Zero-carbon emission road and bridge surface acoustic wave wireless displacement monitoring system and self-calibration method thereof

Abstract

The invention provides a zero-carbon-emission road and bridge surface acoustic wave wireless displacement monitoring system and a self-calibration method thereof, and relates to the field of traffic infrastructure health monitoring. According to the system, environment energy collection and a low-power-consumption SAW label are integrated, so that a battery-free design is realized, the power supply requirement of equipment is thoroughly eliminated, and zero carbon emission is achieved. The system adopts a self-calibration mechanism, and corrects an environmental drift error in real time by embedding a reference SAW unit and a temperature compensation algorithm, thereby ensuring long-term stability. In addition, the dynamic topology protocol based on LoRaWAN reduces the radio frequency excitation power consumption and prolongs the service life of the system. The system is suitable for displacement monitoring of key parts such as bridges and roads, covers the whole life cycle of the construction period and the operation period, and has the characteristics of high precision, real-time performance, greenness and energy conservation.

Description

Technical Field

[0001] The present invention relates to the technical field of displacement detection, and particularly to a zero-carbon road and bridge surface acoustic wave wireless displacement monitoring system and its self-calibration method. Background Art

[0002] In the context of the intensifying global climate change and the promotion of low-carbon circular and green development of industries, the field of infrastructure health monitoring is undergoing a profound green and intelligent transformation. Traditional road and bridge displacement monitoring technologies such as total station, fiber optic sensing, and GPS systems, although playing an important role in bridge and road monitoring, generally have problems such as complex wiring, high energy consumption, and difficult maintenance. For example, the total station requires frequent manual operations and has low measurement efficiency. Although fiber optic sensing has high precision, it is vulnerable to electromagnetic interference and has high wiring costs. The GPS system has unstable signals and insufficient accuracy in complex terrains. These problems not only limit the long-term stable operation of the monitoring system but also make the carbon emission problem in its whole life cycle increasingly prominent.

[0003] Surface Acoustic Wave (SAW) technology, as an emerging passive wireless sensing solution, provides an innovative solution for realizing zero-carbon, high-precision, and intelligent road and bridge displacement monitoring. SAW sensors use elastic waves propagating on the surface of piezoelectric substrates to achieve sensing. Its unique advantage lies in its completely passive working characteristics, without the need for external power supply, and it can work only through electromagnetic wave excitation. This fundamentally eliminates the carbon emission problems caused by battery replacement and cable laying in traditional monitoring systems. Previous studies have shown that the carbon emissions of monitoring systems using SAW technology in the whole life cycle can be reduced by more than 90% compared with traditional systems. At the same time, this technology also has outstanding characteristics such as strong anti-electromagnetic interference ability, high measurement precision (up to the micron level), and multi-parameter synchronous monitoring, and is particularly suitable for displacement monitoring of key parts such as bridge expansion joints, bearings, and suspension cables.

[0004] However, the practical application of SAW technology in road and bridge monitoring still faces several technical challenges. First, environmental adaptability is the primary problem. The complex climate conditions (such as temperature fluctuations, rain erosion, ultraviolet radiation, etc.) where bridges are located will affect the performance stability of piezoelectric materials. Experimental data show that in the temperature range of -20°C to 60°C, the displacement measurement error of uncompensated SAW sensors can reach ±1.2%. Secondly, the signal multipath effect caused by metal structures and the energy attenuation during long-distance transmission also restrict the monitoring precision. In a typical bridge environment, the reliable reading distance of SAW signals usually does not exceed 15 meters. In addition, existing SAW monitoring systems lack an effective self-calibration mechanism, and on-site calibration requires manual intervention, increasing the operation and maintenance costs. These problems limit the wide application of SAW technology in large-scale road and bridge health monitoring.

