混合CNN变压器用于分类煤矿重大事故危险
Qiang Tu1, Yijiang Zong1, Zequan Li2
1School of Transportation Engineering, Jiangsu Vocational Institute of Architectural Technology, Xuzhou, China.
Scientific reports
|September 1, 2025
概括
新的混合CNN变压器模型显著提高了煤矿事故危险分类的准确性. 这种先进的人工智能方法通过更好地识别重大和小危险,减少手工工作量来增强安全系统.
科学领域:
- 采矿工程
- 人工智能
- 数据科学
背景情况:
- 中国的煤矿企业使用安全信息系统,但未充分利用事故危险数据.
- 现有的系统缺乏有效的方法来根据既定标准对各种事故危险进行分类.
研究的目的:
- 开发和评估用于分类煤矿事故危险的先进人工智能模型.
- 提高安全生产系统中危险识别的准确性和效率.
主要方法:
- 根据中国紧急情况管理部的2021年标准,建立了15个主要和79个次要危险类别的分类系统.
- 采用混合卷积神经网络 (CNN) 变压器模型来对事故危险数据进行层次性文本分类.
- 使用变压器的双向编码器表示 (BERT) 模型作为比较的基线.
主要成果:
- 与BERT相比,混合CNN变压器模型在重大危险分类中获得了更高的准确性,回忆力和F1分数 (提高了3%).
- 混合模型在轻微危险分类中达到98%的性能,通常超过BERT模型.
- 该模型表现出更快的融合,需要比BERT更少的计算资源,F1得分高达3-4%.
结论:
- 混合CNN变压器模型为煤矿事故危险分类提供了显著的有效性.
- 这种方法为危险识别系统提供了有效和快速的输入,提高了矿山的安全性.
- 该模型的部署在操作设置中减少了约30%的手动危险分类工作量.
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