煤炭行业长久以来一直是我国能源生产的重要支柱。近年来，伴随着煤炭行业的高速发展，我国煤矿安全生产取得了明显的成效，但重特大事故尚未杜绝，较大事故时有发生。研究与调查表明，人类的不安全行为和失误是事故的主要原因和直接原因，而情景意识，即对特定时间空间内的感知、理解预测能力，如执行功能、工作记忆、注意力和信息处理速度等认知功能是安全工作的重要保证。因此，从脑科学的角度出发，探究煤矿工人情景意识-不安全行为/人因失误的内在认知神经机制，对有效降低煤矿工人的失误率和伤害率，全面落实对“人的隐患”精准排查，全方位做到风险预控、关口前移，切实提升煤矿安全管理水平，具有重要理论意义与实用价值。

本研究以煤矿工人脑功能连接为研究对象，采用fNIRS脑影像实验平台，融合安全科学与认知神经科学研究方法，从安全科学和认知神经科学融合的视角，构建了煤矿工人情景意识全要素模型；对陕煤集团H公司的煤矿工人开展了两项实验——实验一：煤矿工人人因失误倾向者脑功能连接特征分析；实验二：轮班工作对煤矿工人脑功能连接的影响；基于实验一和实验二采集的煤矿工人脑功能连接特征数据构建了基于深度学习的煤矿工人情景意识分类识别模型。主要研究结论如下：

（1）构建了煤矿工人情景意识全要素模型。以动态决策情景意识模型、人类行为SOR模型和大脑信息加工理论为基础，将本模型分为刺激、肌体和反应三大模块。环境信息属于刺激模块，为信息的输入层。情景意识，认知功能和个体因素共同构成肌体模块。其中，以感知为基础的情景意识是信息感受器，认知功能为大脑信息加工器。认知功能是情景意识的神经生理表现。个体因素是情景意识和认知功能的重要影响因素。反应模块为不安全行为/人为因素输出层，是信息效应器。当环境信息与个体因素内任一子因素发生不利变化时，个体感知能力将受到影响，情景意识随之下降或者失效，从而引发个体认知功能下降，导致不安全行为/人因失误。此外，个体不安全行为/人因失误也将反馈于环境信息。

（2）揭示了煤矿工人人因失误倾向者的大脑功能连接特征。实验一应用fNIRS脑影像实验平台，采集了106名煤矿工人的静息态数据。采用皮尔逊相关系数分析、脑网络分析和t检验对煤矿工人人因失误倾向者脑功能连接进行了量化。研究发现，煤矿工人人因失误倾向者的脑功能连接特征在其额叶区、眶额区和布洛卡三角区的脑功能连接与一般煤矿工人之间存在显著差异；且其背外侧前额叶皮层的脑网络在聚类系数、局部结点效率和全局结点效率上与一般煤矿工人存在明显差异。上述脑功能连接特征显示，煤矿工人人因失误倾向者的个人特质为抑郁、冲动、高认知负荷和较弱的注意力控制能力、反应能力、执行能力、情绪稳定能力与工作记忆能力。实验一的研究结果显示，fNIRS静息态功能连接研究手段，可以揭示并量化煤矿工人人因失误倾向者脑功能连接的神经生理特征，为现代化煤炭企业实现科学、精准识别煤矿工人人因失误倾向者提供了重要的技术支撑和数据参考。

（3）明晰了轮班工作对煤矿工人脑功能连接的影响。实验二基于fNIRS脑影像实验平台，采集了早班、午班和夜班共54名煤矿工人岗前岗后的静息态数据以测量其脑功能连接特征。研究发现，早班、午班和夜班煤矿工人的脑功能连接在岗前岗后均有显著差异；其中，早班煤矿工人岗前岗后的功能连接差异最大，其次是午班，夜班岗前岗后功能连接差异最小；相较于岗前，早班和午班的煤矿工人岗后功能连接显著降低；而夜班的情况则与早班和午班相反；三班岗前岗后所有状态的前额叶皮层静息态脑网络都具有小世界属性，早班和夜班煤矿工人的前额叶皮层的中介中心性和局部结点效率具有显着差异。上述脑功能连接变化表明：受教育水平较低的轮班煤矿工人的认知功能较低；离异或丧偶的轮班煤矿工人的认知能力较低；早班和午班轮班煤矿工人在工作结束时更容易出现注意力下降、多任务处理能力下降、情绪控制能力下降、疲劳和困倦等认知功能降低并伴有脑网络信息转换效率显著下降；早班轮班煤矿工人的多任务处理能力在工作结束时显著下降；夜班轮班煤矿工人的情绪控制能力在工作结束时显著下降。实验二的研究结果显示，fNIRS静息态功能连接研究手段，可以从认知神经科学的角度量化早班、午班、夜班不同班次工作对煤矿工人脑功能连接神经生理的影响。为进一步提升轮班制度合理性，保障煤矿工人身心健康提供了重要量化指标和分析依据。

