Wireless passive pressure and temperature sensing device and air leakage prediction method

Abstract

The invention discloses a wireless passive pressure and temperature sensing device and an air leakage prediction method, the device comprises a surface acoustic wave delay line, a temperature sensor, a reference resonator and an antenna module, the surface acoustic wave delay line is used for sensing pressure change, and the resonant frequency of the surface acoustic wave delay line is influenced by external air pressure; the resonant frequency of the temperature sensor mainly reflects the environment temperature; the frequency of the reference resonator is used for comparing with the temperature sensor and calculating a frequency difference to judge a vibration state; and the wireless transceiver module excites the system through a sweep frequency signal, acquires the frequency of the sensor, analyzes the frequency of the surface acoustic wave delay line to obtain a pressure value, and judges the pressure instability or gas leakage precursor by using the frequency difference. According to the wireless passive pressure and temperature sensing device and the air leakage prediction method, the pressure and the temperature can be remotely measured without an external power supply, the device is suitable for long-term monitoring, the equipment fault or leakage risk can be predicted in advance through a vibration analysis function, and the reliability and the safety of a system are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless passive sensors, and in particular relates to a wireless passive pressure and temperature sensing device and a gas leakage prediction method. Background Art

[0002] At present, SF6 gas is indicated by oil pressure or barometer, and humidity indication function can also be completed by hygrometer. However, this type of hygrometer usually has large errors, and it is difficult to accurately measure humidity below 5%. Pointer-type pressure gauges also face this problem. SF6 high-pressure equipment usually observes whether it reaches below the warning pressure to determine whether there is a leak. If the needle is damaged, or the fluctuation is small, or the humidity is below 2%, the existing mechanical instrument is difficult to meet; on the other hand, MEMS technology solves the problem of pressure accuracy, but it requires a very stable power supply and is easily affected by temperature. The existing MEMS technology is used for SF6 gas pressure and temperature measurement solutions, which are usually a combination of MEMS and multiple sensors such as semiconductor temperature measurement, which inevitably introduces higher product costs and maintenance costs. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide an SF6 gas source pressure and temperature sensing device and a gas leakage prediction method which can remotely measure pressure and temperature without an external power supply and can predict equipment failure or leakage risks in advance.

[0004] The technical solution of the present invention is: a wireless passive pressure and temperature sensing device, comprising: a surface acoustic wave delay line, a surface acoustic wave temperature sensor, a second surface acoustic wave resonator, an antenna module and a wireless transceiver, the surface acoustic wave delay line is connected to the wireless transceiver, the surface acoustic wave temperature sensor is connected to the wireless transceiver, the second surface acoustic wave resonator is connected to the wireless transceiver, the antenna module is connected to the wireless transceiver, the surface acoustic wave delay line and the surface acoustic wave temperature sensor are connected in series to form a passive pressure and temperature transmitter, and the surface acoustic wave temperature sensor is a first surface acoustic wave resonator. The passive pressure and temperature transmitter is arranged in the pressure cavity of the fully air-filled sealed pointer temperature and humidity instrument, the second surface acoustic wave resonator is arranged on the pressure pointer of the air-filled sealed pointer temperature and humidity instrument, the surface acoustic wave delay line is a three-finger structure, including: a main interpolation electrode, an asymmetric structure electrode A, an asymmetric structure electrode B, an interactive antenna and a reflection grid, the main interpolation electrode is connected to the interactive antenna, the asymmetric structure electrode A and the asymmetric structure electrode B are respectively arranged on both sides of the main interpolation electrode, and the reflection grid is located between the main interpolation electrode and the asymmetric structure electrode B.

[0005] Furthermore, the surface acoustic wave temperature sensor is connected to the antenna module through electromagnetic coupling or directly connected.

[0006] Furthermore, the antenna module is a flexible antenna or a PCB hard copper-clad antenna or a hollow helical antenna.

[0007] Furthermore, the reflection grating is composed of a plurality of parallel vertical reflection gratings.

[0008] Furthermore, the ratio of the distance L12 between the main interpolation electrode and the asymmetric structure electrode A to the distance L13 between the main interpolation electrode and the main interpolation electrode and the asymmetric structure electrode B is greater than 3, that is, L12:L13>3.

