A method for detecting the burial depth of optical cables based on distributed optical fiber sensing technology

Abstract

The present invention proposes an optical cable burial depth detection method based on distributed optical fiber sensing technology, which relates to the field of optical fiber sensing technology. The above method includes: collecting data on changes in the burial state of the optical cable, where the data on changes in the burial state includes temperature data and vibration data; constructing a spatio-temporal data matrix corresponding to the temperature data, and performing data processing on the spatio-temporal data matrix according to the sliding window algorithm to obtain spatial temperature distribution data, selecting multiple reference features of the spatial temperature distribution data in any time period to construct a feature matrix, and sending the feature matrix into the optical cable temperature-burial depth relationship model to obtain the predicted depth of the optical cable corresponding to the temperature data; constructing a ground noise environment model based on the vibration data, and obtaining a noise field distribution characteristic function corresponding to the ground noise environment model based on the ground noise environment model, performing Fourier transform on the noise field distribution characteristic function to obtain a noise correlation function, and calculating the predicted depth of the optical cable corresponding to the vibration data according to the ground environment parameters and the noise correlation function; calculating the burial depth of the optical cable based on the predicted depth of the optical cable corresponding to the temperature data, the predicted depth of the optical cable corresponding to the vibration data, and the depth prediction algorithm. The present invention helps to improve the accuracy of predicting the burial depth of the optical cable.

Description

Technical Field

[0001] The present invention relates to the technical field of optical fiber sensing, and in particular to a method for detecting the burial depth of an optical cable based on distributed optical fiber sensing technology. Background Art

[0002] With the rapid development of communication technology, as the main carrier of information transmission, the safety and stability of optical cables are of crucial importance. However, the burial depth of optical cables directly affects their safety and stability. Traditional methods for measuring the burial depth of optical cables, such as the excavation method, sonar method, etc., have problems such as complex operation, low efficiency, and easy damage to optical cables.

[0003] Chinese Patent with the publication number CN116662877A discloses a sample evaluation method applied to pattern recognition of distributed optical fiber sensing technology. The method includes: determining the evaluation value of the correlation degree of sample features and the evaluation value of the accuracy of the sample class by calculating the covariance between sample features, and assigning different weights to different samples according to these two results. However, the above method still introduces background noise caused by external natural environment interference. At the same time, using a single covariance calculation to evaluate the correlation degree and accuracy of sample features will also increase the measurement error. Therefore, it is very necessary to provide a method for detecting the burial depth of optical cables based on distributed optical fiber sensing technology to improve the accuracy of predicting the burial depth of optical cables. Summary of the Invention

[0004] In view of this, the present invention proposes a method for detecting the burial depth of an optical cable based on distributed optical fiber sensing technology. By constructing a spatio-temporal data matrix and applying a sliding window algorithm for processing, the key features of the spatial temperature distribution are effectively extracted. At the same time, the optical cable temperature-burial depth relationship model can capture short-term and long-term temperature change patterns, and combined with the ground noise environment model and the noise correlation function, the prediction accuracy and model adaptability are improved.

[0005] The present invention provides a method for detecting the burial depth of an optical cable based on distributed optical fiber sensing technology, and the method includes:

[0006] Collecting the data on the change of the burial state of the optical cable, wherein the data on the change of the burial state includes temperature data and vibration data;

[0007] Constructing a spatio-temporal data matrix corresponding to the temperature data, and performing data processing on the spatio-temporal data matrix according to the sliding window algorithm to obtain spatial temperature distribution data, selecting multiple reference features of the spatial temperature distribution data in any time period to construct a feature matrix, and sending the feature matrix into the optical cable temperature-burial depth relationship model to obtain the predicted depth of the optical cable corresponding to the temperature data;

[0008] Construct a ground noise environment model based on vibration data, obtain a noise field distribution characteristic function corresponding to the ground noise environment model based on the ground noise environment model, perform a Fourier transform on the noise field distribution characteristic function to obtain a noise correlation function, and calculate the predicted depth of the optical cable corresponding to the vibration data according to the ground environment parameters and the noise correlation function;

[0009] Calculate the buried depth of the optical cable based on the predicted depth of the optical cable corresponding to the temperature data, the predicted depth of the optical cable corresponding to the vibration data, and a depth prediction algorithm.

[0010] Based on the above technical solutions, preferably, the data processing of the spatio-temporal data matrix according to the sliding window algorithm to obtain spatial temperature distribution data specifically includes:

[0011] Set the length of the sliding window, and perform sliding window partitioning on the spatio-temporal data matrix based on the sliding window length to obtain the window spatial data after sorting within the sliding window of the spatio-temporal data matrix;

[0012] Calculate the window temperature corresponding to each window spatial data based on the window spatial data and the window temperature relationship function, and integrate the window temperatures corresponding to all window spatial data to form spatial temperature distribution data.

