A method and system for fault identification of buried optical fibers

Abstract

The present application provides a method and system for fault identification of buried optical fibers, which relates to the field of computer technology. It can obtain the polarization mode dispersion change rate and the time-frequency double-domain perturbation coefficient of the optical signal of the buried optical fiber to be detected. The time-frequency double-domain perturbation coefficient is determined according to the amplitude fluctuation coefficient and the frequency offset of the optical signal. When the polarization mode dispersion change rate is greater than a preset first threshold and the time-frequency double-domain perturbation coefficient is greater than a preset second threshold, obtain the environmental state information and physical excitation parameters of the buried optical fiber. Input the environmental state information, polarization mode dispersion change rate, physical excitation parameters, and amplitude fluctuation coefficient into a pre-trained micro-bending strain identification model to obtain the micro-bending strain state index of the buried optical fiber. Input the micro-bending strain state index, environmental state information, and time-frequency double-domain perturbation coefficient into a preset optical fiber fault classification model to obtain the fault identification result of the buried optical fiber. It can comprehensively and accurately identify faults in buried optical fibers.

Description

Technical Field

[0001] This application relates to the field of computer technology, and particularly to a method and system for fault identification of buried optical fibers. Background Art

[0002] With the rapid development of information technology, buried optical fibers, as an important carrier for information transmission, are widely used in many fields such as communication, power, and transportation. Buried optical fibers provide a solid guarantee for the stable and high-speed transmission of various types of data with their advantages of large transmission capacity, strong anti-interference ability, and good confidentiality. For example, in long-distance communication trunks, buried optical fibers undertake the task of cross-regional transmission of massive data; in smart power grids, buried optical fibers are used to transmit real-time operation status data of power systems to achieve intelligent monitoring and management of the power grid.

[0003] However, due to the complex laying environment of buried optical fibers, faults are likely to occur. Traditional methods for detecting faults in buried optical fibers mainly rely on single-parameter monitoring, such as using an optical time domain reflectometer (OTDR) to detect the loss of optical signals. Although this method can detect obvious faults such as fiber breaks, its detection sensitivity for some hidden faults is relatively low. Hidden faults will gradually affect the transmission performance of optical fibers and may eventually lead to serious faults. Summary of the Invention

[0004] The technical problem to be solved by this application is to provide a method and system for fault identification of buried optical fibers, which can comprehensively and accurately identify faults in buried optical fibers. The specific solutions are as follows:

[0005] A method for fault identification of buried optical fibers includes:

[0006] In response to a fault detection instruction, obtain the polarization mode dispersion change rate and the time-frequency double-domain perturbation coefficient of the optical signal of the buried optical fiber to be detected, where the time-frequency double-domain perturbation coefficient is determined according to the amplitude fluctuation coefficient and the frequency offset of the optical signal;

[0007] When the polarization mode dispersion change rate is greater than a preset first threshold and the time-frequency double-domain perturbation coefficient is greater than a preset second threshold, obtain the environmental state information and physical excitation parameters of the buried optical fiber;

[0008] Input the environmental state information, polarization mode dispersion change rate, physical excitation parameters, and amplitude fluctuation coefficient into a pre-trained micro-bending strain identification model to obtain the micro-bending strain state index of the buried optical fiber;

[0009] Input the micro-bending strain state index, the environmental state information, and the time-frequency double-domain perturbation coefficient into a preset optical fiber fault classification model to obtain the fault identification result of the buried optical fiber.

[0010] Optionally, the process of determining the time-frequency dual-domain perturbation coefficient according to the amplitude fluctuation coefficient and the optical signal frequency offset of the optical signal includes:

[0011] Perform wavelet packet decomposition on the amplitude fluctuation coefficient of the optical signal to generate a time-domain subband energy distribution matrix; the energy distribution matrix characterizes the energy distribution characteristics under different time-domain subbands;

[0012] Perform window Fourier transform on the frequency offset of the optical signal, extract the singularity index of each frequency point in the time-frequency matrix, and screen out the set of abnormal frequency points through a preset singularity threshold. The singularity index reflects the local mutation characteristics of the frequency-domain signal; the set of abnormal frequency points contains abnormal frequency points with a singularity index less than the singularity threshold;

[0013] Adjust the weight coefficient of each time-domain subband according to the variance characteristics of each time-domain subband in the time-domain subband energy distribution matrix; determine the frequency-domain anomaly intensity according to the maximum frequency offset in the set of abnormal frequency points; determine the statistical correlation characteristics between the time-domain subband energy distribution matrix after adjusting the weight coefficient and the set of abnormal frequency points, and perform non-linear fusion on the adjusted weight coefficient, the frequency-domain anomaly intensity, and the statistical correlation characteristics of each time-domain subband through a dynamic coupling factor to generate an initial perturbation coefficient;

[0014] Generate a compensation factor for the initial perturbation coefficient according to the change rate of the ambient temperature of the buried optical fiber, and adjust the initial perturbation coefficient according to the compensation factor to obtain the time-frequency dual-domain perturbation coefficient.

[0015] Optionally, the microbending strain recognition model includes a temporal convolutional network, a bidirectional long short-term memory network, and a task learning module. The process of inputting the environmental state information, polarization mode dispersion change rate, physical excitation parameters, and amplitude fluctuation coefficient into a pre-trained microbending strain recognition model to obtain the microbending strain state index of the buried optical fiber includes:

[0016] Input the environmental state information, polarization mode dispersion change rate, physical excitation parameters, and amplitude fluctuation coefficient into the temporal convolutional network in the microbending strain recognition model to obtain the temporal features output by the temporal convolutional network;

[0017] Input the temporal features into the bidirectional long short-term memory network in the microbending strain recognition model to obtain the context-aware features output by the bidirectional long short-term memory network;

[0018] Input the context-aware features into the task learning module to obtain the microbending strain state index of the buried optical fiber.

[0019] Optionally, the training process of the micro-bending strain recognition model in the above method includes:

[0020] Obtain an initial recognition model and a first training data set; the first training data set includes a plurality of first training samples and the sample label of each first training sample; the first training sample includes a historical environmental state information sequence, a historical polarization mode dispersion change rate sequence, a historical physical excitation parameter sequence, and a historical amplitude fluctuation coefficient sequence;

[0021] Select a current first target training sample from the first training sample set;

[0022] Input the first target training sample into the initial recognition model to obtain the recognition result output by the initial recognition model;

[0023] Calculate the loss function value according to the recognition result and the sample label of the first target training sample;

[0024] Update the model parameters of the initial recognition model by using the loss function value;

[0025] In the case that the initial recognition model after updating the model parameters does not meet the first training completion condition, return to execute the step of selecting the current first target training sample from the first training sample set;

[0026] In the case that the initial recognition model after updating the model parameters meets the first training completion condition, determine the initial recognition model that meets the first training completion condition as the micro-bending strain recognition model.

