| In recent years,with the steady growth of China’s national economy and the continuous improvement of people’s material living standards,the impact of our human activities on the environment has become more profound,and the ecological environment has become a topic that we need to pay more and more attention to.For a long time,affected by natural disasters and people’s uncontrolled development and utilization of land resources,the quality of China’s land resources has declined.Therefore,the national and local ministry of water resources pay more and more attention to the problem of soil erosion.Through continuous investigation,they collect the data related to soil erosion,and form a set of calculation method system,which provides an effective means for the monitoring of soil erosion.This paper focuses on the Chinese soil loss equation(CSLE),and studies the characteristics and calculation methods of rainfall data,and applies it to the automatic calculation system of soil erosion model factors.The main achievements of this paper are as follows:First,by studying the clustering algorithm in machine learning,an improved interpolation algorithm is proposed,which can be used to supplement the original rainfall data,and can also be applied to the process of rainfall erosion factor calculation.Secondly,through the big data computing framework,the calculation process of the original rainfall data is implemented parallel computing,including:judgment of the end of a rainfall,calculation of hourly rainfall data and rainfall kinetic energy in a rainfall process,calculation of a rainfall and daily rainfall erosivity.By running the computing program on the big data framework,the computing performance of the system is greatly improved,and the disadvantages of low computing efficiency in the past are changed.Thirdly,it realizes the automatic calculation system of soil erosion model factors.And it realizes the real-time calculation and monitoring of soil erosion in the form of calculation tasks,and solves the problem of manpower and material resources consumption in manual calculation. |