Intelligent pressure sensor temperature drift detection method and system

Abstract

The invention relates to the technical field of pressure sensors, in particular to an intelligent pressure sensor temperature drift detection method and system, and the method comprises the following steps: presetting a heat transfer judgment logic which is used for estimating the temperature of a pressure sensitive element; whether a pressure silence point appears or not is recognized online, wherein the pressure silence point is a working condition point of known real pressure; when the pressure silence point is recognized every time, the deviation between the output pressure of the sensor and the known real pressure is obtained; based on the deviation, parameters in the heat transfer judgment logic are adjusted, so that the output pressure of the sensor is kept consistent with the known real pressure; estimating the temperature of the pressure sensitive element by applying the adjusted heat transfer judgment logic, and compensating a pressure measurement result based on the estimated temperature; the actual temperature of the pressure sensitive element is estimated more accurately by introducing heat transfer judgment logic.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of pressure sensors, in particular to a method and system for detecting temperature drift of an intelligent pressure sensor. BACKGROUND

[0002] In modern industrial production, intelligent pressure sensors are widely used in various key process links to ensure accurate control of the production process and stable product quality. Such sensors usually have pressure-sensitive elements, temperature measurement elements, and signal processing units integrated internally. In normal working conditions, the signal processing unit will correct the original signal output by the pressure-sensitive element according to the current temperature obtained by the temperature measurement element using a pre-set compensation relationship, in order to eliminate the influence of environmental temperature changes on pressure measurement results, i.e. temperature drift compensation. This compensation method can provide accurate pressure readings and ensure stable production processes when the device is in stable operation and the environmental temperature changes are gentle and uniform.

[0003] However, in actual industrial applications, the external thermal environment of the sensor is far from ideal uniformity and stability. When the external complex thermal environment and the self-heating effect of the sensor are superimposed, the problem becomes more prominent. The temperature measurement element inside the sensor may rise faster due to its proximity to the heat-generating chip, while the pressure-sensitive element is mainly affected by external local heat sources and process medium temperature. This results in a dynamic temperature difference between the temperature measurement element reading and the actual temperature of the pressure-sensitive element. The traditional compensation method is based on a pre-set, relatively stable temperature relationship, which cannot accurately capture the constantly changing internal temperature gradient formed by the combined effects of external local heat sources, changes in heat dissipation conditions, and sensor self-heating. This continuous and inaccurate temperature compensation may appear as fluctuations in measurement errors in the short term, but in the long term, especially in scenarios where environmental conditions change frequently, it will lead to cumulative deviations in pressure measurement results. Therefore, in the industrial field where intelligent pressure sensors are located, due to the influence of external complex and dynamically changing local heat sources, non-uniform heat dissipation conditions, and self-heating effects generated by the sensor itself, a continuously changing and unpredictable temperature gradient is formed between the pressure-sensitive element and the temperature measurement element used for temperature compensation inside the sensor. In this complex thermal environment, how to accurately identify and quantify the true drift caused by temperature changes to ensure the accuracy and reliability of pressure measurement is a technical problem that needs to be solved.

[0004] In view of the above problems, the prior art needs to be improved. SUMMARY

[0005] The application aims at solving the problems in the prior art and provides a method and system for detecting temperature drift of an intelligent pressure sensor.

[0006] In a first aspect, the application provides a method for detecting temperature drift of an intelligent pressure sensor, comprising the following steps:

[0007] presetting heat transfer judgment logic, which is used to estimate the temperature of a pressure-sensitive element;

[0008] online identifying whether a pressure silent point appears, which is a working condition point with a known real pressure;

[0009] each time the pressure silent point is identified, obtaining a deviation between a sensor output pressure and the known real pressure;

[0010] based on the deviation, adjusting parameters in the heat transfer judgment logic to keep the sensor output pressure consistent with the known real pressure;

[0011] applying the adjusted heat transfer judgment logic to estimate the temperature of the pressure-sensitive element and compensate a pressure measurement result based on the estimated temperature.

[0012] In a second aspect, the application provides a system for detecting temperature drift of an intelligent pressure sensor, comprising:

[0013] a presetting module, which is used to preset heat transfer judgment logic, which is used to estimate the temperature of a pressure-sensitive element;

[0014] an identifying module, which is used to online identify whether a pressure silent point appears, which is a working condition point with a known real pressure;

[0015] an obtaining module, which is used to obtain a deviation between a sensor output pressure and the known real pressure each time the pressure silent point is identified;

[0016] an adjusting module, which is used to adjust parameters in the heat transfer judgment logic based on the deviation to keep the sensor output pressure consistent with the known real pressure;

[0017] an applying module, which is used to apply the adjusted heat transfer judgment logic to estimate the temperature of the pressure-sensitive element and compensate a pressure measurement result based on the estimated temperature.

[0018] Compared with the prior art, the application has the following beneficial effects:

[0019] The temperature of the pressure sensitive element is estimated by preset heat transfer judgment logic, and the pressure silent point is identified online. At each time when the pressure silent point is identified, the deviation between the sensor output pressure and the known real pressure is obtained, and the parameters in the heat transfer judgment logic are dynamically adjusted based on the deviation, so that the sensor output pressure is consistent with the known real pressure. Finally, the temperature of the pressure sensitive element is estimated by applying the adjusted heat transfer judgment logic, and the pressure measurement result is compensated based on the estimated temperature.

[0020] The method effectively solves the problem in the prior art that, due to the influence of external complex and dynamically changing local heat sources, uneven heat dissipation conditions and self-heating effects generated by the intelligent pressure sensor itself in a complex industrial field, a continuously changing and unpredictable temperature gradient is formed between the pressure sensitive element inside the sensor and the temperature measuring element for temperature compensation, so that the traditional compensation method cannot accurately identify and quantify the real drift caused by temperature change. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 The method flowchart of the present application.

