CN115144688A

CN115144688A - Periodic monitoring method and device for submarine cable disturbance sensing data

Info

Publication number: CN115144688A
Application number: CN202210672896.2A
Authority: CN
Inventors: 王磊; 陈科新; 姜明武
Original assignee: Suzhou Guangge Technology Co Ltd
Current assignee: Suzhou Guangge Technology Co Ltd
Priority date: 2022-06-15
Filing date: 2022-06-15
Publication date: 2022-10-04

Abstract

The disclosure relates to a method and a device for periodically monitoring disturbance sensing data of a submarine cable. Target data is obtained by acquiring disturbance sensing data of the submarine cable acquired by the plurality of sensors and performing data preprocessing. Judging the target data by utilizing N algorithms in a preset algorithm pool to obtain a periodic data set with the length of N; selecting M periodic data subsets from a periodic data set with the length of N, wherein the M periodic data subsets correspond to M base classifiers respectively, obtaining a target classifier according to the base classifier with the result meeting a preset threshold and the weight of the base classifier with the result meeting the preset threshold, determining the periodic result of the submarine cable disturbance sensing data according to the output result of the target classifier, and automatically monitoring the state of submarine equipment and the periodic monitoring of the submarine cable disturbance sensing data.

Description

Periodic monitoring method and device for submarine cable disturbance sensing data

Technical Field

The disclosure relates to the technical field of submarine cable monitoring, in particular to a method and a device for periodically monitoring submarine cable disturbance sensing data.

Background

Ocean resources are resource treasuries which are easy to explore by human beings, and the construction of various marine and seabed engineering projects is in order progress at present. However, compared to land, the difficulty and danger level of human working in underwater environment are higher, and the difficulty of monitoring and maintaining the submarine facilities is also higher. The marine environment is extremely complex and subsea installations are more prone to accidental risks, but the costs of maintenance and repair of the installation are high. Therefore, how to automatically monitor the state of the submarine equipment and periodically monitor the disturbance sensing data of the submarine cable, and early warning the possible damage to the submarine equipment in advance, so as to realize the timely monitoring of the state of the submarine equipment and reduce the maintenance and repair cost of the submarine equipment is an urgent matter to be solved.

Disclosure of Invention

In view of the above, there is a need to provide a method and apparatus for periodic monitoring of disturbance sensor data of a submarine cable, which can automatically monitor the status of submarine equipment.

In a first aspect, the present disclosure provides a method for periodic monitoring of submarine cable disturbance sensory data.

The method comprises the following steps:

acquiring disturbance sensing data of the submarine cable acquired by a plurality of sensors;

carrying out data preprocessing on the disturbance sensing data according to a preset mode to obtain target data;

judging the target data by utilizing N algorithms in a preset algorithm pool to obtain a periodic data set with the length of N;

selecting M periodic data subsets from a periodic data set with the length of N, wherein the M periodic data subsets respectively correspond to M base classifiers, and the weights of the M base classifiers are equal;

if the output result of the base classifier does not accord with the preset threshold, the weight of the base classifier of which the output result does not accord with the preset threshold is adjusted upwards according to a preset rule until the output result of the base classifier accords with the preset threshold;

and obtaining a target classifier according to the base classifier with the result meeting the preset threshold and the weight of the base classifier with the result meeting the preset threshold, and determining the periodic result of the submarine cable disturbance sensing data according to the output result of the target classifier.

In one embodiment, the determining the target data by using N algorithms in a preset algorithm pool to obtain a periodic data set with a length of N includes:

expanding the target data into a linear combination of trigonometric functions;

determining a coefficient maximum term in a linear combination of the trigonometric functions;

and obtaining a periodic data set with the length of N according to the maximum coefficient term.

In one embodiment, the method further comprises:

determining a coefficient maximum term set according to the coefficient maximum terms in the linear combination of the trigonometric functions;

determining autocorrelation coefficients of subsets in the coefficient maximum term set;

comparing the autocorrelation coefficients of the subset to a preset autocorrelation coefficient threshold;

determining a subset of the subset whose autocorrelation coefficients are greater than or equal to the preset autocorrelation coefficient threshold;

and determining the period of the maximum coefficient term set according to the subset of the autocorrelation coefficients which are greater than or equal to the preset autocorrelation coefficient threshold.

