MEMS pressure sensor temperature drift compensation method, system, device and medium

Abstract

The embodiment of the invention provides an MEMS pressure sensor temperature drift compensation method, system and device and a medium. The method comprises the following steps: acquiring a pressure value and an environment temperature value of the MEMS pressure sensor; inputting the pressure value and the environment temperature value into a pre-established temperature drift compensation model, and outputting to obtain a compensation value of the MEMS pressure sensor; wherein the temperature drift compensation model is pre-established through a radial basis function neural network comprising a particle swarm optimization algorithm and a Sudoku algorithm. According to the embodiment of the invention, by dynamically adjusting the output of the sensor, the negative influence of the temperature effect can be reduced to the minimum, the measurement result of the sensor is ensured to be more accurate and reliable, and the measurement precision of the MEMS sensor can be remarkably improved; automatic temperature compensation of the sensor is allowed, manual intervention or calibration is not needed, the burden of maintenance and operation is reduced, and the usability of the sensor is improved; through software implementation, the cost can be remarkably reduced, and the competitiveness of the sensor is improved.

Description

Technical Field

[0001] The embodiments of the present disclosure belong to the technical field of temperature drift compensation for pressure sensors, and specifically relate to a method, system, device and medium for temperature drift compensation for a MEMS pressure sensor. Background Art

[0002] MEMS pressure sensors are usually composed of pressure films and pressure-sensitive elements. External vibrations, pressure, etc. cause the sensor's semiconductor elastic film to deform, and the pressure is reflected by the degree of deformation detected by the pressure-sensitive element, thereby realizing pressure detection. Driven by the development of MEMS technology, MEMS pressure sensors are widely used in aerospace, biomedicine, automotive industry, environmental monitoring, industrial automation and other fields due to their advantages such as miniaturization, low power consumption, high precision and high stability.

[0003] The pressure-sensitive element of a MEMS pressure sensor is usually a piezoresistor, which converts the deformation caused by pressure changes into changes in the resistance of the piezoresistor. However, the piezoresistor itself is significantly affected by the external temperature, which causes a large temperature drift in the pressure sensor and seriously affects the accuracy of the sensor. Summary of the invention

[0004] The embodiments of the present disclosure aim to solve at least one of the technical problems existing in the prior art, and provide a method, system, device and medium for compensating temperature drift of a MEMS pressure sensor.

[0005] One aspect of the present disclosure provides a method for compensating a temperature drift of a MEMS pressure sensor, the method comprising:

[0006] Get the pressure value and ambient temperature value of the MEMS pressure sensor;

[0007] The pressure value and the ambient temperature value are input into a pre-established temperature drift compensation model, and the compensation value of the MEMS pressure sensor is output; wherein,

[0008] The temperature drift compensation model is pre-established by a radial basis function neural network including a particle swarm optimization algorithm and a nine-square grid algorithm.

[0009] Furthermore, the temperature drift compensation model is pre-established through the following steps:

[0010] Acquire an original data set of the MEMS pressure sensor; wherein the original data set includes an original pressure value and an original ambient temperature value;

[0011] Calculate the hidden layer parameters of the radial basis function neural network according to the original data set;

[0012] Use the Nine-Grid Algorithm to obtain the optimal inertia weight and learning factor of the particle swarm optimization algorithm;

[0013] Add random variables to the particle swarm optimization algorithm to initialize the position and velocity of particles;

[0014] Update the position and velocity of the particle, calculate the fitness value, and determine whether the fitness value meets the error requirement;

[0015] The particle swarm optimization algorithm is used to obtain the optimal parameters of the radial basis function neural network, and the radial basis function neural network is trained to obtain the temperature drift compensation model.

[0016] Furthermore, the method of using the nine-square grid algorithm to obtain the optimal inertia weight of the particle swarm optimization algorithm includes:

[0017] The optimal inertia weight of the particle swarm optimization algorithm is obtained using the following formula (1):

[0018]

[0019] Where ω is the optimal inertia weight, ω max is the upper limit of the inertia coefficient, ω min is the lower limit of the inertia coefficient, run is the current number of evolutions, run max For evolutionary algebra.