[0013] Based on the above technical solutions, preferably, the method further includes:

[0014] Obtain the buried information of the optical cable, where the buried information includes the sample point position, the sample point depth, and a first set of detected temperatures corresponding to the sample point position and the sample point depth;

[0015] Change the sample point position and the sample point depth of the optical cable to obtain a second set of detected temperatures;

[0016] Based on the first set of detected temperatures, the second set of detected temperatures, and the window sliding algorithm, obtain a sample point temperature distribution data set, and perform normalization processing on the sample point temperature distribution data set to obtain a standard temperature distribution data set;

[0017] Construct an optical cable temperature-buried depth relationship model based on the standard temperature distribution data set and the grey correlation degree function.

[0018] Even more preferably, the expression of the temperature feature vector of the i-th sample point in the feature matrix is:

[0019]

[0020]

[0021] ΔT(i, t) = T t (i, t) - T t (i, t - 1)

[0022]

[0023]

[0024] where M(i) represents the mean function of the i-th sample point, T t (i, t) represents the temperature of the i-th sample point at the t-th moment, n represents the number of sample points, S(i) represents the standard deviation function of the i-th sample point, ΔT(i, t) represents the temperature difference function of the i-th sample point at the t-th moment, v(i, t) represents the change rate function of the i-th sample point at the t-th moment, Δt represents the time difference between two adjacent sample points, ω(i) represents the temperature feature vector of the i-th sample point, max() represents the maximum value function, represents the average change rate function of i sample points.

[0025] More preferably, the grey correlation degree function can be expressed as:

[0026]

[0027] where γ i represents the grey correlation degree function of the i-th sample point, j represents the j-th factor index, x j (i) represents the data of the i-th sample point in the j-th factor index, y(i) represents the output data of the i-th sample point, represents the resolution coefficient, n represents the number of sample points, max() represents the maximum value function, min() represents the minimum value function.

[0028] More preferably, constructing the ground noise environment model based on the vibration data specifically includes:

[0029] Preprocessing the vibration data to obtain standard vibration data, and extracting the frequency features corresponding to the standard vibration data, where the frequency features include the wavenumber of the noise source and the angular frequency of the noise source;

[0030] Constructing the ground noise environment model based on the Helmholtz equation, the wavenumber of the noise source and the angular frequency of the noise source.

[0031] More preferably, the expression of the ground noise environment model is:

[0032]

[0033] where denotes the Laplacian operator, k denotes the wavenumber of the noise source, P(r, z) denotes the sound pressure at the horizontal coordinate r and the vertical coordinate z, ω denotes the angular frequency of the noise source, z s denotes the height difference between the noise source and the ground, N s (ω, r s ) denotes a noise source with an angular frequency of ω and a position of r s The noise source, δ() represents the Dirac delta function.

[0034] More preferably, the expression of the noise correlation function is:

[0035] Γ ω (r 1 , r 2 ; z 1 , z 2 ) = <P(r 1 , z 1 )P * (r 2 , z 2 )>

[0036]

[0037]

[0038] where Γ ω represents the cross-spectral density, r 1 represents the horizontal coordinate of the first detection point, r 2 represents the horizontal coordinate of the second detection point, z 1 represents the height difference between the first detection point and the ground, z 2 represents the height difference between the second detection point and the ground, P(r 1 , z 1 ) represents the sound pressure at the first detection point, P*(r 2 , z 2 ) represents the complex conjugate of the sound pressure at the second detection point, R represents the difference between the horizontal coordinates of the first detection point and the second detection point, Φ τ () represents the modal depth function, J 0 () represents the zero-order Bessel function, κ τ represents the real part of the horizontal wavenumber, α τ represents the imaginary part of the horizontal wavenumber, Γ t (R, z 1 , z 2 ) represents the noise correlation function, q represents the intensity of the noise source, ρ represents the density of the waveguide medium, τ represents the number of modes.

[0039] More preferably, the method further includes:

[0040] Obtain real-time depth data according to the depth prediction algorithm, obtain the buried depth alarm threshold from the database, and determine whether the real-time depth data of the sample point is less than the buried depth alarm threshold;

[0041] If the real-time depth data of the sample point is less than the buried depth alarm threshold, obtain the geographical location of the sample point, and send an alarm signal and the geographical location of the sample point to the mobile terminal of the staff.

[0042] A method for detecting the buried depth of an optical cable based on distributed optical fiber sensing technology provided by the present invention has the following beneficial effects compared with the prior art:

[0043] (1) By constructing a spatio-temporal data matrix and applying a sliding window algorithm for processing, the key features of the spatial temperature distribution are effectively extracted and transformed into spatial temperature distribution data, which can significantly improve the model's ability to capture complex temperature-depth relationships. At the same time, data from any time period can be selected for analysis, enabling the optical cable temperature-buried depth relationship model to adapt to different time scales, so that the optical cable temperature-buried depth relationship model can capture short-term and long-term temperature change patterns, and can handle non-linear relationships and adapt to changes in different time scales, thereby improving the model adaptability. By constructing a ground noise environment model, the complex ground noise environment can be accurately described and characterized. Fourier transform can convert the time-domain signal to the frequency domain, which is conducive to analyzing the frequency components of the signal. At the same time, the noise correlation function is used to reflect the correlation between noise signals at different positions. Combining the optical cable temperature-buried depth relationship model with the ground noise environment model and the noise correlation function can improve the accuracy of predicting the buried depth of the optical cable;

