A method and system for detecting abnormalities in an in-service OPGW optical cable based on multi-modal data

Abstract

The application provides a kind of abnormality detection method and system of OPGW optical cable in operation based on multi-modal data, which comprises: collecting the Brillouin frequency shift parameters of OPGW optical cable under different operating conditions in actual production, and simultaneously collecting the state information of the corresponding OPGW optical cable;Respectively, the Brillouin frequency shift parameters of the collected OPGW optical cable and the state information of the corresponding OPGW optical cable are extracted to obtain the Brillouin frequency shift parameter characteristics of the OPGW optical cable and the state information characteristics of the OPGW optical cable;The Brillouin frequency shift parameter characteristics of the extracted OPGW optical cable and the state information characteristics of the OPGW optical cable are fused in multi-modal, and the OPGW optical cable fault is classified by combining the SVM classifier, so as to complete fault identification.The application can more comprehensively evaluate the health status of optical cable, improve the accuracy and reliability of abnormality detection.

Description

Technical Field

[0001] The present invention relates to the technical field of optical fiber and cable fault diagnosis, and in particular to a method and system for detecting anomalies of an in-service OPGW optical cable based on multimodal data. Background Art

[0002] In modern power and communications systems, optical cables, as a crucial transmission medium, are widely used in the construction of overhead power lines and communication networks. OPGW (Optical Fiber Composite Overhead Ground Wire) cables not only offer excellent optical signal transmission characteristics but also provide additional lightning protection and power transmission capabilities. However, due to the influence of various factors, including environmental factors, natural disasters, and human activities, OPGW cables may experience various anomalies during operation, such as fiber breakage, external damage, and temperature fluctuations. These anomalies can seriously impact the stability and reliability of power and communications. Therefore, timely and effective anomaly detection of OPGW cables is crucial.

[0003] Traditional anomaly detection methods mostly rely on a single data source, such as optical fiber transmission loss, temperature monitoring, or electromechanical sensors, which often cannot fully reflect the operating status of the optical cable, resulting in insufficient detection accuracy and real-time performance. Summary of the Invention

[0004] In view of this, the present invention provides an anomaly detection method for in-service OPGW optical cables based on multimodal data. By integrating data from different sensors, the health status of the optical cable can be more comprehensively evaluated, thereby improving the accuracy and reliability of anomaly detection.

[0005] To achieve the above objectives, according to one aspect of the present invention, a method for detecting anomalies of an in-service OPGW optical cable based on multimodal data is provided, comprising:

[0006] S1. Collecting Brillouin frequency shift parameters of the OPGW optical cable under different operating conditions in actual production, and simultaneously collecting status information of the corresponding OPGW optical cable, wherein the status information of the OPGW optical cable includes: the wind speed when the Brillouin frequency shift parameters of the OPGW optical cable are collected, and the distance and height difference between the towers of the corresponding OPGW optical cable;

[0007] S2. performing feature extraction on the collected Brillouin frequency shift parameters of the OPGW optical cable and the corresponding state information of the OPGW optical cable to obtain Brillouin frequency shift parameter features of the OPGW optical cable and state information features of the OPGW optical cable;

[0008] S3, performing multimodal fusion on the Brillouin frequency shift parameter features of the OPGW optical cable and the status information features of the OPGW optical cable extracted in step S2, and classifying the OPGW optical cable faults in combination with the SVM classifier, thereby completing fault identification.

[0009] Furthermore, the multimodal fusion in step S3 includes feature layer fusion and decision layer fusion, wherein the feature layer fusion includes fusing and analyzing the extracted Brillouin frequency shift parameter features of the OPGW optical cable and the state information features of the OPGW optical cable to obtain a new feature vector; the decision layer fusion includes pre-classifying the extracted Brillouin frequency shift parameter features of the OPGW optical cable and the state information features of the OPGW optical cable, establishing two binary SVM classifiers, combining the two set fault states to be identified to construct an identification framework Θ = {CL1, CL2}, the two fault states to be identified include a fault state and a normal state, and establishing a DS inference allocation function through the output results of the SVM classifier to calculate the probability values ​​(BPA) of the two types of fault states. And through the orthogonal rule, the feature fusion of probability value is performed to complete the decision layer fusion:

[0010]

[0011] In the formula, the feature vectors after multimodal fusion are labeled in the form of m(CL1) and m(CL2), where m(CL1) represents the confidence level of the fault state and m(CL2) represents the confidence level of the normal state. The trust function of each type of fault state is labeled in the form of Bel(CL), the recognition framework is labeled Θ, and the measurement and label of all B subsets in set A is Bel(A).

