Submarine cable status monitoring data compression method, device and electronic equipment

Abstract

The present application discloses a method, device and electronic device for compressing data for submarine cable status monitoring. The method includes: obtaining a two-dimensional matrix image corresponding to a disturbance optical fiber signal in a period of time and space; filtering out the two-dimensional matrix image belonging to the noise segment, and retaining the non-noise frame image containing vibration information; denoising the non-noise frame image; converting the denoised non-noise frame image into a binary image; performing opening and closing operations on the binary image in turn, and obtaining the processed binary image; marking the connected domain of the processed binary image; extracting the original data segment corresponding to each marked connected domain. The method, device and electronic device for compressing data for submarine cable status monitoring of the embodiments of the present application can reduce the memory usage of the distributed acoustic sensing system, the load of the signal processing system, and improve the efficiency of subsequent data processing.

Description

Technical Field

[0001] The present application relates to the technical field of submarine monitoring, and in particular to a method, device and electronic equipment for submarine cable status monitoring data compression. Background Art

[0002] With the increasing demand for cross-sea and cross-river power and communications, as well as the advantages of submarine cables such as good quality, high reliability, long service life, and low maintenance workload, submarine cable transmission is becoming more and more widely used. Due to the extremely complex and special environment in which they are laid, their mechanical and electrical properties are often threatened. Among them, the main causes of submarine cable failures are: pitting, rupture or perforation of submarine cables due to seawater scouring or corrosion; damage to submarine cables caused by fishing, shipping and marine construction operations; threats to submarine cables posed by environmental factors such as Haiti earthquakes, tides, and ocean currents. Therefore, real-time monitoring of submarine cable wear, insulation degradation, leakage, grounding faults, power outages and other status information, timely detection of fault hazards and fault diagnosis are of great significance to ensure the health of submarine cables.

[0003] Distributed acoustic sensing system is a sensing system that uses optical fiber as a sensing sensitive element and a transmission signal medium. It not only has the advantages of ordinary optical fiber sensing technology, such as small size, anti-electromagnetic interference, high temperature corrosion resistance, etc., but also can detect long-distance, distributed, real-time, and highly sensitive dynamic strain along the optical fiber in real time. Therefore, it is often used in many safety, security, and integrity-related monitoring, such as the power industry, oil pipelines, military defense, seismic wave detection, and structural health monitoring of civil engineering.

[0004] The redundant composite single-mode optical fiber inside the submarine cable can be used as a sensor for the application of distributed optical fiber sensing technology, and the distributed acoustic sensing technology can be used to realize the online status monitoring of the submarine cable. However, due to the complex laying environment and long measurement distance of the submarine cable, the sensing optical fiber is easily affected by environmental noise. Generally speaking, most of the signals contained in the collected data are redundant, and the useful target signals often account for only a small proportion. These noises will cause certain interference to the signal, reduce the quality of the acoustic signal, and also cause certain errors in the subsequent calculations, wasting a lot of time and memory. Processing these invalid information will reduce the efficiency of the sensing system. Therefore, to a certain extent, it is necessary to screen the effective vibration signals, reduce the amount of data to reduce the storage space, thereby reducing the interference of noise and improving the transmission, storage and processing efficiency of the system. It can be seen that the extraction of useful information from the massive submarine cable status monitoring data and the practicality and necessity of reducing redundant data methods are necessary.

[0005] Most of the existing redundant processing methods for time series data like this are endpoint detection methods that aim to fuse multiple feature parameters in the frequency domain and time domain, such as the energy, zero-crossing rate, autocorrelation and other characteristics of the time series in the time domain, the cepstrum distance, band variance, spectral entropy characteristics in the frequency domain, etc. For single-channel data processing, the main focus is on extracting temporal correlation information, performing a preliminary denoising process on the data, and filtering out redundant undisturbed areas, thereby compressing the data capacity.

[0006] However, the above method has the following problems: the distributed acoustic sensing system has high memory usage, the signal processing system has a large load, and the data processing efficiency is low. Summary of the invention

[0007] The purpose of this application is to solve one of the above-mentioned technical problems at least to a certain extent.

[0008] To this end, the first object of the present invention is to propose a submarine cable status monitoring data compression method, which can reduce the memory occupancy of the distributed acoustic sensor system, the load of the signal processing system, and improve the efficiency of subsequent data processing.

[0009] The second objective of the present application is to provide a submarine cable status monitoring data compression device.

[0010] The third objective of the present application is to provide an electronic device.

[0011] A fourth objective of the present application is to provide a computer-readable storage medium.

[0012] To achieve the above-mentioned purpose, the first embodiment of the present application proposes a method for compressing submarine cable status monitoring data, the method comprising: obtaining a two-dimensional matrix image corresponding to a disturbed optical fiber signal in a period of time and space; screening out the two-dimensional matrix image belonging to the noise segment, and retaining the non-noise frame image containing vibration information; denoising the non-noise frame image; converting the denoised non-noise frame image into a binary image; performing opening and closing operations on the binary image in turn, and obtaining the processed binary image; marking the connected domain of the processed binary image; and extracting the original data segment corresponding to each marked connected domain.

