Smart cable monitoring system and device

Abstract

The invention relates to the technical field of engineering structure health monitoring, and discloses a smart cable monitoring system, which comprises a smart cable body module, a central steel wire and a fiber bragg grating smart rib are coupled through a hot cast anchor process, and the smart cable body module is used for sensing cable body strain and converting the cable body strain into an optical signal; the signal acquisition device comprises an optical signal transmitting device and a receiving device, the transmitting device directionally transmits a pulse light source to the fiber bragg grating smart rib, and the receiving device acquires multiple groups of light beams refracted and dispersed by a grating; and the data analysis platform solves the strain of the fiber bragg grating by processing the optical signal. The intelligent cable monitoring system and device aim at solving the problems that in the prior art, manual inspection is limited by labor cost and detection frequency, a single sensor is difficult to achieve multi-dimensional data synchronous collection, data transmission delay is high, and the requirement for real-time safety monitoring of an engineering structure cannot be met.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering structure health monitoring, and in particular to a smart cable monitoring system and device. Background Art

[0002] Cable structures play a vital role in the engineering field and are key load-bearing components. In many large-scale buildings and bridge structures, cable structures, with their unique mechanical properties and efficient spatial force characteristics, undertake the main load transfer task. The health of the cable structure is closely related to the safety of the overall structure. Once the cable structure is damaged, such as steel wire corrosion, cable fatigue fracture, etc., its bearing capacity will drop significantly, thereby affecting the stability of the entire structure. If minor damage is not discovered and treated in time, it may gradually expand under the action of long-term loads, eventually leading to structural damage, causing serious safety accidents, and causing huge economic losses and casualties. Therefore, regular health monitoring and assessment of cable structures and timely detection and repair of potential problems are important measures to ensure the safe operation of engineering structures.

[0003] Traditional cable tension monitoring methods mainly rely on manual inspections or single sensors. Their data collection cycle is long, usually measured in days or weeks. It is difficult to capture sudden damage to the cable body in a timely manner, such as sudden stress changes and local fractures. As a result, structural safety hazards cannot be discovered in the early stages, which may lead to catastrophic accidents. Summary of the Invention

[0004] The purpose of the present invention is to solve the problems in the existing technology that manual inspections are limited by labor costs and detection frequency, a single sensor is difficult to achieve synchronous collection of multi-dimensional data, and the data transmission delay is high, which cannot meet the needs of real-time safety monitoring of engineering structures. A smart cable monitoring system and device are proposed.

[0005] The technical solution of the present invention to solve the above technical problems is as follows:

[0006] A smart cable monitoring system, comprising:

[0007] The smart cable module consists of either high-vanadium or sealed cables as the external load-bearing structure. The central steel wire is coupled to the fiber grating smart reinforcement through a hot-cast anchor process to sense cable strain and convert it into optical signals.

[0008] The signal acquisition device includes an optical signal transmitting device and a receiving device. The transmitting device transmits a pulse light source in a directionally directed manner toward the fiber Bragg grating smart rib, and the receiving device obtains multiple groups of light beams after being dispersed by grating refraction.

[0009] The data analysis platform processes optical signals to calculate fiber Bragg grating strain and converts cable stress and cable force using material mechanics models.

[0010] The user terminal includes a wireless communication module, a self-powered module and an interactive interface, which is used to realize remote transmission of monitoring data, energy supply and human-computer interaction.

[0011] On the basis of the above technical solution, the present invention can also be improved as follows.

[0012] Furthermore, the smart cable module uses a vacuum hot-cast anchoring process to embed a fiber Bragg grating (FBG) smart rib with a diameter of 0.8-1.2 mm and a grating spacing of 50-100 mm into the center of the cable. The casting temperature is controlled at 480-520°C, forming a metallurgical bond between the zinc-copper alloy melt, the steel wire, and the FBG. The FBG smart rib utilizes the Bragg grating effect, and its wavelength offset Δλ and strain ε satisfy the following relationship:

[0013] Δλ=2λ0P e ε+λ0α T ΔT

[0014] Where λ0 is the central wavelength, P e is the effective elastic-optical coefficient, α T is the thermo-optical coefficient, and ΔT is the temperature change.

