Temperature drift compensation flexible pressure sensor system based on bionic mechanics

Abstract

The invention relates to a temperature drift compensation flexible pressure sensor system based on bionic mechanics. The temperature drift compensation flexible pressure sensor system comprises a flexible sensing module, a temperature drift compensation module and a signal processing module. The flexible sensing module is designed by adopting a bionic mechanical structure, and the mechanical characteristics of biological tissues are simulated by compounding a plurality of layers of flexible materials, so that high-sensitivity pressure detection is realized; the temperature drift compensation module is integrated with a temperature sensor and a compensation algorithm, monitors the environment temperature change in real time, dynamically adjusts a sensing signal and eliminates temperature interference; and the signal processing module is used for filtering, amplifying and digitally processing the compensated signal and outputting pressure detection data. According to the system, through combination of bionic structure design and intelligent temperature drift compensation, the measurement precision and stability of the flexible pressure sensor in a complex environment are remarkably improved, and the system is suitable for high-precision pressure detection requirements in the fields of medical treatment, robots and the like.

Description

Technical Field

[0001] The present invention relates to the field of sensor technology, and in particular to a temperature drift compensation flexible pressure sensor system based on bionic mechanics. Background Art

[0002] From wearable devices accurately monitoring human physiological parameters, to achieving natural and smooth interactions in human-computer interaction scenarios, to assisting in early diagnosis of diseases and health management in medical monitoring, the performance of flexible pressure sensors directly affects the effectiveness of related applications.

[0003] Currently, traditional flexible pressure sensors face numerous challenges. Regarding temperature stability, due to limitations in material properties and structural design, the sensors are prone to temperature drift when the ambient temperature changes. This causes deviations in the sensor's output signal, severely impacting measurement accuracy and making it unsuitable for applications requiring extremely high accuracy. For example, in the medical field, accurate pressure measurement is crucial for monitoring a patient's physiological condition, and measurement errors caused by temperature drift can lead to misdiagnosis of the condition.

[0004] Traditional flexible pressure sensors have limited sensitivity to pressure changes, making it difficult to accurately capture minute pressure fluctuations. Furthermore, their structural design lacks innovation, and they struggle to strike a balance between flexibility and durability. On the one hand, excessive pursuit of flexibility can lead to structural damage over long periods of use, shortening the sensor's lifespan. On the other hand, achieving durability can sacrifice flexibility, making it difficult to conform to complex surfaces or adapt to the dynamics of human activity.

[0005] Therefore, there is an urgent need for a flexible pressure sensor system based on the principles of bionic mechanics, with efficient temperature drift compensation function and comprehensive optimization in terms of flexibility, sensitivity and durability, so as to break through the limitations of existing technologies. Summary of the Invention

[0006] The purpose of the present invention is to provide a temperature drift compensated flexible pressure sensor system based on bionic mechanics. By introducing an innovative bionic mechanics structure design, an efficient temperature drift compensation algorithm and an intelligent data processing mechanism, the defects of existing flexible pressure sensors in terms of poor temperature stability, low sensing accuracy, and difficulty in balancing flexibility and durability are overcome, thereby significantly improving the measurement accuracy, temperature adaptability and comprehensive mechanical properties of the sensor.

[0007] To achieve the above object, the present invention provides a temperature drift compensation flexible pressure sensor system based on bionic mechanics, comprising a flexible sensing module, a temperature drift compensation module and a signal processing module;

[0008] The flexible sensing module adopts a bionic mechanical structure design and is formed by compounding multiple layers of flexible materials to form a sensitive unit. The sensitive unit simulates the mechanical properties of biological tissue and outputs a pressure detection signal.

[0009] The temperature drift compensation module integrates a temperature sensor and a compensation algorithm. The temperature sensor monitors ambient temperature changes in real time and outputs ambient temperature data. The compensation algorithm dynamically adjusts the pressure detection signal according to the ambient temperature data to eliminate interference of the ambient temperature on the pressure detection signal.

[0010] The signal processing module performs filtering, amplification and digitization on the pressure detection signal output by the temperature drift compensation module, and outputs pressure detection data.

[0011] Furthermore, the multi-layer flexible material includes an elastic base layer, a piezoresistive sensitive layer and a bionic texture layer. The elastic base layer is made of polyimide, the piezoresistive sensitive layer is carbon nanotubes, and the surface of the bionic texture layer is provided with a micron-scale protrusion structure.

