Optical cable external force damage intelligent identification system

Abstract

The invention relates to the technical field of optical cable line external force damage monitoring, and discloses an optical cable external force damage intelligent identification system. The system comprises an optical fiber vibration sensing module, a multi-source feature fusion analysis module, an abnormal event association positioning module and an intelligent checking strategy generation module. The optical fiber vibration sensing module constructs a model based on historical data and outputs a reference vibration characteristic value; the multi-source feature fusion analysis module performs three-dimensional fusion analysis of time domain, frequency domain and phase on the reference value and the measured value to generate a feature difference matrix; the abnormal event association positioning module is used for generating an abnormal probability distribution cloud picture and positioning an abnormal area in combination with the topological parameters and the environment information; and the intelligent checking strategy generation module configures checking parameters according to the cloud picture, starts high-frequency acquisition for a high-probability region, and applies an environment disturbance test to adjacent lines. The system can accurately identify and position optical cable external force damage, and the monitoring efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of optical cable line external force damage monitoring, in particular to an optical cable external force damage intelligent identification system. BACKGROUND

[0002] With the continuous expansion of communication network scale, as the core carrier of information transmission, the safe operation of optical cable line is facing increasingly severe challenges. In actual application scenarios, optical cable lines often need to pass through complex and diverse geographical environments, including urban building groups, mountainous areas, farmland, and rivers and lakes. Frequent human activities in these areas, such as construction excavation, mechanical operation, vehicle collision, etc., are prone to cause external force damage to optical cables, leading to communication interruption and causing great losses to social production and life. Traditional optical cable external force damage monitoring methods have obvious limitations. Early methods rely on manual inspection, and inspection personnel regularly check possible safety hazards along the line. However, this method is limited by labor costs and inspection cycles, and it is difficult to achieve real-time monitoring in all-weather and full-coverage conditions. For sudden external force damage events, the response is often slow, and it is difficult to discover and handle them in time. With the development of technology, monitoring methods based on vibration sensors have gradually emerged. By deploying sensors on optical cable lines to collect vibration signals, it is attempted to analyze the signals to determine whether there is an anomaly. However, most of these methods only analyze single-dimensional vibration characteristics, such as only focusing on changes in vibration amplitude, ignoring the complex characteristics of vibration signals in frequency, phase, and other dimensions. When there are normal vibration disturbances in the external environment, such as vehicles passing by, natural wind movement, etc., it is easy to confuse them with real external force damage vibration signals, resulting in high false alarm rates. In addition, existing monitoring systems lack effective multi-source information fusion mechanisms in the analysis and processing of vibration signals. The vibration data collected by different monitoring points are often processed separately, and cannot be comprehensively analyzed in combination with the topology of the optical cable line, routing information, and surrounding environmental parameters, making it difficult for the system to accurately determine the source and propagation path of abnormal vibration. When locating abnormal areas, it is often only possible to give a rough line segment, and it is not possible to accurately locate a specific physical area, which brings great inconvenience to subsequent verification work. At the same time, existing verification strategies are also relatively simple and extensive. Usually, after an abnormal signal is found, personnel are directly dispatched to the general area for inspection. This method is not only inefficient, but also often difficult to quickly locate the specific damage point when faced with complex terrain or large-scale lines. In addition, for some potential external disturbances that have not caused obvious damage, the existing system lacks effective identification and early warning capabilities, which can easily lead to the accumulation of hidden dangers and ultimately cause serious line failures. In recent years, although some studies have attempted to introduce artificial intelligence algorithms to improve monitoring accuracy, in practical applications, due to the lack of deep mining and multi-dimensional analysis of the vibration characteristics of optical cable lines and effective combination with the actual environment of the line, the recognition accuracy and positioning accuracy of these algorithms still cannot meet the actual needs. How to build an intelligent system that can accurately identify, accurately locate and efficiently verify the optical cable external force damage has become a problem to be solved in the field of communication network operation. SUMMARY

[0003] The purpose of the present application is to provide an optical cable external force damage intelligent identification system to solve the problems raised in the background art.

[0004] To achieve the above purpose, the present application provides an optical cable external force damage intelligent identification system, which comprises: An optical fiber vibration perception module: based on historical vibration data of an optical cable line, a vibration feature perception model is constructed, real-time vibration amplitude sequence, frequency distribution characteristics and phase fluctuation parameters of the current optical cable line are collected, and reference vibration feature values are output through the vibration feature perception model; A multi-source feature fusion analysis module: three-dimensional feature fusion analysis is performed on the reference vibration feature values and the measured vibration feature values of the monitoring terminal, the three-dimensional feature fusion analysis includes time domain fluctuation deviation, frequency domain energy distribution and phase sequence consistency, and a feature difference matrix at the line level is generated; An abnormal event correlation positioning module: the feature difference matrix is input into a spatial correlation analysis network, combined with optical cable routing topology parameters and line environment location information, an abnormal probability distribution cloud map of abnormal vibration propagation path is generated, and an abnormal external force damage physical area is located; An intelligent verification strategy generation module: according to the abnormal probability distribution cloud map, verification parameters are configured, including: Enabling high-frequency vibration signal acquisition mode for high-probability abnormal areas; Applying environmental disturbance test to adjacent paragraph lines.

[0005] Preferably, the optical fiber vibration perception module specifically comprises: Historical data feature extraction: multi-dimensional decomposition processing is performed on the historical vibration data of the optical cable line, including: using non-subsampled wavelet transform to extract the energy proportion of the steady component and the transient component of the vibration amplitude, establishing a correlation map of vibration frequency characteristics and external force types through frequency coupling analysis, and aligning the phase fluctuation sequence mode under different working conditions using dynamic time warping algorithm; Vibration feature perception model construction: the processed historical vibration data is input into a composite perception network, the composite perception network comprises: The timing prediction unit based on the line aging curve is used to generate the basic vibration prediction feature, the convolutional neural network embedded with the frequency domain attention mechanism is used to correct the prediction deviation caused by the frequency distortion, the phase feature compensator is used to dynamically adjust the prediction weight according to the real-time collected phase fluctuation parameters; The multi-channel acquisition unit deployed on the edge synchronously captures the peak mutation rate and duration parameters of the vibration amplitude, the 50-500Hz segmented content and energy distribution of the vibration frequency, and the phase offset gradient and time interval entropy value of the phase fluctuation sequence. The reference value calculation: input the real-time acquisition data into the vibration feature perception model to obtain the reference vibration feature value.

