Power communication network risk identification method and system, and storage medium

Abstract

The invention provides a power communication network risk identification method and system and a storage medium. The method comprises the following steps: acquiring electromagnetic interference environment parameters, acquiring optical cable laying path and tower structure data, and analyzing a distribution rule of grounding points of an electromagnetic shielding layer; the influence of electromagnetic interference on signals is eliminated according to a grounding point distribution rule; acquiring optical cable strain information, and extracting optical cable strain change characteristics; determining an optical cable fault and a fault point initial position; correcting the position of a fault point; evaluating the damage degree caused by the magnetic field; and generating a fault point repair suggestion, updating the optical cable state database, and generating an optical cable health state report focusing high magnetic field influence. By implementing the method, the optical cable fault can be effectively positioned, the damage degree of the optical cable can be accurately evaluated, and the stability of a power communication network can be improved.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to a method, system and storage medium for identifying risks in a power communication network based on automatic control technology. Background Art

[0002] The automated inspection network integrates advanced communications, computing, networking, and control technologies to enable autonomous operation, self-management, and optimization. The system can perceive network status in real time, intelligently analyze data, and make autonomous decisions, enabling dynamic allocation of network resources and performance optimization.

[0003] In environments with high electromagnetic interference, locating faults in power communication network optical cables faces multiple technical challenges. Strong electromagnetic interference generated by high-voltage transmission lines and substation equipment can easily distort the measurement signals of traditional optical time-domain reflectometers (OTDRs), leading to inaccurate fault location. The densely distributed metal towers and grounding devices along the optical cable create a non-uniform electromagnetic shielding layer. The varying distribution of grounding points further alters the propagation characteristics of the fault reflection signal, significantly increasing the difficulty of analyzing OTDR technology based on single-point measurements. Furthermore, electromagnetic interference-induced potential differences in the optical cable's metallic components can generate discharge arcs, and the temperature rise caused by long-term induced currents can accelerate material aging. This indirect damage mechanism complicates damage assessment.

[0004] Differences in tower structures and the diversity of optical cable installation methods can exacerbate the uneven distribution of electromagnetic shielding grounding points, creating a multi-dimensional interference effect. Traditional single-point measurement methods struggle to fully suppress temporally and spatially varying electromagnetic interference. While fiber Bragg grating (FBG) sensing technology offers inherent resistance to electromagnetic interference and can provide distributed optical cable strain information, the stability of its accompanying demodulation equipment in strong electromagnetic fields still requires specialized verification through shielding design or optical demodulation solutions. Summary of the Invention

[0005] The technical problem to be solved by the present invention is that the present invention proposes a method, system and storage medium for identifying risks in power communication networks, which can effectively locate optical cable faults, accurately assess the degree of damage to optical cables, and improve the stability of power communication networks.

[0006] As one aspect of the present invention, a method for identifying risks in a power communication network is provided, which comprises the following steps:

[0007] S11, collect electromagnetic interference environment parameters, obtain optical cable laying path and tower structure data, and analyze the distribution pattern of electromagnetic shielding layer grounding points;

[0008] S12, based on the distribution pattern of the grounding points, collecting the optical cable transmission signal, measuring the amplitude and phase of the optical cable transmission signal, and analyzing the spectrum characteristics and amplitude fluctuations of the optical cable transmission signal to determine whether electromagnetic interference has reduced the signal-to-noise ratio or increased the bit error rate of the optical cable transmission signal. If so, an adaptive filtering algorithm is used to eliminate the influence of the electromagnetic interference on the signal;

[0009] S13, using fiber Bragg grating sensing technology to obtain optical cable strain information, combined with the amplitude and phase of the optical cable transmission signal, to extract the cable strain change characteristics;

[0010] S14, comparing the strain magnitude and the strain change rate with a preset strain change characteristic threshold. If both the strain magnitude and the strain change rate exceed the preset strain change threshold, it is determined that there is a fault in the optical cable. The preliminary location of the fault point is determined by combining the grounding point distribution and the tower structure data;

[0011] S15, extracting the strain distribution gradient change caused by the high magnetic field around the fault point based on the amplitude, phase, and strain information of the transmission signal, analyzing the strain distribution pattern around the fault point, and correcting the fault point location based on the optical cable laying method;

[0012] S16, using optical fiber sensing data and strain change characteristics, calculates the strain change amplitude at the fault point caused by the high magnetic field. Combined with the elastic modulus and yield strength of the optical cable material, it assesses the degree of damage caused by the magnetic field.

[0013] S17: If the damage caused by the high magnetic field exceeds the set target level, a fault point repair suggestion is generated. Combined with the tower structure and optical cable laying method, the fault point location and the damage degree assessment results, the optical cable status database is updated. Combined with multi-point differential measurement and optical fiber sensing data, a cable health status report focusing on the impact of high magnetic fields is generated.

[0014] Wherein, the step S11 further includes:

[0015] The electromagnetic sensor collects the three-dimensional electromagnetic field intensity value at the preset monitoring point, performs spectrum scanning for the high frequency band and the low frequency band respectively, obtains the electromagnetic spectrum data from the spectrum analyzer, and generates the electromagnetic frequency distribution matrix;

[0016] The point cloud data along the optical cable is collected by the laser radar scanner, and the three-dimensional spatial coordinates of the point cloud data are reconstructed using the least squares method. The optical cable laying trajectory and the geometric size parameters of the tower structure are extracted from the reconstructed data matrix.

[0017] Performing wavelet transform filtering on the electromagnetic spectrum data, extracting the characteristic frequency of electromagnetic interference using Fourier transform, and identifying the position coordinates of the interference source from the frequency domain characteristics;

[0018] Establishing an electromagnetic field distribution function based on the electromagnetic field intensity value and the electromagnetic interference characteristic frequency, and obtaining an electromagnetic field intensity threshold range from the function curve;

[0019] If the electromagnetic field intensity at the measurement point exceeds the threshold range, the gradient descent method is used to calculate the optimal layout position of the grounding point, and the distribution spacing of the grounding points is obtained from the optical cable laying trajectory and the geometric dimension parameters of the tower structure;

[0020] A neural network model is established for the grounding point distribution spacing. The input layer parameters include the electromagnetic field strength value, the interference source position coordinates and the optical cable trajectory line. The output layer parameters are the electromagnetic shielding layer thickness value and the grounding resistance value.

[0021] Wherein, the step S12 further includes:

[0022] The measurement sections are divided according to the distribution data of the optical cable grounding points. The optical cable transmission signal is collected at the boundary points of each section using an optical time domain reflectometer. The amplitude and phase values ​​of the optical signal are obtained from the time domain sampling data.

[0023] Performing multi-point differential calculation on the time domain sampling data, using fast Fourier transform to obtain spectrum components, and extracting the signal center frequency band power density and background noise power density from the spectrum diagram;

[0024] Calculating a signal-to-noise ratio parameter according to the power density ratio, establishing a reference curve for the signal-to-noise ratio parameter, and obtaining a signal-to-noise ratio threshold value from the reference curve;

[0025] If the signal-to-noise ratio parameter is lower than the threshold value, a neural network is used to extract features of the optical signal amplitude and phase values, and the input feature parameters include the signal waveform slope, peak ratio and phase offset;

[0026] Calculating a bit error rate value of the optical signal based on the characteristic parameters, establishing a signal quality evaluation matrix from the bit error rate value, and obtaining a signal quality classification threshold;

[0027] According to the signal quality classification, a wavelet adaptive filter is used to perform denoising on the optical signal, the filter parameters are automatically adjusted according to the signal characteristic parameters, and a waveform of the optical signal after filtering is obtained;

[0028] The signal-to-noise ratio (SNR) of the filtered optical signal is recalculated, and the degree of improvement of the signal quality is determined based on the SNR value to obtain a final optical cable transmission signal.

