Wearable respiration sensing system based on near-field impedance measurement and detection method

Abstract

The invention discloses a wearable breathing sensing system based on near-field impedance measurement and a detection method, and belongs to the field of wearable breathing detection.The wearable breathing sensing system comprises a breathing mask, a near-field sensing electrode and an impedance measurement system, and the near-field sensing electrode is electrically connected with the impedance measurement system; the near-field sensing electrode is installed on a breathing opening of the mask, and the impedance measuring system is installed on the mask. The detection method comprises steps S1 to S4. According to the scheme, the wearable breathing waveform sensing equipment with high sensitivity and comfort is designed, real-time high-sensitivity detection of the impedance of a sensing element can be achieved in a wireless mode on the basis of the near-field impedance measurement technology, magnetic attraction detachable design is adopted between the near-field sensing electrode and the mask, and wearing, cleaning and replacement of the device are facilitated. The invention further designs a calibration and calibration method for the impedance of the whole system, and the quantitative detection requirement of respiration data in the actual application process can be met.

Description

Technical Field

[0001] The present invention relates to the field of wearable respiratory detection, and in particular to a wearable respiratory sensing system and a detection method based on near-field impedance measurement. Background Art

[0002] Respiration is an important vital sign, and changes in its frequency and peak shape can reflect a variety of physiological and pathological information. Therefore, accurate and real-time monitoring of respiratory waveforms is expected to provide a non-invasive means of analyzing and detecting physiological health conditions, which has important application value in the diagnosis of various respiratory diseases and sports health.

[0003] There are currently two main forms of respiratory waveform sensing: one is to achieve respiratory sensing by detecting exhaled air from the mouth and nose, and the other is to detect respiratory waveforms by detecting chest movement. The former can achieve more accurate respiratory waveform measurements and is used in clinical testing, but it still relies on large instruments and supporting pipelines, which are cumbersome to operate and have poor comfort. Although sleep respiratory monitoring devices represented by chest straps can guarantee comfort to the greatest extent, their applicable scenarios and measurement accuracy are limited. Therefore, it is very important to develop wearable respiratory sensing devices that are both high-precision and comfortable.

[0004] In the field of wearable sensing, the invention patent (CN112798651B) proposes a passive humidity detection system. Similar passive wireless sensing technology has unique advantages in the development of miniature high-sensitivity sensor devices. It is suitable for designing and developing high-performance wearable sensing devices based on relevant principles. However, this patent only provides the basic principles of near-field impedance sensing and humidity sensing applications. For wearable respiratory waveform detection, it is still necessary to design the sensing principle, device design, detection method and other contents in a targeted manner to solve the key problems in the actual respiratory detection application process. Summary of the invention

[0005] In view of the above-mentioned deficiencies in the prior art, the present invention provides a wearable respiratory sensing system and detection method based on near-field impedance measurement, which can realize real-time and highly sensitive detection of the respiratory process in a wireless form.

[0006] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is:

[0007] Provided is a wearable respiratory sensing system based on near-field impedance measurement, which includes a respiratory mask, a near-field sensing electrode and an impedance measurement system, wherein the near-field sensing electrode and the impedance measurement system are electrically connected; the near-field sensing electrode is mounted on the respiratory port of the mask, and the impedance measurement system is mounted on the mask;

[0008] The near-field sensing electrode includes a breathing sensor element, a mutual inductance coil and a calibration switch; the breathing sensor element is used to detect humidity change data during the breathing process, the calibration switch is used to control the impedance measurement process, and the control state of the calibration switch includes an open circuit state, a short circuit state and a connected state;

[0009] A near-field signal readout coil is provided in the breathing mask, and the near-field signal readout coil is electrically connected to the impedance measurement system;

[0010] The impedance measurement system includes an impedance signal acquisition module, a control module, a wireless communication module and a power supply module; the impedance signal acquisition module is used to collect the impedance data of the near-field signal readout coil and the mutual inductance coil, the control module is used to process the data collected by the impedance signal acquisition module, and the wireless communication module is used to send the data processed by the control module to the data analysis center.

[0011] Furthermore, the respiratory sensor element is an interdigital electrode modified with GO or GO material.

[0012] Furthermore, the near-field signal readout coil is fitted with the mutual inductance coil.

[0013] Furthermore, the near-field sensing electrode is installed on the breathing port through a magnetic attraction structure.