In one embodiment, the determining the period of the maximum coefficient term set according to the subset of the autocorrelation coefficients greater than or equal to the preset autocorrelation coefficient threshold value includes:

determining any two subsets of which the autocorrelation coefficients are greater than or equal to the preset autocorrelation coefficient threshold;

and performing difference operation on the starting times of any two subsets of which the autocorrelation coefficients are greater than or equal to the preset autocorrelation coefficient threshold value and taking an absolute value to obtain the period of the maximum coefficient term set.

In one embodiment, the obtaining target data after performing data preprocessing on the disturbance sensing data according to a preset mode includes:

resampling the disturbance sensing data to obtain resampled data, wherein the resampling at least comprises one of resampling according to the dimension of a day, resampling according to the dimension of a week and resampling according to the dimension of a month;

carrying out data outlier filtering on the resampled data to obtain outlier filtered data;

and carrying out non-dimensionalization processing on the abnormal value filtering data to obtain target data, wherein the non-dimensionalization processing at least comprises one of linear non-dimensionalization processing and nonlinear non-dimensionalization processing.

In one embodiment, the performing data outlier filtering on the resampled data to obtain the outlier filtered data includes:

performing rule calculation on the resampled data to obtain rule calculation data, wherein the rule calculation at least comprises one of averaging the resampled data and calculating variance of the resampled data;

taking an absolute value of a difference value between the resampled data and the rule calculation data to obtain deviation data;

comparing target deviation data in the deviation data with a preset deviation threshold;

and if the target deviation data is larger than the preset deviation threshold, setting the resample data corresponding to the target deviation data as an abnormal value and removing the abnormal value from the resample data to obtain abnormal value filter data.

In a second aspect, the present disclosure also provides a device for periodically monitoring disturbance sensing data of a submarine cable. The device comprises:

the data acquisition module is used for acquiring disturbance sensing data of the submarine cable acquired by the sensors;

the target data module is used for carrying out data preprocessing on the disturbance sensing data according to a preset mode to obtain target data;

the periodic data set module is used for judging the target data by utilizing N algorithms in a preset algorithm pool to obtain a periodic data set with the length of N;

the subset combining module is used for selecting M periodic data subsets from a periodic data set with the length of N, wherein the M periodic data subsets correspond to M base classifiers respectively, and the weights of the M base classifiers are equal;

the weight adjusting module is used for adjusting the weight of the base classifier of which the output result does not conform to the preset threshold value up according to a preset rule if the output result of the base classifier does not conform to the preset threshold value until the output result of the base classifier conforms to the preset threshold value;

and the result determining module is used for obtaining a target classifier according to the base classifier with the result meeting the preset threshold and the weight of the base classifier with the result meeting the preset threshold, and determining the periodic result of the submarine cable disturbance sensing data according to the output result of the target classifier.

In a third aspect, the present disclosure also provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the method according to any of the embodiments of the present disclosure when executing the computer program.

In a fourth aspect, the present disclosure also provides a computer-readable storage medium. The computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the method of any one of the embodiments of the present disclosure.

In a fifth aspect, the present disclosure also provides a computer program product. The computer program product comprising a computer program that when executed by a processor implements the method of any of the embodiments of the present disclosure.

The method and the device for periodically monitoring the submarine cable disturbance sensing data acquire the disturbance sensing data of the submarine cable acquired by a plurality of sensors; carrying out data preprocessing on the disturbance sensing data according to a preset mode to obtain target data; judging the target data by utilizing N algorithms in a preset algorithm pool to obtain a periodic data set with the length of N; selecting M periodic data subsets from a periodic data set with the length of N, wherein the M periodic data subsets respectively correspond to M base classifiers, and the weights of the M base classifiers are equal; if the output result of the base classifier does not accord with the preset threshold, the weight of the base classifier of which the output result does not accord with the preset threshold is adjusted upwards according to a preset rule until the output result of the base classifier accords with the preset threshold; the method comprises the steps of obtaining a target classifier according to a base classifier with a result meeting a preset threshold value and the weight of the base classifier with the result meeting the preset threshold value, determining a periodic result of submarine cable disturbance sensing data according to an output result of the target classifier, automatically monitoring the state of submarine equipment and the periodic monitoring of the submarine cable disturbance sensing data, early warning damage possibly met by the submarine equipment in advance, monitoring the state of the submarine equipment in time, and reducing the maintenance cost of the submarine equipment.

