多传感器数据时空融合在温室中的应用

Application of multi-sensor data spatiotemporal fusion in greenhouse

设施农业在我国已经广泛应用于种植蔬菜和中药等单体附加值较高的作物,但WSN采集节点数据冗余和寿命普遍较短的问题制约着设施农业的长足发展。为此文章结合WSN存在的问题提出了时空数据融合模型,该模型在时间和空间序列上采用TiNA、DPS阈值管理算法和自适应加权融合算法,实现误差数据清洗和空间环境参数整合。通过时空融合算法实验,数据发送量平均减少了6.09%,同时温度数据的精度提高了2.85%,验证了该方法的可行性和有效性。

Facility agriculture has been widely used in my country to grow vegetables and traditional Chinese medicines and other high-value-added crops,but the problem of data redundancy and generally short life span of WSN collection nodes restricts the rapid development of facility agriculture.Therefore,this paper proposes a spatiotemporal data fusion model for solving the existing problems of WSN.The model uses TiNA,DPS threshold management algorithm and adaptive weighted fusion algorithm to achieve error data cleaning and spatial parameter integration on time series and space series.The experiment of space-time fusion algorithm show that the amount of data sent is reduced by 6.09%on average,and the accuracy of temperature data is increased by 2.85%,which verifies the feasibility and effectiveness of the method.