Accuracy Calibration Method for Differential Pressure Sensors Used for High-Temperature Environment Monitoring

Abstract

This invention discloses a method for calibrating the accuracy of a differential pressure sensor for high-temperature environment monitoring, belonging to the field of sensor calibration technology. The method includes: extracting high-temperature factors and designing simulation parameters from the application scenario information of the differential pressure sensor to obtain a high-temperature test parameter table; setting up a standard differential pressure source in a controllable high-temperature environment simulation chamber; acquiring a set of multi-high-temperature parameter differential pressure test data from the standard differential pressure source; establishing a set of multi-high-temperature parameter differential pressure compensation models; obtaining a set of high-temperature calibration coefficient differential pressure compensation models; and performing calibration matching calibration on the application scenario information of the differential pressure sensor to be calibrated to obtain the accuracy calibration result of the differential pressure sensor. This method solves the technical problem of existing differential pressure sensor accuracy calibration methods, which struggle to effectively calibrate the measurement accuracy of differential pressure sensors in high-temperature environments, leading to poor sensor measurement accuracy and stability. It achieves the technical effect of improving the measurement accuracy, stability, and reliability of differential pressure sensors.

Description

Technical Field

[0001] This application relates to the field of sensor calibration technology, and in particular to a method for calibrating the accuracy of differential pressure sensors used for high-temperature environment monitoring. Background Technology

[0002] With the continuous improvement of industrial automation, differential pressure sensors, as an important measurement tool, have been widely used in many fields, especially in monitoring and control scenarios in high-temperature environments such as industrial production, aerospace, and energy exploration. Differential pressure sensors can provide accurate differential pressure measurement data for various key equipment. However, in high-temperature environments, due to factors such as material thermal expansion, electronic component performance drift, and thermal noise interference during signal transmission, the performance of differential pressure sensors is often affected by temperature fluctuations, which can easily lead to measurement errors and decreased accuracy. How to accurately calibrate and compensate for the impact of high-temperature environments on the accuracy of differential pressure sensors has become a technical problem that urgently needs to be solved. Traditional differential pressure sensor calibration methods are usually carried out at room temperature and do not take into account the complex influencing factors in high-temperature environments. The material properties of the sensor, the working state of the components, and the stability are all significantly affected by temperature, resulting in deviations in the sensor's output signal. If effective compensation and calibration cannot be performed, the accuracy of the measurement results of the differential pressure sensor cannot be guaranteed.

[0003] Currently, there is a technical problem in differential pressure sensor accuracy calibration technology: it is difficult to effectively calibrate the measurement accuracy of differential pressure sensors in high-temperature environments, resulting in poor sensor measurement accuracy and stability. Summary of the Invention

[0004] This application provides a method for calibrating the accuracy of differential pressure sensors for high-temperature environment monitoring. This method solves the technical problem that existing differential pressure sensor accuracy calibration methods are difficult to use in high-temperature environments, resulting in poor sensor measurement accuracy and stability. The method achieves the technical effect of improving the measurement accuracy, stability and reliability of differential pressure sensors.

[0005] This application provides a method for calibrating the accuracy of a differential pressure sensor for high-temperature environment monitoring, comprising: acquiring application scenario information of the differential pressure sensor; extracting high-temperature factors and designing simulation parameters based on the application scenario information of the differential pressure sensor to obtain a sensor high-temperature test parameter table; establishing a controllable high-temperature environment simulation chamber; setting a standard differential pressure source in the controllable high-temperature environment simulation chamber; installing a target differential pressure sensor in the controllable high-temperature environment simulation chamber and connecting it to the standard differential pressure source; performing high-temperature simulation control on the controllable high-temperature environment simulation chamber according to the sensor high-temperature test parameter table, while simultaneously acquiring a set of multi-high-temperature parameter differential pressure test data of the standard differential pressure source through the target differential pressure sensor; performing differential pressure influence analysis on the multi-high-temperature parameter differential pressure test data set and the standard differential pressure source respectively, and establishing a set of multi-high-temperature parameter differential pressure compensation models; calibrating and setting the set of multi-high-temperature parameter differential pressure compensation models using the sensor high-temperature test parameter table to obtain a set of high-temperature calibration coefficient differential pressure compensation models; and performing calibration matching calibration based on the application scenario information of the differential pressure sensor to be calibrated to obtain the differential pressure sensor accuracy calibration result.

[0006] In a possible implementation, obtaining the sensor high-temperature test parameter table further involves the following processing: extracting high-temperature factors from the differential pressure sensor application scenario information to obtain high-temperature application scenario factor information, including temperature range, temperature rise fluctuation, high-temperature duration, and temperature rise rate; performing simulated parameter analysis on the differential pressure sensor application scenario information based on the high-temperature application scenario factor information to obtain a multi-scenario sensor high-temperature test parameter set; performing sensor accuracy correlation analysis on the multi-scenario sensor high-temperature test parameter set to map and obtain a sensor accuracy correlation parameter set; and arranging simulated parameters based on the multi-scenario sensor high-temperature test parameter set and the sensor accuracy correlation parameter set to obtain the sensor high-temperature test parameter table.

[0007] In a possible implementation, the establishment of the multi-temperature parameter differential pressure compensation model set further includes the following processing: performing noise characteristic analysis and filter algorithm matching on the multi-temperature parameter differential pressure test data set to determine the differential pressure data digital filter; performing filtering preprocessing on the multi-temperature parameter differential pressure test data set based on the differential pressure data digital filter to obtain a multi-temperature parameter standard differential pressure test data set; performing sensor performance influence fitting on the multi-temperature parameter differential pressure test data set to generate a multi-temperature parameter sensor performance influence model set; and updating the multi-temperature parameter sensor performance influence model set with differential pressure compensation based on the standard differential pressure source to establish the multi-temperature parameter differential pressure compensation model set.