[0009] A method for predicting air leakage of a wireless passive pressure and temperature sensing device comprises the following steps:

[0010] Step 1, system initialization: determine the resonant frequency f2 of the surface acoustic wave temperature sensor and the resonant frequency f3 of the second surface acoustic wave resonator;

[0011] Step 2, transmitting a frequency sweep signal and collecting data: the wireless transceiver transmits a frequency sweep signal, covering the frequency range of f2 and f3, the wireless transceiver collects the reflected frequency signal, and extracts the resonant frequency f2′ of the surface acoustic wave temperature sensor at the current moment and the resonant frequency f3′ of the second surface acoustic wave resonator at the current moment;

[0012] Step 3: Calculate the vibration amount. The calculation formula of the vibration amount is:

[0013] δf′=|f2′-f3′|,

[0014] Wherein, δf′ represents the frequency difference, f2′ represents the resonant frequency of the surface acoustic wave temperature sensor at the current moment, and f3′ represents the resonant frequency of the second surface acoustic wave resonator at the current moment;

[0015] Step 4: Analyze the change of vibration amount: set a vibration amount threshold δf th , if δf′>δf th , it is determined that there is abnormal vibration, indicating the risk of air leakage;

[0016] Step 5: Output vibration determination result: According to the calculated δf′ value and the threshold δf th By comparing the pressure gauges, it is determined whether there is a gas leak and an alarm signal is issued.

[0017] Another method for predicting air leakage of a wireless passive pressure and temperature sensing device comprises the following steps:

[0018] Step 1, transmitting a sweep frequency signal to cover frequencies: the wireless transceiver transmits a sweep frequency signal to cover the resonant frequency f1 of the surface acoustic wave delay line under pressure, the resonant frequency f2 of the surface acoustic wave temperature sensor, and the resonant frequency f3 of the second surface acoustic wave resonator;

[0019] Step 2, receiving and collecting reflected signals: after receiving the reflected signals, the wireless transceiver extracts the resonant frequency f1′ of the surface acoustic wave delay line under pressure at the current moment, the resonant frequency f2′ of the surface acoustic wave temperature sensor at the current moment, and the resonant frequency f3′ of the second surface acoustic wave resonator at the current moment;

[0020] Step 3: Calculate the frequency difference. The calculation formula of the frequency difference is:

[0021] δf′=|f2′-f3′|,

[0022] Wherein, δf′ represents the frequency difference, f2′ represents the resonant frequency of the surface acoustic wave temperature sensor at the current moment, and f3′ represents the resonant frequency of the second surface acoustic wave resonator at the current moment;

[0023] Step 4: Analyze the pressure value through the resonant frequency f1′ of the surface acoustic wave delay line under pressure at the current moment. The pressure value analysis method is to calculate the pressure value through frequency change. The pressure value calculation formula is:

[0024] P = k·(f1′-f10),

[0025] Wherein, P represents the current air pressure value, k represents the proportional constant related to the characteristics of the surface acoustic wave delay line, f1′ represents the resonant frequency of the surface acoustic wave delay line under the pressure at the current moment, and f10 represents the resonant frequency of the surface acoustic wave delay line under standard atmospheric pressure;

[0026] Step 5, analyzing the pressure change trend: by continuously measuring f1′, drawing a frequency change curve, analyzing the pressure change trend, if the change rate of f1′ exceeds a predetermined threshold, it is determined that there is a risk of air leakage or a large leakage;

[0027] Step 6: Output pressure determination result: Based on the analysis result, determine whether the current pressure state is normal. If the pressure drops rapidly or fluctuates too much, an early warning signal is issued to indicate the existence of a gas leak.