[0029] Wherein, the step S13 further includes:

[0030] According to the fiber Bragg grating reflection spectrum, a narrow-band laser is used to scan within a preset wavelength band, and the central wavelength value and reflection intensity value are extracted from the reflection spectrum data;

[0031] Performing wavelet noise reduction processing on the reflection spectrum data, using a temperature sensor to obtain the ambient temperature value of the optical cable, performing compensation calculation on the center wavelength drift based on the temperature value, and obtaining the axial strain value of the optical cable;

[0032] Constructing a time series for the axial strain value of the optical cable, performing multi-scale decomposition on the strain series data using wavelet transform, and extracting the strain amplitude and strain period from the decomposition coefficients;

[0033] constructing a feature vector according to the strain amplitude and strain period, training the feature vector using a neural network, and obtaining a strain monitoring threshold from the trained parameters;

[0034] If the axial strain value of the optical cable exceeds the monitoring threshold, the time when the strain exceeds the limit and the duration of the strain are recorded, and the strain change rate is extracted from the recorded data;

[0035] The strain change rate is time synchronized with the optical cable transmission signal, and a correlation degree between the strain and the signal amplitude and phase is obtained by cross-correlation calculation;

[0036] A Fourier transform is performed according to the correlation value, and a strain spectrum distribution curve is extracted from the transform result to obtain the strain frequency characteristic.

[0037] Wherein, the step S14 further includes:

[0038] Wavelet transform is used to reduce the noise of the optical cable strain monitoring data. The two characteristic parameters of strain amplitude and duration are extracted from the noise-reduced data to establish a fault feature dataset.

[0039] According to the fault feature data set, a strain fault discriminator is established using a support vector machine, and a strain amplitude threshold and a change rate threshold are obtained from the discriminator output result;

[0040] Performing real-time calculations on the noise-reduced data, using a Bragg grating demodulator to obtain the strain value of the optical cable, and calculating the strain change rate from adjacent sampling points;

[0041] If the cable strain value exceeds the strain amplitude threshold and the rate of change exceeds the rate threshold, the fault time mark is recorded and the strain mutation interval is determined using the cross-correlation method. Based on the strain mutation interval, the distance to the fault point is calculated using optical time domain reflectometry, and the location coordinates of the mutation point are obtained from the optical fiber attenuation curve.

[0042] For the position coordinates of the mutation point, a nearest neighbor search algorithm is used to match adjacent nodes in the grounding point distribution data;

[0043] According to the positions of the adjacent nodes, the tower number and installation position parameters are extracted from the tower structure database to obtain the fault point area range.

[0044] Wherein, the step S15 further includes:

[0045] Pre-process the data collected from the optical cable transmission signal, use a filter to eliminate noise interference, and obtain the fault area signal feature vector from the signal amplitude and phase data;

[0046] Measuring the electromagnetic field strength of the fault area, calculating the magnetic field gradient distribution using a Gaussian distribution model, and obtaining the high magnetic field area range from the magnetic field distribution data;

[0047] extracting strain distribution characteristics using a neural network based on the fiber Bragg grating sensing data within the high magnetic field region, and constructing a strain gradient matrix from the strain data;

[0048] A three-dimensional strain distribution function is established for the strain gradient matrix, a gradient descent method is used to calculate the strain extreme point, and a strain anomaly interval is obtained from the extreme point position;

[0049] According to the boundary coordinates of the strain anomaly interval, the initial position of the fault point is calculated using the triangulation method, and the positioning deviation is corrected using the magnetic field gradient data;

[0050] For the initial position of the fault point, the least square method is used to optimize the spatial coordinates, and the optimization parameters include the cable laying depth, bending radius and direction angle;

[0051] Position matching is performed based on the optimization parameters and the optical cable laying map, and the corrected coordinates of the fault point are obtained from the actual laying path data.

[0052] Wherein, the step S16 further includes:

[0053] The reflected spectrum data collected by the fiber Bragg grating sensor is subjected to noise reduction processing, the central wavelength value is extracted using a narrowband filter, and the initial strain data is obtained from the wavelength drift;

[0054] Performing wavelet transform on the initial strain data, extracting strain components of different frequency bands using multi-scale decomposition, and obtaining strain characteristic parameters in the high magnetic field action area from the time-frequency spectrum;

[0055] Establishing a correlation matrix based on the strain characteristic parameters and magnetic field intensity data, and extracting strain amplitude values ​​using a double hidden layer neural network; performing interval statistics on the strain amplitude values, calculating strain distribution characteristics using a probability density function, and obtaining a magnetic field strain coefficient from the strain peak position;

[0056] Calculating the stress state of the optical cable according to the magnetic field strain coefficient, using material mechanical parameters including elastic modulus and Poisson's ratio to perform stress calculation, and obtaining the strain state value from the stress distribution curve;

[0057] Performing finite element modeling based on the strain state value, dividing the optical cable cross section using a tetrahedral mesh, and obtaining material deformation from the stress-strain relationship;

[0058] The deformation of the material is compared with the yield strength and the damage accumulation function is used to establish an evaluation index, and the magnetic field damage value is obtained from the strain amplitude curve.

[0059] Wherein, the step S17 further includes:

[0060] A damage feature matrix is ​​constructed based on the optical cable damage assessment data. The damage degree is graded using a clustering algorithm. The damage features include strain value, duration, and magnetic field strength. The repair level is obtained from the grading results.

[0061] Perform spatial mapping between the repair level and the optical cable laying environment, use a data preprocessor to extract tower spacing, laying depth, and bending radius parameters, and obtain repair constraints from environmental parameters;

[0062] Establishing a neural network model based on the restoration constraints, wherein the input layer includes the restoration level and environmental parameters, and the output layer generates a restoration plan number;

[0063] Constructing a database index table for the repair solution number, recording the coordinates of the fault point, damage type, and repair suggestion content in an incremental update manner, and obtaining the optical cable status identifier from the data record;

[0064] Perform multi-point differential measurement according to the optical cable status identifier, calculate the state change using a signal comparison method at adjacent measurement points, and obtain the section state value from the differential data;

[0065] Perform feature matching on the segment state value and the fiber Bragg grating sensor data, extract signal feature parameters using a time-frequency analyzer, and determine data consistency based on the matching results;

[0066] The optical cable health index is calculated based on the data consistency, a weighted summer is used to generate a health status score, and status report content is formed from the score data.

[0067] As another aspect of the present invention, there is also provided a power communication network risk identification system, comprising:

[0068] The electromagnetic acquisition module is used to collect electromagnetic interference environment parameters, including electromagnetic field strength and spectrum distribution, obtain optical cable laying paths and tower structure data, and analyze the distribution pattern of electromagnetic shielding layer grounding points;

[0069] The signal measurement module is used to collect the optical cable transmission signal based on the distribution pattern of the grounding points, and measure the amplitude and phase of the optical cable transmission signal using a multi-point differential measurement method. By analyzing the spectrum characteristics and amplitude fluctuations of the optical cable transmission signal, it is determined whether electromagnetic interference has reduced the signal-to-noise ratio or increased the bit error rate of the optical cable transmission signal. If so, an adaptive filtering algorithm is used to eliminate the impact of electromagnetic interference on the signal;

[0070] The strain analysis module is used to obtain optical cable strain information through fiber Bragg grating sensing technology. It combines the amplitude and phase of the optical cable transmission signal to extract the optical cable strain change characteristics, including strain magnitude, change rate and frequency characteristics.

[0071] The fault judgment module is used to compare the strain change characteristic threshold with the preset strain change threshold. If the strain size and change rate both exceed the preset strain change threshold, it is judged that there is a fault in the optical cable. The preliminary location of the fault point is determined by combining the grounding point distribution and tower structure data;

[0072] The position correction module is used to extract the strain distribution gradient change caused by the high magnetic field around the fault point based on the amplitude, phase and strain information of the transmission signal, analyze the strain distribution pattern around the fault point, and correct the fault point position based on the optical cable laying method;

[0073] The damage assessment module is used to calculate the strain change amplitude at the fault point caused by the high magnetic field based on the optical fiber sensing data and strain change characteristics. It also combines the elastic modulus and yield strength of the optical cable material to assess the damage caused by the magnetic field.

[0074] The health report module is used to generate fault point repair suggestions if the damage caused by high magnetic fields exceeds the set target level. It combines the tower structure and optical cable laying method, the fault point location and the damage degree assessment results to update the optical cable status database. It combines multi-point differential measurement and optical fiber sensing data to generate an optical cable health status report focusing on the impact of high magnetic fields.

[0075] As another aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0076] The implementation of the present invention has the following beneficial effects:

[0077] This invention provides a method, system, and storage medium for identifying risks in power communication networks. By collecting electromagnetic interference environmental parameters and optical cable installation data, the system analyzes the distribution of electromagnetic shielding layer grounding points. Multi-point differential measurement and fiber Bragg grating sensing technology are then used to acquire optical cable transmission signals and strain information. By analyzing signal characteristics and strain variations, the system determines the impact of electromagnetic interference and optical cable faults, and locates the fault point. In combination with the cable material properties, the system assesses the extent of damage caused by high magnetic fields, generating repair recommendations and health status reports.