Drawings

In order to more clearly illustrate the embodiments of the present specification or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the specification, and other drawings can be obtained by those skilled in the art without inventive labor.

FIG. 1 is a diagram of an embodiment of an environment in which a method for periodic monitoring of submarine cable disturbance sensor data is implemented;

FIG. 2 is a schematic flow diagram of a method for periodic monitoring of submarine cable disturbance sensory data according to one embodiment;

FIG. 3 is a schematic flow diagram of a method for periodic monitoring of submarine cable disturbance sensory data according to one embodiment;

FIG. 4 is a schematic flow chart diagram of a method for periodic monitoring of subsea cable disturbance sensory data in one embodiment;

FIG. 5 is a schematic flow diagram of a method for periodic monitoring of submarine cable disturbance sensory data according to one embodiment;

FIG. 6 is a schematic flow diagram of a method for periodic monitoring of submarine cable disturbance sensory data according to one embodiment;

FIG. 7 is a schematic flow chart diagram of a method for periodic monitoring of subsea cable disturbance sensory data in one embodiment;

FIG. 8 is a schematic flow chart diagram of a method for periodic monitoring of subsea cable disturbance sensory data in one embodiment;

FIG. 9 is a schematic flow chart illustrating a method for periodic monitoring of disturbance sensor data of a submarine cable according to an embodiment;

FIG. 10 is a schematic diagram of a system for periodic monitoring of submarine cable disturbance sensor data according to an embodiment;

FIG. 11 is a block diagram of a device for periodic monitoring of ocean bottom cable disturbance sensory data according to one embodiment;

FIG. 12 is a diagram illustrating an internal structure of a computer device according to an embodiment.

Detailed Description

In order to make the objects, technical solutions and advantages of the present disclosure more clearly understood, the present disclosure is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the disclosure and are not intended to limit the disclosure.

The method for periodically monitoring the submarine cable disturbance sensing data provided by the embodiment of the disclosure can be applied to the application environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. The data storage system may store data that the server 104 needs to process. The data storage system may be integrated on the server 104, or may be located on the cloud or other network server. The terminal 102 acquires disturbance sensing data of the submarine cable acquired by a plurality of sensors; the server 104 or the terminal 102 performs data preprocessing on the disturbance sensing data according to a preset mode to obtain target data; judging the target data by utilizing N algorithms in a preset algorithm pool to obtain a periodic data set with the length of N; selecting M periodic data subsets from a periodic data set with the length of N, wherein the M periodic data subsets respectively correspond to M base classifiers, and the weights of the M base classifiers are equal; if the output result of the base classifier does not accord with the preset threshold, the weight of the base classifier of which the output result does not accord with the preset threshold is adjusted upwards according to a preset rule until the output result of the base classifier accords with the preset threshold; and obtaining a target classifier according to the base classifier with the result meeting the preset threshold and the weight of the base classifier with the result meeting the preset threshold, and determining the periodic result of the submarine cable disturbance sensing data according to the output result of the target classifier. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things devices, and portable wearable devices, and the internet of things devices may be smart speakers, smart televisions, smart air conditioners, smart car-mounted devices, and the like. The portable wearable device can be a smart watch, a smart bracelet, a head-mounted device, and the like. The server 104 may be implemented as a stand-alone server or a server cluster comprised of multiple servers.

In one embodiment, as shown in fig. 2, a method for periodically monitoring disturbance sensing data of a submarine cable is provided, which is described by taking the method as an example applied to the terminal in fig. 1, and comprises the following steps:

s202, disturbance sensing data of the submarine cable collected through the sensors are obtained.

The sensing data may include data sensed, measured, and transmitted by the sensing device or devices. The sensing device or sensing device may comprise 1 or more sensors.

In particular, values may be obtained for a plurality of sensors for measuring submarine cable disturbance sensory data.

S204, carrying out data preprocessing on the disturbance sensing data according to a preset mode to obtain target data.

Where data pre-processing may include some processing of the data before the main processing is performed. For example, before most geophysical areal observation data are subjected to conversion or enhancement processing, an irregularly distributed measuring network is firstly subjected to interpolation processing and conversion processing into a regular network, so that the calculation of a computer is facilitated.

Specifically, the disturbance sensing data may be subjected to data preprocessing before the trend of the data is predicted according to a preset mode, and the disturbance sensing data is subjected to data preprocessing to obtain target data.

