面向物联网的时空数据处理算法设计

Design of Temporal-spatial Data Processing Algorithm for IoT

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。

With the rapid development of Internet of Things(IoT)and 5G technology,there are more and more applications of artificial intelligence based on deep learning,which makes medical imaging,urban security,autonomous driving and other visual fields based on temporal-spatial data become research hotpots in the direction of IoT.At the same time,the video data,picture data,temperature and humidity data and gas concentration data collected by the IoT system also grow rapidly,which eventually makes the processing speed and feedback speed of the IoT system slower and slower.In view of the large amount of temporal-spatial data collected by IoT nodes and the problem of transient anomalies,this paper designs an EPLSN(Exception Processing Long and Short Memory Network)algorithm based on long and short memory network.This paper designs logic structure of the input gate and improves the network model structure,solving the problem of the classification of transient abnormal data and temporal-spatial data,improving the classification accuracy of the IoT temporal-spatial data,and cleaning the abnormal data.According to the characteristics of the temporal-spatial data collected by the IoT sensor,the data is stored in different data blocks.At the same time,a time-series database is used to temporarily store temporal-spatial data,and an IoT search architecture based on temporal-spatial data is proposed.The architecture is suitable for the real-time search system in IoT environment and accele-rates the search speed of the IoT system.