[0013] Optionally, the two-dimensional matrix image belonging to the noise segment is screened out, and the non-noise frame image containing vibration information is retained, including:

[0014] The short-time average energy of the two-dimensional matrix image is calculated based on Formula 1: Where E(X) is the short-time average energy, M is the time, and N is the N sampling data of the distributed optical fiber sensors at different positions of the submarine cable at the same time;

[0015] The background noise energy of the two-dimensional matrix image is obtained based on Formula 2: E noise =u noise E noise +(1-u noise )E, where E noise is the background noise energy, E is the total energy of the previous noise frame, u noise is the regulating factor;

[0016] The two-dimensional matrix image belonging to the noise segment is screened out according to the short-time average energy and the background noise energy.

[0017] Optionally, performing denoising processing on the non-noise frame image includes:

[0018] The non-noise frame image is denoised based on Formula 3, Formula 3: Among them, W is the weighting coefficient, the adaptive filter working window is S×S, the weight matrix size is the same as the working window, X M×N is a non-noise frame image, and Y(m,n) is the output value of the adaptive filter.

[0019] Optionally, the method further includes:

[0020] The weighting coefficient W of the adaptive filtering output value is iteratively updated.

[0021] Optionally, iteratively updating the weighted coefficient W of the adaptive filtering output value includes:

[0022] The estimated error of the adaptive filter is calculated based on formula 4: E j (m,n)=D(m,n)-Y(m,n), where E j (m,n) is the estimation error, D(m,n) is the expected response, and Y(m,n) is the adaptive filtering output value;

[0023] According to the estimation error, the weighting coefficient W is iteratively updated based on Formula 5, Formula 5: in, l,k=0,1,…,S-1,u is the convergence factor,a is the preset parameter,0 <a<1。

[0024] Optionally, converting the denoised non-noise frame image into a binary image includes:

[0025] Calculating a threshold of the non-noise frame image after the denoising process;

[0026] The non-noise frame image after the denoising process is converted into a binary image according to the threshold.

[0027] Optionally, calculating a threshold of the non-noise frame image after the denoising process includes:

[0028] Obtaining a histogram and an overall grayscale average value U of the non-noise frame image after the denoising process, wherein the histogram represents the distribution of the number of pixels of each grayscale level in the image;

[0029] Determine the probability P of each gray level according to the histogram i =h i / h, where h i is the number of pixels occupied by the gray level i, h is the total number of pixels in the image, P i is the probability value;

[0030] Setting a threshold K, and dividing the denoised non-noise frame image into two regions A and B according to the threshold K;

[0031] Calculate the grayscale probabilities P(A) and P(B) corresponding to area A and area B respectively, where P(A) = h A / h, P(B)=h B / h,h A h is the number of pixels in area A whose gray level is less than or equal to the gray threshold K, B is the number of pixels in the B region whose gray level is greater than the gray threshold K, and h is the total number of pixels in the image;

[0032] The optimal threshold of the non-noise frame image after the denoising process is calculated according to Formula 6: in, is the optimal threshold, P(A) is the expectation of region A, P(B) is the expectation of region B, u A is the average gray value of area A, u B is the average gray value of area B.

[0033] Optionally, marking connected domains on the processed binary image includes:

[0034] Scanning the processed binary image line by line, and marking the connected areas in each line with label numbers (1, 2, 3 ... n);

[0035] When the current pixel is found to be white, if the grayscale values ​​of the neighboring pixels of the current pixel are all black, a new label number is marked for the current pixel; if there is a white pixel among the neighboring pixels of the current pixel, the white pixel is marked as the minimum label number, and the minimum label number is the minimum label number of the white pixels in the neighborhood;

[0036] Recording the equality relationship between each label in the neighborhood pixel points and establishing an equivalence table;

[0037] The processed binary image is scanned for the second time, and the equivalent labels are converted into the minimum labels in the equivalence table, so that the pixel points with equivalent labels are mapped into a connected domain starting from 1.

[0038] Optionally, the method further includes:

[0039] After extracting the original data segments corresponding to each marked connected domain, the original data segments are converted into a log format and stored.

[0040] The submarine cable status monitoring data compression method of the embodiment of the present application can reduce the memory usage of the distributed acoustic sensor system, the load of the signal processing system, and improve the efficiency of subsequent data processing.

[0041] To achieve the above-mentioned purpose, the second embodiment of the present application proposes a submarine cable status monitoring data compression device, the device comprising: an acquisition module, used to acquire a two-dimensional matrix image corresponding to a disturbance optical fiber signal of a period of time and space; a screening module, used to screen out the two-dimensional matrix image belonging to the noise segment, and retain the non-noise frame image containing vibration information; a denoising module, used to denoise the non-noise frame image; a conversion module, used to convert the non-noise frame image after denoising into a binary image; an operation module, used to perform opening operation and closing operation on the binary image in turn, and obtain the processed binary image; a marking module, used to mark the connected domain of the processed binary image; an extraction module, used to extract the original data segment corresponding to each marked connected domain.

[0042] Optionally, the screening module is used to:

[0043] The short-time average energy of the two-dimensional matrix image is calculated based on Formula 1: Where E(X) is the short-time average energy, M is the time, and N is the N sampling data of the distributed optical fiber sensors at different positions of the submarine cable at the same time;

[0044] The background noise energy of the two-dimensional matrix image is obtained based on Formula 2: E noise =u noise E noise +(1-u noise )E, where E noise is the background noise energy, E is the total energy of the previous noise frame, u noise is the regulating factor;

[0045] The two-dimensional matrix image belonging to the noise segment is screened out according to the short-time average energy and the background noise energy.