S206, judging the target data by utilizing N algorithms in a preset algorithm pool to obtain a periodic data set with the length of N.

The N algorithms in the preset algorithm pool may include fourier transform, and may also include an autocorrelation coefficient method. The pre-set algorithm pool may include ensemble learning algorithms.

Specifically, the target data may be judged by using algorithms such as fourier transform and autocorrelation coefficient method in a preset algorithm pool, so as to obtain a periodic data set with a length of N.

S208, M periodic data subsets are selected from the periodic data set with the length of N, the M periodic data subsets correspond to M base classifiers respectively, and the weights of the M base classifiers are equal.

The equal weight of the base classifiers may include that the M base classifiers occupy equal proportion in the total base classifiers, for example, the proportion occupied by the M base classifiers is 1/M.

Specifically, M periodic data subsets may be selected from the periodic data set with the length of N, where the M periodic data subsets respectively correspond to M base classifiers and weights of the M base classifiers are equal.

S210, if the output result of the base classifier does not accord with the preset threshold, the weight of the base classifier of which the output result does not accord with the preset threshold is adjusted upwards according to a preset rule until the output result of the base classifier accords with the preset threshold.

The preset threshold may be a value set in advance according to experience and the like, and may be exemplarily 0.2, 0.8 and the like. The preset rule may include an empirically or actually set value of the weight up-regulation, for example, the weight of the base classifier that does not meet the preset threshold may be up-regulated by 3%,8%, etc.

Specifically, when the output result of the base classifier does not meet the preset threshold, the weight of the base classifier whose output result does not meet the preset threshold is adjusted up according to the preset rule until the output result of the base classifier meets the preset threshold.

S212, obtaining a target classifier according to the base classifier with the result meeting the preset threshold and the weight of the base classifier with the result meeting the preset threshold, and determining the periodic result of the submarine cable disturbance sensing data according to the output result of the target classifier.

The periodic result of the submarine cable disturbance sensing data may include that the submarine cable disturbance sensing data is not periodic, and may also include periodicity, and for example, the periodic period may include 1 week and the like.

Specifically, the target classifier may be obtained according to the basis classifier whose result meets a preset threshold and the weight of the basis classifier whose result meets the preset threshold; for example, the result of the base classifier may be multiplied by the weight of the base classifier to obtain a multiplied value, and then all the multiplied values are added to obtain the output result of the target classifier. And determining a periodic result of the submarine cable disturbance sensing data according to the output result of the target classifier.

In the periodic monitoring method of the submarine cable disturbance sensing data, the disturbance sensing data of the submarine cable collected by a plurality of sensors is obtained; carrying out data preprocessing on the disturbance sensing data according to a preset mode to obtain target data; judging the target data by utilizing N algorithms in a preset algorithm pool to obtain a periodic data set with the length of N; selecting M periodic data subsets from a periodic data set with the length of N, wherein the M periodic data subsets respectively correspond to M base classifiers, and the weights of the M base classifiers are equal; if the output result of the base classifier does not accord with the preset threshold, the weight of the base classifier of which the output result does not accord with the preset threshold is adjusted upwards according to a preset rule until the output result of the base classifier accords with the preset threshold; the method comprises the steps of obtaining a target classifier according to a base classifier with a result meeting a preset threshold value and the weight of the base classifier with the result meeting the preset threshold value, determining a periodic result of submarine cable disturbance sensing data according to an output result of the target classifier, automatically monitoring the state of submarine equipment and the periodic monitoring of the submarine cable disturbance sensing data, early warning damage possibly met by the submarine equipment in advance, monitoring the state of the submarine equipment in time, and reducing the maintenance cost of the submarine equipment.

In an embodiment, as shown in fig. 3, the step S206 of determining the target data by using N algorithms in a preset algorithm pool to obtain a periodic data set with a length of N includes the following steps:

s302, expanding the target data into a linear combination of trigonometric functions.

Wherein the unfolding of the target data into a linear combination of trigonometric functions may comprise a fourier transform.

Specifically, the target data may be expanded into a linear combination of trigonometric functions by fourier transform or the like.

S304, determining a coefficient maximum term in the linear combination of the trigonometric functions.

Wherein the coefficient maximum term in the linear combination of trigonometric functions may comprise a maximum of fourier coefficients.

In particular, the coefficient maximum term in the linear combination of trigonometric functions may be determined by fourier coefficients.

