High-concentration optical fiber state monitoring method and system based on multi-parameter fusion

Abstract

The invention discloses a method and a system for monitoring the state of a high-concentration optical fiber based on multi-parameter fusion, and particularly relates to the technical field of situation awareness and monitoring of the high-concentration optical fiber, and the method comprises the steps: obtaining a multi-type physical sensing path deployed in a high-concentration optical fiber link, carrying out the periodic interception, peak value calibration and inversion compression operation after the data collection operation is executed, and obtaining the state of the high-concentration optical fiber. Generating a track expression unit set, executing time sequence similarity construction and physical channel synchronous mapping operation on the track expression unit set, and outputting a track memory expression structure; by introducing perturbation excitation, trajectory reconstruction and behavior consistency variable extraction processes before fusion processing, parameter paths with inconsistent response rhythms, trend direction conflicts or springback form instability are identified and eliminated, and only parameters meeting consistency criteria are reserved as fusion input. The problem that a multi-parameter fusion process lacks a consistency identification mechanism, and a false feature structure is generated in a non-cooperative state, so that state misjudgment is caused is solved.

Description

Technical Field

[0001] This invention relates to the field of situational awareness and monitoring technology for high-density optical fibers, and more specifically, to a method and system for monitoring the status of high-density optical fibers based on multi-parameter fusion. Background Technology

[0002] Existing high-density fiber condition monitoring methods generally employ multi-parameter fusion techniques, jointly constructing a situation feature expression by combining various physical parameters such as fiber reflection loss, dispersion frequency shift, stress response curve, and temperature change trajectory to support fault detection and condition assessment. Multi-parameter fusion models are typically based on weighted superposition, principal component extraction, modal compression, or deep learning network structures to combine various input signals into a unified state expression vector. However, the multi-parameter fusion process lacks a judgment mechanism for the cooperative relationship between parameters, lacks structural constraints to verify whether multi-dimensional inputs meet the fusion conditions, and does not introduce a multi-parameter fusion consistency recognition mechanism.

[0003] Current technology assumes that all input parameters are synergistic in response time, physical causes, and trend evolution, without setting judgment nodes to identify inconsistencies between parameters. In state monitoring tasks deployed in complex links, frequent interference, or heterogeneous environments, multiple input parameters often exhibit inconsistent response rhythms, opposite trend directions, or abrupt changes in evolutionary morphology, resulting in a loss of fusion compatibility between different parameters. For example, temperature parameters may experience continuous transitions due to external thermal disturbances, stress parameters may experience short-term disturbances due to local contact, while the optical power curve remains stable. In such cases, if parameters lacking consistency are still subjected to fusion processing, the fusion model will output pseudo-feature structures that cannot reflect the true state.

[0004] When information fusion is performed under inconsistent conditions, it will combine originally unrelated disturbance signals into a statistically significant fusion expression, thereby triggering the fault identification model to make incorrect judgments on non-fault behaviors, or when real faults exist, abnormal features will be submerged in fusion noise due to fusion interference, ultimately causing situation identification to fail. Continuous misjudgments will affect the stability and reliability of situation awareness and destroy the global judgment chain built on continuous fiber optic monitoring. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a high-density fiber state monitoring method and system based on multi-parameter fusion. By introducing a disturbance excitation, trajectory reconstruction, and behavior consistency variable extraction process before fusion processing, parameter paths with inconsistent response rhythms, conflicting trend directions, or unstable rebound patterns are identified and eliminated. Only parameters that meet the consistency criteria are retained as fusion inputs. This solves the problem of the lack of a consistency recognition mechanism in the multi-parameter fusion process, the generation of pseudo-feature structures in non-cooperative states, and the resulting misjudgment of the state.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for monitoring the condition of high-density optical fibers based on multi-parameter fusion, comprising:

[0007] S1: Obtain the various types of physical sensing paths deployed in the high-density fiber optic link, perform data acquisition operations, and then perform periodic interception, peak calibration and inversion compression operations to generate a trajectory expression unit set. Perform time-series similarity construction and physical channel synchronization mapping operations on the trajectory expression unit set to output the trajectory memory expression structure.

[0008] S2: Based on the trajectory memory expression structure, after executing the action process control method, trajectory reconstruction, response misalignment quantization and time window alignment operations are performed to output the high-precision fiber path response interferometric expression spectrum;

[0009] S3: Extract behavioral variables from the interferometric expression spectrum of the path response of high-density optical fiber and make joint judgments to determine whether to output the set of trajectory path segments or revert to the output stage of the set of trajectory expression units.

[0010] S4: Based on the set of trajectory path segments, after performing path structure aggregation and cooperative behavior compression operations, expression vector construction and multi-channel fusion expression extraction are performed, and the fused input expression vector is output.

[0011] S5: Perform situation mapping and state transition trend analysis operations on the fused input expression vector, output the high-density fiber state monitoring result set, perform difference verification by combining the mismatch record intervals corresponding to the unfused paths in the trajectory memory expression structure, and update the high-density fiber path response interference expression spectrum.

[0012] In a preferred embodiment, in S1, multiple types of physical sensing paths deployed in the high-density fiber optic link are acquired, and data acquisition operations are performed through the multiple types of physical sensing paths to generate a raw physical response data set. The raw physical response data set includes temperature response sequence, stress evolution trajectory, reflection loss change process and frequency shift expression.