[0020] Furthermore, the method of using the Nine-squares algorithm to obtain the learning factor of the particle swarm optimization algorithm includes:

[0021] The learning factor of the particle swarm optimization algorithm is obtained using the following formula (2):

[0022]

[0023] Where c1 and c2 are learning factors, ↑ indicates increase, ↓ indicates decrease, and → indicates unchanged; each time the optimal inertia weight ω changes, the learning factors c1 and c2 are trained once according to the nine-square algorithm table.

[0024] Another aspect of the present disclosure provides a MEMS pressure sensor temperature drift compensation system, the system comprising:

[0025] An acquisition module is used to obtain the pressure value and ambient temperature value of the MEMS pressure sensor;

[0026] The compensation module is used to input the pressure value and the ambient temperature value into a pre-established temperature drift compensation model, and output a compensation value of the MEMS pressure sensor; wherein the temperature drift compensation model is pre-established by a radial basis function neural network including a particle swarm optimization algorithm and a nine-square grid algorithm.

[0027] Furthermore, the system also includes a model module for pre-establishing the temperature drift compensation model;

[0028] The model module is specifically used for:

[0029] Acquire an original data set of the MEMS pressure sensor; wherein the original data set includes an original pressure value and an original ambient temperature value;

[0030] Calculate the hidden layer parameters of the radial basis function neural network according to the original data set;

[0031] Use the Nine-Grid Algorithm to obtain the optimal inertia weight and learning factor of the particle swarm optimization algorithm;

[0032] Add random variables to the particle swarm optimization algorithm to initialize the position and velocity of particles;

[0033] Update the position and velocity of the particle, calculate the fitness value, and determine whether the fitness value meets the error requirement;

[0034] The particle swarm optimization algorithm is used to obtain the optimal parameters of the radial basis function neural network, and the radial basis function neural network is trained to obtain the temperature drift compensation model.

[0035] Furthermore, the method of using the nine-square grid algorithm to obtain the optimal inertia weight of the particle swarm optimization algorithm includes:

[0036] The optimal inertia weight of the particle swarm optimization algorithm is obtained using the following formula (1):

[0037]

[0038] Where ω is the optimal inertia weight, ω max is the upper limit of the inertia coefficient, ω min is the lower limit of the inertia coefficient, run is the current number of evolutions, run max For evolutionary algebra.

[0039] Furthermore, the method of using the Nine-squares algorithm to obtain the learning factor of the particle swarm optimization algorithm includes:

[0040] The learning factor of the particle swarm optimization algorithm is obtained using the following formula (2):

[0041]

[0042] Where c1 and c2 are learning factors, ↑ indicates increase, ↓ indicates decrease, and → indicates unchanged; each time the optimal inertia weight ω changes, the learning factors c1 and c2 are trained once according to the nine-square algorithm table.

[0043] Another aspect of the present disclosure provides an electronic device, comprising:

[0044] at least one processor; and,

[0045] The memory communicatively connected to the at least one processor is used to store one or more programs, and when the one or more programs are executed by the at least one processor, the at least one processor can implement the MEMS pressure sensor temperature drift compensation method described above.

[0046] Another aspect of the present disclosure provides a computer-readable storage medium storing a computer program, wherein the computer program implements the above-mentioned method for compensating the temperature drift of the MEMS pressure sensor when executed by a processor.

[0047] A method, system, device and medium for compensating temperature drift of a MEMS pressure sensor according to an embodiment of the present disclosure have at least the following technical effects:

[0048] 1. Improve sensor accuracy: The embodiments of the present disclosure can minimize the negative impact of temperature effects by dynamically adjusting sensor output, ensuring that sensor measurement results are more accurate and reliable, and can significantly improve the measurement accuracy of MEMS sensors;

[0049] 2. Improve environmental adaptability: The embodiments of the present disclosure enhance the environmental adaptability of the sensor, enabling it to operate in a wide temperature range without being disturbed by temperature fluctuations, which helps to expand the application field of the sensor;

[0050] 3. Automated temperature compensation; the embodiments of the present disclosure allow the sensor to automatically perform temperature compensation without manual intervention or calibration, thereby reducing the burden of maintenance and operation and improving the ease of use of the sensor.

