数据挖掘在温室大棚上的应用研究

随着物联网的兴起,数据的积累速度、维度以及体积等也越来越大,成了真正的大数据范畴。在农业温室大棚中部署的大量各种各样的传感器产生了大量多源异构的传感数据,而且这些数据中存在需要清洗的各种脏乱数据。本文按照数据清洗,模型构建和模型应用三个部分进行详述,首先介绍数据清洗技术和多源异构数据的融合技术,然后列举了常见的预测模型构建方法并分别指出了每种方法的适用情况,最后对常见的应用领域进行了综述和总结,并提出了目前还存在的问题,以及对未来的展望。

Application of Data Mining in Greenhouse

With the rise of the Internet of Things, data accumulation speed, dimension and volume are also growing, and has become a real big data category. The large variety of sensors deployed in agricultural greenhouses produces a large number of multi-source heterogeneous sensing data, and there are various types of dirty data that need to be cleaned. In this paper, data cleaning, model building and model application are described in detail. Firstly, data cleaning technology and multi-source heterogeneous data fusion technology are introduced. Then, common forecasting model construction methods are listed. Finally, common application fields are introduced. Summarizes and summarizes, and puts forward the existing problems, as well as the prospect of the future.