[0013] Perform cycle truncation, peak calibration and inversion compression operations on the original physical response data set, and output a set of trajectory representation units;

[0014] The trajectory representation unit set is subjected to temporal similarity construction and physical channel synchronous mapping operation to output the trajectory memory representation structure.

[0015] In a preferred embodiment, S1 further includes the periodic interception by using external disturbances or periodic running events injected into the high-density fiber link as time window boundaries, by detecting the start and end times of external disturbances or periodic events in the original response data, extracting the multi-parameter response sequence within a continuous time period between the start and end times as an independent disturbance response periodic segment, and dividing the original physical response data set into several disturbance response periodic segments.

[0016] Peak calibration is achieved by performing local extremum detection and continuous derivative change analysis on the temperature response sequence, stress evolution trajectory, reflection loss change process and frequency shift expression within each disturbance response period. The time points during the disturbance period where the amplitude change rate exceeds the preset slope threshold or the response amplitude reaches the preset amplitude threshold are extracted as the corresponding response peak positions and amplitude change nodes, and index numbers are established for these positions.

[0017] The inversion compression operation establishes a time-series mapping structure for the disturbance propagation process by performing time-series arrangement and reverse path backtracking analysis on the response peaks and amplitude change nodes that have been indexed in the peak calibration. This forms a response function structure with reversible characteristics. Based on the time-series mapping structure, time compression and amplitude normalization are performed on the response trajectory in each disturbance response period segment. Feature segments with response duration and response amplitude stability thresholds not less than the preset thresholds are extracted from each disturbance response period segment. Each disturbance response period segment is then reconstructed into a trajectory expression unit, forming a set of trajectory expression units.

[0018] In a preferred embodiment, in S2, the calibrated perturbation response period segments in the trajectory representation unit set are extracted, a perturbation signal sequence for response excitation is constructed, and an action process control method is executed to apply the perturbation signal sequence to the index position segment corresponding to the paired path in the trajectory memory representation structure to obtain the perturbation excitation response set.

[0019] Perform trajectory reconstruction, response misalignment quantization, and time window alignment operations on the interference excitation response set to output a high-density fiber path response interferometric representation spectrum.

[0020] The trajectory reconstruction process restores the standardized response evolution process by performing morphological backtracking and key point alignment on the trajectory response sequence generated by each path in the disturbance excitation response set under the disturbance. This is based on the set of feature segments extracted by the inversion compression operation in the original trajectory memory expression structure that are not less than the preset response duration threshold and response amplitude stability threshold.

[0021] Response misalignment quantization extracts key temporal nodes such as disturbance trigger points, response peak positions, and rebound segment boundaries from the reconstructed trajectory sequence. It then calculates point-by-point differences between these nodes and the original response rhythm in the trajectory memory representation structure to output the response offset and phase misalignment indices between paths.

[0022] The time window alignment operation constructs a unified analysis interval based on the portion of the offset difference between paired paths in the trajectory memory representation structure that is not less than a preset offset length threshold. Window clipping and alignment interpolation are then performed on all trajectory response sequences to ensure that the responses of each channel in the high-density fiber path response interferometric representation spectrum are comparable on the same time axis.

[0023] In a preferred embodiment, in S3, behavioral variables are extracted from the path response interferometric expression spectrum of the high-density fiber and combined into behavioral consistency variables, which include response offset, response guidance trend difference and historical trajectory rebound stability.

[0024] The behavioral variable extraction is performed by calculating the position difference of the disturbance trigger point in the trajectory reconstruction process for the paired trajectory pairs in the interferometric expression spectrum of the path response of high-density fiber. Based on the absence of external interference in the trajectory memory expression structure and the reference position of the corresponding disturbance trigger point in the natural operating cycle segment, the initial temporal offset of the disturbance response between each path is calculated to obtain the response offset.

[0025] Trend-oriented calculations are performed on the trajectory response sequences of the segments after perturbation in the same trajectory pair. Based on the response rhythm sequence of the synchronous mapping operation in the trajectory memory representation structure, the trend direction differences of the segments after perturbation are identified, and the response-oriented trend difference is obtained.

[0026] The morphological change amount is extracted from the boundary region of the rebound segment in the trajectory response sequence. Based on the set of feature segments extracted by the inversion compression operation in the trajectory expression unit set that are not less than the preset response duration threshold and response amplitude stability threshold, the amplitude change rate, continuous fluctuation amplitude and response envelope characteristics are calculated to form the historical trajectory rebound stability index.

[0027] Response offset, response-oriented trend difference, and historical trajectory rebound stability together constitute behavioral consistency variables.

[0028] In a preferred embodiment, S3 further includes a joint judgment on behavioral consistency variables, judging whether the response offset is lower than a preset response offset threshold, whether the response-oriented trend difference is continuously lower than a preset response-oriented trend difference threshold, and whether the historical trajectory rebound stability continuously meets a preset historical trajectory rebound stability threshold, as three judgment conditions.

[0029] If all three conditions are met, the set of trajectory path segments is output; otherwise, the process reverts to the trajectory expression unit set output stage, excludes the response segment corresponding to the current path, and updates the trajectory memory expression structure.

[0030] In a preferred embodiment, in S4, based on the set of trajectory path segments, path structure aggregation and cooperative behavior compression operations are performed to obtain the parameter fusion path mapping structure;

[0031] The parameter fusion path mapping structure is used to construct expression vectors and extract multi-channel fusion expression, and outputs fused input expression vectors.

