实验室安全ISBOA-KELM多传感器数据融合预警模型

doi:10.16265/j.cnki.issn1003-3033.2026.01.1133

中国安全科学学报 ›› 2026, Vol. 36 ›› Issue (1): 63-71.doi: 10.16265/j.cnki.issn1003-3033.2026.01.1133

实验室安全ISBOA-KELM多传感器数据融合预警模型

葛亮¹^,²(), 周女青¹, 车洪磊³, 肖国清⁴, 赖希¹, 曾文⁵

¹ 西南石油大学机电工程学院,四川成都 610500
² 西南石油大学油气藏地质及开发工程全国重点实验室,四川成都 6105003
³ 中国安全生产科学研究院,北京 100012
⁴ 西南石油大学化学化工学院,四川成都 610599
⁵ 重庆大学材料科学与工程学院,重庆 400045

收稿日期:2025-09-14 修回日期:2025-11-22 出版日期:2026-01-28
作者简介:
葛亮 (1985—),男,湖北咸宁人,博士,教授,主要从事油气安全监检测与控制等方面的研究。E-mail: cgroad@swpu.edu.cn。
车洪磊, 正高级工程师。
肖国清, 教授。
曾文, 教授。
基金资助:
国家自然基金面上项目(52374234); 省科技计划项目(2023ZHCG0020); 四川省国际港澳台科技创新合作项目(25GJHZ0475); 中国石油集团科学技术研究院项目(2025ZD1406800)

ISBOA-KELM multi-sensor data fusion model for early warning method in laboratory safety

GE Liang¹^,²(), ZHOU Nüqing¹, CHE Honglei³, XIAO Guoqing⁴, LAI Xi¹, ZENG Wen⁵

¹ School of Mechanical and Electrical Engineering, Southwest Petroleum University, Chengdu Sichuan 610500, China
² National Key Laboratory of Reservoir Geology and Development Engineering, Southwest Petroleum University, Chengdu Sichuan 6105003, China
³ China Academy of Safety Science and Technology, Beijing 100012, China
⁴ School of Chemistry and Chemical Engineering, Southwest Petroleum University, Chengdu Sichuan 610599, China
⁵ College of Materials Science and Engineering, Chongqing University, Chongqing 400045, China

Received:2025-09-14 Revised:2025-11-22 Published:2026-01-28

摘要/Abstract

摘要：

为解决传统实验室环境信息复杂、单传感器检测不准确且精度有限等问题,提出一种面向实验室安全的改进型鹭鹰优化算法(ISBOA)-核极限学习机(KELM)多传感器数据融合预警算法模型。首先,分析KELM的数据融合机制,并通过引入正则化项来有效缓解模型过拟合问题;然后,利用改进ISBOA对KELM中的正则化参数C和核参数σ进行自适应优化,构建ISBOA-KELM多传感器数据融合模型,从而避免人工选取KELM参数所导致的故障诊断准确率低的问题;最后,以模拟数据和试验数据为基础,分别与未改进的鹭鹰优化算法(SBOA)、粒子群算法(PSO)以及灰狼优化算法(GWO)进行性能对比分析。试验结果表明:ISBOA-KELM算法模型相较于其他3种模型准确率分别提高4%、3%、2%,且在实际测试实验室环境下火灾等4种情况的准确率均高于96%,漏报率低于6%,显著提升安全事故预警的可靠性与鲁棒性。

关键词: 实验室安全, 改进型鹭鹰优化算法(ISBOA), 核极限学习机(KELM), 多传感器数据融合, 智能预警

Abstract:

To address the challenges of complex data environments, low accuracy of single-sensor detection, and limited precision in traditional laboratory safety systems, this study presented a multi-sensor fusion early warning model based on an ISBOA and algorithm KELM. First, the KELM framework was employed to integrate heterogeneous sensor data and construct the warning model, where a regularization term was introduced to alleviate overfitting. Then, the improved ISBOA adaptively optimized the regularization coefficient C and kernel parameterσ of the KELM, thereby enhancing parameter robustness and diagnostic accuracy. Finally, simulation and experimental analyses were conducted using both synthetic and real laboratory datasets, and the proposed ISBOA-KELM model was compared with the unimproved Secretary Bird Optimization Algorithm (SBOA), Particle Swarm Optimization (PSO), and Gray Wolf Optimization (GWO) algorithms. The experimental results show that the ISBOA-KELM model improved accuracy by 4%, 3%, and 2%, respectively, compared with the other three models. In four representative laboratory safety scenarios, including fire and gas leakage, the detection accuracy exceeds 96% with the false negative rate below 6%, which significantly improves the reliability and robustness of safety accident early warning.

Key words: laboratory safety, improved secretary bird optimization algorithm (ISBOA), kernel extreme learning machine (KELM), multi-sensor data fusion, intelligent early warning

中图分类号:

X948

葛亮, 周女青, 车洪磊, 肖国清, 赖希, 曾文. 实验室安全ISBOA-KELM多传感器数据融合预警模型[J]. 中国安全科学学报, 2026, 36(1): 63-71.

GE Liang, ZHOU Nüqing, CHE Honglei, XIAO Guoqing, LAI Xi, ZENG Wen. ISBOA-KELM multi-sensor data fusion model for early warning method in laboratory safety[J]. China Safety Science Journal, 2026, 36(1): 63-71.

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事故类型	主要成因	造成后果
火灾	易燃化学品靠近热源; 操作不当引发明火	设备毁损、人员伤亡、火势蔓延
爆炸	可燃气体泄漏;反应剧烈未控温控压;器皿密封不当	爆炸冲击、实验室破坏、重大伤亡
气体泄漏	管道老化、密封失效、阀门操作错误	中毒、环境污染、人员健康受损
电气故障	超负荷运行、私拉乱接、线路老化	短路、触电、引发火灾
漏水	水管破裂、接头松动、无人值守操作	设备受潮、数据丢失、电气隐患

检测模型	参数类型	准确率	总体精度
SVM	火灾	72.5	84
	可燃气体泄漏	87.5
	电气故障	77.5
	渗水漏水	100
	正常状态	82.5
CNN	火灾	75	83.5
	可燃气体泄漏	92.5
	电气故障	80
	渗水漏水	95
	正常状态	75
ELM	火灾	72.5	81.5
	可燃气体泄漏	85
	电气故障	72.5
	渗水漏水	100
	正常状态	77.5
KELM	火灾	75	85
	可燃气体泄漏	87.5
	电气故障	82.5
	渗水漏水	100
	正常状态	80

事故类型	样本数	特征维数	状态标签
正常状态	1~200	10	1
火灾	201~400	10	2
可燃气体泄漏	401~600	10	3
电气故障	601~800	10	4
渗水漏水	801~1 000	10	5

序号	试验类型	试验次数	报警次数	准确率/%	未报警次数	漏报率/%	平均报警时间/ s
1	火灾	50	47	94	3	6	13.58
2	可燃气体泄漏	50	49	98	1	2	17.33
3	电气事故	50	48	96	2	4	11.6
4	渗水漏水	50	50	100	0	0	3.5

实验室安全ISBOA-KELM多传感器数据融合预警模型

ISBOA-KELM multi-sensor data fusion model for early warning method in laboratory safety

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