[0028] A signal processing method for a wireless passive pressure and temperature sensing device comprises the following steps:

[0029] Step 1, define the state variable of the system: define the frequency signal as the state variable of the system, and the model expression of the state variable is:

[0030] x t =A·x t-1 +w t ,

[0031] Among them, x trepresents the actual frequency value at time t, A represents the state transfer matrix, x t-1 represents the actual frequency value at time t-1, w t represents process noise;

[0032] Step 2: Define the measurement model: The measurement model represents the relationship between the noisy frequency data and the true state. The expression of the measurement model is:

[0033] z t =H·x t +v t ,

[0034] Among them, z t represents the frequency signal measured by the sensor, H represents the measurement matrix, v t represents the measurement noise;

[0035] Step 3, initialize the initial state of the Kalman filter: The initial state is set according to the frequency measurement value, and the initial error covariance matrix is ​​set. The expression of the initial state is:

[0036]

[0037] in, represents the initial state, f10 represents the initial frequency value of the surface acoustic wave delay line, f20 represents the initial frequency value of the surface acoustic wave temperature sensor, and f30 represents the initial frequency value of the second surface acoustic wave resonator;

[0038] The expression of the initial error covariance matrix is:

[0039]

[0040] Where P0 represents the initial error covariance matrix, σ 2 represents the variance of the initial measurement error, f1 represents the resonant frequency of the overlay SAW delay line under pressure, f2 represents the resonant frequency of the SAW temperature sensor, and f3 represents the resonant frequency of the second SAW resonator;

[0041] Step 4, prediction phase and update phase of Kalman filter: the prediction phase includes: prediction state and prediction error covariance, the update phase includes: Kalman gain, update state estimation and update error covariance, the expression of the predicted state is:

[0042]

[0043] in, represents the predicted state at time t, represents the state estimation value at time t-1, and A represents the state transfer matrix;

[0044] The expression of the prediction error covariance is:

[0045]

[0046] in, represents the prediction error covariance matrix, A represents the state transfer matrix, P t-1 represents the error covariance matrix at time t-1, A T represents the transpose of the state transfer matrix, Q represents the process noise covariance matrix;

[0047] The expression of the Kalman gain is:

[0048]

[0049] Among them, K t represents the Kalman gain, represents the prediction error covariance matrix, H represents the measurement matrix, and H T represents the transpose of the measurement matrix, R represents the observation noise covariance matrix;

[0050] The expression for updating the state estimate is:

[0051]

[0052] in, represents the state estimate at time t after correction by the observed data, represents the estimated value of the predicted state at time t, K t represents the Kalman gain, H represents the measurement matrix, z t Represents the frequency signal data measured by the sensor at time t;

[0053] The expression of the update error covariance is:

[0054]

[0055] Among them, P t represents the updated prediction error covariance matrix, I represents the identity matrix, K t represents the Kalman gain, H represents the measurement matrix, represents the prediction error covariance matrix;

[0056] Step 5: Repeat the prediction-update cycle: By continuously executing the prediction and update steps, the Kalman filter filters the frequency signal in real time at each time step, and finally obtains a smooth frequency signal estimate.

[0057] Furthermore, the expression of the observation matrix H is:

[0058]

[0059] Where m represents the number of observation data measured by the sensor, and n represents the number of state variables;

[0060] The expression of the state transfer matrix A is:

[0061]

[0062] Where n represents the number of state variables;

[0063] The expression of the process noise covariance matrix Q is:

[0064]

[0065] Where n represents the number of state variables, q nn Represents the state variable x n The process noise variance of

[0066] The expression of the observation noise covariance matrix R is:

[0067]

[0068] Where m represents the number of observed variables, r mm represents the observed variable z m The noise covariance of

[0069] The expression of the identity matrix I is:

[0070]

[0071] Among them, I n represents the n×n identity matrix, where n represents the number of units.

[0072] Beneficial effects of the present invention:

[0073] 1. Passive operation, suitable for remote and long-term monitoring: The transmitter of the present invention adopts a wireless passive design and can operate without an external power supply. It obtains energy by receiving wireless excitation signals, making it suitable for use in power systems, pipeline monitoring, and unattended remote environments, reducing maintenance costs and power consumption;

[0074] 2. High-precision pressure and temperature measurement: The present invention uses a surface acoustic wave delay line to sense pressure changes, and compares the frequency between the temperature sensor and the reference resonator to achieve high-precision temperature and pressure measurement. The surface acoustic wave sensor has high sensitivity and can effectively capture subtle environmental changes.