[0032] In a preferred embodiment, in S5, a situation mapping and state transition trend analysis operation is performed on the fused input expression vector to output a set of high-density fiber state monitoring results.

[0033] Perform difference verification between the set of high-density fiber state monitoring results and the mismatch record intervals corresponding to the unfused paths in the trajectory memory expression structure, and update the high-density fiber path response interference expression map.

[0034] A high-density fiber optic status monitoring system based on multi-parameter fusion includes a trajectory construction module, an interferometric analysis module, a consistency determination module, a path reorganization module, and an evolution update module.

[0035] The trajectory construction module is used to acquire multiple types of physical sensing paths deployed in high-density fiber optic links. After performing data acquisition operations, it performs periodic truncation, peak calibration and inversion compression operations to generate a trajectory expression unit set. It then performs temporal similarity construction and physical channel synchronization mapping operations on the trajectory expression unit set to output the trajectory memory expression structure.

[0036] The interferometric analysis module is based on the trajectory memory expression structure. After executing the action process control method, it performs trajectory reconstruction, response misalignment quantization and time window alignment operations, and outputs the interferometric expression spectrum of the high-precision fiber path response.

[0037] The consistency determination module is used to extract behavioral variables from the interferometric expression spectrum of the path response of high-density fiber and perform joint judgment to determine whether to output the set of trajectory path segments or revert to the output stage of the set of trajectory expression units.

[0038] The path reorganization module is based on the set of trajectory path segments. After performing path structure aggregation and collaborative behavior compression operations, it constructs expression vectors and extracts multi-channel fusion expression vectors, and outputs fused input expression vectors.

[0039] The evolution update module is used to perform situation mapping and state transition trend analysis operations on the fused input expression vector, output the high-density fiber state monitoring result set, and perform difference verification by combining the mismatch record intervals corresponding to the unfused paths in the trajectory memory expression structure to update the high-density fiber path response interference expression spectrum.

[0040] The technical effects and advantages of this invention are as follows:

[0041] 1. This solution introduces behavioral consistency variables as a pre-judgment mechanism for multi-parameter fusion, which solves the problem that traditional multi-parameter fusion defaults to parameter coordination but actually has conflicting response rhythms and trends, avoids false feature interference, and improves the reliability of high-density fiber status monitoring.

[0042] 2. Construct trajectory representation units and trajectory memory representation structures to achieve standardized modeling and multi-channel alignment of physical path response;

[0043] 3. By quantizing the misalignment between disturbance excitation and response, we can identify the temporal offset and rebound stability differences between paths and eliminate auxiliary paths;

[0044] 4. Perform fusion input expression vector construction and trend mapping to improve the ability to detect anomalies in the co-evolution of multi-parameter states;

[0045] 5. Establish a difference verification and trajectory memory update process to dynamically correct the response of unfused paths and enhance the reliability of monitoring results. Attached Figure Description

[0046] Figure 1 This is a flowchart outlining the method steps of the present invention;

[0047] Figure 2 This is a schematic diagram of the system module structure of the present invention;

[0048] Figure 3 This is a flowchart illustrating the trajectory representation construction and path pairing process of the present invention.

[0049] Figure 4 This is a flowchart of the disturbance excitation and consistency variable extraction process of the present invention;

[0050] Figure 5 This is a flowchart of the consistency judgment and fusion input generation process of the present invention. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] Refer to the instruction manual appendix Figure 1-5 An embodiment of the present invention provides a high-precision optical fiber status monitoring method based on multi-parameter fusion, comprising:

[0053] S1: Obtain the various types of physical sensing paths deployed in the high-density fiber optic link, perform data acquisition operations, and then perform periodic interception, peak calibration and inversion compression operations to generate a trajectory expression unit set. Perform time-series similarity construction and physical channel synchronization mapping operations on the trajectory expression unit set to output the trajectory memory expression structure.

[0054] S2: Based on the trajectory memory expression structure, after executing the action process control method, trajectory reconstruction, response misalignment quantization and time window alignment operations are performed to output the high-precision fiber path response interferometric expression spectrum;

[0055] S3: Extract behavioral variables from the interferometric expression spectrum of the path response of high-density optical fiber and make joint judgments to determine whether to output the set of trajectory path segments or revert to the output stage of the set of trajectory expression units.

[0056] S4: Based on the set of trajectory path segments, after performing path structure aggregation and cooperative behavior compression operations, expression vector construction and multi-channel fusion expression extraction are performed, and the fused input expression vector is output.

[0057] S5: Perform situation mapping and state transition trend analysis operations on the fused input expression vector, output the high-density fiber state monitoring result set, perform difference verification by combining the mismatch record intervals corresponding to the unfused paths in the trajectory memory expression structure, and update the high-density fiber path response interference expression spectrum.

[0058] It should be noted that in the formula structure involved in this scheme, dimensionless terms can be used as proportional or structural adjustment factors. When combined with quantities with units, they only play a role in numerical scaling and do not introduce new physical dimensions. Therefore, they will not change or confuse the overall unit system. This combination of "dimensionless terms and terms with units" can be understood as a composite structural expression commonly used in mathematical physics modeling. It conforms to the principle of dimensional consistency and has a clear physical interpretation basis.

[0059] Secondly, in the formula structure of this scheme, if multiple variables with different physical units are involved, including but not limited to time, mass or energy variables, their joint appearance is to express the collaborative modeling relationship of multiple physical mechanisms. Each variable can form a unified structure through function mapping, ratio combination or normalization adjustment, with clear units and clear meaning. The overall expression conforms to the principle of dimensional consistency and the conventional formula of engineering modeling.