[0014] A breathing detection method using the wearable breathing sensor system based on near-field impedance measurement is provided, which comprises the following steps:

[0015] S1: Establishing a calculation model for the impedance value output by the impedance measurement system;

[0016] S2: using an impedance value calculation model to calibrate the impedance of the system, obtain characteristic variables related to the resistance of the respiratory sensor element, and fit a calibration model that characterizes the relationship between the characteristic variables and the resistance;

[0017] S3: blowing set air to the breathing sensor element to simulate the breathing process, and outputting the resistance value of the simulated breathing process, using the calibration model to calculate the corresponding characteristic variables, and fitting the relationship model between the characteristic variables and the gas flow data in the simulated breathing process;

[0018] S4: After the user puts on the breathing mask, the breathing sensor element produces a resistance change during the breathing process. The impedance measurement system outputs the resistance value, which is input into the fitted relationship model and outputs the detected breathing gas flow data.

[0019] Furthermore, the impedance value calculation model is:

[0020]

[0021] Among them, Z Inis the impedance value of the output of the measurement system, R c1 , L c1 are the resistance and inductance of the near-field signal readout coil loop, R c2 , L c2 are the resistance and inductance of the mutual inductance coil, M is the mutual inductance coefficient between the near-field signal readout coil and the mutual inductance coil, and Z E is the impedance of the respiratory sensor element, ω is the angular frequency, and ω = 2πf, f is the frequency of the AC signal applied by the impedance measurement system, j is the imaginary unit,

[0022] Further, step S2 includes:

[0023] S21: Switch the standard switch to the off state, input an AC signal into the impedance measurement system, and use the impedance value calculation model to output the near-field signal to read the impedance value Z of the coil. oc ;

[0024] S22: Switch the standard switch to the short-circuit state, input an AC signal into the impedance measurement system, and use the impedance value calculation model to output the near-field signal to read the impedance value Z of the coil. sc ;

[0025] S23: Switch the standard switch to the external connection state, and connect several fixed resistors with different resistance values ​​in sequence. The impedance measurement system inputs an AC signal, and uses the impedance value calculation model to output the signal under the condition of connecting fixed resistors with different resistance values ​​to read the impedance value Z of the coil. i , the resistance of the fixed resistor is R i , i is the number of the fixed value resistor;

[0026] S24: According to the impedance value Z oc , Z sc And the impedance value Z under the condition of fixed resistance of different resistance values i Calculate impedance characteristic variables under resistance fluctuation conditions;

[0027]

[0028] Among them, x oc ,y oc The impedance values ​​are Z oc The real and imaginary parts of x sc ,y sc The impedance values ​​are Z sc The real and imaginary parts of , r is the relationship variable, x and y are the impedance values ​​Z i The real and imaginary parts of

[0029] S25: The fixed resistance R under different fixed resistance conditions calculated in step S24 iand the corresponding impedance characteristic variable θ i data, and establish characteristic variable data set (lg(R i ),θ i );

[0030] S26: Establish a multi-binomial equation, using the characteristic variable data set (lg(R i ),θ i ) and fitting the multiple binomial equation using the least square method, outputting the coefficients of the multiple binomial equation, obtaining an impedance calibration curve, and obtaining a calibration equation;

[0031] The multinomial binomial equation is: y′=ax′ 5 +bx′ 4 +cx′ 3 +dx′ 2 +jx′; where a, b, c, d, j are the coefficients of the multiple binomial equation, x′ is the variable of the multiple binomial equation, and y′ is the dependent variable of the multiple binomial equation. During the fitting process, the constant resistance R i As variable x′, impedance characteristic variable θ i as the dependent variable y′.

[0032] Further, step S3 includes:

[0033] S31: Air with a relative humidity of 100% is blown to the respiratory sensor element at different temperatures, cycles and gas flow rates, and the impedance measurement system outputs the measured resistance value R 0 and use a standard gas flow meter to record the blowing gas flow rate v;

[0034] S32: Set the resistance value R 0 In the calibration equation, the output resistance value R 0 The corresponding feature variable θ 0 , and integrate the gas flow rate signal to obtain the gas flow data Q: Where A is the cross-sectional area of ​​the calibrated gas pipeline when blowing gas, and t is the time of gas blowing;

[0035] S33: Gas flow data Q and corresponding resistance value R 0 forming a fitting data set, and forming a plurality of fitting data sets during the gas blowing process;