[0012] Furthermore, the temperature sensor is a thin film thermocouple, which is integrated with the sensitive unit on the same substrate, that is, installed together on the elastic substrate layer with a spacing of less than 500 μm, and is used to synchronously collect ambient temperature data.

[0013] Furthermore, the compensation algorithm is an adaptive temperature compensation model; the adaptive temperature compensation model compensates the pressure detection signal by linear correction based on the temperature difference, which is expressed as follows:

[0014] S com =S actual -k T (T-T0)

[0015] Among them, S com is the pressure detection signal after compensation, S actual k is the pressure detection signal before compensation; T is the temperature sensitivity coefficient; T is the real-time ambient temperature, and T0 is the reference ambient temperature.

[0016] Furthermore, the filtering process of the signal processing module adopts an adaptive Kalman filter algorithm, which specifically includes:

[0017] 1) constructing a state space model of the pressure detection signal, wherein the state space model consists of a state equation and an observation equation;

[0018] The state equation is expressed as follows:

[0019] x k =Ax k-1 +w k

[0020] Among them, x k represents the true value of the pressure detection signal at time k; A is the state transition matrix, reflecting the change law of the true value of the pressure detection signal from the current moment to the next moment; w k The environmental noise in the pressure detection environment includes mechanical vibration and electromagnetic interference, which obeys the normal distribution with mean 0 and covariance matrix Q, that is, w k ~N(0,Q);

[0021] The observation equation is expressed as follows:

[0022] z k =Hx k +v k

[0023] Among them, z k is the observed value, H is the observation matrix, that is, the relationship matrix between the true value and the observed value of the pressure detection signal; v k is the observation noise, i.e., the random error generated during the observation process, satisfying v k ~N(0,R); R is the observation noise covariance matrix;

[0024] 2) Calculating the optimal pressure detection signal value through the Kalman gain matrix;

[0025] The Kalman gain matrix K k , which is expressed as follows:

[0026] K k =P k|k-1 H T (HP k|k-1 H T +R) -1

[0027] Among them, k represents the current moment, k-1 represents the previous moment; P k|k-1 is the prior estimation error covariance matrix, P k|k-1 =AP k-1|k-1 A T +Q, A is the state transfer matrix, A T is the transposed matrix of A; through the Kalman gain matrix K k Calculate the optimal pressure detection signal value It is expressed as follows:

[0028]

[0029] in, is the prior pressure detection signal value,

[0030] Furthermore, the amplification processing of the signal processing module adopts a fixed gain amplification combined with a threshold judgment algorithm;

[0031] The fixed gain amplifies the optimal pressure detection signal input Amplify with fixed gain G to obtain the amplified pressure detection signal y k , k represents the current moment; the amplification formula is as follows:

[0032]

[0033] The threshold judgment algorithm pre-sets the upper limit threshold T max and the lower threshold T min ; When y k >T max , then for y k Perform attenuation processing, that is A att is the attenuation coefficient, y adjusted (k) is the pressure detection signal after adjustment; when y k <T min , then y adjusted (k)=0; when T min ≤y k ≤T max , no adjustment is made; the fixed gain G, upper limit threshold T max , lower threshold T min And the attenuation coefficient A att , which are all system preset thresholds.

[0034] Furthermore, the digital processing of the signal processing module adopts oversampling ∑-Δ analog-to-digital conversion technology to convert the pressure detection signal into the pressure detection data, specifically including:

[0035] 1) Fifth-order modulator processing: Assume that the input of the fifth-order modulator is the pressure detection signal y adjusted (k), the output is a 1-bit high-speed digital stream y[n], where n is the discrete time index. For a fifth-order ∑-Δ modulator, its basic principle can be described by the following dynamic equation:

[0036] e1[n]=y adjusted (k)-y[n]

[0037]

[0038] y[n]=sgn(e5[n])

[0039] Among them, e1[n], e2[n], e3[n], e4[n], e5[n] are the error signals of each integral link inside the modulator; a 1i ,a2i ,a 3i ,a 4i are the coefficients of the corresponding integration links, M1, M2, M3, and M4 are the orders of the integration links; sgn(·) is the sign function, which outputs 1 when the input is greater than 0 and -1 when the input is less than 0 (for the representation of a 1-bit digital stream);

[0040] 3) Digital decimation filter processing:

[0041] CIC filter: Let the input of the CIC filter be y[n], and after R times downsampling (R is the decimation factor), the output is y CIC [m]. For an L-level CIC filter, its transfer function is:

[0042]

[0043] In the time domain, the calculation process of the CIC filter can be expressed as:

[0044]

[0045] y CIC [m]=w L [mR]

[0046] Among them, w1[n],w2[n],…,w L [n] is the intermediate calculation result of each level of the CIC filter;

[0047] FIR filter: Let the input of the FIR filter be y CIC [m], the output is high-precision pressure detection data D[m]; the output of the FIR filter can be obtained by convolution calculation:

[0048]

[0049] Where h[i] is the coefficient of the FIR filter and N is the order of the FIR filter.