[0060] In this scheme, constants, weights, adjustment factors, threshold parameters, proportional coefficients, etc., are all adjustable control parameters for different application environments. Their values ​​depend on the target equipment configuration, data input characteristics, and performance optimization goals. During the implementation phase, they are converged within a reasonable range through model verification, performance constraints, or engineering calibration. Although these parameters do not have a unique preset value, they have clear adjustment logic and calculation paths. They belong to the deterministic setting process in engineering implementation. The purpose of this setting is to ensure that the scheme is both universally adaptable and reproducible and operable, without affecting its technical clarity and feasibility.

[0061] In S1, multiple types of physical sensing paths deployed in the high-density fiber optic link are acquired. Data acquisition operations are performed through these multiple types of physical sensing paths to generate a raw physical response data set. The raw physical response data set includes temperature response sequence, stress evolution trajectory, reflection loss change process, and frequency shift expression. The data acquisition operation refers to acquiring the raw response sequence of multi-dimensional physical parameters such as temperature, stress, reflection loss, and frequency shift within a preset time window through multi-channel sensing devices on the high-density fiber optic physical path. This sequence is used as the basic input for constructing the trajectory expression unit.

[0062] Perform cycle truncation, peak calibration and inversion compression operations on the original physical response data set, and output a set of trajectory representation units;

[0063] Define the set of trajectory representation units U i :

[0064]

[0065] Where K i This represents the number of disturbance response period segments identified by the periodic interception operation in the i-th path; This represents the start time of the k-th disturbance cycle of the i-th path; Ψ represents the end time of the k-th disturbance cycle of the i-th path; i (τ) represents the acceleration response function obtained by logarithmic mapping of the second derivative change of the trajectory at time point τ; Φ i (τ) represents the magnitude response of the trajectory at time point τ; dτ represents the integral infinitesimal element at time point τ. Represents the feature segment extraction function; R i (τ) represents the original physical response trajectory function of the i-th path at time τ; d represents the differential operator; Let represent the second derivative of the response trajectory of the i-th path with respect to time; Let represent the first derivative of the response trajectory of the i-th path with respect to time; log(·) represents the logarithmic function with the natural logarithm as the base.

[0066] The trajectory representation unit set is subjected to temporal similarity construction and physical channel synchronization mapping operations to output a trajectory memory representation structure. Temporal similarity construction refers to identifying trajectory pairs with similar response rhythms under external disturbances by performing trend similarity matching and disturbance trigger point comparison on the temporal evolution process of different physical response paths in the trajectory representation unit set. Physical channel synchronization mapping operation refers to performing path pairing and index binding on trajectory pairs whose response synchronization meets the preset similar response rhythm threshold according to the physical channel number, based on the temporal similarity construction, and establishing path number binding relationship in the trajectory pairs. This is used to construct a trajectory memory representation structure for multi-physical parameter co-evolution. The trajectory memory representation structure serves as the path registration basis for unified disturbance excitation and interference response analysis.

[0067] S1 also includes the periodic interception, which uses the external disturbance or periodic running event injected into the high-density fiber link as the time window boundary. By detecting the start time and end time of the external disturbance or periodic event in the original response data, the multi-parameter response sequence in the continuous time period between the start time and the end time is extracted as an independent disturbance response periodic segment, and the original physical response data set is divided into several disturbance response periodic segments.

[0068] Peak calibration is achieved by performing local extremum detection and continuous derivative change analysis on the temperature response sequence, stress evolution trajectory, reflection loss change process and frequency shift expression within each disturbance response period. The time points during the disturbance period where the amplitude change rate exceeds the preset slope threshold or the response amplitude reaches the preset amplitude threshold are extracted as the corresponding response peak positions and amplitude change nodes, and index numbers are established for these positions.

[0069] The inversion compression operation establishes a time-series mapping structure for the disturbance propagation process by performing time-series arrangement and reverse path backtracking analysis on the response peaks and amplitude change nodes with established index numbers in the peak calibration. This forms a response function structure with reversible characteristics. Based on the time-series mapping structure, time compression and amplitude normalization are performed on the response trajectory in each disturbance response period segment. Feature segments with response duration and response amplitude stability thresholds not less than the preset thresholds are extracted from each disturbance response period segment. Each disturbance response period segment is reconstructed into a trajectory expression unit, forming a trajectory expression unit set. The trajectory expression unit set is used for behavior consistency judgment and trajectory memory expression structure construction.

[0070] In S2, the calibrated perturbation response period segments are extracted from the trajectory representation unit set, a perturbation signal sequence for response excitation is constructed, and an action process control method is executed to apply the perturbation signal sequence to the index position segment corresponding to the paired path in the trajectory memory representation structure to obtain the interference excitation response set. The action process control method refers to the accurate control based on preset perturbation application parameters during the process of applying the perturbation signal sequence to the corresponding path index position in the trajectory memory representation structure, including the application order of the perturbation signal, the duration of action, the amplitude adjustment frequency and the signal interval structure, to ensure that different paths receive perturbation excitation under the same conditions, obtain comparable channel response behavior, and use it for interferometric migration analysis and consistency judgment.

[0071] Trajectory reconstruction, response misalignment quantization, and time window alignment are performed on the interference excitation response set to output a high-density fiber path response interferometric expression spectrum, which is used to describe the degree of offset and phase mismatch behavior of the response between paths.

