基于RFID的无源感知技术在生产线动作优化中应用研究  

Research on the Application of RFID-based Passive Sensing Technology to Action Optimization in the Production Line

作　　者：王成鑫 WANG Chengxin

机构地区：[1]上海交通大学中美物流研究院,上海200030

出　　处：《科技创新与应用》2022年第12期1-10,共10页Technology Innovation and Application

摘　　要：目前,随着新兴技术的发展,人类迈入第四次工业革命,即工业4.0时代。传统的生产制造企业也逐渐向“智能工厂”转型。物联网是工业4.0的关键支柱,通过人和机器之间的信息交换提取数据,分析数据之后,可以显著提高企业生产线的生产率和安全性等,并可以解决质量问题、效率问题和劳动力管理等一系列问题。目前针对生产线的流程和动作优化的研究大多都是运用时间动作研究等传统方法,也有少数运用摄像头图像识别等。这些方法往往都需要耗费大量的人力和物力资源,且效果无法保证。生产制造企业以利润为核心,提高成本则意味着利润就会减少。近几年随着射频识别技术(RFID)的发展,给我们提供一种新的方法和可能。为此,文章介绍一种将基于RFID的无源感知技术引入传统的工业领域,并采取深度学习的方法,对特定生产线的效率问题和劳动力管理进行优化和改进。文章中展示一个真实的装配案例,从数据采集、数据分析、关键指标计算及流程优化与改进等全方面提出一个完整的物联网解决方案,并总结研究成果和经验教训。At present,with the development of emerging technology,mankind has entered the fourth industrial revolution,that is,the era of industrial 4.0.The traditional manufacturing enterprises in the manufacturing industry are also gradually transforming to"smart factories".The Internet of Things is the key pillar of Industry 4.0.After extracting data through the exchange of information between people and machines,and analyzing these data,we can significantly improve the productivity and safety of the enterprise production line,andthereby solve a series of problems such as quality,efficiency,and labor management.At present,most of the researches on the process and action optimization of the production line are based on traditional methods such as time action research,and a few use camera image recognition and so on.These methods often require a lot of human and material resources,and the effect can not be guaranteed.Manufacturing enterprises take profit as the core,and increasing costs means that profits will be reduced.In recent years,with the development of radio frequency identification technology(RFID),it provides us with a new method and possibility.For this reason,this paper introduces a RFID-based passive sensing technology into the traditional industrial field,and adopts the method of deep learning to optimize and improve the efficiency and labor management of a specific production line.This paper shows a real assembly case,puts forward a complete solution of the Internet of Things from the aspects of data collection,data analysis,key index calculation,process optimization and improvement,and summarizes the research results and lessons learned.

关 键 词：RFID 无源感知 深度学习 生产线 效率问题 劳动力管理 动作识别与研究 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]