计算机科学 ›› 2020, Vol. 47 ›› Issue (11): 310-315.doi: 10.11896/jsjkx.200400045
所属专题： 物联网技术 虚拟专题
徐鹤1,2, 吴昊1, 李鹏1,2
XU He1,2, WU Hao1, LI Peng1,2
摘要： 随着物联网和5G技术的快速发展,以深度学习为基础的人工智能应用越来越多,使基于时空数据的医疗影像、城市安防、自动驾驶等视觉领域成为物联网方向的研究热点。物联网系统采集到的视频数据、图片数据、温湿度与气体浓度数据同时也急剧增长,最终使得物联网系统的处理速度和反馈速度越来越慢。针对物联网节点采集的时空数据量大且可能存在短暂性异常的问题,文中设计了基于长短记忆网络的EPLSN(Exception Processing Long and Short Memory Network)算法。首先,对输入门的逻辑结构进行设计,并对网络模型结构进行改进,解决了短暂性异常数据与时空数据分类的问题,提高了EPLSN算法对物联网时空数据的分类精度,并能够对异常数据进行数据清洗。其次,依据传感器采集的时空数据特点,将数据存储到不同的数据块中,采用时序数据库对时空数据进行短暂性存储,并提出基于时空数据的物联网搜索架构,加快了物联网系统搜索的速度。
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