A method for intelligent temperature compensation of pressure sensor

Abstract

The present invention discloses a method for intelligent temperature compensation of a pressure sensor, which relates to the field of sensor equipment technology and includes the following steps: constructing a temperature response characteristic model, real-time acquisition of temperature data, loading a temperature compensation algorithm on the real-time acquired temperature data, updating the temperature response characteristic model in real time, performing real-time temperature compensation correction in the output of the pressure sensor, establishing an error prediction and early warning mechanism, and adaptively optimizing and long-term calibrating the temperature compensation method. This method for intelligent temperature compensation of a pressure sensor significantly improves the measurement accuracy and long-term stability of the sensor in complex environments through methods such as temperature response characteristic modeling, temperature compensation algorithm, and real-time model update, and can adaptively respond to rapid temperature difference changes and nonlinear errors. At the same time, it also ensures that the temperature compensation algorithm can be continuously optimized with environmental changes and sensor aging, has high applicability and scalability, and provides technical support for the application of sensors in harsh working conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of sensor equipment, and in particular to an intelligent temperature compensation method for a pressure sensor. Background Art

[0002] Pressure sensors are widely used in industries such as industry, aviation, and medicine. Their measurement accuracy is extremely sensitive to ambient temperature. Temperature fluctuations can cause fluctuations in sensor material properties, leading to zero-point drift and sensitivity variations. Therefore, temperature compensation is a crucial method for improving pressure sensor accuracy. Traditional temperature compensation methods typically rely on linear models or empirical formulas, which are effective only within a limited temperature range and struggle to adapt to complex environments, such as rapid temperature fluctuations or nonlinear temperature effects. Furthermore, existing technologies typically employ static compensation strategies, which are unable to address performance drift caused by sensor aging or long-term use. The lack of a dynamic adjustment mechanism prevents the compensation model from being optimized as actual operating conditions change, limiting the long-term stability of the system. Summary of the Invention

[0003] In view of the deficiencies in the prior art, the present invention provides an intelligent temperature compensation method for a pressure sensor to solve the problems raised in the above background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] In a first aspect, an embodiment of the present invention provides a method for intelligent temperature compensation of a pressure sensor, comprising the following steps:

[0006] S1. Modeling the temperature response characteristics of the pressure sensor to construct a temperature response characteristic model;

[0007] S2, real-time collection of temperature data from pressure sensors;

[0008] S3. Loading a temperature compensation algorithm on the real-time collected temperature data according to the temperature response characteristic model;

[0009] S4. Real-time calibration and dynamic adjustment of the compensated temperature data, and real-time update of the temperature response characteristic model;

[0010] S5. After the calibration of the temperature and pressure deviation relationship is completed, real-time temperature compensation correction is performed on the output of the pressure sensor;

[0011] S6. Establish an error prediction and early warning mechanism during the real-time temperature compensation correction process;

[0012] S7. Adaptively optimize and perform long-term calibration on the temperature compensation method.

[0013] To further optimize the technical solution, in step S1, the output of the pressure sensor at different temperatures is measured experimentally and compared with the output under known standard temperature conditions, so as to establish a relationship between temperature and output error and construct a temperature response characteristic model;

[0014] The temperature response characteristic model is as follows:

[0015] ;

[0016] in,

[0017] : Temperature response error of pressure sensor;

[0018] : current ambient temperature;

[0019] : Polynomial fitting coefficients used to model the response of the pressure sensor to linear and quadratic temperature changes;

[0020] : Periodic fluctuation coefficient, simulating the harmonic interference generated by the pressure sensor in different temperature zones;

[0021] : Parameters that control harmonic frequency and characterize the frequency changes of periodic fluctuations;

[0022] : The weight coefficient of the exponential term, reflecting the dynamic impact of the temperature change rate on the performance of the pressure sensor;

[0023] : Exponential decay parameter, which describes the gradual change characteristics of the pressure sensor response when the temperature rises or falls rapidly.

