摘  要

全球人口老龄化正加速演进为不可逆的历史进程，其引发的劳动力结构性变迁将对21世纪人类社会产生深远影响。在采矿业这一高危基础产业领域，矿工老龄化现象尤为值得关注：随着我国人口预期寿命持续攀升（2024年已达79岁）、“60后”劳动群体进入职业衰退期以及总和生育率持续低位运行，年长矿工群体正成为采掘作业的主力军，这一趋势在未来20年内将呈现持续强化态势。煤矿安全生产作为国家能源安全的战略基石，其风险防控体系正面临前所未有的复合型挑战。高龄劳动者在生理机能、应急反应、技术适应等方面存在天然劣势，企业安全培训体系、人机工程优化和设备适老化改造亟待升级，这一转变直接关涉国家能源供应链韧性、区域经济可持续发展及社会治理效能。因而，开展不同年龄矿工风险感知的认知机理研究是老龄化时代背景下保障煤炭企业安全生产水平亟待解决的重要现实问题。本文聚焦矿工风险感知能力的年龄差异，构建融合个体特征、认知偏差与神经机制的研究框架。通过编制风险感知量表与实地调研，分析年龄等因素对风险感知的影响，提取关键感知特征，划分年龄画像，并结合fNIRS技术测量认知神经差异，最终建立适用于不同年龄矿工的风险感知状态识别模型，为煤矿作业中个体化风险预警与干预提供支持。主要研究内容和结论如下：

（1）揭示了个体生理状态与经验水平在风险认知中的互动关系，发现矿工对风险的主观感知偏差已成为诱发事故的重要心理机制。本文以认知老化理论与Haddon矩阵为理论基础，围绕年龄相关的生理与经验变化如何影响矿工风险感知偏差进行研究设计，构建分析框架并提出6条针对性研究假设。通过对398份有效问卷数据进行信效度检验、相关性分析与层级回归建模，验证了年龄变化-生理衰退和经验积累-风险感知偏差的路径机制，识别了经验积累与生理衰退的双重调节作用。研究结果揭示了个体生理状态与经验水平在风险认知中的互动关系，为提升矿工风险辨识能力和制定差异化干预策略提供了理论依据和实证支持。

（2）揭示了不同年龄矿工风险感知差异及其动态演化规律。综合运用安全科学、心理学与认知科学理论，采用扎根理论提取矿工风险感知影响因素，进行核心维度划分；基于煤矿全年龄层矿工样本，采用问卷调研，通过结构方程模型SEM关联路径分析，解析矿工个体安全状态、安全素养、组织支持与风险感知的交互路径，结合K-means聚类算法，量化年龄对风险感知的调节效应及群体异质性特征，构建年轻矿工与年长矿工风险感知特征标签体系；通过独立样本T检验，检验年轻矿工与年长矿工风险感知的显著敏感标签，以此为依据，构建不同年龄矿工风险感知特征画像。研究发现：年轻矿工风险感知呈现“情绪-环境”双驱动模式，心理状态的焦躁不安、紧张感与工作环境的有毒有害气体显著正向驱动风险感知，而组织支持的负向调节效应揭示了其对管理实效性的高敏感性；年长矿工表现为“经验-知识”协同驱动模式，安全素养与事故细节预警通过认知框架重构增强风险判断，但生理状态的慢性疲劳与心理状态的失控性担忧导致风险响应迟滞；基于风险感知敏感指标的聚类分析，“绘制”年龄分层的风险感知特征画像，将年轻矿工划分为失衡高敏型矿工和规则游离型矿工，年长矿工划分为知识缺位型矿工、意识疏离型矿工和态度钝化型矿工。本文通过融合跨学科理论与大样本分析方法，构建了矿工风险感知的评估框架，为矿山安全管理提供了从“群体普适”到“个体精准”的理论模式突破打开了一个窗口。

