CN117079005A - Optical cable fault monitoring method, system, device and readable storage medium - Google Patents
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Abstract
The invention provides an optical cable fault monitoring method, an optical cable fault monitoring system, an optical cable fault monitoring device and a readable storage medium, wherein the optical cable fault monitoring method comprises the following steps: acquiring optical cable parameters; performing state discretization on the optical cable parameters and dividing the optical cable parameter sequence, obtaining a corresponding state transition matrix according to the optical cable parameter value of each sub-sequence, converting each sub-sequence into a gray value according to the state transition matrix to obtain a gray image, and performing binarization processing and morphological processing on the gray image to obtain an image corresponding to each sub-sequence; and fusing the images corresponding to all the subsequences, extracting image characteristics from the fused images, and judging whether the optical cable fails or not based on the image characteristics. According to the invention, the acquired optical cable parameters are converted into the images for processing, so that the optical cable is automatically monitored, and the fault monitoring accuracy and efficiency are improved.
Description
Technical Field
The invention relates to the technical field of optical cable operation and maintenance, in particular to an optical cable fault monitoring method, an optical cable fault monitoring system, an optical cable fault monitoring device and a readable storage medium.
Background
The optical cable faults include damage to the line cable, the down-lead cable and the optical fiber fittings of the power communication optical cable, so that the optical fiber core loses a given function, or the performance is seriously reduced, and the communication channel carried by the optical cable is interrupted.
At present, the method for monitoring and detecting the optical cable in real time uses hardware devices such as an optical fiber grating sensor or a vibration sensor to collect optical cable parameters, and processes and analyzes the optical cable parameters to judge whether the optical cable has faults. However, these conventional methods have the disadvantages of low accuracy and poor intelligentization, because they collect only a few simple parameters, and lack in-depth understanding and analysis of the complex state inside the optical cable. In addition, the data acquisition and processing modes of the traditional method have certain errors, which can influence the accuracy and stability of monitoring.
The patent document with the patent application number of CN201811542386.3A provides a system for optical cable monitoring and big data analysis based on an intelligent optical fiber distribution frame, and the system comprises a cloud server, a distributed primary area server, a distributed secondary area server, a device for optical cable monitoring based on the intelligent optical fiber distribution frame and a control terminal for real-time monitoring and big data analysis of an optical cable. Although the accuracy is improved, the system is very consumed in this way, and the efficiency is reduced because the data of all the optical cables needs to be continuously integrated and analyzed for big data.
Disclosure of Invention
Aiming at the problems, the invention aims to provide a method, a system, a device and a readable storage medium for monitoring optical cable faults, which are used for realizing automatic monitoring of the optical cable by converting acquired optical cable parameters into images for processing, thereby being beneficial to improving the accuracy and the efficiency of fault monitoring.
The invention aims to achieve the aim, and the aim is achieved by the following technical scheme: a method of cable fault monitoring comprising:
acquiring optical cable parameters;
performing state discretization on the optical cable parameters and dividing the optical cable parameter sequence, obtaining a corresponding state transition matrix according to the optical cable parameter value of each sub-sequence, converting each sub-sequence into a gray value according to the state transition matrix to obtain a gray image, and performing binarization processing and morphological processing on the gray image to obtain an image corresponding to each sub-sequence;
and fusing the images corresponding to all the subsequences, extracting image characteristics from the fused images, and judging whether the optical cable fails or not based on the image characteristics.
Further, the obtaining the corresponding state transition matrix according to the optical cable parameter value of each sub-sequence includes:
taking the optical cable parameter as a discrete random variable, and establishing the optical cable parameter as a first-order Markov chain;
discretizing the value range of the discrete random variable into n states;
dividing an optical cable parameter sequence into a plurality of subsequences with the length of m, wherein each subsequence corresponds to an m+1 order Markov chain;
obtaining a corresponding state transition matrix according to m+1 optical cable parameter values of each sub-sequence: p= [ P ] ij ]n×n, where p ij Representing slave state S i Transition to state S j Is a probability of (2).
Further, the converting each sub-sequence into a gray value according to the state transition matrix includes converting each sub-sequence into a gray value G according to the state transition matrix by the following formula:
where i and j are the rows and columns, respectively, in the state transition matrix.