（4）构建了基于深度学习的煤矿工人情景意识分类识别模型。在实验一和实验二采集的煤矿工人脑功能连接特征数据基础上，优选了脑功能网络特征，构建了四个SVM分类识别模型对不同条件下的煤矿工人情景意识进行检测。结果表明，SVM分类器对煤矿工人人因失误倾向者情景意识的识别准确率为84.21%；对早班煤矿工人岗前岗后情景意识识别准确率为97.06%；对午班煤矿工人岗前岗后情景意识识别率为83.33%；对晚班煤矿工人岗前岗后情景意识识别率为83.33%。为现代化煤矿企业实现科学、精准煤矿工人个体情景意识排查、检测提供了重要的技术支持和量化数据参考。为政府提供了新的煤矿安全管理和公共安全管理量化思路，进一步促进了我国煤矿监察监管的精准化和科学化发展。

总而言之，本研究运用fNIRS脑影像技术，探究了煤矿工人情景意识-不安全行为/人因失误的内在认知神经机制，从安全科学和认知神经科学交叉融合的角度进一步揭示了煤矿工人发生不安全行为或人因失误内在机制和脑功能连接特征，为煤矿工人情景意识量化检测提供了重要的数据依据和决策参考。

For a long time, the coal industry has been an important pillar of China’s energy production. In recent years, with the rapid development of the coal industry, China’s coal mine safety management has achieved significant results, however, serious accidents have not been eliminated, and larger accidents occur from time to time. Studies and surveys have shown that human unsafe behaviors and human errors are the main and direct causes of accidents, while situation awareness, such as executive functions, working memory, attention, and information processing speed are important protections for safety work. Therefore, from the perspective of brain science, it is of great theoretical significance and practical value to investigate the inner cognitive neural mechanism of situation awareness-unsafe behavior/human error of coal miners, to effectively reduce the error rate and injury rate of coal miners, to fully implement the precise screening of “human hazards”, to achieve all-round risk pre-control, to shift the gate forward, and to effectively improve coal mine safety management.

From the perspective of integrating safety science and cognitive neuroscience, this study built the full elements model of situation awareness of coal miners; took the brain functional connectivity of coal miners as the research object, based on the fNIRS brain imaging experimental platform, integrated safety science and cognitive neuroscience research methods, and two experiments were conducted on coal miners of Shaanxi Coal Group H Company --Experiment 1: characterization of functional brain connectivity in coal miners with human error propensity; Experiment 2: effects of shift work on brain function connectivity in coal miners; a deep learning-based situation awareness classification recognition model for coal miners was constructed based on the brain functional connectivity feature data of coal miners collected in Experiment 1 and Experiment 2. The main findings are summarized as follows:

(1) The full elements model of situation awareness of coal miners was constructed. Based on the dynamic decision-making situation awareness model, the SOR model of human behavior and the brain information processing theory, this model is divided into three modules: stimulus, organism and response. Environment factors belong to the stimulus module, which is the input layer of information. Situation awareness, cognitive function and individual factors together constitute the organism module. Among them, based on perception, situation awareness is the information receptor and cognitive function is the brain information processor. Cognitive function is the neurophysiological manifestation of situation awareness. Individual factors are important influences on situation awareness and cognitive function. The response module is the unsafe behavior/human factor output layer, which is the information effector. When there is an adverse change in any subfactor in environment factors or individual factors, the individual perceptual ability will be affected and the situation awareness will subsequently decline or fail, thus triggering a decline in individual cognitive function and leading to unsafe behavior/human factors failures. In addition, the individual’s unsafe behavior/human factor failures will also feedback to the environment factors.