[0022] Figure 2 The system structure schematic diagram of the present application.

[0023] In the figure: 201, preset module; 202, identification module; 203, acquisition module; 204, adjustment module; 205, application module. DETAILED DESCRIPTION

[0024] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference signs represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be understood as a limitation of the present application.

[0025] The terms "first", "second" are only used for description purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more features. In the description of the present application, the meaning of "multiple" is two or more, unless otherwise specifically limited.

[0026] In complex and variable industrial thermal environments, traditional smart pressure sensors suffer from temperature drift due to a dynamic temperature gradient between the pressure-sensitive element and the temperature-measuring element. This makes conventional temperature compensation methods ineffective in accurately eliminating temperature drift, thus affecting the accuracy and reliability of pressure measurements. Failure to address this issue will lead to cumulative deviations in pressure measurement results, impacting precise control of the production process and the stability of product quality.

[0027] like Figure 1 The method for detecting temperature drift using a smart pressure sensor, as shown, includes the following steps:

[0028] S101, Preset heat transfer judgment logic, which is used to estimate the temperature of the pressure-sensitive element;

[0029] It should be noted that the preset heat transfer judgment logic can be understood as a mathematical model or algorithm designed to simulate the heat transfer process inside the sensor, particularly the pressure-sensitive element. This logic can be constructed based on factors such as the sensor's physical structure, material properties, and heat source distribution. For example, it could be a simplified lumped-parameter thermal model or an empirical model simplified from finite element analysis. The logic parameters, such as thermal conductivity, specific heat capacity, and convective heat transfer coefficient, can be preset before the sensor leaves the factory through theoretical calculations, simulations, or initial calibration experiments in a controlled environment. The purpose is to accurately estimate the actual temperature of the pressure-sensitive element based on factors such as the external ambient temperature and the sensor's own heat generation during actual sensor operation.

[0030] S102. Online identification of whether a pressure quiescent point has occurred. The pressure quiescent point is the operating point with known actual pressure.

[0031] It should be noted that online identification of pressure quiescent points refers to the system's ability to automatically determine whether the current operating condition is in a stable state with a known true pressure during normal sensor operation. For example, when the pressure of the measured medium is set to atmospheric pressure, or when the pipeline system is in a shutdown and pressure-maintaining state, a pressure quiescent point can be considered to have occurred. Identification methods can include sending a specific signal from an external control system indicating that the current pressure is a known value; or monitoring the sensor's own raw pressure output signal, where minimal fluctuations over a period of time, coupled with a preset stability threshold, indicate the presence of a pressure quiescent point.

[0032] S103. Each time a pressure quiescent point is detected, obtain the deviation between the sensor output pressure and the known true pressure;

[0033] It should be noted that the above step refers to comparing the current output pressure reading of the sensor with the known true pressure value corresponding to the pressure silent point, and calculating the difference between the two. The deviation directly reflects the measurement error of the current sensor under the known true pressure condition, and is the basis for subsequent parameter adjustment.

[0034] S104, based on the deviation, adjusting the parameters in the heat transfer judgment logic to make the sensor output pressure consistent with the known true pressure;

[0035] It should be noted that the above step is the core adaptive mechanism of the present application. Specifically, after obtaining the above deviation, the system will start a parameter optimization process. For example, an iterative algorithm such as least squares method, gradient descent method or simple proportional integral derivative (PID) control strategy can be used to fine-tune one or more key parameters in the heat transfer judgment logic according to the size and direction of the deviation. The goal of adjustment is to make the sensor output pressure after estimating the temperature and compensation by the heat transfer judgment logic as close as possible to or equal to the known true pressure under the current pressure silent point. This process can continue until the deviation is reduced to within the pre-set allowable range.

[0036] S105, applying the adjusted heat transfer judgment logic to estimate the temperature of the pressure sensitive element, and compensating the pressure measurement result based on the estimated temperature.

[0037] It should be noted that the above step refers to after the parameter adjustment is completed, the optimized heat transfer judgment logic will continue to run. The logic will receive the readings of the internal temperature measuring element of the sensor, the power consumption of the sensor and other inputs in real time, and output a more accurate estimated temperature of the pressure sensitive element combined with its internal model. Subsequently, the estimated temperature will be used to consult the pre-established temperature compensation curve or model, so as to calculate the correction amount of the original pressure measurement signal, and finally obtain the accurate pressure measurement result after temperature drift compensation. The scheme of the present application ensures that the temperature estimation of the pressure sensitive element always maintains high accuracy in complex and variable thermal environment through the adaptive parameter adjustment mechanism, thereby ensuring the accuracy of the pressure measurement result.

[0038] Compared with the prior art, the scheme of the present application has significant progress. The traditional existing intelligent pressure sensor temperature drift compensation method usually relies on a preset fixed compensation relationship or only compensates based on the reading of a certain temperature measuring element inside the sensor. However, in actual industrial applications, due to the external complex and dynamically changing local heat source, uneven heat dissipation conditions and the self-heating effect generated by the sensor itself, a continuously changing and unpredictable temperature gradient is formed between the pressure sensitive element inside the sensor and the temperature measuring element used for temperature compensation. In this case, the fixed compensation relationship cannot accurately reflect the real temperature of the pressure sensitive element, resulting in inaccurate compensation and cumulative error.