[0044] (2) By obtaining the sample point position, depth, and corresponding temperature set, the integrity and representativeness of the data are ensured. At the same time, by changing the sample point position and depth to obtain the second detection temperature set, the model's adaptability to different environments and conditions is improved. Standardizing the sample point temperature distribution data set eliminates the influence of different scales and units, improves the comparability of the data and the stability of the model. Combining the grey correlation degree function to construct the optical cable temperature-buried depth relationship model enables the temperature-buried depth relationship model to handle small sample data and situations with incomplete information, improving the accuracy and reliability of the optical cable temperature-buried depth relationship model. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present invention 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 following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0046] Figure 1 Flow chart of an optical cable burial depth detection method provided by the present invention based on distributed optical fiber sensing technology;

[0047] Figure 2 Schematic diagram of vibration detection for underground burial of optical cables provided by the present invention. Specific implementation manners

[0048] Next, in combination with the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0049] This application embodiment discloses an optical cable burial depth detection method based on distributed optical fiber sensing technology. As Figure 1 shown, the steps of this method include S1 to S4.

[0050] Step S1, collect data on changes in the burial state of the optical cable, where the data on changes in the burial state includes temperature data and vibration data.

[0051] In this step, use a distributed optical fiber temperature sensing system to collect temperature data along the path of the optical cable, and set appropriate spatial sampling intervals (such as 1 meter) and temporal sampling intervals (such as 10 minutes), record the temperature values at different depths and different times, form a three-dimensional temperature-space-time data, that is, continuously collect the temperature data of the measured optical fiber using the distributed optical fiber temperature sensing system, construct a spatio-temporal data matrix carrying two dimensions of time t and space l, as the original signal T t,l .

[0052] The distributed optical fiber temperature sensing system mainly consists of a light source, an optical fiber sensor, a spectral analyzer, and a data processing unit. Its spatial resolution is usually 0.5 - 2 meters, the temperature resolution can generally reach 0.1 °C or higher, and the sampling interval can be set according to requirements, which can be from 5 minutes to 1 hour; continuously collect temperature over a long period to cover different seasons, to capture the temperature change pattern, and at the same time record the day-night temperature change, especially in the shallow burial area, and record the impact of special weather such as rainfall and snowfall on the temperature.

[0053] Use a distributed acoustic sensing system to collect vibration signals along the optical cable. Set an appropriate sampling frequency (e.g., 1000 Hz) and spatial resolution (e.g., 1 meter), and record the vibration waveform data at different positions and times. Among them, the distributed acoustic sensing system consists of a light source, an optical fiber sensor, a photodetector, and a signal processing unit. The sampling frequency is usually set to 500 Hz - 2000 Hz, which is determined according to the highest frequency to be monitored. The spatial resolution is generally 1 - 10 meters, depending on the performance of the optical fiber sensor and the actual requirements. The acquisition duration can be set according to actual needs and can be continuous acquisition or timed acquisition. Observe the characteristics such as vibration amplitude and duration of the acquired vibration waveform data, perform Fourier transform, analyze the frequency characteristics of the vibration signal, and use methods such as short-time Fourier transform or wavelet transform to analyze the time-frequency characteristics of the signal.

[0054] Step S2: Construct a spatio-temporal data matrix corresponding to the temperature data, and perform data processing on the spatio-temporal data matrix according to the sliding window algorithm to obtain the spatial temperature distribution data. Select multiple reference features of the spatial temperature distribution data in any time period to construct a feature matrix, and send the feature matrix into the optical cable temperature-burial depth relationship model to obtain the predicted depth of the optical cable corresponding to the temperature data.

[0055] By constructing a spatio-temporal data matrix and applying the sliding window algorithm for processing, the key features of the spatial temperature distribution are effectively extracted and transformed into spatial temperature distribution data. This process not only optimizes the data structure but also significantly improves the model's ability to capture complex temperature-depth relationships. At the same time, analyzing the data in any time period enables the optical cable temperature-burial depth relationship model to adapt to different time scales, allowing the model to capture short-term and long-term temperature change patterns, handle non-linear relationships, and adapt to changes on different time scales, thereby improving the prediction accuracy and model adaptability.

[0056] Step S2 also includes steps S21 - S22.

[0057] Step S21: Set the length of the sliding window, and divide the spatio-temporal data matrix based on the sliding window length to obtain the window spatial data after sorting within the sliding window of the spatio-temporal data matrix.

[0058] In this step, set a sliding window size of m + 1, and perform sequential sorting on the original signal T within the window, that is: t,l For the sorted spatial signal y of the sliding window

[0059]

[0060] Perform corresponding processing on it, and calculate the temperature t of this sliding window: m+1 ​

[0061]

[0062] Among them, represents the temperature of the th sample point, and t(i) represents the temperature of the i-th sample point after processing.