[0027] Optionally, after obtaining the fault recognition result of the buried optical fiber in the above method, it further includes:

[0028] Generate a fault warning message according to the fault recognition result and output the fault warning message.

[0029] A fault recognition system for a buried optical fiber includes:

[0030] A first acquisition unit, configured to obtain the polarization mode dispersion change rate and the time-frequency double-domain perturbation coefficient of the optical signal of the buried optical fiber to be detected in response to a fault detection instruction, where the time-frequency double-domain perturbation coefficient is determined according to the amplitude fluctuation coefficient and the frequency offset of the optical signal;

[0031] A second acquisition unit, configured to obtain the environmental state information and physical excitation parameters of the buried optical fiber when the polarization mode dispersion change rate is greater than a preset first threshold and the time-frequency double-domain perturbation coefficient is greater than a preset second threshold;

[0032] The first execution unit is configured to input the environmental state information, polarization mode dispersion change rate, physical excitation parameter, and amplitude fluctuation coefficient into a pre-trained micro-bending strain recognition model to obtain the micro-bending strain state index of the buried optical fiber;

[0033] The second execution unit is configured to input the micro-bending strain state index, the environmental state information, and the time-frequency double-domain perturbation coefficient into a preset optical fiber fault classification model to obtain the fault recognition result of the buried optical fiber.

[0034] For the above system, optionally, the first acquisition unit includes:

[0035] The decomposition sub-unit is configured to perform wavelet packet decomposition on the amplitude fluctuation coefficient of the optical signal to generate a time-domain sub-band energy distribution matrix; the energy distribution matrix characterizes the energy distribution characteristics under different time-domain sub-bands;

[0036] The transformation sub-unit is configured to perform window Fourier transform on the frequency offset of the optical signal, extract the singularity index of each frequency point in the time-frequency matrix, and screen an abnormal frequency point set through a preset singularity threshold. The singularity index reflects the local mutation characteristics of the frequency-domain signal; the abnormal frequency point set contains abnormal frequency points with a singularity index less than the singularity threshold;

[0037] The first execution sub-unit is configured to adjust the weight coefficient of each time-domain sub-band according to the variance characteristics of each time-domain sub-band in the time-domain sub-band energy distribution matrix; determine the frequency-domain abnormal intensity according to the maximum frequency offset in the abnormal frequency point set; determine the statistical correlation characteristics between the adjusted time-domain sub-band energy distribution matrix and the abnormal frequency point set, and perform non-linear fusion on the adjusted weight coefficient of each time-domain sub-band, the frequency-domain abnormal intensity, and the statistical correlation characteristics through a dynamic coupling factor to generate an initial perturbation coefficient;

[0038] The second execution sub-unit is configured to generate a compensation factor for the initial perturbation coefficient according to the change rate of the environmental temperature where the buried optical fiber is located, and adjust the initial perturbation coefficient according to the compensation factor to obtain a time-frequency double-domain perturbation coefficient.

[0039] For the above system, optionally, the micro-bending strain recognition model includes a temporal convolutional network, a bidirectional long short-term memory network, and a task learning module. The first execution unit includes:

[0040] The third execution sub-unit is configured to input the environmental state information, polarization mode dispersion change rate, physical excitation parameter, and amplitude fluctuation coefficient into the temporal convolutional network in the micro-bending strain recognition model to obtain the temporal features output by the temporal convolutional network;

[0041] The fourth execution subunit is configured to input the timing features into the bidirectional long short-term memory network in the micro-bending strain recognition model to obtain context-aware features output by the bidirectional long short-term memory network;

[0042] The fifth execution subunit is configured to input the context-aware features into the task learning module to obtain the micro-bending strain state index of the buried optical fiber.

[0043] For the above system, optionally, the first execution unit includes:

[0044] An acquisition subunit, configured to acquire an initial recognition model and a first training data set; the first training data set includes a plurality of first training samples and sample labels of each of the first training samples; the first training sample includes a historical environmental state information sequence, a historical polarization mode dispersion change rate sequence, a historical physical excitation parameter sequence, and a historical amplitude fluctuation coefficient sequence;

[0045] A selection subunit, configured to select a current first target training sample for training from the first training sample set;

[0046] The sixth execution subunit is configured to input the first target training sample into the initial recognition model to obtain a recognition result output by the initial recognition model;

[0047] A calculation subunit, configured to calculate a loss function value according to the recognition result and the sample label of the first target training sample;

[0048] An update subunit, configured to update the model parameters of the initial recognition model by using the loss function value;

[0049] The seventh execution subunit is configured to, when the initial recognition model after updating the model parameters does not meet the first training completion condition, return to execute the step of selecting a current first target training sample for training from the first training sample set;

[0050] A determination subunit, configured to, when the initial recognition model after updating the model parameters meets the first training completion condition, determine the initial recognition model that meets the first training completion condition as the micro-bending strain recognition model.

[0051] For the above system, optionally, it further includes:

[0052] The third execution unit is configured to generate a fault warning message according to the fault recognition result and output the fault warning message.

[0053] Based on the above-mentioned fault identification method and system for buried optical fibers provided by the present application, it is possible to obtain the polarization mode dispersion change rate and the time-frequency dual-domain perturbation coefficient of the optical signal of the buried optical fiber to be detected, and the time-frequency dual-domain perturbation coefficient is determined according to the amplitude fluctuation coefficient and the frequency offset of the optical signal; in the case where the polarization mode dispersion change rate is greater than a preset first threshold and the time-frequency dual-domain perturbation coefficient is greater than a preset second threshold, obtain the environmental state information and physical excitation parameters of the buried optical fiber; input the environmental state information, the polarization mode dispersion change rate, the physical excitation parameters, and the amplitude fluctuation coefficient into a pre-trained micro-bending strain identification model to obtain the micro-bending strain state index of the buried optical fiber; input the micro-bending strain state index, the environmental state information, and the time-frequency dual-domain perturbation coefficient into a preset optical fiber fault classification model to obtain the fault identification result of the buried optical fiber. It can comprehensively and accurately identify faults in buried optical fibers. Description of the Drawings

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on the provided drawings.

[0055] Figure 1 It is a flowchart of a method for fault identification of a buried optical fiber provided by the present application;

[0056] Figure 2 It is a flowchart of a process for determining the time-frequency dual-domain perturbation coefficient provided by the present application;

[0057] Figure 3 It is a flowchart of a process for obtaining the micro-bending strain state index of a buried optical fiber provided by the present application;

[0058] Figure 4 It is a schematic structural diagram of a fault identification system for a buried optical fiber provided by the present application. Detailed Embodiments

[0059] The following will clearly and completely describe the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0060] In this application, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or apparatus comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or apparatus. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or apparatus comprising the element.