[0008] In a possible implementation, the generation of the multi-high-temperature parameter sensor performance influence model set further includes the following processing: Based on the multi-high-temperature parameter differential pressure test data set, determine the multi-scenario sensor high-temperature test parameter type data and the corresponding sensor accuracy-related parameter change data; use the multi-scenario sensor high-temperature test parameter type data as independent variables, and the corresponding sensor accuracy-related parameter change data for each type as dependent variables; perform influence regression fitting on the independent variables and the dependent variables respectively to obtain a multi-scenario sensor accuracy-related parameter influence regression model set; and perform weighted fusion of the multi-scenario sensor accuracy-related parameter influence regression model set according to the high-temperature test parameter type to generate the multi-high-temperature parameter sensor performance influence model set.

[0009] In a possible implementation, establishing the multi-high temperature parameter differential pressure compensation model set further includes the following processes: performing differential pressure prediction based on the multi-high temperature parameter sensor performance influence model set to obtain a multi-high temperature parameter differential pressure prediction information set; calculating and comparing the difference between the standard differential pressure source and the multi-high temperature parameter differential pressure prediction information set to obtain a multi-high temperature parameter differential pressure deviation set; constructing a differential pressure compensation strategy and integrating the differential pressure compensation strategy into the multi-high temperature parameter sensor performance influence model set; and correcting and compensating the multi-high temperature parameter sensor performance influence model set according to the differential pressure compensation strategy based on the multi-high temperature parameter differential pressure deviation set to establish the multi-high temperature parameter differential pressure compensation model set.

[0010] In a possible implementation, obtaining the differential pressure sensor accuracy calibration result further involves the following processing: classifying the application scenario information of the differential pressure sensor to be calibrated using the sensor high-temperature test parameter table to determine the calibration coefficients for the calibration scenario; performing calibration matching based on the calibration coefficients for the calibration scenario and the differential pressure compensation model set based on the high-temperature calibration coefficients to obtain an applicable differential pressure compensation model; and performing accuracy calibration on the differential pressure test data of the differential pressure sensor to be calibrated based on the applicable differential pressure compensation model to obtain the differential pressure sensor accuracy calibration result.

[0011] In a possible implementation, the differential pressure sensor accuracy calibration method for high-temperature environment monitoring further includes the following steps: extracting model parameters of the applicable differential pressure compensation model, initializing the parameter population, and simultaneously performing performance verification and evaluation on the applicable differential pressure compensation model to obtain model performance evaluation parameters; analyzing the model optimization direction of the applicable differential pressure compensation model based on the model performance evaluation parameters to determine the optimization direction of the model parameters; performing crossover mutation and population update on the parameter population according to the optimization direction of the model parameters until a preset termination condition is met, comparing and optimizing to determine the optimal population parameters; and optimizing the configuration of the applicable differential pressure compensation model based on the optimal population parameters to obtain an optimized differential pressure compensation model.

[0012] In a possible implementation, the differential pressure sensor accuracy calibration method for high-temperature environment monitoring further performs the following processing: identifying the uncertainty sources of the differential pressure sensor to be calibrated to obtain the sensor test uncertainty source factors; performing accuracy loss analysis based on the sensor test uncertainty source factors to obtain the sensor test accuracy loss factor; and supplementing and correcting the differential pressure sensor accuracy calibration results based on the sensor test accuracy loss factor.

[0013] This application proposes a differential pressure sensor accuracy calibration method for high-temperature environment monitoring. The method involves extracting high-temperature factors and designing simulation parameters based on the sensor's application scenario information to obtain a high-temperature test parameter table. A standard differential pressure source is set up in a controllable high-temperature environment simulation chamber. A multi-high-temperature parameter differential pressure test data set from the standard differential pressure source is acquired using a target differential pressure sensor. A multi-high-temperature parameter differential pressure compensation model set is established. This model set is calibrated to obtain a high-temperature calibration coefficient differential pressure compensation model set. Finally, the application scenario information of the differential pressure sensor to be calibrated is used for calibration matching to obtain the differential pressure sensor accuracy calibration result. This method solves the technical problem of existing differential pressure sensor accuracy calibration methods, which struggle to effectively calibrate the sensor's measurement accuracy in high-temperature environments, leading to poor sensor measurement accuracy and stability. It achieves the technical effect of improving the measurement accuracy, stability, and reliability of differential pressure sensors. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0015] Figure 1A schematic flowchart illustrating the accuracy calibration method for a differential pressure sensor used for high-temperature environment monitoring, provided in an embodiment of this application.

[0016] Figure 2 This is a schematic diagram illustrating the process of establishing a set of multi-high-temperature parameter differential pressure compensation models in the differential pressure sensor accuracy calibration method for high-temperature environment monitoring provided in the embodiments of this application. Detailed Implementation

[0017] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application.

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of this application will be provided in conjunction with the accompanying drawings. The described embodiments should not be considered as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] In the following description, references to "some embodiments" describe a subset of all possible embodiments. However, it is understood that "some embodiments" may be the same or different subsets of all possible embodiments and may be combined with each other without conflict. The terms "first" and "second" are used merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only.

[0020] This application provides a method for calibrating the accuracy of a differential pressure sensor for high-temperature environment monitoring, such as... Figure 1 As shown, the method includes:

[0021] Step S100: Obtain application scenario information of differential pressure sensor, extract high temperature factors and design simulation parameters based on the application scenario information of differential pressure sensor, and obtain sensor high temperature test parameter table.

