基于分簇数据融合的农产品冷链温度监控方法

针对现有基于无线传感网络的农产品冷链物流监测系统,传感器节点数据传输量大,带宽利用率低、能耗高,网络生命周期短的问题,提出了一种基于算术平均值与分批估计的簇内数据融合及自适应加权的簇间数据融合冷链温度监测方法。首先利用疏失误差对采集数据进行预处理,然后利用平均值与分批估计方法对簇成员节点发送的数据进行融合处理,最后簇头节点利用自适应加权算法对接收到的成员节点数据进行进一步的融合处理。实验证明,基于该数据融合方法的冷链监测系统网络生存周期相比传统方法提高了34.2%,稳定周期相比于传统低功耗自适应集簇分层型协议提高了11.4%,数据融合精度高于传统算术平均值法7.6%,系统能耗每轮降低约32.5%。能够有效降低冗余和可信度较差的数据对测量结果带来的影响,减少不必要数据传输损耗,降低了冷链物流成本,提高了农产品冷链物流信息化程度。

Cold Chain Temperature Monitoring Method of Agricultural Products Based on Clustered Data Fusion

In order to reduce the data transmission and energy consumption of sensor nodes and improve the broadband utilization and life cycle in real-time cold chain logistics monitoring system for agri-food based on wireless sensor networks, a clustering fusion method based on arithmetic mean and batch estimation for the cold chain temperature monitoring of agricultural products was proposed, Firstly, the mistake errors of the collected data was eliminated, and then the data which were sent from the cluster member nodes were merged by the mean and the batch estimation method. Secondly, the cluster head node used the adaptive weighting algorithm to further analyze the fusion data of the member nodes. The experimental results showed that the network lifetime of the cold chain monitoring system based on the data fusion method was 34.2% higher than that of the traditional method, and the stability period was 11.4% higher than that of the traditional low power adaptive cluster clustering protocol. Compared with the traditional arithmetic average method, the accuracy of data fusion was improved by 7.6%, the system energy consumption was decreased by about 32.5% per round, which can not only reduce the influence of redundancy and less reliable data on the measurement results, but also reduce the unnecessary data transmission loss, as well as reduce the cost of cold chain logistics and improve the degree of informatization of cold chain logistics to a certain extent.