[0039] The core innovation of the present application is the introduction of an adaptive temperature drift detection and compensation mechanism. By identifying pressure silent points online, and using these known real pressure working points as calibration opportunities, the deviation between the sensor output pressure and the real pressure is obtained. More importantly, based on this real-time obtained deviation, the present application dynamically adjusts the parameters in the heat transfer judgment logic used to estimate the temperature of the pressure sensitive element. This means that the heat transfer judgment logic is no longer static and unchanging, but can be self-optimized and learned according to the performance of the sensor in the actual complex thermal environment. As a result, the heat transfer judgment logic can more accurately capture and reflect the actual temperature changes of the pressure sensitive element, thereby achieving accurate compensation of the pressure measurement results. This adaptive adjustment capability enables the scheme of the present application to effectively address the dynamic temperature gradient problem that traditional methods cannot solve, significantly improving the measurement accuracy and long-term stability of the intelligent pressure sensor in complex industrial environments, and providing more reliable data support for industrial process control.

[0040] As an embodiment of the present application, before the step of adjusting the parameters in the heat transfer judgment logic based on the deviation, it includes:

[0041] When the pressure silent point is identified, the temperature change rate of the temperature measuring element inside the sensor is monitored at the same time;

[0042] It should be noted that the temperature measuring element can be understood as a temperature sensor integrated inside the sensor, such as a thermistor, platinum resistance or semiconductor temperature sensor, etc., whose purpose is to obtain real-time temperature information of the internal environment of the sensor. The temperature change rate refers to the change amount of temperature per unit time, which can be obtained by continuously collecting temperature readings of the temperature measuring element and calculating the ratio of the temperature difference between adjacent time points and the time interval. For example, temperature data can be collected every certain time interval (such as 1 second), and then the difference between the current temperature and the temperature at the last time is calculated, and then divided by the time interval to obtain the instantaneous temperature change rate.

[0043] determining whether the temperature change rate meets a preset stability condition based on the temperature change rate;

[0044] It should be noted that the preset stability condition refers to one or a group of threshold values or ranges used to define whether the internal temperature of the sensor is in a relatively stable state. For example, an upper threshold value of the temperature change rate can be set, and when the absolute value of the monitored temperature change rate is less than the threshold value, it is considered that the temperature meets the stability condition. The threshold value can be experimentally determined or empirically set according to the specific type of the sensor, the application environment, and the required compensation accuracy. The purpose is to ensure that the internal thermal environment of the sensor is relatively stable when adjusting the parameters, and to avoid introducing additional measurement errors due to rapid fluctuations in temperature.

[0045] When the temperature change rate meets the preset stability condition, the adjustment of the parameters in the heat transfer judgment logic is started based on the deviation.

[0046] It should be noted that the above step means that even if the pressure silent point has been identified and the deviation has been obtained, the parameter adjustment process will not start immediately if the internal temperature of the sensor is still in an unstable state, but will wait until the temperature change rate meets the stability condition. Thus, it is ensured that the parameter adjustment is carried out in a state of thermal equilibrium or close to thermal equilibrium, thereby ensuring the accuracy of the adjustment result.

[0047] The scheme of the present application effectively avoids adjusting the parameters in an unstable or rapidly changing temperature environment by introducing monitoring and judgment of the temperature change rate of the internal temperature measuring element of the sensor before starting the adjustment of the parameters of the heat transfer judgment logic. When the temperature change rate meets the preset stability condition, the adjustment of the parameters is allowed to start, which ensures that the parameter adjustment process is carried out in a relatively stable thermodynamic condition, thereby improving the accuracy and reliability of the adjustment. This pre-stability judgment mechanism can effectively filter out temperature fluctuations caused by transient changes in environmental temperature or the thermal inertia of the sensor, making the parameter adjustment process more robust.

[0048] Through the above technical scheme, the present application can significantly improve the accuracy and stability of the adjustment of the parameters of the heat transfer judgment logic. By avoiding adjusting the parameters when the temperature fluctuates rapidly, it can effectively prevent misadjustment caused by transient thermal effects or changes in external environmental temperature, thereby ensuring more accurate temperature estimation of the pressure sensitive element and more reliable final pressure measurement compensation result, improving the measurement accuracy and robustness of the intelligent pressure sensor under complex working conditions. This optimization mechanism enables the sensor to maintain high-precision pressure measurement performance in various dynamic temperature environments.

[0049] As an embodiment of the present application, the method further comprises the following steps:

[0050] In the process of adjusting the parameters in the heat transfer judgment logic based on the bias, the temperature change rate of the temperature measuring element inside the sensor is continuously monitored;

[0051] Based on the continuously monitored temperature change rate, it is determined whether the temperature change rate meets a preset disturbance recognition condition;

[0052] When the temperature change rate meets the preset disturbance recognition condition, the parameter adjustment is suspended;

[0053] When the temperature change rate returns to meet the preset stability condition, the parameter adjustment is resumed.

[0054] Specifically, continuously monitoring the temperature change rate of the temperature measuring element inside the sensor refers to real-time acquisition and analysis of the temperature data of the temperature measuring element inside the sensor during the entire parameter adjustment period, and calculation of the rate of change thereof with time. The temperature change rate can be calculated in various ways, such as through differential operation on continuously collected temperature data or calculation of the slope after smoothing by the moving average method. The purpose is to timely capture external temperature disturbances that may affect the accuracy of parameter adjustment. The preset disturbance recognition condition refers to one or a group of threshold values for determining whether the current temperature change rate indicates the presence of significant external disturbances. For example, when the absolute value of the temperature change rate exceeds a certain preset upper limit value, or the fluctuation amplitude of the temperature change rate exceeds a preset range within a certain time, it is considered that the disturbance recognition condition is met. This condition should be set according to the specific application environment of the sensor and the requirement for the accuracy of parameter adjustment. When the temperature change rate meets the preset disturbance recognition condition, the parameter adjustment is suspended, which means that the system will temporarily stop the update operation of the parameters in the heat transfer judgment logic. This is intended to avoid parameter adjustment in an unstable or disturbed environment, thereby preventing the introduction of incorrect parameter values. When the temperature change rate returns to meet the preset stability condition, the parameter adjustment is resumed, which means that when it is detected that the external disturbance disappears and the temperature change rate of the temperature measuring element inside the sensor returns to an acceptable stable range, the system will continue the previously suspended parameter adjustment process. The preset stability condition can be the same as or similar to the stability condition used to start the parameter adjustment, ensuring that the parameter adjustment is performed in a reliable environment.