[0036] S34: establishing a relationship fitting equation between the characteristic variable and the gas flow data, fitting the relationship fitting equation using a number of fitting data groups, outputting the coefficients of the relationship fitting equation, and obtaining a fitted relationship model;

[0037] The relationship fitting equation is: y″=a′e b′x″ +c′ed′x″ , where y″ is the dependent variable of the relationship fitting equation, x″ is the variable of the relationship fitting equation, a′, b′, c′, d′ are the coefficients of the relationship fitting equation, and e is a natural constant; during the fitting process of the relationship fitting equation, the resistance value R 0 As variable x″, gas flow data Q serves as dependent variable y″.

[0038] The beneficial effects of the present invention are as follows: This solution designs a wearable respiratory waveform sensing device that is both highly sensitive and comfortable. Based on the near-field impedance measurement technology, it can realize real-time and highly sensitive detection of the impedance of the sensor element in a wireless form. The near-field sensor electrode and the mask are designed to be detachable by magnetic attraction, which is convenient for wearing, cleaning and replacement of the device. The present invention also designs a calibration and calibration method for the impedance of the entire system, which can meet the quantitative detection requirements of respiratory data in practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 Schematic diagram of the structure of the wearable respiratory sensing system based on near-field impedance measurement.

[0040] Figure 2 Graph showing impedance calibration in the embodiment.

[0041] Figure 3 This is the calibration curve fitting diagram.

[0042] Figure 4 This is the calibration fitting result diagram.

[0043] Figure 5 It is a breathing waveform diagram.

[0044] Figure 6 This is the respiratory waveform during constant power exercise.

[0045] Figure 7 This is a respiratory waveform diagram during the incremental load exercise process.

[0046] Among them, 100-near field sensing electrode, 101-respiratory sensing element, 102-mutual inductance coil, 103-calibration switch, 200-respiratory mask, 300-impedance measurement system, 301-signal acquisition module. DETAILED DESCRIPTION

[0047] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0048] like Figure 1 As shown, a wearable respiratory sensing system based on near-field impedance measurement includes a respiratory mask 200, a near-field sensing electrode 100 and an impedance measurement system 300, wherein the near-field sensing electrode 100 and the impedance measurement system 300 are electrically connected; the near-field sensing electrode 100 is installed on the respiratory port of the mask, the near-field sensing electrode 100 is installed on the respiratory port through a magnetic attraction structure, the impedance measurement system 300 is installed on the mask, and the respiratory mask 200 is made of flexible skin-friendly materials such as silicone.

[0049] The near-field sensing electrode 100 includes a breathing sensor element 101, a mutual inductance coil 102 and a calibration switch 103; the breathing sensor element 101 is an interdigital electrode modified with oxidized graphyne or graphene oxide material, and the breathing sensor element 101 is used to detect humidity change data during the breathing process. According to the humidity change of the gas during the breathing process, resistance fluctuations will be generated on the breathing sensor element 101. The calibration switch 103 is used to control the impedance measurement process, and the control states of the calibration switch 103 include an open circuit state, a short circuit state and a connected state.

[0050] During the implementation of the present invention, other sensing elements such as a temperature-sensitive resistor may be added to the near-field sensing electrode 100 and connected to an independent mutual inductance coil 102 to achieve synchronous recording of other parameters such as temperature.

[0051] A near-field signal readout coil is disposed in the breathing mask 200 . The near-field signal readout coil is electrically connected to the impedance measurement system 300 , and the near-field signal readout coil is in contact with the mutual inductance coil 102 .

[0052] The impedance measurement system 300 includes an impedance signal acquisition module 301, a control module, a wireless communication module and a power supply module; the impedance signal acquisition module 301 is used to collect impedance data of the near-field signal readout coil and the mutual inductance coil 102, the control module is used to process the data collected by the impedance signal acquisition module 301, and the wireless communication module is used to send the data processed by the control module to the data analysis center.

[0053] The near-field signal readout coil is embedded in the front end of the mask and is provided with a connector and a line interface connected to the impedance measurement system 300; the near-field signal readout coil is made into a strip using flexible circuit processing technology and cooperates with the replaceable breathing mask 200 to achieve wearability.