[0072] The trajectory reconstruction process restores the standardized response evolution process by performing morphological backtracking and key point alignment on the trajectory response sequence generated by each path in the disturbance excitation response set under the disturbance. This is based on the set of feature segments extracted by the inversion compression operation in the original trajectory memory expression structure that are not less than the preset response duration threshold and response amplitude stability threshold.

[0073] Response misalignment quantization extracts key temporal nodes such as disturbance trigger points, response peak positions, and rebound segment boundaries from the reconstructed trajectory sequence. It then calculates point-by-point differences between these nodes and the original response rhythm in the trajectory memory representation structure to output the response offset and phase misalignment indices between paths.

[0074] The time window alignment operation constructs a unified analysis interval based on the portion of the offset difference between paired paths in the trajectory memory representation structure that is not less than a preset offset length threshold. Window clipping and alignment interpolation are then performed on all trajectory response sequences to ensure that the responses of each channel in the high-density fiber path response interferometric representation spectrum are comparable on the same time axis, serving as the input structure for consistency judgment.

[0075] In S3, behavioral variables are extracted from the path response interferometric expression spectrum of high-density fiber and combined into behavioral consistency variables, which include response offset, response guidance trend difference and historical trajectory rebound stability.

[0076] Define behavioral consistency variables

[0077]

[0078] Where δτ jIndicates the response offset; δθ j Indicates the difference in response-oriented trends; σ j This indicates the stability of the historical trajectory rebound; This represents the first derivative in the time direction; Let represent the standardized response trajectory function of the first path in the j-th trajectory pair; Represents the standardized response trajectory function of the second path in the j-th trajectory pair; symbol Represents the L1 norm; Indicates the starting point of the trend analysis; Indicates the end point of the trend analysis; This represents the trajectory response trend function of the first path in the j-th trajectory pair during the disturbance response process; This represents the trajectory response trend function of the second path during the disturbance response process; Indicates the start time of the rebound phase; Indicates the end time of the rebound phase; A j (τ) represents the trajectory response amplitude sequence; E represents the second derivative in the time direction. j (τ) represents the trajectory envelope function;

[0079] The behavioral variable extraction is performed by calculating the position difference of the disturbance trigger point in the trajectory reconstruction process for the paired trajectory pairs in the interferometric expression spectrum of the path response of high-density fiber. Based on the absence of external interference in the trajectory memory expression structure and the reference position of the corresponding disturbance trigger point in the natural operating cycle segment, the initial temporal offset of the disturbance response between each path is calculated to obtain the response offset.

[0080] Trend-oriented calculations are performed on the trajectory response sequences of the segments after perturbation in the same trajectory pair. Based on the response rhythm sequence of the synchronous mapping operation in the trajectory memory representation structure, the trend direction differences of the segments after perturbation are identified, and the response-oriented trend difference is obtained.

[0081] The morphological change amount is extracted from the boundary region of the rebound segment in the trajectory response sequence. Based on the set of feature segments extracted by the inversion compression operation in the trajectory expression unit set that are not less than the preset response duration threshold and response amplitude stability threshold, the amplitude change rate, continuous fluctuation amplitude and response envelope characteristics are calculated to form the historical trajectory rebound stability index.

[0082] The response offset, response-oriented trend difference, and historical trajectory rebound stability together constitute the behavioral consistency variable. The behavioral consistency variable is used to construct the consistency judgment basis before multi-parameter fusion, ensuring that it only participates in the fusion input construction of high-density fiber status monitoring under the condition that the path response meets the stability condition.

[0083] S3 also includes joint judgment of behavioral consistency variables, judging whether the response offset is lower than the preset response offset threshold, whether the response-oriented trend difference is continuously less than the preset response-oriented trend difference threshold, and whether the historical trajectory rebound stability continuously meets the preset historical trajectory rebound stability threshold, as three judgment conditions.

[0084] If all three conditions are met, the set of trajectory path segments is output; otherwise, the process reverts to the trajectory expression unit set output stage, excludes the response segment corresponding to the current path, and updates the trajectory memory expression structure.

[0085] In S4, based on the trajectory path segment set, path structure aggregation and cooperative behavior compression operations are performed to obtain the parameter fusion path mapping structure. The path structure aggregation and cooperative behavior compression operations refer to merging all trajectory path segments that meet the behavior consistency variable judgment according to the physical channel number in the trajectory path segment set according to the path pairing relationship established in the trajectory memory expression structure, forming a continuous response framework of parameter fusion path. Joint segment alignment, key feature point reorganization and evolution trend simplification operations are performed on the trajectory response sequence in the trajectory path segment set to extract stable behavior features co-occurring between paths within the disturbance response period, compress the behavioral redundancy between multiple paths, construct a representative fusion path response expression, and form a parameter fusion path mapping structure for situational awareness.

[0086] The parameter fusion path mapping structure is subjected to expression vector construction and multi-channel fusion expression extraction, and the output is a fusion input expression vector. The expression vector construction and multi-channel fusion expression extraction refer to the process of extracting key feature points and encoding trends of the perturbation response segments in the trajectory response sequence of each aggregated path in the parameter fusion path mapping structure to construct expression sub-vectors representing single-channel response behavior. Based on the path pairing relationship and response rhythm synchronization structure established in the trajectory memory expression structure, joint permutation, asynchronous alignment and dimension mapping fusion operations are performed on all sub-vectors to generate a fusion input expression vector. The fusion input expression vector serves as a collaborative feature representation of the multi-channel interference response and is used in the state evolution analysis process.

