Research And Development Of Intelligent Animal Medical Monitoring Equipment

Intelligent animal medical monitoring equipment is mainly aimed at the rehabilitation and treatment of animals,and provides a suitable monitoring and recovery environment for the sick and critically ill animals.The device is mainly composed of a relatively closed monitoring chamber,sensors and controllers.The animals will get the appropriate temperature,humidity and oxygen concentration that can be set in the monitoring room.At the same time,the device will provide functions such as lighting,power failure protection and fault alarm.The key and difficult point of the equipment in the research and development process is the temperature and humidity control of the monitoring chamber.On the one hand,it is necessary to control the temperature and humidity to reach the set target value;on the other hand,after the control target is reached,it needs to maintain a constant temperature and humidity.The control of temperature and humidity belongs to a multiple-input multipleoutput(MIMO)system.It has the characteristics of non-linearity,large hysteresis,and strong coupling.The control is very complicated and challenging.This article first analyzes the physical structure and system composition of the equipment.After grasping various factors that may affect temperature and humidity,the energy conservation equation is written according to the first law of thermodynamics,and a temperature model is established.According to the quality of water vapor in the air,Change the water vapor mass conservation equation and establish the humidity model.Based on the established temperature and humidity model,Simulink components of Matlab are used to set up a simulation experiment environment,which verifies the rationality and accuracy of the established temperature and humidity model.Secondly,based on the established temperature and humidity model,a decentralized neural network feedforward compensation decoupling method is studied.The neural network’s ability to fit nonlinear functions is used to fit the coupling between temperature and humidity control channels,and the structure of the feedforward compensation decoupling method is referenced.A decentralized neural network feedforward compensation connection scheme is proposed,so that Training of neural networks becomes simple and easy to implement.Finally,Simulink is used to carry out simulation experiments.The results of the simulation experiments prove the accuracy and effectiveness of the decentralized neural network feedforward compensation decoupling method.Then,based on the decoupled temperature and humidity control model,the PID control algorithm is used to optimize the PID control parameters to achieve the shortcomings of the shortcomings of the complex model.The PID control parameters are optimized online to realize the PID self-tuning.Finally,the control experiment is performed using the decoupled temperature and humidity model.The experimental results show that the PID algorithm optimized by fuzzy control has better control effect and faster system response.Finally,in the realization of the project,the device uses the AVR single-chip microcomputer as the control core,and the relevant hardware circuits are designed around the core controller.According to the functions that the device needs to implement,the power module is designed to provide power to the device,the serial communication module and human-machine interface interaction,the control output module realizes the output of control signals,and the sensor acquisition module obtains information of various sensors.Based on the hardware circuit that has been implemented,the functions of each sub-module are designed and implemented,and the sub-modules are combined according to a certain process to complete the design and development of the single-chip computer program.Finally,the design and implementation of the human-machine interface were described in detail,and the entire research and development task of intelligent animal medical monitoring equipment was completed.