Optical fiber end-to-end coverage fault positioning system based on passive optical switch cascade

Abstract

The invention discloses an optical fiber end-to-end coverage fault positioning system based on passive optical switch cascade, and relates to the related technical field of optical fiber communication fault detection, and the system comprises a mapping construction module which builds a mapping matrix of a switch state and an optical fiber segment based on a preset switch switching sequence; the first identification module is used for injecting probe pulses in different switching states, and inverting local time delay distortion of each section by using a time delay observation set to form a first type of structural characterization; the second identification module is used for collecting the amplitude and phase sequence of the end-to-end signal, calculating the statistical distance between the end-to-end signal and the reference and projecting the statistical distance to the segment-level space to form a second type of structural representation; and the early warning module is used for carrying out damage analysis under multi-source observation and reporting fault abnormity. The technical problem that in the prior art, multiple potential degradation sections in the whole end-to-end process cannot be subjected to synchronous fine fault sensing and positioning is solved, and the technical effects that the health condition of the optical fiber link is sensed in real time through the passive cascade switch network and the fault positioning accuracy is improved are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of optical fiber communication fault detection, and particularly relates to an optical fiber end-to-end coverage fault positioning system based on passive optical switch cascading. BACKGROUND

[0002] Optical fiber links usually span long distances and complex geographical environments. Physical layer damages such as bending, extrusion, aging, joint degradation or human damage of the optical fiber links are prone to cause signal attenuation, time delay distortion and even communication interruption. Traditional optical fiber fault detection is based on optical time domain reflectometry, and the measurement accuracy is limited by the spatial resolution. In a complex optical network with long distances, multiple nodes and dynamic changes, it is difficult to achieve fine, real-time and low-cost end-to-end coverage monitoring, especially to distinguish closely adjacent fault points or to monitor multiple potential degradation sections simultaneously. In addition, existing optical fiber fault detection relies on active monitoring equipment or distributed optical fiber sensing, which needs to insert electro-optical devices in the link, introduces additional fault points, power consumption and cost, and has high system complexity, expensive demodulation equipment and poor sensitivity to faults outside the link terminal, and cannot realize the synchronous identification and positioning of multiple potential fault sections.

[0003] Therefore, in the related art, there is a technical problem that multiple potential degradation sections in the whole end-to-end process cannot be synchronously and finely fault perceived and positioned. SUMMARY

[0004] The present application provides an optical fiber end-to-end coverage fault positioning system based on passive optical switch cascading, which solves the technical problem that multiple potential degradation sections in the whole end-to-end process cannot be synchronously and finely fault perceived and positioned in the prior art, and achieves the technical effect of real-time perception of the health status of the optical fiber link through the passive cascading switch network and improving the fault positioning accuracy.

[0005] The application provides a passive optical switch cascade-based optical fiber end-to-end coverage fault locating system, which comprises a mapping construction module, a first identification module, a second identification module, and an early warning module.

[0006] In a possible implementation, the passive optical switch cascade-based optical fiber end-to-end coverage fault locating system further comprises a third identification module configured to perform complex field detection on an end-to-end received signal in each switch state, construct a complex envelope sequence, construct a complex field cross-coherence function for any two switch states according to the complex envelope sequence, perform time-frequency analysis, extract a coherent texture atlas and calculate a texture degradation index, project the texture degradation index according to a mapping matrix, and construct a third type of structured representation.

[0007] In a possible implementation, the third identification module is further configured to perform gated coherent differential processing based on the complex envelope sequence, use a complex field phase of a reference state as a coherent anchor point in each switch state, perform joint differential processing on a time domain and a frequency domain through a gated window, and obtain a differential coherent field that suppresses a common factor noise of a whole link.

[0008] In a possible implementation, in the first identification module, the constructing a segment-level time delay inversion model comprises: constructing a time delay observation vector according to the set of round-trip time delay observations, and generating a segment-level observation operator corresponding to the on-off state and the optical fiber segment based on the mapping matrix; introducing a pulse diffusion kernel representation factor into the segment-level observation operator to construct an extended mapping matrix capable of simultaneously describing the segment-level time delay cumulative effect and the pulse waveform diffusion effect; taking the segment-level time delay distortion variable as a solution target, taking the extended mapping matrix as a forward operator, and taking the time delay observation vector as an observation operator input to construct a segment-level time delay inversion model.

[0009] In a possible implementation, the second identification module is configured to: perform amplitude-phase joint normalization processing on the amplitude sequence and the phase sequence of the end-to-end received signal, construct an amplitude-phase coupled vector sequence through phase unwrapping, amplitude normalization, and group delay compensation; and calculate distribution divergence distance, phase deviation distance, and complex energy allocation distance on multiple scales based on the amplitude-phase coupled vector sequence and the reference sequence, and combine the multiple-scale distances to construct an on-off state statistical distance sequence.