[0015] Furthermore, the optical signal transmitting device of the signal acquisition device adopts a narrow linewidth distributed feedback laser with a linewidth ≤0.01nm, and transmits a pulse light source with a central wavelength of 1550nm, a pulse width of 50-100ns, and a repetition frequency of 1-10kHz. The light source is coupled to the fiber grating smart rib via a single-mode optical fiber. The optical signal receiving device includes a 7-channel array photodetector, which receives 7 groups of diffracted light beams with a wavelength interval of 0.1-0.2nm. The optical signal is collected by a high-speed ADC circuit with a sampling rate ≥1MHz, and combined with a spectral solution algorithm based on fast Fourier transform, the wavelength offset measurement accuracy can reach 1pm.

[0016] Furthermore, the optical signal processing system of the data analysis platform fits the seven groups of beam spectra based on the Lagrange interpolation method and calculates the fiber Bragg grating strain by comparing it with the standard strain-wavelength database. The calculation formula is:

[0017]

[0018] The mechanical signal processing system calculates stress based on Hooke's law σ=Eε, where E is the Young's modulus of the smart cable, which is 190-210GPa. The cable force is solved by the formula F=σ·A based on the cross-sectional area A of the cable body. The Kalman filter algorithm is used to recursively estimate the data. The state space equation is:

[0019]

[0020] Where x is the state vector, z is the observation vector, A is the state transfer matrix, H is the observation matrix, K is the Kalman gain, w and v are the process noise and observation noise.

[0021] Furthermore, the data analysis platform integrates the LSTM-GARCH hybrid neural network model. The network structure includes three LSTM layers and a GARCH volatility layer. The input variable is the historical tension sequence. Ambient temperature series and vibration acceleration series The output is the predicted value of the cable force for the next hour When the measured value and the predicted value meet When the warning is triggered, the multi-level warning mechanism is triggered.

[0022] Furthermore, the wireless communication module of the user terminal adopts the LoRa / NB-IoT dual-mode architecture, the physical layer supports LoRa modulation with a spreading factor of 6-12 and the NB-IoT B3 / B5 frequency band, the data link layer adopts the adaptive ARQ protocol, and the edge computing unit is based on the ARM Cortex-M4 core, runs the abnormal data recognition algorithm, and sets the cable force change rate threshold. Data is prioritized and only mutation data that satisfies F(t)-F(t-Δt)>γ·F(t-Δt) is uploaded, achieving a data compression ratio of 5:1.

[0023] Furthermore, the self-powered module of the user terminal is composed of a PVDF flexible piezoelectric film and a graphene supercapacitor. The piezoelectric film is attached along the axial direction of the cable body and generates charge based on the positive piezoelectric effect. Its output voltage V(t) and the cable body vibration acceleration a(t) satisfy the following formula:

[0024]

[0025] Among them A p is the area of ​​the piezoelectric film, l p is the length in the polarization direction, C p The MPPT energy management circuit is an equivalent capacitor and adjusts the load resistance in real time through the perturbation observation method. Under the conditions of vibration frequency of 1-10Hz and amplitude of 0.5mm, the energy conversion efficiency is ≥75%, and the device can be supported to work continuously for ≥30 days.

[0026] Furthermore, the smart cable module integrates MEMS acceleration sensors and fiber Bragg grating smart tendons to construct a multi-parameter monitoring array, and establishes a strain-acceleration joint probability model through a cross-validation algorithm: Where P(ε|a) is the strain probability density under acceleration conditions. The historical data is trained to fit the Gaussian mixture model. When the joint probability P(ε,a)<0.05, it is determined to be an abnormal state.

[0027] Furthermore, the data analysis platform adopts a temperature-strain decoupling algorithm based on dual-wavelength reference, and establishes a simultaneous equation of wavelength drift by arranging temperature-compensated fiber Bragg gratings in the non-stressed area of ​​the cable body:

[0028] Where Δλ1 is the wavelength offset of the sensing grating, Δλ2 is the wavelength offset of the temperature compensation grating, and λ 10 =1550nm,λ 20 =1310nm, and the strain is obtained by elimination method Achieve temperature drift error ≤ 0.5με / ℃, eliminating the cross-influence of ambient temperature on monitoring results;

[0029] The smart cable module sets up a dual-fiber redundant self-diagnosis mechanism and periodically sends calibration optical signals through time division multiplexing technology. The receiving end compares the root mean square error of the feedback spectrum with the reference spectrum: When RMSE>3dB, the optical switch automatically switches to the backup optical path, performs a self-test every 24 hours, and the optical path failure response time is ≤100ms, ensuring the continuous operation time of the monitoring system is ≥10 4 hours, meeting long-term engineering monitoring needs.