[0012] Furthermore, in step S3, the OPGW optical cable fault is classified in combination with the SVM classifier to complete fault identification, specifically including: setting a fixed threshold a, if Bel(A) is greater than the set fixed threshold a, it is considered that the OPGW optical cable has a fault, that is, the fault diagnosis is completed.

[0013] Furthermore, a multimodal data-based in-service OPGW optical cable anomaly detection system is characterized by comprising:

[0014] A data acquisition module is used to collect the Brillouin frequency shift parameters of the OPGW optical cable under different operating conditions in actual production, and simultaneously collect the status information of the corresponding OPGW optical cable, wherein the status information of the OPGW optical cable includes: the wind speed when the Brillouin frequency shift parameters of the OPGW optical cable are collected, and the distance and height difference between the towers of the corresponding OPGW optical cable;

[0015] A feature extraction module is used to extract features from the collected Brillouin frequency shift parameters of the OPGW optical cable and the corresponding state information of the OPGW optical cable, respectively, to obtain the Brillouin frequency shift parameter features of the OPGW optical cable and the state information features of the OPGW optical cable;

[0016] The multimodal fusion module is used to perform multimodal fusion on the extracted Brillouin frequency shift parameter features of the OPGW optical cable and the status information features of the OPGW optical cable, and classify the OPGW optical cable faults in combination with the SVM classifier, thereby completing fault identification.

[0017] Furthermore, the multimodal fusion module performs multimodal fusion on the extracted Brillouin frequency shift parameter features of the OPGW optical cable and the state information features of the OPGW optical cable, specifically including: feature layer fusion and decision layer fusion, wherein the feature layer fusion includes fusing and analyzing the extracted Brillouin frequency shift parameter features of the OPGW optical cable and the state information features of the OPGW optical cable to obtain a new feature vector; the decision layer fusion includes pre-classifying the extracted Brillouin frequency shift parameter features of the OPGW optical cable and the state information features of the OPGW optical cable, establishing two binary SVM classifiers, combining the two set fault states to be identified to construct an identification framework Θ={CL1, CL2}, the two fault states to be identified include a fault state and a normal state, and establishing a DS inference distribution function through the output results of the SVM classifier to calculate the probability values ​​(BPA) of the two types of fault states. And through the orthogonal rule, the feature fusion of probability value is performed to complete the decision layer fusion:

[0018]

[0019] In the formula, the feature vectors after multimodal fusion are labeled in the form of m(CL1) and m(CL2), where m(CL1) represents the confidence level of the fault state and m(CL2) represents the confidence level of the normal state. The trust function of each type of fault state is labeled in the form of Bel(CL), the recognition framework is labeled Θ, and the measurement and label of all B subsets in set A is Bel(A).

[0020] Furthermore, the multimodal fusion module combines with the SVM classifier to classify the OPGW optical cable fault, thereby completing fault identification, specifically including: setting a fixed threshold a, if Bel(A) is greater than the set fixed threshold a, it is considered that the OPGW optical cable has a fault, that is, the fault diagnosis is completed.

