警觉度影响下矿工风险感知能力识别模型

doi:10.16265/j.cnki.issn1003-3033.2026.02.0465

中国安全科学学报 ›› 2026, Vol. 36 ›› Issue (2): 1-8.doi: 10.16265/j.cnki.issn1003-3033.2026.02.0465

• 安全科学理论与方法 • 下一篇

警觉度影响下矿工风险感知能力识别模型

田水承¹^,²(), 李红妍¹^,², 石炎彬³, 田方圆¹^,²^,⁴^,^**(), 王亚娟¹^,², 段梦菲¹^,²

¹ 西安科技大学安全科学与工程学院,陕西西安 710054
² 西安科技大学安全与应急管理研究所,陕西西安 710054
³ 洛阳钼业集团股份有限公司选矿二公司,河南栾川 471500
⁴ 西安科技大学管理学院,陕西西安 710054

收稿日期:2025-10-11 修回日期:2025-12-15 出版日期:2026-02-28
通信作者:
^** 田方圆(1990—),女,陕西西安人,博士,讲师,主要从事安全、管理、认知神经和工效学视域下的安全人因工程、应急管理和职业安全与健康等方面的研究。E-mail:tfy@xust.edu.cn。
作者简介:
田水承 (1964—),男,山东淄博人,博士,教授,主要从事安全与应急管理和行为安全方面的研究。E-mail:tiansc@xust.edu.cn。
基金资助:
国家自然科学基金青年科学基金资助(C类)(52504244); 教育部人文社会科学青年基金资助(23XJC630011); 教育部产学合作协同育人实践条件和实践基地建设项目(2507032817); 陕西省自然科学基础研究计划(2024JC-YBQN-0499)

Identification model of miners' risk perception ability under influence of alertness level

TIAN Shuicheng¹^,²(), LI Hongyan¹^,², SHI Yanbin³, TIAN Fangyuan¹^,²^,⁴^,^**(), WANG Yajuan¹^,², DUAN Mengfei¹^,²

¹ College of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an Shaanxi 710054, China
² Institute of Safety and Emergency Management, Xi'an University of Science and Technology, Xi'an Shaanxi 710054, China
³ The Second Company of Luoyang Molybdenum Group Co., Ltd., Luanchuan Henan 471500, China
⁴ College of Management, Xi'an University of Science and Technology, Xi'an Shaanxi 710054, China

Received:2025-10-11 Revised:2025-12-15 Published:2026-02-28

摘要/Abstract

摘要：

为探究矿工警觉度对风险感知的影响规律,基于文献调研,设计开展矿工警觉度测试与风险感知试验,采集被试的近红外信号、行为与外周生理数据,运用正态性检验、单因素方差分析(ANOVA)等方法,探究不同警觉度矿工的风险感知差异性;优选13项显著差异指标作为特征指标,引入正弦混沌映射麻雀搜索算法优化反向传播神经网络(Sine-SSA-BP),构建矿工风险感知能力分类识别模型。结果表明:矿工警觉度对风险感知能力存在显著影响,警觉度升高,矿工风险感知正确率显著提升;随着矿工警觉度的升高,矿工的背外侧前额叶区域以及额极区激活指数β值具有显著差异,皮肤电(EDA)中的皮肤电导平均值(SC_mean)显著升高,心率变异性(HRV)中的平均心跳间期(Mean_IBI)、心跳R-R间期标准差(SDNN)、连续心跳间期差值均方根(RMSSD)显著降低,平均心率(Mean_HR)显著升高;构建的矿工风险感知能力分类识别模型综合性能最优,准确率高达92.30%,模型具备较好的鲁棒性。

关键词: 警觉度, 矿工, 风险感知能力, 识别模型, 功能性近红外光谱技术(fNIRS), 正弦混沌映射麻雀搜索算法优化反向传播神经网络(Sine-SSA-BP)

Abstract:

In order to explore the influence of miners' alertness on risk perception ability, miners' alertness tests and risk perception experiments were designed and implemented. During the experiments, fNIRS, behavioral data, and peripheral physiological signals were collected. Methods such as the normality test and one-way analysis of variance (ANOVA) were applied to investigate the differences in risk perception ability among miners with different alertness levels. Thirteen significantly different indicators were selected as feature variables. Thirteen significant differential indicators were selected as feature indicators, and Sine-SSA-BP was introduced to construct a classification and recognition model for miners' risk perception ability. The results show that miners' alertness significantly affects their risk perception ability. With increasing alertness, the correct rate of risk perception improves notably. As the alertness level rises, significant differences appear in the activation index β values of the dorsolateral prefrontal cortex and frontopolar areas. The mean skin conductance (SC_mean) in electrodermal activity (EDA) increases significantly, while the mean inter-beat interval (Mean_IBI), standard deviation of normal to normal R-R intervals(SDNN), and root mean square of successive differences (RMSSD) in heart rate variability (HRV) decrease significantly, and mean heart rate (Mean_HR) increases. The constructed miners' risk perception ability classification and identification model based on the Sine-SSA-BP achieves an accuracy of 92.30%, demonstrating excellent overall performance and robustness.

Key words: alertness, miners, risk perception ability, identification model, functional near-infrared spectroscopy (fNIRS), sine chaotic mapping sparrow search algorithm-back propagation neural network (Sine-SSA-BP)

中图分类号:

X911安全心理学

田水承, 李红妍, 石炎彬, 田方圆, 王亚娟, 段梦菲. 警觉度影响下矿工风险感知能力识别模型[J]. 中国安全科学学报, 2026, 36(2): 1-8.

TIAN Shuicheng, LI Hongyan, SHI Yanbin, TIAN Fangyuan, WANG Yajuan, DUAN Mengfei. Identification model of miners' risk perception ability under influence of alertness level[J]. China Safety Science Journal, 2026, 36(2): 1-8.

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警觉度水平	RT范围
高警觉度	<I_m-0.5×T_s
中警觉度	[I_m-0.5×T_s,I_m+T_s]
低警觉度	>I_m+T_s

算法	准确率	精准率	召回率	F₁分数
BP₁	76.92	70.00	77.77	73.33
BP₂	76.92	80.00	80.00	80.00
SSA-BP₁	76.92	80.00	80.00	80.00
SSA-BP₂	84.61	81.82	90.00	85.26
Sine-SSA-BP₁	84.61	81.82	81.82	81.82
Sine-SSA-BP₂	92.30	90.91	90.91	90.91

警觉度影响下矿工风险感知能力识别模型

Identification model of miners' risk perception ability under influence of alertness level

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