[0010] In a possible implementation, in the early warning module, reporting the fault anomaly comprises: if the damage confidence exceeds a preset threshold, sending a feedback instruction to the endpoint control end to trigger local enhanced scanning of the on-off disturbance sequence, wherein the local enhanced scanning comprises dynamically combining on-off states in the fault anomaly segment to generate a highly focused sub-mapping matrix, and performing secondary attention acquisition analysis based on the sub-mapping matrix to establish an enhanced observation set; and after performing anomaly verification of the fault anomaly segment according to the enhanced observation set, reporting the fault anomaly.

[0011] In a possible implementation, the early warning module is further configured to: construct an end-to-end residual spectrum based on historical observation sets of different time windows, perform adaptive noise floor reconstruction based on a spectral density drift of the end-to-end residual spectrum; adjust joint analysis weights of the first type of structured representation and the second type of structured representation according to the reconstructed noise floor to suppress the influence of noise floor changes on segment-level time delay inversion stability and amplitude-phase statistical distance, and perform damage analysis based on the joint analysis weights.

[0012] In a possible implementation, the optical fiber end-to-end coverage fault locating system based on the cascade of passive optical switches further comprises a fault isolation module configured to reconfigure the on-off of the passive optical switches according to the fault anomaly, and perform fault isolation management.

[0013] The optical fiber end-to-end coverage fault positioning system based on passive optical switch cascade provided in the application is proposed. A mapping module is used to establish a mapping matrix of switch states and optical fiber segments based on a preset switch switching sequence. A first identification module is used to inject a probe pulse under different switch states, and local time delay distortion of each segment is inversely calculated by using a time delay observation set to form a first type of structural representation. A second identification module is used to collect an amplitude and phase sequence of an end-to-end signal, calculate a statistical distance thereof from a reference, and project the statistical distance to a segment level space to form a second type of structural representation. An early warning module is used to perform damage analysis under multi-source observation and report a fault anomaly. The technical problem that the prior art cannot simultaneously and finely perceive and locate faults of multiple potential deterioration segments in the whole end-to-end process is solved, and the technical effect that the health condition of the optical fiber link is perceived in real time by the passive cascade switch network and the fault positioning accuracy is improved is achieved. BRIEF DESCRIPTION OF DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments of the present disclosure will be briefly introduced below. In the present application, a flowchart is used to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the foregoing or the following operations are not necessarily performed in sequence. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or a step or several steps of operation can be removed from these processes.

[0015] Figure 1 A structure schematic diagram of the optical fiber end-to-end coverage fault positioning system based on passive optical switch cascade provided for the embodiments of the present application.

[0016] Figure 2 A process schematic diagram of the execution steps of the third identification module in the optical fiber end-to-end coverage fault positioning system based on passive optical switch cascade provided for the embodiments of the present application.

[0017] Legend of reference signs: mapping module 10, first identification module 20, second identification module 30, early warning module 40. DETAILED DESCRIPTION

[0018] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following will be described in detail according to the specific embodiments, structures, features and effects of the present application, and the technical solutions of the present application are as follows. The passive optical switch is cascaded in the optical fiber link, the mapping matrix of the switch state and the optical fiber segment is established by controlling the switch state at the end point; then the first type of time delay distortion and the second type of amplitude / phase statistical structural representation are respectively constructed by measuring the round-trip time delay and the amplitude / phase change under different switch states; finally, the damage analysis and fault positioning early warning are fused.

[0019] The embodiment of the present application provides a fiber end-to-end coverage fault locating system based on cascaded passive optical switches, as shown in the figure. Figure 1 The system comprises: A mapping construction module 10 is configured to arrange a plurality of cascaded passive optical switches in a fiber link to be detected, and switch the passive optical switches at an endpoint control end according to a preset disturbance sequence, so as to establish a mapping matrix of switch states and fiber segments.

[0020] Preferably, the mapping construction module deploys passive optical switch hardware, performs ordered state switching control, and finally establishes a mathematical mapping matrix describing the state-path relationship, so as to perform segment-level fault inversion based on end-to-end measurement. Specifically, in the fiber link to be detected, a plurality of passive optical switches such as MEMS optical switches or thermal optical switches are physically accessed in a cascaded manner, each optical switch has two or more ports, and different paths through which an optical signal passes can be selected by controlling the state of the optical switch. The cascaded passive optical switches are arranged at key nodes such as joint boxes and distribution points of the fiber link, so as to divide the whole fiber link into a plurality of independent fiber segments.

[0021] Preferably, one endpoint of the fiber link is provided with a control end such as a signal injection end, the control end sends an optical signal as a control signal to each passive optical switch according to a preset disturbance sequence with a specific coding rule, so as to systematically switch the physical state of each switch, for example,'straight through' or 'cross', wherein the preset disturbance sequence ensures that the combination of different switch states can uniquely change the combination of fiber segments through which the optical signal passes. Then, based on the physical connection relationship of the passive optical switches and the preset disturbance sequence, a mapping matrix M of switch states and fiber segments is established, the rows of the matrix correspond to different switch state combinations, the columns of the matrix correspond to each independent fiber segment, and the matrix element M(i,j) takes a value of 1 or 0, which is used to define whether the optical signal passes through the jth fiber segment under the ith switch state, so as to completely and accurately describe the mapping relationship between the switch state combination and the set of fiber segments through which the optical signal passes.

