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。
It can improve crop growth condition to optimally detect agriculture temperature and humidity data in IOT database. It needs to get rid of false and wrong data and obtain the optimal solution via data close degree during detecting. Traditional increment correlation rules cannot get rid of wrong data via building detecting model. It reduces the accuracy of agriculture temperature and humidity data detection. This paper proposes a detection method based on modified information entropy in IOT database. Full data are disposed to build initial judgment matrix of temperature and humidity information management data. It can calculate the data detecting index entropy. Then the temperature and humidity data weighting normalized matrix and greenhouse soil humidity data objective function are obtained. The rid of sham and wrong data is got, and the temperature and humidity data detecting optimal solution is obtained via data close degree. Simulation results show that the proposed data detection method has higher accuracy than traditional method.
出处
《计算机仿真》
北大核心
2017年第2期338-341,共4页
Computer Simulation
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)
关键词
物联网
数据库
数据检测
Internet of things
Database
Data detection