[0022] Preferably, acquiring differential pressure sensor application scenario information refers to collecting detailed information related to the actual environmental conditions and operational requirements of the differential pressure sensor. This typically includes the temperature range of the application environment (the minimum, maximum, and average temperatures in the sensor's operating environment), temperature fluctuation characteristics (in some high-temperature scenarios, temperatures may fluctuate rapidly or change periodically, such as high-temperature furnaces and engine compartments in the metallurgical industry), differential pressure range under different temperature conditions, and the fact that some high-temperature scenarios involve confined spaces or complex equipment layouts, which may affect sensor installation and operation. The process involves extracting high-temperature factors and designing simulation parameters based on the differential pressure sensor application scenario information; that is, extracting the main high-temperature factors affecting sensor performance based on the collected scenario information. Factors affecting sensor accuracy are identified, and corresponding test and simulation parameters are designed. For example, it is necessary to analyze which high-temperature factors affect sensor accuracy, typically including material expansion, changes in component characteristics, and the impact of temperature on electronic signal transmission. Based on the extracted high-temperature factors, a set of simulation parameters is set. These parameters include the specific temperature value for high-temperature testing, the rate of temperature change, the pressure difference range at different temperatures, and the time period required during the simulation. Finally, the high-temperature factors and simulation parameters are integrated to generate a parameter table for actual testing, namely the sensor high-temperature test parameter table. This table may include the temperature range and rate of change, specify the test conditions at different temperatures, the corresponding pressure difference range at different temperatures, the test cycle and frequency, and simulate the working cycle that the sensor needs to withstand in a high-temperature environment.

[0023] In one possible implementation, step S100 further includes step S110, extracting high-temperature factors from the differential pressure sensor application scenario information to obtain high-temperature application scenario factor information, including temperature range, temperature fluctuation, high-temperature duration, and temperature rise rate; step S120, performing simulation parameter analysis on the differential pressure sensor application scenario information based on the high-temperature application scenario factor information to obtain a multi-scenario sensor high-temperature test parameter set; step S130, performing sensor accuracy correlation analysis on the multi-scenario sensor high-temperature test parameter set to map and obtain a sensor accuracy correlation parameter set; and step S140, arranging simulation parameters based on the multi-scenario sensor high-temperature test parameter set and the sensor accuracy correlation parameter set to obtain the sensor high-temperature test parameter table.

[0024] Preferably, key information related to high temperatures is extracted from the actual application scenarios of differential pressure sensors, including temperature range, temperature fluctuation, high-temperature duration, and heating rate. By analyzing each factor in the high-temperature application scenario, high-temperature parameters in the actual working environment are simulated. Key conditions such as temperature changes and time periods in the application scenario are analyzed to generate corresponding test parameters. The analysis may include temperature changes from low to high, sensor performance predictions within different temperature ranges, and sensor response to temperature fluctuations. A set of test parameters is generated based on different high-temperature application scenarios, namely a multi-scenario sensor high-temperature test parameter set, containing simulated test data of the sensor under different high-temperature conditions, reflecting the sensor's performance in different high-temperature situations. By analyzing the high-temperature test parameters under multiple scenarios, the influence of factors such as temperature on the sensor's measurement accuracy is explored, and the sensor's performance under different high-temperature conditions is identified. The variation pattern of accuracy, for example, within a certain temperature range, the sensor may exhibit specific error characteristics. These errors are related to factors such as temperature range and heating rate, mapping to a set of parameters related to sensor accuracy, namely the accuracy-related parameter set. This set includes various factors closely related to sensor accuracy, such as zero-point and range offset, measurement result deviation, the influence of temperature range on measurement error, and the influence of heating rate on response speed. Combining the multi-scenario sensor high-temperature test parameter set and the sensor accuracy-related parameter set, different temperature ranges, heating rates, high-temperature durations, and other factors are arranged and combined through permutation and combination to cover various possible high-temperature test situations, generating a complete set of test parameter schemes. This ensures that the sensor can be fully tested and calibrated under different high-temperature conditions. The final generated test parameter table contains all the parameters that need to be tested under high-temperature conditions.

[0025] Step S200: Establish a high-temperature environment controllable simulation chamber, set up a standard differential pressure source in the high-temperature environment controllable simulation chamber, and install a target differential pressure sensor in the high-temperature environment controllable simulation chamber and connect it to the standard differential pressure source.

[0026] Preferably, a laboratory capable of precisely controlling and simulating a high-temperature environment is established to test the performance of differential pressure sensors under controlled conditions. Specifically, this simulation chamber must have a precise temperature control system that can set and adjust the temperature range according to testing requirements, typically covering the lowest and highest temperatures in the target application scenario. The temperature control system must provide a stable temperature and simulate temperature fluctuations that may occur in the application scenario, ensuring the accuracy and repeatability of test data. Since the simulation chamber needs to reach high temperatures, it must have good insulation design and safety measures to prevent damage to operators or the external environment from high temperatures. A precise and controllable standard differential pressure generator is installed in the simulation chamber to generate accurately known differential pressure values ​​as reference points for testing and calibration. This standard differential pressure source is a highly accurate and stable device capable of generating a series of known differential pressure values ​​for sensor measurement and comparison. It not only provides accurate differential pressure but also maintains stable differential pressure values ​​at different temperatures, ensuring that the impact of temperature changes on the differential pressure source is negligible. It is typically equipped with high-precision control... The system can precisely regulate the pressure difference by adjusting the internal airflow or liquid flow. A standard differential pressure source provides a precise reference value for the target sensor. The differential pressure data measured by the sensor is compared with the output of the standard differential pressure source to detect its accuracy and response changes under different temperature conditions. The differential pressure sensor to be calibrated is installed in a high-temperature environment simulation chamber and directly connected to the standard differential pressure source through a pipe or interface. Specifically, the target differential pressure sensor needs to be installed in an appropriate location in the simulation chamber to ensure that it is in a high-temperature environment, ensuring that the sensor is exposed to the required temperature conditions and can collect differential pressure data under high-temperature conditions in real time. The input end of the target differential pressure sensor is connected to the standard differential pressure source through a dedicated interface or pipe. It is essential to ensure unobstructed airflow or liquid flow to avoid resistance or leakage in the pipe affecting the test results. Through this connection, the sensor can receive the precise differential pressure value generated by the standard differential pressure source in real time and compare it with the sensor output. By comparing the sensor's output signal with the standard value, the measurement error of the sensor at different temperatures can be obtained, thus providing a basis for the subsequent calibration process.