[0050] Compared with the prior art, the advantages of the present invention are:

[0051] 1) Bionic Mechanical Optimization Design: This invention draws on the mechanical properties of biological structures to design a pressure sensor structure, improving flexibility and durability. Compared with traditional sensor structures, it can better conform to complex curved surfaces, is less susceptible to damage during long-term use, and significantly enhances overall mechanical performance.

[0052] 2) Efficient Drift Compensation: This invention utilizes a drift compensation algorithm, combined with hardware optimization, to precisely correct measurement deviations caused by temperature changes. Compared to existing methods that rely solely on material coatings for drift correction, which are costly and ineffective, this method significantly improves temperature stability and measurement accuracy.

[0053] 3) Intelligent Data Processing: This invention utilizes advanced data processing mechanisms to analyze and process pressure data in real time. Compared to the simple data output of traditional sensors, this can quickly and accurately extract key information, making it better suited for wearable devices, human-computer interaction, and other scenarios that require timely data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present application, 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 application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0055] Figure 1 It is a system module diagram of the present invention;

[0056] Figure 2 This is a structural diagram of the temperature drift compensation module of the present invention;

[0057] Figure 3 This is a structural diagram of the signal processing module of the present invention. DETAILED DESCRIPTION

[0058] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0059] Example 1: Please refer to Figure 1 As shown, the embodiment of the present invention is a temperature drift compensation flexible pressure sensor system based on bionic mechanics, which includes a flexible sensing module, a temperature drift compensation module and a signal processing module;

[0060] The flexible sensing module adopts a bionic mechanical structure design and is formed by compounding multiple layers of flexible materials to form a sensitive unit. The sensitive unit simulates the mechanical properties of biological tissue and outputs a pressure detection signal.

[0061] In this embodiment, the multi-layer flexible material includes an elastic base layer, a piezoresistive sensitive layer and a bionic texture layer;

[0062] The elastic base layer is a polyimide film with a thickness of 125 μm, an elastic modulus of 3.2 GPa, an elongation at break of 18%, and a thermal expansion coefficient of 20 ppm / °C;

[0063] The piezoresistive sensitive layer is a carbon nanotube with a diameter of 10 nm, a length of 1.5 μm, and a thickness of 35 μm; the square resistance is 45 kΩ (in the unpressurized state);

[0064] The surface micron-scale raised structure of the bionic texture layer significantly improves pressure sensitivity through biomimetic design. Its technical features are as follows: Bionic structural features: a fingerprint-like periodic array design is adopted, with individual raised surfaces in the form of truncated cones; structural dimensions are precisely controlled: top plane diameter: 15±0.5μm; bottom diameter: 120±2μm; height: 65±1μm; and center-to-center distance between adjacent raised surfaces: 200±3μm;

[0065] The temperature drift compensation module integrates a temperature sensor and a compensation algorithm. The temperature sensor monitors the ambient temperature changes in real time and outputs ambient temperature data. The compensation algorithm dynamically adjusts the pressure detection signal according to the ambient temperature data to eliminate the interference of the ambient temperature on the pressure detection signal. Figure 2 As shown;

[0066] In this embodiment, the temperature sensor uses a thin-film thermocouple as a key component; the thin-film thermocouple has an extremely light and thin structure and can achieve fast and accurate temperature measurement. This feature is highly consistent with the high requirements of this system for temperature data acquisition.

[0067] The thin-film thermocouple and the sensitive unit are integrated using a common substrate design, with the two being installed together on an elastic substrate layer. This common substrate integration optimizes the spatial layout of the system, effectively reducing the volume of the entire system and making the system structure more compact. The spacing between the thin-film thermocouple and the sensitive unit is strictly controlled to be less than 500μm, ensuring that the temperature sensor can collect environmental data almost synchronously with the sensitive unit. When the ambient temperature changes, the thin-film thermocouple can sense the temperature fluctuation in a short period of time and quickly collect the temperature data. Due to its extremely close distance to the sensitive unit, there is no delay in the time dimension, thereby enabling the synchronous acquisition of ambient temperature data and other data collected by the sensitive unit.