（3）明确了呈现年龄差异的矿工风险感知的认知神经特征。在所有实验任务中，左侧前额叶表现出对风险感知状态的显著敏感性，是识别矿工是否具备风险感知的关键脑区。当矿工处于有风险感知状态时，其左侧前额叶的HbO浓度t值显著升高，而在无风险感知状态下则明显降低。进一步分析显示，不同年龄矿工在风险感知状态下的脑部激活程度存在显著差异，年轻矿工在高风险情境中左侧前额叶的神经响应更为强烈，表现出更高的t值，反映其对风险信息的加工能力较强。此外，在无风险感知状态下，年轻与年长矿工在通道8和13所对应的前额叶区域HbO浓度均值存在显著差异，提示年龄因素在风险感知的神经加工过程中具有重要影响。该研究从神经层面揭示了不同年龄矿工风险感知能力的结构性差异，为理解个体在高危作业中的风险响应机制提供了实证依据。

（4）构建不同年龄矿工风险感知状态识别模型，并验证模型的可行性。采用ReliefF算法和Kruskal Wallis算法，优选矿工风险感知状态相关的多源异构数据关键特征指标，通过决策树、支持向量机、集成学习和神经网络4种机器学习分类器分别训练了年长和年轻矿工风险感知状态4分类和2分类识别模型，对5折交叉验证得出的高准确率模型进行评估，通过测试集测试了4种分类器各自最优模型的鲁棒性。结果表明：所训练矿工风险感知状态4分类和2分类最优模型中，EL模型的4分类平均准确率最高可达40.16%，SVM模型的2分类平均准确率最高可达90.42%；本研究构建的不同年龄矿工风险感知状态识别模型分类结果表明，使用可穿戴式便携fNIRS进行矿工风险感知状态识别是可行的。

综上所述，本研究围绕年龄相关因素对风险感知偏差的作用机制展开分析，识别了生理退化与经验积累在不同年龄群体中的影响路径，并构建了年轻矿工与年长矿工的风险感知特征标签体系；揭示了不同年龄矿工风险感知的认知神经特征和机理；构建了矿工风险感知状态识别模型。研究结果进一步拓展了神经安全管理的理论和实践视野，有助于推动煤矿安全管理模式由粗放型事后结果管理向精准事前预防转型。

ABSTRACT

Global population aging is accelerating into an irreversible historical process, bringing profound structural changes to the labor force that will significantly impact 21st-century human society. In the mining industry—one of the most hazardous foundational sectors—this aging trend among workers warrants particular attention. With China’s life expectancy rising steadily (reaching 79 years in 2024), the “post-60s” labor group entering the late stage of their careers, and the country’s total fertility rate remaining persistently low, older miners are becoming the dominant workforce in extraction operations—a trend expected to intensify over the next two decades. As a strategic cornerstone of national energy security, coal mine safety is now facing unprecedented, multifaceted challenges. Elderly workers have inherent disadvantages in terms of physical function, emergency responsiveness, and adaptability to new technologies. Consequently, enterprise safety training systems, ergonomics optimization, and age-friendly equipment upgrades are urgently needed. This demographic shift directly affects the resilience of national energy supply chains, regional economic sustainability, and the overall effectiveness of social governance. Therefore, investigating the cognitive mechanisms underlying risk perception across different age groups of miners has become a critical and urgent issue for ensuring safety in coal enterprises under the context of aging. This study focuses on age-related differences in miners’ risk perception capabilities and constructs an integrated research framework combining individual characteristics, cognitive bias, and neurophysiological mechanisms. By developing a risk perception scale and conducting field surveys, the study analyzes the influence of age and other factors on risk perception, extracts key perceptual features, and categorizes age-specific profiles. It further incorporates fNIRS (functional near-infrared spectroscopy) technology to measure cognitive neural differences, ultimately establishing risk perception state recognition models tailored to miners of different age groups. This provides scientific support for personalized risk warning and intervention strategies in coal mine operations. The main research contents and conclusions are as follows:

(1) This study reveals the interactive relationship between individuals’ physiological states and experience levels in risk cognition, identifying miners’ subjective risk perception bias as a critical psychological mechanism contributing to accidents. Grounded in cognitive aging theory and the Haddon Matrix, the research is designed to explore how age-related changes in physiology and experience influence miners’ risk perception bias. An analytical framework is constructed, and six targeted research hypotheses are proposed. By conducting reliability and validity tests, correlation analysis, and hierarchical regression modeling on 398 valid questionnaire data, the path mechanisms of age change physiological decline and experience accumulation risk perception bias were validated, and the dual moderating effects of experience accumulation and physiological decline were identified. The research results reveal the interactive relationship between individual physiological status and experience level in risk cognition, providing theoretical basis and empirical support for improving miners’ risk identification ability and formulating differentiated intervention strategies.

