A method and system for structural risk early warning of a composite material cable support

Abstract

This invention discloses a structural risk early warning method and system for composite material cable supports. The method includes: acquiring a first response signal deployed on a first type of cable support and acquiring a second response signal deployed on a second type of cable support; converting the first and second response signals into a first stress characterization value and a second stress characterization value, respectively; using the second stress characterization value as a benchmark, calibrating the first stress characterization value online to obtain a calibrated third stress characterization value; when any of the first, second, or third stress characterization values ​​exceeds a preset early warning threshold, retrieving monitoring data from multimodal sensing units on other cable supports within the spatial neighborhood based on the triggering early warning support, and realizing structural health risk early warning based on the monitoring data and the stress characterization value of the triggering early warning support. Therefore, this invention can improve the accuracy of health early warning for composite material supports.

Description

Technical Field

[0001] This invention relates to the field of risk warning, and in particular to a structural risk warning method and system for composite material cable supports. Background Technology

[0002] The environment inside underground cable tunnels is consistently high in temperature and humidity. The composite material (FRP) cable supports laid within these tunnels must not only withstand the sustained static load of the cables' own weight but also cope with the thermal expansion and contraction stress caused by the cables' heating. If the supports suffer structural damage or even breakage due to material aging, overload, or accidental impact, the heavy cables may fall, causing serious accidents such as phase-to-phase short circuits, power outages, or even fires.

[0003] Existing technologies often rely on a single type of sensor for independent judgment, lacking cross-verification between multiple sources of information. When a sensor emits an abnormal signal due to its own failure or environmental disturbance, the system has difficulty distinguishing between a real-world risk and a sensor false alarm. As a result, in practical engineering, the system often faces the accuracy dilemma of "missed reports" or "frequent false alarms," ​​failing to meet the large-scale, high-reliability online early warning requirements for underground tunnels. Summary of the Invention

[0004] This invention provides a structural risk early warning method and system for composite material cable supports, which can improve the accuracy of health early warning for composite material supports.

[0005] The first aspect of this invention provides a structural risk early warning method for composite material cable supports, comprising: Acquire a first response signal output by a strain sensing unit deployed on a first type of cable bracket, and acquire a second response signal output by a multimodal sensing unit deployed on a second type of cable bracket; wherein the second type of cable bracket is spatially adjacent to the first type of cable bracket; The first response signal and the second response signal are converted into a first stress characterization value and a second stress characterization value, respectively. The second stress characterization value is used as a reference to calibrate the first stress characterization value online to obtain a calibrated third stress characterization value. When any one of the first stress characterization value, the second stress characterization value, or the third stress characterization value exceeds a preset warning threshold, the system retrieves monitoring data from multimodal sensing units on other cable supports within the spatial neighborhood based on the trigger warning support, makes a consistency judgment based on the monitoring data and the stress characterization value of the trigger warning support, and implements structural health risk warning based on the judgment result.

[0006] This invention provides complementary raw monitoring data from different accuracy levels and spatial locations for subsequent data fusion by acquiring a first response signal and a second response signal. By using the more accurate second stress characterization value as a benchmark to calibrate the first stress characterization value online, measurement deviations caused by environmental temperature and humidity changes, long-term drift, or material aging in low-cost strain sensing units can be effectively eliminated. This significantly improves the accuracy and reliability of stress monitoring data for ordinary supports, reducing false alarms caused by sensor inaccuracies at the source. Furthermore, through spatial neighborhood retrieval and multi-source data consistency judgment, it can effectively distinguish between genuine structural overload or damage and false anomalies caused by occasional sensor malfunctions, electromagnetic interference, or environmental noise. When multiple neighboring high-precision multimodal sensing data match the trigger warning value, a genuine risk is confirmed. This cross-validation mechanism greatly reduces the false alarm and missed alarm rates, ensuring a high degree of credibility in the final warning result. This comprehensively solves the technical problem in existing technologies where a single sensing method struggles to balance cost and accuracy, and is prone to misjudgment. It truly achieves efficient, reliable, low-cost online monitoring and accurate early warning of the service status of composite cable supports.

[0007] Further, the step of using the second stress characterization value as a benchmark to perform online calibration of the first stress characterization value to obtain a calibrated third stress characterization value includes: Using the second stress characterization value and combined with the preset finite element model, the sensitivity coefficient of the strain sensing unit on the first type of cable bracket is updated online, so as to calibrate the conversion parameter of the first stress characterization value based on the sensitivity coefficient and obtain the first calibration result. The second stress characterization value is used to compensate for the temperature-induced drift component in the first stress characterization value to obtain a second calibration result. Based on the first calibration result and / or the second calibration result, the calibrated third stress characterization value is obtained.

[0008] By calibrating the sensitivity coefficient and temperature compensation online, measurement errors caused by aging and environmental drift of low-cost sensors are eliminated, making the first stress characterization value closer to the actual stress state and significantly improving the accuracy of health warning for composite material stents.

[0009] Furthermore, the step of using the second stress characterization value, combined with a preset finite element model, to update the sensitivity coefficient of the strain sensing unit on the first type of cable bracket online includes: The second stress characterization value is input into the pre-constructed and modified finite element model to calculate the theoretical strain value at the installation position of the strain sensing unit on the first type of cable bracket. Obtain the echo frequency offset and initial echo frequency in the first response signal of the first type of cable bracket at the current moment; Based on the theoretical strain value, the echo frequency offset, the initial echo frequency, and the pre-stored conversion relationship, the current sensitivity coefficient is calculated by inverse solution.

[0010] This method utilizes high-precision data from the second type of cable support and inverse finite element model to calculate the current sensitivity coefficient of the first type of cable support, enabling real-time calibration. This ensures accurate strain measurement during long-term service, effectively avoids false alarms or missed alarms caused by sensitivity drift, and improves the reliability of early warning.

[0011] Further, the step of using the second stress characterization value to compensate for the temperature-induced drift component in the first stress characterization value to obtain a second calibration result includes: Extract the temperature change without strain from the multimodal sensing unit on the second type of cable support in spatial proximity; The frequency drift is calculated based on the temperature change and the pre-calibrated temperature drift coefficient. The second calibration result after temperature compensation is obtained by subtracting the frequency drift from the total frequency change of the first response signal.

[0012] By using temperature data from nearby critical supports to accurately compensate for the temperature drift component of ordinary supports, the effects of strain and temperature are effectively separated, so that stress characterization values ​​are not affected by ambient temperature fluctuations, significantly reducing the false alarm rate and improving the accuracy of early warning.

[0013] Further, acquiring the first response signal output by the strain sensing unit deployed on the first type of cable bracket includes: A query signal is transmitted to the tag of the strain sensing unit on the surface of the first type of cable bracket by a remote reader, and the first response signal returned by the tag in response to the query signal is received; wherein, the tag is composed of a conductive nanocomposite material coating sprayed on the surface of the first type of cable bracket and an RFID tag chip electrically connected to the conductive nanocomposite material coating.

[0014] This strain sensing unit, which combines a passive wireless RFID tag with a conductive coating, requires no batteries or wiring, is low-cost, and easy to deploy on a large scale. Combined with subsequent calibration, it ensures measurement accuracy, provides a foundation for low-cost, large-area monitoring, and indirectly improves the overall accuracy of early warning.

[0015] Furthermore, acquiring the second response signal output by the multimodal sensing unit deployed on the second type of cable bracket includes: The reflected wavelength signal output by the fiber Bragg grating strain sensor array embedded inside the second type of cable bracket is acquired by an optical fiber demodulator. The resistance signal output by the self-sensing conductive fiber bundle embedded inside the second type of cable support is collected by a resistance measuring device. The inductance signal output by the magnetostrictive alloy wire embedded inside the second type of cable bracket is collected by an induction coil wound on the outer surface of the second type of cable bracket and a connected inductance measuring device. The reflected wavelength signal, the resistance signal, and the inductance signal are used together as the second response signal.