[0024] To further optimize this technical solution, the experiment of measuring the pressure sensor output at different temperatures and comparing it with the output under known standard temperature conditions includes the following process:

[0025] In an environmentally controlled room, the pressure sensor was placed under stable temperature conditions, including -40°C, 0°C, 25°C, 50°C, and 80°C;

[0026] Compare the actual pressure output of the pressure sensor with the standard pressure value to obtain the error data corresponding to the temperature;

[0027] The least squares method is used to estimate the parameters 、 、 、 、 Perform fitting to ensure that the model accurately describes the changing trend of the error.

[0028] Further optimizing the technical solution, in step S2, during the operation of the pressure sensor, the operating temperature of the pressure sensor is monitored in real time;

[0029] By collecting the current ambient temperature of the pressure sensor, it is ensured that the temperature data can provide accurate input for the subsequent compensation process.

[0030] To further optimize this technical solution, in step S3, the temperature compensation algorithm includes:

[0031] Assume that the output of the pressure sensor is , the temperature of the pressure sensor is , the output after compensation is , the algorithm model is as follows:

[0032] ;

[0033] in,

[0034] : Uncompensated output pressure value of the pressure sensor;

[0035] : Actual output pressure value after compensation;

[0036] : Temperature error value calculated by temperature response characteristic model;

[0037] : Dynamic weight factor, describing the importance and sensitivity of compensation in different temperature ranges;

[0038] : Dynamic correction term, based on historical data of pressure sensor and real-time temperature gradient Perform recursive optimization.

[0039] To further optimize this technical solution, the dynamic weight factor is expressed as follows:

[0040] ;

[0041] in,

[0042] : Define the threshold point of the temperature range

[0043] : Reference temperature point, used to optimize the distribution center of weights.

[0044] : The weight scalar of the interval, reflecting the sensitivity of the error in different temperature intervals.

[0045] : Exponential decay factor, used to control the rate of decrease of weight.

[0046] By initialization ,The temperature compensation algorithm prioritizes allocating more error correction weights in low or high temperature environments to improve accuracy.

[0047] Further optimizing the technical solution, in step S4, the temperature response characteristic model is updated in real time by continuously dynamically calibrating the output of the pressure sensor at different temperatures;

[0048] When the temperature changes, the output deviation of the pressure sensor is adjusted by using historical data and real-time temperature data of the pressure sensor and using data fitting or interpolation methods.

[0049] Further optimizing the technical solution, in step S5, the output of the pressure sensor is corrected by a temperature compensation algorithm according to the current operating temperature to eliminate measurement errors caused by temperature fluctuations;

[0050] As the pressure of the pressure sensor changes, the pressure sensor output is accurately corrected according to the latest collected temperature data to improve the stability of the measurement results.

[0051] Further optimizing the technical solution, in step S6, the error prediction and early warning mechanism predicts the measurement errors that may occur in the future by analyzing the historical data of temperature change trends and compensation effects;

[0052] The error prediction and early warning mechanism is based on statistical methods, machine learning algorithms, or adaptive algorithms. It uses data models to predict potential errors and uses alarm systems to warn of possible failures in advance.

[0053] When the temperature changes too drastically and cannot be effectively compensated, the alarm system will issue a warning signal, prompting the need to check the pressure sensor or recalibrate the temperature response characteristic model.

[0054] Further optimizing the technical solution, in step S7, the temperature compensation method is optimized by using the temperature and pressure data accumulated over a long period of time;

[0055] According to the real-time compensation effect and error accumulation, the pressure sensor is recalibrated regularly by setting up a regular automatic calibration mechanism, and the temperature compensation method is adjusted according to actual usage.

[0056] In a second aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of a pressure sensor intelligent temperature compensation method as described in the first aspect of the present invention are implemented.

[0057] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of a pressure sensor intelligent temperature compensation method as described in the first aspect of the present invention are implemented.