[0087] In S5, situation mapping and state transition trend analysis operations are performed on the fused input expression vector to output a set of high-density fiber state monitoring results;

[0088] Define the set of high-fiber condition monitoring results

[0089]

[0090]

[0091] in This represents the position in the multidimensional state space obtained by mapping at time point τ; Represents the state evolution trend; Ψ(·) represents the state fusion output function; Ω(·) represents the situation mapping function; M represents the number of multi-physics channels; α m (τ) represents the dynamic weighting function of the m-th physical channel; Let ||m|| represent the fusion input vector of the m-th physical channel at time τ; the symbol ||·||2 represents the vector L2 norm. This represents the amount of time the high-precision optical fiber trajectory path advances for each unit change in response potential energy; This represents the time increment corresponding to each unit change in the path stability function; This represents the differentiation operation with respect to time point τ;

[0092] The difference verification process involves comparing the set of high-fiber state monitoring results with the mismatched record intervals corresponding to the unfused paths in the trajectory memory representation structure to update the high-fiber path response interference representation spectrum. The difference verification process involves comparing the set of high-fiber state monitoring results with the mismatched record intervals corresponding to the unfused paths item by item to identify the degree of deviation between the two in response characteristics, which is used to determine whether the unfused paths need to be updated and corrected.

[0093] A high-density fiber optic status monitoring system based on multi-parameter fusion includes a trajectory construction module, an interferometric analysis module, a consistency determination module, a path reorganization module, and an evolution update module;

[0094] The trajectory construction module is used to acquire multiple types of physical sensing paths deployed in high-density fiber optic links. After performing data acquisition operations, it performs periodic truncation, peak calibration and inversion compression operations to generate a trajectory expression unit set. It then performs temporal similarity construction and physical channel synchronization mapping operations on the trajectory expression unit set to output the trajectory memory expression structure.

[0095] The interferometric analysis module is based on the trajectory memory expression structure. After executing the action process control method, it performs trajectory reconstruction, response misalignment quantization and time window alignment operations, and outputs the interferometric expression spectrum of the high-precision fiber path response.

[0096] The consistency determination module is used to extract behavioral variables from the interferometric expression spectrum of the path response of high-density fiber and perform joint judgment to determine whether to output the set of trajectory path segments or revert to the output stage of the set of trajectory expression units.

[0097] The path reorganization module is based on the set of trajectory path segments. After performing path structure aggregation and collaborative behavior compression operations, it constructs expression vectors and extracts multi-channel fusion expression vectors, and outputs fused input expression vectors.

[0098] The evolution update module is used to perform situation mapping and state transition trend analysis operations on the fused input expression vector, output the high-density fiber state monitoring result set, and perform difference verification by combining the mismatch record intervals corresponding to the unfused paths in the trajectory memory expression structure to update the high-density fiber path response interference expression spectrum.

[0099] It should be noted that, including but not limited to, current high-density fiber state monitoring methods generally rely on multi-parameter fusion techniques. These methods involve weighted superposition, principal component extraction, or deep learning fusion of multiple physical response parameters such as temperature, stress, reflection loss, and frequency shift to construct a global representation of fiber state evolution.

[0100] However, the above method does not establish a mechanism for judging the consistency between parameters. This makes it easy for parameter fusion to lead to false feature interference, abnormal occlusion or misidentification when the response rhythm of physical channels is inconsistent, which seriously undermines the stability and reliability of the state judgment chain.

[0101] This solution proposes a multi-parameter fusion mechanism based on behavior consistency recognition and trajectory response collaborative reconstruction. By constructing a complete state monitoring method, it ensures that the fusion path is comparable, the intervention response is interpretable, and the final situation expression is accurate.

[0102] This solution includes the trajectory representation construction and channel registration stages:

[0103] The original response sequences of multi-dimensional physical parameters such as temperature, stress, frequency shift, and reflection loss in multiple types of physical sensing paths are obtained within a preset time window to form an original physical response data set. The original physical response data set is subjected to perturbation period truncation, peak calibration, and inversion compression operations to output a trajectory expression unit set. Based on temporal similarity, a synchronous mapping operation with the physical channel is constructed to build a trajectory memory expression structure.

[0104] This trajectory representation construction and channel registration stage is used to form a multi-channel response structure with registration basis, providing a traceable trajectory semantic foundation for unified perturbation excitation and path comparison;

[0105] This scheme includes the stages of interferometric response construction and misalignment quantization:

[0106] The perturbation period in the trajectory representation unit is extracted, the perturbation signal sequence is constructed and applied to the registered path, the set of interference excitation response is collected, the trajectory reconstruction, response misalignment quantization and time window alignment are performed on the set of interference excitation response, and the interferometric representation spectrum of the high-density fiber path response is output.

[0107] The interference response construction and misalignment quantization stage is used to generate a multi-path interference response structure that is comparable and standardized, for consistency identification, and to avoid mismatched paths from participating in the fusion.

[0108] This solution includes the behavioral consistency identification and trajectory path segment selection stages:

[0109] Three behavioral variables—response offset, response guidance trend difference, and historical rebound stability—are extracted from paired paths in the path response interferometric expression spectrum of high-density optical fibers. A joint judgment is made to output a set of trajectory path segments or a backtrack reconstruction.