[0030] A smart cable monitoring device, comprising:

[0031] The smart cable body consists of either a high-vanadium cable or a sealed cable as the outer cable body, and the internal fiber Bragg grating smart tendons are coupled through a hot-cast anchor process to sense the cable body strain;

[0032] The signal acquisition module includes an optical signal transmitter and a receiver. The transmitter transmits light to the fiber Bragg grating (FBG) smart ribs in a directionally controlled manner, and the receiver obtains the light beam after grating dispersion.

[0033] A data processing module is used to convert the optical signal into strain data and calculate the cable stress and cable force;

[0034] The terminal interaction module includes a wireless communication unit and a self-powered unit for data transmission and device power supply.

[0035] Compared with the prior art, the technical solution of this application has the following beneficial technical effects:

[0036] The intelligent cable body module of the present invention forms an external load-bearing structure through high-vanadium cable or sealed cable, and couples the fiber grating intelligent reinforcement and the central steel wire through a hot-cast anchor process, forming a distributed sensing network that directly senses the strain of the cable body. Secondly, the signal acquisition device realizes high-frequency real-time data acquisition through the coordinated work of the optical signal transmitting and receiving devices. The pulse light source emitted by the transmitting device cooperates with the receiving device, so that the system can collect optical signals at a millisecond frequency. Furthermore, the data analysis platform realizes real-time solution from optical signals to mechanical parameters through optical signal processing and mechanical model conversion. This real-time calculation and intelligent early warning mechanism changes the lag of traditional methods that rely on manual analysis, and realizes the early identification of structural safety hazards. Finally, the wireless communication module and self-powered module of the user terminal ensure the continuous real-time operation of the monitoring system, ensure uninterrupted collection of monitoring data, and provide all-weather, high-precision monitoring protection for the safety of engineering structures. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is an overall system block diagram of a smart cable monitoring system of the present invention;

[0038] Figure 2 This is a schematic diagram of the connection structure between the smart cable and the fiber grating smart rib of a smart cable monitoring device of the present invention. DETAILED DESCRIPTION

[0039] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0040] A smart cable monitoring system of the present invention includes:

[0041] The smart cable module consists of either high-vanadium or sealed cables as the external load-bearing structure. The central steel wire is coupled to the fiber grating smart reinforcement through a hot-cast anchor process to sense cable strain and convert it into optical signals.

[0042] The signal acquisition device includes an optical signal transmitting device and a receiving device. The transmitting device transmits a pulse light source in a directionally directed manner toward the fiber Bragg grating smart rib, and the receiving device obtains multiple groups of light beams after being dispersed by grating refraction.

[0043] The data analysis platform processes optical signals to calculate fiber Bragg grating strain and converts cable stress and cable force using material mechanics models.

[0044] The user terminal includes a wireless communication module, a self-powered module and an interactive interface, which is used to realize remote transmission of monitoring data, energy supply and human-computer interaction.

[0045] Among them Figure 2 The outer side is the smart cable body, and the center of the smart cable body is the fiber Bragg grating smart rib.

[0046] In a preferred embodiment, the present invention can be further configured as follows: the smart cable module utilizes a vacuum hot-cast anchoring process to embed a fiber Bragg grating (FBG) smart rib with a diameter of 0.8-1.2 mm and a grating spacing of 50-100 mm into the center of the cable. The casting temperature is controlled at 480-520°C to form a metallurgical bond between the zinc-copper alloy melt, the steel wire, and the fiber Bragg grating. The fiber Bragg grating smart rib utilizes the Bragg grating effect, and its wavelength offset Δλ and strain ε satisfy the following relationship:

[0047] Δλ=2λ0P e ε+λ0α T ΔT

[0048] Where λ0 is the central wavelength, P e is the effective elastic-optical coefficient (value ranges from 0.22 to 0.24), α T Thermo-optical coefficient (10.8×10 -6 / °C), where ΔT is the temperature change. This structure ensures a strain transfer efficiency of ≥98% through metallurgical bonding, achieving a strain measurement resolution of 1με. It also integrates temperature-sensitive items to characterize environmental impacts. By capturing microstrain changes (such as sudden stress changes) in the cable body with high precision and in real time, it elevates data acquisition from "day / week" to "real-time," preventing the spread of sudden damage due to delayed monitoring.