[0027] Step S300: Perform high-temperature simulation control on the high-temperature environment controllable simulation chamber according to the sensor high-temperature test parameter table, and at the same time, acquire the multi-high-temperature parameter differential pressure test data set of the standard differential pressure source through the target differential pressure sensor.

[0028] Preferably, the temperature of the high-temperature environment controllable simulation chamber is set and adjusted according to the high-temperature test parameter table. For example, the parameter table may require the simulation chamber to reach 300°C at a certain stage and maintain it for a period of time, and then gradually increase to 400°C, or simulate temperature fluctuations. These all need to be precisely executed by the temperature control system of the simulation chamber. The control system gradually adjusts the temperature in the high-temperature environment according to the instructions of the sensor's high-temperature test parameter table to ensure that the temperature change process meets the test requirements. For example, gradual heating or cooling in different temperature ranges, maintaining a sufficiently long time at each set temperature point, and simulating temperature fluctuation characteristics (such as periodic heating and cooling). At the same time, a multi-high-temperature parameter differential pressure test data set of the standard differential pressure source is acquired through the target differential pressure sensor. At each temperature point, the differential pressure sensor measures the differential pressure data corresponding to that temperature. For example, at multiple temperature points such as 200℃, 300℃, and 400℃, the differential pressure sensor measures the differential pressure provided by the standard differential pressure source and records this data. Each time the temperature changes, the differential pressure sensor collects differential pressure data at multiple different temperatures, forming a multi-temperature, multi-differential pressure test data set. This process is repeated multiple times, covering all high-temperature conditions specified in the parameter table. Specifically, at each temperature range, the sensor records the output differential pressure value from the standard differential pressure source. The sensor's performance under each high-temperature condition is recorded and compared with the actual output of the standard differential pressure source. Through multiple temperature adjustments and data acquisitions, a set of differential pressure measurement data at multiple temperatures is obtained, namely, a multi-high-temperature parameter differential pressure test data set, including the differential pressure sensor output data at each specific temperature and the differential pressure value of the standard differential pressure source at the same temperature.

[0029] Step S400: Based on the multi-high temperature parameter differential pressure test data set, perform differential pressure influence analysis with the standard differential pressure source respectively, and establish a multi-high temperature parameter differential pressure compensation model set.

[0030] Preferably, under high-temperature conditions, a detailed comparative analysis is conducted between the actual measured values ​​of the differential pressure sensor and the reference values ​​of the standard differential pressure source to identify the impact of high temperature on sensor accuracy. A corresponding set of compensation models is then established to improve the sensor's measurement accuracy under different high-temperature environments. Specifically, the differential pressure test data of multiple high-temperature parameters collected by the sensor is compared with the reference differential pressure values ​​provided by the standard differential pressure source. By comparing the differences between the sensor's output data and the standard differential pressure at different temperatures, the degree of temperature influence on the sensor's measurement results is identified. For example, at 300℃, the differential pressure value provided by the standard differential pressure source is 100 Pa, while the sensor's measured value may be 95 Pa. The effect of temperature on the sensor output is determined to be -5 Pa. Based on the comparison results, the error trend of temperature on the sensor's measurement results is analyzed, which may show that as the temperature rises... The temperature may increase or decrease, or exhibit nonlinear changes. By analyzing the relationship between the sensor output value and the standard differential pressure value at various temperature points, the specific influence of temperature on differential pressure measurement is determined. Based on the above influence analysis, a compensation model is established to correct the error of the differential pressure sensor under different high-temperature conditions. The compensation model is used to adjust the sensor output value so that it is closer to the actual value provided by the standard differential pressure source under high-temperature conditions. Since the error of the sensor may be different at different temperatures, multiple compensation models need to be established for different temperature ranges. Finally, a multi-high-temperature parameter differential pressure compensation model is obtained. Each model is designed for different high-temperature parameters (such as temperature, pressure range, etc.) to help the differential pressure sensor automatically adjust the output results in high-temperature environments to compensate for the influence of high temperature on sensor performance, thereby ensuring the accuracy of the sensor under various temperature conditions.

[0031] In one possible implementation, such as Figure 2 As shown, step S400 further includes step S410, performing noise characteristic analysis and filter algorithm matching on the multi-high temperature parameter differential pressure test data set to determine the differential pressure data digital filter; step S420, performing filtering preprocessing on the multi-high temperature parameter differential pressure test data set based on the differential pressure data digital filter to obtain a multi-high temperature parameter standard differential pressure test data set; step S430, performing sensor performance influence fitting on the multi-high temperature parameter differential pressure test data set to generate a multi-high temperature parameter sensor performance influence model set; step S440, updating the multi-high temperature parameter sensor performance influence model set with differential pressure compensation based on the standard differential pressure source to establish the multi-high temperature parameter differential pressure compensation model set.

[0032] Preferably, differential pressure test data collected in high-temperature environments may be subject to various noise interferences (e.g., electromagnetic interference from the environment, equipment vibration, etc.). The purpose of noise characteristic analysis is to identify the type, frequency distribution, amplitude, and other characteristics of these noises. For example, through spectrum analysis or statistical methods, the distribution of high-frequency noise, low-frequency drift, or random noise in the test data can be analyzed to determine the main sources and characteristics of the noise. Based on the noise characteristic analysis results, appropriate digital filter algorithms (such as low-pass filters, band-pass filters, Kalman filters, etc.) can be selected or designed to eliminate the interference of noise on the differential pressure test data. For example, if the noise is mainly high-frequency components, A low-pass filter can be selected to suppress high-frequency noise. If the noise is randomly drifting, a Kalman filter can be considered for dynamic adjustment. Based on the above analysis results, a digital filter suitable for specific differential pressure data is determined, including the filter type, order, and parameter settings. The collected multi-high-temperature parameter differential pressure test data set is filtered to eliminate noise interference in the data and extract a more realistic and accurate differential pressure signal. Each set of differential pressure data is processed to remove high-frequency noise, drift, and other interference components, making the data smoother and more stable. A multi-high-temperature parameter standard differential pressure test data set is generated to better reflect the sensor's true differential pressure response under high-temperature conditions.