[0078] The present invention can comprehensively consider factors such as the structural characteristics of the tower, the optical cable laying method, the distribution of the grounding points of the electromagnetic shielding layer, etc., and can effectively detect and evaluate the degree of damage to the optical cable in a high magnetic field environment, thereby improving the efficiency and reliability of optical cable operation and maintenance. It provides important technical support for optical cable systems in the fields of electricity, communications, etc., and can ensure the stable operation of the power communication network. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, without inventive work, other drawings derived from these drawings still fall within the scope of the present invention.

[0080] Figure 1 A schematic diagram of the main process of an embodiment of a method for identifying risks in a power communication network based on automatic control technology provided by the present invention;

[0081] Figure 2 This is a structural diagram of an embodiment of a power communication network risk identification system based on automatic control technology provided by the present invention. DETAILED DESCRIPTION

[0082] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention will be described in further detail below with reference to the accompanying drawings.

[0083] like Figure 1 FIG. 1 is a schematic diagram showing the main process of an embodiment of a method for identifying risks in a power communication network provided by the present invention. In this embodiment, the method includes:

[0084] S11. Collect electromagnetic interference environment parameters, including electromagnetic field strength and spectrum distribution, obtain optical cable laying path and tower structure data, and analyze the distribution pattern of electromagnetic shielding layer grounding points;

[0085] S12. Based on the distribution pattern of the grounding points, collect the optical cable transmission signal, measure the amplitude and phase of the optical cable transmission signal, and determine whether electromagnetic interference reduces the signal-to-noise ratio or increases the bit error rate of the optical cable transmission signal by analyzing the spectrum characteristics and amplitude fluctuations of the optical cable transmission signal. If so, eliminate the influence of the electromagnetic interference on the signal through an adaptive filtering algorithm;

[0086] S13. Obtaining optical cable strain information through fiber Bragg grating sensing technology, and extracting optical cable strain variation characteristics based on the amplitude and phase of the optical cable transmission signal, wherein the optical cable strain variation characteristics include strain magnitude, change rate, and frequency characteristics;

[0087] S14. Compare the strain magnitude and the strain change rate with a preset strain change characteristic threshold. If both the strain magnitude and the strain change rate exceed the preset strain change threshold, it is determined that there is a fault in the optical cable. Combined with the grounding point distribution and the tower structure data, the preliminary location of the fault point is determined.

[0088] S15. Extract the strain distribution gradient change caused by the high magnetic field around the fault point based on the amplitude, phase, and strain information of the transmission signal, analyze the strain distribution pattern around the fault point, and correct the fault point location based on the optical cable laying method;

[0089] S16. Calculate the strain variation at the fault point caused by the high magnetic field using the optical fiber sensing data and strain variation characteristics. Combined with the elastic modulus and yield strength of the optical cable material, evaluate the damage caused by the magnetic field.

[0090] S17. If the damage caused by the high magnetic field exceeds the set target level, a fault point repair suggestion is generated. Combined with the tower structure and optical cable laying method, the fault point location and damage degree assessment results, the optical cable status database is updated. Combined with multi-point differential measurement and optical fiber sensing data, a cable health status report focusing on the impact of high magnetic fields is generated.

[0091] The following describes each step in detail with reference to specific examples.

[0092] In step S11, it is necessary to start the power communication network risk identification function based on automatic control technology and collect electromagnetic interference environment parameters, including electromagnetic field strength and spectrum distribution, while obtaining optical cable laying path and tower structure data, and analyzing the distribution pattern of electromagnetic shielding layer grounding points. The steps include:

[0093] S111. At the preset monitoring points, electromagnetic sensors are used to collect three-dimensional electromagnetic field intensity values ​​and generate a spectrum data matrix. At the same time, a laser radar scanner is used to obtain point cloud data along the optical cable and extract the laying trajectory and tower geometric parameters.

[0094] Specifically, in some examples, the electromagnetic sensor uses a three-axis probe to scan in the horizontal, longitudinal and vertical directions at the monitoring point, record the field strength components in each direction and calculate the total intensity value. A monitoring point is set every 50 meters, and the spectrum scan covers the range of 1 kHz to 1 GHz, with a step frequency of 1 MHz; the laser radar collects point cloud data with a pulse frequency of 200 kHz and an angular resolution of 0.02 degrees, with a density of more than 2,000 points per square meter. The three-dimensional coordinates are reconstructed by the least squares method, and parameters such as the sag height of the optical cable and the spacing between towers are extracted. The coordinate accuracy deviation is less than 5 mm.

[0095] S112. Perform wavelet transform filtering on the collected electromagnetic spectrum data matrix to remove noise, use Fourier transform to identify the interference source location coordinates, and establish a distribution function based on the electromagnetic field intensity value to determine the threshold range. At the same time, build a neural network model based on the interference source location, optical cable trajectory and tower parameters to output the electromagnetic shielding layer thickness value and grounding resistance value.

[0096] In an embodiment of the present invention, after wavelet transform filtering, Fourier transform is used to extract frequency domain features, and the interference source is triangulated through multi-point field strength distribution with an accuracy better than 10 meters; the distribution function is fitted with a Gaussian model, and the threshold is set according to the inflection point of the curve; if the field strength exceeds the threshold, the grounding point spacing is optimized by the gradient descent method. The neural network model adopts a three-layer structure with 128 hidden neurons and a training error of less than 0.01, ensuring the reliability of the output parameters.

[0097] In this embodiment of the present invention, S11 lays the foundation for optical cable status assessment through multi-dimensional data collection and intelligent analysis. Three-dimensional measurement of electromagnetic field strength, combined with spectral characteristics, comprehensively reflects the characteristics of the interference environment, while high-precision extraction of point cloud data ensures the accuracy of the laying path and tower structure. Analysis of ground point distribution patterns further optimizes the reliability of fault location. It can be understood that the comprehensive processing of this data provides solid support for eliminating the impact of electromagnetic interference.

[0098] In an optional embodiment of the present invention, if the number of monitoring points is insufficient to cover the entire optical cable line, the monitoring point layout can be dynamically adjusted based on the distance between towers to ensure data representativeness. The specific adjustment method can be determined by technical personnel based on actual scenarios and is not further limited here.

[0099] Furthermore, in practical applications, the collaborative operation of electromagnetic sensors and lidar can adapt to diverse environmental conditions, such as rainy or hot weather, ensuring stable data collection. Accurately identifying interference sources helps quickly locate high-risk areas, thereby improving risk identification efficiency. Finally, the shield thickness and ground resistance values ​​output by the neural network model can provide a reference for optimizing optical cable design, further reducing the impact of electromagnetic interference on power communication networks.

[0100] It should be noted that the embodiments of the present invention do not limit the specific models of electromagnetic sensors or lidars. Appropriate equipment can be selected according to actual needs to ensure the accuracy and efficiency of data collection.

[0101] In step S12, it is necessary to collect the optical cable transmission signal according to the distribution pattern of the grounding points of the electromagnetic shielding layer, use the multi-point differential measurement method to obtain the amplitude and phase characteristics of the optical signal, judge the impact of electromagnetic interference on the signal quality by analyzing the spectrum characteristics and amplitude changes, and use the adaptive filtering algorithm to optimize the processing when interference exists to ensure the reliability of the optical cable transmission signal.

[0102] In an embodiment of the present invention, the optical cable line is divided into several measurement sections based on the distribution of grounding points. Optical time-domain reflectometry (OTDR) is used at the boundaries of each section to collect transmission signal data. The OTDR operates at a sampling rate of 10 GHz with a time window set to 100 microseconds, recording the amplitude and phase values ​​of the optical signal. During the acquisition process, grounding points are set every 3 to 8 kilometers based on the distribution of electromagnetic interference intensity, for a total of 24 grounding points along the 120-kilometer optical cable line. Under normal circumstances, the amplitude of the optical signal fluctuates between -20 and -25 decibels, and the phase jitter remains below 0.1 radian. If the external electromagnetic field intensity exceeds 5 volts per meter, the amplitude fluctuation may increase to 5 decibels and the phase jitter to 0.5 radians, indicating that the signal is significantly interfered with.