[0006] Preferably, in the reference vibration feature value calculation, the following are performed: Adaptive noise suppression processing based on the line operation stage to eliminate measurement interference caused by environmental temperature and humidity; Integrate the correlation features of amplitude, frequency and phase through the spatiotemporal feature fusion algorithm; Output the reference vibration feature value including the normal working condition fluctuation interval, and the reference vibration feature value is dynamically updated with the line aging state.

[0007] Preferably, the multi-source feature fusion analysis module specifically includes: Time domain fluctuation deviation calculation: sliding comparison of the reference vibration feature value and the measured vibration feature value is performed with a preset time window, the dynamic time warping algorithm is used to align the non-synchronous sampled vibration sequence, the fluctuation deviation amount in each window is calculated, and a time domain deviation vector is generated; Frequency domain energy distribution detection: the vibration frequency features of the reference value and the measured value are decomposed by the frequency domain decomposition algorithm, the energy spectrum density ratio is calculated, the energy distribution index of each frequency band is extracted, and a frequency domain distribution vector is constructed; Phase sequence consistency evaluation: based on the structure matching algorithm, the position distribution of the reference value and the measured phase sequence is matched, the synchronization error of the phase fluctuation mutation point is calculated, the KL divergence of the phase interval distribution is quantified, and a phase consistency vector is generated; Feature difference matrix generation: the time domain deviation vector, the frequency domain distribution vector and the phase consistency vector are spliced by tensor, the dimension difference is eliminated through normalized processing of feature importance weighting, and a three-order feature difference matrix with a dimension of [line number x time stamp x feature type] is output.

[0008] Preferably, the frequency domain energy distribution detection specifically includes: In the frequency domain analysis stage, first, the corresponding vibration frequency characteristics in the benchmark and the measured vibration sequence are extracted, wavelet packet decomposition is used to perform multi-scale frequency band analysis on each group of frequency signals, the energy distribution characteristics in the preset sensitive frequency band interval are extracted, the sensitive frequency band interval is selected to cover the vibration frequency range of typical external force damage, after the extraction, the energy density of the benchmark and the measured data in the sensitive frequency band interval is quantitatively calculated respectively, and the distribution index of each frequency band is extracted based on the relative deviation degree of the two, the energy distribution results of all frequency bands are summarized to construct a frequency domain distribution vector.

[0009] Preferably, in the phase sequence consistency evaluation, the structure matching algorithm adopts the Hausdorff distance algorithm to match the positions of the mutation point sets in the two sequences and identify phase synchronization errors.

[0010] Preferably, the abnormal event correlation positioning module specifically comprises: Optical cable topology modeling: constructing an optical cable node connection relationship topology graph according to line environment position information, labeling routing parameters between nodes, superimposing reverse vibration constraint conditions of external interference source access points in the topology graph, and generating a physical topology model including a routing matrix and a node association matrix; Abnormal propagation simulation: mapping the feature difference matrix to the corresponding nodes of the optical cable topology model; performing abnormal propagation deduction based on a graph convolution network, and the calculation of the abnormal propagation deduction includes: i. calculating the attenuation factor of abnormal vibration according to the node routing parameters; ii. capturing cross-regional abnormal correlation features through a multi-head attention mechanism; iii. simulating the diffusion path of abnormal vibration in the topology network by using a Monte Carlo method; Probability distribution generation: counting the frequency of abnormal vibration in the simulation propagation of each route, calculating the abnormal vibration residence probability value in combination with the routing parameters, and generating an abnormal probability distribution cloud map covering the entire line, and labeling a suspicious route set with a probability value exceeding a preset abnormal residence probability threshold; Physical region positioning: performing spatial clustering analysis on the abnormal probability distribution cloud map to identify abnormal probability aggregation areas; and according to the line environment position and the optical cable topology connection relationship, the physical boundary of the abnormal external force damage is delineated.

[0011] Preferably, the abnormal event correlation positioning module further comprises outputting a suspicious line identifier and an abnormal propagation main path, wherein: The suspicious line identifier is based on the optical cable nodes connected by the suspicious route set, binds the optical cable nodes with the actual line sections to form a suspicious line identifier set, and indicates a potential abnormal external force damage source or affected section; The abnormal propagation main path is obtained by recording the node path and its sequence experienced by each round of propagation in the abnormal diffusion process of Monte Carlo simulation, counting the occurrence frequency of each path in all simulation paths, selecting the path sequence with the highest cumulative occurrence frequency as the abnormal propagation main path, and outputting the abnormal propagation main path sequence as a structured and ordered node list, which reflects the main propagation trajectory of abnormal information in the optical cable.

[0012] Preferably, the intelligent verification strategy generation module specifically comprises: When the abnormal probability value of a certain area in the abnormal probability distribution cloud map exceeds the preset abnormal residence probability threshold, a collection mode switching instruction is issued to the monitoring terminal to which the area belongs, and the following is performed: i. The vibration amplitude / frequency sampling frequency is increased to 5-10 times of the original frequency; ii. The frequency feature real-time tracking mode is enabled synchronously to capture 50-500Hz segmented frequency mutations; iii. A transient event recorder is deployed on the edge to record the vibration amplitude sudden change / continuous waveform segment.