(2) The brain functional connectivity characteristics of coal miners with human error propensity were uncovered a fNIRS brain imaging experimental platform was utilized to collect the resting-state data of 106 coal miners. Pearson correlation coefficient analysis, brain network analysis and two-sample t-test were used to quantify the brain functional connectivity of coal miners with accident-prone tendencies or not. It was found that the brain functional connectivity of coal miners with human error propensity was characterized by significant differences between their frontal, orbitofrontal and Broca’s triangle brain functional connectivity and that of the general coal miners; and their brain networks in the dorsolateral prefrontal cortex were significantly different from those of the general coal miners in terms of clustering coefficient, local nodal efficiency and global nodal efficiency. The functional brain connectivity features described above indicate that the personal traits of coal miners with human error tendency are depression, impulsivity, high cognitive load and weak attentional control, reactivity, executive ability, emotional stability and working memory ability. The results of Experiment 1 show that the fNIRS resting-state functional connectivity research approach can reveal the neurophysiological characteristics of quantifying the brain functional connectivity of coal miners with human error propensity, which provides important technical support and data reference for modern coal enterprises to achieve scientific and accurate identification of coal miners with human error propensity.

(3) The effects of shift work on the functional connectivity of coal miners were clarified. Based on the fNIRS brain imaging experimental platform, resting state data were collected from 54 coal miners in the morning, afternoon, and night shifts before and after work to evaluate their cognitive function status. The results showed that the cognitive functions of coal miners in all morning, afternoon, and night shifts were significantly different before and after work; among them, the difference in functional connectivity was the largest in the morning shift, followed by the afternoon shift, and the smallest in the night shift; compared with the pre-shift, the post-shift functional connectivity of coal miners in the morning and afternoon shifts was significantly lower; while the situation in the night shift was the opposite of the morning and afternoon shifts; all the pre-shift and post-shift functional connectivity in all three shifts state prefrontal cortex resting-state brain functional networks had small-world properties, with significant differences in betweenness centrality and local nodal efficiency in prefrontal cortex between morning and night shift coal miners. The functional brain connectivity features described above indicate that shift coal miners with lower levels of education had lower cognitive function; divorced or widowed shift coal miners had lower cognitive ability; morning and afternoon shift coal miners were more likely to have lower cognitive function at the end of work such as decreased attention, decreased multitasking, decreased emotional control, fatigue and sleepiness with a significant decrease in brain network information conversion efficiency; morning shift shift The multitasking ability of coal miners significantly decreased at the end of work; the emotional control ability of coal miners on night shift significantly decreased at the end of work. The results of Experiment 2 show that the fNIRS resting-state functional connectivity research approach can quantify the effects of working different shifts of morning, afternoon and night shifts on the neurophysiology of brain functional connectivity in coal miners from the perspective of cognitive neuroscience. It provides an important quantitative index and analytical basis for further improving the rationality of shift system and safeguarding the physical and mental health of coal miners.

(4) A deep learning-based classification and recognition model for coal miners’ situation awareness was constructed. Based on the brain functional connectivity feature data collected from coal miners in Experiment 1 and Experiment 2, four SVM classification recognition models were constructed to detect the situation awareness of coal miners under different conditions by preferentially selecting brain functional network features. The results showed that the SVM classifier had an accuracy of 84.21% in recognizing the situation awareness of coal miners with human error tendency; 97.06% in recognizing the situation awareness of coal miners before and after the morning shift; 83.33% in recognizing the situation awareness of coal miners before and after the lunch shift; and 83.33% in recognizing the situation awareness of coal miners before and after the evening shift. The results provide important technical support and quantitative data reference for modern coal mining enterprises to achieve scientific and precise coal miners’ individual situation awareness ranking and detection and provide the government with new quantitative ideas for coal mine safety management and public safety management, and further promotes the precise and scientific development of China’s coal mine supervision and regulation.

In conclusion, this study explored the intrinsic cognitive neural mechanisms of coal miners’ situational awareness - unsafe behavior/human errors based on fNIRS brain imaging technology, further revealing the intrinsic mechanisms and characteristics of coal miners’ unsafe behaviors from the perspective of cross-fertilization between safety science and cognitive neuroscience, and providing an important data basis and decision reference for quantitative detection of coal miners’ accident-prone tendencies.

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