[0046] Optionally, the denoising module is used to:

[0047] The non-noise frame image is denoised based on Formula 3, Formula 3: Among them, W is the weighting coefficient, the adaptive filter working window is S×S, the weight matrix size is the same as the working window, X M×N is a non-noise frame image, and Y(m,n) is the output value of the adaptive filter.

[0048] Optionally, the device further comprises:

[0049] The iterative updating module is used to iteratively update the weighted coefficient W of the adaptive filtering output value.

[0050] Optionally, the iterative update module is used to:

[0051] The estimated error of the adaptive filter is calculated based on formula 4: E j (m,n)=D(m,n)-Y(m,n), where E j (m,n) is the estimation error, D(m,n) is the expected response, and Y(m,n) is the adaptive filtering output value;

[0052] According to the estimation error, the weighting coefficient W is iteratively updated based on Formula 5, Formula 5: in, l,k=0,1,…,S-1,u is the convergence factor,a is the preset parameter,0 <a<1。

[0053] Optionally, the conversion module is used to:

[0054] Calculating a threshold of the non-noise frame image after the denoising process;

[0055] The non-noise frame image after the denoising process is converted into a binary image according to the threshold.

[0056] Optionally, the conversion module is specifically used to:

[0057] Obtaining a histogram and an overall grayscale average value U of the non-noise frame image after the denoising process, wherein the histogram represents the distribution of the number of pixels of each grayscale level in the image;

[0058] Determine the probability P of each gray level according to the histogram i =h i / h, where h i is the number of pixels occupied by the gray level i, h is the total number of pixels in the image, P i is the probability value

[0059] Setting a threshold K, and dividing the denoised non-noise frame image into two regions A and B according to the threshold K;

[0060] Calculate the grayscale probabilities P(A) and P(B) corresponding to area A and area B respectively, where P(A) = h A / h, P(B)=h B / h,h A h is the number of pixels in area A whose gray level is less than or equal to the gray threshold K, B is the number of pixels in the B region whose gray level is greater than the gray threshold K, and h is the total number of pixels in the image;

[0061] The optimal threshold of the non-noise frame image after the denoising process is calculated according to Formula 6: in, is the optimal threshold, P(A) is the expectation of region A, P(B) is the expectation of region B, u A is the average gray value of area A, u B is the average gray value of area B.

[0062] Optionally, the marking module is used to:

[0063] Scanning the processed binary image line by line, and marking the connected areas in each line with label numbers (1, 2, 3 ... n);

[0064] When the current pixel is found to be white, if the grayscale values ​​of the neighboring pixels of the current pixel are all black, a new label number is marked for the current pixel; if there is a white pixel among the neighboring pixels of the current pixel, the white pixel is marked as the minimum label number, and the minimum label number is the minimum label number of the white pixels in the neighborhood;

[0065] Recording the equality relationship between each label in the neighborhood pixel points and establishing an equivalence table;

[0066] The processed binary image is scanned for the second time, and the equivalent labels are converted into the minimum labels in the equivalence table, so that the pixel points with equivalent labels are mapped into a connected domain starting from 1.

[0067] Optionally, the device further comprises:

[0068] The storage module is used to convert and store the original data segments into log format after extracting the original data segments corresponding to each marked connected domain.

[0069] The submarine cable status monitoring data compression device of the embodiment of the present application can reduce the memory usage of the distributed acoustic sensor system, the load of the signal processing system, and improve the efficiency of subsequent data processing.

[0070] To achieve the above-mentioned purpose, the third aspect embodiment of the present application proposes an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the submarine cable status monitoring data compression method described in the first aspect embodiment of the present application is implemented.

[0071] To achieve the above-mentioned purpose, the fourth aspect of the present application provides a computer-readable storage medium, and when the computer program is executed by a processor, the submarine cable status monitoring data compression method described in the first aspect of the present application is implemented.

[0072] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 is a flow chart of a method for compressing submarine cable status monitoring data according to an embodiment of the present application;

[0074] Figure 2 is a schematic diagram of a two-dimensional matrix image of an embodiment of the present application;

[0075] Figure 3 It is a flow chart for filtering out noise fragments in a two-dimensional matrix image and generating a non-noise frame image;

[0076] Figure 4 is a flow chart for iteratively updating the weighted coefficient W of the adaptive filtering output value;

[0077] Figure 5 is a schematic diagram of the filtered image;

[0078] Figure 6 is a flowchart for calculating the threshold of the non-noise frame image after denoising;

[0079] Figure 7 is a schematic diagram of a binary image;

[0080] Figure 8 is a schematic diagram of the processed binary image;

[0081] Fig. 9 It is a flow chart for marking connected domains of the processed binary image;

[0082] Fig.10 It is a schematic diagram of the connected domain labels established by the first scan;

[0083] Fig.11 It is a schematic diagram of assigning each connected domain to the minimum label number in the corresponding equivalence table according to the equivalence table;

[0084] Fig.12 It is a schematic diagram of mapping the label numbers of connected domains to continuous areas starting from 1;

[0085] Fig.13 is a schematic diagram of an image after data compression;

[0086] Fig.14 is a flow chart of a method for compressing submarine cable status monitoring data according to another embodiment of the present application;

[0087] Fig.15 It is a structural schematic diagram of a submarine cable status monitoring data compression device according to an embodiment of the present application;

[0088] Fig.16 is a structural schematic diagram of a submarine cable status monitoring data compression device according to another embodiment of the present application;

[0089] Fig.17 It is a structural schematic diagram of a submarine cable status monitoring data compression device according to another embodiment of the present application. DETAILED DESCRIPTION

[0090] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0091] The present invention is further described in detail below in conjunction with specific embodiments. These embodiments should not be construed as limiting the scope of protection claimed by the present invention.