[0068] The compensation algorithm is an adaptive temperature compensation model; the adaptive temperature compensation model compensates the pressure detection signal through a linear correction based on the temperature difference, which is expressed as follows:

[0069] S com =S actual -k T (T-T0)

[0070] Among them, S com is the pressure detection signal after compensation, S actualk is the pressure detection signal before compensation; T is the temperature sensitivity coefficient; T is the real-time ambient temperature, and T0 is the initial ambient temperature.

[0071] The temperature sensitivity coefficient is obtained by defining a physical quantity related to temperature, which in this embodiment is resistance; preparing a high-precision resistance measuring instrument and a temperature control box; placing the sample with the target physical quantity in the temperature control box and connecting it to the high-precision resistance measuring instrument, selecting a reference temperature of 25 degrees Celsius, and measuring the resistance reference value at the reference temperature; then selecting multiple temperature points and measuring the corresponding resistance values one by one; then calculating the temperature difference of each temperature point relative to the reference temperature, as well as the difference between the corresponding resistance value and the reference value; then using the least squares method to perform a linear fit on the temperature difference and resistance difference data to fit k T .

[0072] The signal processing module performs filtering, amplification and digitization on the pressure detection signal after the temperature drift compensation, and outputs pressure detection data.

[0073] In this embodiment, the filtering process of the signal processing module adopts an adaptive Kalman filter algorithm, which specifically includes:

[0074] 1) constructing a state space model of the pressure detection signal, wherein the state space model consists of a state equation and an observation equation;

[0075] The state equation is expressed as follows:

[0076] x k =Ax k-1 +w k

[0077] Among them, x k represents the true value of the pressure detection signal at time k; A is the state transition matrix, reflecting the change law of the true value of the pressure detection signal from the current moment to the next moment; w k The environmental noise in the pressure detection environment includes mechanical vibration and electromagnetic interference, which obeys the normal distribution with mean 0 and covariance matrix Q, that is, w k ~N(0,Q); the covariance matrix Q describes the correlation and intensity of noise in different dimensions;

[0078] The observation equation is expressed as follows:

[0079] z k =Hx k +v k

[0080] Among them, z kis the observed value, H is the observation matrix, that is, the relationship matrix between the true value and the observed value of the pressure detection signal; v k is the observation noise, i.e., the random error generated during the observation process, satisfying v k ~N(0,R); R is the observation noise covariance matrix;

[0081] 2) Calculating the optimal pressure detection signal value through the Kalman gain matrix;

[0082] The Kalman gain matrix K k , which is expressed as follows:

[0083] K k =P k|k-1 H T (HR k|k-1 H T +R) -1

[0084] Among them, k represents the current moment, k-1 represents the previous moment; P k|k-1 is the prior estimation error covariance matrix, P k|k-1 =AP k-1|k-1 A T +Q, A is the state transfer matrix, A T is the transposed matrix of A; through the Kalman gain matrix K k Calculate the optimal pressure detection signal value It is expressed as follows:

[0085]

[0086] in, is the prior pressure detection signal value,

[0087] The amplification processing of the signal processing module adopts fixed gain amplification combined with a threshold judgment algorithm;

[0088] The fixed gain amplifies the optimal pressure detection signal input Amplify with fixed gain G to obtain the amplified pressure detection signal y k , k represents the current moment; the amplification formula is as follows:

[0089]

[0090] The threshold judgment algorithm pre-sets the upper limit threshold T max and the lower threshold T min When y k >T max , then for y k Perform attenuation processing, that is Aatt is the attenuation coefficient, y adjusted (k) is the pressure detection signal after adjustment; when y k <T min , then y adjusted (k)=0; when T min ≤y k ≤T max , no adjustment is made; the fixed gain G, upper limit threshold T max , lower threshold T min And the attenuation coefficient A att , which are all system preset thresholds.

