煤矿中的气体爆炸预警方法通过智能采矿系统和多变量数据分析
Hongxia Li1, Yiru Zhang2,3, Wanli Yang4
1College of Management, Xi'an University of Science and Technology, Xi'an, Shaanxi, China.
PloS one
|November 2, 2023
概括
这项研究开发了一种随机森林模型,用于使用智能采矿数据预测煤矿天然气爆炸. 优化的模型实现了100%的准确性,显著优于SVM模型,提高了矿山安全.
科学领域:
- 采矿工程 采矿工程 采矿工程
- 数据科学数据科学数据科学
- 预防灾害 预防灾害
背景情况:
- 气体爆炸灾害对煤矿安全构成重大威胁.
- 现有的预警系统往往缺乏有效预防灾害所需的准确性和速度.
- 智能采矿系统产生大量的实时数据,这对于提高安全至关重要.
研究的目的:
- 开发和验证煤矿煤气爆炸的高精度和快速预警模型.
- 为了提高安全管理,利用智能采矿系统的多维数据.
- 将随机森林模型的性能与支持矢量机 (SVM) 模型进行比较.
主要方法:
- 煤矿灾难系统的分解成造成灾难的因素,环境和易受伤害的身体.
- 建立气体爆炸早期预警指数系统.
- 使用实时数据 (矿山安全监测,人员定位,视频监控) 开发和优化随机森林分类模型.
- 使用Matlab软件的支持矢量机 (SVM) 模型进行比较分析.
主要成果:
- 优化的随机森林模型在预测气体爆炸灾难方面实现了100%的准确性.
- 支持矢量机 (SVM) 模型的准确度仅为75%.
- 与SVM相比,随机森林模型显示了较低的模型误差和相对误差.
- 案例研究证实了该模型的适用性和高性能.
结论:
- 优化的随机森林模型提供了一种高效和准确的方法,用于早期预警煤矿气爆炸.
- 将智能采矿数据与随机森林等先进算法集成,可以显著提高煤矿安全管理.
- 这种方法提供了一种新而强大的工具,用于采矿业务中积极预防灾害.
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