Further, the fusing the images corresponding to all the sub-sequences includes:
performing wavelet transformation on the image corresponding to each sub-sequence, and converting the image into a wavelet coefficient sequence;
fusing wavelet coefficients of different scales; and performing inverse wavelet transformation on the fused wavelet coefficients.
Further, the image corresponding to the subsequence is subjected to denoising processing by using a wavelet transform-based denoising method, and the denoising processing comprises the following steps:
performing wavelet decomposition on the image to obtain wavelet coefficients:
describing the noise characteristic of the wavelet coefficient through a Markov chain model, and constructing a denoising filter according to the noise characteristic and the Markov chain model;
and reconstructing and inverse wavelet transformation are carried out on the denoised wavelet coefficient, so as to obtain a denoised image.
Further, the extracting image features from the fused image includes:
and extracting image characteristics from the fused image by adopting an improved local binary pattern algorithm.
Further, the improved local binary pattern algorithm is: wherein xc and yc are coordinates of a central pixel, ic is a gray value of the central pixel, and i is a gray value of an adjacent pixel; s is a sign function; LBP is a local binary pattern algorithm; n is the number of images; c is an adjustment coefficient.
Further, the determining whether the optical cable fails based on the image features includes:
and performing fault judgment by adopting an improved support vector machine model based on the image characteristics.
Further, the improved support vector machine model is:
wherein n is the number of training samples, J i For the input feature of the ith sample, P i For the output result of the ith sample, K (x i X) is a kernel function, alpha i B is a constant term, which is a Lagrangian multiplier; w is the filter window size; n is the number of images.
Correspondingly, the invention also discloses an optical cable fault monitoring system, which comprises:
the acquisition module is configured to acquire optical cable parameters;
the image conversion module is configured to perform state discretization on the optical cable parameters and divide the optical cable parameter sequence, obtain a corresponding state transition matrix according to the optical cable parameter value of each sub-sequence, convert each sub-sequence into a gray value according to the state transition matrix to obtain a gray image, and perform binarization processing and morphological processing on the gray image to obtain an image corresponding to each sub-sequence;
and the image fusion and judgment module is configured to fuse the images corresponding to all the subsequences, extract image characteristics from the fused images, and judge whether the optical cable has faults or not based on the image characteristics.
Correspondingly, the invention discloses an optical cable fault monitoring device, which comprises:
the memory is used for storing an optical cable fault monitoring program;
a processor for implementing the steps of the cable fault monitoring method as described in any one of the preceding claims when executing the cable fault monitoring program.
Accordingly, the present invention discloses a readable storage medium having stored thereon an optical cable fault monitoring program which when executed by a processor implements the steps of the optical cable fault monitoring method as described in any one of the preceding claims.
Compared with the prior art, the invention has the beneficial effects that:
the invention discloses an optical cable fault monitoring method, an optical cable fault monitoring system, an optical cable fault monitoring device and a readable storage medium.
The invention converts the cable parameters into images, thereby improving the accuracy of fault monitoring. Because the conventional data analysis algorithm directly analyzes the data value, some potential characteristics are easy to ignore, the invention maps the data into the image, and the image expression of the data is subjected to data fusion of the image, so that the scientificity of the analysis result can be improved; in addition, compared with the real-time analysis of all relevant data, the method and the device for monitoring the faults of the optical cable have the advantages that the analyzed values are limited to the obtained parameter values of the current cable, and the parameter values of other cables are not required to be considered, so that the monitoring efficiency of the faults of the optical cable is improved.
It can be seen that the present invention has outstanding substantial features and significant advances over the prior art, as well as the benefits of its implementation.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method of an embodiment of the present invention;
fig. 2 is a system configuration diagram of an embodiment of the present invention.
In the figure, 1, an acquisition module; 2. an image conversion module; 3. and the image fusion and judgment module.
Detailed Description
In order to better understand the aspects of the present invention, the present invention will be described in further detail with reference to the accompanying drawings and detailed description. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiment one:
as shown in fig. 1, the embodiment provides a method for monitoring faults of an optical cable, which includes the following steps:
s1: and obtaining optical cable parameters.
In particular embodiments, the cable parameters include at least: strain value of the optical cable, temperature of the optical cable, reflection loss of the optical cable, transmission loss of the optical cable and humidity of the optical cable; wherein, parameters such as temperature, strain, displacement and the like of the optical cable are acquired by adopting a temperature sensor, a strain sensor, a displacement sensor and the like.