[0022] A first identification module 20 is configured to inject a time-coded probe pulse from the endpoint under each switch state, obtain an end-to-end round-trip delay observation set of different switch states, construct a segment-level delay inversion model based on the mapping matrix, solve local delay distortion variables of each fiber segment, and construct a first type of structured representation.

[0023] Preferably, the first identification module decomposes and attributes the macroscopic observation to each microcosmic fiber segment by measuring the macroscopic time delay under controllable switch states and using the inverse model established by the mapping relationship, thereby quantitatively constructing the time delay abnormality distribution in units of segments. Specifically, under each preset switch state, one or more time-coded probe light pulses are injected from the end point of the fiber link into the link, the pulse signal propagates in the link, returns to the injection end after being reflected or looped back, and the end-to-end round-trip time delay or propagation time of the signal from injection to return under each switch state is accurately measured and recorded. After measuring and recording all the preset switch states, the end-to-end round-trip time delay observation set under different switch states is obtained, which is one-to-one corresponding to the switch states.

[0024] Preferably, a segment-level time delay inversion model is constructed based on the mapping matrix. The essence of the segment-level time delay inversion model is to solve the equation set: observation time delay vector = mapping matrix x segment-level time delay vector + noise / error term, wherein each element of the observation time delay vector corresponds to an end-to-end round-trip time delay observation value under a switch state, the mapping matrix is used to describe the fiber segments passed by the optical signal under each switch state, each element of the segment-level time delay vector is an unknown quantity to be solved, representing the local time delay contribution or time delay distortion variable of each independent fiber segment; the segment-level time delay inversion model is solved by using the least square method or sparse recovery algorithm, and the local time delay distortion variable of each fiber segment is calculated from the macroscopic end-to-end time delay observation data. The finally calculated segment-level time delay distortion variable constitutes the first type of structured characterization of the link state, and each element corresponds to a specific fiber segment, and the numerical value reflects the signal propagation time abnormality of the fiber segment caused by physical stress, temperature change, microbending or damage, etc.

[0025] The second identification module 30 is configured to synchronously collect the amplitude sequence and the phase sequence of the end-to-end received signal under different switch states, calculate the statistical distance between the synchronous collection result and the reference sequence, form a switch state statistical distance sequence, project the switch state statistical distance sequence to the segment-level space according to the mapping matrix, and construct the second type of structured characterization.

[0026] Preferably, the second identification module traces and allocates the macroscopic and mixed deviation to each microcosmic fiber segment by analyzing the statistical deviation of the amplitude and phase of the signal under multiple switch states relative to the reference, and using the projection model established by the mapping relationship, thereby quantitatively constructing the amplitude and phase abnormality distribution in units of segments. Specifically, under each preset specific switch state, the optical signal transmitted through the link is synchronously collected from the receiving end of the fiber link, and the received optical signal is processed to extract the amplitude sequence and the phase sequence varying with time or frequency, which respectively represent the signal intensity variation and the signal phase variation, and further reflect the comprehensive effects such as loss, dispersion, nonlinearity and the like experienced by the signal in the propagation process.

[0027] Preferably, for each switching state, the amplitude sequence and phase sequence of the end-to-end received signal are compared with a predefined reference sequence, where the reference sequence represents the reference signal characteristics of the link under known healthy or undisturbed conditions. Then, the statistical distance between the synchronous acquisition result and the reference sequence is calculated, such as calculating the correlation coefficient, root mean square error, Kullback-Leibler divergence, or Euclidean distance in the complex domain between the two sets of sequences. By calculating, a corresponding quantized statistical distance is generated for each switching state, which characterizes the degree of deviation of the link signal characteristics under the current switching state from the reference sequence state. The statistical distance calculation is performed on all switching states to obtain a switching state statistical distance sequence that corresponds one-to-one with the switching state sequence.

[0028] Preferably, the statistical distance sequence of switch states defined in the switch state space is transformed into the fiber segment space through mathematical inversion projection based on the mapping matrix. Specifically, the statistical distance of each switch state is the result of the superposition of the combined effects of multiple fiber segments traversed by the optical path corresponding to that switch state. Then, using the switch state-fiber segment mapping relationship described by the mapping matrix, a constrained linear model is constructed to decompose the macroscopic switch state distance sequence, solve for and determine the contribution weight or local offset of each independent fiber segment to the statistical distance, and finally construct the solution results into a second type of structured representation. Each element of this representation corresponds to a specific fiber segment, and its value is used to quantify the contribution of that fiber segment to the overall signal amplitude and phase distortion, thereby reflecting the loss anomaly, phase disturbance, and other state information of that fiber segment. Among them, the second type of structured representation of amplitude and phase statistics and the first type of representation of time delay distortion are complementary in the physical dimension and jointly describe the health status of the fiber link.

[0029] The early warning module 40 is used to perform damage analysis under multi-source observation based on the first type of structured characterization and the second type of structured characterization, and to report fault anomalies.

