云边页协同的WebBIM大场景多粒度兴趣加载调度算法

CEB-Collaboratively Multi-Granularity Interest Scheduling Algorithm for Loading Large WebBIM Scene

当今5G与VR日益兴起,在移动网页浏览器上展示大规模建筑信息模型(BIM)场景的需求越来越多.网页浏览器计算性能弱与网络延迟严重等问题,使大规模WebBIM场景的在线可视化具有较大的挑战性.为此,提出云边页协同的WebBIM大场景多粒度兴趣加载调度算法.首先,将边缘计算引入WebBIM在线可视化中,设计云边页三端协同的WebBIM多粒度在线可视化框架;然后,针对BIM构件给出兴趣度的定义,并提出基于兴趣度的多粒度化传输调度机制;最后,根据视角的全局性、轻量性与兴趣度,给出BIM场景的最优初始加载视点的选取方法.借助于阿里云服务搭建实验平台,并选取若干大型BIM场景进行了网上实测.实验结果表明,该算法能够有效地降低加载BIM场景的数据量,显著地提升WebBIM场景的初始加载速度,在线漫游时没有明显的延迟.

With the rise of 5G and VR,the demand on visualizing large-scale BIM scene on mobile web browsers is increasing rapidly.However,due to the weakness of browser rendering and networking delay,it is still rather challenging to access huge Web3D scenes smoothly.Firstly,edge computing is introduced into WebBIM online visualization,and a cloud-edge-browser(CEB)collaboratively multi-granularity WebBIM framework is designed.Secondly,the interest degree is defined for each of BIM components and the mul-ti-granularity WebBIM transmission scheduling is proposed based on interest degree.Then,the optimal viewpoint selection for initial loading is presented in terms of globality of viewing scope,light weightness and interest degree.Finally,the experimental platform is deployed using Alibaba Cloud service,and some large-scale BIM scenes are used for practical transmission testing.Experimental results show that the pro-posed algorithm can effectively reduce the data amount of loading WebBIM scenes.The initial loading speed is also improved significantly,and there is no obvious delay to occur when users move huge WebBIM scenes through freely.