A zero-carbon emission road and bridge surface acoustic wave wireless displacement monitoring system and its self-calibration method

Abstract

The present invention provides a zero-carbon emission surface acoustic wave wireless displacement monitoring system for roads and bridges and a self-calibration method thereof, relating to the field of transportation infrastructure health monitoring. The system realizes a battery-free design by integrating environmental energy harvesting with low-power SAW tags, completely eliminating the need for equipment power supply and achieving zero carbon emissions. The system adopts a self-calibration mechanism, and by embedding a reference SAW unit and a temperature compensation algorithm, it corrects environmental drift errors in real time to ensure long-term stability. In addition, the dynamic topology protocol based on LoRaWAN reduces radio frequency excitation power consumption and extends the system life. The present invention is suitable for displacement monitoring of key parts such as bridges and roads, covering the entire life cycle of construction and operation periods, and has the characteristics of high precision, real-time performance, and green energy saving.

Description

Technical Field

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

[0002] Against the backdrop of intensifying global climate change and the push for low-carbon, circular, and green industrial development, the field of infrastructure health monitoring is undergoing a profound green and intelligent transformation. Traditional bridge and road displacement monitoring technologies, such as total stations, fiber optic sensors, and GPS systems, while playing an important role in bridge and road monitoring, suffer from common problems such as complex wiring, high energy consumption, and difficult maintenance. For example, total stations require frequent manual operation and suffer from low measurement efficiency. Fiber optic sensors, while highly accurate, are susceptible to electromagnetic interference and require high wiring costs. GPS systems suffer from unstable signals and insufficient accuracy in complex terrain. These issues not only limit the long-term stable operation of monitoring systems but also contribute to the increasing carbon emissions of their entire lifecycle.

[0003] Surface acoustic wave (SAW) technology, an emerging passive wireless sensing solution, offers an innovative solution for achieving zero-carbon, high-precision, and intelligent road and bridge displacement monitoring. SAW sensors utilize elastic waves propagating on the surface of a piezoelectric substrate for sensing. Their unique advantage lies in their completely passive operation, requiring no external power supply and operating solely through electromagnetic excitation. This fundamentally eliminates the carbon emissions associated with battery replacement and cabling in traditional monitoring systems. Previous studies have shown that monitoring systems using SAW technology can reduce carbon emissions by over 90% over their entire lifecycle compared to traditional systems. Furthermore, this technology offers strong resistance to electromagnetic interference, high measurement accuracy (down to the micron level), and simultaneous multi-parameter monitoring, making it particularly suitable for displacement monitoring of critical bridge components such as expansion joints, bearings, and 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 issue. The complex climatic conditions in which bridges are located (such as temperature fluctuations, rain erosion, and ultraviolet radiation) can affect the performance stability of piezoelectric materials. Experimental data show that within 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 energy attenuation during long-distance transmission also restrict monitoring accuracy. In a typical bridge environment, the reliable reading distance of SAW signals is usually no more than 15 meters. In addition, existing SAW monitoring systems lack an effective self-calibration mechanism, and on-site calibration requires manual intervention, increasing operation and maintenance costs. These issues limit the widespread application of SAW technology in large-scale road and bridge health monitoring.

[0005] To address these challenges, this paper proposes a zero-carbon emission surface acoustic wave (SAW) wireless displacement monitoring system for roads and bridges, along with its self-calibration method. By integrating ambient energy harvesting (e.g., vibration-piezoelectric coupling power supply) with low-power SAW tags, this system achieves a battery-free design, completely eliminating the need for device power supply and achieving zero carbon emissions. Furthermore, the system employs a self-calibration mechanism, using an embedded reference SAW unit and a temperature compensation algorithm to correct for environmental drift errors in real time, ensuring long-term stability. Furthermore, the dynamic topology protocol based on LoRaWAN reduces RF excitation power consumption and extends system life. This system is suitable for displacement monitoring of critical areas such as bridges and roads, covering the entire lifecycle from construction to operation, and features high accuracy, real-time performance, and environmental energy conservation. Summary of the Invention

[0006] (1) Technical problems solved

[0007] In view of the shortcomings of the existing technology, the present invention provides a zero-carbon emission road and bridge surface acoustic wave wireless displacement monitoring system and a self-calibration method thereof, which solves the problems raised by the above background technology.