[0110] This behavior consistency identification and trajectory path segment screening stage ensures that only trajectory segments participating in effective fusion are retained by excluding path segments that do not have consistent response under disturbance conditions, thereby enhancing the constraint control of fusion quality.

[0111] This solution includes the structural aggregation and fusion stage and the expression construction stage:

[0112] Based on the set of trajectory path segments, path structure aggregation and cooperative behavior compression operations are performed to construct a parameter fusion path mapping structure. Key response feature points of the parameter fusion path mapping structure are extracted and expression vectors are constructed. The fused input expression vector is then output.

[0113] The aggregation, fusion, and expression construction phase of this structure extracts representative channel behavior expressions after excluding inconsistent paths, and constructs a unified expression vector by fusing channel responses as units, thus ensuring the integrity and representativeness of the fused feature expressions.

[0114] This scheme includes a situation mapping analysis and structure feedback update phase:

[0115] The fused expression vector is subjected to situation mapping and state transition trend analysis, and the set of high-fiber state monitoring results is output. Combined with the mismatch record intervals corresponding to the unfused paths, the difference verification is performed and the high-fiber path response interference expression spectrum is corrected in a feedback manner.

[0116] This situational mapping analysis and structural feedback update stage is used to complete the mapping and trend judgment of the high-dimensional state space based on the fusion expression, form the final monitoring conclusion, and perform structural correction and path update for abnormal behaviors that did not participate in the fusion path, so as to maintain the coordination of the overall state expression structure and the continuity of temporal evolution.

[0117] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for monitoring the condition of high-density optical fibers based on multi-parameter fusion, characterized in that, include: S1: Obtain the various types of physical sensing paths deployed in the high-density fiber optic link, perform data acquisition operations, then perform periodic interception, peak calibration and inversion compression operations to generate a trajectory expression unit set, perform time-series similarity construction and physical channel synchronization mapping operations on the trajectory expression unit set, and output the trajectory memory expression structure; S2: Based on the trajectory memory expression structure, after executing the action process control method, trajectory reconstruction, response misalignment quantization and time window alignment operations are performed to output the high-precision fiber path response interferometric expression spectrum; S3: Extract behavioral variables from the interferometric expression spectrum of the path response of high-density optical fiber and make joint judgments to determine whether to output the set of trajectory path segments or revert to the output stage of the set of trajectory expression units. S4: Based on the set of trajectory path segments, after performing path structure aggregation and cooperative behavior compression operations, expression vector construction and multi-channel fusion expression extraction are performed, and the fused input expression vector is output. S5: Perform situation mapping and state transition trend analysis operations on the fused input expression vector, output the high-density fiber state monitoring result set, perform difference verification by combining the mismatch record intervals corresponding to the unfused paths in the trajectory memory expression structure, and update the high-density fiber path response interference expression spectrum.

2. The high-density optical fiber condition monitoring method based on multi-parameter fusion according to claim 1, characterized in that: In S1, multiple types of physical sensing paths deployed in the high-density fiber optic link are acquired. Data acquisition operations are performed through these multiple types of physical sensing paths to generate a raw physical response data set. The raw physical response data set includes temperature response sequence, stress evolution trajectory, reflection loss change process, and frequency shift expression. Perform cycle truncation, peak calibration and inversion compression operations on the original physical response data set, and output a set of trajectory representation units; The trajectory representation unit set is subjected to temporal similarity construction and physical channel synchronous mapping operation to output the trajectory memory representation structure.

3. The high-density optical fiber condition monitoring method based on multi-parameter fusion according to claim 2, characterized in that: S1 also includes the periodic interception, which uses the external disturbance or periodic running event injected into the high-density fiber link as the time window boundary. By detecting the start time and end time of the external disturbance or periodic event in the original response data, the multi-parameter response sequence in the continuous time period between the start time and the end time is extracted as an independent disturbance response periodic segment, and the original physical response data set is divided into several disturbance response periodic segments. Peak calibration is achieved by performing local extremum detection and continuous derivative change analysis on the temperature response sequence, stress evolution trajectory, reflection loss change process and frequency shift expression within each disturbance response period. The time points during the disturbance period where the amplitude change rate exceeds the preset slope threshold or the response amplitude reaches the preset amplitude threshold are extracted as the corresponding response peak positions and amplitude change nodes, and index numbers are established for these positions. The inversion compression operation establishes a time-series mapping structure for the disturbance propagation process by performing time-series arrangement and reverse path backtracking analysis on the response peaks and amplitude change nodes that have been indexed in the peak calibration. This forms a response function structure with reversible characteristics. Based on the time-series mapping structure, time compression and amplitude normalization are performed on the response trajectory in each disturbance response period segment. Feature segments with response duration and response amplitude stability thresholds not less than the preset thresholds are extracted from each disturbance response period segment. Each disturbance response period segment is then reconstructed into a trajectory expression unit, forming a set of trajectory expression units.