[0058] Compared with the prior art, the present invention provides an intelligent temperature compensation method for a pressure sensor, which has the following beneficial effects:

[0059] This intelligent temperature compensation method for pressure sensors significantly improves the sensor's measurement accuracy and long-term stability in complex environments through temperature response modeling, a temperature compensation algorithm, and real-time model updates. It can adaptively respond to rapid temperature changes and nonlinear errors. It also ensures that the temperature compensation algorithm is continuously optimized with environmental changes and sensor aging, providing high applicability and scalability, and providing technical support for the sensor's application in harsh working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0061] Figure 1 This is a flow chart of an intelligent temperature compensation method for a pressure sensor proposed by the present invention;

[0062] Figure 2 This is a flow chart of the temperature compensation algorithm in the intelligent temperature compensation method for a pressure sensor proposed by the present invention. DETAILED DESCRIPTION

[0063] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0064] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0065] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it constitute a separate or selective embodiment that is mutually exclusive with other embodiments.

[0066] Example 1:

[0067] Reference Figures 1 and 2 , which is the first embodiment of the present invention, provides a pressure sensor intelligent temperature compensation method, comprising the following steps:

[0068] S1. Model the temperature response characteristics of the pressure sensor and build a temperature response characteristic model

[0069] In this embodiment, each sensor may exhibit different zero point offsets and sensitivity changes at different operating temperatures, and these changes may affect its measurement accuracy.

[0070] By experimentally measuring the pressure sensor output at different temperatures and comparing it with the output under known standard temperature conditions, the relationship between temperature and output error is established, and a temperature response characteristic model is constructed.

[0071] The temperature response characteristic model is as follows:

[0072] ;

[0073] in,

[0074] : Temperature response error of pressure sensor;

[0075] : current ambient temperature;

[0076] : Polynomial fitting coefficients used to model the response of the pressure sensor to linear and quadratic temperature changes;

[0077] : Periodic fluctuation coefficient, simulating the harmonic interference generated by the pressure sensor in different temperature zones;

[0078] : Parameters that control harmonic frequency and characterize the frequency changes of periodic fluctuations;

[0079] : The weight coefficient of the exponential term, reflecting the dynamic impact of the temperature change rate on the performance of the pressure sensor;

[0080] : Exponential decay parameter, which describes the gradual change characteristics of the pressure sensor response when the temperature rises or falls rapidly.

[0081] Furthermore, the experiment of measuring the pressure sensor output at different temperatures and comparing it with the output under known standard temperature conditions includes the following process:

[0082] In an environmentally controlled room, the pressure sensor was placed under stable temperature conditions, including -40°C, 0°C, 25°C, 50°C, and 80°C;

[0083] Compare the actual pressure output of the pressure sensor with the standard pressure value to obtain the error data corresponding to the temperature;

[0084] The least squares method is used to estimate the parameters 、 、 、 、 Perform fitting to ensure that the model accurately describes the changing trend of the error.

[0085] In this model,

[0086] Periodic items and These terms can capture the effects of internal sensor electrical properties (such as thermal noise from strain gauges) or structural characteristics (such as nonlinear offsets caused by thermal expansion) during temperature changes. For example, rapidly changing temperature environments can introduce significant fluctuations in certain temperature ranges (such as around 40°C). These terms can more accurately model and compensate for this periodic interference.

[0087] The exponential term is used to describe the dynamic error characteristics when the temperature changes rapidly. For example, when the sensor is suddenly heated or cooled, it may show significant deviation in a short period of time. This term provides a fast-response compensation channel.

[0088] As the sensor ages, its temperature response characteristics may change. The system can periodically refit the model and update the parameters.

[0089] During real-time compensation, the actual compensation effect of the error can be recorded to evaluate the accuracy of the model and gradually adjust the parameters to optimize the model performance.

[0090] S2. Real-time collection of temperature data from pressure sensors

[0091] In this embodiment, during the operation of the pressure sensor, the operating temperature of the pressure sensor is monitored in real time;

[0092] By collecting the current ambient temperature of the pressure sensor, it is ensured that the temperature data can provide accurate input for the subsequent compensation process.

[0093] In this step, pay attention to the selection and installation location of the temperature sensor attached to the pressure sensor to avoid interference from external environmental factors on the measurement results. For example, if the temperature sensor is too far away from the pressure sensor or is installed in an inappropriate location, inaccurate measurement data may result.