(2) Revealed the differences in risk perception among miners of different ages and their dynamic evolution patterns. Integrating safety science, psychology, and cognitive science theories, using grounded theory to extract factors influencing miners' risk perception, and dividing them into core dimensions; Based on a sample of miners of all ages in coal mines, a questionnaire survey was conducted to analyze the interaction paths between individual safety status, safety literacy, organizational support, and risk perception through structural equation modeling SEM correlation path analysis. Combined with K-means clustering algorithm, the moderating effect of age on risk perception and group heterogeneity characteristics were quantified, and a risk perception feature label system for young and older miners was constructed; By conducting independent sample t-test, significant sensitive labels of risk perception between young and older miners are examined, and based on this, risk perception feature profiles of miners of different ages are constructed.Research has found that the risk perception of young miners presents a dual driving mode of ‘emotion environment’. The psychological state of restlessness, tension, and toxic and harmful gases in the work environment significantly positively drive risk perception, while the negative moderating effect of organizational support reveals their high sensitivity to management effectiveness; Older miners exhibit a ‘experience knowledge’ collaborative driving mode, where safety literacy and accident detail warning enhance risk assessment through cognitive framework reconstruction. However, chronic fatigue in physiological states and concerns about loss of control in psychological states lead to delayed risk response; Cluster analysis based on risk perception sensitive indicators is used to “draw” age stratified risk perception feature portraits, dividing young miners into imbalanced high-sensitivity miners and rule free miners, and older miners into knowledge deficiency miners, consciousness alienation miners, and attitude blunting miners. This article constructs an evaluation framework for miners’ risk perception by integrating interdisciplinary theories and large sample analysis methods, providing a breakthrough in theoretical models from ‘group universality’ to "individual precision" for mine safety management.

(3) Identification of age-related cognitive neural characteristics of miners’ risk perception. Across all experimental tasks, the left prefrontal cortex demonstrated significant sensitivity to risk perception states, serving as a key brain region for distinguishing whether miners possess risk awareness. When miners were in a state of risk perception, the HbO concentration t-values in the left prefrontal cortex significantly increased, whereas they notably decreased when no risk perception was present. Further analysis revealed significant differences in brain activation between miners of different age groups under risk perception conditions. Younger miners exhibited stronger neural responses in the left prefrontal cortex under high-risk scenarios, reflected by higher t-values, indicating a greater capacity for processing risk-related information. Moreover, in the absence of risk perception, significant differences were observed in the mean HbO concentrations in prefrontal regions corresponding to channels 8 and 13 between younger and older miners, suggesting that age plays a critical role in the neural processing of risk perception. This study unveils structural differences in risk perception abilities among miners of different ages at the neural level, providing empirical support for understanding individual risk response mechanisms in high-risk operations.

(4) Build a risk perception state recognition model for miners of different ages and verify the feasibility of the model. Using ReliefF algorithm and Kruskal Wallis algorithm, key feature indicators of multi-source heterogeneous data related to miners’ risk perception status were selected. Four machine learning classifiers, DT, SVM, EL, and NN, were used to train 4-classification and 2-classification recognition models for risk perception status of older and younger miners, respectively. The high-precision models obtained by 5-fold cross validation were evaluated, and the robustness of the optimal models of the four classifiers was tested on the test set. The results showed that among the trained miners’ risk perception state 4-classification and 2-classification optimal models, the EL model had the highest average accuracy of 40.16% for 4-classification, and the SVM model had the highest average accuracy of 90.42% for 2-classification; The classification results of the risk perception state recognition model for miners of different ages constructed in this study indicate that using wearable portable fNIRS for miner risk perception state recognition is feasible.

In summary, this study analyzes the mechanism by which age-related factors influence risk perception bias, identifies the impact pathways of physiological degradation and experience accumulation across different age groups, establishes a characteristic labeling system for the risk perception of both younger and older miners, draws a risk perception feature label system for young miners and older miners, constructs a risk perception state recognition model for miners, expands the theoretical and practical perspectives of neural safety management, and helps promote the transformation of coal mine safety management mode from extensive post result management to precise pre prevention.

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