[0075] 3. Vibration analysis and fault prediction function: By calculating the frequency difference, the system can detect changes in environmental vibration, thereby predicting the precursors of pressure instability, air leakage or equipment abnormality, which helps to detect potential faults in advance and reduce downtime or safety hazards caused by sudden faults;

[0076] 4. Wireless data transmission, easy integration and expansion: The wireless passive pressure and temperature sensing device transmits measurement data through a wireless transceiver module, which simplifies the wiring requirements for signal transmission and makes the device easy to integrate with the existing remote monitoring system. At the same time, this wireless data transmission mode facilitates the networking of multi-sensor systems and can realize a wide range of monitoring applications;

[0077] 5. Noise suppression and signal optimization: The present invention uses a Kalman filter to process the frequency signal, which effectively reduces random noise and external interference in the measurement process. The frequency signal is smoothed and optimized by the filter, which improves the signal processing capability of the system, thereby improving the stability and accuracy of the measurement results.

[0078] 6. Strong anti-interference ability and adaptability to complex environments: The surface acoustic wave technology and efficient signal processing algorithm of the present invention enable the transmitter to have strong anti-interference ability in complex electromagnetic environments, and can work stably in high-noise environments to ensure the reliability of measurement data. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 It is a structural schematic diagram of a surface acoustic wave delay line of a wireless passive pressure and temperature sensing device of the present invention.

[0080] Figure 2 The present invention is a schematic structural diagram of a fully air-filled sealed pointer temperature and humidity meter of a wireless passive pressure and temperature sensing device.

[0081] In the figure: 1-main interpolation electrode, 2-asymmetric structure electrode A, 3-asymmetric structure electrode B, 4-interactive antenna, 5-reflection grid, 6-pressure cavity, 7-pressure pointer. DETAILED DESCRIPTION

[0082] like Figure 1-2As shown, a wireless passive pressure and temperature sensing device includes: a surface acoustic wave delay line, a surface acoustic wave temperature sensor, a second surface acoustic wave resonator, an antenna module and a wireless transceiver, the surface acoustic wave delay line is connected to the wireless transceiver, the surface acoustic wave temperature sensor is connected to the wireless transceiver, the second surface acoustic wave resonator is connected to the wireless transceiver, the antenna module is connected to the wireless transceiver, the surface acoustic wave delay line and the surface acoustic wave temperature sensor are connected in series to form a passive pressure and temperature transmitter, the surface acoustic wave temperature sensor is a first surface acoustic wave resonator, and the passive pressure and temperature transmitter is connected to the wireless transceiver. The transmitter is arranged in a pressure cavity 6 of a fully air-filled sealed pointer temperature and humidity meter, the second surface acoustic wave resonator is arranged on a pressure pointer 7 of the air-filled sealed pointer temperature and humidity meter, the surface acoustic wave delay line is a three-finger structure, including: a main interpolation electrode 1, an asymmetric structure electrode A2, an asymmetric structure electrode B3, an interactive antenna 4 and a reflection grid 5, the main interpolation electrode 1 is connected to the interactive antenna 4, the asymmetric structure electrode A2 and the asymmetric structure electrode B3 are respectively arranged on both sides of the main interpolation electrode 1, and the reflection grid 5 is located between the main interpolation electrode 1 and the asymmetric structure electrode B3.

[0083] Preferably, the surface acoustic wave temperature sensor is connected to the antenna module via electromagnetic coupling or directly connected.

[0084] Preferably, the antenna module is a flexible antenna or a PCB hard copper-clad board antenna or a hollow helical antenna.

[0085] Preferably, the reflective grating 5 is composed of a plurality of parallel vertical reflective gratings.

[0086] Preferably, the ratio of the distance L12 between the main interpolation electrode 1 and the asymmetric structure electrode A2 to the distance L13 between the main interpolation electrode 1 and the asymmetric structure electrode B3 is greater than 3, that is, L12:L13>3.

