Research On Intelligent Control Method Of Facility Environment Based On Data-driven

The research on greenhouse environmental monitoring and intelligent control system based on modern information technology and automation technology has been carried out to better meet the growth needs of crops.A data-driven facility environment intelligent regulation system was developed in conjunction with research on intelligent algorithms for facility environment management.The main research content and conclusions are as follows:The method of using artificial neural network to predict the climatic conditions of greenhouse was proposed.Because the traditional greenhouse environmental control system is generally based on the current real-time environmental monitoring data,it is not combined with the prediction of greenhouse environmental change trend,which leads to poor greenhouse environmental control effect.In order to solve the problem of poor control effect of greenhouse environment at present,a prediction method of greenhouse environmental factors based on Elman neural network was proposed.Taking the collected historical data of temperature,humidity and carbon dioxide concentration in the greenhouse as the input of the prediction model,the Elman neural network prediction model was established,and the accurate prediction of greenhouse environmental factors was realized.The results showed that the Elman model is better than the BP and RBF models.The mean square errors of temperature,humidity and carbon dioxide concentration are 0.39%,0.59% and 2.83%,respectively,and the determination coefficients are 99.15%,96.78% and 97.39%,respectively.The prediction result of the research model was good,which can provide some decision support for greenhouse environmental regulation and control.The method of greenhouse vents and phosgene environment control based on phosgene coupling model was studied.The ventilation control of greenhouse is of great significance to the environmental regulation of closed solar greenhouse.In view of the fact that the current greenhouse ventilation control mostly takes the temperature and humidity in the facility as the control goal,and lacks the optimal control of the linkage control with the illumination and air environment in the facility,a greenhouse vent and phosgene environment control method based on phosgene coupling model was proposed.Based on the indoor data collected during the production of cherry tomato,the multi-factor coupled photosynthetic rate prediction model based on regression support vector machine(SVM)was established.The response curve of vent opening and maximum photosynthetic rate was obtained by using genetic nonlinear programming optimization algorithm,and the appropriate vent opening interval in the response curve was obtained by combining mathematical curvature theory.In the range of suitable vent opening,in order to obtain the optimal photosynthetic rate,based on the prediction model of photosynthetic rate with multi-factors,the target value model of phosgene coupling control under suitable vent opening was constructed to obtain the target area of carbon dioxide concentration-light intensity regulation.The results showed that by optimizing the value target,the photosynthetic rate of greenhouse crops can reach the higher level under the condition of less input of light and air.The design of a sophisticated and comprehensive control system for greenhouses was finished.The sensor technology was used to monitor the light intensity,air temperature and humidity,carbon dioxide concentration and other environmental factors in the greenhouse environment in real time,and the obtained data are transmitted to the GRM WEB cloud platform through the USR-LG207 wireless data transmission terminal,so as to accurately control the crop growth environment.The intelligent control system evaluated measurement data from the greenhouse environment,made control decisions based on the output of a machine learning algorithm,and transmitted control instructions via the USR-LG207 to direct the regular functioning of the on-site machinery.The computer online interface or mobile phone APP will prompt an alarm message when environmental parameters in the greenhouse are outside of the pre-set threshold,enabling remote monitoring and early warning of the greenhouse environment.The system can also manage and retrieve historical data according to crop condition,manage a variety of growing environment characteristics,regulate decision rules according to crop types,and save all measurement data.Following thorough testing,the system has demonstrated its ability to accurately and quickly collect greenhouse environmental data,as well as its strong automation and remote control capabilities,all of which meet the original design objectives.