[0094] S3. Load the temperature compensation algorithm on the real-time collected temperature data according to the temperature response characteristic model

[0095] In this embodiment, the temperature compensation algorithm ensures that it can quickly respond to real-time temperature changes so as to accurately correct the output of the pressure sensor. The temperature compensation algorithm includes:

[0096] Assume that the output of the pressure sensor is , the temperature of the pressure sensor is , the output after compensation is , the algorithm model is as follows:

[0097] ;

[0098] in,

[0099] : Uncompensated output pressure value of the pressure sensor;

[0100] : Actual output pressure value after compensation;

[0101] : Temperature error value calculated by temperature response characteristic model;

[0102] : Dynamic weight factor, describing the importance and sensitivity of compensation in different temperature ranges;

[0103] : Dynamic correction term, based on historical data of pressure sensor and real-time temperature gradient Perform recursive optimization.

[0104] Furthermore, when the algorithm is used, it includes:

[0105] Temperature interval segmentation and weight factor initialization

[0106] In order to adapt the algorithm to the error characteristics of different temperature ranges, the temperature compensation algorithm divides the operating temperature range into multiple ranges (such as low temperature range, medium temperature range, and high temperature range). In each range, the weight factor is initialized through experimental fitting. The expression of the dynamic weight factor is:

[0107] ;

[0108] in,

[0109] : Define the threshold point of the temperature interval;

[0110] : Reference temperature point, used to optimize the distribution center of the weight;

[0111] : The weight scalar of the interval, reflecting the sensitivity of the error in different temperature intervals;

[0112] : Exponential decay factor, used to control the rate of decrease of weight.

[0113] By initialization ,The temperature compensation algorithm prioritizes allocating more error correction weights in low or high temperature environments to improve accuracy.

[0114] Error compensation based on real-time temperature

[0115] During operation, the system collects real-time temperature data through sensors and inserts it into the temperature response model to calculate the error value. Then, based on the current temperature range, the corresponding weight factor is selected to calculate the main compensation amount.

[0116] Recursive optimization of dynamic correction terms

[0117] Dynamic correction items It is used to handle temperature change trends that are not captured by static models during system operation, such as sudden temperature changes or sensor aging. Its expression is:

[0118] ;

[0119] in,

[0120] : Real-time temperature change rate, used to capture additional errors when the temperature fluctuates rapidly.

[0121] : Correction coefficient, which determines the response strength of dynamic correction.

[0122] : Adaptive coefficient based on the sensor's historical error compensation effect.

[0123] Feedback loop for dynamic compensation

[0124] Each calculation After that, the cumulative feedback is fed back into the update of the weight factor, and the weight distribution is adjusted using the following formula:

[0125] ;

[0126] in,

[0127] : Represents the weight factor at the last time step (or last temperature reading).

[0128] : Represents the weight factor at the current moment after feedback adjustment.

[0129] : Error direction guided weight correction.

[0130] : Learning rate, controls the speed of weight update.

[0131] Through feedback adjustment, the compensation effect is gradually optimized to make it more stable under various temperature conditions.

[0132] S4. Real-time calibration and dynamic adjustment of the compensated temperature data, and real-time update of the temperature response characteristic model

[0133] In this embodiment, there is a dynamic nonlinear relationship between the outputs of the temperature and pressure sensors, especially when the ambient temperature varies greatly. By continuously and dynamically calibrating the output of the pressure sensor at different temperatures, the temperature response characteristic model is updated in real time.

[0134] When the temperature changes, the pressure sensor's output deviation is adjusted using data fitting or interpolation methods, combining historical data with the pressure sensor's real-time temperature data. Dynamic calibration not only handles simple temperature changes but also accommodates complex temperature fluctuations, such as sudden temperature changes and long-term temperature drift. This dynamic calibration method effectively improves adaptability and accuracy under various temperature conditions.

[0135] S5. After the calibration of the temperature and pressure deviation relationship is completed, real-time temperature compensation correction is performed in the output of the pressure sensor.