[0021] The present invention utilizes the Brillouin frequency shift parameters and status information parameters of the OPGW optical cable to achieve real-time monitoring and fault diagnosis. The innovation of the present invention lies in the selected parameter combination. Compared with other existing multi-mode data fusion solutions, the present invention adds the status information parameters of the OPGW optical cable, namely, the wind speed when collecting the Brillouin frequency shift parameters of the OPGW optical cable and the corresponding OPGW optical cable tower distance and height difference. This real-time and flexibility enables the system to respond to faults quickly and take appropriate measures to ensure the reliability and stability of the OPGW optical cable. At the same time, predictive maintenance of potential faults can be achieved. By identifying problems in advance and taking measures, the system can reduce maintenance time and costs and extend the life of the OPGW optical cable. By comprehensively utilizing this data, OPGW optical cable faults can be diagnosed more accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a flow chart of a method for detecting anomalies of an in-service OPGW optical cable based on multimodal data provided by an embodiment of the present invention;

[0023] Figure 2 This is a structural diagram of an in-service OPGW optical cable anomaly detection system based on multimodal data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0025] like Figure 1 As shown, an embodiment of the present invention provides a method for detecting anomalies of an in-service OPGW optical cable based on multimodal data, comprising the following steps:

[0026] S1. Collect the Brillouin frequency shift parameters of the OPGW optical cable under different operating conditions in actual production and the status information of the corresponding OPGW optical cable, wherein the status information of the OPGW optical cable includes: the wind speed when the Brillouin frequency shift parameters of the OPGW optical cable are collected and the distance and height difference between the towers of the corresponding OPGW optical cable.

[0027] S2. Feature extraction is performed on the two types of data collected above, that is, feature extraction is performed on the collected Brillouin frequency shift parameters of the OPGW optical cable and the collected status information of the corresponding OPGW optical cable, to obtain the Brillouin frequency shift parameter characteristics of the OPGW optical cable and the status information characteristics of the OPGW optical cable.

[0028] The step S2 comprises the following sub-steps:

[0029] S21. De-noise and normalize the collected Brillouin frequency shift parameters of the OPGW optical cable to obtain its characteristic parameter x1.

[0030] S22, performing denoising and normalization on the collected OPGW optical cable status information to obtain its characteristic parameter x2.

[0031] S3. Use multimodal fusion combined with SVM classifier to classify OPGW optical cable faults, thereby completing fault identification. First, a multi-layer classification model is established based on the SVM classifier. The acquired features are fused using multimodal fusion. Finally, the established classification model is used to complete the diagnosis of OPGW optical cable faults.

[0032] S3 can be implemented in the following ways:

[0033] S31, feature layer fusion is as follows:

[0034] The Brillouin frequency shift parameters of the OPGW optical cable obtained above and the collected state information of the corresponding OPGW optical cable are fused and analyzed to obtain a new feature vector. The process is shown in formula (1);

[0035] y=x 1 ⊙x 2 , (1)

[0036] Where the new eigenvector is labeled y and ⊙ is the Hadamard product.

[0037] S32, the decision-making layer fusion is as follows:

[0038] First, the Brillouin frequency shift parameter characteristics of the OPGW optical cable and the state information characteristics of the corresponding OPGW optical cable are pre-classified, and two binary SVM classifiers are established. The two classifiers will classify the input feature y respectively, and the output results will be used for decision-making layer fusion. The two fault states to be identified (fault state and normal state) are combined to construct an identification framework Θ = {CL1, CL2} (fault state and normal state). The DS inference distribution function is established based on the output results of the SVM classifier to calculate the probability value (BPA) of the two types of fault states, (BPA) = m(CL1) s1 、m(CL2) s1 、 They represent the probability value of the fault state considered by classifier S1, the probability value of the normal state considered by classifier S1, the probability value of the fault state considered by classifier S2, and the probability value of the normal state considered by classifier S2, respectively. The feature fusion of the probability value is performed through the orthogonal rule. The result is shown in formula (2):

[0039]

[0040] In the formula, the feature vectors after multimodal fusion are marked in the form of m(CL1) and m(CL2), where m(CL1) represents the confidence level of the fault state and m(CL2) represents the confidence level of the normal state; the trust function of each type of fault state is marked in the form of Bel(CL), the identification framework is marked Θ, and the metric sum of all B subsets in set A is marked as Bel(A). In this invention, Bel(A) represents the trust function value of the fault, which represents the sum of the basic probability distribution functions of all subsets considered to be faulty. Finally, a fixed threshold a is set. If Bel(A) is greater than the set fixed threshold a, the OPGW optical cable is considered to be faulty, and the fault diagnosis is completed.