[0008] (2) Technical solution

[0009] To achieve the above objectives, the present invention is implemented through the following technical solutions: A zero-carbon emission road and bridge surface acoustic wave wireless displacement monitoring system, comprising:

[0010] The sensing layer consists of multiple distributed SAW sensing nodes, each of which integrates a displacement sensor and a temperature compensation unit;

[0011] The transmission layer uses the low-power LoRaWAN protocol to achieve data backhaul, coupled with a solar-vibration hybrid power supply module;

[0012] At the application layer, an adaptive calibration algorithm based on deep learning is deployed;

[0013] The SAW sensor node detects micro-displacement through changes in electromagnetic wave signals and has a passive wireless design without the need for external power supply.

[0014] Preferably, the SAW sensor comprises:

[0015] Piezoelectric substrate, using high coupling coefficient material;

[0016] Interdigital transducers, used to excite and receive surface acoustic waves;

[0017] Reflection grid arrays, used to reflect surface acoustic waves to form delay lines or resonator structures;

[0018] The temperature compensation electrode adopts a dual-channel differential design to suppress common-mode interference. The dual-channel differential measurement design uses two SAW resonators with different frequencies to achieve common-mode interference suppression. Experiments show that the temperature drift error can be reduced by 82%;

[0019] Displacement-sensitive structures, such as cantilever beams or thin-film displacement transmission mechanisms, are used to convert mechanical displacement into frequency changes of surface acoustic waves.

[0020] Integrated antenna for acquiring wireless energy and transmitting signals.

[0021] Preferably, the solar-vibration hybrid energy supply module consists of a flexible solar film, a piezoelectric vibration energy collector and a radio frequency energy collection component. The main energy supply comes from the high-efficiency flexible solar film with a conversion efficiency of 23%; the auxiliary energy supply uses a piezoelectric vibration energy collector, which can generate a maximum power of 5mW when the vehicle passes; the backup solution is radio frequency energy collection to maintain basic functions under extreme weather conditions.

[0022] Preferably, the SAW sensor further includes a wireless read-write unit, which includes a radio frequency transmission module, a SAW signal demodulation module and a data processing MCU.

[0023] A self-calibration method for a zero-carbon emission road bridge surface acoustic wave wireless displacement monitoring system is applied to the above-mentioned zero-carbon emission road bridge surface acoustic wave wireless displacement monitoring system, comprising the following steps:

[0024] Step 1: Data acquisition: collect ambient temperature data and displacement data through the reference SAW resonator and the main SAW sensor respectively;

[0025] Step 2: differential processing, using a differential processor to process the collected data to eliminate common-mode temperature interference;

[0026] Step 3: Temperature compensation: temperature compensation is performed on the processed data based on the temperature-displacement coupling model;

[0027] Step 4: Algorithm compensation: when the reference unit signal quality is lower than the threshold, switch to pure algorithm compensation mode;

[0028] Step 5: Output the calibration results and the compensated displacement values.

[0029] Preferably, the temperature compensation in step 3 includes:

[0030] Establish the frequency shift-temperature relationship, , among which is the frequency drift value after compensation, a0 is the zero-order coefficient, which means when the temperature T = 0, The starting value is a fixed part that does not depend on temperature changes. a1 is the first-order coefficient, reflecting The proportional relationship of linear change with temperature T reflects the linear temperature characteristic. Its positive and negative and magnitude determine the direction and rate of linear change. a2 is the second-order coefficient, which is used to describe The quadratic relationship between the temperature T reflects the nonlinear temperature characteristics and can describe the change rate with temperature. a3 is the third-order coefficient, which represents The cubic relationship between ε and temperature T further refines the nonlinear characteristics and can capture more complex temperature-displacement variation trends. ε is the process discreteness error, and each coefficient is determined by the three-point temperature chamber calibration at the factory.

[0031] Preferably, the algorithm compensation in step 4 includes: using historical data to update the compensation coefficient through a deep learning algorithm; and using Kalman filtering technology to estimate the current temperature.

[0032] Preferably, the calibration method further includes: adaptive triggering logic to automatically start the calibration process according to environmental changes or time periods; and dynamically adjust the calibration frequency to optimize energy consumption according to battery capacity or data stability.