[0051] 4. Reduce costs; Traditional temperature compensation methods usually require expensive hardware equipment or complex circuit design. The method of the embodiment of the present disclosure is implemented through software, which can significantly reduce costs and improve the competitiveness of the sensor. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 A schematic flow chart of a method for compensating a temperature drift of a MEMS pressure sensor according to an embodiment of the present disclosure;

[0053] Figure 2 It is a structural schematic diagram of a collection circuit according to another embodiment of the present disclosure;

[0054] Figure 3 A schematic diagram of the structure of an RBF network according to another embodiment of the present disclosure;

[0055] Figure 4 It is a structural schematic diagram of a temperature drift compensation system for a MEMS pressure sensor according to another embodiment of the present disclosure;

[0056] Figure 5The figure is a schematic diagram of the structure of an electronic device according to another embodiment of the present disclosure. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present disclosure.

[0058] In addition, the described features, structures or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known methods, devices, implementations or operations are not shown or described in detail to avoid blurring the various aspects of the present disclosure.

[0059] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0060] It should be understood that although the terms first, second, third, etc. may be used in this disclosure to describe various components, these components should not be limited by these terms. These terms are used to distinguish one component from another component. Therefore, the first component discussed below can be referred to as the second component without departing from the teachings of the concepts of the present disclosure. As used in this disclosure, the term "and / or" includes any one of the associated listed items and all combinations of one or more.

[0061] Those skilled in the art will appreciate that the drawings are merely schematic diagrams of example embodiments, and the modules or processes in the drawings are not necessarily necessary for implementing the present disclosure, and therefore cannot be used to limit the protection scope of the present disclosure.

[0062] like Figure 1 As shown, an embodiment of the present disclosure provides a method for compensating a temperature drift of a MEMS pressure sensor, comprising:

[0063] Step S1, obtaining the pressure value and ambient temperature value of the MEMS pressure sensor.

[0064] Specifically, the pressure output value of the MEMS pressure sensor is acquired, and the ambient temperature value of the MEMS pressure sensor is acquired using a temperature sensor.

[0065] Step S2, inputting the pressure value and the ambient temperature value into a pre-established temperature drift compensation model, and outputting a compensation value of the MEMS pressure sensor; wherein the temperature drift compensation model is pre-established by a radial basis function neural network including a particle swarm optimization algorithm and a nine-square grid algorithm.

[0066] Specifically, the pressure value and the ambient temperature value acquired in step S1 are respectively input into a pre-established and trained temperature drift compensation model, and the temperature drift compensation model outputs the compensated pressure value of the MEMS pressure sensor, ie, the compensation value.

[0067] The temperature drift compensation model is pre-established using the particle swarm optimization algorithm (PSO), Sudoku algorithm (SK) and radial basis function (RBF) neural network. The specific process is as follows:

[0068] 1. Obtain multiple sets of raw pressure values ​​X of the MEMS pressure sensor p and the original ambient temperature value X t , forming the original data set of the MEMS pressure sensor, and appropriately dividing the original data set into a training set and a validation set; the original pressure value X of the MEMS pressure sensor p and the original ambient temperature value X t Through Figure 2 Each sensor in the acquisition circuit shown collects data and obtains it through the communication interface.

[0069] 2. Create Figure 3 The RBF network shown in the figure has the pressure and temperature values ​​as input layer and the predicted output value, i.e., the compensation value, and the initial values ​​of b1 and W1 of the hidden layer are calculated by the data of the training set to obtain the corresponding relationship p0=f(X p ,X t ), where p0 represents the predicted output value of the RBF network.