[0087] A method for predicting air leakage of a wireless passive pressure and temperature sensing device comprises the following steps:

[0088] Step 1, system initialization: determine the resonant frequency f2 of the surface acoustic wave temperature sensor and the resonant frequency f3 of the second surface acoustic wave resonator;

[0089] Step 2, transmitting a frequency sweep signal and collecting data: the wireless transceiver transmits a frequency sweep signal, covering the frequency range of f2 and f3, the wireless transceiver collects the reflected frequency signal, and extracts the resonant frequency f2′ of the surface acoustic wave temperature sensor at the current moment and the resonant frequency f3′ of the second surface acoustic wave resonator at the current moment;

[0090] Step 3: Calculate the vibration amount. The calculation formula of the vibration amount is:

[0091] δf′=|f2′-f3′|,

[0092] Wherein, δf′ represents the frequency difference, f2′ represents the resonant frequency of the surface acoustic wave temperature sensor at the current moment, and f3′ represents the resonant frequency of the second surface acoustic wave resonator at the current moment;

[0093] Step 4: Analyze the change of vibration amount: set a vibration amount threshold δf th , if δf′>δf th , it is determined that there is abnormal vibration, indicating the risk of air leakage;

[0094] Step 5: Output vibration determination result: According to the calculated δf′ value and the threshold δf th By comparing the pressure gauges, it is determined whether there is a gas leak and an alarm signal is issued.

[0095] Another method for predicting air leakage of a wireless passive pressure and temperature sensing device comprises the following steps:

[0096] Step 1, transmitting a sweep frequency signal to cover frequencies: the wireless transceiver transmits a sweep frequency signal to cover the resonant frequency f1 of the surface acoustic wave delay line under pressure, the resonant frequency f2 of the surface acoustic wave temperature sensor, and the resonant frequency f3 of the second surface acoustic wave resonator;

[0097] Step 2, receiving and collecting reflected signals: after receiving the reflected signals, the wireless transceiver extracts the resonant frequency f1′ of the surface acoustic wave delay line under pressure at the current moment, the resonant frequency f2′ of the surface acoustic wave temperature sensor at the current moment, and the resonant frequency f3′ of the second surface acoustic wave resonator at the current moment;

[0098] Step 3: Calculate the frequency difference. The calculation formula of the frequency difference is:

[0099] δf′=|f2′-f3′|,

[0100] Wherein, δf′ represents the frequency difference, f2′ represents the resonant frequency of the surface acoustic wave temperature sensor at the current moment, and f3′ represents the resonant frequency of the second surface acoustic wave resonator at the current moment;

[0101] Step 4: Analyze the pressure value through the resonant frequency f1′ of the surface acoustic wave delay line under pressure at the current moment. The pressure value analysis method is to calculate the pressure value through frequency change. The pressure value calculation formula is:

[0102] P = k·(f1′-f10),

[0103] Wherein, P represents the current air pressure value, k represents the proportional constant related to the characteristics of the surface acoustic wave delay line, f1′ represents the resonant frequency of the surface acoustic wave delay line under the pressure at the current moment, and f10 represents the resonant frequency of the surface acoustic wave delay line under standard atmospheric pressure;

[0104] Step 5, analyzing the pressure change trend: by continuously measuring f1′, drawing a frequency change curve, analyzing the pressure change trend, if the change rate of f1′ exceeds a predetermined threshold, it is determined that there is a risk of air leakage or a large leakage;

[0105] Step 6: Output pressure determination result: Based on the analysis result, determine whether the current pressure state is normal. If the pressure drops rapidly or fluctuates too much, an early warning signal is issued to indicate the existence of a gas leak.

[0106] A signal processing method for a wireless passive pressure and temperature sensing device comprises the following steps:

[0107] Step 1, define the state variable of the system: define the frequency signal as the state variable of the system, and the model expression of the state variable is:

[0108] x t =A·x t-1 +w t ,

[0109] Among them, x t represents the actual frequency value at time t, A represents the state transfer matrix, x t-1 represents the actual frequency value at time t-1, w t represents process noise;

[0110] Step 2: Define the measurement model: The measurement model represents the relationship between the noisy frequency data and the true state. The expression of the measurement model is:

[0111] z t =H·x t +v t ,

[0112] Among them, z t represents the frequency signal measured by the sensor, H represents the measurement matrix, v t represents the measurement noise;