[0016] This approach integrates three sensing principles—optical fiber, conductive fiber, and magnetostriction—which complement and verify each other, obtaining high-precision, multi-dimensional stress data as a reference signal. This provides a reliable basis for calibrating ordinary stents, ensuring the accuracy of health warnings for composite material stents from the source.

[0017] Further, the step of converting the first response signal and the second response signal into a first stress characterization value and a second stress characterization value, respectively, includes: Based on the pre-calibrated initial sensitivity coefficient of the strain sensing unit, the first response signal is converted into a first strain value on the surface of the first type of cable bracket, and the first strain value is converted into the first stress characterization value based on the elastic modulus of the first type of cable bracket. The reflected wavelength signal in the second response signal is converted into a second strain value according to the first conversion relationship of the fiber Bragg grating; the resistance signal in the second response signal is converted into a third strain value according to the second conversion relationship of the self-sensing conductive fiber; and the inductance signal in the second response signal is converted into a fourth strain value according to the third conversion relationship of the magnetostrictive alloy wire. The second strain value, the third strain value and the fourth strain value are then fused to obtain the second stress characterization value.

[0018] This method unifies and fuses different physical signals such as wavelength, resistance, and inductance into stress values, eliminating random errors from single sensors and obtaining more robust second stress characterization values. This provides a high-confidence benchmark for subsequent calibration and early warning, improving the accuracy of early warnings.

[0019] Furthermore, the step of retrieving monitoring data from multimodal sensing units on other cable supports within the spatial neighborhood of the trigger-warning support, making a consistency judgment between the monitoring data and the stress characterization value of the trigger-warning support, and realizing structural health risk warning based on the judgment result includes: Based on the spatial location tag of the trigger warning bracket, the monitoring data of the multi-modal sensing unit on at least one other cable bracket that is in the same time section and spatially adjacent to the trigger warning bracket, as well as the monitoring data of other types of sensors associated with the trigger warning bracket itself, are automatically retrieved from the real-time database to obtain multi-source monitoring data. The retrieved multi-source monitoring data are compared with the current stress characterization value of the trigger warning bracket to determine whether all data show a consistent abnormal change trend. If the multi-source monitoring data all show a consistent abnormal change trend, it is determined that the triggering early warning stent has a structural health risk and a structural early warning is issued.

[0020] When a support triggers an alarm, it automatically retrieves nearby multimodal data and cross-validates it. Only when the multi-source data are consistent is it determined to be a true anomaly, effectively distinguishing between structural faults and sensor false alarms, greatly improving the accuracy and reliability of the alarm, and avoiding ineffective maintenance.

[0021] Furthermore, the present invention also includes: If only the sensor data used to trigger the warning is abnormal, while other retrieved monitoring data are normal, it is determined to be a sensor malfunction or environmental interference, and a sensor maintenance prompt will be issued.

[0022] Another embodiment of the present invention provides a structural risk early warning system for composite material cable supports, comprising: The acquisition module is used to acquire a first response signal output by a strain sensing unit deployed on a first type of cable bracket, and to acquire a second response signal output by a multimodal sensing unit deployed on a second type of cable bracket; wherein the second type of cable bracket is spatially adjacent to the first type of cable bracket. The conversion module is used to convert the first response signal and the second response signal into a first stress characterization value and a second stress characterization value, respectively, and use the second stress characterization value as a reference to perform online calibration on the first stress characterization value to obtain a calibrated third stress characterization value. The early warning module is used to retrieve monitoring data from multimodal sensing units on other cable supports in the spatial neighborhood based on the triggering early warning bracket when any one of the first stress characterization value, the second stress characterization value, or the third stress characterization value exceeds a preset early warning threshold. It then makes a consistency judgment based on the monitoring data and the stress characterization value of the triggering early warning bracket, and realizes structural health risk early warning based on the judgment result. Attached Figure Description

[0023] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0024] Figure 1 This is a flowchart illustrating an embodiment of the structural risk warning method for composite material cable supports provided in this application; Figure 2 This is a flowchart illustrating one embodiment of steps S202 to S202; Figure 3 This is a flowchart illustrating one embodiment of steps S301 to S303 provided in this application; Figure 4 This is a flowchart illustrating one embodiment of steps S401 to S403 provided in this application; Figure 5 This is a flowchart illustrating another embodiment of the structural risk warning method for composite material cable supports provided in this application; Figure 6 This is a schematic diagram of the multimodal sensing element embedded in the key support structure of the present invention; Figure 7 This is a schematic diagram showing the coating of a sensing layer on the surface of a common support frame of the present invention and the attachment of an RFID strain tag. Figure 8 This is a schematic diagram of the data acquisition and fusion process of the monitoring system of the present invention; Figure 9 This is a flowchart illustrating one embodiment of the structural risk warning system for composite material cable supports provided in this application. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0027] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0028] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0029] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0030] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0031] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0032] See Figure 1 To improve the accuracy of health warnings for composite material cable supports, an embodiment of the present invention provides a structural risk warning method for composite material cable supports, including steps S101 to S103: Step S101: Obtain the first response signal output by the strain sensing unit deployed on the first type of cable bracket, and obtain the second response signal output by the multimodal sensing unit deployed on the second type of cable bracket; wherein the second type of cable bracket is spatially adjacent to the first type of cable bracket. In some embodiments, acquiring the first response signal output by the strain sensing unit deployed on the first type of cable support includes: transmitting a query signal to a tag of the strain sensing unit on the surface of the first type of cable support via a remote reader, and receiving the first response signal returned by the tag in response to the query signal; wherein the tag consists of a conductive nanocomposite material coating sprayed onto the surface of the first type of cable support and an RFID tag chip electrically connected to the conductive nanocomposite material coating. Specifically, firstly, a region with high stress is selected on the surface of the ordinary support (i.e., the first type of cable support), typically near the lower edge where the support connects to the tunnel wall. After cleaning and polishing the surface of this region, a prepared conductive nanocomposite material coating is evenly sprayed onto it using a spraying device to form a sensing coating. After the coating cures, small copper foil electrodes are attached to both ends for signal extraction. A flexible, passive RFID strain sensor tag is then affixed to the center of the coating strip. The tag's antenna, printed with conductive ink on a polyimide film, is designed as an inverted U-shape encircling the coating to enhance sensitive coupling to changes in coating resistance. Inside the tag is a miniature RFID chip, with its pins connected to copper foil electrodes at both ends of the antenna and the coating. The chip stores a unique tag identification code and integrates a measurement circuit to sense changes in coating resistance. After the entire tag is affixed and fixed, an insulating protective varnish is sprayed onto its surface for encapsulation and protection. When the strain state of the support needs to be read, a remote RFID reader deployed in the tunnel (which can be fixed to the tunnel wall or mounted on an inspection robot) transmits a query signal to the tag. Upon receiving the query signal, the tag antenna is activated. Its internal chip detects the current resistance value of the conductive coating through the measurement circuit and modulates this resistance change onto the RF response characteristics of the tag antenna, then returns a corresponding echo signal (i.e., the first response signal).

[0033] It should be noted that conductive nanocomposite coatings can be made by mixing multi-walled carbon nanotubes with epoxy resin. For example, approximately 5% by mass of multi-walled carbon nanotubes are dispersed in a 95% epoxy resin matrix, and surface functionalization treatment improves dispersibility and adhesion. The thickness of the sprayed coating is controlled at approximately 100 micrometers, the width is several centimeters, and it covers tens of centimeters along the axial length of the support.

[0034] It should be noted that the specific modulation method of the first response signal can be: the change in coating resistance causes a slight shift in the resonant frequency, reflection intensity, or phase of the tag antenna; or the tag chip converts the resistance change into a frequency shift of the echo signal, for example, by using the RC oscillator inside the chip to convert the resistance change into a shift in the output oscillation frequency, and carrying strain information in a frequency-encoded manner.

[0035] This strain sensing unit, which combines a passive wireless RFID tag with a conductive coating, requires no batteries or wiring, is low-cost, and easy to deploy on a large scale. Combined with subsequent calibration, it ensures measurement accuracy, provides a foundation for low-cost, large-area monitoring, and indirectly improves the overall accuracy of early warning.

