in parameters on the strategy. (3) We introduce more parameters, which is more in line with real environments. (4) We strive to make the results of this paper conform to common principles of economics and prove the rationality of our conclusions through rigorous mathematics. In terms of our method, we establish a tripartite evolutionary game model, a payment matrix of participants and replicated dynamic equations, and then we conduct a parameter simulation in MATLAB. Figure 1 shows the research content framework of this paper. How does the reward and punishment mechanism affect the safe production of coal enterprises? What conditions are required for the reward and punishment mechanism to work? Are these conditions applicable to local governments? We explore these problems in the following section.
... e research of organizational relationships usually adopts game methods. Yang et al. examined the impact of managers' emotions on miners' behavior using a bilateral signaling game driven by emotion and judgment [23]. Yu et al. described the asymmetry of the game interests between managers and miners with the system dynamics method [24]. ...
This study explores the multiple organizational relationships between frontline miners, managers, and supervisors to reveal the human organizational risks of coal mine safety and health management. Data were collected from six high-risk rock burst underground mining companies operating in western, central, north-eastern, and south-eastern regions of China. A total of 1105 respondents from the three core groups were investigated. Descriptive statistics and paired test methods were used to empirically analyze the deteriorated and dislocated relationships between multiple roles. The specific conclusions are as follows: (1) Miners’ perception of relationship quality is the lowest, and the managers’ perception of relationship quality is the highest. (2) “Closeness” relationship is expressed among peer colleagues for all multiple roles. (3) The deteriorated relation rate of miners averagely reached 19.67%, and that of supervisors averagely reached 17.63%, thereby mostly reaching 27.8% for miners with regard to supervisors. (4) The workers in high positions easily have a phenomenon of “overestimated confidence” in the perception of dislocated relationships, and the “miners-supervisors” and “supervisors-manager” dual-core contradiction have obviously been emerging. (5) The valuable, harmonious, and extent degree are relatively lowest in all relationship items.
The intelligent construction of coal mining enterprises is the fundamental strategy to prevent and curb major coal mine accidents, and the core technical support to realize the high-quality development of the coal industry. Considering the intelligent construction of coal mining enterprises enabled by emerging information technologies such as 5G and artificial intelligence, this paper constructs a Moran process stochastic evolutionary game model of intelligent construction of coal mining enterprises. Based on the fixed point probability of the Moran process, the probability of successful invasion of the "intelligent construction" strategy and the "traditional production" strategy in the limited coal mining enterprise group is calculated. By comparing the probability of individual fixed point and the probability of neutral mutation, the dominant condition of the strategy under strong selection and weak selection is obtained. The research shows that external stochastic factors, the number and scale of enterprises, the intensity of capacity replacement and the cost-benefit of intelligent construction are the main factors affecting the intelligent construction behavior of coal mining enterprises. When the intelligent construction of coal mining enterprises is in the cultivation period, the intelligent construction of underground coal mines dominated by stochastic factors can be effectively promoted by increasing the intensity of capacity reduction replacement. For the open-pit coal mine dominated by expected payoffs, reducing the number of mining rights and improving the concentration of open-pit coal mining industry will have a better effect on promoting its intelligent cultivation process. When the intelligent construction of coal mining enterprises is in the mature stage, with the improvement of the cost and benefits of intelligent construction, "intelligent construction" strategy will become a general consensus of coal mining enterprises. In addition, this paper analyzes the relevant parameters through specific examples to verify the effectiveness of the conclusions, which provides a scientific basis for effectively accelerating the intelligent construction of coal mining enterprises and promoting the transformation and upgrading of the coal industry.