[0033] Preferably, sensor performance impact fitting is performed based on a multi-high-temperature parameter differential pressure test data set to evaluate the specific impact of the high-temperature environment on the differential pressure sensor performance, including measurement error and sensitivity changes. Specifically, based on the multi-high-temperature data, curve fitting is performed between the sensor output and the reference data of the standard differential pressure source to generate a mathematical model (e.g., linear fitting, polynomial fitting, etc.) to describe the sensor performance changes. This model reflects the output change trend of the sensor under different high-temperature conditions and can describe the sensor's error characteristics. A corresponding performance impact model is generated for each high-temperature condition, and each model describes the sensor performance changes at a specific temperature (e.g., decreased sensitivity, slower response time, etc.), forming a multi-high-temperature parameter sensor performance impact model set. After establishing the sensor performance impact model set, these models are further compensated by combining the data from the standard differential pressure source. The update process corrects the sensor's error characteristics, making the sensor output closer to the true value of the standard differential pressure source. Specifically, based on the reference differential pressure value of the standard differential pressure source, the performance impact model is updated one by one, enabling effective compensation for sensor errors under different high-temperature conditions. This can be achieved by adjusting parameters in the model or introducing a compensation function to correct the sensor output. After the differential pressure compensation update, a set of multi-high-temperature parameter differential pressure compensation models is finally generated. This model is used to automatically adjust the sensor's output during actual operation, ensuring that the sensor maintains high measurement accuracy even under different high-temperature conditions. For example, for a certain high-temperature range, the compensation model may automatically add or subtract a correction value based on the sensor's output, making the final measurement result closer to the true value. This dynamic compensation of sensor errors under different high-temperature conditions ensures the sensor's measurement accuracy in high-temperature environments.

[0034] In one possible implementation, step S430 further includes step S431, determining the types of high-temperature test parameters for multiple scenarios and the corresponding sensor accuracy-related parameter change data based on the multi-high-temperature parameter differential pressure test data set; step S432, using the types of high-temperature test parameters for multiple scenarios as independent variables, and using the corresponding types of related parameter data in the sensor accuracy-related parameter change data as dependent variables in sequence; step S433, performing influence regression fitting on the independent variables and the dependent variables respectively to obtain a set of influence regression models for multi-scenario sensor accuracy-related parameters; and step S434, performing weighted fusion of the set of influence regression models for multi-scenario sensor accuracy-related parameters according to the high-temperature test parameter types to generate the set of influence models for multi-high-temperature parameter sensor performance.

[0035] Preferably, based on a set of high-temperature parameter differential pressure test data, the types of high-temperature test parameters for sensors in various scenarios and the variation data of sensor accuracy-related parameters are determined. Specifically, the sensor test parameter data collected under different high-temperature scenarios covers the sensor's operation under different temperature and differential pressure conditions, such as temperature range, measured differential pressure value, sensor operating cycle, and environmental variables (such as humidity, pressure fluctuations, etc.). The variation data of sensor accuracy-related parameters refers to the change in sensor accuracy with parameters under different high-temperature scenarios, i.e., how the high-temperature environment affects the sensor's measurement accuracy, including sensor measurement error, change in response speed, decrease in sensitivity, and drift of output signal. The types of high-temperature test parameter data for sensors in various scenarios, such as temperature, pressure, and working time, which affect sensor accuracy, are used as independent variables in the regression analysis. The various types of parameters in the variation data of sensor accuracy-related parameters, including measurement error, response speed, and sensitivity change, are used as dependent variables. Regression fitting is performed on each type of sensor accuracy-related parameter variation data to find the relationship between each type of accuracy parameter and the test conditions (independent variables), such as the influence of temperature on measurement error, the influence of differential pressure on sensitivity, and the influence of working time on response speed.

[0036] Preferably, regression analysis is used to find the mathematical relationship between independent variables (such as temperature, pressure difference, etc.) and dependent variables (such as measurement error, sensitivity, etc.). That is, using known independent variables, the changing trend of the dependent variable is predicted. Since different accuracy-related parameters (dependent variables) may be affected by different types of high-temperature test parameters (independent variables), each independent variable-dependent variable pair generates a regression model. The set of multiple regression models is the set of regression models for the influence of sensor accuracy-related parameters in multiple scenarios, including regression models for the influence of sensor accuracy-related parameters corresponding to each type of high-temperature test parameter. Each model can describe the influence of a certain independent variable on a specific sensor performance parameter under different high-temperature conditions. The output results of multiple regression models are weighted and averaged, and each model is given equal weight (i.e., average weight) to generate a comprehensive performance influence model. This model integrates the influence of various test parameters on sensor performance, making the model more representative. Finally, a comprehensive model set containing sensor performance changes under multiple high-temperature conditions is obtained. Each model corresponds to a specific type of high-temperature test parameter and can describe the sensor performance changes under different conditions. This model set can be used to predict the performance of sensors in different high-temperature environments.

[0037] In one possible implementation, step S440 further includes step S441, performing differential pressure prediction based on the multi-high temperature parameter sensor performance influence model set to obtain a multi-high temperature parameter differential pressure prediction information set; step S442, calculating and comparing the difference between the standard differential pressure source and the multi-high temperature parameter differential pressure prediction information set to obtain a multi-high temperature parameter differential pressure deviation set; step S443, constructing a differential pressure compensation strategy and integrating the differential pressure compensation strategy into the multi-high temperature parameter sensor performance influence model set; and step S444, correcting and compensating the multi-high temperature parameter sensor performance influence model set according to the differential pressure compensation strategy based on the multi-high temperature parameter differential pressure deviation set to establish the multi-high temperature parameter differential pressure compensation model set.