And S306, obtaining a periodic data set with the length of N according to the coefficient maximum term.

Wherein, the coefficient maximum term may be included as periodic data.

In particular, a periodic data set of length N may be derived from the coefficient maximum term, e.g., the maximum of the fourier coefficients.

In this embodiment, the target data is expanded into a linear combination of trigonometric functions, a maximum coefficient term in the linear combination of trigonometric functions is determined, and then a periodic data set with a length of N is obtained according to the maximum coefficient term, so that the state of the submarine equipment and the periodic monitoring of submarine cable disturbance sensing data can be automatically monitored.

In one embodiment, as shown in fig. 4, the method further comprises the steps of:

s402, determining a coefficient maximum term set according to the coefficient maximum terms in the linear combination of the trigonometric functions.

Wherein, the coefficient maximum term set may include coefficient maximum terms in linear combinations of all trigonometric functions.

Specifically, the coefficient maximum term set may be determined from the coefficient maximum terms in the linear combination of the trigonometric functions.

S404, determining the autocorrelation coefficient of the subset in the coefficient maximum term set.

The autocorrelation Coefficient may be calculated by Pearson Correlation Coefficient method (Pearson Correlation Coefficient).

Specifically, the self-correlation coefficient of the subset in the maximum coefficient term set can be determined by using a Pearson correlation coefficient method.

S406, comparing the autocorrelation coefficients of the subset with a preset autocorrelation coefficient threshold.

The autocorrelation coefficient threshold may be a value preset empirically or the like.

In particular, the autocorrelation coefficients of the subset may be compared to a preset autocorrelation coefficient threshold.

S408, determining that the autocorrelation coefficients of the subset are larger than or equal to the subset of the preset autocorrelation coefficient threshold.

Specifically, the autocorrelation coefficients of the subset may be determined to be greater than or equal to the subset of the preset autocorrelation coefficient threshold.

S410, determining the period of the maximum coefficient item set according to the subset of the autocorrelation coefficients which are greater than or equal to the preset autocorrelation coefficient threshold.

Specifically, the period of the set of maximum coefficient terms may be determined by determining from the subset of autocorrelation coefficients greater than or equal to the preset autocorrelation coefficient threshold.

In this embodiment, a coefficient maximum term set is determined according to a coefficient maximum term in the linear combination of the trigonometric functions; determining autocorrelation coefficients of subsets in the coefficient maximum term set; comparing the autocorrelation coefficients of the subset to a preset autocorrelation coefficient threshold; determining a subset of the subset whose autocorrelation coefficients are greater than or equal to the preset autocorrelation coefficient threshold; and determining the period of the maximum coefficient item set according to the subset of which the autocorrelation coefficient is greater than or equal to the preset autocorrelation coefficient threshold value, so that the state of the submarine equipment and the periodic monitoring of submarine cable disturbance sensing data can be automatically monitored.

In one embodiment, as shown in fig. 5, the step S410 of determining the period of the maximum coefficient term set according to the subset of the autocorrelation coefficients greater than or equal to the preset autocorrelation coefficient threshold includes the following steps:

s502, determining any two subsets of which the autocorrelation coefficients are greater than or equal to the preset autocorrelation coefficient threshold.

The determining of the subset of any two autocorrelation coefficients greater than or equal to the preset autocorrelation coefficient threshold may include determining that any two autocorrelation coefficients determined by an autocorrelation coefficient method are greater than or equal to the preset autocorrelation coefficient threshold.

Specifically, any two subsets of autocorrelation coefficients greater than or equal to the preset autocorrelation coefficient threshold may be determined by using an autocorrelation coefficient method.

S504, performing difference operation on the starting times of the subsets with the arbitrary two autocorrelation coefficients being greater than or equal to the preset autocorrelation coefficient threshold value, and taking an absolute value to obtain the period of the maximum coefficient term set.

Specifically, the period of the maximum coefficient term set may be obtained by performing a difference operation on the start times of the subsets of which the arbitrary two autocorrelation coefficients are greater than or equal to the preset autocorrelation coefficient threshold and taking an absolute value.

In this embodiment, any two subsets of autocorrelation coefficients greater than or equal to the preset autocorrelation coefficient threshold are determined; and performing difference operation on the starting times of any two subsets of which the autocorrelation coefficients are greater than or equal to the preset autocorrelation coefficient threshold values and taking absolute values to obtain the period of the maximum coefficient item set, so that the state of the submarine equipment and the periodic monitoring of submarine cable disturbance sensing data can be automatically monitored.