[0092] Figure 1 It is a flow chart of a submarine cable status monitoring data compression method according to an embodiment of the present application. It should be noted that the submarine cable status monitoring data compression method according to the embodiment of the present application can be applied to the submarine cable status monitoring data compression device according to the embodiment of the present application, and the device can be configured on an electronic device. In the embodiment of the present invention, the electronic device can be a PC or a mobile terminal (such as a mobile phone, a tablet computer, a PAD, a personal digital assistant, and other hardware devices with various operating systems).

[0093] like Figure 1 As shown, the submarine cable status monitoring data compression method includes:

[0094] S1, obtain a two-dimensional matrix image corresponding to a disturbance optical fiber signal in a period of time and space.

[0095] Specifically, a disturbance optical fiber signal of a period of time and space can be composed as follows: Figure 2 As shown is a two-dimensional matrix image, the horizontal axis of the two-dimensional matrix image represents the sampling data of the distributed optical fiber sensors at different positions of the submarine cable at the same time, and the vertical axis represents the time axis.

[0096] S2, screens out the two-dimensional matrix images belonging to the noise segments and retains the non-noise frame images containing vibration information.

[0097] In one embodiment of the present application, Figure 3 As shown, filtering out the two-dimensional matrix image belonging to the noise segment may specifically include:

[0098] S21, calculating the short-time average energy of the two-dimensional matrix image based on formula 1.

[0099] Formula 1: Wherein, E(X) is the short-time average energy, M is the time, and N is the N sampling data of the distributed optical fiber sensors at different positions of the submarine cable at the same time.

[0100] S22, obtaining background noise energy of the two-dimensional matrix image based on Formula 2.

[0101] Formula 2: E noise =u noise E noise +(1-u noise )E, where E noise is the background noise energy, E is the total energy of the previous noise frame, u noise is the adjustment factor.

[0102] Specifically, an initial threshold can be set to preliminarily filter out noise fragments. noise (background noise energy) can adapt to the change of noise and adopt a smoothing process. Through real-time detection, E noise The energy of the previous noise frame that has been detected is used for updating, that is, the update is performed using Formula 2.

[0103] S23, filtering out the two-dimensional matrix image belonging to the noise segment according to the short-time average energy and the background noise energy.

[0104] Calculate the short-time average energy E(X) and background noise energy E noise , with the background noise energy E noise The noisy frame images and the non-noise frame images are screened out for the current threshold, thereby retaining the non-noise frame images.

[0105] S3, performing denoising processing on the non-noise frame image.

[0106] Specifically, the non-noise frame image may be denoised based on Formula 3.

[0107] Formula 3: Among them, W is the weighting coefficient, the adaptive filter working window is S×S, the weight matrix size is the same as the working window, X M×N is a non-noise frame image, and Y(m,n) is the output value of the adaptive filter.

[0108] In one embodiment of the present application, the weighting coefficient W of the adaptive filtering output value may also be iteratively updated. Specifically, Figure 4 As shown, including:

[0109] S31, calculating the estimation error of the adaptive filter based on formula 4.

[0110] Formula 4: E j (m,n)=D(m,n)-Y(m,n), where E j (m,n) is the estimation error, D(m,n) is the expected response, and Y(m,n) is the adaptive filtering output value.

[0111] S32, iteratively update the weighting coefficient W based on formula 5 according to the estimated error.

[0112] Formula: Five in, represents the energy of the signal segment within the adaptive filter window, l,k=0,1,…,S-1. u is the convergence factor, and its value affects the algorithm convergence speed and estimation error. a is a preset parameter, and its role is to avoid the norm being too small and the step size being too large, 0 <a<1。

[0113] For example, the weight coefficient of the adaptive filter can be initialized to W0, and the non-noise frame image is X M×N The weighted coefficient after j iterations is W j Based on formula 3, the corresponding Y(m,n) can be calculated, and then the non-noise frame image can be denoised to obtain Figure 5 The filtered image is shown.

[0114] The weighted coefficient W corresponding to the j+1th time can be updated iteratively later j+1 , to achieve iterative updates.

[0115] S4, converting the non-noise frame image after denoising into a binary image.

[0116] Specifically, the maximum inter-class variance method may be used to calculate a threshold of the non-noise frame image after denoising, and then the non-noise frame image after denoising is converted into a binary image according to the threshold.

[0117] Among them, the threshold of the non-noise frame image after denoising is calculated, such as Figure 6 As shown, including:

[0118] S41, obtaining a histogram and an overall grayscale average value U of the non-noise frame image after denoising.

[0119] Among them, the histogram represents the distribution of the number of pixels at each gray level in the image.

[0120] S42, determining the probability P of each gray level according to the histogram i =h i / h.

[0121] Among them, h i is the number of pixels occupied by the gray level i, h is the total number of pixels in the image, P i is the probability value.

[0122] S43, setting a threshold K, and dividing the non-noise frame image after denoising into two areas A and B according to the threshold K.

[0123] S44, calculating the grayscale probabilities P(A) and P(B) corresponding to the A region and the B region respectively.

[0124] Where P(A) = h A / h, P(B)=h B / h,h A h is the number of pixels in area A whose gray level is less than or equal to the gray threshold K, B is the number of pixels in area B whose gray level is greater than the gray threshold K, and h is the total number of pixels in the image.