[0013] Preferably, the multi-frequency band environmental disturbance comprises: i. A 0.1-5kHz sweep test signal is injected through a controllable vibration source, and the theoretical response vibration spectrum is calculated based on the topology model and the routing matrix; the vibration data of each node after the test signal is injected is recorded to obtain the measured response vibration spectrum; iii. The Euclidean distance between the theoretical response vibration spectrum and the measured response vibration spectrum is calculated; iv. The Euclidean distances of the measured vibration spectrum and the theoretical vibration spectrum are compared, and the abnormal node offset percentage is calculated; when the abnormal node offset percentage exceeds the second threshold, it is marked as an "environmental disturbance associated line".

[0014] Compared with the prior art, the present application has the following advantages: From the monitoring accuracy, the system constructs a vibration feature perception model based on the historical vibration data of the optical cable line, which changes the limitation of traditional monitoring which only relies on a single vibration parameter. By real-time collection of vibration amplitude sequence, frequency distribution characteristics and phase fluctuation parameters, and output of the reference vibration feature value, the normal vibration state of the optical cable line can be more comprehensively described. On this basis, the three-dimensional feature fusion analysis of the multi-source feature fusion analysis module covers the time domain fluctuation deviation, frequency domain energy distribution and phase sequence consistency, which can accurately capture the subtle differences between normal and abnormal vibrations, and the line-level feature difference matrix generated can more accurately reflect the abnormal condition of the line, greatly reducing the false judgment probability caused by environmental normal vibration interference. In terms of abnormal positioning, the abnormal event correlation positioning module inputs the feature difference matrix into the spatial correlation analysis network, combines the cable routing topology parameters and line environment location information, generates an abnormal probability distribution cloud map, and realizes accurate positioning of the physical area of abnormal external force damage. Compared with the traditional method which can only roughly determine the fuzzy positioning of the line segment, this method can clearly present the propagation path and probability distribution of abnormal vibration, so that the operation and maintenance personnel can quickly lock the high-risk area and avoid wasting manpower and time caused by blind investigation. In terms of real-time monitoring and dynamic adaptability, the design of the intelligent verification strategy generation module reflects the active response capability of the system. High-frequency vibration signal collection mode is enabled for high-probability abnormal areas to obtain more intensive and detailed vibration data in key areas, and abnormal changes are captured in time. Environmental disturbance tests are applied to adjacent line segments to further verify the accuracy of the abnormal area through comparative analysis and exclude environmental interference. This dynamic adjustment verification method not only improves the tracking capability of abnormal events, but also effectively distinguishes between real external force damage and environmental interference, reducing unnecessary verification work. In addition, the overall design of the system fully considers the actual operating environment and topology structure of the optical cable line, deeply combines vibration feature analysis with geographic spatial information and line topology parameters, so that abnormal positioning is no longer limited to abstract signal analysis, but is closely related to specific physical areas and environmental characteristics. This analysis method combined with the actual scene enables operation and maintenance personnel to better understand the background of abnormal events and develop more targeted countermeasures. At the same time, the system generates an abnormal probability distribution cloud map to provide clear priority guidance for verification work, enabling resources to be concentrated in high-probability abnormal areas, thereby improving overall operation and maintenance efficiency. The system forms a complete intelligent identification system for optical cable external force damage through multi-dimensional feature analysis, accurate spatial positioning and dynamic verification strategy, effectively solving the problems of high false alarm rate, fuzzy positioning and slow response in traditional monitoring methods, and providing more reliable protection for the safe operation of optical cable lines. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 The working principle diagram of the optical cable external force damage intelligent identification system described in the present application; Figure 2 The flowchart of the optical fiber vibration sensing module; Figure 3 The flowchart of the multi-source feature fusion analysis module; Figure 4 The flowchart of frequency domain energy distribution detection; Figure 5 The flowchart of the abnormal event correlation positioning module. DETAILED DESCRIPTION

[0016] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the scope of protection of the present application.

[0017] Please refer to Figures 1-5 The present application provides an optical cable external damage intelligent identification system, which comprises a fiber vibration sensing module, a multi-source feature fusion analysis module, an abnormal event correlation positioning module and an intelligent verification strategy generation module. The modules work together to realize intelligent identification of optical cable external damage. The specific implementation scheme is as follows: The fiber vibration sensing module constructs a vibration feature sensing model based on historical vibration data of the optical cable line, and real-time collects the vibration amplitude sequence, frequency distribution feature and phase fluctuation parameter of the current optical cable line. The vibration feature sensing model outputs the reference vibration feature value.

[0018] The multi-source feature fusion analysis module performs three-dimensional feature fusion analysis on the reference vibration feature value and the measured vibration feature value of the monitoring terminal. The three-dimensional feature fusion analysis includes time domain fluctuation deviation, frequency domain energy distribution and phase sequence consistency, and generates a feature difference matrix at the line level.

[0019] The abnormal event correlation positioning module inputs the feature difference matrix into a spatial correlation analysis network, combines the optical cable routing topological parameters and the line environment location information, generates an abnormal probability distribution cloud map of the abnormal vibration propagation path, and locates the abnormal external damage physical area.

[0020] The intelligent verification strategy generation module configures verification parameters according to the abnormal probability distribution cloud map, including enabling high-frequency vibration signal collection mode for high-probability abnormal areas, and applying environmental disturbance test to adjacent paragraph lines.

[0021] Embodiment 1: The operation of the fiber vibration sensing module starts from the historical data feature extraction link. When the historical vibration data of the optical cable line is processed by multi-dimensional decomposition, first, the non-subsampled wavelet transform is used to decompose the vibration amplitude. Through the transform, the relatively stable steady component and the transient component of the vibration amplitude can be separated, and then the proportion of each component in the overall energy can be calculated. This process can clearly present the energy contribution of different vibration components, providing a basis for subsequent analysis of vibration source and nature.