[0063] Step S22: Based on the window spatial data and the window temperature relationship function, calculate the window temperature corresponding to each window spatial data, and integrate the window temperatures corresponding to all window spatial data to form spatial temperature distribution data.

[0064] In this step, set the moving step size of the sliding window to k, slide and calculate the temperature of each window, and integrate it into the processed spatial temperature distribution data.

[0065] In this embodiment, select the spatial temperature distribution data for any period of time, extract five statistical data from it: average value, standard deviation, maximum temperature difference, average change rate, and maximum change rate. After normalization processing, use them as feature vectors to construct a full-line feature matrix. The expression of the temperature feature vector of the i-th sample point in the feature matrix is:

[0066]

[0067]

[0068] ΔT(i,t) = T t (i,t) - T t (i,t - 1)

[0069]

[0070]

[0071] Among them, M(i) represents the average value function of the i-th sample point, T t (i,t) represents the temperature of the i-th sample point at the t-th moment, n represents the number of sample points, S(o) represents the standard deviation function of the i-th sample point, ΔT(i,t) represents the temperature difference function of the i-th sample point at the t-th moment, v(i,t) represents the change rate function of the i-th sample point at the t-th moment, Δt represents the time difference between two adjacent sample points, ω(i) represents the temperature feature vector of the i-th sample point, max() represents the maximum value function, represents the average change rate function of the i sample points.

[0072] In one example, the process of constructing the optical cable temperature-burial depth relationship model includes: obtaining the burial information of the optical cable, where the burial information includes the sample point location, the sample point depth, and the first detected temperature set corresponding to the sample point location and the sample point depth; changing the sample point location and the sample point depth of the optical cable to obtain the second detected temperature set; based on the first detected temperature set, the second detected temperature set, and the window sliding algorithm, obtaining the sample point temperature distribution data set, and performing standardization processing on the sample point temperature distribution data set to obtain the standard temperature distribution data set; based on the standard temperature distribution data set and the grey correlation degree function, constructing the optical cable temperature-burial depth relationship model.

[0073] The process of obtaining the burial information of the optical cable includes: planning the sampling points, recording the sample point locations, measuring the sample point depths, recording the environmental parameters, collecting the temperature data, recording the meteorological data, and establishing the sample point information database, where,

[0074] Planning the sampling points includes selecting multiple representative sample points along the optical cable path, and ensuring that the sample points cover different geographical environments and burial conditions (such as different soil types, surface coverages, etc.), setting a sufficient number of sampling points to ensure statistical significance, usually not less than 30 sampling points, and the sampling point spacing can be determined according to the total length of the optical cable and the terrain changes. For example, one point is selected every 100 meters or 500 meters.

[0075] Recording the sample point locations includes using a high-precision GPS device to record the geographical coordinates (longitude, latitude) of each sample point, and at the same time recording the distance relative to the starting point of the optical cable (such as at 1500 meters of the optical cable), and drawing the sampling point distribution map to ensure that the sampling points are evenly distributed and representative.

[0076] Measuring the sample point depths includes using a laser rangefinder or an ultrasonic depth finder to measure the depth of the sample points during burial. For relatively shallow burials, a ruler can be directly used for measurement, and the measured depth is recorded, accurate to the centimeter level.

[0077] Recording the environmental parameters includes the soil type (such as sandy, clay, rock, etc.); the surface coverage (such as bare soil, grassland, asphalt pavement, etc.); the groundwater level; the surrounding environmental characteristics (such as whether it is close to buildings, water bodies, etc.).

[0078] Collecting the temperature data (the first detected temperature set) includes using a distributed optical fiber temperature sensing system for continuous temperature monitoring, setting an appropriate spatial resolution, usually from 0.5 meters to 1 meter, setting a suitable time resolution, such as collecting data every 5 minutes or 10 minutes, and continuously collecting for at least 24 hours, preferably for a week or longer to capture the daily variations and short-term weather impacts.

[0079] Recording meteorological data includes recording meteorological parameters such as air temperature, humidity, precipitation, etc. during the sampling period. Data can be obtained using a portable weather station or from the nearest meteorological observation station, and the weather conditions (sunny, cloudy, rainy, etc.) during the sampling period are recorded.

[0080] Establishing a sample point information database includes assigning a unique identifier to each sample point, creating a data table containing the following geographical coordinates (longitude, latitude), relative distance, measurement depth, soil type, surface cover type, and fields for describing environmental characteristics, associating the temperature data with the sample point information, and at the same time recording any special circumstances that may affect the temperature of the optical cable (such as underground pipelines, geothermal activities, etc.), and marking areas that may require special treatment (such as river crossings, under highways, etc.).