[0033] Preferably, the calibration method further includes: RF signal quality assessment, achieving millisecond-level switching of compensation modes through RF signal quality index; process error self-learning, using a deep belief network to update the compensation coefficient online.

[0034] (3) Beneficial effects

[0035] The present invention provides a zero-carbon emission road and bridge surface acoustic wave wireless displacement monitoring system and its self-calibration method, which has the following beneficial effects:

[0036] Zero-carbon emissions and high-precision monitoring: By integrating ambient energy harvesting technology with low-power SAW tags, this system completely eliminates the need for power supply, achieving true zero-carbon emissions and complying with green infrastructure requirements. Furthermore, the system utilizes a dual-channel differential design and temperature compensation algorithm to significantly minimize the impact of temperature changes on displacement measurement, reducing errors from hundreds of microns to micron levels. This ensures high-precision displacement monitoring data under diverse environmental conditions, meeting the stringent requirements of road and bridge structural health monitoring.

[0037] Self-calibration capability and long-term stability: The self-calibration mechanism of the present invention can correct environmental drift errors in real time, reduce operation and maintenance costs, ensure the reliable operation of the system throughout its life cycle, and provide continuous protection for the safe operation of roads and bridges.

[0038] Dynamic mode switching and intelligent compensation: The system dynamically selects compensation mode based on signal strength, ensuring reliable measurement results in diverse environments. When signal quality is good, compensation is performed using reference cell data. When signal quality is poor or the reference cell fails, an algorithm estimates the temperature and applies compensation. This intelligent compensation mechanism improves the system's adaptability and robustness, enabling it to maintain high performance in complex and changing environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 Schematic diagram of the structure of the SAW device in the present invention;

[0040] Figure 2 Schematic diagram of the interior of the SAW device of the present invention;

[0041] Figure 3 This is a schematic diagram of the principle of the wireless read-write unit in the present invention;

[0042] Figure 4 Schematic diagram of the energy management principle in the present invention;

[0043] Figure 5 Schematic diagram of the principle of the self-calibration module in the present invention.

[0044] 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 shell; 8. Piezoelectric vibration energy collector; 801. Flexible solar film; 802. Transparent cover; 9. Energy management module; 10. Wireless read-write unit; 11. Displacement sensitive structure. DETAILED DESCRIPTION

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0046] Example 1:

[0047] like Figure 1-2 As shown, an embodiment of the present invention provides a zero-carbon emission surface acoustic wave wireless displacement monitoring system for road and bridges, including: a perception layer, a transmission layer, and an application layer. The perception layer is composed of 86 distributed SAW sensor nodes; the transmission layer adopts the LoRaWAN protocol to realize data backhaul, and is equipped with a solar-vibration hybrid energy supply module; the application layer deploys an adaptive calibration algorithm based on deep learning.

[0048] The SAW sensor is made of a protective shell 7 made of high-strength plastic, and a piezoelectric substrate 1, a sensitive area thin film layer 101, an interdigital transducer, a reflective grating array 3, an integrated antenna 5, a temperature compensation electrode 6, an energy management module 9, a displacement sensitive structure and a wireless reader / writer module 10 are arranged inside the protective shell 7. The SAW sensor itself does not contain any active electronic components or power supplies, but obtains energy and generates a response through the radio frequency signal transmitted by the external reader / writer. The electromagnetic wave emitted by the reader / writer is received by the integrated antenna 5 of the sensor and converted into a surface acoustic wave by 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 / writer, completing a sensing process.

[0049] like Figure 5 As shown, the SAW sensor chip integrates a reference unit with the same process as the sensing unit. Both use a coplanar waveguide design, but the reference unit maintains mechanical isolation. When the ambient temperature changes, the sensing unit and the reference unit simultaneously produce frequency shifts. and , through differential operation Eliminates common-mode temperature interference while retaining frequency shifts caused by pure mechanical displacement. The SAW sensor has a reading distance of ≤15m, power consumption in standby mode is <1mW, and power consumption in active mode is 80mW. It supports multi-tag access using the TDMA time division multiplexing protocol.