[0036] In some embodiments, acquiring the second response signal output by the multimodal sensing unit deployed on the second type of cable bracket includes: acquiring the reflected wavelength signal output by the fiber Bragg grating strain sensor array embedded inside the second type of cable bracket using a fiber optic demodulator; acquiring the resistance signal output by the self-sensing conductive fiber bundle embedded inside the second type of cable bracket using a resistance measuring device; acquiring the inductance signal output by the magnetostrictive alloy wire embedded inside the second type of cable bracket using an induction coil wound around the outer surface of the second type of cable bracket and a connected inductance measuring device; and using the reflected wavelength signal, the resistance signal, and the inductance signal together as the second response signal. Specifically, three different types of sensing elements are pre-embedded during the manufacturing process of the second type of cable bracket (i.e., the critical bracket). First, during the composite material layup of the support structure, a fiber Bragg grating sensor chain with a diameter of approximately 250 micrometers is placed between several layers of glass fiber cloth. After the support structure is installed and put into operation, the outgoing optical fibers are connected to a fiber optic demodulator (usually installed in the tunnel monitoring room or a nearby fiber optic junction box). Broadband light emitted by the broadband light source in the demodulator is transmitted through the optical fibers to each grating sensing point. Specific wavelengths of light satisfying the Bragg condition are reflected back. The demodulator polls the reflection center wavelength of each grating sensing point, detects its offset, and outputs the reflected wavelength signal. Second, a bundle of carbon fiber filaments coated with an insulating coating is laid parallel to the fiber Bragg grating as a self-sensing conductive fiber sensing element. The two ends of the carbon fiber bundle are led out to pre-reserved terminals on the support structure surface through thin wires. The wires and the carbon fiber bundle joints are insulated to prevent moisture. After the support structure is installed, these leads are connected to a high-resistivity meter or a wireless resistance measurement module (such as a miniature low-power wireless resistance measurement unit, which can be installed near the support structure). The high-resistivity meter applies excitation and measures the resistance value at both ends of the carbon fiber bundle. When the support is deformed by stress, the carbon fiber bundles are stretched or compressed, and the internal conductive network structure changes, resulting in a corresponding change in resistance. The high-resistivity meter outputs the measured resistance signal. Third, a magnetostrictive alloy wire (such as an iron-gallium alloy wire or an iron-silicon-boron amorphous alloy wire) with a diameter of approximately 0.5 mm is embedded axially at the center of the support cross-section. After embedding, the alloy wire does not require direct cable connection. On the outer surface of the support, near the root, a loop-shaped induction coil is wound with insulated wire and fixed to the support surface. The coil is connected to a multi-channel LCR meter via a coaxial cable. The LCR meter applies a specific frequency excitation signal to the induction coil and measures the coil's equivalent inductance or resonant frequency. When the support is stressed, causing a change in the stress state of the alloy wire, its permeability changes, resulting in a corresponding change in the coil's inductance. The LCR meter outputs the measured inductance signal. The reflected wavelength signal output by the fiber optic demodulator, the resistance signal output by the high-resistivity meter or wireless resistance measurement module, and the inductance signal output by the LCR meter together serve as the second response signal for this second type of cable support.

[0037] It should be noted that the fiber Bragg grating sensor chain is laid along the axis of the support, and multiple grating sensing points are buried at intervals at stress concentration locations such as the root and the middle of the cantilever. For example, a sensing point is set every 20 centimeters. One end of the optical fiber is led out from the root of the support as a signal interface.

[0038] It should be noted that fiber optic grating (FBG) strain sensors are wavelength-modulated sensors. Their working principle involves detecting changes in the center wavelength of reflected light to sense external physical quantities. When the temperature and strain in the grating region change, it causes a change in the grating period and an elastic-optical effect, resulting in a shift in the reflected wavelength. Self-sensing conductive fiber sensors are based on the piezoresistive effect, meaning that when a material is deformed under stress, its internal conductive network structure changes, leading to a change in macroscopic resistance. For continuous carbon fiber bundles, the rate of resistance change has a linear relationship with strain. Magnetostrictive alloy wire sensors are based on the magnetoelastic effect (or inverse magnetostrictive effect / piezomagnetic effect), meaning that when a ferromagnetic material is subjected to external force, its internal stress state changes, causing a change in the material's permeability μ. In these sensors, the alloy wire runs through the main stress area, is slightly shorter than the cantilever length of the support, and is fixed at both ends inside the support and electrically isolated from the support material.

[0039] This approach integrates three sensing principles—optical fiber, conductive fiber, and magnetostriction—which complement and verify each other, obtaining high-precision, multi-dimensional stress data as a reference signal. This provides a reliable basis for calibrating ordinary stents, ensuring the accuracy of health warnings for composite material stents from the source.

[0040] Step S102: Convert the first response signal and the second response signal into a first stress characterization value and a second stress characterization value, respectively. Use the second stress characterization value as a reference to calibrate the first stress characterization value online to obtain a calibrated third stress characterization value. Please refer to Figure 2 The step of converting the first response signal and the second response signal into a first stress characterization value and a second stress characterization value, respectively, includes steps S201 to S202: Step S201: Based on the pre-calibrated initial sensitivity coefficient of the strain sensing unit, the first response signal is converted into a first strain value on the surface of the first type of cable bracket, and the first strain value is converted into the first stress characterization value based on the elastic modulus of the first type of cable bracket. In some embodiments, when the remote reader receives the first response signal returned by the tag (such as echo frequency offset), After that, the system first uses the pre-calibrated initial sensitivity coefficient. and known RFID chip conversion coefficients According to the formula The first strain value in the region where the conductive coating is located was calculated by inverse kinematics. ,in The initial echo frequency under no-strain conditions. The conversion coefficient of the RFID chip (converting resistance change into frequency change, determined by the chip circuit design, and is a known constant). The first strain value is obtained. Then, based on the elastic modulus of the FRP composite material used in the first type of cable support... According to Hooke's Law The first strain value Converted to the first stress characterization value.

[0041] Step S202: The reflected wavelength signal in the second response signal is converted into a second strain value according to the first conversion relationship of the fiber Bragg grating; the resistance signal in the second response signal is converted into a third strain value according to the second conversion relationship of the self-sensing conductive fiber; the inductance signal in the second response signal is converted into a fourth strain value according to the third conversion relationship of the magnetostrictive alloy wire; and the converted second strain value, third strain value and fourth strain value are then fused to obtain the second stress characterization value.

[0042] In some embodiments, firstly, the reflected wavelength signal output by the fiber Bragg grating (FBG) strain sensor array is processed according to the wavelength-strain conversion relationship of the fiber Bragg grating. The specific formula is: ,in This represents the drift of the center wavelength of the FBG. The initial center wavelength of the FBG. The effective elastic-optic coefficient is approximately 0.22 (for silica optical fiber). This represents the strain along the fiber grating axis. The coefficient of thermal expansion of the optical fiber material. The thermo-optic coefficient of the optical fiber material. Given the temperature change, according to Hooke's Law, within the elastic range, the stress σ of the support is proportional to the strain ε. Where E is the elastic modulus of the FRP composite scaffold. Therefore, the temperature term is eliminated by an independent temperature compensation grating in the system. Then, the pure strain can be obtained, and the second strain value can be calculated. Secondly, for the resistance signal output by the self-sensing conductive fiber bundle, the piezoresistive effect formula is applied. Perform the conversion, where This represents the initial resistance value of the conductive fiber. Let σ be the change in resistance after being subjected to force, k be the sensitivity coefficient of the conductive fiber (also known as the strain gauge factor, which needs to be calibrated experimentally), and ε be the average strain of the conductive fiber along the axial direction. Similarly, using Hooke's law σ = E * ε, the measured strain is converted into a third strain value. Third, for the inductance signal of the magnetostrictive alloy wire measured by an external induction coil, based on the magnetoelastic effect formula... ,in This is the initial inductance value under stress-free conditions. This is the change in inductance. Let σ be the magnetostrictive stress sensitivity coefficient of the material (experimentally calibrated), and σ be the stress along the direction of the alloy wire. First, calculate the stress value from the change in inductance, then apply Hooke's law. The stress values ​​are then converted into a fourth strain value. Finally, the stress values ​​converted from different physical signals such as wavelength, resistance, and inductance are fused to eliminate random errors from a single sensor and obtain a more robust second stress characterization value.