[0081] For changing the sample point location and sample point depth of the optical cable, a change strategy can be planned, such as determining the purpose of the change (such as verifying the performance of the model at different depths, testing the impact of location on temperature, etc.), deciding the scope of the change (such as changing 20% of the sample points, or adding 1 - 2 new points near each original sample point), etc. New locations can be selected near the original sample points, usually within a range of 5 - 10 meters, ensuring that the new locations do not interfere with the original optical cable path or other infrastructure. For some of the original sample points, change their burial depth. The new depth can be deeper or shallower than the original, usually within a range of 0.5 - 2 meters. For the newly added locations, small-scale excavation and burial work are required. For the points with changed depth, it may be necessary to re-excavate and adjust the depth of the optical cable.

[0082] Repeat the steps of obtaining the burial information of the optical cable and changing the sample point location and sample point depth of the optical cable to obtain more diverse data samples and enhance the generalization ability of the optical cable temperature-burial depth relationship model.

[0083] Clean the temperature data obtained in the above steps, remove outliers and noise, handle missing data, which can be done using interpolation methods or deleting incomplete records, and align the temperature data with timestamp and location information. According to the time resolution of the data and the research objective, select an appropriate sliding window length (such as 24 hours), set the sliding step size, usually smaller than the window length (such as 1 hour), to ensure overlap between windows. Sort the temperature data within each sliding window, and at the same time calculate the basic statistics within the window (average temperature, maximum and minimum temperatures, temperature standard deviation, temperature median and quartiles, and temperature change feature extraction); calculate the rate of change of temperature within the window (such as temperature change per hour), extract the temperature change trend, which can be obtained by simple linear regression to get the slope, identify temperature fluctuation patterns, such as daily cycles or other periodic changes. Combine all the extracted features into a comprehensive dataset, with each sample point corresponding to a row of data, containing the following: sample point ID, location information (latitude and longitude or relative coordinates), timestamp, statistical features (average temperature, standard deviation, etc.), change features (rate of change, trend, etc.), spatial features (spatial gradient, etc.), time series features (autocorrelation coefficient, etc.), frequency domain features, and environment-related features. Calculate the correlation between features, remove highly correlated redundant features, and use principal component analysis (PCA) or other dimensionality reduction techniques to reduce the dimensionality of the feature space. Standardize all numerical features, usually using the Z-score or Min-Max method, to ensure that all features are within the same numerical range for subsequent model processing.

[0084] For constructing a model based on the standard temperature distribution dataset and the grey relational degree function, this step includes selecting an appropriate reference sequence, which is usually the data of sample points with known depths, calculating the grey relational degree between the comparison sequence and the reference sequence, and constructing a relational degree matrix to reflect the relationship strength between different features and the burial depth.

[0085] The grey relational degree function can be expressed as:

[0086]

[0087] where γ i represents the grey relational degree function of the i-th sample point, j represents the j-th factor index, x j (i) represents the data of the i-th sample point in the j-th factor index, y(i) represents the output data of the i-th sample point, represents the distinguishing coefficient, n represents the number of sample points, max() represents the maximum value function, and min() represents the minimum value function.

[0088] Randomly shuffle the organized dataset and divide it into a training set, a validation set, and a test set according to a preset ratio, such as 70% for the training set, 15% for the validation set, and 15% for the test set, to ensure the independence and effectiveness of the dataset. Build a support vector machine (SVM) model based on the training set, and use the validation set for cross-validation to optimize the parameters and performance of the model. Then, use the test set to test, evaluate, optimize, and adjust the model, and finally obtain a trained model.

[0089] Step S3: Build a ground noise environment model based on the vibration data, obtain a noise field distribution characteristic function corresponding to the ground noise environment model based on the ground noise environment model, perform a Fourier transform on the noise field distribution characteristic function to obtain a noise correlation function, and calculate the predicted depth of the optical cable corresponding to the vibration data according to the ground environment parameters and the noise correlation function.

[0090] By building a ground noise environment model, the complex ground noise environment can be accurately described and characterized. The Fourier transform can convert the time-domain signal to the frequency domain, which is beneficial to analyzing the frequency components of the signal, thereby obtaining more useful information. At the same time, the noise correlation function is used to reflect the correlation between noise signals at different positions, and by combining the ground environment parameters and the noise correlation function, the depth corresponding to the vibration data can be estimated.

[0091] Step S3 also includes steps S31 to S32.

[0092] Step S31: Preprocess the vibration data to obtain standard vibration data, and extract the frequency characteristics corresponding to the standard vibration data, where the frequency characteristics include the wave number of the noise source and the angular frequency of the noise source.

[0093] In this step, remove outliers and fill in missing data by cleaning the vibration data, use a low-pass, high-pass, or band-pass filter to remove unnecessary frequency components in the vibration data, standardize the signal amplitude of the vibration data, and at the same time divide the long-time series data into appropriate time windows, and perform spectral analysis using methods such as short-time Fourier transform or wavelet transform, and extract key characteristics such as the wave number of the noise source and the angular frequency of the noise source from the spectrum.

[0094] Step S32: Build a ground noise environment model based on the Helmholtz equation, the wave number of the noise source, and the angular frequency of the noise source.