[0030] Preferably, the early warning module integrates segment-level inversion results from two independent observation dimensions, time delay and amplitude phase, and performs cross-validation and comprehensive analysis to achieve fault location and alarm with higher reliability and lower false alarm rate. Specifically, it receives the first type of structured representation characterizing the local time delay distortion vector of each fiber segment and the second type of structured feature characterizing the amplitude phase statistical anomaly contribution vector of each fiber segment. It performs time synchronization and alignment on the segment-level data of time-domain propagation characteristics and amplitude phase transmission characteristics to ensure that they correspond to the same monitoring period and link status. Subsequently, it performs damage analysis under multi-source observation, including correlation analysis, contradiction identification, pattern recognition and confidence assessment.

[0031] Preferably, correlation analysis is used to determine whether the same fiber segment exhibits abnormal indicators simultaneously in the first and second types of characterization. For example, if a fiber segment simultaneously shows a significant increase in time delay and a sharp increase in amplitude statistical distance, then the segment has physical damage, such as severe bending or compression. Inconsistency identification is used to analyze whether there are physically inconsistent indications between the two types of characterization. For example, if a segment has a large time delay distortion but a small change in amplitude statistical distance, it may indicate that the abnormality is caused by a uniform change in refractive index, such as a temperature gradient. Pattern recognition refers to combining the characterization values ​​of each fiber segment into a multi-dimensional feature vector, and using a classification model pre-trained based on support vector machines or neural networks or a decision tree based on physical rules to identify whether its feature pattern belongs to "normal", "slight degradation", "suspected fault" or "definite fault", while outputting a damage confidence score for each fiber segment's fault anomaly determination. When the damage confidence of any fiber segment exceeds a preset threshold, a fault alarm signal is generated and sent. This includes identifying the specific fiber segment number or location that is determined to be abnormal or faulty, and reporting the type and severity level of the fault based on pattern recognition results and damage confidence scores. This ensures real-time awareness of the health status of the fiber link and improves the accuracy of fault location.

[0032] Furthermore, the specific configuration of the fiber optic end-to-end coverage fault location system based on passive optical switch cascading also includes: a third identification module, used to perform complex field detection on the end-to-end received signal in each switching state, construct a complex envelope sequence, construct a complex field cross-coherence function for any two switching states based on the complex envelope sequence, perform time-frequency analysis, extract coherent texture maps and calculate texture degradation indices, and construct a third type of structured representation after projection based on the mapping matrix; and a location verification module, used to perform fault trust authentication based on the third type of structured representation, reconstruct the fault anomaly level based on the fault trust authentication result, and report the fault location.

[0033] Preferably, the third identification module provides third-dimensional diagnostic information that is highly sensitive to microscopic consistency disturbances in the fiber optic link by analyzing the coherence texture between signals from different optical paths. Specifically, in each preset switching state, complex field detection is performed on the optical signals received end-to-end, including measuring the signal intensity and phase, thereby obtaining a complex form signal containing real and imaginary part information. This complex form signal is then processed by Hilbert transform to construct a complex envelope sequence describing the evolution of its amplitude and phase with events / frequency, so as to completely preserve the amplitude and phase information of the optical signal. Then, using the complex envelope sequences obtained in different switching states, a complex field cross-coherence function is calculated for signals in any two switching states. The complex field cross-coherence function is used to quantify the amplitude and phase correlation between the received signals in two different optical paths defined by different switching states.

[0034] Preferably, short-time Fourier transform or wavelet transform is used to perform time-frequency analysis on the complex field cross-coherence function, transforming the one-dimensional complex field cross-coherence function into a two-dimensional time-frequency joint distribution map, i.e., a coherent texture map, to reveal how coherence changes with time and frequency. Then, quantitative indicators that can characterize its structural properties are extracted from the coherent texture map to determine texture degradation indicators, such as the width and symmetry of the coherence peak, the energy distribution entropy value of the time-frequency plane, and the roughness of the texture. These indicators are extremely sensitive to microscopic perturbations such as Rayleigh scatterer changes, nonlinear effects, and polarization state perturbations in the fiber link. Finally, using the mapping relationship between the switch state and the fiber segment described by the mapping matrix, the texture degradation indicator sequence calculated based on the switch state pair is transformed into the fiber segment space through mathematical inversion projection, and the contribution of each independent fiber segment to the overall coherent texture degradation characteristics is determined as the third type of structured characterization.

[0035] Preferably, the location verification module independently verifies the initial warning using the third type of structured representation. Through fusion decision-making, it improves the accuracy of fault diagnosis, reduces false alarms, and outputs a more reliable and refined final fault location result. Specifically, it receives the third type of structured representation and cross-compares and verifies the anomalies of the third type of representation in the corresponding suspicious fiber segment with the anomalies of the first and second types of structured representation. If the third type of structured representation also shows a clear anomaly at the same location, and is physically explainable by the first and second types of anomalies (e.g., increased latency, increased loss, and a sharp decrease in coherence), then the authentication is successful, increasing the confidence in the existence of a real physical fault at that location. If the third type of structured representation shows normal or only a weak anomaly at the suspicious location, then a more in-depth analysis is triggered or the fault confidence at that location is reduced. Based on the trust authentication result, the fault anomaly level is updated and reconstructed; for example, a fault jointly confirmed by the three representations is upgraded to a high-confidence fault. Finally, a verified fault location report is output, including the fault location, the updated fault level, a consistency description of multi-source evidence, and detailed information about the nature of the fault provided by coherent texture analysis.