[0043] Please refer to Figure 3 The step of using the second stress characterization value as a benchmark to calibrate the first stress characterization value online to obtain the calibrated third stress characterization value includes steps S301 to S303: Step S301: Using the second stress characterization value and combined with the preset finite element model, the sensitivity coefficient of the strain sensing unit on the first type of cable bracket is updated online, so as to calibrate the conversion parameter of the first stress characterization value based on the sensitivity coefficient and obtain the first calibration result. In some embodiments, the step of using the second stress characterization value and combining it with a preset finite element model to update the sensitivity coefficient of the strain sensing unit on the first type of cable bracket online includes: inputting the second stress characterization value into a pre-constructed and modified finite element model to calculate the theoretical strain value at the installation position of the strain sensing unit on the first type of cable bracket; obtaining the echo frequency offset and initial echo frequency in the first response signal of the first type of cable bracket at the current moment; and calculating the current sensitivity coefficient based on the theoretical strain value, the echo frequency offset, the initial echo frequency, and a pre-stored conversion relationship. Specifically, the second stress characterization value at the current moment is obtained from a neighboring second type of cable bracket (critical bracket), and this second stress characterization value is input into a pre-constructed and modified finite element model. After receiving the second stress characterization value as input, the model performs finite element calculations to calculate the theoretical strain value at the installation position of the strain sensing unit on a common bracket (first type of cable bracket) spatially adjacent to the critical bracket, denoted as... Subsequently, the response relationship of the ordinary bracket RFID strain sensing unit is as follows: , where β is the conversion coefficient of the RFID chip (determined by the chip circuit design and is a known constant). Let ε be the sensitivity coefficient of the conductive coating (i.e., the amount that needs to be updated online), and ε be the strain in the region where the coating is located. Then, the theoretical strain value obtained earlier through the finite element model is used... The measured echo frequency offset will be used as the actual strain value ε. and initial frequency By substituting into the formula, the true sensitivity coefficient of the conductive coating at the current moment can be calculated: =( ) / (β* ).

[0044] It should be noted that the finite element model construction process is as follows: First, based on the precise geometric dimensions, material properties (such as anisotropic elastic modulus and Poisson's ratio) and boundary conditions (such as the end fixed to the tunnel wall) of the FRP cable support, a high-precision initial model is established in finite element software (such as ANSYS or Abaqus). This model can simulate the stress and strain distribution field under various theoretical loads (cable self-weight, thermal expansion force). Next, in the finite element model, nodes or elements that perfectly correspond to the installation positions of the physical sensors (FBG, carbon fiber bundles, magnetostrictive wires, coatings) are identified. Depending on the sensor type, the physical quantities that need to be output from the model, such as axial strain along the fiber direction, are defined. Average strain along the carbon fiber bundle path Stress on the magnetostrictive wire path Theoretically, the output of the finite element model (e.g., strain) should correspond to the sensor's measured values ​​(e.g., wavelength drift of the FBG). However, due to uncertainties in material parameters and simplifying assumptions, the initial model calculations may deviate from the measured values. Therefore, a transformation function f needs to be constructed to correlate the model output with the sensor readings. This can be achieved through model correction techniques, i.e., by adjusting uncertain input parameters in the finite element model (e.g., the stiffness of boundary conditions, the true elastic modulus of the composite material) to minimize the error between the model's output at the sensor location and the processed sensor measurement data. The formula for the finite element model is: ,in, These are the actual measured data of the sensor at time t (such as wavelength, resistance, inductance, and frequency). These are the physical quantities (such as strain ε) calculated by the finite element model at the corresponding sensor location under load P at time t; K and These are the conversion coefficients and offsets obtained through calibration and model correction. The corrected finite element model then calculates the full-field stress and strain distribution, which becomes a precise and reliable "digital reference." New measurement data from any sensor can be converted using the aforementioned calibrated conversion function. (Right now The stress and deformation of the entire model are mapped back to this unified finite element model framework in real time, thus obtaining the stress and deformation state of the entire model at this moment. In this way, whether it is the point strain from the high-precision FBG or the overall average strain from the carbon fiber bundle, it can ultimately be presented and compared on the model in the form of a unified stress or deformation contour map.

[0045] This method utilizes high-precision data from the second type of cable support and inverse finite element model to calculate the current sensitivity coefficient of the first type of cable support, enabling real-time calibration. This ensures accurate strain measurement during long-term service, effectively avoids false alarms or missed alarms caused by sensitivity drift, and improves the reliability of early warning.

[0046] In some embodiments, the conversion parameter of the first stress characterization value is calibrated based on the sensitivity coefficient to obtain a first calibration result. Specifically, the system uses this newly calculated... Replace the previously stored initial sensitivity coefficient or the last calibration value with the value. This allows for the online updating of the sensitivity coefficient of the strain sensing unit.

[0047] Step S302: Using the second stress characterization value, compensate for the temperature-induced drift component in the first stress characterization value to obtain a second calibration result; In some embodiments, step S302 includes: extracting the temperature change without strain from the multimodal sensing unit on the second type of cable support spatially adjacent to it; calculating the frequency drift based on the temperature change and a pre-calibrated temperature drift coefficient; and subtracting the frequency drift from the total frequency change of the first response signal to obtain the second calibration result after temperature compensation. Specifically, the temperature change is extracted from the multimodal sensing unit on a key support (second type of cable support) spatially adjacent to the ordinary support (first type of cable support). The key support is internally equipped with a fiber Bragg grating strain sensor array, which includes a temperature-compensated grating specifically designed to be unaffected by strain (e.g., placing one grating in a free state, sensing only temperature). The change in the reflected wavelength output by this temperature-compensated grating is entirely caused by temperature, and the temperature change of the current environment relative to the initial state can be calculated using the fiber Bragg grating temperature sensing formula. Since the critical support and the ordinary support are located in the same tunnel environment and are spatially adjacent, it can be assumed that their ambient temperatures are the same. Therefore, this... This represents the actual temperature change at the location of a standard support without strain. Next, the system calculates the frequency shift caused by the temperature change based on a pre-calibrated temperature drift coefficient γ (unit: Hz / ℃). The calculation formula is as follows: Then, the system obtains the total frequency change in the first response signal of a standard stent at the current moment. The total frequency change is the result of the combined effect of the resistance change caused by the deformation of the conductive coating (useful signal) and the resistance change caused by the change in ambient temperature (interference signal). To eliminate temperature interference, the system subtracts the frequency drift caused by temperature, calculated above, from the total frequency change. .this This is the pure frequency offset caused solely by the deformation of the support under stress, which is the second calibration result after temperature compensation.

[0048] It should be noted that the temperature drift coefficient γ was obtained through laboratory calibration during the installation of the RFID strain sensing unit on the ordinary bracket. The specific method is to place the bracket in a temperature-controlled chamber, change the temperature under no external strain conditions, measure the change in the RFID tag echo frequency, and thus fit the rate of change of frequency with temperature γ.

[0049] By using temperature data from nearby critical supports to accurately compensate for the temperature drift component of ordinary supports, the effects of strain and temperature are effectively separated, so that stress characterization values ​​are not affected by ambient temperature fluctuations, significantly reducing the false alarm rate and improving the accuracy of early warning.

[0050] Step S303: Based on the first calibration result and / or the second calibration result, obtain the calibrated third stress characterization value.