[0091] The digital processing of the signal processing module adopts oversampling ∑-Δ analog-to-digital conversion technology to convert the pressure detection signal into the pressure detection data, specifically including:

[0092] 1) Processing process of the fifth-order modulator: Assume that the input of the fifth-order modulator is the pressure detection signal y adjusted (k), the output is a 1-bit high-speed digital stream y[n] (n is the discrete time index); for the fifth-order ∑-Δ modulator, its basic principle can be described by the following dynamic equation:

[0093] e1[n]=y adjusted (k)-y[n]

[0094]

[0095] y[n]=sgn(e5[n])

[0096] Among them, e1[n], e2[n], e3[n], e4[n], e5[n] are the error signals of each integral link inside the modulator; a 1i ,a 2i ,a 3i ,a 4i are the coefficients of the corresponding integration links, M1, M2, M3, and M4 are the orders of the integration links; sgn(·) is the sign function, which outputs 1 when the input is greater than 0 and -1 when the input is less than 0 (for the representation of a 1-bit digital stream);

[0097] 2) Digital decimation filter processing: CIC filter (cascaded integrator comb filter): Let the input of the CIC filter be y[n], and after R times downsampling (R is the decimation factor), the output is y CIC [m]. For an L-level CIC filter, its transfer function is:

[0098]

[0099] In the time domain, the calculation process of the CIC filter can be expressed as:

[0100]

[0101] y CIC [m]=w L [mR]

[0102] Among them, w1[n],w2[n],…,w L [n] is the intermediate calculation result of each stage of the CIC filter.

[0103] FIR filter (finite impulse response filter): Let the input of the FIR filter be y CIC [m], the output is high-precision pressure detection data D[m]. The output of the FIR filter can be obtained by convolution calculation:

[0104]

[0105] Where h[i] is the coefficient of the FIR filter and N is the order of the FIR filter;

[0106] The pressure detection signal y is finally converted into adjusted (k) Converted into high-precision pressure detection data D[m].

[0107] The embodiment of the present invention provides a flexible pressure sensor system with temperature drift compensation based on biomimetic mechanics. This system, based on the principles of biomimetic mechanics, simulates the way biological organisms perceive pressure and respond to temperature changes, and is used in pressure sensor design. During pressure detection, the flexible sensing structure accurately captures the pressure value, distribution, and dynamic changes. The temperature drift compensation mechanism constructed based on biomimetic mechanics effectively solves the problem of deviation caused by temperature in traditional pressure sensors, thereby improving detection stability and accuracy. Compared with conventional systems, this system has strong resistance to temperature drift, high sensitivity, and excellent flexibility, and can accurately obtain pressure data in real time in complex temperature environments. Advanced materials and processes are used to create biomimetic flexible sensing elements, combined with intelligent algorithms to dynamically compensate for temperature drift. This transforms traditional rigid sensors that are easily affected by temperature into intelligent, adaptive, flexible pressure detection and temperature drift compensation systems. This significantly improves measurement accuracy and expands application scenarios. It has great application potential in fields such as wearable medical care, robotic tactile sensing, and industrial precision testing, and will promote technological upgrades in related industries.

[0108] The above formulas are dimensionless and use only numerical values for calculations. These formulas are based on extensive data and software simulations, and are intended to be as close to actual conditions as possible. Those skilled in the art can adjust the preset parameters in the formulas based on their specific needs.

[0109] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0110] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A temperature drift compensation flexible pressure sensor system based on bionic mechanics, characterized in that: include: The flexible sensing module adopts a bionic mechanical structure design and is formed by combining multiple layers of flexible materials to form a sensitive unit. The sensitive unit simulates the mechanical properties of biological tissue and outputs a pressure detection signal. A temperature drift compensation module integrates a temperature sensor and a compensation algorithm. The temperature sensor monitors ambient temperature changes in real time and outputs ambient temperature data. The compensation algorithm dynamically adjusts the pressure detection signal based on the ambient temperature data to eliminate interference from the ambient temperature on the pressure detection signal. The signal processing module performs filtering, amplification and digitization on the pressure detection signal output by the temperature drift compensation module, and outputs pressure detection data.

2. The system according to claim 1, wherein: The multi-layer flexible material includes an elastic base layer, a piezoresistive sensitive layer and a bionic texture layer. The elastic base layer is made of polyimide, the piezoresistive sensitive layer is carbon nanotubes, and the surface of the bionic texture layer is provided with a micron-scale protrusion structure.

3. The system according to claim 1, wherein: The temperature sensor is a thin film thermocouple, which is integrated with the sensitive unit on the same substrate, that is, installed together on the elastic substrate layer with a spacing of less than 500 μm, and is used for synchronously collecting ambient temperature data.