[0049] The vacuum hot casting anchor process eliminates the interface slip loss of traditional adhesive sensors through the metallurgical bonding of zinc-copper alloy melt at 480-520℃. The grating spacing of the fiber Bragg grating smart rib is 50-100mm to form a distributed sensing array, which can synchronously monitor the strain distribution of the cable body along the axial direction. In addition, the temperature sensitive term λ0α T The introduction of ΔT enables the system to compensate for the impact of ambient temperature on wavelength drift in real time, avoiding misjudgment caused by temperature changes.

[0050] In a preferred embodiment, the present invention can be further configured as follows: the optical signal transmitter of the signal acquisition device utilizes a narrow-linewidth distributed feedback laser with a linewidth ≤ 0.01 nm, emitting a pulsed light source with a central wavelength of 1550 nm, a pulse width of 50-100 ns, and a repetition rate of 1-10 kHz. The light source is coupled to the fiber Bragg grating (FBG) smart rib via a single-mode optical fiber. The optical signal receiver includes a 7-channel array photodetector that receives seven groups of diffracted beams with wavelength intervals of 0.1-0.2 nm. The optical signal is acquired by a high-speed ADC circuit with a sampling rate ≥ 1 MHz. Combined with a spectrum decomposition algorithm based on fast Fourier transform, the wavelength offset measurement accuracy reaches 1 pm. The device establishes a real-time spectrum-strain mapping relationship through multi-channel parallel detection and high-frequency sampling. The signal acquisition device achieves high-frequency real-time acquisition and high-precision decomposition of optical signals through the collaboration of the narrow-linewidth laser and the 7-channel photodetector, establishing a real-time spectrum-strain mapping relationship. This enables the system to capture cable strain transients at millisecond frequencies, ensuring that sudden strain changes caused by sudden damage, such as local fractures, are immediately perceived.

[0051] The distributed feedback (DFB) laser uses the 1550nm communication band to reduce fiber transmission losses. The 50-100ns pulse width combined with a 1-10kHz repetition rate reduces power consumption while ensuring light source energy. The 7-channel detector receives diffracted beams with wavelength intervals of 0.1-0.2nm, corresponding to different grating positions of the fiber Bragg grating. The spectrum is converted into the time-frequency domain through the fast Fourier transform (FFT) algorithm, which can separate the wavelength drift and noise interference caused by strain, achieving a wavelength measurement accuracy of 1pm, providing high-frequency and accurate raw data for subsequent mechanical parameter calculations.

[0052] In a preferred embodiment, the present invention can be further configured as follows: the optical signal processing system of the data analysis platform fits the seven sets of light beam spectra based on the Lagrange interpolation method, and calculates the fiber Bragg grating strain by comparing it with the standard strain-wavelength database. The calculation formula is:

[0053]

[0054] The mechanical signal processing system calculates stress based on Hooke's law σ=Eε, where E is the Young's modulus of the smart cable, which is 190-210GPa. The cable force is solved by the formula F=σ·A based on the cross-sectional area A of the cable body. The Kalman filter algorithm is used to recursively estimate the data. The state space equation is:

[0055]

[0056] Where x is the state vector, z is the observation vector, A is the state transfer matrix, H is the observation matrix, K is the Kalman gain, w and v are the process noise and observation noise, achieving a cable force calculation error of ≤0.5% FS. The Lagrange interpolation method performs n-order polynomial fitting on seven sets of light beam spectra, improves the spectral restoration by adding interpolation nodes, and ensures the accuracy of strain solution. The Kalman filter algorithm uses the cable force state as the state vector x and the optical signal observation value as z. A recursive relationship is established between the state transfer matrix A and the observation matrix H, and the noise covariance matrix is ​​updated in real time to suppress random interference such as environmental vibration. In cable-stayed bridge cable force monitoring, this algorithm can filter out high-frequency noise caused by wind vibration and retain the true change trend of the cable force.

[0057] In a preferred embodiment, the present invention can be further configured as follows: the data analysis platform integrates an LSTM-GARCH hybrid neural network model, the network structure includes three LSTM layers (128 neurons per layer) and a GARCH (1,1) volatility layer, and the input variable is the historical tension sequence Ambient temperature series and vibration acceleration series The output is the predicted value of the cable force for the next hour When the measured value and the predicted value meet When the cable is moving, a multi-level warning mechanism is triggered. The model adaptively learns the time-varying characteristics and fluctuation aggregation of the cable's mechanical behavior to build a dynamic warning threshold. The LSTM-GARCH hybrid neural network model learns historical cable force, temperature, and acceleration data to build a dynamic warning threshold. When the deviation between the measured value and the predicted value exceeds 3%, a warning is triggered.