[0036] Furthermore, such as Figure 2As shown, the specific configuration of the third identification module further includes: performing gated coherent differential processing based on the complex envelope sequence; using the complex field phase of the reference state as the coherent anchor point in each switching state; performing joint differential processing in the time and frequency domains through a gated window to obtain a differential coherent field that suppresses the noise of the entire link common factor; inputting the differential coherent field to a multi-scale complex wavelet packet decomposer; performing amplitude-phase coupled feature decomposition on the complex field perturbations of different switching states at multiple scales to extract scale feature clusters that reflect the perturbation response of scatterers within the fiber segment; constructing an intra-segment perturbation fingerprint matrix based on the mapping matrix between the scale feature clusters and the switching states; and performing sparse inversion based on spectral domain reversible reconstruction to obtain the perturbation-dominant scale and effect weights within each fiber segment, generating deep coherent texture features; and outputting the deep coherent texture features as an enhancement term for the third type of structured representation.

[0037] Preferably, gated coherent differential processing is performed with the complex envelope sequence of each switching state as input. One switching state is selected as the reference state, and its complex field phase sequence is used as a stable phase reference, i.e., the "coherent anchor point". For each other switching state, its complex envelope sequence is compared with the reference state sequence. Joint differential processing is performed in the time domain and frequency domain through a gated window. That is, within the gated window, the complex field phase difference between the current switching state and the reference state is calculated, including the amplitude difference and the phase difference. The differential operation suppresses the noise and slow-varying disturbances common to the entire fiber link, such as laser phase noise and overall length changes caused by ambient temperature, as the common factor noise of the entire link. The differential coherent field is output, highlighting the signal changes corresponding to the optical path differences introduced by the switching state switching.

[0038] Preferably, the differential coherent field is input to a multi-scale complex wavelet packet decomposer to perform fine analysis of the time-frequency structure of the signal at different frequency bandwidths and time windows. Amplitude-phase coupling feature decomposition is performed on the complex field perturbations of different switching states at multiple scales. That is, the multi-scale complex wavelet packet decomposer simultaneously analyzes the coupling relationship between amplitude and phase changes in the differential coherent field at each scale. For example, the perturbations of scatterers in the optical fiber will affect the amplitude and phase of the signal in a specific way. Then, from the decomposition results of all scales, a set of features that can reflect the response characteristics of scatterers or structural perturbations inside the optical fiber segment is extracted as a scale feature cluster to characterize the amplitude-phase coupling perturbation mode of different switching state changes distributed at multiple scales. Each scale feature may correspond to statistics such as energy and correlation at different scales.

[0039] Preferably, a correlation model between scale feature clusters and each fiber segment is established using the mapping matrix of each switching state. This model represents the extracted multi-scale features, which are the perturbations within each fiber segment. These perturbations are linearly or nonlinearly superimposed according to their on / off states under different switching states. This results in the construction of an intra-segment perturbation fingerprint matrix, where rows represent fiber segments, columns represent corresponding scale features, and matrix elements represent the "weights" of the perturbations generated by that fiber segment at that feature scale. Then, based on spectral domain reversible reconstruction, sparse inversion is performed to solve for the perturbation fingerprint within each fiber segment, obtaining the dominant perturbation scale and effect weights within each fiber segment, i.e., assuming true... The actual faults or disturbances occur only in a few fiber segments, and within these segments, only a few characteristic scales are dominant. The solutions are sparsity, which allows for the estimation of the dominant disturbance scales and their corresponding effect weights within each fiber segment. This leads to the output of deep coherent texture features, which are used to indicate the main time-frequency scale characteristics and intensity of disturbances within anomalous fiber segments and their internal disturbances. Finally, the deep coherent texture features are output as an enhancement term for the third type of structured characterization, thereby greatly enhancing the resolution, anti-interference capability, and diagnostic depth of the third type of characterization, enabling it to detect and characterize weaker and earlier distributed damage or local defects.

[0040] Furthermore, the specific configuration of the first identification module 20 also includes: constructing a time delay observation vector based on the round-trip time delay observation set, and generating a segment-level observation operator corresponding to the switching state and the fiber segment based on the mapping matrix; introducing a pulse diffusion kernel characterization factor into the segment-level observation operator to construct an extended mapping matrix that can simultaneously describe the segment-level time delay accumulation effect and the pulse waveform diffusion effect; taking the segment-level time delay distortion as the solution objective, using the extended mapping matrix as the forward operator, and the time delay observation vector as the observation operator input, to construct a segment-level time delay inversion model.