S2: performing state discretization on the optical cable parameters and dividing the optical cable parameter sequence, obtaining a corresponding state transition matrix according to the optical cable parameter value of each sub-sequence, converting each sub-sequence into a gray value according to the state transition matrix to obtain a gray image, and performing binarization processing and morphological processing on the gray image to obtain an image corresponding to each sub-sequence.
The purpose of this step is to convert each acquired cable parameter into a corresponding image, which may be either a statistically drawn image from a numerical value or a direct conversion of the data into a corresponding image value for further analysis. The two modes have no deviation in the subsequent image processing process, and only different templates are needed to be executed when the final fault judgment is carried out. Such as: the data is converted into a statistically significant image, such as a histogram, which can be determined by conventional contrast analysis. In this embodiment, the feature difference of the two images can be directly compared to obtain a judgment result, but this method will cause waste of system resources, because the complexity of the algorithm for matching the image features of the histogram is significantly higher than that of the direct comparison. Therefore, if the simple statistical images are directly compared, a certain degree of resource waste is caused, and if the images are directly mapped and converted, the higher efficiency is still maintained under the more scientific condition.
In a specific embodiment, the implementation process of this step is specifically as follows:
(1) Performing filtering treatment on the optical cable parameters; let the optical cable parameter sequence be x 1 ,x 2 ,...,x n And if the size of the filtering window is w, the filtering result of the kth optical cable parameter sampling value is as follows:
wherein y is k Is the filtering result.
(2) Normalizing the filtered optical cable parameters:
wherein, let x be the original parameter, x min And x max Respectively the minimum value and the maximum value of the parameter, and y is the normalized parameter.
(3) Taking the normalized optical cable parameter as a discrete random variable, and establishing the discrete random variable as a first-order Markov chain;
discretizing the value range of the discrete random variable into n states S 1 ，S 2 ，...，S n ；
Dividing the normalized optical cable parameter sequence into a plurality of subsequences with the length of m by utilizing a sliding window, wherein each subsequence corresponds to an m+1 order Markov chain;
according to m+1 optical cable parameter values of each subsequence, a state transition matrix corresponding to the subsequence is obtained:
P＝[p ii ]n×n
wherein p is ij Representing slave state S i Transition to state S j Probability of (2);
according to the state transition matrix, the subsequence is converted into a gray value G:
where i and j represent the rows and columns, respectively, in the state transition matrix.
(4) And carrying out binarization processing on the gray level image.
(5) Morphological processing is carried out on the binarized gray level image; the morphological processing is used for changing the shape, size and structure of the binary image, and when the binarized image is subjected to morphological processing, proper morphological operation and parameters are generally required to be selected so as to achieve the purposes of removing noise, filling holes, enhancing a target area and the like, thereby improving the judging accuracy of the state of the optical cable.
It should be noted that some basic morphological operations, such as expansion (scaling), erosion (erosion), open operation (open) and close operation (close), are usually used; specifically, the dilation is to enlarge the boundary of the target area by adding pixels around the target area; erosion is the narrowing of the boundary of a target area by removing pixels from around the target area; the open operation is to firstly corrode and then expand, and is usually used for removing noise points in the image; the closed operation is to expand and then erode, and is typically used to fill voids in the image.
S3: and fusing the images corresponding to all the subsequences, extracting image characteristics from the fused images, and judging whether the optical cable fails or not based on the image characteristics.
In a specific embodiment, a specific procedure of fusing images corresponding to all sub-sequences is as follows:
(1) The denoising processing for each image by using a denoising method based on wavelet transformation specifically comprises the following steps:
performing wavelet decomposition on the image to obtain each decomposition coefficient:
wherein d j，k Wavelet coefficient of j-th layer, a J-1，k Wavelet coefficients for layer J-1; x is a decomposition coefficient;
for each wavelet coefficient, describing the noise characteristics of the wavelet coefficient through a Markov chain model; assuming a certain wavelet coefficient d j，k Is subject to gaussian distribution, the noise characteristics are: d' j，k ＝d j，k +n j，k The method comprises the steps of carrying out a first treatment on the surface of the Wherein n is j，k Noise that is the wavelet coefficient; if n j，k Obeying gaussian distribution and satisfying a markov chain model, namely: p (n) j，k |n j-1，k )＝P(n j，k |n j-1，k-1 ，n j-1，k ，n j-1，k+1 )；
For each wavelet coefficient, constructing an optimal denoising filter according to the noise characteristic and the Markov chain model; the denoising filter is calculated by maximum posterior estimation, and specifically comprises the following steps:
wherein,representing the denoised wavelet coefficients;
and re-synthesizing the denoised wavelet coefficient into a signal, and reversely performing wavelet transformation to obtain a denoised image.
