基于5G+AI技术的智慧电厂安全风险线上边云协同检测方法

Online edge cloud collaborative detection method for security risks of smart power plants based on 5G+AI technology

智慧电厂是工业互联网的重要组成部分,伴随着智能、信息化程度的不断提升,在智慧电厂的建设和运行过程中,会出现一系列的安全风险,影响智慧电厂的稳定运行。为此,提出基于5G+AI技术的智慧电厂安全风险线上边云协同检测方法。基于5G+AI技术,结合边云协同技术,设计5G+AI技术下智慧电厂安全风险线上边云协同检测模型架构。采用加权平均法,灰度化处理智慧电厂安全风险图像。利用边缘计算与云计算结合的MapReduce并行编程模型,提取智慧电厂安全风险特征。采用YOLO-v1算法,构建智慧电厂安全风险检测模型并进行训练,确定模型最佳参数取值,输出智慧电厂安全风险检测结果,实现智慧电厂安全风险线上边云协同检测。实验结果表明,所研究方法的智慧电厂安全风险线上边云协同检测效果较好,能够有效提高智慧电厂安全风险线上边云协同检测精度和效率。

Smart power plants are an important part of the Industrial Internet.With the continuous improvement of intelligence and informatization,a series of safety risks will appear during the construction and operation of smart power plants,affecting the stable operation of smart power plants.To this end,a collaborative online edge cloud detection method for safety risks in smart power plants based on 5G+AI technology is studied.Based on 5G+AI technology and combined with edge cloud collaboration technology,design an online edge cloud collaborative detection model architecture for safety risks in smart power plants using 5G+AI technology.The weighted average method is used to grayscale the safety risk images of smart power plants.The MapReduce parallel programming model combining edge computing and cloud computing is used to extract the security risk characteristics of smart power plants.Using the YOLO-v1 algorithm,a smart power plant safety risk detection model is constructed and trained to determine the optimal parameter values of the model,output the smart power plant safety risk detection results,and achieve online and cloud collaborative detection of smart power plant safety risks.The experimental results show that the proposed method has good performance in online edge cloud collaborative detection of safety risks in smart power plants,and can effectively improve the accuracy and efficiency of online edge cloud collaborative detection of safety risks in smart power plants.