Research And Implement On The Distributed Storage System For The Intelligent Agriculture

With the continuous development of China's intelligent agriculture,the quantity of various agricultural data types has been growing explosively.At the same time,the agricultural data contains several disadvantages,such as low storage efficiency,easy loss of core data,backward of data storage platforms and insufficient openness of data sharing,that seriously restrict the development of the intelligent agriculture in China.In order to ensure the reliability and scalability of data storage system,and achieve effective data sharing,as well as promote the rapid development of the intelligent agriculture,this paper proposes a distributed data storage scheme based on the REST and the Mongo DB.Firstly,this paper introduces the key technologies related to the intelligent agricultural data storage scheme.Furthermore,the data characteristics and storage requirements in the intelligent agriculture are analyzed.The Mongo DB database is selected to play the role as the data storage.Lastly,combined with the data characteristics of the intelligent agriculture and the Mongo DB's document model,this paper proposes the agricultural data storage model based on the Mongo DB database and designs the storage model for the representative agricultural data in details.Secondly,the swing door trending(SDT)algorithm,which is used to compress the agricultural sensing data,has the disadvantages of weak resistance to noise,large recovery error and difficult selection of compression accuracy parameter.To overcome the above-mentioned disadvantages,a linear adaptive swing door trending(LA-SDT)algorithm is proposed in this paper.Experimental results show that the compression performance of the LA-SDT algorithm is improved by 18.3% compared with the SDT algorithm,and the compression error is increased by 62.7%.Finally,in order to improve the sharing of the agricultural data and theextensibility of the distributed systems,this paper designs a data service platform for the intelligent agriculture based on the REST and the Mongo DB.The main functional modules of the platform can be summarized as follows: a data model building module based on the Mongoose tool,a REST style of data access module based on the Mongoose and the Express frameworks,and a data processing module applying the Map Reduce computing model.Finally deploy Mongo DB distributed storage clusters in an experimental environment and test the reliability,availability,and scalability of the storage clusters.