[0061] An embodiment of the present invention provides a method for fault identification of buried optical fibers, which is applied to an electronic device. The method flowchart of the method is as Figure 1 shown, and specifically includes:

[0062] S101: In response to a fault detection instruction, obtain the polarization mode dispersion change rate and the time-frequency double-domain perturbation coefficient of the optical signal of the buried optical fiber to be detected. The time-frequency double-domain perturbation coefficient is determined according to the amplitude fluctuation coefficient and the frequency offset of the optical signal.

[0063] In this embodiment, the fault detection instruction may be an instruction generated when a three-dimensional microelectromechanical system sensor detects that the acceleration standard deviation exceeds the acceleration threshold in a continuous plurality of sampling periods (sampling rate 500 Hz), the distributed optical fiber temperature measurement system monitors that the interval temperature gradient between adjacent regions > the temperature gradient threshold, or the data collected by an optical time domain reflectometer indicates that the optical power drops by more than the preset power threshold within a preset time.

[0064] In this embodiment, the polarization mode dispersion change rate may refer to the change rate of the propagation time delay difference between two orthogonal polarization modes of an optical signal in an optical fiber due to the birefringence effect. The polarization mode dispersion change rate can be calculated by a polarization diversity receiver monitoring the Stokes parameters in real time.

[0065] Optionally, the time-frequency double-domain perturbation coefficient is a comprehensive perturbation index that fuses the time-domain amplitude fluctuation and the frequency-domain frequency offset of the optical signal, and characterizes the intensity and frequency response characteristics of external interference.

[0066] In this embodiment, the amplitude fluctuation coefficient may be the degree of change of the optical signal amplitude over time; an optical detector can be used to convert the optical signal into an electrical signal, and after sampling by a high-speed analog-to-digital converter, the statistical value can be calculated; data can also be obtained by combining an optical power meter or an optical time domain reflectometer to obtain the amplitude fluctuation coefficient. In some embodiments, the amplitude fluctuation coefficient can be obtained by calculating the standard deviation or variance of the amplitude time series and then normalizing it by dividing by the average amplitude.

[0067] In this embodiment, the frequency offset may be the deviation between the actual frequency and the nominal frequency of the optical signal. The frequency offset can be calculated by the phase detection method or the spectrum analysis method. For example, the phase change can be tracked using a phase-locked loop to calculate the frequency offset, or the spectrum can be analyzed to detect the center frequency offset. It can also be directly measured using an optical spectrum analyzer, or the phase change can be analyzed after demodulating the signal by a coherent receiver, and the digital signal processing technology can be combined to calculate the frequency offset.

[0068] S102: When the polarization mode dispersion change rate is greater than a preset first threshold and the time-frequency double-domain perturbation coefficient is greater than a preset second threshold, obtain the environmental state information and physical excitation parameters of the buried optical fiber.

[0069] In this embodiment, the first threshold is set according to the type of the buried optical fiber. The preset configuration table can be queried according to the type of the buried optical fiber to obtain the first threshold. The second threshold is set according to the burial depth of the buried optical fiber. The setting method of the second threshold is as follows:

[0070]

[0071] Among them, is the second threshold, and d is the burial depth of the buried optical fiber.

[0072] Optionally, the environmental state information may include at least one of soil temperature, chemical corrosion parameters, electromagnetic environment parameters, biological activity parameters, and groundwater level, etc. The physical excitation parameters may include at least one of mechanical dynamics parameters (such as shock energy spectral density, stress wave propagation speed, and plastic deformation index, etc.), three-dimensional vibration acceleration, and sound pressure level, etc.

[0073] In this embodiment, the polarization mode dispersion change rate reflects the change rate of the propagation time delay difference caused by the birefringence effect of the optical signal in the optical fiber and is sensitive to the change of the internal structure of the optical fiber. The time-frequency double-domain perturbation coefficient combines the time-domain amplitude fluctuation and the frequency-domain frequency offset characteristics of the optical signal and characterizes the intensity and frequency response characteristics of the external interference. The combination of the two can more accurately judge whether the optical fiber is in an abnormal state, reduce the possibility of misjudgment and missed judgment, and effectively improve the accuracy of fault identification.

[0074] In this embodiment, the first threshold is set according to the type of the buried optical fiber. Different types of optical fibers have different sensitivities to faults due to differences in materials, structures, etc. The corresponding threshold can be obtained by querying the preset configuration table, making the detection more targeted. The second threshold is set according to the burial depth of the buried optical fiber. Considering that the degree of influence of the optical fiber by the environment is different for different burial depths, for example, the optical fiber with a shallower burial depth is more easily affected by ground activities. This way of setting the threshold according to the actual installation parameters further improves the accuracy of fault judgment.

[0075] S103: Input the environmental status information, polarization mode dispersion change rate, physical excitation parameter, and amplitude fluctuation coefficient into a pre-trained micro-bending strain recognition model to obtain the micro-bending strain status index of the buried optical fiber.

[0076] In this embodiment, the micro-bending strain recognition model includes a temporal convolutional network, a bidirectional long short-term memory network, and a task learning module. The temporal convolutional network can be used to extract temporal features, and the bidirectional long short-term memory network can be used to extract context-aware features. The task learning module can be a multi-task learning module, which can output the micro-bending strain status index, a pre-classification result, and the confidence level of the micro-bending strain status index, etc. That is, the output results of the micro-bending strain recognition model can include the micro-bending strain status index, a pre-classification result, and the confidence level of the micro-bending strain status index, etc.

[0077] Optionally, the micro-bending strain status index can comprehensively reflect the degree of micro-bending deformation and its potential risk of the buried optical fiber caused by external mechanical stress or environmental changes. The micro-bending strain status index can take values between [0, 1]. The larger the value, the higher the risk.

[0078] In this embodiment, the micro-bending strain recognition model is a time series parameter, which includes environmental status information, polarization mode dispersion change rate, physical excitation parameter, and amplitude fluctuation coefficient.

[0079] In this embodiment, the micro-bending strain recognition model consists of a temporal convolutional network, a bidirectional long short-term memory network, and a multi-task learning module. The temporal convolutional network effectively extracts the temporal features of the input parameters through a specific convolutional structure, and can capture the change rules of environmental status, optical signal parameters, etc. over time. The bidirectional long short-term memory network uses its bidirectional structure to fully consider the front and back information of the time series, extracts context-aware features, and enhances the understanding ability of complex time series data. The multi-task learning module outputs the micro-bending strain status index, a pre-classification result, and the confidence level based on the context-aware features, and evaluates the micro-bending strain from multiple perspectives. This way of multi-module collaborative work can evaluate the degree of micro-bending deformation and its potential risk of the buried optical fiber caused by external mechanical stress or environmental changes more comprehensively and accurately than traditional single-structure models, providing a reliable basis for subsequent fault classification.