[0041] Preferably, the measured end-to-end round-trip time delay values ​​under all switching states are arranged in a fixed order to form a time delay observation vector, the elements of which correspond to the time delay observation values ​​of each switching state. A mapping matrix is ​​used as the initial segment-level observation operator corresponding to the switching state and the fiber segment. It is assumed that the contribution of each fiber segment to the total time delay is simply superimposed and the pulse shape remains unchanged. The basic linear model is y=M*x, where x is the segment-level time delay distortion vector to be determined. However, in actual optical pulses, waveform broadening and diffusion will occur due to dispersion, scattering and other effects when propagating in the optical fiber. The total optical path corresponding to different switching states will vary. Different fiber lengths and segment combinations result in varying diffusion effects on the pulse, thus affecting the accurate measurement of round-trip time. Therefore, a pulse diffusion kernel characterization factor is introduced into the segment-level observation operator for more precise modeling. A function is defined for each fiber segment to characterize the diffusion or convolution effect of the pulse waveform on the time axis after the signal passes through that segment. This constructs an extended mapping matrix that simultaneously describes the segment-level delay accumulation effect and the pulse waveform diffusion effect. Given the delay and diffusion characteristics of each segment, the pulse waveform received end-to-end in any switching state can be calculated, allowing the deduction of the observed round-trip time. Finally, the segment-level delay distortion is used as the solution objective, with the extended mapping matrix as the forward operator. This forward operator includes the delay superposition information of each fiber segment and the pulse diffusion effect. The actual measured delay observation vector is used as the input to the observation operator to construct a segment-level delay inversion model. Ultimately, the segment-level delay distortion distribution that best explains the delay observation data in all switching states is determined, thereby improving the detection sensitivity and positioning accuracy for dispersion abrupt changes and nonlinear effects.

[0042] Furthermore, the specific configuration of the second identification module 30 also includes performing amplitude-phase joint consistency processing on the amplitude sequence and phase sequence of the end-to-end received signal, constructing an amplitude-phase coupled vector sequence through phase expansion, amplitude normalization and group delay compensation; and calculating the distribution divergence distance, phase deviation distance and complex domain energy distribution distance at multiple scales based on the amplitude-phase coupled vector sequence and the reference sequence, and combining the multi-scale distances to construct a switch-state statistical distance sequence.

[0043] Preferably, amplitude and phase sequences of the end-to-end received signals are subjected to amplitude-phase joint unification processing. Specifically, since the directly measured phase is usually wrapped within the principal value range, a continuous, non-jumping true phase sequence is recovered through phase expansion. Then, the amplitude sequence is normalized to eliminate the overall amplitude scaling effect caused by laser power fluctuations, changes in total link loss, etc., for example, normalizing it to a fixed total energy or peak value, so that the comparison focuses on the relative changes in the amplitude distribution shape. Then, group delay compensation is performed to calculate and compensate for the inherent group delay of the signal transmission in the link, so as to ensure that the signals of different switching states are aligned on the time axis or frequency axis, avoiding the phase linear slope and amplitude time shift introduced by different propagation times. Finally, an amplitude-phase coupling vector sequence is constructed to completely preserve the inherent coupling relationship between amplitude and phase.

[0044] Preferably, multi-scale analysis or wavelet transform is used to decompose the amplitude-phase coupling vector sequence and the reference sequence into different scales. At each scale, the distribution divergence distance, phase deviation distance, and complex domain energy distribution distance are calculated. The distribution divergence distance is calculated using KL divergence to determine the difference between the probability distribution of the amplitude at the current scale and the reference distribution at the corresponding scale, and is used to capture changes in amplitude statistical characteristics caused by loss inhomogeneity and scattering variations. The phase deviation distance is calculated to determine the statistical deviation between the unfolded phase sequence at the current scale and the reference phase sequence, and is used to capture changes in phase stability caused by polarization mode dispersion, nonlinear phase noise, etc. The complex domain energy distribution distance is calculated to determine the correlation and coherence of the energy distribution of the two complex sequences in the time-frequency domain, or the difference in their point cloud distribution on the complex plane, and is used to capture changes in the coupling relationship between amplitude and phase. For each analysis scale of each switching state, the multi-scale distances are combined in the switching state order to construct a switching state statistical distance sequence, which is used to quantify the overall deviation of the entire fiber optic link signal characteristics from the reference state at each switching state.

[0045] Furthermore, the specific configuration of the early warning module 40 also includes sending a feedback command to the endpoint control terminal if the damage confidence exceeds a preset threshold, triggering a local enhanced scan of the switch disturbance sequence. The local enhanced scan includes dynamically combining switch states in the fault abnormal segment, generating a highly focused sub-mapping matrix, and performing secondary attention acquisition analysis based on the sub-mapping matrix to establish an enhanced observation set. After performing anomaly verification of the fault abnormal segment based on the enhanced observation set, the fault abnormality is reported.

