基于环境监测的两级数据融合模型与算法

Two-Stage Data Fusion Model and Algorithm Based on Environmental Monitoring

利用多源传感器采集的数据不仅存在大量冗余,而且会影响最终监测结果.为了提高监测的准确度,本文提出一种面向草原环境监测的两级数据融合模型.在一级数据融合中,首先采用自适应加权平均法对各区域内的同类传感器进行融合,然后利用BP神经网络对该区域内的异类传感器进行训练和融合,从而得到对各区域环境状况的初步判断.由于经BP神经网络融合的结果具有不确定性,因此,二级融合利用D-S证据理论对一级融合结果进行综合分析,从而得到对草原环境的决策判断.最后对模型及算法进行了有效性验证与分析,实验结果表明本文的方法能够较准确地监测草原环境状况,同时对草原环境的高效管理和科学养护等提供一些有价值的指导和决策依据.

The data collected by multi-source sensors not only have a lot of redundancy, but also affect the final monitoring results. In order to improve the accuracy of monitoring, this study proposes a two-level data fusion model and algorithm for grassland environment monitoring. In the first-level data fusion, the adaptive weighted averaging method is used to fuse the similar sensors in each region, and then the BP neural network is used to train and fuse the heterogeneous sensors in the region, thus a preliminary judgment on the environmental conditions of each region is obtained. Because of the uncertainty of the fusion result by BP neural network, the secondary fusion uses DES evidence theory to analyze the primary fusion result and get the decision-making judgment of grassland environment. Finally, the validity and analysis of the model and algorithm are carried out. The experimental results show that the proposed method can accurately monitor the grassland environment. At the same time, it provides some valuable guidance and decision-making basis for the efficient management and scientific conservation of grassland environment.