电子测量与仪器学报

程王峰,郑晓亮,薛生.多视窗低浓度瓦斯燃烧状态协同监测[J].电子测量与仪器学报,2026,40(5):30-41

多视窗低浓度瓦斯燃烧状态协同监测

Multi-window coordinated monitoring of low-concentrationgas combustion states

DOI：

英文关键词:low-concentration gas direct combustion system combustion state multi-window monitoring model embedded platform

基金项目:十四五国家重点研发计划课题(2023YFB3211003)、安徽省高校协同创新项目(GXXT-2022-017)资助

作者	单位
程王峰	1.安徽理工大学深部煤炭安全开采与环境保护全国重点实验室淮南232001；2.安徽理工大学煤炭安全精准开采国家地方联合工程研究中心淮南232001；3.安徽理工大学电气与信息工程学院淮南232001
郑晓亮	1.安徽理工大学深部煤炭安全开采与环境保护全国重点实验室淮南232001；2.安徽理工大学煤炭安全精准开采国家地方联合工程研究中心淮南232001；4.安徽理工大学公共安全与应急管理学院合肥231131
薛生	1.安徽理工大学深部煤炭安全开采与环境保护全国重点实验室淮南232001；2.安徽理工大学煤炭安全精准开采国家地方联合工程研究中心淮南232001

Author	Institution
Cheng Wangfeng	1.National Key Laboratory of Deep Coal Safety Mining and Environmental Protection, Anhui University of Science and Technology, Huainan 232001, China; 2.National and Local Joint Engineering Research Center for Safe and Precise Coal Mining, Anhui University of Science and Technology, Huainan 232001, China; 3.School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China
Zheng Xiaoliang	1.National Key Laboratory of Deep Coal Safety Mining and Environmental Protection, Anhui University of Science and Technology, Huainan 232001, China; 2.National and Local Joint Engineering Research Center for Safe and Precise Coal Mining, Anhui University of Science and Technology, Huainan 232001, China; 4.School of Public Safety and Emergency Management, Anhui University of Science and Technology, Hefei 231131, China
Xue Sheng	1.National Key Laboratory of Deep Coal Safety Mining and Environmental Protection, Anhui University of Science and Technology, Huainan 232001, China; 2.National and Local Joint Engineering Research Center for Safe and Precise Coal Mining, Anhui University of Science and Technology, Huainan 232001, China

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中文摘要:

针对煤矿低浓度瓦斯直燃系统，传统温度探针与离子棒等传感器不足以准确反映燃烧过程中的细微波动，针对此问题提出了多视窗低浓度瓦斯燃烧状态协同监测策略。首先利用不同视窗的高速摄像机（high speed camera, HSC）和高清摄像机（high definition camera, HDC）构建低浓度瓦斯燃烧状态数据集，对数据集进行高斯模糊等数据增强扩展数据集多样性，提升模型的适应性；进一步构建低浓度瓦斯燃烧状态监测模型，分别建立主干模型、颈部模型、头部模型，主干模型提升对瓦斯燃烧状态局部特征的提取能力；颈部模型增强对瓦斯燃烧动态变化趋势；头部模型优化对瓦斯燃烧状态特征提取效率，减少冗余特征的提取；最后在模型的参数量满足部署的前提，将低浓度瓦斯燃烧状态监测模型部署至嵌入式平台，实时监测瓦斯复杂的燃烧状态变化。实验结果表明，低浓度瓦斯燃烧状态识别模型精确率为96.11%、准确率为95.80%、监测速率为87.5 fps，模型的参数量为40.1×106，误差均值不超过0.16，部署在嵌入式平台的精确率均值为96.10%，监测速度均值为30.10 fps。基本满足低浓度瓦斯直燃系统瓦斯燃烧状态监测需求的精确性、实时性、适应性，具备一定的工业应用价值。

英文摘要:

For the low-concentration gas direct combustion system in coal mines, traditional sensors such as temperature probes and ionization rods are insufficient to accurately reflect the subtle fluctuations in the combustion process. To address this problem, a collaborative monitoring strategy for multi-window low-concentration gas combustion states is proposed. Firstly, a low-concentration gas combustion state dataset is constructed using high-speed cameras (HSC) and high-definition cameras (HDC) with different viewing windows. Data enhancement analysis, such as Gaussian blur, is performed on the dataset to expand its diversity to improve the adaptability of the model. Further, a low-concentration gas combustion state monitoring model is constructed, and backbone, neck, and head models are established, respectively. The backbone model improves the ability to extract local features; the neck model enhances the sensitivity to the dynamic change trend; the head model optimizes the efficiency of gas combustion state feature extraction and reduces the extraction of redundant features. Finally, on the premise that the model parameter quantity meets the deployment requirements, the low-concentration gas combustion state monitoring model is deployed to the embedded platform to monitor the complex combustion state changes of the gas in real-time. The experimental results show that the precision of the low-concentration gas combustion state monitoring model is 96.11%, the accuracy is 95.80%, the monitoring rate is 87.5 fps, the number of model parameters is 40.1×106, the average error is no more than 0.14, the average precision of the model deployed on the embedded platform is 96.10%, and the average monitoring speed is 30.10 fps. It meets the precision, real-time, and adaptability requirements of the gas combustion state monitoring of the low-concentration gas direct combustion system, and has certain industrial application value.

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