(2) And fusing the denoised images by adopting a wavelet transformation fusion algorithm. The specific sub-process is as follows:
(2-1) after performing wavelet transform decomposition on the image corresponding to each sub-sequence, converting it into a wavelet coefficient sequence:
where j is the scale, k is the translation, ψ j，k (N) is a wavelet basis function and N is the sequence length.
(2-2) determining a wavelet coefficient fusion rule:
wherein m represents the number of the sensors,represents the wavelet coefficient, mu, acquired by the ith sensor i Representing the weight of the i-th sensor.
(2-3) coefficient reconstruction:
wherein X is n Representing the reconstructed signal sequence.
(2-4) inverse wavelet transform:
(2-5) fusing the wavelet coefficients of different scales to obtain a new wavelet coefficient matrix Wfusion; in the process, a weighted average mode can be adopted to weighted average the wavelet coefficients under different scales to obtain a new wavelet coefficient matrix;
wherein W is k (i, j) represents the wavelet coefficients at the kth scale, W fusion (i, j) represents the wavelet coefficient after fusion, K represents the scale number of wavelet transformation decomposition, w represents a weight coefficient matrix for adjusting the importance of the wavelet coefficient of each scale, and the weight coefficient matrix w can be designed according to specific situations.
(2-6) performing inverse wavelet transformation on the fused wavelet coefficients to obtain a denoised signal matrix S: s=idwt (W fusion )。
In a specific embodiment, step S3 adopts an improved local binary pattern algorithm to extract image features; the improved local binary pattern algorithm is as follows:
wherein x is c And y c Coordinates of the central pixel, i c The gray value of the center pixel, i is the gray value of the adjacent pixel; s is a sign function; LBP () represents a local binary pattern algorithm; n is the number of images and is equal to the type of optical cable parameters acquired by the sensor; c is an adjustment coefficient, and the value range is 3-5.
In a specific embodiment, after converting the image features into feature vectors, training by adopting an improved support vector machine model; and (3) classifying and judging according to the trained model, judging whether the optical cable has faults, and alarming in modes of sound, light and the like when judging that the optical cable has faults.
The improved support vector machine specifically comprises the following components:
in the above formula, n is the number of training samples, J i For the input feature of the ith sample, P i For the output result of the ith sample, K (x i X) is a kernel function, alpha i B is a constant term, which is a Lagrangian multiplier; f (x) is a functional representation of the improved support vector machine; w is the filter window size; n is the number of images.
The embodiment provides an optical cable fault monitoring method, which is used for converting each acquired optical cable parameter into a corresponding image, extracting image characteristics after data fusion of all the images, judging whether an optical cable has faults or not based on the fused image characteristics, realizing automatic monitoring of the optical cable, and being beneficial to improving the accuracy and efficiency of fault monitoring.
Embodiment two:
based on the first embodiment, as shown in fig. 2, the invention further discloses an optical cable fault monitoring system, which comprises: the system comprises an acquisition module 1, an image conversion module 2 and an image fusion and judgment module 3.
And the acquisition module 1 is configured to acquire the optical cable parameters.
The image conversion module 2 is configured to perform state discretization on the optical cable parameters and divide the optical cable parameter sequence, obtain a corresponding state transition matrix according to the optical cable parameter value of each sub-sequence, convert each sub-sequence into a gray value according to the state transition matrix, obtain a gray image, and perform binarization processing and morphological processing on the gray image to obtain an image corresponding to each sub-sequence.
And the image fusion and judgment module 3 is configured to fuse the images corresponding to all the subsequences, extract image features from the fused images, and judge whether the optical cable has faults or not based on the image features.
The embodiment provides an optical cable fault monitoring system, which converts the acquired optical cable parameters into images for processing, so that the optical cable is automatically monitored, and the accuracy and the efficiency of fault monitoring are improved.
