Research On Association Mining And Visualization Of Agricultural Statistical Data

With the deepening of agricultural information,agricultural data is increasing day by day,and many agricultural-related websites have accumulated a large amount of agricultural data,but these types of data are complex,and the data is difficult to obtain,which makes these data unable to play their due role.How to effectively obtain,integrate and mine multi-source agricultural statistics has become a problem to be solved by current researcher.Use data mining technology to mine hidden agricultural knowledge from massive agricultural statistics,and combine visualization technology to display data and mining results with simple and simple graphical symbols,so as to better serve agriculture.In this paper,according to the disperse and broad characteristics of agricultural statistical data and the problems that are difficult to obtain,the acquisition of data from different sources is realized by using web crawler technology,the selection of impact factors and the construction of prediction models is designed by data mining technology,and the agricultural statistical data mining and visualization platform is implemented using the Django framework.The main research contents of this article are as follows:(1)Through the application analysis of domestic and foreign agricultural data mining,the research content and research objectives are determined,and the collection and processing of agricultural statistical data are analyzed.Using the technology of web crawler,we can capture the data of web pages directionally,extract the agricultural statistical data of web pages,supplement,transform and collect them,and provide effective data support for data mining.(2)Design of data mining model.Aiming at the problem of a large number of fuzzy and uncertain factors in agricultural statistical data,a multi-factor selection model combining gray correlation and lasso regression algorithm was designed to solve the local optimal solution and collinear problems in traditional data mining methods;In order to verify the accuracy of the multi-factor selection model,a prediction verification model that combines GM(1,1)gray prediction and BP neural network is designed and implemented,which can improve the adaptability and fault tolerance of the model.(3)Realization of mining and visualization system.Design and development of Web agriculture statistics data mining and visualization system based on Django,studied data mining process,data visualization technology and data management technology,and implement the modules of data management,user management,data mining and data visualization,including:The system makes use of Xadmin to realize data management and user permission setting.Data mining technology and pyechart technology are used to realize the chart display of data mining results and make the user experience better.Using Echart visualization technology and django custom routing configuration method to realize the multi-graph correlation display of agricultural statistics data and provide dynamic interaction function;Gulp optimization technology and debug-toolbar technology are used to optimize the front-end and back-end of the system.(4)Application and testing of the system.Using Hebei's grain output data for testing,analyze the multi-factor relationship that affects grain yield,and display the multiple impact factors in combination through visualization technology.Finally,it is verified by the prediction model,and the prediction effect is good.In this paper,a multi-source heterogeneous agricultural statistical data is used as the basic data source.By studying the grey correlation,lasso regression,GM(1,1)prediction and BP neural network algorithm,an agricultural statistical data mining and visualization system is designed to mine agricultural data.