Research On Temperature Control Model Of Cucumber Cultivation In Solar Greenhouse Based On Data-Driven

Intelligent control of the greenhouse environment in solar greenhouse cultivation is a key technology to improve crop yield and quality.At present,the research on the cultivation environment management technology of some common facility vegetables has reached a high theoretical level,but most of the research results are based on specific test conditions,and cannot cover all kinds of complex environmental conditions in actual production.On the other hand,the greenhouse environment control model based on the theoretical basis of this type of test results cannot achieve ideal results in practical applications.In order to solve the above problems,this experiment proposes to establish a data-driven solar greenhouse environmental temperature control model.The specific method is to establish the BP neural network simulation model of cucumber cultivation temperature by analyzing the environmental data in the high-yield cucumber greenhouse,and input the environmental data of the ordinary greenhouse into the corresponding model after data cleaning and clustering.It is the ambient temperature of the high-yield greenhouse under the corresponding environmental conditions.Taking this temperature value as the control target of the ordinary greenhouse environment temperature regulation can theoretically achieve the purpose of improving the ordinary greenhouse temperature management level.Aiming at the automatic processing of greenhouse environmental data,a method for cleaning greenhouse environmental data that combines wavelet packet analysis and light intensity outlier correction program and a clustering method of similar environmental condition data based on light intensity are proposed.Under the background of extensive control of cucumber production environment in domestic solar greenhouses and backward hardware equipment,the simulation model of greenhouse cucumber cultivation temperature established in this experiment is of great significance for guiding the cultivation temperature control of cucumber cultivation and improving the production level of cucumber production.The main findings are as follows:1.The experiment confirmed the BP neural network topology suitable for the temperature simulation of solar greenhouse.The model is a three-layer structure of 5-11-1.The five parameters of light intensity(L),CO2 concentration(CO2),air humidity(Hair),soil temperature(Tsoil),and soil humidity(Hsoil)are used as input.Temperature(Tair)is the output;the transfer function between the input layer and the hidden layer is a logarithmic sigmoid transfer function Logsig,the transfer function between the hidden layer and the output layer is a linear transfer function Purelin;trainlm function is used for network training function.2.Aiming at the problems of noise and outliers in the greenhouse environment data,this experiment used wavelet packet analysis to eliminate the data noise,and based on the VBA language,a program for correcting the outliers of light intensity was developed.Through the combination of the two methods above,automatic cleaning of greenhouse environmental data is achieved.After data cleaning,the average relative error of the simulation was reduced by 0.46%,and the simulation accuracy of the model in the complex section of environmental changes was significantly improved.3.Similar day clustering was performed on the data of cucumber initial blooming and fruiting stage based on light intensity.According to the clustering effect,the optimal number of data in the initial flowering period was 2 and the optimal number of data in the fruiting stage was 4.Among them,19 days of the first flowering period were classified as class I,and 6 days were classified as type II;On the 31 st day,it was classified as class I,on the 61 st day it was classified as class II,on the 9th day it was classified as class III,and on the 32 nd day it was classified as class IV.Compared with the control,the accuracy of the models established by the clustered data is significantly improved.4.After cleaning and clustering the data,it was used to train the BP neural network model.Finally,there are 6 types greenhouse temperature simulation model of first flowering stage ?,first flowering stage ?,fruiting stage ?,fruiting stage ?,fruiting stage ?,and fruiting stage ?.After testing the model,it was found that the simulation effect of the model was good overall.Among them,the fitting degree of the first flowering stage ? and the fruiting stage ? model was relatively low due to insufficient training data.The fitting degree of the first flowering stage ? model was 0.62,and the melon stage ? model is 0.87.The data-driven solar greenhouse cucumber cultivation temperature control model established in this experiment has a good application effect in the control greenhouse.The temperature reference value output by the model for the control greenhouse conforms to the theoretical temperature control law of high-yield cucumber cultivation in facilities.