油气物联网数据污染检测算法研究  

Research on Detection Algorithm of Oil and Gas IoT Data Contamination

作　　者：郭亚茹 刘苗[2,3] 聂中文 GUO Yaru;LIU Miao;NIE Zhongwen(College of Physics and Electronic Engineering,Northeast Petroleum University,Daqing 163318,China;Qinhuangdao Campus,Northeast Petroleum University,Qinhuangdao 066044,China;School of Electronic Information Engineering,Wuxi University,Wuxi 210044,China;Smart Energy Institute,Shanghai Gas Engineering Design and Research Company Limited,Shanghai 200120,China)

机构地区：[1]东北石油大学物理与电子工程学院,黑龙江大庆163318 [2]东北石油大学秦皇岛校区,河北秦皇岛066044 [3]无锡学院电子信息工程学院,江苏无锡210044 [4]上海燃气工程设计研究有限公司智慧能源院,上海200120

出　　处：《吉林大学学报（信息科学版）》2024年第2期307-311,共5页Journal of Jilin University（Information Science Edition）

基　　金：黑龙江省自然科学基金资助项目(LH2022F004)。

摘　　要：针对油气物联网(OGIoT:Oil and Gas Internet of Things)连接设备的数量暴增导致边缘计算(EC:Edge Computing)系统中的边缘节点算力不足,且难以有效识别其他边缘节点的恶意攻击而导致的服务崩溃问题,提出针对油气物联网数据污染检测改进的高效机器学习算法(EMLDI:Efficient Machine Learning Method for Improved Data Contamination Detection of Oil and Gas IoT),解决了因边缘节点鲁棒性不强,数据失真或遭到轻度质变导致边缘节点运算结果波动大且不准确问题。通过随机选择批量样本加入高斯噪声(GN:Gaussian Noise)扩充数据集训练网络,使网络具有更宽泛的数据拟合能力和预测能力,解决了数据被严重破坏时边缘节点难以实施正确运算导致系统性崩溃问题。实验结果表明,该算法能更有效地识别噪声污染以及随机标签污染的样本,并且算法在规定的训练批次内能达到最好的效果。In order to address the problem that the number of connected devices in the OGIoT(Oil and Gas IoT)has increased dramatically,resulting in insufficient computing power of the edge nodes in the EC(Edge Computing)system,and it is difficult to effectively identify the service collapse caused by malicious attacks from other edge nodes,an EMLDI(Efficient Machine Learning method for Improved Data Contamination Detection of Oil and Gas IoT algorithm)is proposed,which solves the problem of fluctuating and inaccurate results of edge nodes due to their poor robustness,data distortion or mild qualitative changes.The problem of large and inaccurate edge node results due to robustness of edge nodes and data distortion or mild qualitative changes is solved.The network is trained by adding GN(Gaussian Noise)to the expanded data set through randomly selected batch samples,which enables the network to have broader data fitting and prediction capabilities,and solves the problem of systemic collapse due to the difficulty of implementing correct operations at the edge nodes when the data is severely corrupted.The algorithm is able to identify noise contaminated and random label contaminated samples more effectively and the algorithm achieves the best results within the specified training batches.

关 键 词：油气物联网 高斯噪声 数据污染 机器学习 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]