| Agricultural Industrial production refers to the use of modern technology for the agricultural industry to provide a stable environment for the growth, get rid of the restrictions by the natural environment and provide a stable large-scale production. In order to ensure the smooth progress of the agricultural industrial production, the need for the entire production process monitoring and control, namely process control. Traditional industrial process control and other industrial equipment by model and implement acquisition and control, mostly used in coal, iron and steel, machinery, chemicals, textiles and other fields, and for the emerging field of agriculture production factory, there is no better process control methods. This paper aims to study agricultural control technology, and to achieve agricultural industrial production process control through the system and has mainly completed the work listed below:1, The traditional process control system analysis software to explore, summarize the development and maintenance of complex, high cost, discusses the B / S’s Web application software design and advantages, and Web application server system architecture to explore research. Through comparative analysis Webform and MVC programming model, as well as Apache and Nginx Web server tests compared designed for the operating system to Linux, Nginx + uWSGI for the Web server, Mysql database, Django MTV for the Web server daemons model system architecture, improved process control software server development efficiency, stability and concurrency.2, Studied based on the improved BP neural network PID process control. Through the agricultural industrial production process is analyzed to find the key to the whole production process control environment for the growth of crops that air temperature, humidity, etc. The purpose is when the crop growth environment changes, make the environment through control systems and methods to achieve fast and stable and kept in the controller’s request. For large inertia agricultural factory production control system exists, nonlinear problems, an improved conjugate gradient algorithm for BP neural network PID control method, effectively improve the performance of the algorithm, make up BP neural network training algorithm the existence of slow convergence and easy to fall into local minimum problems and improve the control efficiency.3, For process control actuator failure problems faced studied the vibration signal analysis method based on fault detection. By ICP acceleration sensors and capture card collection to convert the incoming computer actuators mechanical vibration signals were analyzed using Labview G language. Frequency fields through the fast Fourier transform and Hilbert-Huang transform the vibration signal processing and analysis, and achieved some success in the future for the time-frequency domain analysis of the lack of innovative analytical methods proposed based on the energy distribution of the wavelet transform, effectively improve the efficiency of fault detection, improve the accuracy of actuator fault detection.4, B a fungus production plant process control specific analysis of the situation, to build a whole system solutions, and complete the design verification of each service module, to achieve the process control system platform. Finally, the control system platform to verify the performance of the specific application and process control Web application service system architecture proposed in this paper.The results show that, Django based Web services architecture proposed reducing the coupling of software systems, so that the stability, development and maintenance of the system has been improved; improved conjugate gradient algorithm BP neural network PID control method based on make up the shortcomings of traditional BP algorithm to improve BP neural network PID controller availability and robustness; fault detection method based on vibrational energy analysis to improve the efficiency and accuracy of actuator fault detection. |
PDF Full Text Request