In an embodiment, as shown in fig. 6, after the step S204 performs data preprocessing on the disturbance sensing data according to a preset manner, obtaining target data includes the following steps:

s602, resampling the disturbance sensing data to obtain resampled data, wherein the resampling at least comprises one of resampling according to a day dimension, resampling according to a week dimension and resampling according to a month dimension.

Wherein resampling may comprise resampling the perturbation sensing data as it is typically non-uniform or discontinuous as it is collected by the sensor. According to the dimension of trend analysis, resampling is generally carried out for the dimension of day, week and month.

Specifically, the resampling data may be obtained by one of resampling the disturbance sensing data according to a day dimension, resampling according to a week dimension, and resampling according to a month dimension.

And S604, performing data abnormal value filtering on the resampled data to obtain abnormal value filtering data.

Specifically, the outlier filtered data may be obtained by performing data outlier filtering on the resampled data. In some embodiments, filtering outliers may be performed using programmatic filter methods.

And S606, performing non-dimensionalization on the abnormal value filtering data to obtain target data, wherein the non-dimensionalization at least comprises one of linear non-dimensionalization and non-linear non-dimensionalization.

The non-dimensionalization processing can help the system to improve the accuracy of the model, and further avoid influence of data with a large value range on distance calculation.

Specifically, the abnormal value filtering data may be subjected to non-dimensionalization processing to obtain target data. The data may be non-dimensionalized linearly or non-linearly. The non-dimensionalization of the linearity includes a centering process and a scaling process. The essence of centralization may include all records minus a fixed value, i.e., having the data sample data shifted to a certain location. The nature of the scaling may be such that the data is fixed within a certain range by dividing by a fixed value, and the scaling process may also include taking the logarithm.

In this embodiment, resampling processing is performed on the disturbance sensing data to obtain resampled data; carrying out data outlier filtering on the resampled data to obtain outlier filtered data; and carrying out non-dimensionalization processing on the abnormal value filtering data to obtain target data, so that the periodic monitoring result of the disturbance data of the submarine cable can be more accurate.

In one embodiment, as shown in fig. 7, the step S604 of performing data outlier filtering on the resampled data, and obtaining the outlier filtered data includes the following steps:

s702, carrying out rule calculation on the resampling data to obtain rule calculation data, wherein the rule calculation at least comprises one of averaging the resampling data and calculating variance of the resampling data.

Specifically, the rule calculation data may be obtained by one of averaging the resampled data and averaging the resampled data.

S704, taking an absolute value of a difference value of the resampled data and the rule calculation data to obtain deviation data.

Specifically, the deviation data may be obtained by subtracting the resampled data from the rule calculation data, and taking the absolute value of the subtracted difference.

And S706, comparing the target deviation data in the deviation data with a preset deviation threshold value.

Specifically, the target deviation data in the deviation data may be compared with a deviation threshold value set in advance.

S708, if the target deviation data is larger than the preset deviation threshold, setting the resample data corresponding to the target deviation data as an abnormal value and removing the abnormal value from the resample data to obtain abnormal value filter data.

Specifically, the abnormal value filtering data may be obtained by setting the resample data corresponding to the target deviation data as an abnormal value and removing the abnormal value from the resample data if the target deviation data is greater than a preset deviation threshold.

In the embodiment, the abnormal value filtering data is obtained by filtering the data abnormal value of the resampled data, so that the result of the periodic monitoring of the disturbance data of the submarine cable is more accurate.

In one embodiment, as shown in fig. 8, there is provided a method for periodic monitoring of submarine cable disturbance sensory data, the method comprising the steps of:

s802, disturbance sensing data of the submarine cable collected through the sensors are obtained.

S804, resampling the disturbance sensing data to obtain resample data, wherein the resampling at least comprises one of resampling according to a dimension of a day, resampling according to a dimension of a week and resampling according to a dimension of a month.

S806, performing rule calculation on the resampling data to obtain rule calculation data, wherein the rule calculation at least comprises one of averaging the resampling data and calculating variance of the resampling data.

And S808, taking an absolute value of the difference value between the resampled data and the rule calculation data to obtain deviation data.

And S810, comparing target deviation data in the deviation data with a preset deviation threshold value.