[0022] In terms of frequency feature analysis, the correlation between vibration frequency characteristics and external force types is explored through frequency coupling analysis. Specifically, the vibration frequencies generated under different external forces in historical data are sorted and compared to find the corresponding relationship between a specific frequency range and a certain type of external force, thereby establishing a correlation map of vibration frequency characteristics and external force types. This map can intuitively reflect the distribution characteristics of vibration frequencies under different external forces, helping to quickly identify possible external force disturbances.

[0023] For the phase fluctuation sequence, dynamic time warping algorithm is used to align the sequence patterns under different working conditions. Under different working conditions, the rhythm and change law of phase fluctuation may be different. Through this algorithm, these different mode sequences can be adjusted to the same time reference, making them comparable in time dimension, which is convenient for subsequent unified analysis and processing of phase characteristics.

[0024] After completing the multi-dimensional decomposition of historical data, the vibration feature perception model construction stage is entered. The processed historical data is input into the composite perception network, which is composed of multiple functional units. Among them, the time series prediction unit based on the line aging curve plays a role in generating basic vibration prediction features. The vibration characteristics of the line will show certain regular changes at different aging stages. The time series prediction unit predicts the basic vibration features of the line in the future according to the regularity reflected by the line aging curve, forming the initial prediction result.

[0025] The convolutional neural network embedded with frequency domain attention mechanism focuses on correcting the prediction deviation caused by frequency distortion. During the transmission and collection of vibration signals, frequency distortion may occur due to various factors, causing prediction deviation. Convolutional neural network can focus on the frequency components that are prone to distortion through in-depth learning and analysis of frequency characteristics, combined with frequency domain attention mechanism, to correct the prediction deviation and improve the prediction accuracy.

[0026] The role of the phase feature compensator is to dynamically adjust the prediction weight according to the real-time collected phase fluctuation parameters. Phase fluctuation parameters can reflect the subtle changes in current line vibration, and the phase feature compensator will use these real-time parameters as a basis to flexibly adjust the prediction weight of each part in the composite perception network, so that the prediction result of the model can better adapt to the current vibration state.

[0027] At the same time, the multi-channel acquisition unit deployed on the edge side continues to work, synchronously capturing multiple vibration parameters. For the vibration amplitude, the peak mutation rate and the duration parameter are collected, the peak mutation rate can reflect the change speed of the vibration amplitude in a short time, and the duration parameter records the time length of the vibration in a certain state. In terms of vibration frequency, the content and energy distribution of 50-500Hz segments are collected, which covers a variety of vibration frequencies generated by external force damage. By analyzing the content and energy distribution of each segment, rich frequency information can be obtained. For the phase fluctuation sequence, the phase offset gradient and time interval entropy value are collected, the phase offset gradient reflects the speed of phase change, and the time interval entropy value can reflect the disorder degree and complexity of the phase fluctuation sequence.

[0028] Finally, the vibration amplitude sequence, frequency distribution characteristics and phase fluctuation parameters collected by the multi-channel acquisition unit in real time are input into the vibration feature perception model constructed. The model comprehensively processes and analyzes these real-time data, combines historical data characteristics and current line state, and finally outputs the reference vibration feature value. The reference value integrates the influence of historical vibration law of the line, current real-time vibration parameters and line aging, and can accurately reflect the vibration characteristics of the line in the normal operation state.

[0029] Example 2: The calculation process of the reference vibration feature value starts from the adaptive noise suppression processing based on the line operation stage. The line will be affected by the change of environmental temperature and humidity in different operation stages, and these factors will cause interference signals to be mixed in the vibration measurement data. In the processing process, first, the operation stage of the line is divided, and different stages correspond to different environmental interference characteristics. For example, in the summer with high temperature and humidity, the change of physical properties of optical cable materials may cause vibration interference of a specific frequency; while in the winter with low temperature and dryness, the frequency distribution and amplitude characteristics of the interference signal will show different characteristics. By analyzing the correlation mode between environmental temperature and humidity and measurement interference in each operation stage, the corresponding noise model is constructed, and then according to the real-time monitored temperature and humidity data, the collected vibration signal is processed by targeted filtering. For the low-frequency vibration interference caused by the thermal expansion and contraction of the optical cable due to temperature change, adaptive low-frequency filtering method is used for suppression; for the cable surface friction vibration interference caused by humidity change, high-frequency noise filtering mechanism is used for processing. After such processing, the interference caused by environmental temperature and humidity factors to the measurement can be effectively eliminated, and the vibration data is closer to the real line vibration state.

[0030] After the adaptive noise suppression processing is completed, the application link of the space-time feature fusion algorithm is entered. Vibration amplitude, frequency, and phase are three important features reflecting the vibration state of the optical cable, and there is an inherent correlation among them. The space-time feature fusion algorithm integrates the correlation features of the three from the time and space dimensions. In the time dimension, the linkage relationship between the amplitude variation, frequency drift, and phase fluctuation of the same monitoring point at different times is analyzed. For example, when the amplitude suddenly changes, the corresponding frequency component and phase state will also change. By tracking the change correlation in the time sequence, the dynamic evolution process of the vibration features can be captured. In the space dimension, the amplitude transmission law between adjacent monitoring points, the spatial attenuation of the frequency component, and the spatial consistency of the phase are compared in combination with the position distribution of different monitoring points on the optical cable line, so as to establish the spatial correlation between the vibration features at different positions. Through the fusion processing in the time and space dimensions, the originally independent amplitude, frequency, and phase features are organically combined to form a more comprehensive feature set, which can more completely reflect the vibration characteristics of the optical cable line.