[0125] like Figure 7 As shown, area A is white pixels and area B is black pixels.

[0126] S45, calculating the optimal threshold of the non-noise frame image after denoising according to Formula 6.

[0127] Formula 6: in, is the optimal threshold, P(A) is the expectation of region A, P(B) is the expectation of region B, u A is the average gray value of area A, u B is the average gray value of area B.

[0128] S5, performing opening operation processing and closing operation processing on the binary image in sequence, and obtaining the processed binary image.

[0129] Among them, the opening operation is to erode first and then expand, and the closing operation is to expand first and then erode. The function of expansion is to merge the background in contact with the target area into the target object, so that the target boundary expands outward; erosion is to eliminate some boundary points of the target figure, so that its boundary shrinks inward.

[0130] Specifically, the binary image is first opened and then closed to eliminate Figure 7 The isolated points shown in and the filled holes generate Figure 8 The processed binary image is shown.

[0131] S6, marking the connected components of the processed binary image.

[0132] The processed binary image is marked with connected domains, that is, the image area composed of pixels with the same pixel value and adjacent positions in the binary image is found and marked. By setting the area threshold, the isolated small area connected domains are removed, thereby removing redundant information more effectively.

[0133] Specifically, Fig. 9 As shown, the connected domain of the processed binary image is marked, including:

[0134] S61, scanning the processed binary image line by line, and marking the connected areas in each line with label numbers (1, 2, 3 . . . n).

[0135] S62, when the current pixel is accessed and is white, if the grayscale values ​​of the neighboring pixels of the current pixel are all black, a new label number is marked for the current pixel; if there is a white pixel among the neighboring pixels of the current pixel, the white pixel is marked as the minimum label number.

[0136] The minimum label number is the minimum label number of the white pixels in the neighborhood.

[0137] Specifically, when the current white pixel is accessed, if the values ​​of the neighborhood pixels of the scanning point (the top, bottom, left, right, upper left corner, lower left corner, upper right corner, and lower right corner of the pixel) are all 0, a new label number is assigned to the white pixel; if there are white pixels in the neighborhood, they are considered to be connected, and the smallest label number among the white pixels in the neighborhood is assigned to the scanning point as its label number.

[0138] S63, recording the equality relationship between each label in the neighborhood pixel points of the current white pixel point, and establishing an equivalence table.

[0139] The labels of each white pixel in the neighborhood of the current white pixel are stored in an array, so as to establish an equivalence relationship between each label, that is, a label equivalence table.

[0140] S64, scanning the processed binary image for the second time, converting the equivalent labels into the minimum labels in the equivalence table, so that the pixels with equivalent labels are mapped into a connected domain starting from 1.

[0141] All equivalent labels in the equivalence table are converted into the smallest label number in its equivalence table, and finally the label number of each pixel in the connected component is the same, and the label is mapped to a connected domain starting from 1.

[0142] For example, the connected domain labels created by the first scan can be Fig.10 As shown; then according to the equivalence table [1,2], [3,5], each connected domain is assigned the minimum label number in the corresponding equivalence table, such as Fig.11 As shown; finally, the label number of the connected domain is mapped to a continuous area starting from 1, as Fig.12 shown.

[0143] S7, extracting the original data segment corresponding to each marked connected domain.

[0144] Extract the original data fragment corresponding to each marked connected domain, and its corresponding image is as follows Fig.13 The image shown is the compressed data.

[0145] Considering that in practical applications, the submarine cable status data collected by the distributed fiber optic sensing system is multi-channel massive data, and there is no fault in the cable positions corresponding to most of the sensing channels, this method converts the extracted signal into two-dimensional data based on time and space, processes it in the form of images, improves the timeliness of data processing, compresses the two-dimensional image, screens out noise signals and irrelevant signals, retains valid status signals as much as possible, and thus reduces data redundancy and storage space.

[0146] The submarine cable status monitoring data compression method of the embodiment of the present application can reduce the memory usage of the distributed acoustic sensor system, the load of the signal processing system, and improve the efficiency of subsequent data processing.

[0147] In another embodiment of the present application, Fig.14 As shown, the method also includes:

[0148] S8, after extracting the original data segments corresponding to each marked connected domain, converting the original data segments into a log format and storing them.

[0149] Based on the distributed fiber optic acoustic sensing system, this application proposes an effective signal fragment detection method that combines the normalized adaptive minimum mean square error filtering method and the binary morphology method for the generation mechanism and characteristics of various disturbance signals of submarine cables in complex environments. Without losing useful information, data compression is achieved, which can provide accurate signal recognition for the safety monitoring of submarine cables and conduct safety alerts. When detecting effective signals, this application removes a large amount of irrelevant noise interference data on the basis of retaining effective data as much as possible, ensuring the real-time writing, storage and reading of massive time series data.

[0150] To achieve the above objectives, the present application also proposes a submarine cable status monitoring data compression device.

[0151] Fig.15 It is a structural schematic diagram of a submarine cable status monitoring data compression device according to an embodiment of the present application.

[0152] like Fig.15 As shown, the device may include: an acquisition module 710 , a screening module 720 , a denoising module 730 , a conversion module 740 , a calculation module 750 , a marking module 760 and an extraction module 770 .

[0153] The acquisition module 710 is used to acquire a two-dimensional matrix image corresponding to the disturbance optical fiber signal in a period of time and space.

