融合RFID和激光信息技术的资源标签分类系统  

A library resource label classification system integrating RFID and laser information

作　　者：曹雪琦[1] 杨晓军 CAO Xueqi;YANG Xiaojun(Yan’an University,Yan'an Shaanxi 716000,China)

机构地区：[1]延安大学,陕西延安716000

出　　处：《激光杂志》2022年第7期164-168,共5页Laser Journal

基　　金：陕西省自然科学基金项目(No.2018JM6069)。

摘　　要：图书馆资源标签分类问题是众多学者研究方向之一,面对当前已有的图书馆资源标签分类系统中存在标签信息缺失等问题,设计融合RFID和激光信息的图书馆资源标签分类系统。该系统利用控制模块分别控制RFID读写模块和激光传感器模块获取图书馆资源标签的RFID信息和激光信息后,通过WSN网络协调器节点传输到数据中心模块,数据中心模块的卷积神经网络算法,以标签的RFID信息和激光信息作为输入,经全连接层特征提取和权重优化后,实现图书馆资源标签分类,并使用RS232接口传输到PC端。实验结果表明:该系统采集信息完整度为99.59%,信息采集能力较强;分类错误次数少;存在噪声干扰时,数据传输丢失率仅为0.296%,具有较强数据传输能力。The label classification of library resources is one of the research objectives of many scholars.In the face of the label information missing in the current existing library resource label classification system,a library resource label classification system that integrates RFID and laser information is designed.The system uses the control module to control the RFID reading and writing module and the laser sensor module to obtain the RFID information and laser information of the library resource tag,and then transmits it to the data center module through the WSN network coordinator node.The convolutional neural network algorithm of the data center module,taking the tag’s RFID information and laser information as input,after fully connecting the layer feature extraction and weight optimization,the library resource tag classification is realized,and the RS232 interface is used to transmit to the PC.The experimental results show that the completeness of the information collected by the system is 99.59%,and the information collection ability is strong;the number of classification errors is few;when there is noise interference,the data transmission loss rate is only 0.296%,which has strong data transmission ability.

关 键 词：RFID 激光传感器 读写器 图书馆 标签分类 卷积神经网络 

分 类 号：TN247[电子电信—物理电子学]