[0058] The three LSTM layers in the model capture the long-term dependencies of the cable force sequence through the forget gate, input gate, and output gate mechanisms. The GARCH(1,1) layer fits the conditional variance of the cable force fluctuations to characterize their "fluctuation clustering." The Adam optimizer is used for training, with the mean squared error (MSE) as the loss function. The input window m = 1000 (corresponding to approximately 17 hours of data) outputs a predicted value for the next hour. In a certain suspension bridge monitoring example, the model predicted abnormal fluctuations in the main cable force 48 hours in advance.

[0059] In a preferred embodiment, the present invention can be further configured as follows: the wireless communication module of the user terminal adopts the LoRa / NB-IoT dual-mode architecture, the physical layer supports LoRa modulation with a spreading factor of 6-12 (communication distance ≥ 3km, receiving sensitivity -148dBm) and NB-IoT B3 / B5 frequency band (supporting mobility management), the data link layer adopts the adaptive ARQ protocol, the edge computing unit is based on the ARMCortex-M4 core, runs the abnormal data recognition algorithm, and sets the cable force change rate threshold. Data is prioritized, and only mutation data satisfying F(t)-F(t-Δt)>γ·F(t-Δt) is uploaded. This achieves a data compression ratio of 5:1. By uploading only mutation data with a cable force change rate greater than 5% / min, data transmission volume is reduced by 80%, which not only reduces communication power consumption but also avoids massive data transmission delays, ensuring that warning information reaches the terminal in real time.

[0060] In the physical layer, LoRa uses a spreading factor of 6-12 for adaptive adjustment, with a communication distance of up to 3km in an open environment and a receiving sensitivity of -148dBm, suitable for large outdoor structures. NB-IoT accesses the operator network and supports mobility management, meeting the monitoring needs of scenarios such as bridge expansion joint displacement. The adaptive ARQ protocol in the data link layer dynamically adjusts the retransmission strategy according to the channel quality, with a bit error rate of ≤10 -6 The edge computing unit is based on the ARMCortex-M4 core and runs the gradient descent optimized anomaly recognition algorithm. It marks the mutation data corresponding to the sudden damage in real time by calculating the cable force change rate.

[0061] In a preferred embodiment, the present invention can be further configured as follows: the self-powered module of the user terminal is composed of a PVDF flexible piezoelectric film (thickness 50μm, d31 coefficient -35pC / N) and a graphene supercapacitor (10F / 3.8V). The piezoelectric film is attached along the axial direction of the cable body and generates charge based on the positive piezoelectric effect. Its output voltage V(t) and the cable body vibration acceleration a(t) satisfy the following formula:

[0062]

[0063] Among them A p is the area of ​​the piezoelectric film, l p is the length in the polarization direction, C p The MPPT energy management circuit uses the perturbation observation method to adjust the load resistance in real time. Under the conditions of a vibration frequency of 1-10Hz and an amplitude of 0.5mm, the energy conversion efficiency is ≥75%, supporting the device to operate continuously for ≥30 days. The self-powered system composed of PVDF flexible piezoelectric film and graphene supercapacitor uses the vibration energy of the cable body to continuously supply power, breaking away from the limitations of traditional external power supplies. Under the conditions of a vibration frequency of 1-10Hz and an amplitude of 0.5mm, the device can operate continuously for ≥30 days, ensuring monitoring continuity.

[0064] PVDF film (thickness 50μm) is pasted along the axial direction of the cable body, and the piezoelectric effect with a d31 coefficient of -35pC / N is used to convert vibration mechanical energy into electrical energy, with an output power density of ≥0.5mW / cm 2 The MPPT energy management circuit adjusts the load resistance to the optimal matching point in real time through the perturbation observation method, with an energy conversion efficiency of ≥75%. The graphene supercapacitor adopts a double-layer energy storage mechanism, with a charge and discharge cycle life of >105 Second, the capacity retention rate is ≥80% in the temperature range of -40℃ to 85℃, which is suitable for harsh engineering environments.