[0103] Step S12 specifically includes the following steps:

[0104] S121. Utilize multi-point differential measurement technology to calculate the signal difference between adjacent monitoring points to eliminate common-mode interference, extract spectral components through fast Fourier transform, analyze the power density of the signal center frequency segment and the background noise power density, and then calculate the signal-to-noise ratio parameters and establish a reference curve to determine the threshold value. Specifically, multi-point differential measurement filters out consistent interference in the environment by calculating the signal difference between adjacent points, and the differential result is converted into frequency domain data through fast Fourier transform. Near the 2.5 GHz center frequency, a 3 MHz bandwidth is selected for power analysis. The in-band power density of the normal signal is approximately -30 dB per Hz, the noise power density is -60 dB per Hz, and the signal-to-noise ratio reference value reaches 30 dB. The reference curve is generated by statistical fitting of multiple sampling data, and the threshold value is set to 25 dB as the basis for interference judgment.

[0105] S122. If the signal-to-noise ratio parameter is lower than the threshold, the waveform features of the optical signal are extracted through a neural network, including the rising and falling edge slopes, peak ratio, and phase offset. A signal quality assessment matrix is ​​constructed based on these features to determine the signal quality grading threshold to quantify the impact of interference. In an embodiment of the present invention, the neural network adopts a convolutional structure. The input layer receives amplitude and phase data, and the hidden layer extracts feature parameters through multiple rounds of convolution calculations, such as the slope of the signal waveform, the ratio of peak to trough, and the phase offset within a 100-picosecond window. The signal quality assessment matrix is ​​5×5 in size and maps the feature parameters to quality levels. The grading thresholds include a signal-to-noise ratio of 25 decibels, a phase jitter of 0.2 radians, and an amplitude fluctuation of 2 decibels. If the signal-to-noise ratio drops below 20 decibels, the bit error rate may rise to 10 to the power of -10, indicating a significant decline in transmission quality.

[0106] An adaptive wavelet filter is used to denoise the interfered signal. The filter uses Daubechies orthogonal wavelet basis functions, with a decomposition layer set to four. The filter coefficients are dynamically adjusted based on signal characteristics. For example, when phase jitter exceeding 0.2 radians is detected, the filter uses iterative optimization to enhance its ability to suppress high-frequency interference. After processing, the optical signal amplitude fluctuation is reduced to less than 2 decibels, the phase jitter is reduced to 0.15 radians, the signal-to-noise ratio is improved to approximately 28 decibels, and the bit error rate is reduced to 10 to the power of -12, meeting the requirements of high-speed transmission. This filtering method, through adaptive adjustment, can adapt to different interference types, enhancing the flexibility of signal recovery.

[0107] In an optional embodiment of the present invention, if the optical cable line is long or the grounding points are unevenly distributed, the measurement segment division can be dynamically adjusted according to the electromagnetic field strength to ensure the coverage of the differential measurement. The specific division method can be set by technicians according to the site conditions and is not further restricted here. The optical signal after filtering and optimization is verified by recalculating the signal-to-noise ratio to verify the degree of quality improvement. The final output transmission signal shows strong anti-interference ability in various experimental scenarios. For example, it can still maintain stable data transmission performance near high-voltage lines.

[0108] It should be noted that the embodiments of the present invention, through the combination of multi-point differential measurement and neural network feature extraction, can effectively identify the specific impact of electromagnetic interference on optical cable signals, while the introduction of adaptive filtering further ensures the stability of signal quality. The coordinated work of these steps not only improves the accuracy of fault analysis.

[0109] In step S13, it is necessary to obtain the optical cable strain information through fiber Bragg grating sensing technology, and extract the strain change characteristics, including strain size, change rate and frequency characteristics, in combination with the amplitude and phase characteristics of the optical cable transmission signal, to achieve accurate monitoring and analysis of the optical cable status.

[0110] In an embodiment of the present invention, the light reflection characteristics of a fiber Bragg grating are utilized to obtain strain-related data through narrowband laser scanning. The grating structure is formed by inscribing periodic refractive index changes in the core layer of the optical fiber. The reflection center wavelength is set to 1550 nanometers, the reflection bandwidth is approximately 0.2 nanometers, and the reflectivity can reach over 90%. When the optical cable is subjected to external stress, the grating period changes slightly, causing the reflection wavelength to drift. By measuring this drift, the strain value can be inferred. The narrowband laser scans in the range of 1549.5 to 1550.5 nanometers with a step size of 0.01 nanometer to generate a reflection spectrum curve. In actual measurements, the strain amplitude is less than 1000 microstrain under normal conditions, but external force may cause it to suddenly increase to more than 3000 microstrain, reflecting the stress state change of the optical cable.

[0111] Step S13 specifically includes the following steps:

[0112] S131. Wavelet noise reduction is performed on the reflection spectrum data and combined with temperature compensation calculations to obtain the axial strain value of the optical cable. At the same time, the strain amplitude and period characteristics are extracted through multi-scale wavelet transform. Specifically, the reflection spectrum data is decomposed through a four-layer wavelet to filter out high-frequency noise, and the Haar wavelet basis function is used to ensure computational efficiency. The ambient temperature is collected in real time by a temperature sensor, ranging from -20 to 60 degrees Celsius. The temperature influence coefficient on wavelength drift is 10 picometers per degree Celsius. The wavelength drift is corrected by a linear compensation algorithm to obtain the axial strain value with a sensitivity of 1.2 picometers per microstrain. The measurement range covers 0 to 10,000 microstrain. Multi-scale decomposition divides the strain time series into three layers, extracting amplitude and period data at different time scales, where the amplitude represents the strain intensity and the period reflects the persistence of the change.

[0113] A neural network is used to train the extracted strain features to determine the monitoring threshold. The feature vector, consisting of strain amplitude and period, is fed into a three-layer neural network with 16 neurons in the hidden layer. A backpropagation algorithm optimizes the weights based on historical data, ultimately outputting the strain state judgment. After training, a monitoring threshold of 2000 microstrain is set. If the strain value exceeds this threshold, the time and duration of the violation are recorded. For example, in a sudden strain event caused by external force, the strain change rate can reach 500 microstrains per minute, indicating a potential risk to the optical cable.

[0114] S132. Perform time-synchronous analysis on the strain data and the optical cable transmission signal. Cross-correlation calculations are used to evaluate the correlation between strain and signal amplitude and phase, and Fourier transforms are used to extract the strain frequency characteristics. In this embodiment of the present invention, both the strain data and the transmission signal are recorded at a 1-second sampling interval. Cross-correlation calculations show that the correlation between sudden strain changes and signal anomalies can exceed 0.8, indicating a significant linkage effect between the two. After Fourier transforming the correlation data, characteristic peaks corresponding to the external force frequency can be observed in the spectrum distribution curve of the response. For example, construction vibrations may form significant peaks in the low-frequency range, providing a basis for fault cause analysis.

[0115] In an optional embodiment of the present invention, if the optical cable installation environment is complex, the compensation algorithm's weights can be adjusted based on the magnitude of on-site temperature fluctuations to improve the accuracy of strain calculations. The specific adjustment method can be set by technical personnel based on actual needs and is not specifically defined here. Field measurements have demonstrated that strain monitoring can effectively capture external force damage events, such as abnormal strain caused by tower swaying due to wind or construction compression, with the monitoring data highly consistent with actual fault records.

[0116] Furthermore, by combining amplitude and phase analysis of the transmitted signal, this method further reveals the impact of strain changes on optical cable transmission performance. For example, a sudden increase in strain may cause abnormal fluctuations in signal amplitude and a corresponding increase in phase jitter. This simultaneous analysis allows for more accurate location of potential fault points. This multi-dimensional data fusion enhances comprehensive monitoring.

[0117] In step S14, it is necessary to preset a threshold value for the strain change characteristics of the optical cable, identify the optical cable fault by judging whether the strain magnitude and change rate exceed the threshold value, and determine the preliminary location of the fault point in combination with the grounding point distribution and tower structure data. At the same time, wavelet transform noise reduction and support vector machine classification technology are used to optimize fault identification, and ultimately achieve accurate positioning of the fault area.