S812, if the target deviation data is larger than the preset deviation threshold, setting the resample data corresponding to the target deviation data as an abnormal value and removing the abnormal value from the resample data to obtain abnormal value filter data.

S814, performing non-dimensionalization processing on the abnormal value filtering data to obtain target data, wherein the non-dimensionalization processing at least comprises one of linear non-dimensionalization processing and nonlinear non-dimensionalization processing.

And S816, unfolding the target data into a linear combination of trigonometric functions.

And S818, determining a coefficient maximum term in the linear combination of the trigonometric functions.

And S820, determining a coefficient maximum term set according to the coefficient maximum terms in the linear combination of the trigonometric functions.

And S822, determining the autocorrelation coefficient of the subset in the coefficient maximum term set.

S824, comparing the autocorrelation coefficients of the subset with a preset autocorrelation coefficient threshold.

And S826, determining that the autocorrelation coefficients of the subset are larger than or equal to the subset of the preset autocorrelation coefficient threshold.

S828, determining any two subsets of which the autocorrelation coefficients are greater than or equal to the preset autocorrelation coefficient threshold.

S830, performing difference operation on the starting times of the subsets of which the autocorrelation coefficients are greater than or equal to the preset autocorrelation coefficient threshold value, and taking an absolute value to obtain the period of the maximum coefficient term set.

And S832, obtaining a periodic data set with the length of N according to the coefficient maximum term.

S834, selecting M periodic data subsets from the periodic data sets with the length of N, wherein the M periodic data subsets correspond to M base classifiers respectively, and the weights of the M base classifiers are equal.

And S836, if the output result of the base classifier does not accord with the preset threshold, adjusting the weight of the base classifier of which the output result does not accord with the preset threshold up according to a preset rule until the output result of the base classifier accords with the preset threshold.

S838, obtaining a target classifier according to the base classifier with the result meeting the preset threshold and the weight of the base classifier with the result meeting the preset threshold, and determining the periodic result of the submarine cable disturbance sensing data according to the output result of the target classifier.

In one embodiment, as shown in fig. 9, a schematic flow chart of a periodic monitoring method for submarine cable disturbance sensing data is provided, in which raw data collected by a sensor is subjected to normalization preprocessing, resampling preprocessing and normalization preprocessing, the preprocessed data is processed by fourier transform (FFT) and then by an autocorrelation coefficient method (ACF), the obtained data is used as an input of a Boosting ensemble learning algorithm, and an output of the Boosting ensemble learning algorithm is a specific periodic value or non-periodic result of the submarine cable disturbance sensing data.

In one embodiment, as shown in fig. 10, a system for periodic monitoring of subsea cable disturbance sensory data is provided, the system consisting of a policy module 1002, a database 1004, and a front end 1006. The policy module 1002 is configured to read original data in the database 1004, determine and output a period index according to the original data; the database 1004 is configured to store the raw data, and is further configured to receive and store the period index output by the policy module 1002; the front end 1006 is configured to read a period index of the database 1004, obtain a period intensity curve by using the period index, and obtain a data periodicity monitoring result according to the period intensity curve. The policy module 1002 is further configured to: reading original data in a database 1004, and performing data preprocessing on the original data to obtain time sequence data; determining a period index according to the time sequence data; the period indicator is sent to the database 1004. The front end 1006 is further configured to read raw data in the database 1004, obtain a disturbance curve according to the raw data, and perform periodic monitoring on submarine cable disturbance sensing data by using the disturbance curve. The front end 1006 is further configured to read alarm data in the database 1004, obtain an alarm message according to the alarm data, and send the alarm message when the submarine cable disturbance sensing data is in an abnormal state.

It should be understood that, although the steps in the flowcharts of fig. 2 to 8 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2 to 8 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of performing the steps or stages is not necessarily sequential, but may be performed alternately or alternately with other steps or at least some of the other steps or stages.

Based on the same inventive concept, the embodiment of the present disclosure further provides a periodic monitoring device for submarine cable disturbance sensing data, which is used for implementing the above-mentioned periodic monitoring method for submarine cable disturbance sensing data. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the method, so that specific limitations in the following embodiment of the periodic monitoring device for one or more submarine cable disturbance sensing data may refer to the limitations in the above periodic monitoring method for submarine cable disturbance sensing data, and details are not repeated herein.