[0046] Preferably, when the damage confidence of any fiber segment exceeds a preset alarm threshold, a suspected fault is determined. First, a feedback command is sent to the endpoint control terminal, requesting a pause or temporary modification of the preset global scan sequence. This triggers a local enhancement scan of the switching disturbance sequence, dynamically generating new switching state combinations. This ensures the optical path passes through the suspected faulty segment and its adjacent area as concentrated and differentiatedly as possible, while minimizing the number of times the optical path passes through other unrelated healthy segments. Then, based on the dynamically generated combination of switching states, a highly focused sub-mapping matrix is ​​recalculated. Finally, based on the sub-mapping matrix, a secondary focus acquisition analysis is performed; that is, the endpoint control terminal drives the passive optical switch to switch to this group according to the new command. In the switch-on state, the control signal input, acquisition, time delay measurement, amplitude and phase acquisition, and complex field coherence analysis are re-executed to obtain an enhanced observation set for the suspected area. Finally, using the enhanced observation set, the anomaly verification of the faulty segment is performed through segment-level inversion. That is, the sub-mapping matrix is ​​used to invert the new time delay or amplitude and phase data. If the inversion result still clearly indicates that the segment has an anomaly and the confidence level is increased or remains high, the verification is passed, and the fault is finally confirmed. If the analysis result of the enhanced observation set shows that the anomaly is not obvious or can be attributed to noise, the damage confidence of the fiber segment is reduced, it is judged as a false alarm or temporary disturbance, no final alarm is issued, and the global scan mode may be restored.

[0047] Furthermore, the specific configuration of the early warning module 40 also includes: constructing an end-to-end residual spectrum based on historical observation sets of different time windows; performing adaptive noise floor reconstruction based on the spectral density drift of the end-to-end residual spectrum; adjusting the joint analysis weights of the first type of structured characterization and the second type of structured characterization according to the reconstructed noise floor to suppress the influence of noise floor changes on segment-level time delay inversion stability and amplitude-phase statistical distance; and performing damage analysis under multi-source observations according to the joint analysis weights.

[0048] Preferably, for each historical observation set of a historical time window, the assumed segment-level state vector is used to calculate the predicted end-to-end observations through an extended mapping matrix. Then, the actual historical observations are subtracted from the predicted observations to obtain the residual sequence, which mainly contains noise, errors, and unmodeled dynamic disturbances not explained by the model. The residual sequence is subjected to spectral analysis to determine its energy distribution at different frequency components, i.e., the end-to-end residual spectrum. Then, the residual spectrum of the recent time window is compared with the residual spectrum of the long-term historical baseline to calculate the drift of the spectral density in key frequency bands. For example, the low frequency band corresponds to slow temperature drift, and the high frequency band corresponds to random noise. Then, the noise floor reflecting the latest noise environment is dynamically reconstructed to characterize the noise level of different frequency bands.

[0049] Preferably, the joint analysis weights of the first and second type of structured representations are adjusted according to the reconstructed noise floor to suppress the impact of noise floor changes on the stability of segment-level time delay inversion and amplitude-phase statistical distance. If the reconstructed noise floor shows an increase in time delay observation noise, the weight of the time delay inversion result in the current analysis is reduced, i.e., the analysis weight of the first type of structured representation is reduced. Increased phase noise may directly lead to an increase in the benchmark value of phase deviation distance. The statistical distance threshold in the calculation of the second type of representation is dynamically adjusted or normalized and compensated according to the noise floor, and its analysis weight in the fusion decision is adjusted accordingly. Then, the first and second type of structured representations are weighted and fused using the joint analysis weights adaptively adjusted according to the noise floor, and damage analysis under multi-source observation is performed to ensure that the final fault judgment takes into account the uncertainty of the current measurement environment, thereby effectively suppressing the risk of misjudgment caused by noise changes.

[0050] Furthermore, the fiber optic end-to-end coverage fault location system based on cascaded passive optical switches also includes a fault isolation module, used to reconfigure the switching on / off state of the passive optical switches according to the fault anomaly, and to perform fault isolation management.

[0051] Preferably, the fault isolation module receives and parses faulty data to determine the target fiber segment that needs to be isolated. Based on the network topology and mapping matrix, it calculates and determines a set of reconfiguration instructions for passive optical switches, dynamically reconstructs the optical path at the physical layer, and ensures that the optical paths of all normal service signals completely bypass the identified faulty fiber segment by changing the state of one or more passive optical switches. Then, the generated switch control instructions are sent to the endpoint control terminal, which drives the corresponding passive optical switches to change their physical state. For example, if a fault occurs, the signal is switched to a pre-deployed backup fiber path or logically removed directly from the ring network by controlling the switches at both ends. After the switches are reconfigured, a fast connectivity test or status scan is automatically performed to verify that the faulty segment has been successfully bypassed and the new optical path is working normally. At the same time, the faulty segment is marked as "isolated / disabled".