[0070] 3. Initialize the number of iterations and population size of PSO, and use the SK algorithm to obtain the optimal inertia weight ω, learning factor c1 and learning factor c2 of the PSO algorithm; the optimal inertia weight ω and learning factors c1 and c2 are obtained by the following formula (1) and formula (2) respectively:

[0071]

[0072] Where ω is the optimal inertia weight, ω max is the upper limit of the inertia coefficient, ω min is the lower limit of the inertia coefficient, run is the current number of evolutions, runmax for evolutionary algebra;

[0073]

[0074] Where c1 and c2 are learning factors, ↑ indicates increase, ↓ indicates decrease, and → indicates unchanged; each time the optimal inertia weight ω changes, the learning factors c1 and c2 are trained once according to the nine-square algorithm table.

[0075] Fourth, combine the RBF network parameters b1 and W1 into a particle, and add a random variable system to PSO within a certain range to initialize the particle's position x and velocity v.

[0076] 5. Update the particle position x and velocity v, and use the difference between the predicted output value p0 and the calibrated pressure value as the fitness function value as follows (3):

[0077] F=p-p0(3)

[0078] In the formula, F is the value of the optimization objective function, p is the calibration pressure value, and p0 is the predicted output value. The fitness function value is optimized, the fitness value fitness is calculated, and it is judged whether it meets the error requirements; if it meets the requirements, the next step is performed; if it does not meet the requirements, the particle position x and velocity v are updated again.

[0079] 6. Obtain the optimal parameters of the RBF network optimized by the PSO algorithm, train the optimized RBF network again and test it to determine whether it meets the training termination requirements; if not, return to 3. Initialize the number of iterations and population size of PSO, if they meet the requirements, terminate the training and obtain the temperature compensation model.

[0080] The pressure value and the ambient temperature value of the MEMS pressure sensor to be temperature compensated are input into the temperature drift compensation model established through the above steps, and the predicted compensated temperature value can be output.

[0081] A method for compensating a temperature drift of a MEMS pressure sensor according to an embodiment of the present disclosure has at least the following technical effects:

[0082] 1. Improve sensor accuracy: The embodiments of the present disclosure can minimize the negative impact of temperature effects by dynamically adjusting sensor output, ensuring that sensor measurement results are more accurate and reliable, and can significantly improve the measurement accuracy of MEMS sensors;

[0083] 2. Improve environmental adaptability: The embodiments of the present disclosure enhance the environmental adaptability of the sensor, enabling it to operate in a wide temperature range without being disturbed by temperature fluctuations, which helps to expand the application field of the sensor;

[0084] 3. Automated temperature compensation; the embodiments of the present disclosure allow the sensor to automatically perform temperature compensation without manual intervention or calibration, thereby reducing the burden of maintenance and operation and improving the ease of use of the sensor.

[0085] 4. Reduce costs; Traditional temperature compensation methods usually require expensive hardware equipment or complex circuit design. The method of the embodiment of the present disclosure is implemented through software, which can significantly reduce costs and improve the competitiveness of the sensor.

[0086] like Figure 4 As shown, another embodiment of the present disclosure provides a MEMS pressure sensor temperature drift compensation system, comprising:

[0087] An acquisition module 410 is used to acquire a pressure value and an ambient temperature value of a MEMS pressure sensor;

[0088] The compensation module 420 is used to input the pressure value and the ambient temperature value into a pre-established temperature drift compensation model, and output a compensation value of the MEMS pressure sensor; wherein the temperature drift compensation model is pre-established by a radial basis function neural network including a particle swarm optimization algorithm and a nine-square grid algorithm.

[0089] For example, Figure 4 As shown, the system further includes a model module 430 for pre-establishing the temperature drift compensation model. The model module 430 is specifically used for:

[0090] 1. Obtain the original data set of the MEMS pressure sensor; wherein the original data set includes the original pressure value and the original ambient temperature value; 2. Calculate the hidden layer parameters of the radial basis function neural network according to the original data set; 3. Use the nine-square grid algorithm to obtain the optimal inertia weight and learning factor of the particle swarm optimization algorithm; 4. Add a random variable system to the particle swarm optimization algorithm to initialize the position and velocity of the particles; 5. Update the position and velocity of the particles, calculate the fitness value, and determine whether the fitness value meets the error requirements; 6. Use the particle swarm optimization algorithm to obtain the optimal parameters of the radial basis function neural network, and train the radial basis function neural network to obtain a temperature drift compensation model.