[0031] The finally output reference vibration feature value contains a normal working condition fluctuation interval. This interval is determined by statistical analysis of a large amount of vibration data in the normal running state, and covers the vibration variation range that may occur in the normal running process of the line. At the same time, the reference vibration feature value will be dynamically updated as the line aging state changes. The line aging is a gradual process. As the running time increases, the physical properties of the optical cable will gradually change, and its vibration characteristics will also change accordingly. For example, line aging may cause the rigidity of the optical cable to decrease, and under the same external force, the change amplitude of the vibration amplitude may increase. Or because of material fatigue, the inherent vibration frequency characteristics may also shift. In order to adapt to such changes, the system will continuously monitor the aging state parameters of the line, such as the running time, cumulative load, etc., and adjust the reference vibration feature value according to these parameters. When the line aging degree reaches a certain threshold, the normal working condition fluctuation interval will be recalculated, so that the reference vibration feature value is always matched with the current actual vibration characteristics of the line, so that the normal vibration and abnormal vibration can be accurately distinguished. Through such a dynamic updating mechanism, it is ensured that the reference vibration feature value can be long-term and effectively used as a reference standard for judging whether the line is damaged by external force.

[0032] Example 3 The operation of the multi-source feature fusion analysis module starts from the time-domain fluctuation deviation calculation. The system sets a preset time window, the length of which is determined according to the actual operation characteristics of the optical cable line and the time distribution characteristics of the historical data, and can cover a complete change cycle of the vibration signal. The reference vibration characteristic value and the measured vibration characteristic value obtained by the monitoring terminal are compared in a sliding manner in the time window. Since the sampling times of the reference vibration characteristic value and the measured vibration characteristic value may be out of sync, a dynamic time warping algorithm is used to align the vibration sequences of the two. The algorithm finds the optimal matching path between the two sequences by locally stretching or compressing the time axis, eliminating the sequence misalignment caused by the difference in sampling time. In each time window, the fluctuation deviation between the reference value and the measured value is calculated, and the deviations of all windows are arranged in time sequence to form a time-domain deviation vector, which can intuitively reflect the difference between the two at different time points.

[0033] In the frequency domain energy distribution detection link, the vibration frequency characteristics of the reference vibration characteristic value and the measured vibration characteristic value are first decomposed by a frequency domain decomposition algorithm. The frequency domain decomposition algorithm can decompose complex vibration signals into the superposition of different frequency components, clearly presenting the proportion of each frequency component in the overall signal. After decomposition, the energy spectrum density ratio of the reference value and the measured value at the same frequency point is calculated, which can reflect the difference in energy distribution between the two. The energy distribution index of each frequency band is extracted, which considers factors such as the total energy, peak energy, and uniformity of energy distribution in the frequency band. These indices are combined in frequency band order to construct a frequency domain distribution vector. In the frequency domain analysis process, the corresponding vibration frequency characteristics are extracted from the reference and measured vibration sequences to ensure consistency in frequency analysis. Wavelet packet decomposition is used to analyze each group of frequency signals at multiple scales, which can divide the frequency range into multiple fine sub-bands, achieving multi-resolution analysis of the signal. The energy distribution characteristics in the preset sensitive frequency band interval are extracted, which is determined by statistical analysis of the vibration frequencies generated by common external damage types (such as excavation, impact, and rolling), and can cover the main frequency range of these external damage behaviors. After extraction, the energy density of the reference data and the measured data in the sensitive frequency band interval is calculated, and the energy density is the energy value in a unit frequency range. By comparing the energy densities of the two, the relative deviation degree is determined, and the distribution index of each frequency band is extracted based on the deviation degree. The energy distribution results of all frequency bands are summarized in a certain order to form a complete frequency domain distribution vector.

[0034] In the phase sequence consistency evaluation, the Hausdorff distance algorithm is used as the structure matching algorithm to process the phase sequences of the reference vibration characteristic values and the measured vibration characteristic values. The algorithm realizes the matching of the mutation point positions by calculating the distance between the mutation point sets in the two sequences. Specifically, the mutation points at which the phase changes significantly are first identified from the reference phase sequence and the measured phase sequence to form two mutation point sets. Then, the maximum value of the distance from a point in one set to the nearest point in the other set, and the maximum value of the distance from a point in the other set to the nearest point in one set are calculated. The smaller of the two maximum values is taken as the Hausdorff distance, which can reflect the matching degree of the two mutation point sets. According to the matching result of the mutation points, the synchronization error of the phase fluctuation mutation points, i.e., the difference in time corresponding to the mutation points, is calculated. The KL divergence of the phase interval distribution is quantified. The KL divergence is used to measure the difference between two probability distributions. The distribution of the phase interval is regarded as a probability distribution, and the KL divergence between the reference phase interval distribution and the measured phase interval distribution is calculated. The larger the divergence value is, the greater the difference between the two is. The synchronization error and the KL divergence are integrated to generate a phase consistency vector, which can comprehensively reflect the consistency degree of the reference phase sequence and the measured phase sequence.

[0035] In the feature difference matrix generation stage, the time domain deviation vector, the frequency domain distribution vector, and the phase consistency vector are tensor spliced. Tensor splicing can combine vectors of three different dimensions into a multi-dimensional array, retaining the original feature information and dimension information of each vector. The dimension difference is eliminated through normalization processing with feature importance weighting. The feature importance weight is determined according to the contribution of each feature in the historical abnormal event identification. The higher the contribution, the greater the feature weight. The normalization processing converts each feature value to the same numerical range, and the formula used is:

[0036] wherein, is the normalized and weighted feature value, is the original feature value, is the minimum value of the feature, is the maximum value of the feature, is the importance weight of the feature. After processing, a three-order feature difference matrix with dimensions of [line number x timestamp x feature type] is output. The line number is used to distinguish different optical cable line sections, the timestamp marks the time point corresponding to the feature difference, and the feature type corresponds to the features in the time domain, frequency domain, and phase. The matrix completely records the feature difference of different lines at different times.