[0055] The scheme of the present application effectively solves the problem of the influence of external temperature disturbance on the adjustment accuracy in the parameter adjustment process by introducing a dynamic temperature disturbance monitoring and control mechanism in the parameter adjustment process. Specifically, the temperature change rate of the internal temperature measurement element of the sensor is continuously monitored, so that the system can perceive the stability of the environment in real time. When it is detected that the temperature change rate meets the preset disturbance identification condition, it indicates that there may be significant temperature fluctuations in the current environment, and the parameter adjustment is suspended at this time, which can effectively avoid parameter updating under unstable conditions, thereby preventing the measurement error caused by external disturbance from being misjudged as temperature drift and being fixed in the parameters of the heat transfer judgment logic. Once the temperature change rate meets the preset stability condition, i.e., the environment returns to stability, the parameter adjustment process is resumed, ensuring the accuracy and effectiveness of the adjustment. This dynamic suspension and recovery mechanism makes the parameter adjustment process more robust and can adapt to complex and variable environmental conditions in actual applications.

[0056] As an embodiment of the present application, after the step of identifying whether a pressure silence point occurs online, the method comprises:

[0057] When the pressure silence point is identified, the pressure output reading of the sensor itself is monitored;

[0058] It should be noted that the monitoring process is aimed at obtaining the original pressure measurement data of the sensor within a certain period of time under a specific working condition. These original readings are the basis for subsequent judgment and calculation. Among them, the monitored pressure output reading will be used to judge whether it meets the preset stability condition. The stability condition can be understood as a series of criteria for evaluating the fluctuation degree of the pressure reading within a certain period of time. For example, the stability condition can include that the standard deviation of the pressure reading is less than a certain threshold, the difference between the maximum and minimum values of the pressure reading is less than a certain threshold, or the change rate of the pressure reading within a certain time window is lower than a certain preset value, etc. The purpose is to ensure that the identified pressure silence point is indeed in a relatively stable pressure state, avoiding false judgment caused by transient disturbance or noise.

[0059] Judging whether the pressure output reading meets the preset stability condition;

[0060] When the pressure output reading meets the preset stability condition, determining a statistical value according to the pressure output reading, and taking the statistical value as the true pressure reference value of the pressure silence point.

[0061] It is noted that when the pressure output readings are determined to satisfy the preset stability condition, a statistical value will be determined based on these stable pressure output readings. The statistical value aims to obtain a more representative and robust true pressure reference value through data processing. For example, the statistical value can be the arithmetic mean, median, weighted average or other statistical quantity of all pressure output readings in the time period that satisfies the stability condition. By calculating the statistical value, instantaneous fluctuations can be effectively smoothed, and the influence of random noise can be reduced, so as to obtain a reference value closer to the true pressure. The statistical value is then used as the true pressure reference value of the pressure quiet point, providing an accurate benchmark for subsequent parameter adjustment of the heat transfer judgment logic.

[0062] The scheme of the present application effectively solves the accuracy and reliability problem of obtaining the "known true pressure" in the basic scheme by introducing the process of monitoring the sensor's own pressure output readings, stability judgment and statistical value determination after identifying the pressure quiet point. Specifically, when the system identifies a potential pressure quiet point, it does not directly adopt a certain instantaneous pressure reading as the true pressure, but continuously monitors the pressure output readings for a period of time and strictly filters them in combination with the preset stability condition. Only when the pressure readings show sufficient stability, the pressure data in this period is considered reliable. On this basis, by statistically analyzing the stable data, such as calculating the average value, the influence of random noise and measurement error can be further eliminated, so as to obtain a more accurate and robust true pressure reference value. It is precisely due to this multiple verification and data optimization mechanism that the subsequent adjustment of the heat transfer judgment logic parameters based on the true pressure reference value is more accurate, thereby improving the accuracy of the entire temperature drift detection and compensation method.

[0063] Through the above technical scheme, the present application can significantly improve the accuracy and reliability of obtaining the true pressure reference value at the pressure quiet point. Compared with directly using the instantaneous pressure reading, the introduction of the stability judgment and statistical value determination mechanism effectively avoids the problem of inaccurate reference value caused by sensor noise, environmental instantaneous disturbance or measurement error. Thus, a more solid and accurate benchmark is provided for the subsequent parameter adjustment of the heat transfer judgment logic, so that the pressure measurement result compensation of the intelligent pressure sensor at different temperatures is more accurate, effectively suppressing the influence of temperature drift on the measurement accuracy, and improving the overall performance and reliability of the sensor.

[0064] As an embodiment of the present application, the step of determining the statistical value based on the pressure output readings comprises:

[0065] dividing the pressure output readings into multiple time periods;

[0066] It is noted that dividing the pressure output readings into multiple time segments aims to conduct fine-grained analysis on the continuous output data of the sensor under the pressure silent point working condition. These time segments can be divided according to a preset time length, such as every second, every ten seconds or longer, or adaptively divided according to data characteristics to capture pressure output behaviors under different time scales. This division helps to identify transient changes or local unstable regions in the data.