[0005] To address the above challenges, the present invention proposes a road and bridge surface acoustic wave (SAW) wireless displacement monitoring system with zero carbon emissions and its self-calibration method. By integrating ambient energy harvesting (such as vibration-piezoelectric coupling power supply) and low-power SAW tags, the system achieves a battery-free design, completely eliminating the power supply requirement for the device and achieving zero carbon emissions. At the same time, the system adopts a self-calibration mechanism. By embedding a reference SAW unit and a temperature compensation algorithm, it can correct the environmental drift error in real time to ensure long-term stability. In addition, the dynamic topology protocol based on LoRaWAN reduces the radio frequency excitation power consumption and extends the system life. The present invention is applicable to displacement monitoring of key parts such as bridges and roads, covering the entire life cycle of the construction period and the operation period, and has the characteristics of high precision, real-time performance, and green energy conservation. Summary of the Invention

[0006] (I) Technical Problems to be Solved In view of the deficiencies of the prior art, the present invention provides a road and bridge SAW wireless displacement monitoring system with zero carbon emissions and its self-calibration method, which solves the problems raised in the above background technology.

[0007] (II) Technical Solutions To achieve the above objectives, the present invention is realized through the following technical solutions: A road and bridge SAW wireless displacement monitoring system with zero carbon emissions includes: A sensing layer, which consists of multiple distributed SAW sensing nodes, and each node integrates a displacement sensitive element and a temperature compensation unit; A transmission layer, which uses the low-power LoRaWAN protocol to achieve data backhaul, and is equipped with a solar-vibration energy hybrid power supply module; An application layer, which deploys an adaptive calibration algorithm based on deep learning; Among them, the SAW sensing node detects micro-displacements through changes in electromagnetic wave signals, and has a passive wireless design without external power supply.

[0008] Preferably, the SAW sensor includes: A piezoelectric substrate, which uses a high coupling coefficient material; Interdigital transducers, which are used to excite and receive surface acoustic waves; A reflection grating array, which is used to reflect surface acoustic waves to form a delay line or resonator structure; Temperature compensation electrodes, which adopt a dual-channel differential design to suppress common-mode interference. Using a dual-channel differential measurement design, common-mode interference suppression is achieved by using two SAW resonators with different frequencies. Experiments show that the temperature drift error can be reduced by 82%; A displacement sensitive structure, such as a cantilever beam or a thin-film displacement transfer mechanism, which is used to convert mechanical displacement into a frequency change of surface acoustic waves.

[0009] An integrated antenna, which is used to obtain wireless energy and transmit signals.

[0010] Preferably, the solar-vibration hybrid energy supply module is composed of a flexible solar thin film, a piezoelectric vibration energy harvester, and a radio frequency energy harvester. The main energy supply comes from the high-efficiency flexible solar thin film with a conversion efficiency of 23%. The auxiliary energy supply uses the piezoelectric vibration energy harvester, which can generate a maximum power of 5 mW when a vehicle passes by. The backup solution is radio frequency energy harvesting to maintain basic functions under extreme weather conditions.

[0011] Preferably, the SAW sensor further includes a wireless reading and writing unit, and the wireless reading and writing unit includes a radio frequency transmitting module, a SAW signal demodulation module, and a data processing MCU.

[0012] A self-calibration method for a zero-carbon road and bridge surface acoustic wave wireless displacement monitoring system is applied to the above zero-carbon road and bridge surface acoustic wave wireless displacement monitoring system, and includes the following steps: Step 1, data acquisition, collect ambient temperature data and displacement data through a reference SAW resonator and a main SAW sensor respectively; Step 2, differential processing, use a differential processor to process the collected data to eliminate common-mode temperature interference; Step 3, temperature compensation, perform temperature compensation on the processed data based on the temperature-displacement coupling model; Step 4, algorithm compensation, when the signal quality of the reference unit is lower than the threshold, switch to the pure algorithm compensation mode; Step 5, output the calibration result, and output the compensated displacement value.

[0013] Preferably, the temperature compensation in step 3 includes: Establish a frequency shift-temperature relationship formula, , where is the compensated frequency drift value, a0 is the zero-order coefficient, representing when the temperature T = 0, the starting value of, which is a fixed part independent of temperature change, a1 is the first-order coefficient, reflecting the proportional relationship that changes linearly with temperature T, reflecting the linear temperature characteristic, and its positive and negative values and magnitude determine the direction and rate of the linear change. a2 is the second-order coefficient, used to describe the quadratic relationship between and temperature T, reflecting the non-linear temperature characteristic, and can depict the change of the change rate with temperature. a3 is the third-order coefficient, representing the cubic relationship between and temperature T, further refining the non-linear characteristic, and can capture more complex temperature-displacement change trends. ε is the process discreteness error, and each coefficient is determined by calibration in a three-point temperature chamber during factory production.