Embodiment III:
the embodiment discloses an optical cable fault monitoring device, which comprises a processor and a memory; the processor performs the following steps when executing the optical cable fault monitoring program stored in the memory:
1. and obtaining optical cable parameters.
2. Performing state discretization on the optical cable parameters and dividing the optical cable parameter sequence, obtaining a corresponding state transition matrix according to the optical cable parameter value of each sub-sequence, converting each sub-sequence into a gray value according to the state transition matrix to obtain a gray image, and performing binarization processing and morphological processing on the gray image to obtain an image corresponding to each sub-sequence.
3. And fusing the images corresponding to all the subsequences, extracting image characteristics from the fused images, and judging whether the optical cable fails or not based on the image characteristics.
Further, the optical cable fault monitoring device in this embodiment may further include:
the input interface is used for acquiring an externally imported optical cable fault monitoring program, storing the acquired optical cable fault monitoring program into the memory, and acquiring various instructions and parameters transmitted by external terminal equipment and transmitting the various instructions and parameters into the processor so that the processor can develop corresponding processing by utilizing the various instructions and parameters. In this embodiment, the input interface may specifically include, but is not limited to, a USB interface, a serial interface, a voice input interface, a fingerprint input interface, a hard disk reading interface, and the like.
And the output interface is used for outputting various data generated by the processor to the terminal equipment connected with the output interface so that other terminal equipment connected with the output interface can acquire various data generated by the processor. In this embodiment, the output interface may specifically include, but is not limited to, a USB interface, a serial interface, and the like.
And the communication unit is used for establishing remote communication connection between the optical cable fault monitoring device and the external server so that the optical cable fault monitoring device can mount the image file to the external server. In this embodiment, the communication unit may specifically include, but is not limited to, a remote communication unit based on a wireless communication technology or a wired communication technology.
And the keyboard is used for acquiring various parameter data or instructions input by a user by knocking the key cap in real time.
And the display is used for displaying the related information of the operation optical cable fault monitoring process in real time.
A mouse may be used to assist a user in inputting data and to simplify user operations.
Embodiment four:
the present embodiment also discloses a readable storage medium, where the readable storage medium includes Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art. The readable storage medium stores an optical cable fault monitoring program which when executed by a processor realizes the following steps:
1. and obtaining optical cable parameters.
2. Performing state discretization on the optical cable parameters and dividing the optical cable parameter sequence, obtaining a corresponding state transition matrix according to the optical cable parameter value of each sub-sequence, converting each sub-sequence into a gray value according to the state transition matrix to obtain a gray image, and performing binarization processing and morphological processing on the gray image to obtain an image corresponding to each sub-sequence.
3. And fusing the images corresponding to all the subsequences, extracting image characteristics from the fused images, and judging whether the optical cable fails or not based on the image characteristics.
In summary, the invention converts the acquired optical cable parameters into the images for processing, thereby realizing the automatic monitoring of the optical cable and being beneficial to improving the accuracy and efficiency of fault monitoring.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, so that the same or similar parts between the embodiments are referred to each other. For the method disclosed in the embodiment, since it corresponds to the system disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative elements and steps are described above generally in terms of functionality in order to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the several embodiments provided by the present invention, it should be understood that the disclosed systems, and methods may be implemented in other ways. For example, the system embodiments described above are merely illustrative, e.g., the division of the elements is merely a logical functional division, and there may be additional divisions when actually implemented, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interface, system or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each module may exist alone physically, or two or more modules may be integrated in one unit.
Similarly, each processing unit in the embodiments of the present invention may be integrated in one functional module, or each processing unit may exist physically, or two or more processing units may be integrated in one functional module.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software modules may be disposed in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The method, the system, the device and the readable storage medium for monitoring the optical cable faults are described in detail. The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to facilitate an understanding of the method of the present invention and its core ideas. It should be noted that it will be apparent to those skilled in the art that various modifications and adaptations of the invention can be made without departing from the principles of the invention and these modifications and adaptations are intended to be within the scope of the invention as defined in the following claims.
Claims (10)
1. A method of monitoring for a fiber optic cable fault, comprising:
acquiring optical cable parameters;
performing state discretization on the optical cable parameters and dividing the optical cable parameter sequence, obtaining a corresponding state transition matrix according to the optical cable parameter value of each sub-sequence, converting each sub-sequence into a gray value according to the state transition matrix to obtain a gray image, and performing binarization processing and morphological processing on the gray image to obtain an image corresponding to each sub-sequence;
and fusing the images corresponding to all the subsequences, extracting image characteristics from the fused images, and judging whether the optical cable fails or not based on the image characteristics.