[0118] In an embodiment of the present invention, the optical cable strain monitoring data is collected at a sampling frequency of 100 Hz. The raw data is mixed with environmental noise and equipment errors. In order to improve the analysis accuracy, wavelet transform is used for noise reduction. The db4 wavelet basis function is selected for 4-layer decomposition. The signal-to-noise ratio of the decomposed data is improved by about 12 decibels, and the strain measurement accuracy reaches less than 5 microstrain. The strain amplitude and duration are extracted from the noise-reduced data as characteristic parameters. During normal operation, the strain amplitude is usually less than 1000 microstrain and the duration does not exceed 10 seconds. However, these parameters will change significantly under fault conditions. By analyzing these characteristics, a data set containing 500 sets of historical fault records is constructed. Each set of data covers information such as strain amplitude, duration and fault type.

[0119] Step S13 specifically includes the following steps:

[0120] S141. A strain fault discriminator was established using a support vector machine (SVM) based on the fault signature dataset. An optimization algorithm was used to determine strain amplitude and rate of change thresholds. The SVM, combined with a Bragg grating (FBG) demodulator, calculated strain values ​​and rates of change in real time to identify anomalies. Specifically, the SVM employed a radial basis kernel function, with kernel parameters adjusted through cross-validation to achieve a classification accuracy exceeding 95%. Based on statistical analysis of historical data, the strain amplitude threshold was set at 2000 microstrain, and the rate of change threshold was set at 200 microstrain per second. The FBG demodulator operated in the 1525-1565 nanometer wavelength range with a wavelength resolution of 1 picometer. The grating reflectance spectrum was measured using a wavelength scanning method. The shift in the peak reflection wavelength was linearly correlated with the strain, with a sensitivity of 1.2 picometers per microstrain. When a sudden strain event occurred, the reflection wavelength shift could exceed 2.4 nanometers, indicating significant external force on the optical cable. The strain rate of change was calculated by the difference between adjacent sampling points. If both the strain value and rate exceeded the threshold, a fault event was flagged.

[0121] S142. A cross-correlation method is used to determine the strain mutation interval for the fault event, and optical time-domain reflectometry is used to calculate the distance to the fault point. The fault area is precisely located by combining grounding point distribution and tower structure data. In this embodiment of the present invention, the cross-correlation method identifies the start and end times of the strain mutation by calculating the correlation of signals from adjacent measurement points. The length of the mutation interval is typically related to the range of the external force. The optical time-domain reflectometry test uses a 10 nanosecond laser pulse with a spatial resolution of 1 meter. The distance to the fault point is calculated by analyzing the time delay of the reflected light pulse, with a ranging accuracy of less than 5 meters. After extracting the location coordinates of the mutation point from the fiber attenuation curve, a nearest neighbor search algorithm is used to match the two closest nodes in the grounding point distribution data. The grounding point coordinates are based on GPS records, with a positioning accuracy of better than 1 meter. The tower structure database stores information such as the tower's spatial position, height, and crossarm structure. The tower number is associated with the optical cable route to ultimately determine the fault area.

[0122] In real-world applications, such as during a fiber optic cable line test, an excavator collision caused a sudden strain change. This method, using the aforementioned steps, accurately located the fault point with an error of less than 10 meters, providing critical support for rapid repairs. It can be understood that the combination of wavelet transform noise reduction and support vector machines improves the reliability of fault identification, while the coordinated application of optical time domain reflectometry and nearest neighbor search ensures location accuracy.

[0123] In an optional embodiment of the present invention, if the optical cable line is long or the grounding points are sparsely distributed, the sampling point density can be dynamically increased to improve the resolution of the sudden change interval. The specific density adjustment can be determined by technicians based on site conditions and is not strictly limited here. In addition, this method has demonstrated strong adaptability in the verification of multiple important optical cable lines, can effectively cope with different types of external force interference, and provides technical support for the stable operation of the power communication network.

[0124] In step S15, it is necessary to analyze the amplitude, phase and strain information of the optical cable transmission signal, extract the strain distribution gradient change law caused by the high magnetic field around the fault point, and correct the fault point position in combination with the optical cable laying method to achieve high-precision positioning.

[0125] In this embodiment of the present invention, the optical cable transmission signal data is first preprocessed to extract fault area characteristics. The signal acquisition frequency is set to 10 GHz, and the time window is 100 microseconds. The raw data is often mixed with high-frequency noise and electromagnetic interference. To ensure data quality, a fourth-order Butterworth low-pass filter is used for processing. After filtering, the signal amplitude fluctuation is controlled within 0.5 decibels, and the phase jitter is reduced to less than 0.05 radians. Feature vectors are extracted from the filtered data, including the amplitude mean, phase mean, and their standard deviation. These parameters can effectively reflect the abnormal signal characteristics of the fault area.

[0126] Step S15 specifically includes the following steps:

[0127] S151. Electromagnetic field intensity measurements were conducted within the fault area. A Gaussian distribution model was used to determine the high magnetic field range. A strain gradient matrix was constructed from fiber Bragg grating (FBG) sensor data to analyze the strain distribution characteristics. Electromagnetic field measurements were performed using a three-axis magnetic field probe with a sensitivity of 0.1 microtesla and a measurement range of 0 to 100 millitesla. A grid scan was performed at 0.5-meter intervals within a 5-meter radius around the fault point to obtain a three-dimensional magnetic field intensity distribution. Gaussian distribution model fitting results showed that the high magnetic field region was ellipsoidal in shape, with magnetic field intensity reaching 50 millitesla along the major axis and gradually decreasing to below 10 millitesla along the minor axis. Microscopic deformation of fiber Bragg grating (FBG) in high magnetic fields causes reflection wavelength shift. For example, in a 50 millitesla magnetic field, strain values ​​can increase to 500 microstrains. Based on this, a neural network was used to process the FBG sensor data. The network structure consists of three layers: the input layer receives magnetic field intensity and wavelength shift values, and the hidden layer, configured with 32 neurons, outputs strain gradient values. Training data was obtained from 200 sets of field measurements to ensure the model accurately extracts strain distribution characteristics. The constructed strain gradient matrix clearly reflects the spatial variation pattern around the fault point. The gradient extreme points usually coincide with the fault location with a deviation of less than 1 meter.

[0128] S152: A three-dimensional strain distribution function is established based on the strain gradient matrix. The precise coordinates of the fault point are determined using triangulation combined with magnetic field correction and least-squares optimization. In this embodiment of the present invention, the three-dimensional strain distribution function uses a gradient descent method to calculate extreme points and identify strain anomaly intervals. The boundary coordinates of the anomaly interval are used as input, and triangulation is used to calculate the initial fault location based on the strain data from three measurement points, with a positioning accuracy of approximately 2 meters. To further improve accuracy, magnetic field gradient data is introduced as a correction factor to adjust the positioning deviation to within 1 meter. Least-squares optimization comprehensively considers the physical constraints of optical cable installation, such as a burial depth between 0.8 and 1.2 meters, a bend radius greater than 20 times the cable diameter, and a strike angle variation of less than 30 degrees. Initial spatial coordinates are generated through iterative optimization. The optimization results are then matched to the optical cable installation map, which records details such as turning point coordinates and terrain undulations. This map fully accounts for construction errors and environmental impacts. The final corrected coordinates are kept within 0.5 meters of the actual fault location.

[0129] In an optional embodiment of the present invention, if the high magnetic field area is large, the density of magnetic field measurement points can be increased to improve the accuracy of the distribution model. The specific density is adjusted by technicians based on site conditions and is not limited here. It can be understood that this multi-level data fusion and optimization method can not only reveal the impact of high magnetic fields on strain distribution, but also effectively address positioning challenges in complex installation environments.

[0130] Furthermore, this method has demonstrated significant practical results. For example, during a fault investigation, the corrected coordinates of the fault point directly guided repair crews to locate the damaged area, shortening investigation time compared to traditional methods while also avoiding ineffective excavation due to positioning errors. This high-precision positioning capability provides crucial technical support for the rapid restoration and long-term stable operation of the power communication network.

[0131] In step S16, it is necessary to use the optical fiber sensing data and strain change characteristics to calculate the strain amplitude of the fault point caused by the high magnetic field, and combine the mechanical properties of the optical cable material to evaluate the degree of damage caused by the magnetic field, so as to provide a scientific analysis and maintenance basis for the health status of the optical cable.