[0136] In this embodiment, once the calibration of the relationship between temperature and pressure deviation is completed, the output of the pressure sensor is corrected by a temperature compensation algorithm according to the current operating temperature to eliminate measurement errors caused by temperature fluctuations.

[0137] As the pressure sensor changes, the output of the pressure sensor is accurately corrected based on the latest collected temperature data to improve the stability of the measurement results. The real-time compensation algorithm can be a gradual update type or based on fast calculation methods such as Kalman filtering or adaptive filtering to improve the response speed under temperature changes.

[0138] S6. Establish an error prediction and early warning mechanism during the real-time temperature compensation correction process

[0139] In this embodiment, the sensor may experience some unforeseen deviations due to long-term use, environmental factors, etc. The error prediction and early warning mechanism analyzes the historical data of temperature change trends and compensation effects to predict possible measurement errors in the future.

[0140] The error prediction and early warning mechanism is based on statistical methods, machine learning algorithms, or adaptive algorithms. It uses data models to predict potential errors and uses alarm systems to warn of possible failures in advance.

[0141] When the temperature changes too drastically and cannot be effectively compensated, the alarm system will issue a warning signal, prompting the need to check the pressure sensor or recalibrate the temperature response characteristic model.

[0142] S7. Adaptive optimization and long-term calibration of temperature compensation methods

[0143] In this embodiment, the temperature compensation method is optimized through the long-term accumulation of temperature and pressure data;

[0144] Based on the real-time compensation effect and error accumulation, a regular automatic calibration mechanism is set up to regularly recalibrate the pressure sensor and adjust the temperature compensation method according to actual usage. This maintains high accuracy throughout the entire life cycle and avoids error accumulation caused by aging or environmental changes.

[0145] Example 2:

[0146] This embodiment also provides a computer device suitable for a pressure sensor intelligent temperature compensation method, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement a pressure sensor intelligent temperature compensation method proposed in the above embodiment.

[0147] This embodiment further provides a storage medium storing a computer program, which, when executed by a processor, implements a pressure sensor intelligent temperature compensation method as proposed in the above embodiment.

[0148] The computer device may be a terminal, comprising a processor, memory, a communication interface, a display, and an input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores an operating system and computer programs. The internal memory provides an environment for the operating system and computer programs stored in the non-volatile storage media. The communication interface of the computer device is used to communicate with external terminals via wired or wireless communication. Wireless communication may be achieved via Wi-Fi, a carrier network, NFC (near-field communication), or other technologies. The display of the computer device may be a liquid crystal display or an electronic ink display. The input device may be a touchscreen overlay on the display, buttons, a trackball, or a touchpad on the computer device housing, or an external keyboard, touchpad, or mouse.

[0149] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0150] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0151] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting, or processing it in another suitable manner as necessary, and then storing it in a computer memory.