[0080] S104: Input the micro-bending strain status index, the environmental status information, and the time-frequency dual-domain perturbation coefficient into a preset optical fiber fault classification model to obtain the fault recognition result of the buried optical fiber.

[0081] In this embodiment, the micro-bending strain state index, the environmental state information, and the time-frequency dual-domain perturbation coefficient are synchronously input into a preset optical fiber fault classification model. Through multi-modal feature fusion and non-linear decision boundary calculation, a fault recognition result of the buried optical fiber is generated. Among them, the micro-bending strain state index characterizes the strain energy accumulation state of the microscopic deformation of the optical fiber. The environmental state information includes soil temperature and humidity, groundwater level, and geostress distribution parameters. The time-frequency dual-domain perturbation coefficient is jointly calculated based on the time-domain derivative of the optical signal amplitude fluctuation and the frequency-domain power spectrum entropy. The optical fiber fault classification model is pre-trained by a generative adversarial network, and its decision space is divided into four orthogonal sub-spaces: mechanical damage, chemical corrosion, geological deformation, and environmental interference. The output result is a fault type label with confidence evaluation, and may also include a positioning topology map.

[0082] In this embodiment, the optical fiber fault classification model synchronously inputs the micro-bending strain state index, the environmental state information, and the time-frequency dual-domain perturbation coefficient through multi-modal feature fusion, fully utilizes the information carried by different parameters, and comprehensively characterizes the state of the optical fiber. Through non-linear decision boundary calculation, complex feature relationships can be processed, and a fault recognition result can be accurately generated. Compared with traditional simple classification methods, this method can more accurately identify the fault type and improve the accuracy of fault classification.

[0083] Optionally, three parallel input channels can be established to perform normalization encoding on the micro-bending strain state index, the environmental state information, and the time-frequency dual-domain perturbation coefficient respectively.

[0084] In some embodiments, the output result of the micro-bending strain recognition model, the environmental state information, and the time-frequency dual-domain perturbation coefficient can be input into a preset optical fiber fault classification model to obtain the fault recognition result of the buried optical fiber.

[0085] Applying the method provided in the embodiments of the present application can comprehensively and accurately identify faults in buried optical fibers.

[0086] In an embodiment provided by the present application, based on the above solution, optionally, the process of determining the time-frequency dual-domain perturbation coefficient according to the amplitude fluctuation coefficient of the optical signal and the optical signal frequency offset is as Figure 2 shown and includes:

[0087] S201: Perform wavelet packet decomposition on the amplitude fluctuation coefficient of the optical signal to generate a time-domain sub-band energy distribution matrix; the energy distribution matrix characterizes the energy distribution characteristics under different time-domain sub-bands.

[0088] In this embodiment, the optical signal amplitude fluctuation coefficient is decomposed by K-layer wavelet packet decomposition. The multi-Bessel 4 wavelet basis function is used, and the optimal basis function is adaptively selected from the basis function library according to the minimum energy entropy criterion. After decomposition, k time-domain subbands are generated, and each subband corresponds to a different time resolution level (for example, the first layer corresponds to the instantaneous fluctuation of 0-10 ms, and the kth layer corresponds to the long-term trend of 1-10 s). Energy integration is performed on the signals of each subband to construct a time-domain subband energy distribution matrix , where e i represents the normalized energy value of the ith layer subband, k is an integer greater than or equal to 2, for example, it can be 8; i is an integer not greater than k. Through multi-scale time-frequency analysis, the transient and steady-state components of the signal are separated, overcoming the limitation that traditional wavelet decomposition only decomposes low frequencies. The basis function that makes the subband energy distribution the most concentrated is selected to suppress noise interference and enhance the characteristics of the effective signal.

[0089] S202: Perform window Fourier transform on the frequency offset of the optical signal, extract the singularity index of each frequency point in the time-frequency matrix, and screen the abnormal frequency point set through a preset singularity threshold. The singularity index reflects the local mutation characteristics of the frequency-domain signal; the abnormal frequency point set contains abnormal frequency points with singularity indices less than the singularity threshold.

[0090] In this embodiment, perform Hamming window short-time Fourier transform on the optical signal frequency offset. The window length is 256 points and the overlap rate is 75% to generate a time-frequency matrix . Calculate the singularity index of each frequency point as follows:

[0091]

[0092] Among them, is the singularity index, and this singularity index can be the Hlder index; s is the scale parameter, represents time, represents frequency.

[0093] Optionally, screen abnormal frequency points that satisfy <0.5 to form an abnormal frequency point set .

[0094] In this embodiment, the singularity threshold can be a preset critical value of the Hlder index (such as 0.5) for distinguishing normal frequency points from abnormal frequency points.

[0095] In this embodiment, the abnormal frequency point set contains all frequency points that satisfy <0.5, which characterizes the frequency-domain burst interference event.

[0096] S203: Adjust the weight coefficients of each time-domain sub-band according to the variance characteristics of each time-domain sub-band in the time-domain sub-band energy distribution matrix; determine the frequency-domain anomaly intensity according to the maximum frequency offset in the set of abnormal frequency points; determine the statistical correlation characteristics between the adjusted time-domain sub-band energy distribution matrix and the set of abnormal frequency points, and non-linearly fuse the adjusted weight coefficients, the frequency-domain anomaly intensity, and the statistical correlation characteristics of each time-domain sub-band through a dynamic coupling factor to generate an initial perturbation coefficient.

[0097] In this embodiment, the variance of the energy sequence of each time-domain sub-band can be calculated , and the weight coefficient is used to suppress the noise interference of high-fluctuation sub-bands.

[0098] Optionally, the maximum frequency offset in the set of abnormal frequency points can be extracted to quantify the frequency-domain anomaly intensity, as follows:

[0099]

[0100] Among them, represents the maximum frequency offset, represents the frequency offset with respect to the frequency in absolute value. represents the frequency offset at the frequency , that is, the difference between the actual frequency and the reference frequency. Taking the absolute value is to uniformly measure the magnitude of the frequency offset without considering the offset direction. is the set of abnormal frequency points.

[0101] Optionally, calculate the Pearson correlation coefficient ρ between the time-domain sub-band energy distribution matrix and the abnormal frequency distribution, and screen the significantly correlated sub-bands with |ρ| > 0.7.