[0067] Stability evaluation is performed on the pressure output readings in each time segment to obtain a stability evaluation result for each time segment.

[0068] It is noted that the stability evaluation of the pressure output readings in each time segment aims to quantify the fluctuation degree or noise level of the pressure readings in each time segment. Stability evaluation can be achieved through various statistical methods, such as calculating the standard deviation, variance, difference between maximum and minimum values of the pressure readings in each time segment, or by analyzing the rate of change. The evaluation result reflects the reliability of the pressure output in that time segment.

[0069] According to the stability evaluation result, the time segment with the highest stability is determined.

[0070] It is noted that the above step means selecting a time window with the most stable pressure output, the smallest noise and the closest to the true pressure. This step ensures that the data used for subsequent statistical calculations has the highest reliability. For example, the time segment with the smallest standard deviation can be selected as the time segment with the highest stability.

[0071] The arithmetic mean of the pressure output readings in the time segment with the highest stability is calculated as a statistical value.

[0072] It is noted that the arithmetic mean, as a commonly used statistical quantity, can effectively smooth random noise in the data and provide the central tendency value of the pressure output in the stable time segment. This arithmetic mean is used as the true pressure reference value of the pressure silent point, providing an accurate benchmark for parameter adjustment of the subsequent heat transfer judgment logic.

[0073] The scheme of the present application can identify and select the most stable output data of the sensor at the pressure silent point by dividing the pressure output readings into time periods and performing stability evaluation on each time period. The traditional method may directly process the data during the entire silent point, but in actual working conditions, even at the theoretical silent point, the sensor output may be affected by instantaneous disturbances or noise. Through fine time period division and stability evaluation, the interference of these unstable factors on the determination of the true pressure reference value can be effectively avoided. The time period with the highest stability is selected, and the pressure output readings within it are considered to be the part that is least affected by external non-pressure related factors and best represents the true pressure. The arithmetic mean of the readings in this time period further reduces the influence of random errors, making the determined statistical value more accurate and reliable. This process ensures that the true pressure reference value used to calibrate the heat transfer judgment logic has high precision, thereby providing a solid foundation for subsequent temperature drift compensation.

[0074] Through the above technical scheme, the determination accuracy of the true pressure reference value at the pressure silent point can be significantly improved. By performing detailed time period division and stability evaluation on the pressure output readings, transient fluctuations and noise interference in the data can be effectively identified and excluded, ensuring that the selected data segment for calculating the statistical value has the highest reliability. As a result, the statistical value obtained as the true pressure reference value of the pressure silent point has greatly improved accuracy, which further makes the adjustment of the heat transfer judgment logic parameters based on the reference value more accurate. This is crucial for the overall performance of the intelligent pressure sensor temperature drift detection method, as it directly affects the accuracy of temperature estimation of the pressure sensitive element and the compensation effect of the final pressure measurement result, thereby improving the measurement stability and reliability of the sensor under complex working conditions.

[0075] As an embodiment of the present application, the step of performing stability evaluation on the pressure output readings in each time period to obtain the stability evaluation result of each time period includes:

[0076] Performing signal feature analysis on the pressure output readings in each time period to identify specific signal features caused by external non-pressure related factors;

[0077] It should be noted that the signal feature analysis of the pressure output readings in each time period refers to identifying specific patterns or abnormalities related to external non-pressure-related factors by analyzing the time domain or frequency domain characteristics of the pressure output readings. For example, frequency domain analysis methods such as Fourier transform, wavelet analysis, etc. can be used to detect whether there are vibration noises or periodic interferences of specific frequencies. In addition, pattern recognition algorithms can also be used to identify signal features such as spikes, drifts or irregular fluctuations caused by transient impacts or electromagnetic interferences. The purpose is to accurately distinguish between signals caused by real pressure changes and noises caused by external interferences.

[0078] The pressure output readings in the time period are denoised to remove the influence of specific signal features;

[0079] It should be noted that the denoising of the pressure output readings in the time period can be understood as applying various signal processing techniques aimed at eliminating or significantly reducing the influence of identified specific signal features on the pressure output readings. For example, digital filters such as low-pass filters, band-stop filters can be used to filter out noise of specific frequencies; or adaptive filtering algorithms can be used to dynamically adjust filtering parameters according to real-time monitored interference signal characteristics, to more effectively remove noise. In addition, nonlinear denoising methods such as wavelet denoising, empirical mode decomposition (EMD) can also be used to remove noise while preserving the original information of the pressure signal to the greatest extent. The purpose is to obtain purer and more accurate pressure output readings, providing a reliable data basis for subsequent stability evaluation.

[0080] The denoised pressure output readings are statistically analyzed to obtain the stability evaluation results of the time period.

[0081] It should be noted that the statistical analysis of the denoised pressure output readings specifically refers to quantitative analysis of the sequence of denoised pressure output readings to evaluate their fluctuation degree and stability in a specific time period. For example, statistical quantities such as standard deviation, variance, root mean square error or maximum minimum difference of the denoised pressure output readings can be calculated. Smaller standard deviation or variance usually indicates higher stability. In addition, moving average, exponential smoothing and other methods can be used to further smooth the data and observe its trend, to more accurately judge whether the pressure output readings meet the preset stability conditions. The purpose is to provide a quantitative stability evaluation result, so as to accurately identify the truly stable pressure silent points.