[0050] The piezoelectric substrate uses 128° YX LiNbO3 and has dimensions of 10×5×0.5 mm³. The IDT consists of a transmitting IDT 2 and a receiving IDT 4. Both IDT 2 and IDT 4 are configured with aluminum electrodes, with a finger width of 3 μm, a spacing of 3 μm, and an operating frequency of 915 MHz. The device uses a dual-channel IDT layout to offset common-mode interference and uses an orthogonal coded reflector to reduce the impact of multipath effects.

[0051] The patterning of IDTs is usually achieved using a photolithography process, which includes the following steps: cleaning and surface treatment of the piezoelectric substrate, spin coating and pre-baking of photoresist, UV exposure through a mask, development and post-baking, metal deposition, and lift-off or etching to form the final pattern.

[0052] Reflection grating array, with gradient spacing design and displacement sensitivity Δf / Δx≈50Hz / nm, is used to reflect surface acoustic waves to form a delay line or resonator structure. The period of the reflection grating is the same as that of the interdigital transducer, and the reflection coefficient depends on the number of bars, metal thickness and degree of acoustic impedance mismatch. In terms of patterning process, the reflection grating and the interdigital transducer are manufactured simultaneously using the same materials and processes. The reflection grating array adopts a "spring-mass block" design, with a displacement transmission efficiency of ≥90%, a dynamic range of ±5mm, and a resolution of 0.01mm.

[0053] Temperature compensation electrode, dual-channel differential design, used to suppress common mode interference;

[0054] 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). The sensitive area thin film layer is connected to the interdigital transducer through a microstrip line, and a polyimide temperature-sensitive film with a thickness of 300nm is spin-coated on the delay path.

[0055] The displacement monitoring process of this system is as follows: When an external physical quantity (such as displacement) changes, it causes changes in the propagation characteristics of surface acoustic waves (SAWs), such as velocity, frequency, and phase. By detecting these changes, displacement can be measured. The SAW sensor's reflector array is designed to detect displacement changes. When the monitored structure (such as a bridge or road) shifts, the spacing of the reflectors changes, thereby altering the SAW reflection characteristics. The SAW sensor integrates a displacement-sensitive structure to convert mechanical displacement into SAW frequency changes. The reflector array uses a gradient spacing design, allowing displacement changes to induce specific frequency changes, with a displacement sensitivity of up to 50 Hz / nm. The wireless reader / writer's RF transmitter module transmits an electromagnetic wave signal, which is received by the SAW sensor's antenna and converted into a SAW by an interdigital transducer (IDT). The SAW propagates on the piezoelectric substrate 1, passes through the reflector array 3, and is reflected back to the IDT, where it is converted back into an electromagnetic wave signal and reflected back to the wireless reader / writer. The wireless reader / writer 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 and, combined with a temperature compensation algorithm and self-calibration mechanism, calculates the actual displacement value. Using the embedded reference SAW cell and temperature compensation algorithm, it corrects frequency drift errors caused by ambient temperature changes in real time. The self-calibration mechanism further improves measurement accuracy and stability through a dual-channel differential measurement design and a dynamic weight allocation algorithm.

[0056] The solar-vibration energy hybrid power supply module includes: a flexible solar film 801, a piezoelectric vibration energy collector 8, a capacitor energy storage module, an energy management module 9, and a radio frequency energy collection component. The conversion efficiency of the flexible solar film 801 is 23%. The surface of the flexible solar film 801 is covered with a transparent cover to protect the flexible solar film 801. The piezoelectric vibration energy collector 8 generates a maximum power of 5mW when a vehicle passes. The resonant frequency of the vibration energy collector 8 is 30Hz, and the output is ≥3mW. The radio frequency energy collection component maintains the basic functions of the device in continuous rainy weather; the rated voltage of the capacitor energy storage module is 5.5V and the rated capacitance is 1F to ensure stable operation of the system.

[0057] Energy management module 9 adopts Figure 4In the management process shown, the flexible solar film 801 supplies energy to the capacitor energy storage module, and the capacitor energy storage module continuously supplies energy to the low-power wake-up circuit. When the vehicle passes through the low-power wake-up circuit, the wireless read-write unit is activated to work. The energy management module 9 uses timestamp synchronization technology to align the RF emission with the energy collection peak time, thereby improving efficiency by 40%.