[0095] In this step, describe the propagation of sound waves in the ground medium using the Helmholtz equation, set appropriate boundary conditions according to the actual environment, and establish a noise source model based on the extracted wave number and angular frequency.

[0096] The expression of the ground noise environment model is:

[0097]

[0098] Among them, represents the Laplace operator, k represents the wavenumber of the noise source, P(r, z) represents the sound pressure at the abscissa r and ordinate z, ω represents the angular frequency of the noise source, and z s represents the height difference between the noise source and the ground, that is, the vertical component of r s , N s (ω, r s ) represents the noise source with angular frequency ω and position r s , and δ() represents the Dirac delta function.

[0099] For the ground noise environment model, the following boundary conditions are usually considered:

[0100] Free surface condition (ground surface): At z = 0 (assuming the ground surface is the z = 0 plane), it should satisfy This means that the vertical gradient of the sound pressure at the ground surface is zero, that is, the sound wave is completely reflected at the ground surface.

[0101] Radiation condition (deep part): When z → ∞, it should satisfy: This is the Sommerfeld radiation condition, which ensures that the wave energy decays at a distance and does not reflect back from infinity.

[0102] Periodic or attenuation condition in the horizontal direction: According to the specific problem, it may be necessary to set periodic boundary conditions or attenuation conditions in the horizontal direction.

[0103] If the model contains multiple strata, the continuity of pressure and particle velocity needs to be satisfied at the interface between strata:

[0104] P1 = P2

[0105]

[0106] Among them, ρ is the density of the waveguide medium, ρ 1 represents the medium on one side of the interface, and ρ 2 represents the medium on the other side of the interface.

[0107] Please refer to Figure 2 , Figure 2 , for the schematic diagram of vibration detection, where the expression of the noise correlation function is:

[0108] Γ ω (r 1 , r 2 ; z 1 , z 2 ) = <P(r 1 , z 1 )P * (r 2 , z2 )>

[0109]

[0110]

[0111] where Γ ω represents the cross-spectral density, r 1 represents the horizontal coordinate of the first detection point, r 2 represents the horizontal coordinate of the second detection point, z 1 represents the height difference between the first detection point and the ground, z 2 represents the height difference between the second detection point and the ground, P(r 1 , z 1 ) represents the sound pressure at the first detection point, P*(r 2 , z 2 ) represents the complex conjugate of the sound pressure at the second detection point, R represents the difference between the horizontal coordinate of the first detection point and the horizontal coordinate of the second detection point, Φ τ () represents the modal depth function, J 0 () represents the zero-order Bessel function, κ τ represents the real part of the horizontal wave number, α τ represents the imaginary part of the horizontal wave number, Γ t (R, z 1 , z 2 ) represents the noise correlation function, q represents the intensity of the noise source, ρ represents the density of the waveguide medium, and τ represents the number of modes.

[0112] By differentiating and integrating the above formula, the time derivative of the noise correlation function can be obtained:

[0113]

[0114] After the ground noise conditions are determined, q, ρ, κ τ and α τ can be regarded as fixed values, then the amplitude A C of the time derivative of the noise correlation function depends on the depth relationship between the two receiving points, and its expression is:

[0115]

[0116] The above formula reflects a vertical coherence characteristic. For an ideal solid space, such as the bedrock layer and the sedimentary layer, this characteristic is related to the relative distance between the two receiving points, that is, it is independent of the absolute position. Therefore, the relationship can be written as:

[0117] A C ∝Φ′|z 2 -z 1

[0118] Therefore, the depth change between two receiving points can be estimated by extracting the amplitude of the time derivative of the noise correlation function.

[0119] In one example, since the integration in the time derivative of the noise correlation function is performed in the frequency domain, represents the contributions of different modes in the time derivative of the noise correlation function, and Φ τ (z 1 )Φ τ (z 2 ) directly reflects the influence of the depth of the receiving point.

[0120] To establish the depth - amplitude relationship, if only the amplitude of a single dominant mode is considered, the amplitude can be expressed as:

[0121]

[0122] When z 1 is fixed and only the amplitude varying with z 2 is considered, it can be expressed as:

[0123] A(z 2 ,ω) ∝ Φ τ (z 2 )gC(ω)

[0124] where C(ω) contains all terms independent of z 2 .

[0125] For the modal function approximation, in general, a simple function can be used to approximate the modal function, for example: or where H represents the medium thickness and β represents the attenuation coefficient related to z 2 .

[0126] When using the sin approximation:

[0127] When using the exp approximation: A(z 2 ,ω) ∝ exp(-βz 2 )C(ω)

[0128] Taking the logarithmic transformation of the amplitude corresponding to z 2 when using the exp approximation, we can obtain: Log(A) = -βz 2 + log(C).

[0129] Then, repeating the above process for different frequencies, the frequency - related model parameters can be expressed as:

[0130] A(z 2 ,ω) = C(ω)exp(-β(ω)z2 )

[0131] Similarly, when z 1 is fixed, only consider the amplitude variation of z 2 , and the frequency-dependent model parameters can be expressed as:

[0132] A(z 1 , ω) = D(ω)exp(-μ(ω)z 1 )

[0133] where D(ω) contains all terms independent of z 1 , and μ represents the attenuation coefficient related to z 1 .