[0038] Preferably, models from a multi-high-temperature parameter sensor performance influence model set are used to predict pressure differences under different high-temperature scenarios. Under different temperature and pressure difference conditions, the potential pressure difference values ​​that the sensor might measure under these conditions are calculated, forming a multi-high-temperature parameter pressure difference prediction information set. This set represents a series of predicted pressure difference values ​​based on the sensor's current performance under various high-temperature scenarios. The predicted pressure difference values ​​are compared with the actual values ​​from a standard pressure difference source to obtain the sensor's pressure difference error or deviation, forming a multi-high-temperature parameter pressure difference deviation set. This set reflects the degree of deviation between the pressure difference measured by the sensor and the standard value under different high-temperature conditions. Based on the pressure difference deviation set, a compensation strategy is formulated to correct the error in the sensor's pressure difference measurement under high-temperature conditions; this is the pressure difference compensation strategy. This adjusts the sensor's predicted values ​​to be close to the standard values, thereby improving the accuracy of its measurement. The pressure difference compensation strategy is then integrated into existing... In some high-temperature parameter sensor performance influence models, a compensation strategy is automatically applied to the prediction results each time the model outputs a differential pressure prediction, thereby improving the model's accuracy. For example, a compensation value is added or subtracted from the model's predicted value to reduce the deviation from the standard value. Based on the established differential pressure compensation strategy, the deviation data in the differential pressure deviation set of multiple high-temperature parameters is used to adjust and correct the sensor performance influence model. It is automatically adjusted according to the predicted differential pressure error. By applying the compensation strategy, the model takes the original error value into account and adjusts the model's output in real time according to the deviation data, improving the model's prediction accuracy and making the sensor's measurement more accurate under high-temperature conditions. The set of models after correction and compensation is the final differential pressure compensation model set. These models can accurately predict differential pressure under different high-temperature conditions and automatically compensate according to the actual deviation, ensuring the accuracy of the measurement results.

[0039] Step S500: Use the sensor high temperature test parameter table to calibrate the multi-high temperature parameter differential pressure compensation model set to obtain the high temperature calibration coefficient differential pressure compensation model set.

[0040] Preferably, the generated multi-high-temperature parameter differential pressure compensation model is further calibrated and precisely adjusted using the sensor's high-temperature test parameter table. This ensures that these models can accurately reflect the sensor's differential pressure measurement under various high-temperature environments. Specifically, calibration refers to comparing and adjusting the differential pressure compensation model with actual test data to make the model's output more accurately reflect the sensor's true measurement value under high-temperature environments. Various parameters (such as temperature and differential pressure) from the sensor's high-temperature test parameter table are input into the compensation model, and by adjusting model parameters (such as compensation coefficients and correction values), the model's output (i.e., the predicted differential pressure value) is made as close as possible to the value of the actual standard differential pressure source. During the calibration process, key coefficients in the model are continuously adjusted to ensure that the model responds more accurately to high-temperature environments and reduce the measurement error of the sensor under different temperature conditions. The high-temperature calibration coefficient is a correction coefficient generated during the calibration process, which reflects the sensor's error and performance deviation under different high-temperature scenarios. By introducing calibration coefficients to adjust the compensation model, the model output becomes more accurate. That is, by introducing high-temperature calibration coefficients, the original multi-high-temperature parameter differential pressure compensation model is optimized to obtain a set of high-temperature calibration coefficient differential pressure compensation models, which can better reflect the actual performance of the sensor in high-temperature environments, further reduce measurement errors, and ensure its measurement accuracy under different high-temperature conditions.

[0041] Step S600: Based on the application scenario information of the differential pressure sensor to be calibrated using the high-temperature calibration coefficient differential pressure compensation model set, calibration matching calibration is performed to obtain the differential pressure sensor accuracy calibration result.

[0042] Preferably, an established high-temperature calibration coefficient differential pressure compensation model is used to calibrate and match the differential pressure sensor to be calibrated under the specific operating conditions of the application scenario. This ensures the sensor's measurement accuracy under these conditions. Specifically, based on the application scenario information of the sensor to be calibrated, the most suitable model is selected from the set of high-temperature calibration coefficient differential pressure compensation models. This includes finding the corresponding compensation model from the model set based on the sensor's application temperature, differential pressure range, and other conditions. The sensor's operating data is then input into the model, and the sensor's output is corrected through the calibration coefficients and compensation mechanism in the model. In the actual calibration process, the sensor's measurement data will be compared with the compensated model. The model output is compared to confirm whether the sensor can accurately reflect the true differential pressure value under high temperature conditions. If there is a deviation, the model will automatically apply compensation and adjust the sensor output until the accuracy requirements are met. If the sensor's application scenario information (such as temperature, pressure, etc.) changes during actual operation, the compensation model will adjust in real time according to these dynamic changes to ensure that the sensor output remains accurate under different conditions. Finally, the differential pressure sensor accuracy calibration result is obtained, which reflects the calibration effect of the sensor in a specific application scenario. Through multiple calibration adjustments to the sensor output, the error is minimized throughout the high temperature range, ensuring its reliability and accuracy in practical applications.

[0043] In one possible implementation, step S600 further includes step S610, classifying the application scenario information of the differential pressure sensor to be calibrated using the sensor high-temperature test parameter table, and determining the calibration coefficients for the calibration scenario; step S620, performing calibration matching based on the calibration coefficients for the calibration scenario and the differential pressure compensation model set of the high-temperature calibration coefficients to obtain an applicable differential pressure compensation model; and step S630, performing accuracy calibration on the differential pressure test data of the differential pressure sensor to be calibrated based on the applicable differential pressure compensation model to obtain the accuracy calibration result of the differential pressure sensor.