[0054] A breathing detection method using the wearable breathing sensor system based on near-field impedance measurement comprises the following steps:

[0055] S1: Establishing an impedance value calculation model output by the impedance measurement system 300, the impedance value calculation model is:

[0056]

[0057] Among them, Z In is the impedance value of the output of the measurement system, R c1 , L c1 are the resistance and inductance of the near-field signal readout coil loop, R c2 , L c2 are the resistance and inductance of the mutual inductance coil 102, M is the mutual inductance coefficient between the near-field signal readout coil and the mutual inductance coil 102, and Z E is the impedance of the respiratory sensor element 101, ω is the angular frequency, and ω=2πf, f is the frequency of the AC signal applied by the impedance measurement system 300, j is an imaginary unit,

[0058] S2: Using the impedance value calculation model to calibrate the impedance of the system, obtain characteristic variables related to the resistance of the respiratory sensor element 101, and fit a calibration model that characterizes the relationship between the characteristic variables and the resistance. Step S2 specifically includes:

[0059] S21: Switch the standard switch to the off state, the impedance measurement system 300 inputs an AC signal, and uses the impedance value calculation model to output a near-field signal to read the impedance value Z of the coil. oc ;

[0060] S22: Switch the standard switch to the short-circuit state, the impedance measurement system 300 inputs an AC signal, and uses the impedance value calculation model to output a near-field signal to read the impedance value Z of the coil. sc ;

[0061] S23: Switch the standard switch to the external connection state, and connect several fixed resistors with different resistance values ​​in sequence. The impedance measurement system 300 inputs an AC signal, and uses the impedance value calculation model to output signals under the condition of connecting fixed resistors with different resistance values ​​to read the impedance value Z of the coil. i , the resistance of the fixed resistor is R i , i is the serial number of the fixed value resistor; in this embodiment, fixed value resistors with resistance values ​​of 10Ω, 25Ω, 50Ω, 100Ω, 200Ω, 500Ω and 1000Ω are selected and connected to the impedance measurement system 300 respectively;

[0062] S24: According to the impedance value Z oc , Z sc And the impedance value Z under the condition of fixed resistance of different resistance values i Calculate impedance characteristic variables under resistance fluctuation conditions;

[0063]

[0064] Among them, x oc ,y oc The impedance values ​​are Zoc The real and imaginary parts of x sc ,y sc The impedance values ​​are Z sc The real and imaginary parts of , r is the relationship variable, x and y are the impedance values ​​Z i The real and imaginary parts of

[0065] S25: The fixed resistance R under different fixed resistance conditions calculated in step S24 i and the corresponding impedance characteristic variable θ i data, and establish characteristic variable data set (lg(R i ),θ i );

[0066] S26: Establish a multi-binomial equation, using the characteristic variable data set (lg(R i ),θ i ) and use the least squares method to fit the multiple binomial equation, output the coefficients of the multiple binomial equation, and obtain the impedance calibration curve, such as Figure 2 The impedance calibration curve fitted in this embodiment is shown, and the calibration equation is obtained;

[0067] The multinomial binomial equation is: y′=ax′ 5 +bx′ 4 +cx′ 3 +dx′ 2 +jx′; where a, b, c, d, j are the coefficients of the multiple binomial equation, x′ is the variable of the multiple binomial equation, and y′ is the dependent variable of the multiple binomial equation. During the fitting process, the constant resistance R i As variable x′, impedance characteristic variable θ i as the dependent variable y′.

[0068] The calibration curve is determined by the shape and electromagnetic characteristics of the system device and circuit. The same calibration curve can be used for devices with the same design and processing parameters, without repeated calibration.

[0069] S3: Blowing set air to the breathing sensor element 101 to simulate the breathing process, and outputting the resistance value of the simulated breathing process, using the calibration model to calculate the corresponding characteristic variables, and fitting the relationship model between the characteristic variables and the gas flow data in the simulated breathing process. Step S3 specifically includes:

[0070] S31: Air with a relative humidity of 100% is blown to the respiratory sensor element 101 at different temperatures, different cycles and gas flow rates, and the impedance measurement system 300 outputs the measured resistance value R 0 and use a standard gas flow meter to record the blowing gas flow rate v;

[0071] S32: Set the resistance value R 0 In the calibration equation, the output resistance value R 0 The corresponding feature variable θ 0 , and integrate the gas flow rate signal to obtain the gas flow data Q: Where A is the cross-sectional area of ​​the calibrated gas pipeline when blowing gas, and t is the time of gas blowing;

[0072] S33: Gas flow data Q and corresponding resistance value R 0 forming a fitting data set, and forming a plurality of fitting data sets during the gas blowing process;

[0073] S34: Establish a relationship fitting equation between the characteristic variable and the gas flow data, use a number of fitting data groups to fit the relationship fitting equation, output the coefficients of the relationship fitting equation, and obtain a fitted relationship model; Figure 3 The relationship curve between the characteristic variables obtained by fitting in this embodiment and the gas flow data is shown in FIG. 1 . The relationship curve is used as a calibration curve. The fitting result is shown in FIG. Figure 4 shown.

