Abstract: This paper introduces the circuit structure and theoretical basis of an intelligent pressure transmitter developed using the 8031 ​​microcontroller. The hardware and software design focuses on the information fusion technology and application of two sensors. Keywords: Transmitter; Information fusion; Control 1 Introduction In recent years, traditional sensors have played a significant role in the control field due to their inherent characteristics. However, with the rapid development of computer technology, instruments are required to automatically perform multiple functions, and bus control requires traditional sensors to have self-calibration, self-diagnosis, and self-compensation functions. Thus, by applying a microcontroller to appropriately control traditional sensors, these requirements can be met. For transmitters, this means standardizing the sensor's output signal; the internationally accepted standard is a current output of 4–20 mA. This project, based on the transmitter, applies a microcontroller to control it, achieving self-calibration, self-diagnosis, and self-compensation capabilities. With the development of microelectronics technology, the accuracy of sensors, especially pressure and temperature sensors, has become relatively stable. The rapidly developing bus technology also inherently requires transmitters (or sensors) to have good reliability and higher accuracy. Some foreign companies (such as Honeywell and Rosemount) have launched intelligent transmitters, while China is basically blank in this field, and the required products must be imported. Therefore, developing intelligent transmitters independently has important theoretical and practical significance. 2 Theoretical Basis The sampling in this project is completed by differential pressure sensors and temperature sensors. Sensors usually exhibit cross-sensitivity, meaning that the sensor's output value is not determined by a single parameter; when other parameters change, the output value also changes. For example, a pressure (differential) sensor, when the pressure (differential) parameter is constant but the temperature or static pressure parameter changes, its output value also changes. Therefore, this pressure (differential) sensor has cross-sensitivity to temperature or static pressure parameters. Sensors with cross-sensitivity have unstable performance and low measurement accuracy. Multi-sensor information fusion technology aims to improve the measurement accuracy of each parameter by monitoring multiple parameters and using certain information processing methods. This paper only discusses the principle of two-sensor information fusion. Two sensors can measure two parameters and obtain information about both parameters. There are various algorithms for fusing two pieces of information, and surface fitting is one of the fundamental ones, also known as two-dimensional regression analysis. The basic principle of surface fitting is discussed below. Given the pressure sensor's output voltage U[sub]p[/sub], and its temperature sensitivity, only a one-dimensional calibration experiment is performed on the pressure sensor. If the measured pressure value is obtained from the input (pressure P) and output (voltage U[sub]p[/sub]) characteristic curves, there will be a significant error (because the measured value P is not a univariate function of the output value U[sub]p[/sub]). The output voltage U[sub]t[/sub] of the other temperature sensor represents the temperature information t; therefore, the pressure parameter P can be more completely represented by the binary function U[sub]p[/sub] and U[sub]t[/sub]. Similarly, we can also describe the pressure sensor output voltage U[sub]p[/sub] as a binary function of the pressure parameter P and the temperature sensor output U[sub]t[/sub]. In this case, we can use the quadratic surface fitting equation, i.e., the two-dimensional regression equation, to describe it. If the constant coefficients in equations (1) and (2) are known, then the binary input-output characteristics used to detect P and the output U[sub]p[/sub], i.e., the surface fitting equations (1) and (2), are determined. When the output values ​​U[sub]p[/sub] and U[sub]t[/sub] of the two sensors are collected, the measured parameter P of the sensor can be calculated by substituting them into equation (1). For this purpose, a two-dimensional calibration experiment must first be carried out, and then the constant coefficients are determined by the least squares method based on the calibrated input and output values. (1) Experimental calibration: Determine one pressure calibration point (set to 6) within the range of the pressure sensor and m temperature scale points (set to m=5) within the working temperature range. Then, the standard input values ​​generated by the pressure P and temperature t standard value generator at each calibration point are: The corresponding output values ​​are read according to the standard input values ​​of each calibration point. In this way, we perform static calibration on the pressure sensor at m different temperature states and obtain the input-output characteristics corresponding to the different temperature states, i.e., P-u, characteristic cluster, as shown in Figure 1(a). Similarly, we can also obtain the n input-output characteristics (t~U[sub]t[/sub] ) of the temperature sensor corresponding to different pressure states, i.e., t-U[sub]t[/sub] characteristic cluster, as shown in Figure 1(b). (2) Determination of the undetermined constants of the quadratic surface fitting equation: The constant coefficients of the quadratic surface fitting equation characterized by equations (1) and (2) are usually obtained according to the least squares principle, and the coefficients satisfy the minimum mean square error condition. The coefficient a in the intelligent pressure transmitter data fusion technology. ~a[sub]t[/sub] is controlled and output by a microcontroller. 3 Hardware Design The intelligent pressure transmitter consists of a front-end circuit composed of pressure, static pressure and temperature sensors; it is connected to an A/D converter via a multiplexer; it uses an 8o31 microcontroller, which has the advantages of a 16-bit high-speed microcontroller, strong computing power, and simple interface; it is externally connected to F, PROM and RAM storage space, latches, a display and printer. The microcontroller processes the signal and then sends it to the D/A converter, and outputs a standard current through the V/I conversion circuit. The system hardware structure block diagram is shown in Figure 2. 4 Software Design This system develops three software programs: the main program is used to control the operation of the entire system {the data fusion technology flowchart is used to determine constant coefficients and calculate outputs {the third software system is the intelligent transmitter nonlinear correction (scaling transformation) subroutine. 5 Test Results Through simulation experiments and software processing of intelligent transmitter data fusion technology, the experimental calibration values ​​are shown in Table 1. As shown in Table 1, the pressure output signal UP decreases as the working temperature increases. Within the working temperature range of -10℃ to 50℃, when the input pressure is 2500 Pa, the maximum change in output value with temperature is ΔU[sub]max[/sub]. Therefore, a[sub]t[/sub] = 23.6/60×118.5 = 3.3×10 /℃. It can be seen that when the temperature changes by 1℃, the pressure output value of the sensor changes somewhat. (2) Calculate the zero-point temperature coefficient of the pressure sensor. According to the definition: It can be seen that when the temperature changes by 1℃, the drift of the pressure sensor is very small. 6 Conclusion In summary , although the intelligent pressure transmitter uses sensor sampling signals, due to the use of multi-sensor fusion technology and single-chip microcomputer control and processing, the cross sensitivity of the sensor is very small. The measurement accuracy of the self-developed intelligent pressure transmitter is high, basically meeting the main performance indicators of the instrument. It has stable performance and reliable operation, and is widely used in petroleum, chemical metallurgy, power, light industry and other industries. [References] [1] Proceedings of the 4th National Conference on Sensitive Documents and Sensors, 1995, 5. 39-41. [2] Ji Junhua et al., Intelligent Sensor Systems [M]. Journal of Electronics, 1999. [3] Journal of Instrumentation, 1997. (5). 202 503. Research on Intelligent Pressure Transmitters: PDF