Construction And Optimization Of The Control Model Of Greenhouse Crop Growth Environment Parameters From The Perspective Of Energy Consumption Reduction

Greenhouses can not only provide the most suitable growing environment for crops,but also improve the economic benefits of producing crops.They have become the core of modern facility agriculture development.With the development and popularization of emerging technologies,modern greenhouses are developing in the direction of intelligent control.While satisfying the normal growth of greenhouse crops,choose the most suitable greenhouse control equipment,and to a certain extent reduce the energy consumption generated in the process of greenhouse environmental control,improve production efficiency.This paper takes the cucumber seedling greenhouse in the greenhouse base of Jiangsu University as the experimental object,considers the needs of the control device in the greenhouse for the greenhouse environment,and builds an ARX(Auto Regressive eXogenous)model of temperature and room temperature and humidity based on the ARX model and multi-objective genetic algorithm optimization theory.Control the cost model,and find the best way to control the greenhouse environment through multi-objective genetic algorithm.Finally,the algorithm is applied to a greenhouse production in an enterprise in Wuxi.The results show that while ensuring that the greenhouse is always in the most suitable growth environment,it saves the power consumption of greenhouse regulation.The main research contents and results are as follows:(1)Construct the temperature and humidity ARX model and regulation cost model of cucumber greenhouse environment.In order to meet the needs of greenhouse control equipment for greenhouse environment control,on the basis of the ARX model,increase the control equipment factor to form a greenhouse environment temperature and humidity ARX model.The validity of the model is verified by simulation.The results show that the temperature rms error of the model is 0.026 and the relative error is 0.0332%.The root mean square error of humidity is 0.3383,and the relative error is 0.4316%.It can be concluded that the temperature and humidity model is suitable for the cucumber greenhouse selected in the experiment,and it also lays the foundation for the subsequent optimal control of the greenhouse environment.Construct a greenhouse control cost model based on the basic power formula,use the energy consumption of the control equipment during the greenhouse environment control as the target of economic savings,and establish a control cost model.The control cost is measured by the amount of power consumption.The smaller the power consumption,the control cost Lower.(2)To achieve optimal control and decision-making of the greenhouse environment through multi-objective genetic algorithms.Based on the genetic algorithm,combined with the multi-objective optimization idea,the multi-objective genetic optimization algorithm is used to find the optimal value of the greenhouse environment control.Through simulation experiments,the multi-objective genetic optimization algorithm reduced the temperature in the cucumber greenhouse environment from 31.4 ℃ to 24.4 ℃,the relative humidity was increased from 47.1% to 59.24%,and the energy consumption was reduced by 17.6%.Compared with linear weighted sum method and standard genetic algorithm,it has advantages in control effect and energy consumption.(3)Achieve the multi-objective optimization of the greenhouse in the field application of agricultural enterprises.The actual greenhouse cucumber production process of Wuxi XX enterprises was selected as the research object,and the temperature and humidity ARX model and regulation cost model designed in this paper were applied to it.Through simulation,the temperature change fit of the model reached 99.55%,and the humidity change reached 94.86.%,And optimal control of the temperature and humidity system in the greenhouse environment through a multi-objective genetic optimization algorithm.As a result,energy consumption was reduced by 34.8% compared to before optimization.Controlling the greenhouse environment through a multi-objective genetic optimization algorithm can enable crops to achieve the most suitable growth environment while ensuring the lowest energy consumption generated by the greenhouse control,thereby enhancing the economic benefits of the greenhouse and contributing to the rapid development of China’s facility agriculture influences.