在科技迅速发展的今天，我国煤矿事故的总量虽有所下降，但平均死亡人数却呈逐年上升趋势，煤矿安全形势依然严峻。现有研究表明，不安全行为是导致煤矿事故频发的核心因素，有效控制该行为是降低事故率的关键。在煤矿作业环境中，井下噪声是普遍存在的一大挑战，对工人的心理和生理具有重要影响。然而，现有研究对于噪声如何影响个体不安全行为决策仍存在争议，尤其缺乏在不同噪声环境下，通过脑电和行为特征来预测个体不安全行为的相关研究。鉴于此，本文聚焦于探究不同风险情境下，井下噪声对个体不安全行为决策的影响，旨在揭示噪声的影响机制，并构建用于预测个体不安全行为发生的监督机器学习模型，从而提升煤矿安全管理水平。

本论文以探索井下噪声对煤矿工人不安全行为的影响机制为核心，基于双系统理论和风险情感理论，建立了解释井下噪声诱发不安全行为的理论模型。其次，论文通过设计模拟风险情境的实验任务，采集了被试的行为数据和脑电数据。最后，利用构建的脑电-行为特征数据集，通过统计分析验证了理论模型与假设的有效性，并应用支持向量机和随机森林算法构建了预测模型，得到以下主要结果：（1）井下噪声显著影响个体行为决策的晚期阶段，导致决策冲突和情绪强度受到干扰，进而影响个体不安全行为决策；（2）在高水平噪声环境下，个体的P3和LPP波幅降低，表明个体的决策过程和情绪强度受到显著干扰，导致其对行为的收益与损失的评估变得模糊，引发非理性和犹豫的行为；（3）经由网格搜索优化的随机森林模型，在交叉验证和分类报告中展现出93.20%的高准确率和96.00%的综合得分，证明了模型的优秀泛化能力和分类性能；（4）针对线性判别分析降维后的随机森林模型，本文创新性地提出了协同特征重要性的概念和计算方法，旨在解开模型的“黑箱”，并将之应用于脑电活动特征的模式识别。

综上所述，本文深入分析了井下噪声对煤矿工人不安全行为的心理及神经机制，建立了一个有效的行为预测模型，并提出了模型解释的新方法，为煤矿安全管理领域提供了新的研究思路和见解。

In the rapid development of technology today, although the total number of coal mine accidents in our country has decreased, the average number of deaths has shown an upward trend year by year, indicating that the safety situation in coal mines remains severe. Existing research indicates that unsafe behavior is the core factor leading to frequent coal mine accidents, and effectively controlling this behavior is key to reducing accident rates. In the coal mining work environment, underground noise is a common challenge with significant impacts on workers' psychological and physiological states. However, existing research on how noise affects individual unsafe behavior decisions remains controversial, particularly lacking studies predicting individual unsafe behavior through EEG and behavioral characteristics in different noise environments. In view of this, this paper focuses on exploring the impact of underground noise on individual unsafe behavior decisions in different risk situations, aiming to reveal the mechanisms of noise impact and to construct a supervised machine learning model for predicting individual unsafe behavior, thereby enhancing coal mine safety management.

This paper centers on exploring the mechanism by which underground noise influences coal miners' unsafe behavior, based on dual-system theory and risk emotion theory, establishing a theoretical model explaining how underground noise induces unsafe behavior. Secondly, through designing experimental tasks that simulate risk situations, the study collected behavioral data and EEG data from the subjects. Finally, using the constructed EEG-behavioral feature dataset, statistical analysis was conducted to verify the validity of the theoretical model and hypotheses. Support Vector Machine (SVM) and Random Forest algorithms were applied to build the prediction model, yielding the following main results: (1) Underground noise significantly affects the late stages of individual behavioral decision-making, causing decision conflict and emotional intensity interference, thereby impacting individual unsafe behavior decisions; (2) In high-noise environments, individuals' P3 and LPP amplitudes decrease, indicating significant interference in decision-making processes and emotional intensity, leading to blurred evaluations of behavioral gains and losses, causing irrational and hesitant behaviors; (3) The Random Forest model optimized by grid search exhibited a high accuracy rate of 93.20% and a comprehensive score of 96.00% in cross-validation and classification reports, demonstrating excellent generalization ability and classification performance; (4) For the Random Forest model after linear discriminant analysis dimensionality reduction, this paper innovatively proposed the concept and calculation method of collaborative feature importance, aiming to uncover the model's "black box" and apply it to pattern recognition of EEG activity features.

In summary, this paper deeply analyzes the psychological and neural mechanisms of underground noise on coal miners' unsafe behavior, establishes an effective behavior prediction model, and proposes a new method for model interpretation, providing new research ideas and insights for the field of coal mine safety management.

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