基于农田墒情监测的轻量级语义传感网络  

Lightweight Semantic Sensor Network Based on Farmland Moisture Monitoring

作　　者：赵小虎[1,2] 蔡长煦 ZHAO Xiaohu;CAI Changxu(National and Local Joint Engineering Laboratory of Mining IoT Application Technology,China University of Mining and Technology,Xuzhou 221008,China;School of Information and Control Engineering,China University of Mining and Technology,Xuzhou 221008,China)

机构地区：[1]矿山物联网应用技术国家地方联合工程实验室,中国矿业大学,江苏徐州221008 [2]中国矿业大学信息与控制工程学院,江苏徐州221008

出　　处：《湖南大学学报（自然科学版）》2023年第8期181-193,共13页Journal of Hunan University:Natural Sciences

基　　金：中央高校基本科研业务费专项资金资助(2020ZDPY0223)。

摘　　要：针对现有农田传感节点信息化查询和本体知识构建的问题,分析农田墒情监测系统,提出了轻量级语义传感网络本体.从农田信息数据中抽取关系,设计FSSN本体注释方法,使用Tf-Idf算法分析农田本体语义权重,提出FSSN-SDRM算法构建农田墒情轻量级本体模型,对农作物生长环境进行分析,设定适宜性推理规则,使用Jena API对注释本体模型推理.根据农田传感节点采集的墒情信息,在轻量级中间件NVIDIA TX2平台上进行实验,查询数据库中推理信息的正确性,比较设备的响应时间.实验结果表明,轻量级FSSN注释本体将平均响应时间压缩至81 ms,相较于未注释本体缩短了41.4%的响应时间,TX2中时间比主机端缩短了12.81%,能够迅速并准确地进行农作物生长环境的适宜性判断,为农业信息化采集提供新的思路.Aiming at the problems of current farmland sensor nodes information query and ontology knowledge construction,this paper conducts an analysis of the farmland moisture collection system.It proposes a lightweight semantic sensor network ontology and extracts the relationships from farmland information data.Additionally,it designs the FSSN ontology annotation method and utilizes Tf-Idf algorithm to analyze the semantic weight of farmland Ontology.FSSN-SDRM algorithm is proposed to construct the farmland moisture lightweight ontology model,analyze the growing environment of crops,set suitability reasoning rules,and the Jena API is employed for reasoning the annotated ontology model.According to the soil moisture information collected by the farmland sensor nodes,experiments are conducted on the lightweight middleware NVIDIA TX2 platform to query the correctness of reasoning information in the database,and compare the response time of the equipment.The experimental results show that lightweight FSSN annotation ontology compresses the average response time to 81 ms,which is 41.4%shorter than the uncommented ontology,and the time in TX2 platform is 12.81%shorter than that of the host side.The ontology model can quickly and accurately judge the suitability of crop growth environment and provides new ideas for agricultural information production.

关 键 词：墒情 轻量级本体 农田语义传感网络 适宜性分析 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]