[0132] In an embodiment of the present invention, a fiber Bragg grating sensor achieves high-precision strain measurement through periodic changes in the refractive index of the optical fiber core layer. The center wavelength of the reflection spectrum is 1550 nanometers, the reflection bandwidth is approximately 0.2 nanometers, and the wavelength resolution is better than 1 picometer. The collected reflection spectrum data is first subjected to noise reduction processing through a 50 Hz bandpass filter to effectively filter out environmental interference, improve the signal-to-noise ratio by approximately 12 decibels, and achieve a wavelength drift measurement accuracy of 2 picometers. The center wavelength value is extracted from the filtered spectrum, and the initial strain data is calculated based on the wavelength drift. In actual measurements, under high magnetic field conditions, such as at an intensity of 50 millitesla, the strain amplitude may increase to 2000 microstrain, indicating a significant effect of the magnetic field on strain.

[0133] Step S16 specifically includes the following steps:

[0134] S161. Perform a multi-scale wavelet transform on the initial strain data to extract characteristic parameters of the high magnetic field region. A dual-hidden-layer neural network is then used to analyze the correlation between strain and magnetic field intensity. In this embodiment of the present invention, the wavelet transform uses the db4 wavelet basis function, with a decomposition layer of four, generating strain components in four frequency bands: 0 to 0.5 Hz, 0.5 to 2 Hz, 2 to 8 Hz, and 8 to 32 Hz. Time-frequency spectrum analysis shows that strain changes induced by high magnetic fields are primarily concentrated in the 2 to 8 Hz frequency band. Characteristic parameters include strain amplitude, frequency center, and bandwidth, which clearly reflect the dynamic characteristics of the magnetic field. Next, a correlation matrix containing strain characteristics and magnetic field intensity data is constructed and input into a dual-hidden-layer neural network for processing. The first hidden layer of the network is configured with 64 neurons, responsible for capturing the nonlinear relationship between strain and magnetic field; the second hidden layer is configured with 32 neurons, further compressing the characteristic information and ultimately outputting the strain amplitude value. This structural design, through layered extraction and optimization, ensures high accuracy in strain amplitude calculation.

[0135] Interval statistics were performed on the strain amplitude values, and a probability density function was used to analyze the strain distribution characteristics and extract the magnetic field strain coefficient. The statistical results show that the strain distribution has a bimodal characteristic, with a low strain peak corresponding to normal background strain and a high strain peak related to the magnetic field. The magnetic field strain coefficient was calculated based on the position of the high strain peak, with a typical value of 40 microstrain per millitesla. This coefficient quantifies the sensitivity of the optical cable to magnetic field intensity. Based on this coefficient, the stress state was calculated in combination with the mechanical parameters of the optical cable material. The elastic modulus of the optical cable outer sheath is 2.5 GPa, the Poisson's ratio is 0.35, and the elastic modulus of the optical fiber core is 70 GPa. These parameters can be used to derive the stress distribution curve, revealing the specific influence of the magnetic field on strain.

[0136] S162. Analyze the stress-strain relationship and evaluate the degree of damage through finite element modeling, and use the damage accumulation function to quantify the material damage under the action of the magnetic field. In an embodiment of the present invention, the finite element modeling uses a tetrahedral mesh to divide the optical cable cross section, with a minimum mesh size of 0.1 mm, generating a total of approximately 8,000 units. Calculations show that stress concentration mainly occurs at the interface between the optical fiber core and the cladding. Combining the Mises criterion to compare the material deformation and yield strength, the outer sheath yield strength is 50 MPa. When the strain exceeds 5,000 microstrain, plastic deformation may occur. The damage accumulation function comprehensively considers the strain amplitude and action time. For example, when the magnetic field lasts for 2 hours and the strain peak exceeds 3,000 microstrain, the optical cable material begins to show microscopic damage. This quantitative assessment can accurately predict the time window and extent of damage.

[0137] In an optional embodiment of the present invention, if the magnetic field intensity fluctuates significantly, the frequency range of the wavelet decomposition can be dynamically adjusted based on real-time data to more accurately capture strain characteristics. The specific adjustment method can be set by technicians based on site requirements and is not mandatory here. Field tests have demonstrated that this method can effectively identify the risk of optical cable damage in high-magnetic field environments. For example, in one test, potential fracture points in areas of stress concentration were promptly identified, providing critical information for preventive maintenance.

[0138] In step S17, if the degree of damage to the optical cable caused by the high magnetic field exceeds the preset target level, repair suggestions are generated based on the damage assessment results combined with the tower structure, optical cable laying method and fault point location. At the same time, the optical cable status database is updated, and a cable health status report reflecting the impact of the high magnetic field is generated through multi-point differential measurement and fiber optic sensor data analysis.

[0139] In an embodiment of the present invention, optical cable damage assessment constructs a damage feature matrix based on multidimensional feature analysis, encompassing three core dimensions: strain value, duration, and magnetic field strength. Strain values ​​typically range from 0 to 5000 microstrain, durations from a few minutes to several hours, and magnetic field strengths from 0 to 100 millitesla. A clustering algorithm is used to classify the degree of damage into four levels: Level 1 damage corresponds to strains less than 1000 microstrain and durations less than one hour, while Level 4 damage corresponds to strains exceeding 3000 microstrain or durations greater than four hours. A repair level identifier is generated based on the clustering results. In actual measurements, for example, a section of optical cable subjected to an 80 millitesla magnetic field for two hours developed a strain value of 4000 microstrain, resulting in Level 4 damage, demonstrating the significant destructive effect of high magnetic fields on optical cables.

[0140] Step S17 specifically includes the following steps:

[0141] S171. A neural network model is established based on the repair level and the optical cable installation environment parameters. A repair plan number is generated through spatial mapping, and repair recommendations and status information are recorded in a database index table. Specifically, environmental parameters include tower spacing, installation depth, and bend radius. Tower spacing generally ranges from 200 to 400 meters, installation depth is controlled between 0.8 and 1.2 meters, and the bend radius is no less than 20 times the cable diameter. These parameters are normalized to the range of 0 to 1 using a data preprocessor to ensure input data consistency. The neural network employs a three-layer architecture: the input layer contains the repair level and seven environmental parameter nodes, the hidden layer has 128 neurons and uses the ReLU activation function, and the output layer generates a repair plan number. Different repair levels are combined with environmental constraints to generate targeted recommendations. For example, in a Level 4 damage area, the tower load-bearing capacity must be assessed to determine whether the support structure should be replaced. The repair plan number is then used to construct a database index table. An incremental update mechanism is used to record the global positioning coordinates of the fault point, the damage type code, and the specific repair recommendations, thereby updating the optical cable status indicator.

[0142] Multi-point differential measurements are used to verify changes in the optical cable's condition. Measurement points are set every 50 meters along the cable, and the status of each section is assessed by calculating the signal difference between adjacent points. The differential value fluctuation in healthy sections is typically less than 0.2 decibels, while it may rise to over 2 decibels in damaged sections, reflecting significant differences in local conditions. Fiber Bragg grating sensor data is cross-validated with the differential measurement results. Time-frequency analysis is performed using a short-time Fourier transform (SFT) with a time window set to 100 milliseconds and a frequency resolution of 1 Hz. Data consistency is determined by extracting signal characteristic parameters. Cosine similarity calculations show that when the similarity exceeds 0.95, the data is considered highly consistent, providing a reliable basis for health status assessment.

[0143] S172. Calculate the health index of the optical cable based on multi-dimensional data and generate a status report, and comprehensively evaluate the influence of strain, signal quality and environmental parameters through weighted summation. In an embodiment of the present invention, the weight distribution for calculating the health index is 40% for strain, 30% for signal quality, and 30% for environmental parameters. The calculation process is implemented through a weighted summer, and finally a health status score is generated and the report content is formed. Actual measurements show that after a section of optical cable has been running in a high magnetic field environment for 1000 hours, the health index dropped from the initial 95 points to 75 points, mainly due to increased strain and decreased signal quality. This quantitative assessment not only reveals the long-term impact of high magnetic fields on optical cables, but also provides data support for maintenance decisions.

[0144] In an optional embodiment of the present invention, if the installation environment is complex, the measurement point density can be dynamically adjusted based on changes in tower spacing to improve differential measurement coverage. The specific adjustments are determined by technicians based on site conditions and are not strictly limited here. It can be understood that this method, through the combination of cluster analysis, neural network prediction, and multi-point verification, can generate highly targeted and operational repair recommendations, while ensuring that status reports fully reflect the health of the optical cable in high-magnetic field environments, providing technical support for the reliable operation of the power communication network.