[0014] Preferably, the algorithm compensation in step 4 includes: updating the compensation coefficient through a deep learning algorithm using historical data; estimating the current temperature using Kalman filtering technology.

[0015] Preferably, the calibration method further includes: an adaptive trigger logic that automatically starts the calibration process according to environmental changes or time periods; dynamically adjusting the calibration frequency to optimize energy consumption based on battery capacity or data stability.

[0016] Preferably, the calibration method further includes: radio frequency signal quality assessment to achieve millisecond-level switching of the compensation mode through the radio frequency signal quality index; process error self-learning to update the compensation coefficient online using a deep belief network.

[0017] (III) Beneficial Effects The present invention provides a zero-carbon road and bridge surface acoustic wave wireless displacement monitoring system and its self-calibration method, having the following beneficial effects: Zero-carbon emissions and high-precision monitoring: By integrating environmental energy harvesting technology and low-power SAW tags, the present invention completely eliminates the power supply requirement for the device, realizes true zero-carbon emissions, and meets the requirements of green infrastructure. At the same time, the system adopts a dual-channel differential design and a temperature compensation algorithm, which can significantly reduce the influence of temperature changes on displacement measurement, reduce the error from hundreds of micrometers to the micrometer level, and ensure high-precision displacement monitoring data can be obtained under different environmental conditions, meeting the stringent requirements of road and bridge structural health monitoring.

[0018] Self-calibration ability and long-term stability: The self-calibration mechanism of the present invention can real-time correct environmental drift errors, reduce operation and maintenance costs, and ensure the reliable operation of the system throughout its life cycle, providing continuous guarantee for the safe operation of road and bridges.

[0019] Dynamic mode switching and intelligent compensation: The system dynamically selects the compensation mode according to the signal strength to ensure reliable measurement results under different environments. When the signal quality is good, the reference unit data is used for compensation; when the signal quality is poor or the reference unit fails, the algorithm is relied on to estimate the temperature and perform compensation. This intelligent compensation mechanism improves the adaptability and robustness of the system, enabling it to maintain high performance in complex and changing environments. Description of the Drawings

[0020] Figure 1 It is a schematic structural diagram of the SAW device in the present invention; Figure 2 It is an internal schematic diagram of the SAW device in the present invention; Figure 3 It is a schematic principle diagram of the wireless reading and writing unit in the present invention; Figure 4 It is a schematic principle diagram of the energy management in the present invention; Figure 5 This is a schematic diagram of the principle of the self - calibration module in the present invention.

[0021] Among them, 1. Piezoelectric substrate; 101. Sensitive area thin film layer; 2. Transmitting interdigital transducer; 3. Reflection grating array; 4. Receiving interdigital transducer; 5. Integrated antenna; 6. Temperature compensation electrode; 7. Protective housing; 8. Piezoelectric vibration energy harvester; 801. Flexible solar thin film; 802. Transparent cover; 9. Energy management module; 10. Wireless reading and writing unit; 11. Displacement sensitive structure. Specific embodiments

[0022] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0023] Embodiment 1:

[0024] As Figure 1-2 shown, the embodiment of the present invention provides a zero - carbon - emission road and bridge surface acoustic wave wireless displacement monitoring system, including: a sensing layer, a transmission layer, and an application layer. The sensing layer is composed of 86 distributed SAW sensing nodes; the transmission layer uses the LoRaWAN protocol to realize data back - transmission, and is equipped with a solar - vibration energy hybrid power supply module; the application layer deploys an adaptive calibration algorithm based on deep learning.