[0051] In some embodiments, if only sensitivity coefficient calibration (first calibration result) has been performed, the updated sensitivity coefficient is used. As a conversion parameter, the total frequency change in the first response signal without temperature compensation is also used. According to the conversion formula Calculate the strain and multiply it by the elastic modulus E to obtain the third stress characterization value. If only temperature compensation calibration (second calibration result) has been completed, use the initial sensitivity coefficient. and the compensated frequency offset ,according to Calculate the strain and multiply it by E to obtain the third stress characterization value. If both calibrations have been completed (the system typically performs both simultaneously for optimal accuracy), then update the sensitivity coefficient from the first calibration result. Compensated frequency offset in the second calibration result Combining these, and substituting them into the unified conversion formula: ε = ( ) / ( The calculated strain value is then multiplied by the elastic modulus E to obtain the calibrated third stress characterization value.

[0052] By calibrating the sensitivity coefficient and temperature compensation online, measurement errors caused by aging and environmental drift of low-cost sensors are eliminated, making the first stress characterization value closer to the actual stress state and significantly improving the accuracy of health warning for composite material stents.

[0053] Step S103: When any one of the first stress characterization value, the second stress characterization value, or the third stress characterization value exceeds a preset warning threshold, the monitoring data of multimodal sensing units on other cable supports in the spatial neighborhood are retrieved based on the trigger warning support, and a consistency judgment is made between the monitoring data and the stress characterization value of the trigger warning support, and a structural health risk warning is realized based on the judgment result.

[0054] In some embodiments, when any one of the first stress characterization value, the second stress characterization value, or the third stress characterization value exceeds a preset warning threshold, the following steps are taken: For each type of stress characterization value, the system internally stores a corresponding warning threshold. Within each sampling period, the system compares the first stress characterization value, the second stress characterization value, and the third stress characterization value calculated at the current moment with their respective preset thresholds. The comparison employs real-time judgment logic: once any stress characterization value is found to continuously exceed the corresponding threshold (e.g., exceeding it in three consecutive samples to eliminate instantaneous noise interference), the system determines that the value "exceeds the preset warning threshold." Specifically for ordinary stents, the calibrated third stress characterization value is typically used as the primary judgment criterion, but the uncalibrated first stress characterization value is also monitored as an auxiliary reference; for critical stents, the second stress characterization value is directly used for threshold comparison. When the stress monitoring value of any ordinary stent exceeds 80% of its threshold three times consecutively, subsequent retrieval is automatically triggered.

[0055] It should be noted that the warning threshold is set comprehensively based on the design load-bearing capacity of the support, the fatigue limit of the material, and relevant power engineering specifications. For example, 60% of the yield strength of the support material or 80% of the long-term creep limit can be set as the warning threshold.

[0056] Please refer to Figure 4 In some embodiments, the step of retrieving monitoring data from multimodal sensing units on other cable supports within the spatial neighborhood of the trigger-warning support, and making a consistency judgment based on the monitoring data and the stress characterization value of the trigger-warning support, and realizing structural health risk warning based on the judgment result, includes steps S401 to S403: Step S401: Based on the spatial location tag of the trigger warning bracket, automatically retrieve the monitoring data of the multimodal sensing unit on at least one other cable bracket that is in the same time section and spatially adjacent to the trigger warning bracket, as well as the monitoring data of other types of sensors associated with the trigger warning bracket itself, in the real-time database to obtain multi-source monitoring data. In some embodiments, the spatial location tag of the triggering early warning bracket (e.g., ordinary bracket B-12) is first acquired. Based on this tag, the system automatically performs the following searches in the real-time database: First, it searches for monitoring data of multimodal sensing units on other cable brackets that are in the same time segment as the triggering early warning bracket (e.g., within ±1 second before and after the triggering time) and spatially adjacent. Spatially adjacent can be defined as other sensors on the same bracket (if the bracket itself is a critical bracket, then data on its internal fiber optic gratings, conductive fibers, and magnetostrictive wires are searched), or several upstream and downstream adjacent brackets (e.g., brackets A-12 and C-12). Second, it searches for monitoring data of other types of sensors associated with the triggering early warning bracket itself. For example, if the bracket triggering the early warning is an ordinary bracket that only has an RFID strain sensing unit, then "other types of sensors" may include an additional environmental temperature and humidity sensor installed on the bracket, or if the bracket is also a critical bracket (i.e., it has both an RFID tag and a multimodal sensor installed), then data on its internal fiber optic gratings, conductive fibers, and magnetostrictive wires are searched. After the search is completed, the system aggregates all this data to form a multi-source monitoring dataset for use in the next step of consistency judgment.

[0057] It should be noted that all sensor data is stored in a real-time database, and each data point is accompanied by a precise timestamp and spatial location tag. The spatial location tags are identified according to the numbering rules of the supports within the tunnel, such as "Tunnel No. 3, Tower No. 5, Phase B Support, RFID-1" or "Critical Support A-12".

[0058] Step S402: The retrieved multi-source monitoring data is compared with the current stress characterization value of the trigger warning bracket to determine whether all data show a consistent abnormal change trend. In some embodiments, the retrieved multi-source monitoring data are extracted one by one and compared with the stress characterization value of the triggering early warning support at the current moment (i.e., the value that triggers the early warning, such as the first stress characterization value, the second stress characterization value, or the third stress characterization value). The comparison method is not simply numerical equality, but judging whether the trend of change is consistent. Specifically, the system checks whether each retrieved monitoring data has also shown a significant abnormal increase or decrease on its own historical baseline. For example, if the third stress characterization value of the triggering early warning support exceeds the early warning threshold and shows a continuous upward trend, the system checks whether the fiber grating strain data on adjacent key supports also shows an increase in strain beyond the normal range in the same time period, checks whether the inductance data of the magnetostrictive alloy wire has undergone corresponding stress changes, and checks whether the theoretical strain calculated by the finite element model based on the stress of these adjacent supports also matches the actual trend. If all retrieved multi-source monitoring data show the same abnormal change direction as the stress characterization value of the triggering early warning support (e.g., all are increasing) and the change magnitude is mutually corroborated within a reasonable error range, it is judged as "consistent"; if only some data are abnormal while other data are normal, it is judged as "inconsistent".

[0059] It should be noted that, in order to quantify consistency, data fusion algorithms (such as fusion methods based on Kalman filtering or DS evidence theory) can be used to make a comprehensive judgment on consistency.

[0060] Step S403: If the multi-source monitoring data all show a consistent abnormal change trend, it is determined that the trigger warning support has a structural health risk and a structural warning is issued.

[0061] In some embodiments, when "all multi-source monitoring data show a consistent abnormal trend," the system confirms that the triggering support does indeed pose a real structural health risk, rather than a false alarm caused by sensor failure or environmental interference. At this point, a clear warning message is generated, including the triggering support's number, spatial location (tunnel section, tower number, support phase), current stress level, over-limit amplitude, and a summary of other sensor data used to support the consistency assessment. The system then reports the warning message to the power dispatch control center or the mobile terminal of maintenance personnel via a network (preferably using fiber optic Ethernet within the tunnel, but also supplemented by wireless communication). Simultaneously, the system highlights the support's location and risk level (e.g., marked in red) on the local monitoring platform interface. Upon receiving the warning, maintenance personnel can immediately arrange on-site inspection or replacement of the support to prevent major accidents such as cable drops or phase-to-phase short circuits caused by support failure.

[0062] When a support triggers an alarm, it automatically retrieves nearby multimodal data and cross-validates it. Only when the multi-source data are consistent is it determined to be a true anomaly, effectively distinguishing between structural faults and sensor false alarms, greatly improving the accuracy and reliability of the alarm, and avoiding ineffective maintenance.

[0063] In some embodiments, the present invention further includes: if only the sensor data on which the warning is based is abnormal, while other retrieved monitoring data are normal, then it is determined to be a sensor malfunction or environmental interference, and a sensor maintenance prompt is issued. Specifically, if it is determined that the multi-source data is inconsistent (for example, only the RFID tag data is abnormal while the adjacent critical support data is normal), the system determines that it may be a malfunction or interference with the indicator sensor itself. In this case, no structural warning will be issued, but a "sensor malfunction" prompt (yellow warning) will be generated, suggesting that maintenance personnel prioritize the maintenance of the sensor, thereby effectively preventing false alarms.