[0134] For a receiving point with an unknown depth, the following steps can be used to calculate the corresponding depth of z 2 :

[0135] For each frequency ω, calculate z 2 : z 2 (ω) = -log(A(ω) / C(ω)) / β(ω).

[0136] For the multi-frequency results, perform a synthesis, and z 2 = Σ(w(ω)*z2(ω)) / Σw(ω).

[0137] where w(ω) represents the weight coefficient, and this weight coefficient can be determined based on the signal-to-noise ratio or fitting quality of each frequency.

[0138] Step S4, based on the predicted depth of the optical cable corresponding to the temperature data, the predicted depth of the optical cable corresponding to the vibration data, and the depth prediction algorithm, calculate the buried depth of the optical cable.

[0139] In this step, the depth prediction algorithm can be expressed as: Z = λz t + ξz sa

[0140] where λ represents the weight coefficient corresponding to the predicted depth of the optical cable, and z t represents the predicted depth of the optical cable, and ξ represents the weight coefficient corresponding to the predicted depth of the optical cable, and z sa represents the predicted depth of the optical cable.

[0141] Step S4 also includes steps S41 to S42.

[0142] Step S41, obtain the real-time depth data according to the depth prediction algorithm, and obtain the buried depth alarm threshold from the database, and judge whether the real-time depth data of the sample point is less than the buried depth alarm threshold.

[0143] In this step, the real-time depth data obtained according to the depth prediction algorithm is compared with the preset threshold obtained from the system configuration file or database.

[0144] In step S42, if the real-time depth data of the sample point is less than the buried depth alarm threshold, obtain the geographical location of the sample point, and send an alarm signal and the geographical location of the sample point to the mobile terminal of the staff.

[0145] In this step, the buried depth alarm threshold can be set to a reasonable value according to the actual burial requirements of the optical cable. For example, if the standard burial depth of the optical cable is 1 meter, the buried depth alarm threshold can be set to 0.8 meters. The buried depth alarm threshold can be a fixed value or dynamically adjusted according to different environmental conditions and soil characteristics. Combining information such as the mileage stake number or GPS coordinates of the optical cable line, the geographical location of the abnormal point can be accurately located. The GIS system or electronic map can be used to intuitively display the specific location of the abnormal point on the optical cable line. The location information of the abnormal point can include longitude and latitude coordinates, kilometer marks, nearby landmarks, etc., which is convenient for maintenance personnel to quickly find the problem. The alarm information can be sent to relevant maintenance personnel through various methods such as mobile APP, SMS, and email. The alarm information can include the location coordinates of the abnormal point, buried depth data, temperature change trend, etc., providing a basis for subsequent maintenance. Different levels of alarms can be set, such as slight deviation, serious abnormality, etc., to trigger different levels of response measures. Establish a perfect alarm management system, record the alarm history, analyze the fault mode, and optimize the alarm strategy. After receiving the alarm, the maintenance personnel can quickly rush to the scene and take corresponding maintenance measures, such as replenishing backfill soil, adjusting the buried depth, etc. Combining historical data analysis, possible problem areas can be predicted, and preventive maintenance can be taken in advance.

[0146] By constructing a spatio-temporal data matrix and applying the sliding window algorithm for processing, the key features of the spatial temperature distribution are effectively extracted and transformed into spatial temperature distribution data, which can significantly improve the model's ability to capture complex temperature-depth relationships. At the same time, selecting data from any time period for analysis enables the optical cable temperature-buried depth relationship model to adapt to different time scales, enabling the model to capture short-term and long-term temperature change patterns and handle non-linear relationships and adapt to changes at different time scales, thereby improving the model's adaptability. By constructing a ground noise environment model, the complex ground noise environment can be accurately described and characterized. Fourier transform can convert the time-domain signal to the frequency domain, which is beneficial for analyzing the frequency components of the signal. At the same time, the noise correlation function is used to reflect the correlation between noise signals at different positions. Combining the optical cable temperature-buried depth relationship model with the ground noise environment model and the noise correlation function can improve the accuracy of predicting the buried depth of the optical cable.

