Characteristic Analysis And Prediction Of Farmland Microclimate Time Series Data

Farmland microclimate is an important environmental condition affecting crop growth and yield formation.On the basis of the Internet of things(Io T)system built in the "Bohai granary" project,this paper takes the accumulated monitoring time sequence data as the research object,analyzes the dynamic characteristics of the various variables of the agricultural microclimate,and studies and constructs the short-term and long-term prediction model of the various variables of the agricultural microclimate.Firstly,the characteristics of microclimate in farmland were analyzed.The time series data of the key factors affecting the growth of crops,such as air temperature and humidity,soil temperature and humidity,carbon dioxide concentration,light intensity and wind speed,were selected as experimental samples.The characteristics of long memory,periodicity and chaotic dynamics of each variable were analyzed by R/S test and power spectrum.On this basis,the minimum mutual information method is used to calculate the time delay of phase space reconstruction,and the CAO method is used to determine the embedding dimension m,and the phase space reconstruction of the field microclimate variables is realized.At the same time,we use correlation dimension,Kolmogcorev entropy and Lyapunov index to quantitatively analyze the chaotic and fractal characteristics of each microclimate factor monitoring time series.Secondly,on the basis of the time delay embedded phase space reconstruction,the shortage of chaotic neural network model in multi step prediction is analyzed,and a time delay based chaotic neural network model based on the dynamic correlation of each component in the reconstruction vector is proposed.Based on adaptive and self learning modeling,the model has the advantages of one step reconstruction and multi-step prediction.At the same time,the combined optimization scheme of different prediction time length and prediction accuracy is provided,which can achieve more accurate prediction effect on the basis of the same known amount of data.Finally,the improved neural network model is used to analyze the maximum prediction time and prediction accuracy of the variables in the farmland microclimate.In order to meet the trend forecast demand of the longer period of agricultural microclimate,a multi scale farmland microclimate prediction model based on the empirical mode decomposition was proposed,which realized the trend prediction function of the longer period under differenttime scales,and the prediction accuracy of the model reached 90%.To a certain extent,the prediction time of farmland microclimate is prolonged.Through the study of the characteristic analysis and prediction model of the time series data of the agricultural microclimate,it provides a theoretical and technical support for the better grasp of the change law of farmland meteorological environment and the early warning of agro meteorological disasters.