[0037] Example 4: The operation of the abnormal event correlation positioning module starts from the optical cable topology modeling. According to the line environment location information, the system will sort out various geographic areas through which the optical cable passes, such as under urban roads, underground farmland, gullies in mountainous areas, etc., and mark out the specific locations of the optical cable nodes (such as joint boxes, fiber distribution boxes, monitoring points, etc.) in these areas, and construct an optical cable node connection relationship topology graph. In this topology graph, the routing parameters between nodes will be marked in detail, including node spacing, optical cable laying mode (direct burial, pipeline, overhead, etc.), optical cable model and core number, etc. At the same time, the system will collect information on possible external interference sources around the line, such as construction sites, traffic arteries, industrial equipment areas, etc., and superimpose the reverse vibration constraint conditions of these interference source access points into the topology graph. The reverse vibration constraint condition is determined based on the attenuation law of the vibration generated by the interference source propagating in the optical cable, for example, the vibration of a construction site propagates in the optical cable, the farther the distance, the weaker the vibration intensity. This attenuation characteristic will be converted into a constraint parameter and included in the topology graph. Through these operations, a physical topology model containing a routing matrix and a node association matrix is generated, and the routing matrix records the routing parameters between nodes, and the node association matrix reflects the connection relationship between nodes.

[0038] In the abnormal propagation simulation, the data in the feature difference matrix generated by the multi-source feature fusion analysis module is mapped to each node of the optical cable topology model according to the corresponding line location, so that each node has corresponding feature difference data. Based on the graph convolution network, the abnormal propagation deduction is performed. First, the attenuation factor of abnormal vibration is calculated according to the node routing parameters. Different routing parameters will result in different attenuation factors, for example, the vibration attenuation speed of direct buried optical cable is different from that of overhead optical cable, and the corresponding attenuation factor will also be different. Cross-regional abnormal correlation features are captured through a multi-head attention mechanism, which can simultaneously focus on the node features of multiple different regions to discover the correlation relationship between abnormal vibrations in different regions, for example, the abnormal vibration in one region may have a certain synchronous change pattern with the vibration in another region. The Monte Carlo method is used to simulate the diffusion path of abnormal vibration in the topology network. This method simulates the process of abnormal vibration propagating from the initial node to other nodes through multiple random simulations. Each simulation is based on the current attenuation factor and correlation feature to determine the propagation direction and strength, thereby obtaining multiple possible propagation paths.

[0039] The probability distribution generation stage counts the abnormal vibration occurrence frequency of each route in multiple Monte Carlo simulation propagation. The higher the occurrence frequency, the greater the possibility of abnormal vibration of the route. The abnormal vibration residence probability value is calculated in combination with the route parameters. The residence probability value comprehensively considers the frequency of abnormal vibration and the physical characteristics of the route itself. For example, if the abnormal vibration occurrence frequency of a certain route is high and the anti-interference ability of the cable of the route is weak, the residence probability value will be relatively high. According to the residence probability values of all routes, an abnormal probability distribution cloud map covering all routes is generated. Different colors or gray scales in the cloud map represent different probability values. The deeper the color or the higher the gray scale, the greater the abnormal probability. At the same time, a suspicious route set whose probability value exceeds a preset abnormal residence probability threshold is marked. The threshold is determined according to historical abnormal data of the line and safety requirements.

[0040] When the physical area is located, spatial clustering analysis is performed on the abnormal probability distribution cloud map. The clustering algorithm is used to cluster the areas with high abnormal probability values and adjacent in space in the cloud map, and identify the abnormal probability aggregation area. According to the line environment location information such as the geographic coordinates corresponding to the abnormal probability aggregation area, the surrounding landmarks, and in combination with the cable topology connection relationship, the actual physical range corresponding to the aggregation area is determined, and the physical boundary of the abnormal external force damage is further delimited. For example, the abnormal probability aggregation area is mapped to a certain farmland area, and in combination with the laying path of the cable in the area, a certain width area centered on the cable path is delimited as the physical boundary of the abnormal external force damage.

[0041] In addition, the abnormal event correlation positioning module also outputs a suspicious line identifier and an abnormal propagation main path. The suspicious line identifier is based on the cable nodes connected by the suspicious route set, and binds these cable nodes with the actual line sections. For example, if a suspicious route connects node A and node B, the line section between node A and node B is marked as a suspicious line. Multiple such suspicious lines constitute a suspicious line identifier set, indicating that there may be a source of external force damage or an affected section. The abnormal propagation main path records the node path and its order experienced by each round of propagation in the Monte Carlo simulation abnormal diffusion process. For example, the path of a certain simulation is node C→node D→node E, and the path of another simulation is node C→node F→node E. In all simulation paths, the occurrence frequency of each path is counted, and the path sequence with the highest cumulative occurrence frequency is selected as the abnormal propagation main path. The output abnormal propagation main path sequence is a structured and ordered node list, such as node C→node F→node E→node G. This list reflects the main propagation trajectory of abnormal information in the cable.

[0042] Example 5: The operation of the intelligent verification strategy generation module is based on the abnormal event correlation positioning module output abnormal probability distribution cloud map. When the abnormal probability value of a certain area in the cloud map exceeds the preset abnormal residence probability threshold, the system issues a collection mode switching instruction to the monitoring terminal to which the area belongs, and starts the high-frequency vibration signal collection mode.

[0043] After the switching instruction is triggered, the monitoring terminal first adjusts the sampling frequency of the vibration amplitude and frequency, which is increased to 5-10 times the original frequency. The original sampling frequency is set according to daily monitoring needs, and the high-frequency sampling after the increase can more densely capture the detailed changes of the vibration signal, especially those with short duration and rapid changes. For example, originally 100 data are collected per second, and after the increase, 500-1000 data can be collected per second, so that the slight fluctuations of the vibration amplitude and the instantaneous changes of the frequency can be recorded completely.

[0044] The frequency feature real-time tracking mode enabled synchronously focuses on capturing mutations of 50-500Hz segmented frequencies. This frequency segment covers the vibration frequencies generated by various typical external force destruction behaviors, such as mechanical excavation, which produces vibrations mainly concentrated in 100-300Hz, and vehicle rolling, which causes vibrations possibly distributed in 50-200Hz. The real-time tracking mode continuously monitors the energy changes of each segmented frequency, and when the frequency energy of a certain segment increases or decreases significantly within a short time, the time point and change amplitude of the mutation event are immediately recorded.