[0064] This invention provides complementary raw monitoring data from different accuracy levels and spatial locations for subsequent data fusion by acquiring a first response signal and a second response signal. By using the more accurate second stress characterization value as a benchmark to calibrate the first stress characterization value online, measurement deviations caused by environmental temperature and humidity changes, long-term drift, or material aging in low-cost strain sensing units can be effectively eliminated. This significantly improves the accuracy and reliability of stress monitoring data for ordinary supports, reducing false alarms caused by sensor inaccuracies at the source. Furthermore, through spatial neighborhood retrieval and multi-source data consistency judgment, it can effectively distinguish between genuine structural overload or damage and false anomalies caused by occasional sensor malfunctions, electromagnetic interference, or environmental noise. When multiple neighboring high-precision multimodal sensing data match the trigger warning value, a genuine risk is confirmed. This cross-validation mechanism greatly reduces the false alarm and missed alarm rates, ensuring a high degree of credibility in the final warning result. This comprehensively solves the technical problem in existing technologies where a single sensing method struggles to balance cost and accuracy, and is prone to misjudgment. It truly achieves efficient, reliable, low-cost online monitoring and accurate early warning of the service status of composite cable supports.

[0065] The FRP cable support monitoring system provided by this invention innovatively integrates multiple sensing mechanisms: it leverages the high precision and reliability of FBG fiber optic sensing and magnetostrictive sensing, while also taking into account the low cost and large-scale coverage of conductive coating / wireless tag sensing, enabling hierarchical monitoring of a large number of FRP supports in underground cable tunnels. Compared with existing solutions that only use a single type of sensor, this system has the following significant advantages: (1) Online real-time monitoring: All sensors can work online for a long time. Combined with fiber optic network transmission and remote data platform, unattended real-time monitoring can be achieved, and abnormal stress conditions can be detected at the first time, providing support for tunnel operation. This ensures the safe operation of the power supply system.

[0066] (2) Non-destructive integration: The sensing elements are integrated into the bracket structure by pre-embedding or surface coating, without drilling holes or slots on the bracket, thus avoiding weakening the structural strength; and all sensing components are reliably insulated or protected, without affecting the electrical insulation strength and durability of the bracket.

[0067] (3) Balancing accuracy and coverage: High-precision multi-sensor arrays are used for key supports to provide accurate and detailed strain data as a benchmark, while sensor tags are used for general supports to achieve widespread distributed monitoring, successfully combining macro-network monitoring with micro-level fine monitoring. While ensuring monitoring quality, the cost per point is controlled, which can support large-scale deployment in long-distance tunnels.

[0068] (4) Excellent scalability and intelligence: The system architecture is modularly designed, allowing for flexible addition or reduction of the number of key sensor supports or adjustment of sensor types according to actual needs; the data fusion platform supports the access of other environmental monitoring data (such as temperature, humidity, water level, fire alarm, etc. in the tunnel), enabling comprehensive analysis of the stress state of the supports and environmental factors. Combining IoT and big data technologies, this system can be connected to the intelligent operation and maintenance management platform for power tunnels, enabling trend prediction of the health status of supports and support for maintenance decisions, thereby improving the intelligent operation and maintenance level of underground power transmission tunnels.

[0069] In summary, the non-destructive online monitoring system for FRP supports in underground cable tunnels provided by this invention is highly innovative and practical, and can significantly improve the safety monitoring capabilities of power supply line supports in tunnels, with broad prospects for engineering applications.

[0070] For easier understanding, please refer to Figure 5 , Figure 5 This is a flowchart illustrating another embodiment of the structural risk early warning method for composite material cable supports provided in this application. In this method, FRP cable supports are divided into two categories in underground cable tunnels: critical supports (embedded with fiber optic gratings, conductive fiber bundles, and magnetostrictive alloy wires) and ordinary supports (surface coated with a conductive coating and RFID tags). Multimodal sensing signals from critical supports are collected using fiber optic demodulators, high-resistivity meters, and LCR meters, while strain data from ordinary supports is wirelessly read by RFID readers or inspection robots deployed along the tunnel. All front-end collected data is aggregated via fiber optic Ethernet to the central processing unit in the tunnel monitoring room. The central unit performs time synchronization, filtering, noise reduction, and temperature compensation on the multi-source data, and uses a finite element digital twin model to uniformly convert different physical quantities into stress / strain indices. When the stress of any support exceeds a threshold, the system automatically retrieves multimodal data from neighboring supports for cross-validation, and finally uploads the early warning information to the dispatch center or maintenance terminal, achieving hierarchical and reliable online monitoring.

[0071] The implementation of the present invention will be further illustrated below with reference to specific embodiments.

[0072] Example 1: Embedded Multi-Sensor Arrangement on a Key Support This embodiment addresses FRP cable supports (e.g., fiberglass reinforced composite supports used in 220-500kV cable lines) used in underground power transmission tunnels. Multiple sensors are pre-embedded during the manufacturing process to enable focused monitoring of the support's service status. The support is a beam-like structure (which can be a solid rectangular cross-section, an I-beam cross-section, or a hollow box-shaped cross-section) fixed to the tunnel wall at one end and cantilevered at the other. Based on mechanical analysis, sensors are suitable for placement at the root of the support near the connection to the tunnel wall (where the support bears the maximum bending moment) and in the middle of the cantilevered region, which are key locations for stress concentration. The specific implementation steps are as follows: First, during the composite material layup of the support, a fiber Bragg grating (FBG) sensor chain (e.g., approximately 250µm in diameter) is placed between several layers of fiberglass cloth. Figure 6 (As shown by mark ① in the middle). The FBG sensing fiber is laid along the axis of the support, with multiple grating sensing points embedded at intervals at the root and the middle of the cantilever (for example, one sensing point every 20 cm). One end of the fiber extends from the root of the support as a signal interface. Next, a bundle of carbon fiber filaments coated with an insulating coating is laid parallel to the FBG fiber as a self-sensing strain sensing element (e.g., ...). Figure 6 (Marked ② in the middle). The two ends of the carbon fiber bundle are led out through thin wires to pre-drilled terminals on the support surface for measuring its resistance change (both the leads and the carbon fiber bundle joints are insulated to prevent moisture). Then, a magnetostrictive alloy filament (e.g., approximately 0.5 mm in diameter) is axially embedded at the center of the support cross-section. Figure 6 (Marked ③ in the middle) This alloy wire runs through the main stress area, is slightly shorter than the cantilever length of the support, and is fixed at both ends inside the support and electrically isolated from the support material. After the above steps are completed, the prepared FRP support contains three types of sensing media: FBG grating, conductive fiber bundle, and magnetostrictive alloy wire. Before the support is installed and put into operation, its leading sensing interface needs to be connected to a signal reading device: connect the optical fiber to the optical fiber demodulator (installed in the tunnel monitoring room or a nearby optical fiber junction box), connect the lead wire of the carbon fiber sensing bundle to a high resistance meter or wireless resistance measurement module (such as a miniature low-power wireless resistance measurement unit, which can be installed near the support), and the magnetostrictive alloy wire does not need to be directly connected to a cable; its stress change can be read by an induction coil installed on the outer surface of the support (such as... Figure 6(Marked ④ in the middle). The induction coil is made of insulated wire wound into a loop and fixed near the root of the support. It is connected to a multi-channel LCR measuring instrument via a coaxial cable. During the actual operation of the support, the strain response of this key support under various loads will be captured in real time by embedded multi-sensors and transmitted to the ground monitoring system: the FBG grating array provides the strain distribution at different locations of the support; the high-precision fiber optic demodulator can poll the center wavelength shift of each grating sensing point to obtain the strain curve along the length of the support; the carbon fiber bundle sensor provides information on the overall stress level of the support; and the magnetostrictive alloy wire can sense dynamic stress fluctuations and sudden damage information. Through fiber optic and cable transmission, the above three types of sensor signals will be converged to the data fusion unit in the tunnel monitoring room for comprehensive analysis. The multi-sensor data of this key support is not only used for accurate assessment of its own condition, but also serves as a calibration reference for the monitoring data of nearby ordinary supports, improving the accuracy and reliability of the entire tunnel support monitoring network.