[0074] The relationship fitting equation is: y″=a′e b′x″ +c′e d′x″ , where y″ is the dependent variable of the relationship fitting equation, x″ is the variable of the relationship fitting equation, a′, b′, c′, d′ are the coefficients of the relationship fitting equation, and e is a natural constant; during the fitting process of the relationship fitting equation, the resistance value R 0 As variable x″, gas flow data Q serves as dependent variable y″.

[0075] S4: After the user puts on the breathing mask 200, the breathing sensor element 101 generates resistance changes during the breathing process

[0076] The impedance measurement system 300 outputs the resistance value, which is input into the fitted relationship model, and outputs the detected respiratory gas flow data. Figure 5 The respiratory waveform is shown.

[0077] In this embodiment, the detection method of the present invention is used to detect the breathing process of the constant power exercise process and the load-increasing exercise process, and the following results are obtained: Figure 6 and Figure 7 The respiratory waveform shown.

[0078] The present invention designs a wearable respiratory waveform sensing device with both high sensitivity and comfort. Based on the near-field impedance measurement technology, it can realize real-time high-sensitivity detection of the impedance of the sensor element in a wireless form. The near-field sensing electrode 100 and the mask are designed to be detachable by magnetic attraction, which is convenient for wearing, cleaning and replacement of the device. The present invention also designs a calibration and calibration method for the impedance of the entire system, which can meet the quantitative detection requirements of respiratory data in practical applications.

Claims

1. A wearable respiratory sensing system based on near-field impedance measurement, characterized in that: It includes a breathing mask, a near-field sensing electrode and an impedance measurement system, wherein the near-field sensing electrode and the impedance measurement system are electrically connected; the near-field sensing electrode is installed on the breathing port of the mask, and the impedance measurement system is installed on the mask; The near-field sensing electrode comprises a breathing sensor element, a mutual inductance coil and a calibration switch; the breathing sensor element is used to detect humidity change data during the breathing process, the calibration switch is used to control the impedance measurement process, and the control state of the calibration switch includes an open circuit state, a short circuit state and a connected state; A near-field signal readout coil is provided in the breathing mask, and the near-field signal readout coil is electrically connected to the impedance measurement system; The impedance measurement system includes an impedance signal acquisition module, a control module, a wireless communication module and a power supply module; the impedance signal acquisition module is used to collect impedance data of the near-field signal readout coil and the mutual inductance coil, the control module is used to process the data collected by the impedance signal acquisition module, and the wireless communication module is used to send the data processed by the control module to the data analysis center.

2. The wearable respiratory sensing system based on near-field impedance measurement according to claim 1, characterized in that: The respiratory sensor element is an interdigital electrode modified with oxidized Graphene or oxidized Graphene material.

3. The wearable respiratory sensing system based on near-field impedance measurement according to claim 1, characterized in that: The near-field signal readout coil is attached to the mutual inductance coil.

4. The wearable respiratory sensing system based on near-field impedance measurement according to claim 1, characterized in that: The near-field sensing electrode is installed on the breathing port through a magnetic attraction structure.

5. A breathing detection method using the wearable breathing sensor system based on near-field impedance measurement according to any one of claims 1 to 4, characterized in that: The following steps are involved: S1: Establishing a calculation model for the impedance value output by the impedance measurement system; S2: using an impedance value calculation model to calibrate the impedance of the system, obtain characteristic variables related to the resistance of the respiratory sensor element, and fit a calibration model that characterizes the relationship between the characteristic variables and the resistance; S3: blowing set air to the breathing sensor element to simulate the breathing process, and outputting the resistance value of the simulated breathing process, using the calibration model to calculate the corresponding characteristic variables, and fitting the relationship model between the characteristic variables and the gas flow data in the simulated breathing process; S4: After the user puts on the breathing mask, the breathing sensor element produces a resistance change during the breathing process. The impedance measurement system outputs the resistance value, which is input into the fitted relationship model and outputs the detected breathing gas flow data.