[0091] Exemplarily, the method of using the Nine-squares algorithm to obtain the optimal inertia weight of the particle swarm optimization algorithm includes:

[0092] The optimal inertia weight of the particle swarm optimization algorithm is obtained using the following formula (1):

[0093]

[0094] Where ω is the optimal inertia weight, ω max is the upper limit of the inertia coefficient, ω minis the lower limit of the inertia coefficient, run is the current number of evolutions, run max For evolutionary algebra.

[0095] Exemplarily, the method of using the Nine-squares algorithm to obtain a learning factor of a particle swarm optimization algorithm includes:

[0096] The learning factor of the particle swarm optimization algorithm is obtained using the following formula (2):

[0097]

[0098] Where c1 and c2 are learning factors, ↑ indicates increase, ↓ indicates decrease, and → indicates unchanged; each time the optimal inertia weight ω changes, the learning factors c1 and c2 are trained once according to the nine-square algorithm table.

[0099] Specifically, a MEMS pressure sensor temperature drift compensation system according to an embodiment of the present disclosure is used to implement the MEMS pressure sensor temperature drift compensation method described in the above embodiments. The specific implementation process has been described in detail in the above embodiments and will not be repeated herein.

[0100] like Figure 5 As shown, another embodiment of the present disclosure provides an electronic device, including:

[0101] At least one processor 501; and a memory 502 communicatively connected to the at least one processor 501, for storing one or more programs, which, when executed by the at least one processor 501, enable the at least one processor 501 to implement the MEMS pressure sensor temperature drift compensation method described above.

[0102] The memory 502 and the processor 501 are connected in a bus manner, and the bus may include any number of interconnected buses and bridges, and the bus connects various circuits of one or more processors 501 and the memory 502 together. The bus can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be one element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices on a transmission medium. The data processed by the processor 501 is transmitted on a wireless medium via an antenna, and further, the antenna also receives data and transmits the data to the processor 501.

[0103] The processor 501 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management and other control functions. The memory 502 can be used to store data used by the processor 501 when performing operations.

[0104] Yet another embodiment of the present disclosure provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the above-mentioned method for compensating for temperature drift of a MEMS pressure sensor.

[0105] The computer-readable storage medium may be included in the system or electronic device of the present disclosure, or may exist independently.

[0106] Computer-readable storage media may be any tangible media that contains or stores a program, which may be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device. More specific examples include, but are not limited to, an electrical connection with one or more wires, a portable computer disk, a hard disk, optical fiber, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0107] The computer-readable storage medium may also include a data signal propagated in baseband or as part of a carrier wave, in which the computer-readable program code is carried. Specific examples include but are not limited to electromagnetic signals, optical signals, or any suitable combination thereof.

[0108] It is to be understood that the above embodiments are merely exemplary embodiments used to illustrate the principles of the present disclosure, but the present disclosure is not limited thereto. For those of ordinary skill in the art, various modifications and improvements can be made without departing from the spirit and substance of the present disclosure, and these modifications and improvements are also considered to be within the scope of protection of the present disclosure.

Claims

1. A method for compensating temperature drift of a MEMS pressure sensor, characterized in that: The method comprises: Get the pressure value and ambient temperature value of the MEMS pressure sensor; The pressure value and the ambient temperature value are input into a pre-established temperature drift compensation model, and the compensation value of the MEMS pressure sensor is output; wherein, The temperature drift compensation model is pre-established by a radial basis function neural network including a particle swarm optimization algorithm and a nine-square grid algorithm.