[0025] The SAW sensor is made of a protective housing 7 made of high - strength plastic. Inside the protective housing 7, there are a piezoelectric substrate 1 and a sensitive area thin film layer 101; interdigital transducers, a reflection grating array 3, an integrated antenna 5, a temperature compensation electrode 6, an energy management module 9, a displacement sensitive structure, and a wireless reading and writing module 10. The SAW sensor itself does not contain any active electronic components or power sources, but obtains energy and generates a response through the radio frequency signal emitted by an external reader. After the electromagnetic wave emitted by the reader is received by the integrated antenna 5 of the sensor, it is converted into a surface acoustic wave through the interdigital transducer. After the acoustic wave propagates on the piezoelectric substrate, it is converted back into an electromagnetic wave by the IDT and reflected back to the reader to complete a sensing process.

[0026] As Figure 5 shown, a reference unit that is completely consistent with the sensing unit process is integrated on the SAW sensor chip. The two adopt a coplanar waveguide design, but the reference unit is mechanically isolated. When the ambient temperature changes, both the sensing unit and the reference unit generate frequency shifts and , and through differential operation Eliminate common-mode temperature interference and retain the frequency shift caused by pure mechanical displacement. The SAW sensor reads distances: ≤15m, standby power consumption <1mW, active state power consumption 80mW, supports multi-tag access: TDMA time division multiplexing protocol.

[0027] The piezoelectric substrate uses 128°Y-X LiNbO3 with dimensions of 10×5×0.5mm³; the interdigital transducer consists of a transmitting interdigital transducer 2 and a receiving interdigital transducer 4. Both the transmitting interdigital transducer 2 and the receiving interdigital transducer 4 are set as aluminum electrodes, with a finger width of 3μm and a spacing of 3μm, and an operating frequency of 915MHz; this device uses a dual-channel interdigital transducer layout to cancel common-mode interference and reduces the influence of multipath effects through an orthogonal coding reflector grating.

[0028] The patterning of the interdigital transducer is usually achieved by photolithography technology, including the following steps: cleaning and surface treatment of the piezoelectric substrate, spin coating and pre-baking of the photoresist, ultraviolet exposure through a mask, development and post-baking, metal deposition, and lift-off or etching to form the final pattern.

[0029] The reflector grating array has a pitch gradient design, with a displacement sensitivity of Δf / Δx≈50Hz / nm. The reflector grating array is used to reflect surface acoustic waves to form a delay line or resonator structure. The period of the reflector grating is the same as that of the interdigital transducer. The reflection coefficient depends on the number of fingers, the metal thickness, and the degree of acoustic impedance mismatch. In the patterning process, the reflector grating and the interdigital transducer are fabricated simultaneously using the same materials and processes. The reflector grating array adopts a "spring-mass block" design, with a displacement transfer efficiency ≥90%, a dynamic range of ±5mm, and a resolution of 0.01mm.

[0030] The temperature compensation electrode has a dual-channel differential design and is used to suppress common-mode interference; The integrated antenna is used to obtain wireless energy and transmit signals. The integrated antenna is set as a dipole antenna with an arm length of 75mm (λ / 4), and is connected to the sensitive area thin film layer of the interdigital transducer through a microstrip line. A polyimide temperature-sensitive film with a thickness of 300nm is spin-coated on the delay path.

[0031] The displacement monitoring process of the system is as follows: When an external physical quantity (such as displacement) changes, it will cause changes in the propagation characteristics of surface acoustic waves (such as wave velocity, frequency, phase, etc.). By detecting the changes in these parameters, the measurement of displacement can be achieved. The reflection grating array of the SAW sensor is designed to detect displacement changes. When the monitored structure (such as a bridge or a road) undergoes displacement, the spacing of the reflection grating will change, thereby changing the reflection characteristics of surface acoustic waves. A displacement-sensitive structure is integrated in the SAW sensor to convert mechanical displacement into a frequency change of surface acoustic waves. The reflection grating array adopts a spacing gradient design, enabling displacement changes to cause a change in a specific frequency, and the displacement sensitivity can reach 50 Hz / nm. The radio frequency transmission module of the wireless reading and writing unit transmits an electromagnetic wave signal, which is received by the antenna of the SAW sensor and converted into a surface acoustic wave through an interdigital transducer (IDT). The surface acoustic wave propagates on the piezoelectric substrate 1, is reflected back to the IDT after passing through the reflection grating array 3, and is converted into an electromagnetic wave signal again, which is reflected back to the wireless reading and writing unit. The wireless reading and writing unit receives the reflected electromagnetic wave signal and extracts the frequency information through the SAW signal demodulation module. The data processing MCU analyzes the extracted frequency information, combines the temperature compensation algorithm and the self-calibration mechanism, calculates the actual displacement value, and uses the embedded reference SAW unit and the temperature compensation algorithm to correct the frequency drift error caused by environmental temperature changes in real time. The self-calibration mechanism further improves the measurement accuracy and stability through the dual-channel differential measurement design and the dynamic weight allocation algorithm.