6. The breathing detection method of the wearable breathing sensor system based on near-field impedance measurement according to claim 5, characterized in that: The impedance value calculation model is: Among them, Z In is the impedance value of the output of the measurement system, R c1 , L c1 are the resistance and inductance of the near-field signal readout coil loop, R c2 , L c2 are the resistance and inductance of the mutual inductance coil, M is the mutual inductance coefficient between the near-field signal readout coil and the mutual inductance coil, and Z E is the impedance of the respiratory sensor element, ω is the angular frequency, and ω = 2πf, f is the frequency of the AC signal applied by the impedance measurement system, j is the imaginary unit, 7. The breathing detection method of the wearable breathing sensor system based on near-field impedance measurement according to claim 6, characterized in that: The step S2 comprises: S21: Switch the standard switch to the off state, input an AC signal into the impedance measurement system, and use the impedance value calculation model to output the near-field signal to read the impedance value Z of the coil. oc ; S22: Switch the standard switch to the short-circuit state, input an AC signal into the impedance measurement system, and use the impedance value calculation model to output the near-field signal to read the impedance value Z of the coil. sc ; S23: Switch the standard switch to the external connection state, and connect several fixed resistors with different resistance values ​​in sequence. The impedance measurement system inputs an AC signal, and uses the impedance value calculation model to output the signal under the condition of connecting fixed resistors with different resistance values ​​to read the impedance value Z of the coil. i , the resistance of the fixed resistor is R i , i is the number of the fixed value resistor; S24: According to the impedance value Z oc , Z sc And the impedance value Z under the condition of fixed resistance of different resistance values i Calculate impedance characteristic variables under resistance fluctuation conditions; Among them, x oc ,y oc The impedance values ​​are Z oc The real and imaginary parts of x sc ,y sc The impedance values ​​are Z sc The real and imaginary parts of , r is the relationship variable, x and y are the impedance values ​​Z i The real and imaginary parts of S25: The fixed resistance R under different fixed resistance conditions calculated in step S24 i and the corresponding impedance characteristic variable θ i data, and establish characteristic variable data set (lg(R i ),θ i ); S26: Establish a multi-binomial equation, using the characteristic variable data set (lg(R i ),θ i ) and fitting the multiple binomial equation using the least square method, outputting the coefficients of the multiple binomial equation, obtaining an impedance calibration curve, and obtaining a calibration equation; The multi-order binomial equation is: y′=ax′ 5 +bx′ 4 +cx′ 3 +dx′ 2 +jx′; where a, b, c, d, j are the coefficients of the multiple binomial equation, x′ is the variable of the multiple binomial equation, and y′ is the dependent variable of the multiple binomial equation. During the fitting process, the constant resistance R i As variable x′, impedance characteristic variable θ i as the dependent variable y′.

8. The breathing detection method of the wearable breathing sensor system based on near-field impedance measurement according to claim 7, characterized in that: The step S3 comprises: S31: Air with a relative humidity of 100% is blown to the respiratory sensing element at different temperatures, at different cycles and gas flow rates, the impedance measurement system outputs the measured resistance value R0, and the blown gas flow rate v is recorded using a standard gas flow meter; S32: Input the resistance value R0 into the calibration equation, output the characteristic variable θ0 corresponding to the resistance value R0, and integrate the gas flow rate signal to obtain the gas flow data Where A is the cross-sectional area of ​​the calibrated gas pipeline when blowing gas, and t is the time of gas blowing; S33: The gas flow data Q and the corresponding resistance value R0 form a fitting data set, and several fitting data sets are formed during the gas blowing process; S34: establishing a relationship fitting equation between the characteristic variable and the gas flow data, fitting the relationship fitting equation using a number of fitting data groups, outputting the coefficients of the relationship fitting equation, and obtaining a fitted relationship model; The relationship fitting equation is: y″=a′e b′x″ +c′e d′x″ , where y″ is the dependent variable of the relationship fitting equation, x″ is the variable of the relationship fitting equation, a′, b′, c′, d′ are the coefficients of the relationship fitting equation, and e is a natural constant; in the fitting process of the relationship fitting equation, the resistance value R0 is used as the variable x″, and the gas flow data Q is used as the dependent variable y″.

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