2. The method of claim 1, wherein obtaining a corresponding state transition matrix from the values of the cable parameters for each sub-sequence comprises:
taking the optical cable parameter as a discrete random variable, and establishing the optical cable parameter as a first-order Markov chain;
discretizing the value range of the discrete random variable into n states;
dividing an optical cable parameter sequence into a plurality of subsequences with the length of m, wherein each subsequence corresponds to an m+1 order Markov chain;
m+1 according to each subsequenceThe corresponding state transition matrix is obtained by the optical cable parameter values: p= [ P ] ij ]n×n, where p ij Representing slave state S i Transition to state S j Is a probability of (2).
3. The method of claim 2, wherein converting each sub-sequence into gray values according to the state transition matrix comprises,
each sub-sequence is converted into a gray value G according to a state transition matrix by the following formula:
where i and j are the rows and columns, respectively, in the state transition matrix.
4. The method for monitoring the fault of the optical cable according to claim 1, wherein the fusing the images corresponding to all the sub-sequences comprises:
performing wavelet transformation on the image corresponding to each sub-sequence, and converting the image into a wavelet coefficient sequence;
fusing wavelet coefficients of different scales; and performing inverse wavelet transformation on the fused wavelet coefficients.
5. The method for monitoring the fault of the optical cable according to claim 1, wherein the image corresponding to the subsequence is subjected to denoising processing by using a denoising method based on wavelet transform, and the denoising processing comprises the following steps:
performing wavelet decomposition on the image to obtain wavelet coefficients:
describing the noise characteristic of the wavelet coefficient through a Markov chain model, and constructing a denoising filter according to the noise characteristic and the Markov chain model;
and reconstructing and inverse wavelet transformation are carried out on the denoised wavelet coefficient, so as to obtain a denoised image.
6. The method for monitoring the fault of the optical cable according to claim 1, wherein the extracting the image features from the fused image includes:
extracting image characteristics from the fused image by adopting an improved local binary pattern algorithm;
the improved local binary pattern algorithm is as follows:wherein x is c And y c Coordinates of the central pixel, i c The gray value of the center pixel, i is the gray value of the adjacent pixel; s is a sign function; LBP is a local binary pattern algorithm; n is the number of images; c is an adjustment coefficient.
7. The method for monitoring the fault of the optical cable according to claim 1, wherein the determining whether the optical cable has a fault based on the image features comprises:
performing fault judgment by adopting an improved support vector machine model based on image characteristics;
the improved support vector machine model is as follows:
wherein n is the number of training samples, J i For the input feature of the ith sample, P i For the output result of the ith sample, K (x i X) is a kernel function, alpha i B is a constant term, which is a Lagrangian multiplier; w is the filter window size; n is the number of images.
8. An optical cable fault monitoring system, comprising:
the acquisition module is configured to acquire optical cable parameters;
the image conversion module is configured to perform state discretization on the optical cable parameters and divide the optical cable parameter sequence, obtain a corresponding state transition matrix according to the optical cable parameter value of each sub-sequence, convert each sub-sequence into a gray value according to the state transition matrix to obtain a gray image, and perform binarization processing and morphological processing on the gray image to obtain an image corresponding to each sub-sequence;
and the image fusion and judgment module is configured to fuse the images corresponding to all the subsequences, extract image characteristics from the fused images, and judge whether the optical cable has faults or not based on the image characteristics.
9. An optical cable fault monitoring device, comprising:
the memory is used for storing an optical cable fault monitoring program;
a processor for implementing the steps of the cable fault monitoring method as claimed in any one of claims 1 to 7 when executing the cable fault monitoring program.
10. A readable storage medium, characterized by: the readable storage medium has stored thereon a cable fault monitoring program which when executed by a processor implements the steps of the cable fault monitoring method as claimed in any one of claims 1 to 7.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117289014A (en) * | 2023-11-24 | 2023-12-26 | 昆明英派尔科技有限公司 | Detection design method and device for ODF electrification caused by optical cable aging |
| CN117289014B (en) * | 2023-11-24 | 2024-01-23 | 昆明英派尔科技有限公司 | Detection design method and device for ODF electrification caused by optical cable aging |
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