[0032] The solar-vibration energy hybrid supply module includes: a flexible solar thin film 801, a piezoelectric vibration energy harvester 8, a capacitive energy storage module, an energy management module 9, and a radio frequency energy harvesting component. The conversion efficiency of the flexible solar thin film 801 is 23%. A transparent cover is covered on the surface of the flexible solar thin film 801 to protect the flexible solar thin film 801. The piezoelectric vibration energy harvester 8 generates a maximum power of 5 mW when the vehicle passes by. The resonant frequency of the vibration energy harvester 8 is 30 Hz, and the output is ≥3 mW. The radio frequency energy harvesting component maintains the basic functions of the device under continuous rainy weather. The rated voltage of the capacitive energy storage module is 5.5 V, and the rated capacitance is 1 F to ensure the stable operation of the system.

[0033] The energy management module 9 adopts the management process as Figure 4 shown. The flexible solar thin film 801 supplies energy to the capacitive energy storage module. The capacitive energy storage module continuously supplies energy to the low-power wake-up circuit. When the vehicle passes by the low-power wake-up circuit, the wireless reading and writing unit is activated to work. The energy management module 9 adopts the timestamp synchronization technology to align the peak moments of radio frequency transmission and energy harvesting, and the efficiency is increased by 40%.

[0034] As Figure 3As shown, the wireless reading and writing unit includes a radio frequency transmitting module, a SAW signal demodulation module, and a data processing MCU. The operating frequency of the radio frequency transmitting module is 902 - 928 MHz; the SAW signal demodulation module uses a correlation detection algorithm; the data processing MCU uses the STM32 series. The SAW sensor works through backscatter modulation. The electromagnetic wave emitted by the wireless reader provides both energy and carries information. The power consumption of a traditional strain gauge is approximately 50 mW, while the power consumption of this design is 0 mW.

[0035] The self - calibration method includes: Step 1: Data acquisition, collect ambient temperature data and displacement data through a reference SAW resonator and a main SAW sensor respectively.

[0036] Step 2: Differential processing, use a differential processor to process the collected data to eliminate common - mode temperature interference. Integrate a reference unit on the SAW sensor chip with the same process as the sensing unit. Both use a coplanar waveguide design, but the reference unit is kept mechanically isolated. When the ambient temperature changes, both the sensing unit and the reference unit generate frequency shifts and , and eliminate the common - mode temperature interference through differential operation to retain the frequency shift caused by pure mechanical displacement.

[0037] Step 3: Temperature compensation, perform temperature compensation on the processed data based on the temperature - displacement coupling model. The frequency - shift - temperature relationship is: .

[0038] where ε is the process discreteness error, and each coefficient is determined through three - point temperature chamber calibration (-20°C / 25°C / 60°C) during factory production.

[0039] The temperature compensation algorithm flow is def auto_compensation(f_sense, f_ref, RSSI): # Input: sensing unit frequency f_sense, reference unit frequency f_ref, signal strength RSSI α = sigmoid((RSSI - 85) / 5) # Confidence calculation if α>0.7: # Reference unit valid mode Δf_temp = lookup_table(f_ref) f_compensated = f_sense - Δf_temp else: # Pure algorithm mode T_est = kalman_filter(history_f) Δf_temp = a0 + a1*T_est + a2*T_est**2 + a3*T_est***3 +ε f_compensated = f_sense - Δf_temp return smooth(f_compensated, window=5) Among them, the input parameters: f_sense: the frequency measured by the sensing unit; f_ref: the frequency measured by the reference unit; RSSI: Received Signal Strength Indicator, used to evaluate the signal quality.