[0045] The transient event recorder deployed on the edge side is started synchronously and is specially used to record the sudden changes of the vibration amplitude and the continuous waveform segments. The sudden change of the vibration amplitude includes sudden peak rise or fall, and the continuous waveform segment refers to the vibration signal maintaining a specific mode of fluctuation within a certain period of time. The recorder uses a circular storage mechanism, which automatically covers the earliest non-important record when a new transient event occurs, ensuring that critical data is not lost, while avoiding the depletion of storage resources.

[0046] For adjacent paragraph lines, the intelligent verification strategy generation module will apply environmental disturbance tests. The test injects a 0.1-5kHz sweep test signal to the adjacent line through a controllable vibration source. The sweep signal starts from 0.1kHz and gradually rises to 5kHz at a certain frequency interval, and each frequency point lasts for a certain period of time to ensure that the signal can fully stimulate the vibration response of the line. The installation position of the controllable vibration source is determined according to the line direction and is usually deployed near the starting node of the adjacent paragraph to ensure that the signal can uniformly propagate to the entire paragraph.

[0047] Based on the cable topology model and the routing matrix, the system pre-computes the theoretical response vibration spectrum of the swept-frequency test signal under normal conditions. The theoretical response vibration spectrum comprehensively considers the length, laying method, and surrounding environment of the line, and reflects the energy attenuation and frequency shift of the test signal at different frequencies during the propagation process. For example, the energy attenuation rate of a signal at a certain frequency propagating in an underground cable will be faster than that in an overhead cable, and the theoretical response spectrum will reflect this difference.

[0048] During the test signal injection process, the monitoring terminal along the line synchronously records the vibration data of each node to form the measured response vibration spectrum. The measured data includes the vibration amplitude, frequency component, and phase change of each node under the action of different frequency test signals. The data acquisition time is synchronized with the sweep rhythm of the test signal to ensure that each frequency point has corresponding measured data.

[0049] The system calculates the Euclidean distance between the theoretical response vibration spectrum and the measured response vibration spectrum, which reflects the difference between the two in overall characteristics. By comparing the Euclidean distance, the abnormal node offset percentage is further calculated, i.e., the proportion of the number of nodes whose deviation between the measured data and the theoretical data exceeds the allowed range to the total number of nodes. When this percentage exceeds a second threshold value, the corresponding line is marked as an "environmental disturbance associated line", indicating that the vibration response characteristics of the line are significantly different from the normal situation and may be indirectly affected by external force damage.

[0050] The environmental disturbance test and the high-frequency acquisition mode cooperate with each other. The high-frequency acquisition mode focuses on the detailed features of the high-probability abnormal area, and the environmental disturbance test actively applies signals to investigate potential abnormalities of adjacent lines. The two together constitute a multi-level verification system that comprehensively covers the line areas that may be affected.

[0051] It should be noted that, in this text, relational terms such as first and second are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such a process, method, article or device.

[0052] Although embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and alterations can be made thereto without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. An optical cable external force damage intelligent identification system, characterized in that, Comprise: Optical fiber vibration sensing module: based on the historical vibration data of optical cable line to build vibration feature perception model, real-time acquisition of the current optical cable line vibration amplitude sequence, frequency distribution characteristics and phase fluctuation parameters, through the vibration feature perception model output reference vibration characteristic value; Multi-source feature fusion analysis module: the reference vibration characteristic value and the measured vibration characteristic value of the monitoring terminal are analyzed by three-dimensional feature fusion, the three-dimensional feature fusion includes time domain fluctuation deviation, frequency domain energy distribution and phase sequence consistency, and the feature difference matrix of the line level is generated; Abnormal event correlation positioning module: input the feature difference matrix into the spatial correlation analysis network, combine the optical cable routing topology parameters and the line environment location information, generate the abnormal probability distribution cloud map of the abnormal vibration propagation path, and locate the abnormal external force damage physical area; Intelligent verification strategy generation module: configure verification parameters according to the abnormal probability distribution cloud map, including: Enable high-frequency vibration signal acquisition mode for high-probability abnormal area; Apply environmental disturbance test to adjacent paragraph line.

2. The optical cable external force damage intelligent identification system according to claim 1, characterized in that, The optical fiber vibration sensing module specifically comprises: Historical data feature extraction: multi-dimensional decomposition processing is performed on the historical vibration data of optical cable line, including: using non-subsampled wavelet transform to extract the energy proportion of steady component and transient component of vibration amplitude, establishing vibration frequency feature and external force type correlation graph through frequency coupling analysis, and aligning phase fluctuation sequence mode under different working conditions by using dynamic time warping algorithm; Vibration feature perception model construction: input the processed historical vibration data into the composite perception network, the composite perception network comprises: Time series prediction unit based on line aging curve, used to generate basic vibration prediction features, convolutional neural network embedded with frequency domain attention mechanism, used to correct prediction deviation caused by frequency distortion, phase feature compensator, used to dynamically adjust prediction weight according to real-time collected phase fluctuation parameters; Through the multi-channel acquisition unit deployed on the edge, the peak mutation rate and duration parameters of vibration amplitude, the 50-500Hz segmented content and energy distribution of vibration frequency, and the phase offset gradient and time interval entropy value of phase fluctuation sequence are synchronously captured; Reference value calculation: input real-time acquisition data into the vibration feature perception model to obtain reference vibration characteristic value.

3. The optical cable external force damage intelligent identification system according to claim 2, characterized in that, In the reference vibration characteristic value calculation, including: Adaptive noise suppression processing based on line operation stage, eliminating measurement disturbance caused by environmental temperature and humidity; Integrate the correlation features of amplitude, frequency and phase through space-time feature fusion algorithm; Output reference vibration characteristic value including normal working condition fluctuation interval, and the reference vibration characteristic value is dynamically updated with the line aging state.