[0154] The screening module 720 is used to screen out the two-dimensional matrix image belonging to the noise segment and retain the non-noise frame image containing vibration information.

[0155] The screening module 720 is used for:

[0156] The short-time average energy of the two-dimensional matrix image is calculated based on formula 1: Where E(X) is the short-time average energy, M is the time, and N is the N sampling data of the distributed optical fiber sensors at different positions of the submarine cable at the same time;

[0157] The background noise energy of the two-dimensional matrix image is obtained based on Formula 2: E noise =u noise E noise +(1-u noise )E, where E noise is the background noise energy, E is the total energy of the previous noise frame, u noise is the regulating factor;

[0158] The two-dimensional matrix image belonging to the noise fragments is screened out according to the short-time average energy and the background noise energy.

[0159] The denoising module 730 is used to perform denoising processing on the non-noise frame image.

[0160] The denoising module 730 is used to:

[0161] The non-noise frame image is denoised based on formula 3: Among them, W is the weighting coefficient, the adaptive filter working window is S×S, the weight matrix size is the same as the working window, X M×N is a non-noise frame image, and Y(m,n) is the output value of the adaptive filter.

[0162] The conversion module 740 is used to convert the non-noise frame image after the denoising process into a binary image.

[0163] The conversion module 740 is specifically used to: calculate the threshold of the non-noise frame image after denoising; and convert the non-noise frame image after denoising into a binary image according to the threshold.

[0164] The conversion module 740 is specifically used for:

[0165] Obtaining a histogram and an overall grayscale average value U of the non-noise frame image after denoising, wherein the histogram represents the distribution of the number of pixels of each grayscale level in the image;

[0166] Determine the probability P of each gray level according to the histogram i =h i / h. Among them, h i is the number of pixels occupied by the gray level i, h is the total number of pixels in the image, P i is the probability value.

[0167] A threshold K is set, and the non-noise frame image after denoising is divided into two areas A and B according to the threshold K;

[0168] Calculate the expected P(A) and P(A)=h for area A and area B respectively A / h, where P(A) = h A / h, P(B)=h B / h,h A h is the number of pixels in area A whose gray level is less than or equal to the gray threshold K, B is the number of pixels in area B whose gray level is greater than the gray threshold K, and h is the total number of pixels in the image.

[0169] The optimal threshold of the non-noise frame image after denoising is calculated according to Formula 6: in, is the optimal threshold, P(A) is the expectation of region A, P(B) is the expectation of region B, u Ais the average gray value of area A, u B is the average gray value of area B.

[0170] The operation module 750 is used to perform opening operation processing and closing operation processing on the binary image in sequence, and obtain the processed binary image.

[0171] The marking module 760 is used to mark the connected components of the processed binary image.

[0172] The marking module 760 is used to:

[0173] Scanning the processed binary image line by line, and marking the connected areas in each line with label numbers (1, 2, 3 ... n);

[0174] When the current pixel is found to be white, if the grayscale values ​​of the neighboring pixels of the current pixel are all black, a new label number is marked for the current pixel; if there is a white pixel among the neighboring pixels of the current pixel, the white pixel is marked as the minimum label number, and the minimum label number is the minimum label number of the white pixels in the neighborhood;

[0175] Record the equality relationship between each label in the neighborhood pixels and establish an equivalence table;

[0176] The processed binary image is scanned a second time, and the equivalent labels are converted into the minimum labels in the equivalence table, so that the pixels with equivalent labels are mapped to connected domains starting from 1.

[0177] The extraction module 770 is used to extract the original data segment corresponding to each marked connected domain.

[0178] The submarine cable status monitoring data compression device of the embodiment of the present application can reduce the memory usage of the distributed acoustic sensor system, the load of the signal processing system, and improve the efficiency of subsequent data processing.

[0179] In another embodiment of the present application, Fig.16 As shown, the apparatus further includes an iterative update module 780 .

[0180] The iterative updating module 780 is used to iteratively update the weighting coefficient W of the adaptive filtering output value.

[0181] The iterative update module 780 is used to:

[0182] The estimated error of the adaptive filter is calculated based on formula 4: E j (m,n)=D(m,n)-Y(m,n), where E j (m,n) is the estimation error, D(m,n) is the expected response, and Y(m,n) is the adaptive filtering output value;

[0183] According to the estimation error, the weighting coefficient W is iteratively updated based on Formula 5, Formula 5: in, represents the energy of the signal segment within the adaptive filter window, l,k=0,1,…,S-1, u is the convergence factor, a is the preset parameter, 0 <a<1。

[0184] In another embodiment of the present application, Fig.17 As shown, the device also includes a storage module 790.

[0185] The storage module 790 is used to convert the original data segments corresponding to each marked connected domain into a log format and store the log data segments after extracting the original data segments.

[0186] To achieve the above objectives, the present application also proposes an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for compressing submarine cable status monitoring data as described in the first aspect of the embodiment is implemented.

[0187] To achieve the above-mentioned purpose, the present application also proposes a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the method for compressing submarine cable status monitoring data as described in the first aspect of the embodiment is implemented.

[0188] It should be noted that the computer-readable medium of the present invention can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal can take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0189] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0190] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0191] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: obtains at least two Internet Protocol addresses; sends a node evaluation request including the at least two Internet Protocol addresses to a node evaluation device, wherein the node evaluation device selects an Internet Protocol address from the at least two Internet Protocol addresses and returns it; receives the Internet Protocol address returned by the node evaluation device; wherein the obtained Internet Protocol address indicates an edge node in a content distribution network.