[0044] Preferably, based on the sensor's application scenario information, it is assigned to a predefined high-temperature test scenario. For example, a certain application scenario may be classified as a high pressure differential range of 300°C to 400°C. According to the classification result, a calibration coefficient matching the scenario is determined to correct the sensor's error under different high temperature and pressure differential conditions. According to the calibration coefficient of the scenario to be calibrated, it is matched with the generated high-temperature calibration coefficient pressure differential compensation model set. That is, the compensation model most suitable for the current application scenario is selected from the model set. According to the temperature and pressure differential conditions of the scenario to be calibrated, specific calibration coefficients are applied for adjustment to ensure the most accurate compensation result. The appropriate pressure differential compensation model is used to calibrate the sensor's pressure differential test data. Specifically, the actual measurement data in the scenario to be calibrated is used as input. The compensation model will apply calibration coefficients to correct the sensor's measurement error according to the sensor's actual output value and the corresponding temperature and pressure differential conditions, ensuring that the sensor's output pressure differential value is closer to the actual standard value. Finally, the sensor output corrected by the compensation model is the accuracy calibration result of the pressure differential sensor. The calibration result reflects the sensor's measurement accuracy under actual working conditions and confirms whether the sensor meets the accuracy requirements.

[0045] In one possible implementation, step S620 further includes step S621, extracting model parameters of the applicable differential pressure compensation model, initializing the parameter population, and simultaneously performing performance verification and evaluation on the applicable differential pressure compensation model to obtain model performance evaluation parameters; step S622, performing model optimization direction analysis on the applicable differential pressure compensation model based on the model performance evaluation parameters to determine the optimization direction of the model parameters; step S623, performing crossover mutation and population update on the parameter population according to the optimization direction of the model parameters until a preset termination condition is met, and comparing and optimizing to determine the optimal population parameters; step S624, optimizing the configuration of the applicable differential pressure compensation model based on the optimal population parameters to obtain an optimized differential pressure compensation model.

[0046] Preferably, model parameters suitable for the differential pressure compensation model are extracted, which may include differential pressure correction coefficients, temperature compensation coefficients, nonlinearity correction parameters, etc. A certain number of population individuals are randomly generated based on the model parameters, with each population individual representing a set of model parameters. The parameter population is a set of several possible parameter values. Using the currently extracted model parameters, the performance of the suitable differential pressure compensation model is evaluated to determine the model's effectiveness and accuracy in compensating for differential pressure sensor errors. Evaluation criteria may include the model's error correction accuracy, computational efficiency, and model stability. Through performance verification and evaluation, the obtained evaluation parameters are used to quantify the current performance of the differential pressure compensation model. By analyzing the model performance evaluation parameters, the optimization direction of the model is determined, identifying which model parameters have a significant impact on overall performance and deciding how to adjust these parameters. Based on the analysis of the model's current performance, it is decided how to adjust each parameter to optimize model performance. Prioritization is performed based on performance indicators, selecting the direction that best improves model performance. Optimization directions may include improving model accuracy (e.g., reducing differential pressure error), reducing computational complexity, and improving model stability. Cross-referencing involves combining two or more parameters... Several population combinations generate new parameter sets. Mutation involves randomly making small adjustments to certain parameters to explore new parameter combinations. Through parameter recombination and random perturbation, new and better parameter populations are generated to find better model parameter combinations. Population update refers to continuously eliminating poorly performing parameter sets and retaining well-performing ones, iteratively generating better model parameters, i.e., evaluating the performance of new parameter combinations, selecting the best-performing parameter combination to enter the next round of optimization, until a preset termination condition is met. By continuously comparing the performance of different parameter sets, the optimal parameter combination, i.e., the optimal population parameters, is finally found, which can maximize the performance of the applicable differential pressure compensation model and ensure its accuracy and stability when compensating for sensor errors in high-temperature environments. Finally, the applicable differential pressure compensation model is optimized and configured using the optimal population parameters. That is, the found optimal parameters are applied to the compensation model, adjusting the model's structure and parameter settings so that the model can achieve the best compensation effect, resulting in an applicable differential pressure optimized compensation model that can not only correct sensor errors but also maintain high measurement accuracy and compensation effect in high-temperature and complex environments.

[0047] In one possible implementation, step S600 further includes step S640, identifying the uncertainty sources of the differential pressure sensor to be calibrated and obtaining the sensor test uncertainty source factors; step S650, performing accuracy loss analysis based on the sensor test uncertainty source factors and obtaining the sensor test accuracy loss factor; and step S660, supplementing and correcting the differential pressure sensor accuracy calibration results based on the sensor test accuracy loss factor.

[0048] Preferably, the differential pressure sensor to be calibrated undergoes uncertainty source identification, identifying all inherent state factors that may affect the sensor's test accuracy, i.e., sensor test uncertainty source factors, such as sensor housing damage, service life, loose interfaces, electrical connection performance, etc. Each factor will have a different degree of impact on the final measurement result. For each uncertainty source factor, its specific impact on sensor measurement accuracy is evaluated. Based on the analysis of each uncertainty source factor, the impact coefficient of each factor on the overall test accuracy is calculated. The sensor test accuracy loss factor reflects the accuracy loss that occurs in the sensor during the measurement process due to uncertainty sources. Finally, the differential pressure sensor accuracy calibration result is supplemented and corrected based on the sensor test accuracy loss factor, taking into account the accuracy loss caused by uncertainty, so that the final differential pressure sensor has higher accuracy and smaller error, ensuring higher accuracy and stronger reliability of the differential pressure sensor calibration result.