[0145] like Figure 2 FIG. 1 is a schematic diagram showing a structure of an embodiment of a power communication network risk identification system based on automatic control technology provided by the present invention. In this embodiment, the system 1 includes:

[0146] Electromagnetic acquisition module 10, used to collect electromagnetic interference environment parameters, including electromagnetic field strength and spectrum distribution, obtain optical cable laying path and tower structure data, and analyze the distribution pattern of electromagnetic shielding layer grounding points;

[0147] The signal measurement module 11 is used to collect the optical cable transmission signal according to the distribution pattern of the grounding points, measure the amplitude and phase of the optical cable transmission signal using a multi-point differential measurement method, and determine whether electromagnetic interference has reduced the signal-to-noise ratio or increased the bit error rate of the optical cable transmission signal by analyzing the spectrum characteristics and amplitude fluctuations of the optical cable transmission signal. If so, an adaptive filtering algorithm is used to eliminate the influence of electromagnetic interference on the signal;

[0148] The strain analysis module 12 is used to obtain the strain information of the optical cable through the fiber Bragg grating sensing technology, and extract the strain variation characteristics of the optical cable in combination with the amplitude and phase of the optical cable transmission signal. The strain variation characteristics of the optical cable include the strain magnitude, change rate and frequency characteristics;

[0149] The fault judgment module 13 is used to compare the strain change characteristic threshold with the preset strain change threshold. If the strain magnitude and change rate both exceed the preset strain change threshold, it is judged that there is a fault in the optical cable. The preliminary location of the fault point is determined by combining the grounding point distribution and the tower structure data;

[0150] The position correction module 14 is used to extract the strain distribution gradient change caused by the high magnetic field around the fault point based on the amplitude, phase and strain information of the transmission signal, analyze the strain distribution pattern around the fault point, and correct the fault point position based on the optical cable laying method;

[0151] The damage assessment module 15 is used to calculate the strain change amplitude at the fault point caused by the high magnetic field based on the optical fiber sensing data and the strain change characteristics, and to assess the damage degree caused by the magnetic field in combination with the elastic modulus and yield strength of the optical cable material;

[0152] The health report module 16 is used to generate fault point repair suggestions if the damage caused by the high magnetic field exceeds the set target level. It combines the tower structure and optical cable laying method, the fault point location and the damage degree assessment results to update the optical cable status database, and combines multi-point differential measurement and optical fiber sensing data to generate an optical cable health status report focusing on the impact of high magnetic fields.

[0153] For more details, please refer to and combine the above Figure 1 The description is not repeated here.

[0154] As another aspect of the present invention, a computer readable storage medium is provided on which a computer program is stored. When the computer program is executed by the processor module, the computer program can achieve the following Figure 1 For more details, please refer to and combine the above Figure 1 The description is not repeated here.

[0155] The implementation of the embodiments of the present invention has the following beneficial effects:

[0156] This invention provides a method, system, and storage medium for identifying risks in power communication networks. By collecting electromagnetic interference environmental parameters and optical cable installation data, the system analyzes the distribution of electromagnetic shielding layer grounding points. Multi-point differential measurement and fiber Bragg grating sensing technology are then used to acquire optical cable transmission signals and strain information. By analyzing signal characteristics and strain variations, the system determines the impact of electromagnetic interference and optical cable faults, and locates the fault point. In combination with the cable material properties, the system assesses the extent of damage caused by high magnetic fields, generating repair recommendations and health status reports.

[0157] The present invention can comprehensively consider factors such as the structural characteristics of the tower, the optical cable laying method, the distribution of the grounding points of the electromagnetic shielding layer, etc., and can effectively detect and evaluate the degree of damage to the optical cable in a high magnetic field environment, thereby improving the efficiency and reliability of optical cable operation and maintenance. It provides important technical support for optical cable systems in the fields of electricity, communications, etc., and can ensure the stable operation of the power communication network.

[0158] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0159] The above disclosure is only a preferred embodiment of the present invention and certainly cannot be used to limit the scope of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope of the present invention.

Claims

1. A method for identifying risks in a power communication network, characterized in that: The steps include: S11, collect electromagnetic interference environment parameters, obtain optical cable laying path and tower structure data, and analyze the distribution pattern of electromagnetic shielding layer grounding points; S12, based on the distribution pattern of the grounding points, collecting the optical cable transmission signal, measuring the amplitude and phase of the optical cable transmission signal, and analyzing the spectrum characteristics and amplitude fluctuations of the optical cable transmission signal to determine whether electromagnetic interference has reduced the signal-to-noise ratio or increased the bit error rate of the optical cable transmission signal. If so, an adaptive filtering algorithm is used to eliminate the influence of the electromagnetic interference on the signal; S13, using fiber Bragg grating sensing technology to obtain optical cable strain information, combined with the amplitude and phase of the optical cable transmission signal, to extract the cable strain change characteristics; S14, comparing the strain magnitude and the strain change rate with a preset strain change characteristic threshold. If both the strain magnitude and the strain change rate exceed the preset strain change threshold, it is determined that there is a fault in the optical cable. The preliminary location of the fault point is determined by combining the grounding point distribution and the tower structure data; S15, extracting the strain distribution gradient change caused by the high magnetic field around the fault point based on the amplitude, phase, and strain information of the transmission signal, analyzing the strain distribution pattern around the fault point, and correcting the fault point location based on the optical cable laying method; S16, using optical fiber sensing data and strain change characteristics, calculates the strain change amplitude at the fault point caused by the high magnetic field. Combined with the elastic modulus and yield strength of the optical cable material, it assesses the degree of damage caused by the magnetic field. S17: If the damage caused by the high magnetic field exceeds the set target level, a fault point repair suggestion is generated. Combined with the tower structure and optical cable laying method, the fault point location and the damage degree assessment results, the optical cable status database is updated. Combined with multi-point differential measurement and optical fiber sensing data, a cable health status report focusing on the impact of high magnetic fields is generated.

2. The method according to claim 1, wherein The step S11 further includes: The electromagnetic sensor collects the three-dimensional electromagnetic field intensity value at the preset monitoring point, performs spectrum scanning for the high frequency band and the low frequency band respectively, obtains the electromagnetic spectrum data from the spectrum analyzer, and generates the electromagnetic frequency distribution matrix; The point cloud data along the optical cable is collected by the laser radar scanner, and the three-dimensional spatial coordinates of the point cloud data are reconstructed using the least squares method. The optical cable laying trajectory and the geometric size parameters of the tower structure are extracted from the reconstructed data matrix. Performing wavelet transform filtering on the electromagnetic spectrum data, extracting the characteristic frequency of electromagnetic interference using Fourier transform, and identifying the position coordinates of the interference source from the frequency domain characteristics; Establishing an electromagnetic field distribution function based on the electromagnetic field intensity value and the electromagnetic interference characteristic frequency, and obtaining an electromagnetic field intensity threshold range from the function curve; If the electromagnetic field intensity at the measurement point exceeds the threshold range, the gradient descent method is used to calculate the optimal layout position of the grounding point, and the distribution spacing of the grounding points is obtained from the optical cable laying trajectory and the geometric dimension parameters of the tower structure; A neural network model is established for the grounding point distribution spacing. The input layer parameters include the electromagnetic field strength value, the interference source position coordinates and the optical cable trajectory line. The output layer parameters are the electromagnetic shielding layer thickness value and the grounding resistance value.

3. The method according to claim 2, wherein The step S12 further comprises: The measurement sections are divided according to the distribution data of the optical cable grounding points. The optical cable transmission signal is collected at the boundary points of each section using an optical time domain reflectometer. The amplitude and phase values ​​of the optical signal are obtained from the time domain sampling data. Performing multi-point differential calculation on the time domain sampling data, using fast Fourier transform to obtain spectrum components, and extracting the signal center frequency band power density and background noise power density from the spectrum diagram; Calculating a signal-to-noise ratio parameter according to the power density ratio, establishing a reference curve for the signal-to-noise ratio parameter, and obtaining a signal-to-noise ratio threshold value from the reference curve; If the signal-to-noise ratio parameter is lower than the threshold value, a neural network is used to extract features of the optical signal amplitude and phase values, and the input feature parameters include the signal waveform slope, peak ratio and phase offset; Calculating a bit error rate value of the optical signal based on the characteristic parameters, establishing a signal quality evaluation matrix from the bit error rate value, and obtaining a signal quality classification threshold; According to the signal quality classification, a wavelet adaptive filter is used to perform denoising on the optical signal, the filter parameters are automatically adjusted according to the signal characteristic parameters, and a waveform of the optical signal after filtering is obtained; The signal-to-noise ratio (SNR) of the filtered optical signal is recalculated, and the degree of improvement of the signal quality is determined based on the SNR value to obtain a final optical cable transmission signal.