[0082] The scheme of the present application can effectively identify and eliminate the influence of external non-pressure related factors on pressure output readings by introducing signal feature analysis and denoising processing before stability evaluation. It is precisely because the original pressure data is purified that the subsequent statistical analysis can more accurately reflect the true stability of the pressure output readings, thereby avoiding false judgments caused by external interference. Thus, the true pressure reference value of the determined pressure silent point can be ensured to be more accurate and reliable.

[0083] Through the above technical scheme, the accuracy and robustness of determining the true pressure reference value of the pressure silent point can be significantly improved. By identifying and removing specific signal features caused by external non-pressure related factors, the negative effects of noise and interference on stability evaluation are avoided, and the determined statistical value can more truly reflect the actual pressure of the pressure silent point. This not only improves the overall accuracy of the temperature drift detection method, but also enhances the adaptability and reliability of the system in complex industrial environments.

[0084] As an embodiment of the present application, the step of identifying specific signal features caused by external non-pressure related factors includes:

[0085] Obtaining auxiliary sensor data;

[0086] It should be noted that the auxiliary sensor data can refer to non-pressure parameter data related to the pressure measurement environment, for example, it can be environmental temperature, humidity, vibration, electromagnetic interference, air flow speed, etc. These data can reflect external factors that may interfere with the output of the pressure sensor. Obtaining auxiliary sensor data aims to provide necessary reference information for subsequent identification of non-pressure related signal features.

[0087] Correlating the auxiliary sensor data with the specific patterns observed in the pressure output readings within the corresponding time period;

[0088] Based on the correlation results, identify specific signal features caused by external non-pressure related factors.

[0089] It is noted that the above step refers to determining whether there is a causal or correlational relationship between the auxiliary sensor data and the pressure output readings by analyzing the synchronicity or correlation of the two in time, frequency, or amplitude. For example, when the auxiliary sensor detects a specific vibration frequency, if the pressure output readings also synchronously appear fluctuations of the same frequency, it can be considered to be associated. The specific pattern can include periodic fluctuations, transient spikes, drift trends, etc., which can be caused by external non-pressure related factors. Thus, based on the correlation result, once the specific pattern in the auxiliary sensor data and the pressure output readings is successfully correlated, the specific pattern is identified as a signal feature caused by external non-pressure related factors. For example, if the vibration sensor data shows that the vibration of a specific frequency is highly correlated with the fluctuation of a specific frequency output by the pressure sensor, the fluctuation is identified as noise caused by vibration.

[0090] The scheme of the present application can more accurately identify the interference signal caused by non-pressure factors in the pressure measurement process by introducing auxiliary sensor data and correlating it with the pattern in the pressure output readings. Traditional methods may have difficulty distinguishing between true pressure changes and external interference, leading to errors in stability evaluation. However, by obtaining auxiliary sensor data directly related to potential sources of interference and correlating patterns, a clear mapping relationship between the interference source and the specific signal feature in the pressure readings can be established. It is precisely because of the establishment of this mapping relationship that the system can effectively separate the signal features caused by external non-pressure related factors from the true pressure signal, thereby laying an accurate foundation for subsequent noise reduction and stability evaluation.

[0091] As an embodiment of the present application, the step of performing noise reduction processing on the pressure output readings in the time period to remove the influence of the specific signal feature includes:

[0092] Inputting the auxiliary sensor data as a reference;

[0093] It is noted that the above step refers to using the data collected by other sensors related to the environment in which the pressure sensitive element is located (such as vibration sensors, acoustic sensors, temperature sensors, or electromagnetic field sensors, etc.) as a reference for identifying and canceling the specific signal feature. These auxiliary sensor data can reflect the real-time state or characteristics of external non-pressure related factors that cause the specific signal feature.

[0094] Based on the matching degree of the reference input and the frequency characteristics of the pressure output readings in the corresponding time period, dynamically adjusting the filter parameters for processing the pressure output readings in the corresponding time period;

[0095] It should be noted that the above step can be understood as follows: first, the auxiliary sensor data and the pressure output reading are subjected to frequency domain analysis, such as by fast Fourier transform (FFT) or other methods, to obtain their frequency components and energy distribution. Second, the energy spectrum density, coherence or phase relationship of the two in a certain frequency range is compared to evaluate the form of specific signal characteristics caused by external non-pressure related factors in the pressure output reading and the degree of association with the auxiliary sensor data. For example, when the auxiliary sensor data shows high energy in a certain frequency range, and the pressure output reading also shows similar energy peak or coherence in the frequency range, it can be judged that the signal components in the frequency range are mainly derived from external disturbance. Therefore, according to the degree of matching, the parameters of the de-noising filter (such as band-stop filter, adaptive filter or Kalman filter, etc.) are adjusted in real time, including but not limited to cutoff frequency, bandwidth, gain or filter coefficient, etc. The purpose of this dynamic adjustment is to enable the filter to accurately suppress the specific signal characteristics detected at the moment, avoiding excessive filtering of non-noise components.

[0096] According to the dynamically adjusted filter parameters, the pressure output reading in the time period is de-noised to offset the influence of specific signal characteristics on the pressure reading and to retain the pressure change information in the pressure output reading.

[0097] It should be noted that the above step is specifically referred to as follows: after the filter parameters are dynamically adjusted, the filter is applied to the original pressure output reading. The filter can selectively attenuate or eliminate specific frequency components or patterns caused by external non-pressure related factors that are highly related to the auxiliary sensor data, thereby effectively offsetting their influence on the pressure reading. At the same time, since the filter parameters are dynamically adjusted based on the degree of frequency characteristic matching, the signal components caused by real pressure changes in the pressure output reading can be maximally retained, ensuring the accuracy of the pressure measurement results.