[0040] Confidence calculation: Use the sigmoid function to calculate the confidence α, and the input is (RSSI - 85) / 5. The sigmoid function maps the input to a value between 0 and 1, indicating the reliability of the reference unit data.

[0041] Compensation mode selection: If the confidence α is greater than 0.7, it is considered that the reference unit data is reliable, and enter the reference unit valid mode: obtain the temperature frequency shift Δf_temp corresponding to the reference unit frequency f_ref through the lookup table lookup_table. Subtract Δf_temp from the sensing unit frequency f_sense to get the compensated frequency f_compensated. If the confidence α is less than or equal to 0.7, it is considered that the reference unit data is unreliable, and enter the pure algorithm mode: use the Kalman filter kalman_filter to process the historical frequency data history_f to estimate the current temperature T_est; calculate the frequency shift Δf_temp caused by temperature according to the polynomial model, where a0, a1, and a2 are the model coefficients; subtract Δf_temp from the sensing unit frequency f_sense to get the compensated frequency f_compensated.

[0042] Output: Smooth the compensated frequency f_compensated (window size is 5) to reduce the influence of noise, and return the final compensated frequency.

[0043] Step 4: Algorithm compensation, set the confidence factor β∈[0,1] to evaluate the reliability of the reference unit in real time. When the signal strength of the reference unit is lower than the threshold, automatically switch to the pure algorithm compensation mode, and use the historical data to update the compensation coefficient through the deep learning algorithm; adopt the Kalman filtering technology to estimate the current temperature.

[0044] Step 5: Output the calibration result, and output the compensated displacement value.

[0045] Adaptive triggering logic that automatically initiates the calibration process based on environmental changes or time periods; dynamically adjusts the calibration frequency to optimize energy consumption according to battery capacity or data stability.

[0046] Radio frequency signal quality assessment enables millisecond - level switching of compensation modes through the radio frequency signal quality index (RQI). The switching process satisfies: 。

[0047] Process error self - learning uses deep belief networks to update compensation coefficients online. The following MATLAB code example shows how to compensate for the errors of SAW sensors using deep learning algorithms (specifically, multi - layer feed - forward neural networks) to improve the accuracy of the monitoring system.

[0048] matlab net = newff(T_input, f_error, [10 5]); train(net, historical_data); T_input: Input data, which can be environmental temperature, historical displacement data, etc.

[0049] f_error: Output data, usually the frequency error measured by the SAW sensor.

[0050] newff: A MATLAB function used to create a feed - forward neural network. The parameters represent input data, output data, and network structure (the number of neurons in the hidden layer) respectively.

[0051] train: A MATLAB function used to train the neural network. historical_data is the historical data set used for training.

[0052] Initiate emergency calibration when |df / dt| > 10kHz / s, and default to full - parameter calibration every 24 hours, dynamically adjusting the calibration frequency according to battery capacity.

[0053] To verify the effectiveness and reliability of the zero - carbon - emission road bridge surface acoustic wave wireless displacement monitoring system and its self - calibration method of the present invention, we conducted the following experimental tests: Temperature compensation effect test: Under different temperature change conditions, record the displacement measurement errors before and after using the temperature compensation and the self - calibration method of the present invention respectively. The specific data is shown in the following table: , As can be seen from the data in the table, the self-calibration method of the present invention can significantly reduce the influence of temperature changes on displacement measurement, reducing the error from hundreds of micrometers to the micrometer level.

[0054] These experimental data fully demonstrate that the monitoring system of the present invention has excellent accuracy under different environmental conditions and can meet the high requirements of bridge and road structure health monitoring.

[0055] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A road and bridge surface acoustic wave wireless displacement monitoring system with zero carbon emissions, characterized in that, Including: The perception layer, which consists of multiple distributed SAW sensing nodes, and each node integrates a displacement sensitive element and a temperature compensation unit; The transmission layer, which uses the low-power LoRaWAN protocol to achieve data backhaul, and is equipped with a solar-vibrational energy hybrid power supply module; The application layer, which deploys an adaptive calibration algorithm based on deep learning; Among them, the SAW sensing node detects micro-displacement through the change of electromagnetic wave signal, with a passive wireless design and no external power supply required.