[0052] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. An end-to-end optical fiber coverage fault location system based on passive optical switch cascades, characterized in that, The system comprises: a mapping construction module, configured to arrange a plurality of cascaded passive optical switches in a fiber link to be detected, switch the passive optical switches at an endpoint control end according to a preset perturbation sequence, and establish a mapping matrix of switch states and fiber segments; a first identification module, configured to inject a time-encoded probe pulse from the endpoint in each switch state, obtain an end-to-end round-trip delay observation set in different switch states, construct a segment-level delay inversion model based on the mapping matrix, solve local delay distortion variables of each fiber segment, and construct a first type of structured representation; a second identification module, configured to synchronously collect amplitude sequences and phase sequences of end-to-end received signals in different switch states, calculate statistical distances between the synchronous collection results and reference sequences, form a switch state statistical distance sequence, project the switch state statistical distance sequence to a segment-level space according to the mapping matrix, and construct a second type of structured representation; a warning module, configured to perform damage analysis under multi-source observation according to the first type of structured representation and the second type of structured representation, and report a fault anomaly.

2. The passive optical switch cascade based fiber end-to-end coverage fault location system as claimed in claim 1, wherein, The system further comprises: a third identification module, configured to perform complex field detection on the end-to-end received signals in each switch state, construct a complex envelope sequence, construct a complex field cross-coherence function according to the complex envelope sequence for any two switch states, perform time-frequency analysis, extract a coherent texture atlas and calculate a texture degradation index, project after the mapping matrix, and construct a third type of structured representation; a positioning verification module, configured to perform fault trust authentication of the fault anomaly according to the third type of structured representation, reconstruct a fault anomaly level according to a fault trust authentication result, and report a fault positioning position.

3. The passive optical switch cascade based fiber end-to-end coverage fault location system as claimed in claim 2, wherein, The third identification module is further configured to: perform gated coherent differential processing based on the complex envelope sequence, use a complex field phase of a reference state as a coherent anchor point in each switch state, perform joint differential on the time domain and the frequency domain through a gating window, and obtain a differential coherent field that suppresses common factor noise of the whole link; input the differential coherent field into a multi-scale complex wavelet packet decomposer, perform amplitude-phase coupling feature decomposition on complex field perturbations of different switch states in multiple scales, and extract a scale feature cluster reflecting perturbation response of a scatterer in a fiber segment; construct an intra-segment perturbation fingerprint matrix according to the scale feature cluster and the mapping matrix of the switch states, and perform sparse inversion based on spectral domain reversible reconstruction to obtain a dominant scale and an effect weight of perturbation inside each fiber segment, and generate deep coherent texture features; output the deep coherent texture features as an enhancement item of the third type of structured representation.

4. The passive optical switch cascade based fiber end-to-end coverage fault location system as claimed in claim 1, wherein, In the first identification module, the construction of the segment-level delay inversion model comprises: constructing a delay observation vector according to the round-trip delay observation set, and generating a segment-level observation operator corresponding to the switch state and the fiber segment based on the mapping matrix; introducing a pulse diffusion kernel representation factor into the segment-level observation operator, constructing an extended mapping matrix capable of describing both segment-level delay cumulative effect and pulse waveform diffusion effect, and taking the segment-level delay distortion variable as a solving target, taking the extended mapping matrix as a forward operator, and taking the delay observation vector as an observation operator input, to construct the segment-level delay inversion model.

5. The passive optical switch cascade based fiber end-to-end coverage fault location system as claimed in claim 4, wherein, The second identification module is configured to: Performing amplitude-phase joint alignment processing on the amplitude sequence and the phase sequence of the end-to-end received signal, constructing an amplitude-phase coupling vector sequence through phase unwrapping, amplitude normalization and group delay compensation; Based on the amplitude-phase coupling vector sequence and the reference sequence, respectively calculate the distribution divergence distance, the phase deviation distance and the complex energy allocation distance on multiple scales, and combine the multiple scale distances to construct an on-off state statistical distance sequence.

6. The passive optical switch cascade based fiber end-to-end coverage fault location system as claimed in claim 1, wherein, In the early warning module, the fault anomaly includes: If the damage confidence exceeds the preset threshold, send a feedback instruction to the end control end to trigger a local enhanced scan of the switch disturbance sequence, the local enhanced scan includes dynamically combining the on-off state in the fault anomaly segment, generating a highly focused sub-mapping matrix, and performing secondary attention collection analysis based on the sub-mapping matrix to establish an enhanced observation set; After performing anomaly verification on the fault anomaly segment according to the enhanced observation set, the fault anomaly is reported.

7. The passive optical switch cascade based fiber end-to-end coverage fault location system as claimed in claim 1, wherein, The early warning module is also used for: Based on the end-to-end residual spectrum constructed based on the historical observation set of different time windows, performing adaptive noise floor reconstruction based on the spectral density drift of the end-to-end residual spectrum; According to the reconstructed noise floor, adjust the joint analysis weight of the first type of structured representation and the second type of structured representation to suppress the influence of noise floor change on segment-level time delay inversion stability and amplitude-phase statistical distance, and perform damage analysis under multi-source observation according to the joint analysis weight.

8. The passive optical switch cascade based fiber end-to-end coverage fault location system as claimed in claim 1, wherein, The system also includes: A fault isolation module is configured to reconfigure the on-off of the passive optical switch according to the fault anomaly, and perform fault isolation management.

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