[0073] Example 2: Sensing coating and RFID tag unit for ordinary brackets This embodiment addresses the issue of numerous existing FRP cable supports in underground tunnels by employing a spray-applied strain-sensing coating combined with RFID passive tags to achieve low-cost online strain monitoring of the supports (see [link]). Figure 7 The specific implementation steps are as follows: First, select the area with the greatest stress on each bracket to be monitored as the location for the sensor coating. For cantilever brackets, the lower edge near the fixed end to the wall experiences the greatest tensile stress when bearing the vertical load of the cable, making it suitable as the monitoring area. The bracket surface in this area is then polished and cleaned to increase coating adhesion. Then, a spraying device is used to evenly spray the prepared conductive nano-coating onto the selected area, forming a long strip of sensor coating. Figure 7 (Middle gray shaded area). The coating used in this embodiment is a mixture of multi-walled carbon nanotubes (MWCNTs) and epoxy resin, with the carbon nanotube content accounting for approximately 5% (by mass). Surface treatments such as acidification were used to improve its dispersibility and adhesion within the resin matrix. After spraying, the coating thickness is approximately 100 micrometers, the width is approximately 5 centimeters, and it covers approximately 30 centimeters along the length of the support. After the coating cures, a small copper foil electrode is attached to each end as a lead-out terminal (used for experimental calibration of the support's initial strain zero point; no external power supply is required during normal operation). Next, a flexible RFID strain sensing tag is attached to the center of the coating strip. Figure 7(As shown by mark ⑤ in the image). The antenna of this RFID tag is printed with silver paste ink on a polyimide film and designed in an inverted U-shape around the coating to enhance sensitive coupling to changes in coating resistance. A miniature RFID chip is encapsulated within the tag, with its pins connected to both ends of the antenna and the copper foil electrodes of the coating. The chip pre-stores a unique tag identification code and integrates a bridge circuit for sensing changes in coating resistance. After the tag is attached and fixed, an insulating protective varnish is sprayed onto its surface for encapsulation and protection. With these modifications, a passive wireless strain sensor is integrated into the surface of a standard FRP bracket in service. Its working principle is as follows: when the bracket undergoes slight bending deformation under load, the sprayed conductive coating is stretched or compressed along with the bracket, causing a change in the conductive path within the coating, resulting in an increase or decrease in resistance. This resistance change modulates the radio frequency signal reflected by the tag antenna through the measurement circuit inside the RFID tag chip. When an RFID reader installed in the tunnel (e.g., a reader fixed to the tunnel wall with its antenna facing the support, or a reading module carried by a tunnel inspection robot) sends a query signal, the passive RFID tag on the support is activated and responds with an echo. At this time, the intensity, phase, or frequency characteristics of the tag echo signal will subtly change due to differences in coating resistance. The remote reader receives the echo, analyzes these changes, and can then calculate the corresponding strain value of the support. To enhance sensitivity, the RFID tag chip can operate in frequency measurement mode: converting changes in coating resistance into a frequency offset of the tag's return signal, which the reader then precisely measures to decode the strain information. Laboratory calibration results show that the above-mentioned coating + RFID tag sensing unit exhibits a basically linear relationship in sensing the support strain within the range of 0–500 micro-strains, with a measurement resolution of approximately 10 micro-strains, meeting the accuracy requirements for daily strain monitoring of the support. The manufacturing and installation cost of each ordinary support sensing unit is less than 100 yuan, and it requires no on-site power supply, making it ideal for large-scale deployment in tunnels.

[0074] Example 3: Data Acquisition and Fusion Analysis System The various sensing components described in Embodiments 1 and 2 together form a support stress monitoring network covering the entire tunnel line. See also... Figure 8The data acquisition and fusion analysis system of this invention includes the following units: a fiber optic demodulation subsystem, installed in a monitoring room or substation control room at one end of the tunnel, consisting of a broadband light source, a fiber optic spectrometer demodulator, and fiber optic switching switches, used to poll and read the wavelength signals of FBG strain sensors in all key supports. For long-distance tunnels, multi-channel fiber optic demodulation equipment and fiber optic splitting / time-division technology can be used to access dozens or even hundreds of FBG sensing points; an RFID reading subsystem, consisting of several UHF RFID fixed readers and their matching directional antennas, deployed at intervals along the tunnel line (e.g., one set every several hundred meters, with the antenna covering several supports in a certain area), responsible for periodically waking up and reading data from RFID strain sensor tags on nearby ordinary supports. If an intelligent inspection robot is equipped in the tunnel, this... The robot can also integrate an RFID reading module to obtain tag data for each support structure through mobile inspection. The sensing and measurement subsystem includes induction coils installed near each key support structure, connecting cables, and a centrally located multi-channel LCR measuring instrument. This subsystem periodically measures the electrical parameters (such as equivalent inductance and resonant frequency) of the magnetostrictive alloy wire within each key support structure. The system can apply excitation to each induction coil at set intervals and read the response, thereby acquiring stress-related signals for all key supports. The data processing and communication subsystem, located in the tunnel monitoring room or higher-level dispatch center, includes an industrial computer, data fusion analysis software, and communication interface modules. The communication module preferentially uses the existing fiber optic Ethernet within the tunnel to connect the front-end acquisition units to the central processing unit, while also using wireless communication as a backup. The central processing unit centrally stores and fuses the data uploaded from each source. The fusion analysis software first performs preprocessing on the multi-source data (including signal filtering, noise suppression, and compensation for environmental temperature effects), then synchronizes the data from FBG sensors, RFID tags, and induction coils according to a unified time base. By comparing the data with the digital twin model of the tunnel supports, the data from different sources is converted into comparable stress-strain indices. During normal operation, the system uses high-precision FBG measurements of key supports as a benchmark to continuously correct the reference thresholds and sensitivity coefficients of ordinary support sensor data, reducing the impact of environmental drift on low-cost sensing units. When any support sensor data is detected to exceed the normal range, the system automatically executes the aforementioned multi-sensor information cross-validation algorithm to determine the authenticity and severity of the event, ultimately issuing an alarm conclusion and sending the warning information to the power operation and maintenance cloud platform or the terminal of on-duty personnel via wired / wireless network.

[0075] In the implementation of the monitoring system of this invention, various modifications and combinations of the above embodiments can be made according to actual needs. For example, for particularly important tunnel sections, the number of FRP supports used for key monitoring can be increased, so that every few supports there is a set of key supports with embedded multi-sensors; for existing old supports already in operation, stress monitoring can be upgraded by using surface spraying of sensing coatings and external fiber optic winding without replacing the support body. Furthermore, in certain scenarios, different combinations of sensing elements can be selected. For example, for tunnel environments with large temperature variations, fiber optic temperature compensation gratings can be added to key supports, or temperature and humidity sensing functions can be integrated into ordinary support labels to obtain environmental parameters to assist in judgment. Moreover, in tunnel sections with limited communication conditions, distributed data relay stations or active inspection robots can be used to collect data and then transmit it centrally. All the above equivalent substitution techniques fall within the protection scope of this invention.