2. The zero-carbon-emission road and bridge surface acoustic wave wireless displacement monitoring system according to claim 1, wherein: Each of the SAW sensing nodes includes a SAW sensor, and the SAW sensor includes: A piezoelectric substrate, which uses a high coupling coefficient material; Interdigital transducers, which are used to excite and receive surface acoustic waves; A reflection grating array, which is used to reflect surface acoustic waves to form a delay line or resonator structure; Temperature compensation electrodes, which adopt a dual-channel differential design to suppress common-mode interference; An integrated antenna, which is used to obtain wireless energy and transmit signals; A displacement sensitive structure, which is used to convert mechanical displacement into a frequency change of surface acoustic waves.

3. The zero-carbon-emission road and bridge surface acoustic wave wireless displacement monitoring system according to claim 1, characterized in that: The solar-vibrational energy hybrid power supply module consists of a flexible solar thin film, a piezoelectric vibration energy collector, and a radio frequency energy collection component.

4. The zero-carbon-emission road and bridge surface acoustic wave wireless displacement monitoring system according to claim 2, characterized in that: The SAW sensor also includes a wireless reading and writing unit, and the wireless reading and writing unit includes a radio frequency transmission module, a SAW signal demodulation module, and a data processing MCU.

5. A self - calibration method for a zero - carbon - emission road - bridge surface acoustic wave wireless displacement monitoring system, which is applied to a zero - carbon - emission road - bridge surface acoustic wave wireless displacement monitoring system according to any one of claims 1 - 4, characterized in that, Including the following steps: Step 1, data acquisition, respectively acquire ambient temperature data and displacement data through a reference SAW resonator and a main SAW sensor; Step 2, differential processing, use a differential processor to process the acquired data to eliminate common-mode temperature interference; Step 3, temperature compensation, perform temperature compensation on the processed data based on the temperature-displacement coupling model; Step 4, algorithm compensation, when the signal quality of the reference unit is lower than the threshold, switch to the pure algorithm compensation mode; Step 5, output the calibration result, and output the compensated displacement value.

6. The self-calibration method of a zero-carbon-emission road and bridge surface acoustic wave wireless displacement monitoring system according to claim 5, characterized in that The temperature compensation in the said Step 3 includes: Establish a frequency shift-temperature relationship, , where is the compensated frequency drift value, a0 is the zero-order coefficient, representing the starting value of when the temperature T = 0, a1 is the first-order coefficient, reflecting the proportional relationship of changing linearly with the temperature T, embodying the linear temperature characteristic, and its positive or negative sign and magnitude determine the direction and rate of the linear change. a2 is the second-order coefficient, used to describe the quadratic relationship between and the temperature T, embodying the non-linear temperature characteristic. a3 is the third-order coefficient, characterizing the cubic relationship between and the temperature T, further refining the non-linear characteristic. ε is the process discreteness error, and each coefficient is determined by the three-point temperature chamber calibration during factory production.

7. The self-calibration method of a zero-carbon-emission road and bridge surface acoustic wave wireless displacement monitoring system according to claim 5, characterized in that, The algorithm compensation in the said Step 4 includes: updating the compensation coefficient through a deep learning algorithm using historical data; using the Kalman filtering technique to estimate the current temperature.

8. The self-calibration method of a zero-carbon-emission road and bridge surface acoustic wave wireless displacement monitoring system according to claim 5, characterized in that The self-calibration method also includes: an adaptive trigger logic, which automatically starts the calibration process according to environmental changes or time periods; dynamically adjusting the calibration frequency to optimize energy consumption according to the battery capacity or data stability.

9. The self-calibration method of a zero-carbon emission road and bridge surface acoustic wave wireless displacement monitoring system according to claim 5, characterized in that, The self-calibration method also includes: radio frequency signal quality assessment, achieving millisecond-level switching of the compensation mode through a radio frequency signal quality index; process error self-learning, using a deep belief network to update the compensation coefficient online.

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