基于时空图卷积网络的瓦斯体积分数预警效果研究
Research on performance of gas volume fraction early-warning based on spatio-temporal graph convolutional network
高翼飞 1张晓航 1畅明 2葛帅帅 3陈伟4
作者信息
- 1. 北京邮电大学 经济管理学院,北京 100876
- 2. 运城职业技术大学 信息技术应用创新学院,山西 运城 044031
- 3. 运城职业技术大学 继续教育学院,山西 运城 044031
- 4. 运城职业技术大学 建筑工程学院,山西 运城 044031
- 折叠
摘要
为了提升瓦斯体积分数预警效果,提出1 种融合时空特征的瓦斯体积分数预警模型(STGCN),以图神经网络作为基本框架对同一工作面多传感器进行统一的训练和推断,并通过图卷积的方式捕捉瓦斯体积分数数据的时空特征.在此基础上,提出瓦斯体积分数分级预警方法,将预测扩展为分级预警.研究结果表明:STGCN在瓦斯体积分数预测和预警任务上取得更好的准确率和效率.研究结果可为矿井瓦斯灾害防治提供参考.
Abstract
To enhance the performance of gas volume fraction early-warning,a gas volume fraction early-warning model in-tegrating the spatio-temporal features(STGCN)was proposed.The graph neural network was taken as the foundational frame-work for unified training and inference of multiple sensors on the same working face,and the spatio-temporal characteristics of gas volume fraction data were captured through graph convolution.On this basis,a grading early-warning method of gas volume fraction was proposed,which extended the prediction to the grading early-warning.The results show that STGCN achieves bet-ter accuracy and efficiency in gas volume concentration prediction and early-warning tasks.The research results can provide a reference for the prevention and control of mine gas disasters.
关键词
瓦斯体积分数预警/图卷积网络/时空数据/可学习矩阵/分级预警方法/煤矿安全Key words
gas volume fraction early-warning/graph convolutional network/spatial-temporal data/learnable matrix/grad-ing early-warning method/coal mine safety引用本文复制引用
基金项目
国家自然科学基金项目(72271034)
北京邮电大学博士研究生创新基金项目(CX2021132)
出版年
2024
NETL
NSTL
维普
万方数据