[0065] In a preferred embodiment, the present invention can be further configured as follows: the smart cable module integrates a MEMS acceleration sensor (range ±50g, resolution 0.01g) and a fiber Bragg grating smart rib to construct a multi-parameter monitoring array, and a strain-acceleration joint probability model is established using a cross-validation algorithm: Where P(ε|a) is the probability density of strain under acceleration conditions. A Gaussian mixture model was trained using historical data. When the joint probability P(ε,a) < 0.05, an abnormal state was identified. This mechanism reduced the false alarm rate from 15% to < 2%, resolving the ambiguity inherent in single-parameter monitoring. MEMS accelerometers and fiber Bragg grating (FBG) smart reinforcements are orthogonally arranged to simultaneously collect cable vibration acceleration and strain signals. A Gaussian mixture model (GMM) trained using the EM algorithm clustered the strain-acceleration data into K = 3 Gaussian distributions, one for each of the three distributions: normal, minor damage, and severe damage. When the joint probability P(ε,a) < 0.05, an abnormal state was identified. This mechanism reduced false alarms caused by temperature changes from 12 / month to 0.5 / month in monitoring a cable-supported grid structure.

[0066] In a preferred embodiment, the present invention can be further configured as follows: the data analysis platform uses a temperature-strain decoupling algorithm based on a dual-wavelength reference, and by arranging a temperature-compensated fiber Bragg grating (central wavelength 1310 nm) in the non-stressed area of ​​the cable body, a simultaneous equation for wavelength drift is established:

[0067] Where Δλ1 is the wavelength offset of the sensing grating, Δλ2 is the wavelength offset of the temperature compensation grating, and λ 10 =1550nm,λ 20 =1310nm, and the strain is obtained by elimination method Achieve temperature drift error ≤ 0.5με / ℃, eliminating the cross-influence of ambient temperature on monitoring results;

[0068] The smart cable module has a dual-fiber redundant self-diagnosis mechanism. It periodically sends a calibration optical signal (wavelength 1625nm) through time-division multiplexing technology. The receiving end compares the root mean square error (RMS) of the feedback spectrum with the reference spectrum: When RMSE>3dB, the optical switch automatically switches to the backup optical path (loss ≤0.5dB), performs a self-test every 24 hours, and the optical path fault response time is ≤100ms, ensuring the continuous operation time of the monitoring system ≥10 4 hours, meeting the needs of long-term engineering monitoring. The dual-wavelength temperature-strain decoupling algorithm and dual-fiber redundant self-diagnosis mechanism eliminate the cross-influence of temperature on monitoring results and ensure monitoring continuity through automatic optical path switching.

[0069] A temperature-compensated fiber Bragg grating with a central wavelength of 1310nm is arranged in the non-stressed area of ​​the cable body, forming a dual-wavelength reference with the 1550nm sensing grating. In the dual-fiber redundant design, the main and backup optical paths use single-mode polarization-maintaining optical fibers, and the optical switch response time is ≤1ms. When the RMSE of the feedback spectrum and the reference spectrum is greater than 3dB, the switching is triggered by time-division multiplexing technology, and the optical path loss is ≤0.5dB, ensuring uninterrupted monitoring data.

[0070] The external load-bearing structure composed of high-vanadium cables or sealed cables in the smart cable module and the fiber Bragg grating smart reinforcement coupled to the central steel wire through the hot-cast anchor process utilizes the Bragg grating effect to convert the cable strain into an optical wavelength signal. The wavelength offset Δλ satisfies Δλ=2λ0P with the strain ε and temperature ΔT. e ε+λ0α T ΔT, the optical signal transmitter of the signal acquisition device transmits a pulse light source with a central wavelength of 1550nm and a pulse width of 50-100ns to the fiber Bragg grating smart rib. After being refracted and dispersed by the grating, the seven groups of light beams are captured by the receiving device. The data is collected by a high-speed ADC circuit with a sampling rate of ≥1MHz and combined with a fast Fourier transform spectrum solution algorithm to achieve a measurement accuracy of 1pm for the wavelength offset. The data analysis platform fits the spectrum based on the Lagrange interpolation method to solve the fiber Bragg grating strain. Then, the stress and cable force are calculated based on σ = Eε and F = σ·A. At the same time, the Kalman filter algorithm is used for denoising. Combined with the LSTM-GARCH hybrid neural network model, it is trained based on historical cable force, temperature, and acceleration data. When the deviation between the measured value and the predicted value exceeds 3%, an early warning is triggered. The wireless communication module of the user terminal adopts the LoRa / NB-IoT dual-mode architecture, and the edge computing unit is based on the cable force change rate. The data is uploaded in priority. The self-powered module converts the cable vibration energy into electrical energy through the PVDF flexible piezoelectric film, which is stored in the graphene supercapacitor through MPPT management. The device can work for ≥30 days. The WEB terminal / mobile phone APP realizes data visualization and early warning reception. In addition, the smart cable module integrates MEMS acceleration sensors to build a multi-parameter monitoring array. Through the joint probability model To reduce the false alarm rate, a dual-wavelength reference temperature-strain decoupling algorithm is used to eliminate temperature effects. The dual-fiber redundant self-diagnosis mechanism automatically switches the optical path through spectral RMSE monitoring to ensure continuous system operation.