[0041] According to another aspect of the present invention, Figure 2 As shown, a system for detecting anomalies of an in-service OPGW optical cable based on multimodal data is provided, including:

[0042] The data acquisition module is used to collect the Brillouin frequency shift parameters of the OPGW optical cable under different operating conditions in actual production, and at the same time collect the status information of the corresponding OPGW optical cable, where the status information of the OPGW optical cable includes: the wind speed when the Brillouin frequency shift parameters of the OPGW optical cable are collected and the distance and height difference between the towers of the corresponding OPGW optical cable.

[0043] The feature extraction module is used to extract features of the collected Brillouin frequency shift parameters of the OPGW optical cable and the corresponding state information of the OPGW optical cable, respectively, to obtain the Brillouin frequency shift parameter features of the OPGW optical cable and the state information features of the OPGW optical cable.

[0044] The multimodal fusion module is used to perform multimodal fusion on the extracted Brillouin frequency shift parameter features of the OPGW optical cable and the status information features of the OPGW optical cable, and classify the OPGW optical cable faults in combination with the SVM classifier, thereby completing fault identification.

[0045] The present invention's multimodal data-based anomaly detection method for in-service OPGW optical cables combines multiple data sources, including the Brillouin frequency shift parameters of the OPGW cables and OPGW cable status information parameters. While wind speed affects the wind pressure ratio of the cable, it more significantly causes the cable to vibrate and dance at high altitudes. This vibration and dancing not only affects the cable's operating status but also poses a serious threat to the normal operation of power transmission lines. When continuous OPGW cables are subjected to wind, the vertical and horizontal stress components of the cables differ due to the different spans and elevation angles between the cables. Consequently, the displacements of each suspension point along different lines also vary. Therefore, compared to other existing multimodal data fusion solutions, this method adds OPGW cable status information parameters: namely, the wind speed used to collect the OPGW cable's Brillouin frequency shift parameters and the corresponding OPGW cable's tower distance and elevation difference. By incorporating environmental factors such as wind speed into the analysis, the performance of the cable under actual operating conditions can be more accurately assessed. This multidimensional data fusion helps identify potential anomalies, thereby reducing false positives and missed detections. Wind speed and tower information can reflect the dynamic response of optical cables under different environmental conditions, enabling the system to promptly monitor changes in cable status and issue early warnings to prevent damage or accidents caused by factors such as wind. Furthermore, varying wind speeds, tower distances, and height differences can cause variations in stress components in the optical cable in different directions. By comprehensively considering these parameters, a more comprehensive analysis of the stress on the cable can be achieved, thereby assessing its safety and reliability.

[0046] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for detecting anomalies of an in-service OPGW optical cable based on multimodal data, characterized in that: include: S1. Collecting Brillouin frequency shift parameters of the OPGW optical cable under different operating conditions in actual production, and simultaneously collecting status information of the corresponding OPGW optical cable, wherein the status information of the OPGW optical cable includes: the wind speed when the Brillouin frequency shift parameters of the OPGW optical cable are collected, and the distance and height difference between the towers of the corresponding OPGW optical cable; S2. performing feature extraction on the collected Brillouin frequency shift parameters of the OPGW optical cable and the corresponding state information of the OPGW optical cable to obtain Brillouin frequency shift parameter features of the OPGW optical cable and state information features of the OPGW optical cable; S3, performing multimodal fusion on the Brillouin frequency shift parameter features of the OPGW optical cable and the status information features of the OPGW optical cable extracted in step S2, and classifying the OPGW optical cable faults in combination with the SVM classifier, thereby completing fault identification.