[0113] Step 3, initialize the initial state of the Kalman filter: The initial state is set according to the frequency measurement value, and the initial error covariance matrix is ​​set. The expression of the initial state is:

[0114]

[0115] in, represents the initial state, f10 represents the initial frequency value of the surface acoustic wave delay line, f20 represents the initial frequency value of the surface acoustic wave temperature sensor, and f30 represents the initial frequency value of the second surface acoustic wave resonator;

[0116] The expression of the initial error covariance matrix is:

[0117]

[0118] Where P0 represents the initial error covariance matrix, σ 2 represents the variance of the initial measurement error, f1 represents the resonant frequency of the overlay SAW delay line under pressure, f2 represents the resonant frequency of the SAW temperature sensor, and f3 represents the resonant frequency of the second SAW resonator;

[0119] Step 4, prediction phase and update phase of Kalman filter: the prediction phase includes: prediction state and prediction error covariance, the update phase includes: Kalman gain, update state estimation and update error covariance, the expression of the predicted state is:

[0120]

[0121] in, represents the predicted state at time t, represents the state estimation value at time t-1, and A represents the state transfer matrix;

[0122] The expression of the prediction error covariance is:

[0123]

[0124] in, represents the prediction error covariance matrix, A represents the state transfer matrix, P t-1 represents the error covariance matrix at time t-1, A T represents the transpose of the state transfer matrix, Q represents the process noise covariance matrix;

[0125] The expression of the Kalman gain is:

[0126]

[0127] Among them, K t represents the Kalman gain, represents the prediction error covariance matrix, H represents the measurement matrix, and H T represents the transpose of the measurement matrix, R represents the observation noise covariance matrix;

[0128] The expression for updating the state estimate is:

[0129]

[0130] in, represents the state estimate at time t after correction by the observed data, represents the estimated value of the predicted state at time t, K t represents the Kalman gain, H represents the measurement matrix, z t Represents the frequency signal data measured by the sensor at time t;

[0131] The expression of the update error covariance is:

[0132]

[0133] Among them, P t represents the updated prediction error covariance matrix, I represents the identity matrix, K t represents the Kalman gain, H represents the measurement matrix, represents the prediction error covariance matrix;

[0134] Step 5: Repeat the prediction-update cycle: By continuously executing the prediction and update steps, the Kalman filter filters the frequency signal in real time at each time step, and finally obtains a smooth frequency signal estimate.

[0135] Furthermore, the expression of the observation matrix H is:

[0136]

[0137] Where m represents the number of observation data measured by the sensor, and n represents the number of state variables;

[0138] The expression of the state transfer matrix A is:

[0139]

[0140] Where n represents the number of state variables;

[0141] The expression of the process noise covariance matrix Q is:

[0142]

[0143] Where n represents the number of state variables, q nn Represents the state variable x n The process noise variance of

[0144] The expression of the observation noise covariance matrix R is:

[0145]

[0146] Where m represents the number of observed variables, r mm represents the observed variable z m The noise covariance of

[0147] The expression of the identity matrix I is:

[0148]

[0149] Among them, I n represents the n×n identity matrix, where n represents the number of units.

[0150] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A wireless passive pressure and temperature sensing device, characterized in that: include: A surface acoustic wave delay line, a surface acoustic wave temperature sensor, a second surface acoustic wave resonator, an antenna module and a wireless transceiver, wherein the surface acoustic wave delay line is connected to the wireless transceiver, the surface acoustic wave temperature sensor is connected to the wireless transceiver, the second surface acoustic wave resonator is connected to the wireless transceiver, the antenna module is connected to the wireless transceiver, the surface acoustic wave delay line and the surface acoustic wave temperature sensor are connected in series to form a passive pressure and temperature transmitter, the surface acoustic wave temperature sensor is a first surface acoustic wave resonator, and the passive pressure and temperature transmitter is arranged in a pressure cavity of a fully air-filled sealed pointer temperature and humidity instrument (6 ), the second surface acoustic wave resonator is arranged on the pressure pointer (7) of the air-filled sealed pointer temperature and humidity instrument, the surface acoustic wave delay line is a three-finger structure, comprising: a main interpolation electrode (1), an asymmetric structure electrode A (2), an asymmetric structure electrode B (3), an interactive antenna (4) and a reflection grid (5), the main interpolation electrode (1) is connected to the interactive antenna (4), the asymmetric structure electrode A (2) and the asymmetric structure electrode B (3) are respectively arranged on both sides of the main interpolation electrode (1), and the reflection grid (5) is located between the main interpolation electrode (1) and the asymmetric structure electrode B (3).