[0071] A smart cable monitoring device, comprising:

[0072] The smart cable body consists of either a high-vanadium cable or a sealed cable as the outer cable body, and the internal fiber Bragg grating smart tendons are coupled through a hot-cast anchor process to sense the cable body strain;

[0073] The signal acquisition module includes an optical signal transmitter and a receiver. The transmitter transmits light to the fiber Bragg grating (FBG) smart ribs in a directionally controlled manner, and the receiver obtains the light beam after grating dispersion.

[0074] A data processing module is used to convert the optical signal into strain data and calculate the cable stress and cable force;

[0075] The terminal interaction module includes a wireless communication unit and a self-powered unit for data transmission and device power supply.

[0076] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0077] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A smart cable monitoring system, characterized in that: include: The smart cable module consists of either high-vanadium or sealed cables as the external load-bearing structure. The central steel wire is coupled to the fiber grating smart reinforcement through a hot-cast anchor process to sense cable strain and convert it into optical signals. The signal acquisition device includes an optical signal transmitting device and a receiving device. The transmitting device transmits a pulse light source in a directionally directed manner toward the fiber Bragg grating smart rib, and the receiving device obtains multiple groups of light beams after being dispersed by grating refraction. The data analysis platform processes optical signals to calculate fiber Bragg grating strain and converts cable stress and cable force using material mechanics models. The user terminal includes a wireless communication module, a self-powered module and an interactive interface, which is used to realize remote transmission of monitoring data, energy supply and human-computer interaction.

2. The smart cable monitoring system according to claim 1, characterized in that: The smart cable module uses a vacuum hot-cast anchoring process to embed a fiber Bragg grating (FBG) smart rib with a diameter of 0.8-1.2 mm and a grating spacing of 50-100 mm into the center of the cable. The casting temperature is controlled at 480-520°C to form a metallurgical bond between the zinc-copper alloy melt, the steel wire, and the fiber Bragg grating. The fiber Bragg grating smart rib utilizes the Bragg grating effect, and its wavelength offset Δλ and strain ε satisfy the following relationship: Δλ=2λ0P e e+λ0a T ΔT Where λ0 is the central wavelength, P e is the effective elastic-optical coefficient, α T is the thermo-optical coefficient, and ΔT is the temperature change.

3. The smart cable monitoring system according to claim 1, characterized in that: The optical signal transmitting device of the signal acquisition device adopts a narrow-linewidth distributed feedback laser with a linewidth of ≤0.01nm, and transmits a pulse light source with a central wavelength of 1550nm, a pulse width of 50-100ns, and a repetition frequency of 1-10kHz. The light source is coupled to the fiber Bragg grating smart rib via a single-mode optical fiber. The optical signal receiving device includes a 7-channel array photodetector, which receives 7 groups of diffracted light beams with a wavelength interval of 0.1-0.2nm. The optical signal is collected by a high-speed ADC circuit with a sampling rate of ≥1MHz. Combined with a spectral solution algorithm based on fast Fourier transform, the wavelength offset measurement accuracy reaches 1pm.

4. The smart cable monitoring system according to claim 1, characterized in that: The optical signal processing system of the data analysis platform fits the seven groups of beam spectra based on the Lagrange interpolation method and calculates the fiber Bragg grating strain by comparing it with the standard strain-wavelength database. The calculation formula is: The mechanical signal processing system calculates stress based on Hooke's law σ=Eε, where E is the Young's modulus of the smart cable, which is 190-210GPa. The cable force is solved by the formula F=σ·A based on the cross-sectional area A of the cable body. The Kalman filter algorithm is used to recursively estimate the data. The state space equation is: Where x is the state vector, z is the observation vector, A is the state transfer matrix, H is the observation matrix, K is the Kalman gain, w and v are the process noise and observation noise.