[0102] Optionally, through the dynamic coupling factor perform weighted superposition on the time-domain weight coefficient, the frequency-domain anomaly intensity, and the statistical correlation characteristics to generate an initial perturbation coefficient, and the initial perturbation coefficient is expressed as follows:

[0103]

[0104] Among them, tanh represents the hyperbolic tangent function, which is used to map the integral result to the interval (−1, 1), playing a role of normalization and non-linear transformation. Through this non-linear transformation, the response of the coupling factor to the frequency offset can be made more in line with the actual needs, avoiding the limitations that may be brought by linear transformation, and enhancing the adaptability of the model to complex situations. T represents the length of the historical time window, and T can be determined according to the application scenario.

[0105] The dynamic coupling factor can adaptively adjust the fusion ratio of time-domain and frequency-domain features according to the historical frequency offset trend. Specifically, it can be based on the normalization parameter of the frequency offset integral value to balance the fusion weights of time-domain and frequency-domain features.

[0106] S204: Generate a compensation factor for the initial perturbation coefficient according to the change rate of the ambient temperature of the buried optical fiber, and adjust the initial perturbation coefficient according to the compensation factor to obtain a time-frequency dual-domain perturbation coefficient.

[0107] In this embodiment, the change rate of temperature ΔT / Δt along the optical fiber can be obtained through a distributed optical fiber temperature measurement system.

[0108] Optionally, a logical function can be used to calculate the temperature-sensitive compensation factor:

[0109]

[0110] where is the temperature-sensitive compensation factor, is the reference temperature change rate, k is the slope coefficient, and t is the time.

[0111] In this embodiment, temperature compensation can be performed on the initial perturbation coefficient:

[0112]

[0113] where γ is the temperature-sensitive coefficient (default 0.1), which is calibrated according to the thermal expansion coefficient of the optical fiber material.

[0114] In this embodiment, the signal parameter drift caused by the natural change of the ambient temperature can be suppressed, and the stability of perturbation detection can be improved.

[0115] In an embodiment provided by the present application, based on the above solution, optionally, the micro-bending strain recognition model includes a temporal convolutional network, a bidirectional long short-term memory network, and a task learning module. The process of inputting the environmental state information, polarization mode dispersion change rate, physical excitation parameter, and amplitude fluctuation coefficient into a pre-trained micro-bending strain recognition model to obtain the micro-bending strain state index of the buried optical fiber is as Figure 3 shown, and includes:

[0116] S301: Input the environmental state information, polarization mode dispersion change rate, physical excitation parameter, and amplitude fluctuation coefficient into the temporal convolutional network in the micro-bending strain recognition model to obtain the temporal features output by the temporal convolutional network.

[0117] In this embodiment, the Temporal Convolutional Network (TCN) consists of five dilated causal convolutional layers, and the convolutional kernel size of each layer is set to 5. Its dilation coefficients increase layer by layer, being [1, 2, 4, 8, 16] respectively. Through this structural design, the network can effectively capture the dependency relationships and local features of the input parameters at different time scales.

[0118] Specifically, the first convolutional layer performs a convolution operation on the input data with a convolutional kernel of size 5 and a dilation coefficient of 1 to initially extract features on a relatively short time scale; as the number of layers increases and the dilation coefficient increases, subsequent convolutional layers can gradually capture feature information on longer time scales.

[0119] For example, when processing data on the change of soil temperature over time, the TCN can extract features from short-term temperature fluctuations (such as hourly temperature changes) to long-term temperature trends (such as daily temperature changes), thereby outputting a temporal feature vector containing information on different time scales.

[0120] S302: Input the temporal features into the bidirectional long short-term memory network in the micro-bending strain recognition model to obtain context-aware features output by the bidirectional long short-term memory network.

[0121] In this embodiment, the bidirectional long short-term memory network (Bi-LSTM) has a two-layer hidden structure, and the dimension of each hidden layer is set to 64. The Bi-LSTM can consider both the forward and backward information of the input sequence, thereby better capturing context dependency relationships. When receiving the temporal features output by the TCN, the forward and backward hidden layers of the Bi-LSTM process the temporal features respectively. The forward hidden layer starts from the beginning of the sequence and gradually learns the information before the current moment; the backward hidden layer starts from the end of the sequence and obtains the information after the current moment.

[0122] For example, when analyzing physical excitation parameters (such as the change of vibration frequency over time), the Bi-LSTM can not only know the historical change situation of the vibration frequency but also consider possible future change trends. After fusing this information, it outputs a context-aware feature vector, which integrates the context information of the input parameters over the entire time series and provides a more comprehensive feature representation for accurately evaluating the micro-bending strain state subsequently.

[0123] S303: Input the context-aware features into the task learning module to obtain the micro-bending strain state index of the buried optical fiber.

[0124] In this embodiment, the task learning module is a multi-task learning module, which adopts a dual-task branch structure. One branch outputs the micro-bending strain state index through an activation function, and the value range of this index is between [0, 1]. The larger the value, the higher the degree of micro-bending deformation and its potential risk of the buried optical fiber caused by external mechanical stress or environmental changes. The other branch outputs pre-classification labels, such as "normal", "micro-bending", "extrusion", etc., through a normalization exponential function to preliminarily judge the fault type of the optical fiber. At the same time, to balance the training processes of the two tasks, the loss functions of the two tasks are adjusted through dynamic weight coefficients.

[0125] For example, in the initial stage of training, since the accuracy requirement for the micro-bending strain state index is relatively high, the weight of the loss function of this task can be appropriately increased; as the training progresses, when the accuracy improvement space of the pre-classification label is relatively large, the weight of the loss function of the pre-classification task is increased. Through this multi-task learning mechanism, the model can more effectively learn the complex relationship between the input features and the micro-bending strain state, so as to accurately output the micro-bending strain state index, providing an important basis for subsequent optical fiber fault classification.

[0126] In an embodiment provided by the present application, based on the above solution, optionally, the training process of the micro-bending strain recognition model includes:

[0127] Obtain an initial recognition model and a first training data set; the first training data set includes a plurality of first training samples and the sample label of each first training sample; the first training sample includes a historical environmental state information sequence, a historical polarization mode dispersion change rate sequence, a historical physical excitation parameter sequence, and a historical amplitude fluctuation coefficient sequence;

[0128] Select a current first target training sample from the first training sample set;

[0129] Input the first target training sample into the initial recognition model to obtain the recognition result output by the initial recognition model;

[0130] Calculate the loss function value according to the recognition result and the sample label of the first target training sample;

[0131] Update the model parameters of the initial recognition model using the loss function value;

[0132] In the case that the initial recognition model after updating the model parameters does not meet the first training completion condition, return to execute the step of selecting a current first target training sample from the first training sample set;

[0133] When the initial recognition model after updating the model parameters meets the first training completion condition, the initial recognition model that meets the first training completion condition is determined as the micro-bending strain recognition model.

