物联网数据库中农业温湿度数据检测仿真研究

发布时间：2018-07-10 04:15

本文选题：物联网 + 数据库　；参考：《计算机仿真》2017年02期

【摘要】：对物联网数据库中的农业温湿度数据进行优化检测,可改善农作物的生长条件。进行温湿度数据检测时,需要利用数据贴近度的概念剔除虚假、错误的的数据,获取农业温湿度数据检测的最优解,而传统的采用增量关联规则通过建立检测基本模型,不能剔除模型中的错误数据,降低了农业温湿度数据检测的精度。提出改进信息熵的物联网数据库中农业温湿度数据检测方法。对物联网数据库全部的数据进行处理,建立农业温湿度信息管理数据初始判断矩阵,可计算出农业温湿度数据检测指标的熵,得到农业温湿度数据加权规范化矩阵和温室土壤湿度数据的目标函数,利用数据贴近度的概念剔除虚假、错误的的数据,获取农业温湿度数据检测的最优解。仿真结果表明,提出的农业温湿度数据检测方法,相比传统的数据检测精确度高。
[Abstract]:The optimum detection of agricultural temperature and humidity data in the Internet of things database can improve the growth conditions of crops. In the process of temperature and humidity data detection, the concept of data closeness should be used to eliminate false and wrong data, and the optimal solution of agricultural temperature and humidity data detection should be obtained, while the traditional incremental association rule is used to establish the basic model of detection. The error data in the model can not be eliminated, and the precision of agricultural temperature and humidity data detection is reduced. This paper presents an improved information entropy method for detecting agricultural temperature and humidity data in Internet of things database. By processing all the data in the Internet of things database and establishing the initial judgment matrix of the agricultural temperature and humidity information management data, the entropy of the agricultural temperature and humidity data detection index can be calculated. The weighted normalization matrix of agricultural temperature and humidity data and the objective function of greenhouse soil moisture data are obtained. The false and wrong data are eliminated by using the concept of data closeness, and the optimal solution of agricultural temperature and humidity data detection is obtained. The simulation results show that the proposed method is more accurate than the traditional method.
【作者单位】：吉林农业大学信息技术学院;吉林农业大学计算机学院;
【基金】：吉林省省级经济结构战略调整引导资金专项项目(2014Y108) 吉林省教育厅“十二五”科学技术研究项目(2015175) 长春市科技局计划项目(14nk029)
【分类号】：S126;TP391.44;TN929.5

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