[0152] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0153] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A pressure sensor intelligent temperature compensation method, characterized in that: The following steps are involved: S1. Modeling the temperature response characteristics of the pressure sensor to construct a temperature response characteristic model; By experimentally measuring the pressure sensor output at different temperatures and comparing it with the output under known standard temperature conditions, the relationship between temperature and output error is established, and a temperature response characteristic model is constructed. The temperature response characteristic model is as follows: ; in, : Temperature response error of pressure sensor; : current ambient temperature; : Polynomial fitting coefficients used to model the response of the pressure sensor to linear and quadratic temperature changes; : Periodic fluctuation coefficient, simulating the harmonic interference generated by the pressure sensor in different temperature zones; : Parameters that control harmonic frequency and characterize the frequency changes of periodic fluctuations; : The weight coefficient of the exponential term, reflecting the dynamic impact of the temperature change rate on the performance of the pressure sensor; : Exponential decay parameter, which describes the gradual change characteristics of the pressure sensor response when the temperature rises or falls rapidly; S2, real-time collection of temperature data from pressure sensors; S3. Loading a temperature compensation algorithm on the real-time collected temperature data according to the temperature response characteristic model; The temperature compensation algorithm includes: Assume that the output of the pressure sensor is , the temperature of the pressure sensor is , the output after compensation is , the algorithm model is as follows: ； in, : Uncompensated output pressure value of the pressure sensor; : Actual output pressure value after compensation; : Temperature error value calculated by temperature response characteristic model; : Dynamic weight factor, describing the importance and sensitivity of compensation in different temperature ranges; : Dynamic correction term, based on historical data of pressure sensor and real-time temperature gradient Perform recursive optimization; The dynamic weight factor is expressed as: ； in, : Define the threshold point of the temperature interval; : Reference temperature point, used to optimize the distribution center of the weight; : The weight scalar of the interval, reflecting the sensitivity of the error in different temperature intervals; : Exponential decay factor, used to control the decreasing rate of weight; By initialization ,The temperature compensation algorithm prioritizes allocating more error correction weights in low or high temperature environments to improve accuracy; The dynamic correction term is expressed as: ； in, : Real-time temperature change rate, used to capture additional errors when the temperature fluctuates rapidly; : Correction coefficient, which determines the response strength of dynamic correction; : Adaptive coefficient based on the historical error compensation effect of the sensor; S4. Real-time calibration and dynamic adjustment of the compensated temperature data, and real-time update of the temperature response characteristic model; S5. After the calibration of the temperature and pressure deviation relationship is completed, real-time temperature compensation correction is performed on the output of the pressure sensor; S6. Establish an error prediction and early warning mechanism during the real-time temperature compensation correction process; S7. Adaptively optimize and perform long-term calibration on the temperature compensation method.

2. The intelligent temperature compensation method for a pressure sensor according to claim 1, characterized in that: The experiment of measuring the pressure sensor output at different temperatures and comparing it with the output under known standard temperature conditions includes the following steps: In an environmentally controlled room, the pressure sensor was placed under stable temperature conditions, including -40°C, 0°C, 25°C, 50°C, and 80°C; Compare the actual pressure output of the pressure sensor with the standard pressure value to obtain the error data corresponding to the temperature; The least squares method is used to estimate the parameters 、 、 、 、 Perform fitting to ensure that the model accurately describes the changing trend of the error.

3. The intelligent temperature compensation method for a pressure sensor according to claim 1, characterized in that: In step S2, during the operation of the pressure sensor, the operating temperature of the pressure sensor is monitored in real time; By collecting the current ambient temperature of the pressure sensor, it is ensured that the temperature data can provide accurate input for the subsequent compensation process.

4. The intelligent temperature compensation method for a pressure sensor according to claim 1, characterized in that: In step S4, the temperature response characteristic model is updated in real time by continuously dynamically calibrating the output of the pressure sensor at different temperatures; When the temperature changes, the output deviation of the pressure sensor is adjusted by using historical data and real-time temperature data of the pressure sensor and using data fitting or interpolation methods.

5. The intelligent temperature compensation method for a pressure sensor according to claim 1, characterized in that: In step S5, the output of the pressure sensor is corrected by a temperature compensation algorithm according to the current operating temperature to eliminate measurement errors caused by temperature fluctuations; As the pressure of the pressure sensor changes, the pressure sensor output is accurately corrected according to the latest collected temperature data to improve the stability of the measurement results.

6. The intelligent temperature compensation method for a pressure sensor according to claim 1, characterized in that: In step S6, the error prediction and early warning mechanism analyzes the historical data of temperature change trends and compensation effects to predict possible measurement errors in the future. The error prediction and early warning mechanism is based on statistical methods, machine learning algorithms, or adaptive algorithms. It uses data models to predict potential errors and uses alarm systems to warn of possible failures in advance. When the temperature changes too drastically and cannot be effectively compensated, the alarm system will issue a warning signal, prompting the need to check the pressure sensor or recalibrate the temperature response characteristic model.

7. The intelligent temperature compensation method for a pressure sensor according to claim 1, characterized in that: In step S7, the temperature compensation method is optimized through the long-term accumulated temperature and pressure data; According to the real-time compensation effect and error accumulation, the pressure sensor is recalibrated regularly by setting up a regular automatic calibration mechanism, and the temperature compensation method is adjusted according to actual usage.

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