[0134] In this embodiment, an initial recognition model and a first training data set are obtained. The initial recognition model is the starting architecture for constructing the micro-bending strain recognition model. It has model parameters that can be adjusted subsequently and provides a basic framework for model training.

[0135] The first training data set includes multiple first training samples and the sample labels corresponding to each of the first training samples. Among them, the first training samples include a historical environmental state information sequence, a historical polarization mode dispersion change rate sequence, a historical physical excitation parameter sequence, and a historical amplitude fluctuation coefficient sequence. These sequence data reflect the relevant characteristics of the buried optical fiber under different past times and environmental conditions and are important bases for the model to learn the relationship between micro-bending strain and various parameters. The historical environmental state information sequence covers information such as soil temperature, humidity, chemical corrosion parameters, electromagnetic environment parameters, biological activity parameters, and groundwater level, reflecting the comprehensive situation of the environment where the buried optical fiber is located; the historical polarization mode dispersion change rate sequence records the change rate of the propagation time difference between two orthogonal polarization modes caused by the birefringence effect of the optical signal over time, and this parameter is sensitive to the micro-bending strain of the optical fiber; the historical physical excitation parameter sequence includes mechanical dynamics parameters (such as impact energy spectral density, stress wave propagation speed, and plastic deformation index, etc.), three-dimensional vibration acceleration, and sound pressure level, etc., reflecting the physical effects of the outside world on the optical fiber; the historical amplitude fluctuation coefficient sequence characterizes the fluctuation of the optical signal amplitude, and its change is related to the micro-bending strain of the optical fiber and external interference. The sample labels clearly mark the actual state of the micro-bending strain corresponding to each training sample and provide an accurate learning target for model training.

[0136] In this embodiment, a sample is selected from the rich first training sample set as the input for the current training by means of random sampling or sampling according to specific rules. The purpose is to enable the model to gradually learn the characteristics and laws contained in different samples in the data set, avoid the model overfitting to specific samples, and enhance the generalization ability of the model.

[0137] Optionally, based on its current model parameters, the initial recognition model performs feature extraction and analysis on the input first target training sample, attempts to predict the micro-bending strain state corresponding to the sample, and outputs an identification result. This identification result is the initial judgment of the model on the micro-bending strain situation of the sample based on its current learning state.

[0138] The loss function is used to measure the degree of difference between the recognition results predicted by the model and the actual labels of the samples, and it is an important indicator for evaluating the current performance of the model. By reasonably selecting the loss function (such as mean squared error loss function, cross-entropy loss function, etc.), the deviation between the predicted value and the true value of the model can be quantified, providing a clear direction for subsequent model parameter updates, that is, by adjusting the model parameters to minimize the loss function value, thereby improving the prediction accuracy of the model.

[0139] Adopt optimization algorithms such as the stochastic gradient descent algorithm, calculate the gradients of the model parameters according to the loss function values, and adjust the model parameters according to the gradient directions, so that the model can be closer to the true labels of the samples in subsequent predictions. This is one of the core steps in model training. By continuously adjusting the parameters, the model gradually learns the complex mapping relationship between the input features and the micro-bending strain state.

[0140] Optionally, the first training completion condition can be that the preset number of training rounds reaches the upper limit, the loss function value converges below a certain threshold, the validation set accuracy reaches a specific standard, etc. If the model does not meet this condition, it means that the model has not fully learned the laws in the data, and new samples need to be selected from the training dataset for training, repeating the above steps to further optimize the model parameters until the first training completion condition is met.

[0141] In an embodiment provided by the present application, based on the above solution, optionally, after obtaining the fault recognition result of the buried optical fiber, it further includes:

[0142] Generate a fault warning message according to the fault recognition result, and output the fault warning message.

[0143] In this embodiment, after generating the fault warning message, the system will output the fault warning message through a preset output channel. The output channels may include but are not limited to the following ways:

[0144] Local display: On the local device (such as a monitoring terminal) responsible for monitoring the buried optical fiber, display the fault warning message in the form of a visual interface, facilitating on-site staff to obtain the fault situation in a timely manner. For example, a prompt box pops up on the display screen of the monitoring terminal, clearly showing key information such as the fault type, cause, location, and severity.

[0145] Remote transmission: Transmit the fault warning message through a network (such as a wired network, wireless network, etc.) to a remote management center for managers to remotely monitor the operation status of the buried optical fiber. For example, send the information to a cloud server through Internet of Things technology, and managers can log in to the management platform through terminal devices such as computers and mobile phones to view the detailed fault warning content.

[0146] Alarm Notification: Send fault alarm notifications to relevant maintenance personnel via text messages, voice announcements, etc. For example, the system automatically sends text messages containing fault information to the mobile phones of maintenance personnel, or issues voice alarms through on-site voice broadcast devices to notify nearby staff to handle the faults in a timely manner.

[0147] Through the above steps of generating and outputting fault warning information, the fault situation of the buried optical fiber can be effectively conveyed to relevant personnel in a timely manner, so as to take corresponding measures for processing and ensure the normal operation of the buried optical fiber.

[0148] See Figure 4 , which is a schematic structural diagram of a fault identification method system for a buried optical fiber provided by an embodiment of the present application. The system includes:

[0149] The first acquisition unit 401 is configured to, in response to a fault detection instruction, acquire the polarization mode dispersion change rate and the time-frequency double-domain perturbation coefficient of the optical signal of the to-be-detected buried optical fiber, where the time-frequency double-domain perturbation coefficient is determined according to the amplitude fluctuation coefficient and the frequency offset of the optical signal;

[0150] The second acquisition unit 402 is configured to, when the polarization mode dispersion change rate is greater than a preset first threshold and the time-frequency double-domain perturbation coefficient is greater than a preset second threshold, acquire the environmental state information and physical excitation parameters of the buried optical fiber;

[0151] The first execution unit 403 is configured to input the environmental state information, the polarization mode dispersion change rate, the physical excitation parameter, and the amplitude fluctuation coefficient into a pre-trained microbend strain identification model to obtain the microbend strain state index of the buried optical fiber;

[0152] The second execution unit 404 is configured to input the microbend strain state index, the environmental state information, and the time-frequency double-domain perturbation coefficient into a preset optical fiber fault classification model to obtain the fault identification result of the buried optical fiber.

[0153] In an embodiment provided by the present application, based on the above solution, optionally, the first acquisition unit 401 includes:

[0154] The decomposition subunit is configured to perform wavelet packet decomposition on the amplitude fluctuation coefficient of the optical signal to generate a time-domain sub-band energy distribution matrix; the energy distribution matrix characterizes the energy distribution characteristics under different time-domain sub-bands;

[0155] A transform sub - unit, configured to perform a window Fourier transform on the frequency offset of the optical signal, extract the singularity index of each frequency point in the time - frequency matrix, and screen out an abnormal frequency point set through a preset singularity threshold. The singularity index reflects the local mutation characteristics of the frequency - domain signal; the abnormal frequency point set contains abnormal frequency points with singularity indices less than the singularity threshold.