4. The optical cable external force damage intelligent identification system of claim 1, wherein, The multi-source feature fusion analysis module specifically comprises: Time domain fluctuation deviation calculation: compare the reference vibration characteristic value and the measured vibration characteristic value with a preset time window, align the vibration sequence sampled asynchronously by using dynamic time warping algorithm, calculate the fluctuation deviation amount in each window, and generate time domain deviation vector; Frequency domain energy distribution detection: the vibration frequency characteristics of the reference value and the measured value are decomposed by a frequency domain decomposition algorithm, the energy spectrum density ratio is calculated, the energy distribution index of each frequency band is extracted, and a frequency domain distribution vector is constructed; Phase sequence consistency evaluation: based on a structure matching algorithm, the position distribution of the reference value and the measured phase sequence is matched, the synchronization error of the phase fluctuation mutation point is calculated, the KL divergence of the phase interval distribution is quantified, and a phase consistency vector is generated; Feature difference matrix generation: the time domain deviation vector, the frequency domain distribution vector, and the phase consistency vector are spliced into a tensor, the dimension difference is eliminated through normalized processing of feature importance weighting, and a three-order feature difference matrix with a dimension of [line number x timestamp x feature type] is output.

5. The optical cable external force damage intelligent identification system according to claim 4, characterized in that, The frequency domain energy distribution detection specifically includes: In the frequency domain analysis stage, first, the corresponding vibration frequency characteristics in the reference and measured vibration sequence are extracted, wavelet packet decomposition is used to analyze each frequency signal in multiple scales, the energy distribution characteristics in the preset sensitive frequency band interval are extracted, the sensitive frequency band interval is selected to cover the vibration frequency range of typical external force damage, after the extraction is completed, the energy density of the reference and measured data in the sensitive frequency band interval is quantitatively calculated, and the distribution index of each frequency band is extracted based on the relative deviation degree of the two, the energy distribution results of all frequency bands are summarized, and a frequency domain distribution vector is constructed.

6. The optical cable external force damage intelligent identification system according to claim 4, characterized in that, In the phase sequence consistency evaluation, the structure matching algorithm uses the Hausdorff distance algorithm to match the position of the mutation point set in the two sequences, and identify the phase synchronization error.

7. The optical cable external force damage intelligent identification system of claim 1, wherein, The abnormal event correlation positioning module specifically includes: Optical cable topology modeling: constructing an optical cable node connection relationship topology graph according to line environment position information, labeling routing parameters between nodes, superimposing reverse vibration constraint conditions of external interference source access points in the topology graph, and generating a physical topology model including a routing matrix and a node association matrix; Abnormal propagation simulation: mapping the feature difference matrix to the corresponding nodes of the optical cable topology model; performing abnormal propagation deduction based on a graph convolution network, which includes: i. calculating the attenuation factor of abnormal vibration according to the node routing parameters; ii. capturing cross-regional abnormal correlation features through a multi-head attention mechanism; iii. simulating the diffusion path of abnormal vibration in the topology network by using the Monte Carlo method; Probability distribution generation: counting the frequency of abnormal vibration in the simulation propagation of each route, calculating the abnormal vibration residence probability value combined with the routing parameters, generating an abnormal probability distribution cloud map covering the entire line, and labeling a suspicious route set with a probability value exceeding a preset abnormal residence probability threshold; Physical region positioning: performing spatial clustering analysis on the abnormal probability distribution cloud map to identify an abnormal probability aggregation area; and delineating the physical boundary of the abnormal external force damage according to the line environment position and the optical cable topology connection relationship.

8. The optical cable external force damage intelligent identification system according to claim 7, characterized in that, The abnormal event correlation positioning module further includes outputting a suspicious line identifier and an abnormal propagation main path, wherein: The suspicious line identifier is connected to the optical cable node based on the suspicious route set, binds the optical cable node with the actual line segment, forms a suspicious line identifier set, and indicates a potential abnormal external force damage source or affected section; The abnormal propagation main path is recorded in the abnormal diffusion process of the Monte Carlo simulation, and the node path and its order experienced in each round of propagation are recorded. In all simulation paths, the occurrence frequency of each path is counted, the path sequence with the highest cumulative occurrence frequency is selected as the abnormal propagation main path, and the output abnormal propagation main path sequence is a structured and ordered node list, reflecting the main propagation trajectory of the abnormal information in the optical cable.

9. The optical cable external force damage intelligent identification system of claim 1, wherein, The intelligent verification strategy generation module specifically includes: When the abnormal probability value of a certain area in the abnormal probability distribution cloud map exceeds the preset abnormal residence probability threshold, a collection mode switching instruction is issued to the monitoring terminal to which the area belongs, and the following is executed: i. The vibration amplitude / frequency sampling frequency is increased to 5-10 times the original frequency; ii. Synchronously enable the frequency feature real-time tracking mode to capture 50-500Hz segmented frequency mutations; iii. Deploy a transient event recorder on the edge to record the vibration amplitude sudden change / continuous waveform segment.

10. The optical cable external force damage intelligent identification system of claim 9, wherein, The multi-frequency band environmental disturbance includes: i. Inject a 0.1-5kHz sweep test signal through a controllable vibration source, calculate the theoretical response vibration spectrum based on the topology model and the routing matrix, record the vibration data of each node after the test signal is injected, and obtain the measured response vibration spectrum; iii. Calculate the Euclidean distance between the theoretical response vibration spectrum and the measured response vibration spectrum; iv. Compare the Euclidean distances of the measured vibration spectrum and the theoretical vibration spectrum, calculate the abnormal node offset percentage, and when the abnormal node offset percentage exceeds the second threshold, mark it as an "environmental disturbance associated line".

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