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顾闯. 煤炭企业工控网络安全防护与预测方法研究[J]. 煤炭科学技术, 2019, (11).
引用本文: 顾闯. 煤炭企业工控网络安全防护与预测方法研究[J]. 煤炭科学技术, 2019, (11).
GU Chuang. Study on safety protection and prediction method of industrial control network in coal enterprises[J]. COAL SCIENCE AND TECHNOLOGY, 2019, (11).
Citation: GU Chuang. Study on safety protection and prediction method of industrial control network in coal enterprises[J]. COAL SCIENCE AND TECHNOLOGY, 2019, (11).

煤炭企业工控网络安全防护与预测方法研究

Study on safety protection and prediction method of industrial control network in coal enterprises

  • 摘要: 针对煤矿企业工控网络的安全防护和预警在煤矿安全生产中的重要性,分析了煤矿工控网络安全防护的3个主要区域:井上安全防护体系、井下传输通信、井下控制执行及相关安全防护策略,并提出在数字监控系统网络安全防护中,尝试引入大数据分析的思路,应用LM神经网络预测方法,利用网络数据的包方向、包间隔、CAN协议数据位为体征信息集合,建立网络异常预测系统的架构和模型程序设计。并通过试验结果表明:通过对3层神经网络的隐含层神经元数量由3个分别增加到5个和7个之后,该模型趋近稳定、预测精度误差趋近0.007%,能够较好地对监控系统网络异常做出分类,以实现预测预警功能。

     

    Abstract: According to important of safety protection and early warning of industrial control network safety production in coal mine,this paper analyzes three main areas of coal mine industrial control network security protection:the safety protection system on the well,the underground transmission and communication,the implementation of underground control and related safety protection strategies.In the network security protection of digital monitoring system,we try to introduce the idea of large data analysis,using the LM neural network prediction method,using the packet direction of network data,the packet interval,the data bit of the CAN protocol as collection of information,and establish the architecture and model program design of the network anomaly prediction system.The experimental results show that trough increasing the number of hidden layer neurons in the 3 layer neural network from 3 to 5 and 7,the model is stable and the error of prediction accuracy is close to 0.007%.It can classify the network anomaly of the monitoring system well to realize the prediction and early warning power.

     

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