[0156] A first execution sub - unit, configured to adjust the weight coefficient of each time - domain sub - band according to the variance characteristics of each time - domain sub - band in the time - domain sub - band energy distribution matrix; determine the frequency - domain abnormal intensity according to the maximum frequency offset in the abnormal frequency point set; determine the statistical correlation characteristics between the adjusted time - domain sub - band energy distribution matrix and the abnormal frequency point set, and perform non - linear fusion on the adjusted weight coefficients of each time - domain sub - band, the frequency - domain abnormal intensity, and the statistical correlation characteristics through a dynamic coupling factor to generate an initial perturbation coefficient.

[0157] A second execution sub - unit, configured to generate a compensation factor for the initial perturbation coefficient according to the change rate of the ambient temperature where the buried optical fiber is located, and adjust the initial perturbation coefficient according to the compensation factor to obtain a time - frequency dual - domain perturbation coefficient.

[0158] In an embodiment provided by the present application, based on the above - mentioned solution, optionally, the micro - bend strain recognition model includes a temporal convolutional network, a bidirectional long short - term memory network, and a task learning module. The first execution unit 403 includes:

[0159] A third execution sub - unit, configured to input the environmental state information, polarization mode dispersion change rate, physical excitation parameters, and amplitude fluctuation coefficient into the temporal convolutional network in the micro - bend strain recognition model to obtain the temporal features output by the temporal convolutional network.

[0160] A fourth execution sub - unit, configured to input the temporal features into the bidirectional long short - term memory network in the micro - bend strain recognition model to obtain context - aware features output by the bidirectional long short - term memory network.

[0161] A fifth execution sub - unit, configured to input the context - aware features into the task learning module to obtain the micro - bend strain state index of the buried optical fiber.

[0162] In an embodiment provided by the present application, based on the above - mentioned solution, optionally, the first execution unit 403 includes:

[0163] An acquisition subunit, configured to acquire an initial recognition model and a first training data set; the first training data set includes a plurality of first training samples and sample labels of each of the first training samples; the first training samples include a historical environmental state information sequence, a historical polarization mode dispersion change rate sequence, a historical physical excitation parameter sequence, and a historical amplitude fluctuation coefficient sequence;

[0164] A selection subunit, configured to select a first target training sample currently used for training from the first training sample set;

[0165] A sixth execution subunit, configured to input the first target training sample into the initial recognition model to obtain a recognition result output by the initial recognition model;

[0166] A calculation subunit, configured to calculate a loss function value according to the recognition result and the sample label of the first target training sample;

[0167] An update subunit, configured to update the model parameters of the initial recognition model by using the loss function value;

[0168] A seventh execution subunit, configured to, when the initial recognition model after updating the model parameters does not meet the first training completion condition, return to execute the step of selecting a first target training sample currently used for training from the first training sample set;

[0169] A determination subunit, configured to, when the initial recognition model after updating the model parameters meets the first training completion condition, determine the initial recognition model that meets the first training completion condition as a micro-bending strain recognition model.

[0170] In an embodiment provided by the present application, based on the above solution, optionally, it further includes:

[0171] A third execution unit, configured to generate a fault warning message according to the fault recognition result and output the fault warning message.

[0172] It should be noted that the embodiments in this specification are all described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The same and similar parts among the embodiments can be referred to each other.

[0173] Finally, it should also be noted that in this article, relational terms such as first and second are only used 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.

[0174] For the convenience of description, when describing the above system, it is divided into various units according to functions for separate description. Of course, when implementing this application, the functions of each unit can be realized in the same or multiple software and / or hardware.

[0175] From the description of the above embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of this application, in essence, or the part that makes a contribution to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0176] The above has introduced in detail a method for fault identification of buried optical fibers provided by this application. Specific examples are used in this article to elaborate on the principle and implementation manner of this application. The description of the above embodiments is only used to help understand the method and its core idea of this application; at the same time, for those of ordinary skill in the art, according to the idea of this application, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to this application.

Claims

1. A method for fault identification of buried optical fibers, characterized in that, Including: In response to a fault detection instruction, obtaining the polarization mode dispersion change rate and the time-frequency dual-domain perturbation coefficient of the optical signal of the buried optical fiber to be detected, where the time-frequency dual-domain perturbation coefficient is determined according to the amplitude fluctuation coefficient and the frequency offset of the optical signal; When the polarization mode dispersion change rate is greater than a preset first threshold and the time-frequency dual-domain perturbation coefficient is greater than a preset second threshold, obtaining the environmental state information and physical excitation parameters of the buried optical fiber; Inputting the environmental state information, the polarization mode dispersion change rate, the physical excitation parameters, and the amplitude fluctuation coefficient into a pre-trained micro-bending strain recognition model to obtain the micro-bending strain state index of the buried optical fiber; Inputting the micro-bending strain state index, the environmental state information, and the time-frequency dual-domain perturbation coefficient into a preset optical fiber fault classification model to obtain the fault recognition result of the buried optical fiber.

2. The method according to claim 1, wherein The process of determining the time-frequency dual-domain perturbation coefficient according to the amplitude fluctuation coefficient and the optical signal frequency offset of the optical signal includes: Performing wavelet packet decomposition on the amplitude fluctuation coefficient of the optical signal to generate a time-domain sub-band energy distribution matrix; the energy distribution matrix characterizes the energy distribution characteristics under different time-domain sub-bands; Performing window Fourier transform on the frequency offset of the optical signal, extracting the singularity index of each frequency point in the time-frequency matrix, and screening the abnormal frequency point set through a preset singularity threshold, where the singularity index reflects the local mutation characteristics of the frequency-domain signal; the abnormal frequency point set contains abnormal frequency points with a singularity index less than the singularity threshold; Adjusting the weight coefficient of each time-domain sub-band according to the variance characteristics of each time-domain sub-band in the time-domain sub-band energy distribution matrix; determining the frequency-domain abnormal intensity according to the maximum frequency offset in the abnormal frequency point set; determining the statistical correlation characteristics between the adjusted time-domain sub-band energy distribution matrix and the abnormal frequency point set, and non-linearly fusing the adjusted weight coefficients of each time-domain sub-band, the frequency-domain abnormal intensity, and the statistical correlation characteristics through a dynamic coupling factor to generate an initial perturbation coefficient; Generating a compensation factor for the initial perturbation coefficient according to the change rate of the environmental temperature where the buried optical fiber is located, and adjusting the initial perturbation coefficient according to the compensation factor to obtain the time-frequency dual-domain perturbation coefficient.