[0192] Alternatively, the computer-readable medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device: receives a node evaluation request including at least two Internet Protocol addresses; selects an Internet Protocol address from the at least two Internet Protocol addresses; and returns the selected Internet Protocol address; wherein the received Internet Protocol address indicates an edge node in a content distribution network.

[0193] Computer program code for performing the operations of the present invention may be written in one or more programming languages ​​or a combination thereof, including, but not limited to, object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0194] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0195] The units involved in the embodiments of the present invention may be implemented by software or hardware. The name of a unit does not limit the unit itself in some cases. For example, the first acquisition unit may also be described as a "unit for acquiring at least two Internet Protocol addresses".

[0196] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0197] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0198] The above description is only a preferred embodiment of the present invention and an explanation of the technical principles used. Those skilled in the art should understand that the disclosure scope involved in the present invention is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present invention (but not limited to) to form a technical solution.

[0199] In addition, although each operation is described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the present invention. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0200] Although the subject matter has been described in language specific to structural features and / or methodological logical actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

Claims

1. A method for compressing submarine cable status monitoring data, characterized in that: include: Obtaining a two-dimensional matrix image corresponding to a disturbance optical fiber signal in a period of time and space, wherein the horizontal axis of the two-dimensional matrix image represents the sampling data of the distributed optical fiber sensors at different positions of the submarine cable at the same time, and the vertical axis represents the time axis; Screen out the two-dimensional matrix image belonging to the noise segment and retain the non-noise frame image containing vibration information; Performing denoising processing on the non-noise frame image; Convert the non-noise frame image after denoising into a binary image; Performing an opening operation and a closing operation on the binary image in sequence, and obtaining a processed binary image; Marking connected domains on the processed binary image; Extract the original data fragment corresponding to each marked connected domain.

2. The method according to claim 1, characterized in that Screen out the two-dimensional matrix images belonging to the noise fragments and retain the non-noise frame images containing vibration information, including: The short-time average energy of the two-dimensional matrix image is calculated based on Formula 1: Where E(X) is the short-time average energy, M is the time, and N is the N sampling data of the distributed optical fiber sensors at different positions of the submarine cable at the same time; The background noise energy of the two-dimensional matrix image is obtained based on Formula 2: in, is the estimated value of the background noise energy of the tth frame, is the historical background noise energy of the t-1th frame, E (t-1) is the total energy of the previous noise frame, u noise is the regulating factor; The two-dimensional matrix image belonging to the noise segment is screened out according to the short-time average energy and the background noise energy.

3. The method according to claim 1, characterized in that The non-noise frame image is subjected to denoising processing, comprising: The non-noise frame image is denoised based on Formula 3, Formula 3: Among them, W is the weighting coefficient, the adaptive filter working window is S×S, the weight matrix size is the same as the working window, X M×N is a non-noise frame image, and Y(m,n) is the output value of the adaptive filter.

4. The method according to claim 3, characterized in that Also includes: The weighting coefficient W of the adaptive filtering output value is iteratively updated.

5. The method according to claim 4, characterized in that Iteratively updating the weighted coefficient W of the adaptive filtering output value comprises: The estimated error of the adaptive filter is calculated based on formula 4: E j (m,n)=D(m,n)-Y(m,n), where E j (m,n) is the estimation error, D(m,n) is the expected response, and Y(m,n) is the adaptive filtering output value; According to the estimation error, the weighting coefficient W is iteratively updated based on Formula 5, Formula 5: in, u is the convergence factor, a is the preset parameter, 0 <a<1。 6. The method according to claim 1, characterized in that Convert the denoised non-noise frame image into a binary image, including: Calculating a threshold of the non-noise frame image after the denoising process; The non-noise frame image after the denoising process is converted into a binary image according to the threshold.

7. The method according to claim 6, characterized in that Calculating the threshold of the non-noise frame image after the denoising process includes: Obtaining a histogram and an overall grayscale average value U of the non-noise frame image after the denoising process, wherein the histogram represents the distribution of the number of pixels of each grayscale level in the image; Determine the probability P of each gray level according to the histogram i =h i / h, where h i is the number of pixels occupied by the gray level i, h is the total number of pixels in the image, P i is the probability value; Setting a grayscale threshold K, and dividing the denoised non-noise frame image into two regions A and B according to the threshold K; Calculate the probability values ​​P(A) and P(B) of the number of pixels corresponding to area A and area B to the total number of pixels respectively, where P(A) = h A / h, P(B)=h B / h,h A h is the number of pixels in area A whose gray level is less than or equal to the gray threshold K, B is the number of pixels in the B region whose gray level is greater than the gray threshold K, and h is the total number of pixels in the image; The optimal threshold of the non-noise frame image after the denoising process is calculated according to Formula 6: in, is the optimal threshold, P(A) is the probability value of the number of pixels in area A accounting for the total number of pixels, P(B) is the probability value of the number of pixels in area B accounting for the total number of pixels, u A is the average gray value of area A, u B is the average gray value of area B.

8. The method according to claim 1, characterized in that Marking connected domains on the processed binary image includes: Scanning the processed binary image line by line, and marking the connected areas in each line with label numbers (1, 2, 3 ... n); When the current pixel is found to be white, if the grayscale values ​​of the neighboring pixels of the current pixel are all black, a new label number is marked for the current pixel; if there is a white pixel among the neighboring pixels of the current pixel, the white pixel is marked as the minimum label number, and the minimum label number is the minimum label number of the white pixels in the neighborhood; Recording the equality relationship between each label in the neighborhood pixel points and establishing an equivalence table; The processed binary image is scanned for the second time, and the equivalent labels are converted into the minimum labels in the equivalence table, so that the pixel points with equivalent labels are mapped into a connected domain starting from 1.

