我国是一个煤炭资源丰富但石油和天然气资源相对匮乏的国家，煤炭在我国一次性能源生产和消费中长期占据主导地位。目前我国煤炭资源总量为5.97万亿吨，其中深埋在1000米以下的资源占53%，深部开采已然成为发展的必然趋势。随着煤矿开采深度不断增加，采掘工作面的温度湿度也随之升高。湿热环境不仅影响矿工的生理健康和工作效率，还会加速设备老化，增加安全隐患。研究表明，湿热条件可能导致矿工的认知功能下降，包括注意力、判断力和反应时间的减慢。这些变化会直接影响矿工对安全操作规程的遵循，以及对潜在危险的识别能力。因此研究湿热环境下矿工不安全行为的认知神经机理，对保障煤矿安全生产至关重要。然而目前的研究多侧重于高热环境对矿工生理反应的影响，温度是否也会对矿工的认知神经造成影响，温湿度之间是否具有结合效应尚未明确。因此，论文从认知神经科学视角出发，探讨不同温湿度环境对矿工不安全行为的影响机制，并提出相应的预警模型，以期在实际生产中提升矿工行为安全管理的有效性和针对性。本研究的主要工作及研究成果如下：

本研究基于SOR（Stimulus-Organism-Response）模型，构建了湿热环境下矿工不安全行为的认知神经理论模型。在该模型中，“刺激（S）”代表不同的温湿度条件；“有机体反应（O）”涵盖了这些环境刺激如何影响矿工的认知状态；“响应（R）”部分则聚焦于这些内部状态变化如何影响实际的行为表现，尤其是不安全行为。研究发现，矿工在湿热环境下的行为反应并非孤立的生理或心理事件，而是两者交互作用的结果。通过理论模型，不仅揭示了这些交互过程，还为后续实验的设计和实施提供了理论依据。

为验证湿热环境对矿工行为和认知神经机制的负面影响，设计并实施了警觉性实验和工作记忆实验，系统评估了温度和湿度对矿工认知神经机制和行为的影响。实验结果表明，随着温度和湿度的升高，矿工的反应时间显著延长，失误率增加，工作记忆容量降低，其中温度对矿工认知的影响更甚于湿度。而氧合血红蛋白浓度仅受温湿度结合效应的影响，不受其单独影响，这揭示了湿热环境条件下复杂的生理应激反应。此外，研究发现，湿热环境特别影响矿工的前额叶区域，而辅助运动区的反应没有显示出同等显著性。该结果为后续预警模型的建立提供了数据支持和理论基础。

为实现对采掘工作面湿热环境下矿工不安全行为的检测与预警，利用生成对抗网络（GAN）粒子群优化（PSO）算法对数据集和随机森林模型进行优化，并在此基础上构建了湿热环境中矿工不安全行为分级预警模型。GAN的应用确保在数据受限情况下模型训练的充足性和多样性，而PSO的使用提高了随机森林模型在处理复杂数据时的参数配置效率。优化后的随机森林模型在混合数据上表现出的预测准确率最高可达90%，说明模型能基于环境参数、生理和认知特征，对不安全行为进行有效分类和预警。此外，预警模型在辨识不同级别不安全行为风险方面也表现出了较高的准确性，对于安全管理人员在实时监测和干预中有效决策具有重要意义，有助于管理人员及时采取相应预防措施，有效降低事故发生率。

综上所述，本研究通过理论模型、实验分析和预警模型的构建，系统揭示了湿热环境对矿工不安全行为影响的认知神经机理，并提出切实可行的安全预警方案。研究不仅为科学制定煤矿安全政策、改善矿工工作条件、提升安全管理水平提供理论和技术支持，对推动煤矿行业的安全生产和可持续发展意义重大。

China has abundant coal resources but is relatively short on oil and gas, making coal the primary energy source. Currently, the total coal resources amount to 597 trillion tons, with 53% buried deeper than 1,000 meters, necessitating deep mining. The humid and hot conditions in coal mines affect miners' health and efficiency, accelerate equipment aging, and increase safety risks. Research indicates that such environments impair cognitive functions like attention, judgment, and reaction time, impacting miners' adherence to safety protocols. However, most current studies focus on the influence of high thermal environment on miners' physiological response. It is not clear whether temperature will also affect miners' cognitive nerves, and whether there is a combination effect between temperature and humidity. This paper examines how hot and humid environments influence miners' unsafe behavior from a cognitive neuroscience perspective and proposes an early warning model to enhance safety management in mining operations. The main findings of this study are as follows:

Based on SOR (Stimulus-Organism Response) model, this study constructed a cognitive neural theory model of miners' unsafe behavior in hot and humid environment. In this model, "stimulus (S)" represents different temperature and humidity conditions; "Organic Response (O)" covers how these environmental stimuli affect the cognitive state of miners; The "Response (R)" section focuses on how these internal state changes affect actual behavior, especially unsafe behavior. It is found that the behavior of miners in hot and humid environment is not an isolated physiological or psychological event, but the result of the interaction of the two. The theoretical model not only reveals these interaction processes, but also provides a theoretical basis for the design and implementation of subsequent experiments.

In order to verify the negative effects of hot and humid environment on miners' behavior and cognitive neural mechanism, alertness experiment and working memory experiment were designed and implemented, and the effects of temperature and humidity on miners' cognitive neural mechanism and behavior were systematically evaluated. The experimental results show that with the increase of temperature and humidity, the reaction time of miners is significantly extended, the error rate is increased, and the working memory capacity is decreased. The oxygenated hemoglobin concentration was only affected by combination effect of temperature and humidity, and was not affected by it alone, which revealed the complex physiological stress response under the condition of hot and humid environment. In addition, the study found that the hot and humid environment particularly affected the miners' prefrontal regions, while the response in the auxiliary motor area did not show the same significance. The results provide data support and theoretical basis for the establishment of subsequent early warning models.

In order to realize the detection and early warning of miners' unsafe behavior in wet and hot environment of mining face, the data set and random forest model were optimized by PSO algorithm based on GAN, and the hierarchical early warning model of miners' unsafe behavior in wet and hot environment was constructed. The application of GAN ensures the adequacy and diversity of model training under data constraints, while the use of PSO improves the parameter configuration efficiency of random forest models when processing complex data. The optimized random forest model showed a prediction accuracy of up to 90% on mixed data, indicating that the model can effectively classify and warn unsafe behaviors based on environmental parameters, physiological and cognitive characteristics. In addition, the early warning model also shows high accuracy in identifying different levels of unsafe behavior risks, which is of great significance for safety managers in real-time monitoring and intervention decision-making, and helps managers to take appropriate preventive measures in time to effectively reduce the accident rate.

In summary, this study systematically revealed the cognitive neural mechanism of the influence of humid and hot environment on miners' unsafe behavior, and proposed a feasible safety early warning scheme. The research not only provides theoretical and technical support for scientific formulation of coal mine safety policies, improvement of miners' working conditions, and enhancement of safety management level, but also has great significance for promoting safe production and sustainable development of the coal mine industry.

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