Stable Measurement And Rapid Control Of Petroleum Viscosity Instruments In Interference Environment

Based on the vigorous development of the Internet of Things industry,intelligent manufacturing of various intelligent devices has gradually entered people's field of vision.The development of intelligent instruments is a little later than the development of other intelligent devices,but the instruments are very important for the industrial development of a country.Intelligent instruments have gradually become the focus of current research.Based on this,the traditional oil viscosity-measuring instrument is a singular,mechanized and manufactured traditional instrument.It proposes intelligent and automatic transformation and upgrading of traditional oil viscosity measuring instruments based on the Internet of Things and intelligent instruments.In this paper,we focus on studying the accurate temperature control algorithm for oil viscosity measuring instruments in an unstable environment and the noise-carrying data collected by sensors under complex conditions.For the fast and precise control of temperature,we design three schemes according to the traditional PID technology,which are parameter-variable parametric PID control,Smith-predicted PID-Smith temperature control and fuzzy PID control.After theoretical analysis and actual verification,we comprehensive anti-jamming performance and convergence speed and temperature control accuracy,and the fuzzy PID control performance is the best.On the other hand,in order to reduce the signal noise in the sampling process and reduce the jitter smoothing data,we propose to use the Kalman filter.Through the MATLAB simulation and actual measurement results of the experimental data,we find that the Kalman filter is the best.And effectively reducing the error and smoothing the data and reducing the jitter is better than the arithmetic average filtering method and the first-order lag filtering algorithm commonly used in the industry.If there is a "Bad Value" in the process of measuring errors during the measurement process,if the data is fitted by least squares method,the error will increase due to the "Bad Value".For this problem,we propose a Huber based on this problem.The robust regression of the function,by weighted iterative least squares,can reduce the influence of "bad value" on the fitting curve,reduce the error,and enhance the anti-interference of the data.Finally,we applied the research content to the oil viscosity measuring instrument desktop detection control software,and merged into the IoT architecture to realize a set of functions including automatic operation,intelligent control,analysis data,data sharing and so on.The instrument has become the IoT high-precision intelligent oil viscosity measuring instrument.The transition from traditional instruments to smart instruments has been realized.