[0098] The scheme of the present application solves the problem that the traditional fixed denoising method is not effective in dealing with dynamic external disturbances by introducing auxiliary sensor data as reference input and dynamically adjusting filter parameters based on the degree of matching between the frequency characteristics of the auxiliary sensor data and the pressure output readings. Specifically, when external non-pressure-related factors (such as vibration, electromagnetic interference, etc.) affect the pressure sensor, these factors will usually introduce specific frequency components or patterns in the pressure output readings. At the same time, the auxiliary sensor can synchronously capture real-time information of these external factors. By analyzing the frequency characteristics of the auxiliary sensor data and the pressure output readings, the frequency range and intensity of the noise components caused by external disturbances can be accurately identified. It is precisely because of this accurate frequency matching and correlation that the filter parameters can be adjusted in real time and adaptively, ensuring that the filter can accurately suppress the current noise characteristics and avoid damaging the real pressure signal. Thus, the scheme of the present application can effectively cancel specific signal characteristics while maximizing the preservation of effective information reflecting real pressure changes in the pressure output readings. Through the above technical scheme, the present application can achieve more accurate and adaptive denoising processing of specific signal characteristics caused by external non-pressure-related factors in the output readings of intelligent pressure sensors.

[0099] As an embodiment of the present application, the step of correlating the auxiliary sensor data with the specific patterns observed in the pressure output readings within the corresponding time period includes:

[0100] Performing frequency domain transformation on the auxiliary sensor data to obtain frequency information of the auxiliary sensor data;

[0101] It should be noted that the frequency domain transformation of the auxiliary sensor data aims to convert the time domain signal into a frequency domain representation, thereby revealing its inherent frequency components and energy distribution. For example, Fast Fourier Transform (FFT) or other suitable spectral analysis methods can be used. Through this step, the frequency information of the auxiliary sensor data can be obtained, such as its dominant frequency, harmonic components, and noise distribution, etc.

[0102] Performing frequency domain transformation on the pressure output readings within the time period to obtain frequency information of the pressure output readings;

[0103] It should be noted that the frequency domain transformation of the pressure output readings within the time period aims to obtain the characteristics of the pressure output readings in the frequency domain. This helps to identify possible periodic disturbances or specific frequency patterns in the pressure signal, which may be related to external non-pressure-related factors.

[0104] Comparing the energy spectral density or coherence of the frequency information of the auxiliary sensor data and the frequency information of the pressure output readings within a specific frequency range to obtain a comparison result;

[0105] It is noted that comparing the energy spectral density or coherence of the frequency information of the auxiliary sensor data and the frequency information of the pressure output readings within a specific frequency range is the key to identifying the correlation between the two. The energy spectral density can reflect the energy distribution of the signals at different frequencies, while the coherence can measure the linear correlation degree of the two signals in the frequency domain. By comparing these frequency domain characteristics, the similarity or coupling degree between the external disturbance represented by the auxiliary sensor data and the specific pattern observed in the pressure output readings can be quantified. For example, if the auxiliary sensor data (such as vibration sensor data) has a high energy spectral density within a certain frequency range, and the pressure output readings also exhibit a similar energy spectral density or high coherence within that frequency range, it can be inferred that the specific pattern is caused by external vibration.

[0106] Based on the comparison result, the correlation between the auxiliary sensor data and the specific pattern observed in the pressure output readings within the time period is identified.

[0107] It is noted that the identification of such correlation helps to accurately distinguish the signal characteristics caused by external non-pressure-related factors from the real pressure changes, providing accurate basis for subsequent denoising processing.

[0108] The scheme of the present application can more effectively reveal the potential frequency correlation between the auxiliary sensor data and the pressure output readings by converting both into the frequency domain for analysis. In the time domain, external disturbances may appear as complex waveforms, making it difficult to directly match with specific patterns in the pressure signal. However, in the frequency domain, these disturbances often have specific frequency characteristics, such as periodic vibrations that may exhibit sharp frequency peaks, and temperature changes that may cause slow frequency drift. By comparing the energy spectral density or coherence of the auxiliary sensor data and the pressure output readings within a specific frequency range, the similarity of the two in frequency can be quantitatively evaluated. This frequency domain correlation analysis enables the system to accurately identify specific signal characteristics caused by external non-pressure-related factors (such as mechanical vibration, electromagnetic interference, etc.), thereby providing accurate guidance for subsequent denoising processing, ensuring that only disturbance signals are removed while preserving the real pressure change information. Through the above technical scheme, accurate identification of the correlation between the auxiliary sensor data and the specific pattern in the pressure output readings can be achieved. Compared with simple correlation analysis in the time domain, frequency domain analysis can more effectively capture periodic or specific frequency disturbance patterns and quantify their coupling degree with the pressure signal. This enables the system to more accurately identify signal characteristics caused by external non-pressure-related factors, thereby providing more reliable basis for subsequent denoising processing, significantly improving the accuracy and stability of the determination of the real pressure reference value at the pressure quiet point, and thus enhancing the overall precision of the intelligent pressure sensor temperature drift detection.

[0109] AsFigure 2 An intelligent pressure sensor temperature drift detection system is shown, the system comprises:

[0110] A preset module 201 is configured to preset heat transfer judgment logic, the heat transfer judgment logic is used for estimating the temperature of the pressure sensitive element;

[0111] An identification module 202 is configured to identify whether a pressure silent point appears online, the pressure silent point is a working condition point with a known true pressure;

[0112] An acquisition module 203 is configured to acquire a deviation between a sensor output pressure and the known true pressure each time the pressure silent point is identified;

[0113] An adjustment module 204 is configured to adjust parameters in the heat transfer judgment logic based on the deviation, so that the sensor output pressure is consistent with the known true pressure;

[0114] An application module 205 is configured to apply the adjusted heat transfer judgment logic to estimate the temperature of the pressure sensitive element, and compensate the pressure measurement result based on the estimated temperature.