[0049] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application. In some cases, the actions or steps described in this application can be performed in a different order than that shown in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for calibrating the accuracy of a differential pressure sensor used for high-temperature environment monitoring, characterized in that, The method includes: Obtain application scenario information of differential pressure sensor, extract high temperature factors and design simulation parameters from the application scenario information of differential pressure sensor, and obtain sensor high temperature test parameter table; A high-temperature environment controllable simulation chamber is established, a standard differential pressure source is set up in the high-temperature environment controllable simulation chamber, and a target differential pressure sensor is installed in the high-temperature environment controllable simulation chamber and connected to the standard differential pressure source; According to the sensor high temperature test parameter table, the high temperature environment controllable simulation chamber is subjected to high temperature simulation control, and at the same time, the target differential pressure sensor is used to collect a set of multi-high temperature parameter differential pressure test data of the standard differential pressure source. Based on the multi-high temperature parameter differential pressure test data set, differential pressure influence analysis was performed on the standard differential pressure source, and a multi-high temperature parameter differential pressure compensation model set was established. The high-temperature test parameter table of the sensor is used to calibrate and standardize the set of multi-high-temperature parameter differential pressure compensation models to obtain the set of high-temperature calibration coefficient differential pressure compensation models. Based on the application scenario information of the differential pressure sensor to be calibrated using the high-temperature calibration coefficient differential pressure compensation model set, calibration and matching calibration are performed to obtain the differential pressure sensor accuracy calibration result. The establishment of a set of multi-high temperature parameter differential pressure compensation models includes: Noise characteristics analysis and filter algorithm matching are performed on the multi-high temperature parameter differential pressure test data set to determine the digital filter for differential pressure data; Based on the differential pressure data digital filter, the multi-high temperature parameter differential pressure test data set is filtered and preprocessed to obtain the multi-high temperature parameter standard differential pressure test data set. Based on the set of differential pressure test data for multiple high-temperature parameters, the sensor performance influence is fitted to generate a set of sensor performance influence models for multiple high-temperature parameters. Based on the standard differential pressure source, the differential pressure compensation model set of the multi-high temperature parameter sensor performance influence model set is updated by performing differential pressure compensation, and the multi-high temperature parameter differential pressure compensation model set is established. The set of models for generating multiple high-temperature parameter sensor performance impacts includes: Based on the multi-high temperature parameter differential pressure test data set, determine the multi-scenario sensor high temperature test parameter type data and the corresponding sensor accuracy-related parameter change data; The data of the high temperature test parameters of the multi-scenario sensors are used as independent variables, and the data of each type of correlation parameter in the corresponding sensor accuracy correlation parameter change data are used as dependent variables in turn. By performing influence regression fitting on the independent variable and the dependent variable respectively, a set of influence regression models for the sensor accuracy correlation parameters in multiple scenarios is obtained. The set of regression models for the influence of multi-scenario sensor accuracy correlation parameters is fused by weighting according to the type of high temperature test parameters to generate the set of multi-high temperature parameter sensor performance influence models. The establishment of the multi-high temperature parameter differential pressure compensation model set includes: Based on the set of multi-high temperature parameter sensor performance influence models, pressure difference prediction is performed to obtain a set of multi-high temperature parameter pressure difference prediction information. The difference between the standard differential pressure source and the multi-high temperature parameter differential pressure prediction information set is calculated and compared to obtain the multi-high temperature parameter differential pressure deviation set. A differential pressure compensation strategy is constructed and integrated into the set of performance influence models of the multi-high temperature parameter sensor. According to the pressure difference compensation strategy, based on the pressure difference deviation set of multiple high temperature parameters, the set of multi-high temperature parameter sensor performance influence models is corrected, compensated and updated to establish the multi-high temperature parameter pressure difference compensation model set. The obtained differential pressure sensor accuracy calibration results include: The application scenario information of the differential pressure sensor to be calibrated is classified into test scenarios using the high temperature test parameter table of the sensor, and the calibration coefficient of the scenario to be calibrated is determined. Based on the calibration coefficients of the scenario to be calibrated and the set of differential pressure compensation models for high temperature calibration coefficients, calibration matching is performed to obtain an applicable differential pressure compensation model. The differential pressure test data of the differential pressure sensor to be calibrated are calibrated based on the applicable differential pressure compensation model to obtain the accuracy calibration result of the differential pressure sensor.

2. The method for calibrating the accuracy of a differential pressure sensor for high-temperature environment monitoring as described in claim 1, characterized in that, The obtained sensor high-temperature test parameter table includes: High-temperature factors are extracted from the application scenario information of the differential pressure sensor to obtain high-temperature application scenario factor information, which includes temperature range, temperature rise fluctuation, high-temperature duration and temperature rise rate. Based on the high-temperature application scenario factor information, the differential pressure sensor application scenario information is sequentially simulated and analyzed to obtain a set of high-temperature test parameters for multiple scenarios. A sensor accuracy correlation analysis is performed on the set of high-temperature test parameters for the multi-scenario sensors to obtain a set of sensor accuracy correlation parameters. Based on the set of high-temperature test parameters for the multi-scenario sensors and the set of sensor accuracy-related parameters, the simulated parameters are arranged to obtain the high-temperature test parameter table for the sensors.

3. The method for calibrating the accuracy of a differential pressure sensor for high-temperature environment monitoring as described in claim 1, characterized in that, The method includes: Extract the model parameters of the applicable differential pressure compensation model, initialize the parameter population, and simultaneously perform performance verification and evaluation on the applicable differential pressure compensation model to obtain model performance evaluation parameters; Based on the model performance evaluation parameters, the applicable differential pressure compensation model is analyzed for optimization direction to determine the optimization direction of model parameters. According to the optimization direction of the model parameters, the parameter population is subjected to crossover mutation and population update until the preset termination condition is met, and the optimal population parameters are determined by comparison and optimization. Based on the optimal population parameters, the applicable differential pressure compensation model is optimized to obtain an optimized differential pressure compensation model.

4. The method for calibrating the accuracy of a differential pressure sensor for high-temperature environment monitoring as described in claim 1, characterized in that, The method includes: Uncertainty sources are identified for the differential pressure sensor to be calibrated, and the factors contributing to the sensor's test uncertainty are obtained. Based on the aforementioned sources of uncertainty in sensor testing, an accuracy loss analysis is performed to obtain the sensor testing accuracy loss factor. The accuracy calibration results of the differential pressure sensor are supplemented and corrected based on the sensor test accuracy loss factor.

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