2. A wireless passive pressure and temperature sensing device according to claim 1, characterized in that: The surface acoustic wave temperature sensor is connected to the antenna module through electromagnetic coupling or directly connected.

3. A wireless passive pressure and temperature sensing device according to claim 1, characterized in that: The antenna module is a flexible antenna or a PCB hard copper-clad antenna or a hollow spiral antenna.

4. A wireless passive pressure and temperature sensing device according to claim 1, characterized in that: The reflection grid (5) is composed of a plurality of parallel vertical reflection grids.

5. A wireless passive pressure and temperature sensing device according to claim 1, characterized in that: The ratio of the distance L12 between the main interpolation electrode (1) and the asymmetric structure electrode A (2) to the distance L13 between the main interpolation electrode (1) and the asymmetric structure electrode B (3) is greater than 3, that is, L12:L13>3.

6. A method for predicting air leakage of a wireless passive pressure and temperature sensing device according to claim 1, characterized in that: The following steps are involved: Step 1, system initialization: determine the resonant frequency f2 of the surface acoustic wave temperature sensor and the resonant frequency f3 of the second surface acoustic wave resonator; Step 2, transmitting a frequency sweep signal and collecting data: the wireless transceiver transmits a frequency sweep signal, covering the frequency range of f2 and f3, the wireless transceiver collects the reflected frequency signal, and extracts the resonant frequency f2′ of the surface acoustic wave temperature sensor at the current moment and the resonant frequency f3′ of the second surface acoustic wave resonator at the current moment; Step 3: Calculate the vibration amount. The calculation formula of the vibration amount is: δf′=|f2′-f3′|, Wherein, δf′ represents the frequency difference, f2′ represents the resonant frequency of the surface acoustic wave temperature sensor at the current moment, and f3′ represents the resonant frequency of the second surface acoustic wave resonator at the current moment; Step 4: Analyze the change of vibration amount: set a vibration amount threshold δf th , if δf′>δf th , it is determined that there is abnormal vibration, indicating the risk of air leakage; Step 5: Output vibration determination result: According to the calculated δf′ value and the threshold δf th By comparing the pressure gauges, it is determined whether there is a gas leak and an alarm signal is issued.

7. A method for predicting air leakage of a wireless passive pressure and temperature sensing device according to claim 1, characterized in that: The following steps are involved: Step 1, transmitting a sweep frequency signal to cover frequencies: the wireless transceiver transmits a sweep frequency signal to cover the resonant frequency f1 of the surface acoustic wave delay line under pressure, the resonant frequency f2 of the surface acoustic wave temperature sensor, and the resonant frequency f3 of the second surface acoustic wave resonator; Step 2, receiving and collecting reflected signals: after receiving the reflected signals, the wireless transceiver extracts the resonant frequency f1′ of the surface acoustic wave delay line under pressure at the current moment, the resonant frequency f2′ of the surface acoustic wave temperature sensor at the current moment, and the resonant frequency f3′ of the second surface acoustic wave resonator at the current moment; Step 3: Calculate the frequency difference. The calculation formula of the frequency difference is: δf′=|f2′-f3′|, Wherein, δf′ represents the frequency difference, f2′ represents the resonant frequency of the surface acoustic wave temperature sensor at the current moment, and f3′ represents the resonant frequency of the second surface acoustic wave resonator at the current moment; Step 4: Analyze the pressure value through the resonant frequency f1′ of the surface acoustic wave delay line under pressure at the current moment. The pressure value analysis method is to calculate the pressure value through frequency change. The pressure value calculation formula is: P = k·(f1′-f10), Wherein, P represents the current air pressure value, k represents the proportional constant related to the characteristics of the surface acoustic wave delay line, f1′ represents the resonant frequency of the surface acoustic wave delay line under the pressure at the current moment, and f10 represents the resonant frequency of the surface acoustic wave delay line under standard atmospheric pressure; Step 5, analyzing the pressure change trend: by continuously measuring f1′, drawing a frequency change curve, analyzing the pressure change trend, if the change rate of f1′ exceeds a predetermined threshold, it is determined that there is a risk of air leakage or a large leakage; Step 6: Output pressure determination result: Based on the analysis result, determine whether the current pressure state is normal. If the pressure drops rapidly or fluctuates too much, an early warning signal is issued to indicate the existence of a gas leak.