4. The high-density optical fiber condition monitoring method based on multi-parameter fusion according to claim 3, characterized in that: In S2, the calibrated disturbance response periodic segments are extracted from the trajectory representation unit set, a disturbance signal sequence for response excitation is constructed, and the action process control method is executed to apply the disturbance signal sequence to the index position segment corresponding to the paired path in the trajectory memory representation structure to obtain the disturbance excitation response set. Perform trajectory reconstruction, response misalignment quantization, and time window alignment operations on the interference excitation response set to output a high-density fiber path response interferometric representation spectrum. The trajectory reconstruction process restores the standardized response evolution process by performing morphological backtracking and key point alignment on the trajectory response sequence generated by each path in the disturbance excitation response set under the disturbance. This is based on the set of feature segments extracted by the inversion compression operation in the original trajectory memory expression structure that are not less than the preset response duration threshold and response amplitude stability threshold. Response misalignment quantization extracts key temporal nodes such as disturbance trigger points, response peak positions, and rebound segment boundaries from the reconstructed trajectory sequence. It then calculates point-by-point differences between these nodes and the original response rhythm in the trajectory memory representation structure to output the response offset and phase misalignment indices between paths. The time window alignment operation constructs a unified analysis interval based on the portion of the offset difference between paired paths in the trajectory memory representation structure that is not less than a preset offset length threshold. Window clipping and alignment interpolation are then performed on all trajectory response sequences to ensure that the responses of each channel in the high-density fiber path response interferometric representation spectrum are comparable on the same time axis.

5. The high-density optical fiber condition monitoring method based on multi-parameter fusion according to claim 4, characterized in that: In S3, behavioral variables are extracted from the path response interferometric expression spectrum of high-density fiber and combined into behavioral consistency variables, which include response offset, response guidance trend difference and historical trajectory rebound stability. The behavioral variable extraction is performed by calculating the position difference of the disturbance trigger point in the trajectory reconstruction process for the paired trajectory pairs in the interferometric expression spectrum of the path response of high-density fiber. Based on the absence of external interference in the trajectory memory expression structure and the reference position of the corresponding disturbance trigger point in the natural operating cycle segment, the initial temporal offset of the disturbance response between each path is calculated to obtain the response offset. Trend-oriented calculations are performed on the trajectory response sequences of the segments after perturbation in the same trajectory pair. Based on the response rhythm sequence of the synchronous mapping operation in the trajectory memory representation structure, the trend direction differences of the segments after perturbation are identified, and the response-oriented trend difference is obtained. The morphological change amount is extracted from the boundary region of the rebound segment in the trajectory response sequence. Based on the set of feature segments extracted by the inversion compression operation in the trajectory expression unit set that are not less than the preset response duration threshold and response amplitude stability threshold, the amplitude change rate, continuous fluctuation amplitude and response envelope characteristics are calculated to form the historical trajectory rebound stability index. Response offset, response-oriented trend difference, and historical trajectory rebound stability together constitute behavioral consistency variables.

6. The high-density optical fiber condition monitoring method based on multi-parameter fusion according to claim 5, characterized in that: S3 also includes joint judgment of behavioral consistency variables, judging whether the response offset is lower than the preset response offset threshold, whether the response-oriented trend difference is continuously less than the preset response-oriented trend difference threshold, and whether the historical trajectory rebound stability continuously meets the preset historical trajectory rebound stability threshold, as three judgment conditions. If all three conditions are met, the set of trajectory path segments is output; otherwise, the process reverts to the trajectory expression unit set output stage, excludes the response segment corresponding to the current path, and updates the trajectory memory expression structure.

7. The high-density optical fiber condition monitoring method based on multi-parameter fusion according to claim 6, characterized in that: In S4, based on the set of trajectory path segments, path structure aggregation and cooperative behavior compression operations are performed to obtain the parameter fusion path mapping structure; The parameter fusion path mapping structure is used to construct expression vectors and extract multi-channel fusion expression, and outputs fused input expression vectors.

8. The high-density optical fiber condition monitoring method based on multi-parameter fusion according to claim 7, characterized in that: In S5, situation mapping and state transition trend analysis operations are performed on the fused input expression vector to output a set of high-density fiber state monitoring results; Perform difference verification between the set of high-density fiber state monitoring results and the mismatch record intervals corresponding to the unfused paths in the trajectory memory expression structure, and update the high-density fiber path response interference expression map.

9. A high-density fiber optic condition monitoring system based on multi-parameter fusion, comprising the high-density fiber optic condition monitoring method based on multi-parameter fusion as described in claim 8, including a trajectory construction module, an interferometry analysis module, a consistency determination module, a path reconstruction module, and an evolution update module, characterized in that: The trajectory construction module is used to acquire multiple types of physical sensing paths deployed in high-density fiber optic links. After performing data acquisition operations, it performs periodic truncation, peak calibration and inversion compression operations to generate a trajectory expression unit set. It then performs temporal similarity construction and physical channel synchronization mapping operations on the trajectory expression unit set to output the trajectory memory expression structure. The interferometric analysis module is based on the trajectory memory expression structure. After executing the action process control method, it performs trajectory reconstruction, response misalignment quantization and time window alignment operations, and outputs the interferometric expression spectrum of the high-precision fiber path response. The consistency determination module is used to extract behavioral variables from the interferometric expression spectrum of the path response of high-density fiber and perform joint judgment to determine whether to output the set of trajectory path segments or revert to the output stage of the set of trajectory expression units. The path reorganization module is based on the set of trajectory path segments. After performing path structure aggregation and collaborative behavior compression operations, it constructs expression vectors and extracts multi-channel fusion expression vectors, and outputs fused input expression vectors. The evolution update module is used to perform situation mapping and state transition trend analysis operations on the fused input expression vector, output the high-density fiber state monitoring result set, and perform difference verification by combining the mismatch record intervals corresponding to the unfused paths in the trajectory memory expression structure to update the high-density fiber path response interference expression spectrum.

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