9. The method according to claim 1, characterized in that: Also includes: After extracting the original data segments corresponding to each marked connected domain, the original data segments are converted into a log format and stored.

10. A submarine cable status monitoring data compression device, characterized in that: include: An acquisition module, used to acquire a two-dimensional matrix image corresponding to a disturbance optical fiber signal in a period of time and space, wherein the horizontal axis of the two-dimensional matrix image represents the sampling data of the distributed optical fiber sensors at different positions of the submarine cable at the same time, and the vertical axis represents the time axis; A screening module is used to screen out the two-dimensional matrix image belonging to the noise segment and retain the non-noise frame image containing vibration information; A denoising module, used for performing denoising processing on the non-noise frame image; A conversion module, used for converting the non-noise frame image after denoising into a binary image; An operation module, used for sequentially performing an opening operation and a closing operation on the binary image, and obtaining a processed binary image; A marking module, used for marking connected domains of the processed binary image; The extraction module is used to extract the original data fragments corresponding to each marked connected domain.

11. The device according to claim 10, characterized in that The screening module is used for: The short-time average energy of the two-dimensional matrix image is calculated based on Formula 1: Where E(X) is the short-time average energy, M is the time, and N is the N sampling data of the distributed optical fiber sensors at different positions of the submarine cable at the same time; The background noise energy of the two-dimensional matrix image is obtained based on Formula 2: in, is the estimated value of the background noise energy of the tth frame, is the historical background noise energy of the t-1th frame, E (t-1) is the total energy of the previous noise frame, u noise is the regulating factor; The two-dimensional matrix image belonging to the noise segment is screened out according to the short-time average energy and the background noise energy.

12. The device according to claim 10, characterized in that The denoising module is used to: The non-noise frame image is denoised based on Formula 3, Formula 3: Among them, W is the weighting coefficient, the adaptive filter working window is S×S, the weight matrix size is the same as the working window, X M×N is a non-noise frame image, and Y(m,n) is the output value of the adaptive filter.

13. The device according to claim 12, characterized in that Also includes: The iterative updating module is used to iteratively update the weighting coefficient W of the adaptive filtering output value.

14. The device according to claim 13, characterized in that The iterative update module is used for: The estimated error of the adaptive filter is calculated based on formula 4: E j (m,n)=D(m,n)-Y(m,n), where E j (m,n) is the estimation error, D(m,n) is the expected response, and Y(m,n) is the adaptive filter output value; According to the estimation error, the weighting coefficient W is iteratively updated based on Formula 5, Formula 5: in, u is the convergence factor, a is the preset parameter, 0 <a<1。 15. The device according to claim 10, characterized in that The conversion module is used for: Calculating a threshold of the non-noise frame image after the denoising process; The non-noise frame image after the denoising process is converted into a binary image according to the threshold.

16. The device according to claim 15, characterized in that The conversion module is specifically used for: Obtaining a histogram and an overall grayscale average value U of the non-noise frame image after the denoising process, wherein the histogram represents the distribution of the number of pixels of each grayscale level in the image; Determine the probability P of each gray level according to the histogram i =h i / h, where h i is the number of pixels occupied by the gray level i, h is the total number of pixels in the image, P i is the probability value; Setting a threshold K, and dividing the denoised non-noise frame image into two regions A and B according to the threshold K; Calculate the probability values ​​P(A) and P(B) of the number of pixels corresponding to area A and area B to the total number of pixels respectively, where P(A) = h A / h, P(B)=h B / h,h A h is the number of pixels in area A whose gray level is less than or equal to the gray threshold K, B is the number of pixels in the B region whose gray level is greater than the gray threshold K, and h is the total number of pixels in the image; The optimal threshold of the non-noise frame image after the denoising process is calculated according to Formula 6: in, is the optimal threshold, P(A) is the probability value of the number of pixels in area A accounting for the total number of pixels, P(B) is the probability value of the number of pixels in area B accounting for the total number of pixels, u A is the average gray value of area A, u B is the average gray value of area B.

17. The device according to claim 10, characterized in that The marking module is used for: Scanning the processed binary image line by line, and marking the connected areas in each line with label numbers (1, 2, 3 ... n); When the current pixel is found to be white, if the grayscale values ​​of the neighboring pixels of the current pixel are all black, a new label number is marked for the current pixel; if there is a white pixel among the neighboring pixels of the current pixel, the white pixel is marked as the minimum label number, and the minimum label number is the minimum label number of the white pixels in the neighborhood; Recording the equality relationship between each label in the neighborhood pixel points and establishing an equivalence table; The processed binary image is scanned for the second time, and the equivalent labels are converted into the minimum labels in the equivalence table, so that the pixel points with equivalent labels are mapped into a connected domain starting from 1.

18. The device according to claim 10, characterized in that Also includes: The storage module is used to convert and store the original data segments into log format after extracting the original data segments corresponding to each marked connected domain.

19. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for compressing submarine cable status monitoring data according to any one of claims 1 to 9 is implemented.

20. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for compressing submarine cable status monitoring data according to any one of claims 1 to 9 is implemented.

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