基于机器学习的激光通信设备状态分类和识别方法  

Classification and recognition method of laser communication equipment status based on artificial intelligence technology

作　　者：吴玲红[1] 王葵[1] WU Linghong;WANG Kui(School of Computer Information Engineering,Nanchang Institute of Technology,Nanchang 330044,China)

机构地区：[1]南昌理工学院计算机信息工程学院,南昌330044

出　　处：《激光杂志》2024年第11期145-150,共6页Laser Journal

基　　金：江西省教育厅科学技术研究项目(No.GJJ212125)。

摘　　要：物联网中,激光通信设备的状态识别对于数据传输与调度的准确性至关重要。当前,设备状态主要依赖DCS设备中的控制器和传感器,通过设定单一阈值来判断。但随着设备复杂性的增加,这种方法的准确性受到影响。为此,研究了一种基于机器学习的激光通信设备状态分类与识别方法。采用时间序列滑动窗口模式,划分激光通信设备状态特征向量;以具有告警作用的特征属性,定义激光通信设备异常状态等级;基于机器学习融合告警特征,构建激光通信设备状态识别模型。实验结果表明:以不同类型的激光通信设备作为测试对象,分别在不同场景中设置其故障状态,所研究方法可以实现各个场景内测试设备的状态识别,具有应用价值。In the Internet of Things,the state recognition of laser communication equipment is crucial for the accuracy of data transmission and scheduling.Currently,the device status mainly relies on the controllers and sensors in the DCS equipment,which are determined by setting a single threshold.But as the complexity of the equipment increases,the accuracy of this method is affected.To this end,a machine learning based method for laser communication equipment state classification and recognition was studied.Using time series sliding window mode to partition the state feature vectors of laser communication equipment;Define the abnormal status level of laser communication equipment based on its characteristic attributes that have an alarm effect;Building a laser communication equipment state recognition model based on machine learning fusion of alarm features.The experimental results show that by using different types of laser communication equipment as test objects and setting their fault states in different scenarios,the research method can achieve state recognition of test equipment in various scenarios,which has practical value.

关 键 词：机器学习 激光通信设备 分类方法 识别方法 设备状态 

分 类 号：TN911[电子电信—通信与信息系统]