煤矿工作面的气体多指标警告方法的研究基于MOA变压器
Huan Yang1,2, Jian Wang1,2, Haoliang Zhang3
1Changzhou Research Institute Co., Ltd., China Coal Technology and Engineering Group, Changzhou 213000, China.
ACS omega
|May 27, 2024
概括
本研究引入了煤矿的新型气体多指标预警系统,通过分析实时度,波动和趋势来提高安全性. 先进的方法显著提高了在危险环境中警告的准确性和可靠性.
科学领域:
- 采矿工程 采矿工程 采矿工程
- 计算智能是一种计算智能.
- 安全科学安全科学 安全科学
背景情况:
- 煤矿气体排放是复杂的,受到多种因素的影响,导致独立但相互关联的气体度动态.
- 现有的预警系统往往无法全面捕捉实时值,波动和趋势变化,造成安全风险.
- 准确和及时的气体预警对于防止地下煤矿开采事故至关重要.
研究的目的:
- 为煤矿工作面开发一个全面的气体多指标预警方法.
- 改进气体预警级别的动态值分区.
- 通过先进的机器学习技术,提高气体预警系统的准确性和可靠性.
主要方法:
- 提出了一个智能值分区算法,使用基因固定中心K-平均值 (GF-KMeans),结合基因算法 (GA) 和FC-KMeans.
- 开发了一种基于多头最佳注意力 (MOA) 变压器架构的多指标气体警告方法.
- 利用GA克服K-means中的局部最佳值,增强稳定性,并使用FC-KMeans进行精确的初始集群.
主要成果:
- MOA-变压器方法的准确度为86.17%,精度为88.78%,回忆率为85.23%,宏F1为86.96%.
- 与标准的变压器方法相比,显著改进,精度增加了3.45%,精度增加了3.75%,回忆率增加了4.70%,宏观F1.39的回忆率增加了4.39%.
- GF-KMeans算法为气体预警级别提供了动态和稳定的值分区.
结论:
- 拟议的气体多指标预警方法,特别是MOA变压器,为煤矿安全提供了卓越的性能.
- 智能值和先进的注意力机制的整合提供了一个更强大,更可靠的气体预警系统.
- 这项研究通过改善气体监测和预警能力,有助于加强煤炭开采的安全协议.
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