4. The system according to claim 1, wherein: The compensation algorithm is an adaptive temperature compensation model; the adaptive temperature compensation model compensates the pressure detection signal through a linear correction based on the temperature difference, which is expressed as follows: S com =S actual -k T ·(T-T0) Among them, S com is the pressure detection signal after compensation, S actual k is the pressure detection signal before compensation; T is the temperature sensitivity coefficient; T is the real-time ambient temperature, and T0 is the reference ambient temperature.

5. The system according to claim 1, wherein: The filtering process of the signal processing module adopts an adaptive Kalman filtering algorithm, which specifically includes: 1) constructing a state space model of the pressure detection signal, wherein the state space model consists of a state equation and an observation equation; The state equation is expressed as follows: x k =Ax k-1 +w k Among them, x k represents the true value of the pressure detection signal at time k; A is the state transition matrix, reflecting the change law of the true value of the pressure detection signal from the current moment to the next moment; w k The environmental noise in the pressure detection environment includes mechanical vibration and electromagnetic interference, which obeys the normal distribution with mean 0 and covariance matrix Q, that is, w k ~N(0,Q); The observation equation is expressed as follows: with k =Hz k +v k Among them, z k is the observed value, H is the observation matrix, that is, the relationship matrix between the true value and the observed value of the pressure detection signal; v k is the observation noise, i.e., the random error generated during the observation process, satisfying v k ~N(0,R); R is the observation noise covariance matrix; 2) Calculating the optimal pressure detection signal value through the Kalman gain matrix; The Kalman gain matrix K k , which is expressed as follows: K k =P k|k-1 H T (HP k|k-1 H T +R) -1 Among them, k represents the current moment, k-1 represents the previous moment; P k|k-1 is the prior estimation error covariance matrix, P k|k-1 =AP k-1|k-1 A T +Q, A is the state transfer matrix, A T is the transposed matrix of A; through the Kalman gain matrix K k Calculate the optimal pressure detection signal value It is expressed as follows: in, is the prior pressure detection signal value, 6. The system according to claim 5, characterized in that The amplification processing of the signal processing module adopts fixed gain amplification combined with a threshold judgment algorithm; The fixed gain amplifies the optimal pressure detection signal input Amplify with fixed gain G to obtain the amplified pressure detection signal y k , k represents the current moment; the amplification formula is as follows: The threshold judgment algorithm pre-sets the upper limit threshold T max and the lower threshold T min ; When y k >T max , then for y k Perform attenuation processing, that is A att is the attenuation coefficient, y adjusted (k) is the pressure detection signal after adjustment; when y k <T min , then y adjusted (k)=0; when T min ≤y k ≤T max , no adjustment is made; the fixed gain G, upper limit threshold T max , lower threshold T min And the attenuation coefficient A att , which are all system preset thresholds.

7. The system according to claim 6, characterized in that The digital processing of the signal processing module adopts oversampling ∑-Δ analog-to-digital conversion technology to convert the pressure detection signal into the pressure detection data, specifically including: 1) Fifth-order modulator processing: Assume that the input of the fifth-order modulator is the pressure detection signal y adjusted (k), the output is a 1-bit high-speed digital stream y[n], where n is the discrete time index. For a fifth-order ∑-Δ modulator, its basic principle can be described by the following dynamic equation: e1[n]=y adjusted (k)-y[n] y[n]=sgn(e5[n]) Among them, e1[n], e2[n], e3[n], e4[n], e5[n] are the error signals of each integral link inside the modulator; a 1i ,a 2i ,a 3i ,a 4i are the coefficients of the corresponding integration links, M1, M2, M3, and M4 are the orders of the integration links; sgn(·) is the sign function, which outputs 1 when the input is greater than 0 and -1 when the input is less than 0 (for the representation of a 1-bit digital stream); 2) Digital decimation filter processing: CIC filter: Let the input of the CIC filter be y[n], and after R times downsampling (R is the decimation factor), the output is y CIC [m]. For an L-level CIC filter, its transfer function is: In the time domain, the calculation process of the CIC filter can be expressed as: … y CIC [m]=w L [mR] Among them, w1[n],w2[n],…,w L [n] is the intermediate calculation result of each level of CIC filter; FIR filter: Let the input of the FIR filter be y CIC [m], the output is high-precision pressure detection data D[m]; the output of the FIR filter can be obtained by convolution calculation: Where h[i] is the coefficient of the FIR filter and N is the order of the FIR filter.

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