2. The method according to claim 1, characterized in that: The temperature drift compensation model is pre-established through the following steps: Acquire an original data set of the MEMS pressure sensor; wherein the original data set includes an original pressure value and an original ambient temperature value; Calculate the hidden layer parameters of the radial basis function neural network according to the original data set; Use the Nine-Grid Algorithm to obtain the optimal inertia weight and learning factor of the particle swarm optimization algorithm; Add random variables to the particle swarm optimization algorithm to initialize the position and velocity of particles; Update the position and velocity of the particle, calculate the fitness value, and determine whether the fitness value meets the error requirement; The particle swarm optimization algorithm is used to obtain the optimal parameters of the radial basis function neural network, and the radial basis function neural network is trained to obtain the temperature drift compensation model.

3. The method according to claim 2, characterized in that The method of using the Nine-squares algorithm to obtain the optimal inertia weight of the particle swarm optimization algorithm includes: The optimal inertia weight of the particle swarm optimization algorithm is obtained using the following formula (1): Where ω is the optimal inertia weight, ω max is the upper limit of the inertia coefficient, ω min is the lower limit of the inertia coefficient, run is the current number of evolutions, run max For evolutionary algebra.

4. The method according to claim 3, characterized in that The method of using the Nine-Grid Algorithm to obtain the learning factor of the particle swarm optimization algorithm includes: The learning factor of the particle swarm optimization algorithm is obtained using the following formula (2): Where c1 and c2 are learning factors, ↑ indicates increase, ↓ indicates decrease, and → indicates unchanged; each time the optimal inertia weight ω changes, the learning factors c1 and c2 are trained once according to the nine-square algorithm table.

5. A MEMS pressure sensor temperature drift compensation system, characterized in that: The system comprises: An acquisition module is used to obtain the pressure value and ambient temperature value of the MEMS pressure sensor; The compensation module is used to input the pressure value and the ambient temperature value into a pre-established temperature drift compensation model, and output a compensation value of the MEMS pressure sensor; wherein the temperature drift compensation model is pre-established by a radial basis function neural network including a particle swarm optimization algorithm and a nine-square grid algorithm.

6. The system according to claim 5, characterized in that The system also includes a model module for pre-establishing the temperature drift compensation model; The model module is specifically used for: Acquire an original data set of the MEMS pressure sensor; wherein the original data set includes an original pressure value and an original ambient temperature value; Calculate the hidden layer parameters of the radial basis function neural network according to the original data set; Use the Nine-Grid Algorithm to obtain the optimal inertia weight and learning factor of the particle swarm optimization algorithm; Add random variables to the particle swarm optimization algorithm to initialize the position and velocity of particles; Update the position and velocity of the particle, calculate the fitness value, and determine whether the fitness value meets the error requirement; The particle swarm optimization algorithm is used to obtain the optimal parameters of the radial basis function neural network, and the radial basis function neural network is trained to obtain the temperature drift compensation model.

7. The system according to claim 6, characterized in that The method of using the Nine-squares algorithm to obtain the optimal inertia weight of the particle swarm optimization algorithm includes: The optimal inertia weight of the particle swarm optimization algorithm is obtained using the following formula (1): Where ω is the optimal inertia weight, ω max is the upper limit of the inertia coefficient, ω min is the lower limit of the inertia coefficient, run is the current number of evolutions, run max For evolutionary algebra.

8. The system according to claim 7, characterized in that The method of using the Nine-Grid Algorithm to obtain the learning factor of the particle swarm optimization algorithm includes: The learning factor of the particle swarm optimization algorithm is obtained using the following formula (2): Where c1 and c2 are learning factors, ↑ indicates increase, ↓ indicates decrease, and → indicates unchanged; each time the optimal inertia weight ω changes, the learning factors c1 and c2 are trained once according to the nine-square algorithm table.

9. An electronic device, characterized in that: include: at least one processor; as well as, A memory communicatively connected to the at least one processor, for storing one or more programs, which, when executed by the at least one processor, enables the at least one processor to implement the MEMS pressure sensor temperature drift compensation method according to any one of claims 1 to 4.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for compensating for temperature drift of a MEMS pressure sensor according to any one of claims 1 to 4 is implemented.

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