The Facilities Of Light Environment Control Model Research And Modeling Of Software Development

Light environment is a necessary condition for the growth and development of crops.Due to the constraints of facilities and the natural environment,it is difficult for facility light to meet crop growth needs.Artificial light-filling technology can effectively improve the growing light environment of plant crops and improve crop photosynthesis efficiency.The target value of light environment is used as the basis of artificial light supplement.It determines the efficiency of regulation.The existing light environment control target value model does not take into account the differences in photosynthetic demands at different growth stages of crops,and cannot be applied to the light environment during the whole crop growth period.In terms of control methods,the light saturation point is the main control target,and the control efficiency is low and is easy to cause excessive light compensation.In popularization and application,no general modeling process has been formed.In view of the above problems,this paper studies the whole growth phase photosynthetic rate modeling method associated with environmental factors.Research on the method of obtaining high-efficiency regulation target value.Integrated modeling methods and development of an auxiliary modeling tools.The research contents and conclusions of this article are as follows:?1?Reasearch on the full-growing stage photosynthetic rate predicted modeling method based on multi-population genetic?MPGA?-support vector regression?SVR?algorithm.Taking the cucumber plant as an example,photosynthetic rate samples under different growth period and environmental factors were obtained through photosynthetic rate test.The SVR is used as the nonlinear regression algorithm,and a MPGA algorithm was used to optimize the structural parameters of SVR.Photosynthetic rate model was established during the whole growth period of cucumber.The results of different verification show that the R² between the photosynthetic rate predicted and measured values was 0.998,the mean absolute error?MAE?is 0.280?mol·m^-2·s^-1,and the maximum absolute error is 2.462?mol·m^-2·s^-1,which is better than the results of standard SVR model and back propagation?BP?model and multiple nonlinear regression?MLR?model.The above studies indicate that the photosynthetic rate model based on MPGA-SVR algorithm can achieve accurate prediction of photosynthetic rate under different growth period and different environmental factors,and provide a quantitative physiological model to describe the photosynthetic demand of crop plants for light environment regulation.?2?The method of obtaining efficient target values of light environment based on curvature theory is studied.Studied the definition of curvature and the physiological significance of the light response curve in this paper,associating the light response curve curvature value with the change of photosynthetic rate.Proposed the idea of choosing the maximum value of curve curvature as an regulation target value point.on the basis of this,proposed a method for obtaining the maximum curvature of light response curve based on the trichotomy method by comparing analysis of the timeliness of multiple local optimization algorithms.Furthermore,complete 160 groups of target value optimization under various growth period and environment conditions.Then,established a light environment regulation target model with SVR algorithm.The model determination coefficient R² was 0.987,which realizes the dynamic calculation of the control target value with the growth days,temperature,and CO₂ concentration as input parameters.The results of different verification show that the slope of fitting line was 0.975 between the measured value and predicted value,the intercept was 22.04?mol·m^-2·s^-1,and the maximum relative error was 4.5%.It shows that the target value model has high prediction accuracy.Compared with the control with the target for light saturation point,this control effect is better.The average loss of photosynthetic rate was 5%,and the light energy consumption was reduced by 30.38%,which is effectively improved the light environment control efficiency.?3?Research on an intelligent photosynthesis modeling assistant system based on Py Qt.Based on the analysis of the whole growth period photosynthetic rate model of cucumber and the light environment control target value model algorithm,extracting the generic modeling process module,model structure module and model template module,with Qt application development framework and python language as technical support.Combining with the related demand for the regulation of light environment,an intelligent crop photosynthetic modeling system was designed.The model reconfiguration method was used to verify the versatility and reliability of the system by taking the facility tomato as the object.The results showed that the R² was 0.989,and the average absolute error was 0.473?mol·m^-2·s^-1,which had a high prediction accuracy of photosynthetic rate.The environmental control target model has R² of 0.979 and an average absolute error of 23.69?mol·m^-2·s^-1,which can achieve accurate output of light environment control target values.Therefore,the present photosynthetic modeling system has good versatility and reliability.