[0058] like Figure 3 As shown in the figure, the wireless reader / writer unit includes a radio frequency transmitter module, a SAW signal demodulation module, and a data processing MCU. The operating frequency of the radio frequency transmitter module is 902-928MHz; the SAW signal demodulation module adopts a correlation detection algorithm; the data processing MCU adopts the STM32 series. The SAW sensor works through backscatter modulation. The electromagnetic waves emitted by the wireless reader / writer both provide energy and carry information. The power consumption of traditional strain gauges is ≈50mW, while the power consumption of this design is =0mW.

[0059] The self-calibration method comprises:

[0060] Step 1: Data acquisition, collect ambient temperature data and displacement data through the reference SAW resonator and the main SAW sensor respectively.

[0061] Step 2: Differential processing, using a differential processor to process the collected data, eliminate common-mode temperature interference, and integrate a reference unit with the same process as the sensing unit on the SAW sensor chip. Both use a coplanar waveguide design, but the reference unit maintains mechanical isolation. When the ambient temperature changes, the sensing unit and the reference unit will simultaneously produce frequency shifts. and , through differential operation Eliminate common-mode temperature interference and retain the frequency shift caused by pure mechanical displacement.

[0062] Step 3: Temperature compensation. Perform temperature compensation on the processed data based on the temperature-displacement coupling model. The frequency shift-temperature relationship is: .

[0063] Where ε is the process discrete error, and each coefficient is determined by three-point temperature chamber calibration (-20℃ / 25℃ / 60℃) at the factory.

[0064] The temperature compensation algorithm flow is:

[0065] def auto_compensation(f_sense, f_ref, RSSI):

[0066] # Input: sensor unit frequency f_sense, reference unit frequency f_ref, signal strength RSSI

[0067] α = sigmoid((RSSI - 85) / 5) # Confidence calculation

[0068] if α>0.7: # Reference unit valid mode

[0069] Δf_temp = lookup_table(f_ref)

[0070] f_compensated = f_sense - Δf_temp

[0071] else: # Pure algorithm mode

[0072] T_est = kalman_filter(history_f)

[0073] Δf_temp = a0 + a1*T_est + a2*T_est**2 + a3*T_est***3 +ε

[0074] f_compensated = f_sense - Δf_temp

[0075] return smooth(f_compensated, window=5)

[0076] Among them, the input parameters are: 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 signal quality.

[0077] Confidence calculation: The confidence α is calculated using the sigmoid function with the input being (RSSI - 85) / 5. The sigmoid function maps the input to a value between 0 and 1, indicating the degree of confidence in the reference cell data.

[0078] Compensation mode selection: If the confidence level α is greater than 0.7, the reference cell data is considered reliable and the system enters the reference cell valid mode. The temperature frequency shift Δf_temp corresponding to the reference cell frequency f_ref is obtained through the lookup table lookup_table. Δf_temp is subtracted from the sensing cell frequency f_sense to obtain the compensated frequency f_compensated. If the confidence level α is less than or equal to 0.7, the reference cell data is considered unreliable and the system enters the pure algorithm mode. The Kalman filter kalman_filter is used to process the historical frequency data history_f to estimate the current temperature T_est. The temperature-induced frequency shift Δf_temp is calculated using a polynomial model, where a0, a1, and a2 are the model coefficients. The compensated frequency f_compensated is obtained by subtracting Δf_temp from the sensing cell frequency f_sense.

[0079] Output: Smooth the compensated frequency f_compensated (window size is 5) to reduce the influence of noise and return the final compensated frequency.

[0080] 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, it automatically switches to the pure algorithm compensation mode and uses historical data through a deep learning algorithm to update the compensation coefficient. Kalman filtering technology is used to estimate the current temperature.

[0081] Step 5: Output the calibration results and the compensated displacement value.

[0082] Adaptive trigger logic automatically initiates the calibration process based on environmental changes or time periods; dynamically adjusts the calibration frequency to optimize energy consumption based on battery capacity or data stability.

[0083] Radio frequency signal quality assessment: millisecond-level switching of compensation mode is achieved through the radio frequency signal quality index (RQI). The switching process meets the following requirements: .

[0084] Process error self-learning uses a deep belief network to update compensation coefficients online. The following MATLAB code example demonstrates how to use a deep learning algorithm (specifically a multi-layer feedforward neural network) to compensate for SAW sensor errors to improve the accuracy of the monitoring system.