3. The method according to claim 1, wherein The micro-bending strain recognition model includes a temporal convolutional network, a bidirectional long short-term memory network, and a task learning module. The step of inputting the environmental state information, the polarization mode dispersion change rate, the physical excitation parameters, and the amplitude fluctuation coefficient into the pre-trained micro-bending strain recognition model to obtain the micro-bending strain state index of the buried optical fiber includes: Inputting the environmental state information, the polarization mode dispersion change rate, the physical excitation parameters, and the amplitude fluctuation coefficient into the temporal convolutional network in the micro-bending strain recognition model to obtain the temporal features output by the temporal convolutional network; Inputting the temporal features into the bidirectional long short-term memory network in the micro-bending strain recognition model to obtain the context-aware features output by the bidirectional long short-term memory network; Input the context-aware features into the task learning module to obtain the micro-bending strain state index of the buried optical fiber.

4. The method according to claim 1, wherein The training process of the micro-bending strain recognition model includes: Obtain an initial recognition model and a first training data set; the first training data set includes a plurality of first training samples and sample labels for each of the first training samples; the first training samples include a historical environmental state information sequence, a historical polarization mode dispersion change rate sequence, a historical physical excitation parameter sequence, and a historical amplitude fluctuation coefficient sequence; Select a current first target training sample from the first training sample set. Input the first target training sample into the initial recognition model to obtain the recognition result output by the initial recognition model. Calculate the loss function value according to the recognition result and the sample label of the first target training sample. Update the model parameters of the initial recognition model using the loss function value. If the initial recognition model after updating the model parameters does not meet the first training completion condition, return to execute the step of selecting the current first target training sample from the first training sample set. If the initial recognition model after updating the model parameters meets the first training completion condition, determine the initial recognition model that meets the first training completion condition as the micro-bending strain recognition model.

5. The method according to claim 1, wherein After obtaining the fault recognition result of the buried optical fiber, it further includes: Generate a fault warning message according to the fault recognition result and output the fault warning message.

6. A fault identification system for buried optical fibers, characterized in that It includes: A first acquisition unit for obtaining the polarization mode dispersion change rate and the time-frequency double-domain perturbation coefficient of the optical signal of the buried optical fiber to be detected in response to a fault detection instruction, where the time-frequency double-domain perturbation coefficient is determined according to the amplitude fluctuation coefficient and the frequency offset of the optical signal; A second acquisition unit for obtaining the environmental state information and physical excitation parameters of the buried optical fiber when the polarization mode dispersion change rate is greater than a preset first threshold and the time-frequency double-domain perturbation coefficient is greater than a preset second threshold; A first execution unit for inputting the environmental state information, polarization mode dispersion change rate, physical excitation parameters, and amplitude fluctuation coefficient into a pre-trained micro-bending strain recognition model to obtain the micro-bending strain state index of the buried optical fiber; A second execution unit for inputting the micro-bending strain state index, the environmental state information, and the time-frequency double-domain perturbation coefficient into a preset optical fiber fault classification model to obtain the fault recognition result of the buried optical fiber.

7. The system according to claim 6, characterized in that The first acquisition unit includes: A decomposition subunit for performing wavelet packet decomposition on the amplitude fluctuation coefficient of the optical signal to generate a time-domain sub-band energy distribution matrix; the energy distribution matrix characterizes the energy distribution characteristics under different time-domain sub-bands; A transform sub - unit for performing window Fourier transform on the frequency offset of the optical signal, extracting the singularity index of each frequency point in the time - frequency matrix, and screening the abnormal frequency point set through a preset singularity threshold. The singularity index reflects the local mutation characteristics of the frequency - domain signal; the abnormal frequency point set contains abnormal frequency points with singularity indices less than the singularity threshold. A first execution sub - unit for adjusting the weight coefficients of each time - domain sub - band according to the variance characteristics of each time - domain sub - band in the time - domain sub - band energy distribution matrix; determining the frequency - domain abnormal intensity according to the maximum frequency offset in the abnormal frequency point set; determining the statistical correlation characteristics between the adjusted time - domain sub - band energy distribution matrix and the abnormal frequency point set, and non - linearly fusing the adjusted weight coefficients of each time - domain sub - band, the frequency - domain abnormal intensity, and the statistical correlation characteristics through a dynamic coupling factor to generate an initial perturbation coefficient. A second execution sub - unit for generating a compensation factor for the initial perturbation coefficient according to the change rate of the environmental temperature where the buried optical fiber is located, and adjusting the initial perturbation coefficient according to the compensation factor to obtain a time - frequency dual - domain perturbation coefficient.

8. The system according to claim 6, wherein The micro - bend strain identification model includes a temporal convolutional network, a bidirectional long - short - term memory network, and a task learning module. The first execution unit includes: A third execution sub - unit for inputting the environmental state information, polarization mode dispersion change rate, physical excitation parameters, and amplitude fluctuation coefficient into the temporal convolutional network in the micro - bend strain identification model to obtain the temporal features output by the temporal convolutional network. A fourth execution sub - unit for inputting the temporal features into the bidirectional long - short - term memory network in the micro - bend strain identification model to obtain the context - aware features output by the bidirectional long - short - term memory network. A fifth execution sub - unit for inputting the context - aware features into the task learning module to obtain the micro - bend strain state index of the buried optical fiber.

9. The system according to claim 6, wherein The first execution unit includes: An acquisition sub - unit for acquiring an initial recognition model and a first training data set; the first training data set includes multiple first training samples and the sample label of each first training sample; the first training sample includes a historical environmental state information sequence, a historical polarization mode dispersion change rate sequence, a historical physical excitation parameter sequence, and a historical amplitude fluctuation coefficient sequence. A selection sub - unit for selecting the current first target training sample from the first training sample set. A sixth execution sub - unit for inputting the first target training sample into the initial recognition model to obtain the recognition result output by the initial recognition model. A calculation sub - unit for calculating the loss function value according to the recognition result and the sample label of the first target training sample. An update sub - unit for updating the model parameters of the initial recognition model using the loss function value. The seventh execution subunit is configured to, when the initial recognition model after updating the model parameters does not meet the first training completion condition, return to execute the step of selecting the current first target training sample for training from the first training sample set; The determination subunit is configured to, when the initial recognition model after updating the model parameters meets the first training completion condition, determine the initial recognition model that meets the first training completion condition as the micro-bending strain recognition model.

10. The system according to claim 6, characterized in that, It further includes: The third execution unit is configured to generate a fault warning message according to the fault recognition result and output the fault warning message.

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