[0115] The basic principles, main features and advantages of the present application are shown and described above. It should be understood by those skilled in the art that the present application is not limited to the above embodiments, and the above embodiments and descriptions in the specification are only the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the present application.

Claims

1. A method of intelligent pressure sensor temperature drift detection, the method comprising: The method comprises the following steps: preset heat transfer judgment logic for estimating the temperature of the pressure sensitive element; online identification of whether a pressure silent point occurs, the pressure silent point being a working condition point with a known true pressure; each time the pressure silent point is identified, a deviation between a sensor output pressure and the known true pressure is obtained; based on the deviation, adjusting parameters in the heat transfer judgment logic to keep the sensor output pressure consistent with the known true pressure; applying the adjusted heat transfer judgment logic to estimate the temperature of the pressure sensitive element and compensate the pressure measurement result based on the estimated temperature.

2. The method of claim 1, wherein, Before the step of adjusting the parameters in the heat transfer judgment logic based on the deviation, the method comprises: when the pressure silent point is identified, simultaneously monitoring a temperature change rate of the internal temperature measuring element of the sensor; based on the temperature change rate, judging whether the temperature change rate meets a preset stability condition; when the temperature change rate meets the preset stability condition, starting the adjustment of the parameters in the heat transfer judgment logic based on the deviation.

3. The method of claim 2, wherein, The method further comprises the following steps: during the adjustment of the parameters in the heat transfer judgment logic based on the deviation, continuously monitoring the temperature change rate of the internal temperature measuring element of the sensor; based on the continuously monitored temperature change rate, judging whether the temperature change rate meets a preset disturbance identification condition; when the temperature change rate meets the preset disturbance identification condition, pausing the parameter adjustment; when the temperature change rate meets the preset stability condition again, resuming the parameter adjustment.

4. The method of claim 1, wherein, After the step of online identification of whether a pressure silent point occurs, the method comprises: when the pressure silent point is identified, monitoring a pressure output reading of the sensor itself; judging whether the pressure output reading meets a preset stability condition; when the pressure output reading meets the preset stability condition, determining a statistical value based on the pressure output reading and taking the statistical value as a true pressure reference value of the pressure silent point.

5. The method of claim 4, wherein the temperature drift of the smart pressure sensor is detected by, The step of determining a statistical value based on the pressure output reading comprises: dividing the pressure output reading into multiple time periods; performing stability evaluation on the pressure output reading in each time period to obtain a stability evaluation result of each time period; determining a time period with the highest stability based on the stability evaluation results; calculating an arithmetic mean value of the pressure output reading in the time period with the highest stability as the statistical value.

6. The method of claim 5, wherein, The step of performing stability evaluation on the pressure output reading in each time period to obtain a stability evaluation result of each time period comprises: performing signal feature analysis on the pressure output reading in each time period to identify specific signal features caused by external non-pressure related factors; performing denoising processing on the pressure output reading in the time period to remove the influence of the specific signal features; performing statistical analysis on the denoised pressure output reading to obtain the stability evaluation result of the time period.

7. The method of claim 6, wherein the method further comprises: The step of identifying the specific signal feature caused by the external non-pressure-related factor comprises: acquiring auxiliary sensor data; associating the auxiliary sensor data with a specific pattern observed in the pressure output readings in the corresponding time period; based on the association result, identifying the specific signal feature caused by the external non-pressure-related factor.

8. The method of claim 7, wherein the method further comprises: The step of denoising the pressure output readings in the time period to remove the influence of the specific signal feature comprises: inputting the auxiliary sensor data as a reference; based on the degree of matching between the reference input and the frequency characteristics of the pressure output readings in the corresponding time period, dynamically adjusting the filter parameters used to process the pressure output readings in the corresponding time period; according to the dynamically adjusted filter parameters, denoising the pressure output readings in the time period to offset the influence of the specific signal feature on the pressure readings and retain the pressure change information in the pressure output readings.

9. The method of claim 7, wherein the method further comprises: The step of associating the auxiliary sensor data with a specific pattern observed in the pressure output readings in the corresponding time period comprises: performing frequency domain transformation on the auxiliary sensor data to obtain frequency information of the auxiliary sensor data; performing frequency domain transformation on the pressure output readings in the time period to obtain frequency information of the pressure output readings; comparing the frequency information of the auxiliary sensor data and the frequency information of the pressure output readings in a specific frequency range to obtain a comparison result; based on the comparison result, identifying the association between the auxiliary sensor data and the specific pattern observed in the pressure output readings in the time period.

10. A system for performing a method of detecting temperature drift of a smart pressure sensor as claimed in any one of claims 1 to 9, wherein, The system comprises: a presetting module configured to preset heat transfer judgment logic, the heat transfer judgment logic being configured to estimate a temperature of a pressure-sensitive element; an identifying module configured to identify whether a pressure silent point appears in real time, the pressure silent point being a working condition point with a known true pressure; an acquiring module configured to acquire a deviation between a sensor output pressure and the known true pressure each time the pressure silent point is identified; an adjusting module configured to adjust a parameter in the heat transfer judgment logic based on the deviation, so that the sensor output pressure is consistent with the known true pressure; an applying module configured to apply the adjusted heat transfer judgment logic to estimate the temperature of the pressure-sensitive element and compensate a pressure measurement result based on the estimated temperature.

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