4. The method according to claim 3, wherein The step S13 further comprises: According to the fiber Bragg grating reflection spectrum, a narrow-band laser is used to scan within a preset wavelength band, and the central wavelength value and reflection intensity value are extracted from the reflection spectrum data; Performing wavelet noise reduction processing on the reflection spectrum data, using a temperature sensor to obtain the ambient temperature value of the optical cable, performing compensation calculation on the center wavelength drift based on the temperature value, and obtaining the axial strain value of the optical cable; Constructing a time series for the axial strain value of the optical cable, performing multi-scale decomposition on the strain series data using wavelet transform, and extracting the strain amplitude and strain period from the decomposition coefficients; constructing a feature vector according to the strain amplitude and strain period, training the feature vector using a neural network, and obtaining a strain monitoring threshold from the trained parameters; If the axial strain value of the optical cable exceeds the monitoring threshold, the time when the strain exceeds the limit and the duration of the strain are recorded, and the strain change rate is extracted from the recorded data; The strain change rate is time synchronized with the optical cable transmission signal, and a correlation degree between the strain and the signal amplitude and phase is obtained by cross-correlation calculation; A Fourier transform is performed according to the correlation value, and a strain spectrum distribution curve is extracted from the transform result to obtain the strain frequency characteristic.

5. The method according to claim 4, wherein The step S14 further comprises: Wavelet transform is used to reduce the noise of the optical cable strain monitoring data. The two characteristic parameters of strain amplitude and duration are extracted from the noise-reduced data to establish a fault feature dataset. According to the fault feature data set, a strain fault discriminator is established using a support vector machine, and a strain amplitude threshold and a change rate threshold are obtained from the discriminator output result; Performing real-time calculations on the noise-reduced data, using a Bragg grating demodulator to obtain the strain value of the optical cable, and calculating the strain change rate from adjacent sampling points; If the cable strain value exceeds the strain amplitude threshold and the rate of change exceeds the rate threshold, the fault time mark is recorded and the strain mutation interval is determined using the cross-correlation method. Based on the strain mutation interval, the distance to the fault point is calculated using optical time domain reflectometry, and the location coordinates of the mutation point are obtained from the optical fiber attenuation curve. For the position coordinates of the mutation point, a nearest neighbor search algorithm is used to match adjacent nodes in the grounding point distribution data; According to the positions of the adjacent nodes, the tower number and installation position parameters are extracted from the tower structure database to obtain the fault point area range.

6. The method according to claim 5, wherein The step S15 further comprises: Pre-process the data collected from the optical cable transmission signal, use a filter to eliminate noise interference, and obtain the fault area signal feature vector from the signal amplitude and phase data; Measuring the electromagnetic field strength of the fault area, calculating the magnetic field gradient distribution using a Gaussian distribution model, and obtaining the high magnetic field area range from the magnetic field distribution data; extracting strain distribution characteristics using a neural network based on the fiber Bragg grating sensing data within the high magnetic field region, and constructing a strain gradient matrix from the strain data; A three-dimensional strain distribution function is established for the strain gradient matrix, a gradient descent method is used to calculate the strain extreme point, and a strain anomaly interval is obtained from the extreme point position; According to the boundary coordinates of the strain anomaly interval, the initial position of the fault point is calculated using the triangulation method, and the positioning deviation is corrected using the magnetic field gradient data; For the initial position of the fault point, the least square method is used to optimize the spatial coordinates, and the optimization parameters include the cable laying depth, bending radius and direction angle; Position matching is performed based on the optimization parameters and the optical cable laying map, and the corrected coordinates of the fault point are obtained from the actual laying path data.

7. The method according to claim 6, wherein The step S16 further comprises: The reflected spectrum data collected by the fiber Bragg grating sensor is subjected to noise reduction processing, the central wavelength value is extracted using a narrowband filter, and the initial strain data is obtained from the wavelength drift; Performing wavelet transform on the initial strain data, extracting strain components of different frequency bands using multi-scale decomposition, and obtaining strain characteristic parameters in the high magnetic field action area from the time-frequency spectrum; Establishing a correlation matrix based on the strain characteristic parameters and magnetic field intensity data, and extracting strain amplitude values ​​using a double hidden layer neural network; performing interval statistics on the strain amplitude values, calculating strain distribution characteristics using a probability density function, and obtaining a magnetic field strain coefficient from the strain peak position; Calculating the stress state of the optical cable according to the magnetic field strain coefficient, using material mechanical parameters including elastic modulus and Poisson's ratio to perform stress calculation, and obtaining the strain state value from the stress distribution curve; Performing finite element modeling based on the strain state value, dividing the optical cable cross section using a tetrahedral mesh, and obtaining material deformation from the stress-strain relationship; The deformation of the material is compared with the yield strength and the damage accumulation function is used to establish an evaluation index, and the magnetic field damage value is obtained from the strain amplitude curve.

8. The method according to claim 7, wherein The step S17 further includes: A damage feature matrix is ​​constructed based on the optical cable damage assessment data. The damage degree is graded using a clustering algorithm. The damage features include strain value, duration, and magnetic field strength. The repair level is obtained from the grading results. Perform spatial mapping between the repair level and the optical cable laying environment, use a data preprocessor to extract tower spacing, laying depth and bending radius parameters, and obtain repair constraints from environmental parameters; Establishing a neural network model based on the restoration constraints, wherein the input layer includes the restoration level and environmental parameters, and the output layer generates a restoration plan number; Constructing a database index table for the repair solution number, recording the coordinates of the fault point, damage type, and repair suggestion content in an incremental update manner, and obtaining the optical cable status identifier from the data record; Perform multi-point differential measurement according to the optical cable status identifier, calculate the state change using a signal comparison method at adjacent measurement points, and obtain the section state value from the differential data; Perform feature matching on the segment state value and the fiber Bragg grating sensor data, extract signal feature parameters using a time-frequency analyzer, and determine data consistency based on the matching results; The optical cable health index is calculated based on the data consistency, a weighted summer is used to generate a health status score, and status report content is formed from the score data.

9. A power communication network risk identification system, characterized in that: At least: The electromagnetic acquisition module is used to collect electromagnetic interference environment parameters, including electromagnetic field strength and spectrum distribution, obtain optical cable laying paths and tower structure data, and analyze the distribution pattern of electromagnetic shielding layer grounding points; The signal measurement module is used to collect the optical cable transmission signal based on the distribution pattern of the grounding points, and measure the amplitude and phase of the optical cable transmission signal using a multi-point differential measurement method. By analyzing the spectrum characteristics and amplitude fluctuations of the optical cable transmission signal, it is determined whether electromagnetic interference has reduced the signal-to-noise ratio or increased the bit error rate of the optical cable transmission signal. If so, an adaptive filtering algorithm is used to eliminate the impact of electromagnetic interference on the signal; The strain analysis module is used to obtain optical cable strain information through fiber Bragg grating sensing technology. It combines the amplitude and phase of the optical cable transmission signal to extract the optical cable strain change characteristics, including strain magnitude, change rate and frequency characteristics. The fault judgment module is used to compare the strain change characteristic threshold with the preset strain change threshold. If the strain size and change rate both exceed the preset strain change threshold, it is judged that there is a fault in the optical cable. The preliminary location of the fault point is determined by combining the grounding point distribution and tower structure data; The position correction module is used to extract the strain distribution gradient change caused by the high magnetic field around the fault point based on the amplitude, phase and strain information of the transmission signal, analyze the strain distribution pattern around the fault point, and correct the fault point position based on the optical cable laying method; The damage assessment module is used to calculate the strain change amplitude at the fault point caused by the high magnetic field based on the optical fiber sensing data and strain change characteristics. It also combines the elastic modulus and yield strength of the optical cable material to assess the damage caused by the magnetic field. The health report module is used to generate fault point repair suggestions if the damage caused by high magnetic fields exceeds the set target level. It combines the tower structure and optical cable laying method, the fault point location and the damage degree assessment results to update the optical cable status database. It combines multi-point differential measurement and optical fiber sensing data to generate an optical cable health status report focusing on the impact of high magnetic fields.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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