In one embodiment, as shown in fig. 11, there is provided a device 1100 for periodic monitoring of submarine cable disturbance sensory data, comprising: a data acquisition module 1102, a target data module 1104, a cycle data aggregation module 1106, a subset module 1108, a weight adjustment module 1110, and a result determination module 1112, wherein:

a data acquisition module 1102 for acquiring disturbance sensing data of the submarine cable acquired by the plurality of sensors.

And a target data module 1104, configured to perform data preprocessing on the disturbance sensing data according to a preset manner, so as to obtain target data.

And the periodic data set module 1106 is configured to judge the target data by using N algorithms in a preset algorithm pool to obtain a periodic data set with a length of N.

The subset matching module 1108 is configured to select M periodic data subsets from a periodic data set with a length of N, where the M periodic data subsets correspond to M basis classifiers, respectively, and weights of the M basis classifiers are equal to each other.

The weight adjusting module 1110 is configured to, if the output result of the base classifier does not meet the preset threshold, adjust the weight of the base classifier whose output result does not meet the preset threshold up according to a preset rule until the output result of the base classifier meets the preset threshold.

A result determining module 1112, configured to obtain a target classifier according to the basis classifier whose result meets a preset threshold and the weight of the basis classifier whose result meets the preset threshold, and determine a periodic result of the submarine cable disturbance sensing data according to an output result of the target classifier.

The modules in the device for periodically monitoring the submarine cable disturbance sensing data can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.

In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 12. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a method of periodic monitoring of submarine cable disturbance sensory data. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on a shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.

It will be appreciated by those skilled in the art that the configuration shown in fig. 12 is a block diagram of only a portion of the configuration associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.

In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the above-described method embodiments when executing the computer program.

In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.

In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, carries out the steps in the method embodiments described above.

It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, presented data, etc.) referred to in the present disclosure are information and data that are authorized by the user or sufficiently authorized by each party.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, databases, or other media used in the embodiments provided by the present disclosure may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high-density embedded nonvolatile Memory, resistive Random Access Memory (ReRAM), magnetic Random Access Memory (MRAM), ferroelectric Random Access Memory (FRAM), phase Change Memory (PCM), graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others. The databases involved in embodiments provided by the present disclosure may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the embodiments provided in this disclosure may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic, quantum computing based data processing logic, etc., without limitation.

All possible combinations of the technical features in the above embodiments may not be described for the sake of brevity, but should be considered as being within the scope of the present disclosure as long as there is no contradiction between the combinations of the technical features.

The above-mentioned embodiments only express several embodiments of the present disclosure, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present disclosure. It should be noted that, for those skilled in the art, various changes and modifications can be made without departing from the concept of the present disclosure, and these changes and modifications are all within the scope of the present disclosure. Therefore, the protection scope of the present disclosure should be subject to the appended claims.

Claims

1. A method for periodic monitoring of submarine cable disturbance sensory data, the method comprising:

2. The method according to claim 1, wherein the determining the target data by using N algorithms in a preset algorithm pool to obtain a periodic data set with a length of N comprises:

expanding the target data into a linear combination of trigonometric functions;

3. The method of claim 2, further comprising:

4. The method of claim 3, wherein the determining the period of the set of maximum coefficient terms according to the subset of autocorrelation coefficients greater than or equal to the preset autocorrelation coefficient threshold comprises:

and performing difference operation on the starting times of the subsets of which the arbitrary two autocorrelation coefficients are greater than or equal to the preset autocorrelation coefficient threshold value, and taking an absolute value to obtain the period of the coefficient maximum term set.

5. The method according to claim 1, wherein the obtaining target data after the data preprocessing of the disturbance sensing data according to the preset mode comprises:

and carrying out non-dimensionalization processing on the abnormal value filtering data to obtain target data, wherein the non-dimensionalization processing at least comprises one of linear non-dimensionalization processing and non-linear non-dimensionalization processing.

6. The method of claim 5, wherein the performing data outlier filtering on the resampled data comprises:

comparing target deviation data in the deviation data with a preset deviation threshold value;

7. An apparatus for periodic monitoring of submarine cable disturbance sensory data, the apparatus comprising:

the target data module is used for preprocessing the disturbance sensing data according to a preset mode to obtain target data;

8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor realizes the steps of the method of any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that the computer program realizes the steps of the method of any one of claims 1 to 6 when executed by a processor.