[0076] like Figure 9 As shown, based on the above method embodiments, corresponding apparatus embodiments are provided; An embodiment of the present invention provides a structural risk early warning system for composite material cable supports, comprising: The acquisition module 100 is used to acquire a first response signal output by a strain sensing unit deployed on a first type of cable bracket, and to acquire a second response signal output by a multimodal sensing unit deployed on a second type of cable bracket; wherein the second type of cable bracket is spatially adjacent to the first type of cable bracket. The conversion module 200 is used to convert the first response signal and the second response signal into a first stress characterization value and a second stress characterization value, respectively, and use the second stress characterization value as a reference to calibrate the first stress characterization value online to obtain a calibrated third stress characterization value. The early warning module 300 is used to retrieve monitoring data of multimodal sensing units on other cable supports in the spatial neighborhood based on the triggering early warning bracket when any one of the first stress characterization value, the second stress characterization value, or the third stress characterization value exceeds a preset early warning threshold, and to make a consistency judgment based on the monitoring data and the stress characterization value of the triggering early warning bracket, and to realize structural health risk early warning based on the judgment result.

[0077] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can realize the structural risk warning method for composite material cable brackets provided by any of the above-described method embodiments of the present invention.

[0078] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0079] Based on the above embodiments of the structural risk warning method for composite material cable supports, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the structural risk warning method for composite material cable supports of any embodiment of the present invention.

[0080] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0081] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0082] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0083] Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the structural risk warning method for composite material cable brackets as described in any of the above-described method embodiments of the present invention.

[0084] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0085] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A structural risk early warning method for composite material cable supports, characterized in that, include: Acquire a first response signal output by a strain sensing unit deployed on a first type of cable bracket, and acquire a second response signal output by a multimodal sensing unit deployed on a second type of cable bracket; wherein the second type of cable bracket is spatially adjacent to the first type of cable bracket; The first response signal and the second response signal are converted into a first stress characterization value and a second stress characterization value, respectively. The second stress characterization value is used as a reference to calibrate the first stress characterization value online to obtain a calibrated third stress characterization value. When any one of the first stress characterization value, the second stress characterization value, or the third stress characterization value exceeds a preset warning threshold, the system retrieves monitoring data from multimodal sensing units on other cable supports within the spatial neighborhood based on the trigger warning support, makes a consistency judgment based on the monitoring data and the stress characterization value of the trigger warning support, and implements structural health risk warning based on the judgment result.

2. The structural risk early warning method for composite material cable supports according to claim 1, characterized in that, The step of using the second stress characterization value as a benchmark to perform online calibration of the first stress characterization value to obtain the calibrated third stress characterization value includes: Using the second stress characterization value and combined with the preset finite element model, the sensitivity coefficient of the strain sensing unit on the first type of cable bracket is updated online, so as to calibrate the conversion parameter of the first stress characterization value based on the sensitivity coefficient and obtain the first calibration result. The second stress characterization value is used to compensate for the temperature-induced drift component in the first stress characterization value to obtain a second calibration result. Based on the first calibration result and / or the second calibration result, the calibrated third stress characterization value is obtained.

3. The structural risk early warning method for composite material cable supports according to claim 2, characterized in that, The step of using the second stress characterization value, combined with a preset finite element model, to update the sensitivity coefficient of the strain sensing unit on the first type of cable bracket online includes: The second stress characterization value is input into the pre-constructed and modified finite element model to calculate the theoretical strain value at the installation position of the strain sensing unit on the first type of cable bracket. Obtain the echo frequency offset and initial echo frequency in the first response signal of the first type of cable bracket at the current moment; Based on the theoretical strain value, the echo frequency offset, the initial echo frequency, and the pre-stored conversion relationship, the current sensitivity coefficient is calculated by inverse solution.

4. The structural risk early warning method for composite material cable supports according to claim 2, characterized in that, The step of using the second stress characterization value to compensate for the temperature-induced drift component in the first stress characterization value to obtain a second calibration result includes: Extract the temperature change without strain from the multimodal sensing unit on the second type of cable support in spatial proximity; The frequency drift is calculated based on the temperature change and the pre-calibrated temperature drift coefficient. The second calibration result after temperature compensation is obtained by subtracting the frequency drift from the total frequency change of the first response signal.

5. The structural risk early warning method for composite material cable supports according to claim 1, characterized in that, The acquisition of the first response signal output by the strain sensing unit deployed on the first type of cable bracket includes: A query signal is transmitted to the tag of the strain sensing unit on the surface of the first type of cable bracket by a remote reader, and the first response signal returned by the tag in response to the query signal is received; wherein, the tag is composed of a conductive nanocomposite material coating sprayed on the surface of the first type of cable bracket and an RFID tag chip electrically connected to the conductive nanocomposite material coating.

6. The structural risk early warning method for composite material cable supports according to claim 1, characterized in that, The acquisition of the second response signal output by the multimodal sensing unit deployed on the second type of cable bracket includes: The reflected wavelength signal output by the fiber Bragg grating strain sensor array embedded inside the second type of cable bracket is acquired by an optical fiber demodulator. The resistance signal output by the self-sensing conductive fiber bundle embedded inside the second type of cable support is collected by a resistance measuring device. The inductance signal output by the magnetostrictive alloy wire embedded inside the second type of cable bracket is collected by an induction coil wound on the outer surface of the second type of cable bracket and a connected inductance measuring device. The reflected wavelength signal, the resistance signal, and the inductance signal are used together as the second response signal.

7. The structural risk early warning method for composite material cable supports according to claim 6, characterized in that, The step of converting the first response signal and the second response signal into a first stress characterization value and a second stress characterization value, respectively, includes: Based on the pre-calibrated initial sensitivity coefficient of the strain sensing unit, the first response signal is converted into a first strain value on the surface of the first type of cable bracket, and the first strain value is converted into the first stress characterization value based on the elastic modulus of the first type of cable bracket. The reflected wavelength signal in the second response signal is converted into a second strain value according to the first conversion relationship of the fiber Bragg grating; the resistance signal in the second response signal is converted into a third strain value according to the second conversion relationship of the self-sensing conductive fiber; and the inductance signal in the second response signal is converted into a fourth strain value according to the third conversion relationship of the magnetostrictive alloy wire. The second strain value, the third strain value and the fourth strain value are then fused to obtain the second stress characterization value.

8. The structural risk early warning method for composite material cable supports according to claim 1, characterized in that, The method involves retrieving monitoring data from multimodal sensing units on other cable supports within the spatial neighborhood of the trigger-and-warning support, determining the consistency between the monitoring data and the stress characterization value of the trigger-and-warning support, and implementing structural health risk warning based on the determination result. This includes: Based on the spatial location tag of the trigger warning bracket, the monitoring data of the multi-modal sensing unit on at least one other cable bracket that is in the same time section and spatially adjacent to the trigger warning bracket, as well as the monitoring data of other types of sensors associated with the trigger warning bracket itself, are automatically retrieved from the real-time database to obtain multi-source monitoring data. The retrieved multi-source monitoring data are compared with the current stress characterization value of the trigger warning bracket to determine whether all data show a consistent abnormal change trend. If the multi-source monitoring data all show a consistent abnormal change trend, it is determined that the triggering early warning stent has a structural health risk and a structural early warning is issued.

9. The structural risk early warning method for composite material cable supports according to any one of claims 1-8, characterized in that, Also includes: If only the sensor data used to trigger the warning is abnormal, while other retrieved monitoring data are normal, it is determined to be a sensor malfunction or environmental interference, and a sensor maintenance prompt will be issued.

10. A structural risk early warning system for composite material cable supports, characterized in that, include: The acquisition module is used to acquire a first response signal output by a strain sensing unit deployed on a first type of cable bracket, and to acquire a second response signal output by a multimodal sensing unit deployed on a second type of cable bracket; wherein the second type of cable bracket is spatially adjacent to the first type of cable bracket. The conversion module is used to convert the first response signal and the second response signal into a first stress characterization value and a second stress characterization value, respectively, and use the second stress characterization value as a reference to perform online calibration on the first stress characterization value to obtain a calibrated third stress characterization value. The early warning module is used to retrieve monitoring data from multimodal sensing units on other cable supports in the spatial neighborhood based on the triggering early warning bracket when any one of the first stress characterization value, the second stress characterization value, or the third stress characterization value exceeds a preset early warning threshold. It then makes a consistency judgment based on the monitoring data and the stress characterization value of the triggering early warning bracket, and realizes structural health risk early warning based on the judgment result.

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