[0147] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for detecting buried depth of optical cable based on distributed optical fiber sensing technology, characterized in that: The method comprises: Collecting buried state change data of the optical cable, wherein the buried state change data includes temperature data and vibration data; Constructing a spatiotemporal data matrix corresponding to the temperature data, and performing data processing on the spatiotemporal data matrix according to a sliding window algorithm to obtain spatial temperature distribution data, selecting multiple reference features of the spatial temperature distribution data in any time period to construct a feature matrix, and sending the feature matrix to an optical cable temperature-burial depth relationship model to obtain an optical cable predicted depth corresponding to the temperature data; The method further comprises: Acquire burial information of the optical cable, wherein the burial information includes a sample point position, a sample point depth, and a first detection temperature set corresponding to the sample point position and the sample point depth; Changing the sample point position and sample point depth of the optical cable to obtain a second detection temperature set; Based on the first detection temperature set, the second detection temperature set and the window sliding algorithm, a sample point temperature distribution data set is obtained, and the sample point temperature distribution data set is standardized to obtain a standard temperature distribution data set; Based on the standard temperature distribution data set and the grey correlation function, the optical cable temperature-burial depth relationship model is constructed; Select the spatial temperature distribution data of any period of time, extract five statistical data: mean value, standard deviation, maximum temperature difference, average change rate, and maximum change rate, and use them as feature vectors after normalization to construct a feature matrix. The expression of the temperature feature vector of the i-th sample point in the feature matrix is: ΔT(i,t)=T t (i,t)-T t (i,t-1) Where M(i) represents the mean value function of the i-th sample point, T t (i, t) represents the temperature of the i-th sample point at the t-th time, n represents the number of sample points, S(i) represents the standard deviation function of the i-th sample point, ΔT(i, t) represents the temperature difference function of the i-th sample point at the t-th time, v(i, t) represents the rate of change function of the i-th sample point at the t-th time, Δt represents the time difference between two adjacent sample points, ω(i) represents the temperature feature vector of the i-th sample point, max() represents the maximum value function, Represents the average rate of change function of i sample points; Constructing a ground noise environment model based on the vibration data, and obtaining a noise field distribution characteristic function corresponding to the ground noise environment model based on the ground noise environment model, performing Fourier transform on the noise field distribution characteristic function to obtain a noise correlation function, and calculating a predicted depth of the optical cable corresponding to the vibration data according to ground environment parameters and the noise correlation function; The method of constructing a ground noise environment model based on vibration data specifically includes: Preprocessing the vibration data to obtain standard vibration data, and extracting frequency features corresponding to the standard vibration data, wherein the frequency features include a noise source wave number and a noise source angular frequency; Constructing a ground noise environment model based on the Helmholtz equation, the noise source wave number and the noise source angular frequency; The expression of the ground noise environment model is: in, represents the Laplace operator, k represents the wave number of the noise source, P(r, z) represents the sound pressure at the position where the horizontal coordinate is r and the vertical coordinate is z, ω represents the angular frequency of the noise source, z s Represents the height difference between the noise source and the ground, N s (ω,r s ) indicates an angular frequency of ω and a position of r s The noise source, δ() represents the Dirac delta function; The buried depth of the optical cable is calculated based on the predicted depth of the optical cable corresponding to the temperature data, the predicted depth of the optical cable corresponding to the vibration data, and a depth prediction algorithm.

2. The method according to claim 1, characterized in that The processing of the spatiotemporal data matrix according to the sliding window algorithm to obtain spatial temperature distribution data specifically includes: Setting the length of the sliding window, and performing sliding window division on the space-time data matrix based on the sliding window length, and obtaining window space data after the space-time data matrix is ​​sorted within the sliding window; Based on the window space data and the window temperature relationship function, the window temperature corresponding to each window space data is calculated, and the window temperatures corresponding to all the window space data are integrated to form the spatial temperature distribution data.

3. The method according to claim 1, characterized in that The grey relational function can be expressed as: in, γ i represents the grey relational function of the i-th sample point, j represents the j-th factor index, x j (i) represents the i-th sample point data in the j-th factor index, y(i) represents the output data of the i-th sample point, represents the resolution coefficient, n represents the number of sample points, max() represents the maximum function, and min() represents the minimum function.

4. The method according to claim 1, characterized in that The expression of the noise correlation function is: C ω (r1,r2;z1,z2)= <P(r1,z1)P * (r2,z2)> Among them, Γ ω represents the cross spectral density, r1 represents the horizontal coordinate of the first detection point, r2 represents the horizontal coordinate of the second detection point, z1 represents the height difference between the first detection point and the ground, z2 represents the height difference between the second detection point and the ground, P(r1, z1) represents the sound pressure at the first detection point, P*(r2, z2) represents the complex conjugate of the sound pressure at the second detection point, R represents the difference between the horizontal coordinates of the first detection point and the horizontal coordinates of the second detection point, Φ τ () represents the modal depth function, J0() represents the zero-order Bessel function, κ τ represents the real part of the horizontal wave number, α τ represents the imaginary part of the horizontal wave number, Γ t (R,z1,z2) represents the noise correlation function, q represents the intensity of the noise source, ρ represents the density of the waveguide medium, and τ represents the mode number.

5. The method according to claim 1, characterized in that The method further comprises: Acquire real-time depth data according to the depth prediction algorithm, and obtain a burial depth alarm threshold from a database to determine whether the real-time depth data of the sample point is less than the burial depth alarm threshold; If the real-time depth data of the sample point is less than the buried depth alarm threshold, the geographical location of the sample point is acquired, and an alarm signal and the geographical location of the sample point are sent to the mobile terminal of the staff.

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