[0085] Matlab

[0086] net = newff(T_input, f_error, [10 5]);

[0087] train(net, historical_data);

[0088] T_input: input data, which can be ambient temperature, historical displacement data, etc.

[0089] f_error: Output data, usually the frequency error measured by the SAW sensor.

[0090] newff: A MATLAB function that creates a feedforward neural network. The parameters represent the input data, output data, and network structure (number of hidden layer neurons).

[0091] train: MATLAB function used to train the neural network. historical_data is the historical dataset used for training.

[0092] When |df / dt|>10kHz / s, emergency calibration is started. By default, full parameter calibration is performed 24 hours a day, and the calibration frequency is dynamically adjusted according to the battery capacity.

[0093] 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, we conducted the following experimental tests:

[0094] Temperature compensation effect test:

[0095] Under different temperature change conditions, the displacement measurement errors without temperature compensation and with the self-calibration method of the present invention are recorded respectively. The specific data are shown in the following table:

[0096] ,

[0097] It can be seen from the data in the table that the self-calibration method of the present invention can significantly reduce the impact of temperature changes on displacement measurement, reducing the error from hundreds of microns to micron level.

[0098] 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 road and bridge structure health monitoring.

[0099] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A zero-carbon emission surface acoustic wave wireless displacement monitoring system for road and bridges, characterized by: include: The sensing layer consists of multiple distributed SAW sensing nodes, each of which integrates a displacement sensor and a temperature compensation unit; The transmission layer uses the low-power LoRaWAN protocol to achieve data backhaul, coupled with a solar-vibration hybrid power supply module; At the application layer, an adaptive calibration algorithm based on deep learning is deployed; The SAW sensor node detects micro-displacement through changes in electromagnetic wave signals and has a passive wireless design without the need for external power supply.

2. The zero-carbon emission surface acoustic wave wireless displacement monitoring system for road and bridges according to claim 1 is characterized by: Each of the SAW sensing nodes includes a SAW sensor, and the SAW sensor includes: Piezoelectric substrate, using high coupling coefficient material; Interdigital transducers, used to excite and receive surface acoustic waves; Reflection grid arrays, used to reflect surface acoustic waves to form delay lines or resonator structures; Temperature compensation electrode, using dual-channel differential design to suppress common-mode interference; Integrated antenna for acquiring wireless energy and transmitting signals; A displacement-sensitive structure for converting mechanical displacement into frequency changes of surface acoustic waves.

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

4. The zero-carbon emission surface acoustic wave wireless displacement monitoring system for road and bridges according to claim 2 is characterized by: The SAW sensor further includes a wireless read-write unit, which 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, applied to a zero-carbon emission road bridge surface acoustic wave wireless displacement monitoring system according to any one of claims 1 to 4, characterized in that: The following steps are involved: Step 1: Data acquisition: collect ambient temperature data and displacement data through the reference SAW resonator and the main SAW sensor respectively; Step 2: differential processing, using a differential processor to process the collected data to eliminate common-mode temperature interference; Step 3: Temperature compensation: temperature compensation is performed on the processed data based on the temperature-displacement coupling model; Step 4: Algorithm compensation: when the reference unit signal quality is lower than the threshold, switch to pure algorithm compensation mode; Step 5: Output the calibration results and the compensated displacement values.

6. The self-calibration method of a zero-carbon emission road bridge surface acoustic wave wireless displacement monitoring system according to claim 5 is characterized in that: The temperature compensation in step 3 includes: Establish the frequency shift-temperature relationship, ,in is the frequency drift value after compensation, a0 is the zero-order coefficient, which means when the temperature T = 0, The starting value of a1 is the first-order coefficient, reflecting The proportional relationship of linear change with temperature T reflects the linear temperature characteristic. Its positive and negative and magnitude determine the direction and rate of linear change. a2 is the second-order coefficient, which is used to describe The quadratic relationship between the temperature T reflects the nonlinear temperature characteristics. a3 is the third-order coefficient, which represents The cubic relationship between ε and temperature T further refines the nonlinear characteristics. ε is the process discrete error, and each coefficient is determined by the three-point temperature chamber calibration at the factory.

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

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

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

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