5. The smart cable monitoring system according to claim 4, characterized in that: The data analysis platform integrates the LSTM-GARCH hybrid neural network model. The network structure includes three LSTM layers and a GARCH volatility layer. The input variable is the historical tension sequence. Ambient temperature series and vibration acceleration series The output is the predicted value of the cable force for the next hour When the measured value and the predicted value meet When the warning is triggered, the multi-level warning mechanism is triggered.

6. The smart cable monitoring system according to claim 3, characterized in that: The wireless communication module of the user terminal adopts the LoRa / NB-IoT dual-mode architecture. The physical layer supports LoRa modulation with a spreading factor of 6-12 and the NB-IoT B3 / B5 frequency band. The data link layer adopts the adaptive ARQ protocol. The edge computing unit is based on the ARM Cortex-M4 core, runs the abnormal data recognition algorithm, and sets the cable force change rate threshold. Data is prioritized and only mutation data that satisfies F(t)-F(t-Δt)>γ·F(t-Δt) is uploaded, achieving a data compression ratio of 5:

1.

7. The smart cable monitoring system according to claim 5, characterized in that: The self-powered module of the user terminal is composed of a PVDF flexible piezoelectric film and a graphene supercapacitor. The piezoelectric film is attached along the axial direction of the cable body and generates charge based on the positive piezoelectric effect. Its output voltage V(t) and the cable body vibration acceleration a(t) satisfy the following formula: Among them A p is the area of ​​the piezoelectric film, l p is the length in the polarization direction, C p The MPPT energy management circuit is an equivalent capacitor and adjusts the load resistance in real time through the perturbation observation method. Under the conditions of vibration frequency of 1-10Hz and amplitude of 0.5mm, the energy conversion efficiency is ≥75%, and the device can be supported to work continuously for ≥30 days.

8. The smart cable monitoring system according to claim 7, characterized in that: The smart cable module integrates MEMS acceleration sensors and fiber Bragg grating smart tendons to construct a multi-parameter monitoring array, and establishes a strain-acceleration joint probability model through a cross-validation algorithm: Where P(ε|a) is the strain probability density under acceleration conditions. The historical data is trained to fit the Gaussian mixture model. When the joint probability P(ε,a)<0.05, it is determined to be an abnormal state.

9. The smart cable monitoring system according to claim 1, characterized in that: The data analysis platform adopts a temperature-strain decoupling algorithm based on dual-wavelength reference, and establishes the simultaneous equations of wavelength drift by arranging temperature-compensated fiber Bragg gratings in the non-stressed area of ​​the cable body: Where Δλ1 is the wavelength offset of the sensing grating, Δλ2 is the wavelength offset of the temperature compensation grating, and λ 10 =1550nm,λ 20 =1310nm, and the strain is obtained by elimination method Achieve temperature drift error ≤ 0.5με / ℃, eliminating the cross-influence of ambient temperature on monitoring results; The smart cable module sets up a dual-fiber redundant self-diagnosis mechanism and periodically sends calibration optical signals through time division multiplexing technology. The receiving end compares the root mean square error of the feedback spectrum with the reference spectrum: When RMSE>3dB, the optical switch automatically switches to the backup optical path, performs a self-test every 24 hours, and the optical path failure response time is ≤100ms, ensuring the continuous operation time of the monitoring system is ≥10 4 hours, meeting long-term engineering monitoring needs.

10. A smart cable monitoring device, comprising the smart cable monitoring system according to claim 1, wherein: include: The smart cable body consists of either a high-vanadium cable or a sealed cable as the outer cable body, and the internal fiber Bragg grating smart tendons are coupled through a hot-cast anchor process to sense the cable body strain; The signal acquisition module includes an optical signal transmitter and a receiver. The transmitter transmits light to the fiber Bragg grating (FBG) smart ribs in a directionally controlled manner, and the receiver obtains the light beam after grating dispersion. A data processing module is used to convert the optical signal into strain data and calculate the cable stress and cable force; The terminal interaction module includes a wireless communication unit and a self-powered unit for data transmission and device power supply.

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