“互联网+大数据”背景下VI传感器实验教学系统的设计与实现  

Design and implementation of VI sensor experimental teaching system under the background of“Internet+big data”

作　　者：彭丽娟 PENG Lijuan(Xi’an Aeronautical Polytechnic,Xi’an 710089,China)

机构地区：[1]西安航空职业技术学院,西安710089

出　　处：《自动化与仪器仪表》2023年第6期111-114,119,共5页Automation & Instrumentation

基　　金：陕西高校网络思想政治工作中心首批研究课题《陕西高校思政类微信公众号发展策略研究》(2021SPWSKT-C-73);西安航空职业技术学院2020年度科研计划项目《新媒体时代“双高计划”建设院校提升新闻宣传力传播力影响力研究》(20XHSK-04)。

摘　　要：针对传统传感器实验课程存在内容抽象、学生动手实践机会少的问题,提出在互联网+大数据的背景下,从虚拟仿真教学角度入手,设计一个基于碰撞检测算法的VI传感器实验教学系统。首先,以VI传感器实验的实验设备作为研究对象,采用3dMax传感器模型构建和优化;然后通过软件对渲染后的模型进行动画合成;采用量子蚁群算法(QACA)进行虚拟现实碰撞增强检测,以提高检测精度;最后再利用Unity3D实现人机交互。实验结果表明,采样数据特征量为70002时,模型检测的损耗时间增加,但其检测率也随之提升,更利于完成准确的碰撞检测。蚁群规模为70时,碰撞检测率高达95.41%,检测效果最好。提出的量子蚁群算法的检测率为94%,对比于粒子群算法、DNN神经网络和LSTM网络的检测率明显更高,本算法的检测时间仅为280 ms,比另外三种算法耗时更低。系统应用表明,此系统可实现传感器实验的实操联系,具备较强的交互性,可提升学生的学习兴趣和专业水平。content and few practical opportunities for students in traditional sensor experiment courses,a VI sensor experiment teaching system based on collision detection algorithm is designed from the perspective of virtual sim-ulation teaching under the background of Internet+big data.First,the experimental equipment of VI sensor experiment is used as the research object,and use 3 dMax sensor model constructed and optimized;then synthesize the rendered model through Adobe Premiere and Adobe After Effects software;quantum ant colony algorithm(QACA)is used to detect virtual reality collision enhancement to im-prove the detection accuracy;and finally use Unity3D to realize human-computer interaction.The experimental results show that when the feature quantity of sampling data is 70002,the loss time of model detection increases,but the detection rate also increases,which is more conducive to the accurate collision detection.When the ant colony size was 70,the collision detection rate was as high as 95.41%,with the best detection effect.The detection rate of the proposed quantum ant colony algorithm is 94%,which is signifi-cantly higher than that of the particle swarm algorithm,DNN neural network and LSTM network.The detection time of this algorithm is only 280ms,which is less time-consuming than the other three algorithms.The system application shows that this system can real-ize the practical connection of sensor experiment,with strong interactivity,and can improve students'learning interest and profession-profession-al level.

关 键 词：VI传感器 虚拟现实 实验教学 碰撞检测 量子蚁群算法 

分 类 号：TP392[自动化与计算机技术—计算机应用技术]