2. The method for detecting anomalies of an in-service OPGW optical cable based on multimodal data according to claim 1, wherein: The multimodal fusion in step S3 includes feature layer fusion and decision layer fusion, wherein the feature layer fusion includes fusing and analyzing the extracted Brillouin frequency shift parameter features of the OPGW optical cable and the state information features of the OPGW optical cable to obtain a new feature vector; the decision layer fusion includes pre-classifying the extracted Brillouin frequency shift parameter features of the OPGW optical cable and the state information features of the OPGW optical cable, establishing two binary SVM classifiers, combining the two set fault states to be identified to construct an identification framework Θ = {CL1, CL2}, the two fault states to be identified include a fault state and a normal state, and establishing a DS inference allocation function through the output results of the SVM classifier to calculate the probability values ​​(BPA) of the two types of fault states. And through the orthogonal rule, the feature fusion of probability value is performed to complete the decision layer fusion: In the formula, the feature vectors after multimodal fusion are labeled in the form of m(CL1) and m(CL2), where m(CL1) represents the confidence level of the fault state and m(CL2) represents the confidence level of the normal state. The trust function of each type of fault state is labeled in the form of Bel(CL), the recognition framework is labeled Θ, and the measurement and label of all B subsets in set A is Bel(A).

3. The method for detecting anomalies of an in-service OPGW optical cable based on multimodal data according to claim 2, wherein: In step S3, the OPGW optical cable fault is classified in combination with the SVM classifier to complete fault identification, which specifically includes: setting a fixed threshold a. If Bel(A) is greater than the set fixed threshold a, it is considered that the OPGW optical cable has a fault, that is, the fault diagnosis is completed.

4. An in-service OPGW optical cable anomaly detection system based on multimodal data, characterized in that: include: A data acquisition module is used to collect the Brillouin frequency shift parameters of the OPGW optical cable under different operating conditions in actual production, and simultaneously collect the status information of the corresponding OPGW optical cable, wherein the status information of the OPGW optical cable includes: the wind speed when the Brillouin frequency shift parameters of the OPGW optical cable are collected, and the distance and height difference between the towers of the corresponding OPGW optical cable; A feature extraction module is used to extract features from the collected Brillouin frequency shift parameters of the OPGW optical cable and the corresponding state information of the OPGW optical cable, respectively, to obtain the Brillouin frequency shift parameter features of the OPGW optical cable and the state information features of the OPGW optical cable; The multimodal fusion module is used to perform multimodal fusion on the extracted Brillouin frequency shift parameter features of the OPGW optical cable and the status information features of the OPGW optical cable, and classify the OPGW optical cable faults in combination with the SVM classifier, thereby completing fault identification.

5. The in-service OPGW optical cable anomaly detection system based on multimodal data according to claim 4, characterized in that: The multimodal fusion module performs multimodal fusion on the extracted Brillouin frequency shift parameter features and the state information features of the OPGW optical cable, specifically including: feature layer fusion and decision layer fusion, wherein the feature layer fusion includes fusing and analyzing the extracted Brillouin frequency shift parameter features and the state information features of the OPGW optical cable to obtain a new feature vector; the decision layer fusion includes pre-classifying the extracted Brillouin frequency shift parameter features and the state information features of the OPGW optical cable, establishing two binary SVM classifiers, combining two set fault states to be identified to construct an identification framework Θ={CL1, CL2}, wherein the two fault states to be identified include a fault state and a normal state, and establishing a DS inference allocation function through the output results of the SVM classifier to calculate the probability values ​​(BPA) of the two types of fault states. And through the orthogonal rule, the feature fusion of probability value is performed to complete the decision layer fusion: In the formula, the feature vectors after multimodal fusion are labeled in the form of m(CL1) and m(CL2), where m(CL1) represents the confidence level of the fault state and m(CL2) represents the confidence level of the normal state. The trust function of each type of fault state is labeled in the form of Bel(CL), the recognition framework is labeled Θ, and the measurement and label of all B subsets in set A is Bel(A).

6. The in-service OPGW optical cable anomaly detection system based on multimodal data according to claim 5, characterized in that: The multimodal fusion module combines with the SVM classifier to classify the OPGW optical cable fault, thereby completing fault identification, specifically including: setting a fixed threshold a, if Bel(A) is greater than the set fixed threshold a, it is considered that the OPGW optical cable has a fault, that is, the fault diagnosis is completed.

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