8. A signal processing method for the wireless passive pressure and temperature sensing device according to claim 1, characterized in that: The following steps are involved: Step 1, define the state variable of the system: define the frequency signal as the state variable of the system, and the model expression of the state variable is: x t =H·x t -1+w t , Among them, x t represents the actual frequency value at time t, A represents the state transfer matrix, x t-1 represents the actual frequency value at time t-1, w t represents process noise; Step 2: Define the measurement model: The measurement model represents the relationship between the noisy frequency data and the true state. The expression of the measurement model is: z t =H·x t +v t , Among them, z t represents the frequency signal measured by the sensor, H represents the measurement matrix, v t represents the measurement noise; Step 3, initialize the initial state of the Kalman filter: The initial state is set according to the frequency measurement value, and the initial error covariance matrix is ​​set. The expression of the initial state is: in, represents the initial state, f10 represents the initial frequency value of the surface acoustic wave delay line, f20 represents the initial frequency value of the surface acoustic wave temperature sensor, and f30 represents the initial frequency value of the second surface acoustic wave resonator; The expression of the initial error covariance matrix is: Where P0 represents the initial error covariance matrix, σ 2 represents the variance of the initial measurement error, f1 represents the resonant frequency of the overlay SAW delay line under pressure, f2 represents the resonant frequency of the SAW temperature sensor, and f3 represents the resonant frequency of the second SAW resonator; Step 4, prediction phase and update phase of Kalman filter: the prediction phase includes: prediction state and prediction error covariance, the update phase includes: Kalman gain, update state estimation and update error covariance, the expression of the predicted state is: in, represents the predicted state at time t, represents the state estimation value at time t-1, and A represents the state transfer matrix; The expression of the prediction error covariance is: in, represents the prediction error covariance matrix, A represents the state transfer matrix, P t-1 represents the error covariance matrix at time t-1, A T represents the transpose of the state transfer matrix, Q represents the process noise covariance matrix; The expression of the Kalman gain is: Among them, K t represents the Kalman gain, represents the prediction error covariance matrix, H represents the measurement matrix, and H T represents the transpose of the measurement matrix, R represents the observation noise covariance matrix; The expression for updating the state estimate is: in, represents the state estimate at time t after correction by the observed data, represents the estimated value of the predicted state at time t, K t represents the Kalman gain, H represents the measurement matrix, z t Represents the frequency signal data measured by the sensor at time t; The expression of the update error covariance is: Among them, P t represents the updated prediction error covariance matrix, I represents the identity matrix, K t represents the Kalman gain, H represents the measurement matrix, represents the prediction error covariance matrix; Step 5: Repeat the prediction-update cycle: By continuously executing the prediction and update steps, the Kalman filter filters the frequency signal in real time at each time step, and finally obtains a smooth frequency signal estimate.

9. The signal processing method of a wireless passive pressure and temperature sensing device according to claim 8, characterized in that: The expression of the observation matrix H is: Where m represents the number of observation data measured by the sensor, and n represents the number of state variables; The expression of the state transfer matrix A is: Where n represents the number of state variables; The expression of the process noise covariance matrix Q is: Where n represents the number of state variables, q nn Represents the state variable x n The process noise variance of The expression of the observation noise covariance matrix R is: Where m represents the number of observed variables, r mm represents the observed variable z m The noise covariance of The expression of the identity matrix I is: Among them, I n represents the n×n identity matrix, where n represents the number of units.

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