Submarine cable insulation abnormity detection and state evaluation method based on optical fiber temperature measurement

Abstract

The invention provides a submarine cable insulation abnormity detection and state evaluation method based on optical fiber temperature measurement, and relates to the technical field of submarine cable insulation abnormity monitoring, and the method comprises the steps: obtaining the temperature data of a dynamic cable, building a temperature data set and a position set corresponding to the temperature data, screening an effective peak interval set to obtain an effective maximum value set and an effective maximum value position set, screening an effective peak interval set to obtain an effective minimum value set and an effective minimum value position set, screening an effective sunken interval set, and adding peak intervals which are overlapped with the peak interval set and the sunken interval set and exceed a preset threshold value into a shallow-buried abnormal peak interval set; screening out a shallow-buried abnormal peak interval set from the peak interval set to obtain an insulation abnormal peak set, and evaluating the state of the insulation abnormal peak; and defining abnormal observation points, establishing a temperature sequence of the abnormal observation points, establishing a temperature sequence of the contrast observation points, and evaluating the abnormal state of the submarine cable by analyzing the temperature difference growth trend of the abnormal observation points and the contrast observation points.

Description

Technical Field

[0001] The present invention relates to the technical field of insulation anomaly monitoring of submarine cables, and in particular to a method for detecting and evaluating insulation anomaly of submarine cables based on optical fiber temperature measurement. Background Art

[0002] Submarine cables are laid in complex ocean and seabed environments and are important power transmission media. Submarine cables are affected by external factors such as seabed pressure, ocean current erosion, microbial corrosion, external wear, and internal factors such as material aging, process defects, and continuous loading. Submarine cables are at risk of insulation failure and require real-time online monitoring. Distributed fiber optic temperature sensing technology uses optical fiber as the sensing medium. It can obtain temperature data of optical fibers at various locations in the submarine cable without changing the operation mode of the submarine cable. It is an important method for current submarine cable temperature monitoring.

[0003] The current submarine cable monitoring technology based on optical fiber temperature measurement mainly solves the problem of abnormal temperature alarm, that is, when the absolute temperature of the optical fiber or submarine cable is too high, the temperature rise is too fast, etc., the alarm is triggered. The temperature anomaly caused by abnormal insulation of the submarine cable is a slowly changing process, which is difficult to capture by the criterion of too fast temperature rise. On the other hand, it is affected by factors such as the dynamic changes in the marine hydrological environment, the dynamic changes in the submarine cable load, and the dynamic changes in the depth of the submarine cable laying. As a small signal anomaly, the temperature anomaly caused by insulation is difficult to extract and identify.

[0004] Therefore, a submarine cable insulation anomaly detection and status assessment method based on optical fiber temperature measurement is urgently needed to solve the above technical problems. Summary of the invention

[0005] The purpose of the present invention is to provide a method for detecting and evaluating the insulation anomaly of a submarine cable based on optical fiber temperature measurement, so as to overcome the problems of temperature peak extraction, type discrimination and trend state analysis in the detection of insulation anomaly of a submarine cable based on optical fiber temperature measurement, and realize the detection and evaluation of insulation anomaly of a submarine cable. The preferred technical scheme among the many technical schemes provided by the present invention can produce many technical effects as described below.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The present invention provides a method for detecting insulation anomalies and evaluating the state of a submarine cable based on optical fiber temperature measurement, comprising the following steps:

[0008] S1: acquiring temperature data corresponding to a dynamic cable target position and a target time point based on a distributed optical fiber acoustic wave sensing device, wherein the temperature data at least includes historical temperature data of a first time period close to the dynamic cable target position and the current time t;

[0009] S2: Obtain the optical fiber temperature measurement value corresponding to the current time t to establish the temperature data set A and the location set L corresponding to the temperature data, and obtain the effective maximum value set D * and the set of valid maximum positions F * , filter the effective peak interval set M;

[0010] Search forward from the current time t to select the temperature data of the second time period most recently, obtain the date with the lowest temperature in the second time period, and obtain a time t within the date * The corresponding optical fiber temperature measurement value establishes the temperature data set A * The location set L corresponding to the temperature data * , and obtain the effective minimum value set S * and the effective minimum position set R * , and screen the effective concave interval set N;

[0011] S3: define the anomaly types as shallow buried anomaly and insulation anomaly, add the peak interval set M and the peak interval set N whose overlap exceeds the preset threshold λ to the shallow buried anomaly peak interval set P; the peak interval set M filters out the shallow buried anomaly peak interval set P to obtain the insulation anomaly peak set Q, and evaluate the status of the insulation anomaly peaks;

[0012] S4: Establish a temperature sequence H of the abnormal observation point X; establish a temperature sequence W of the comparison observation point Y for the abnormal observation point X; define a temperature difference sequence Z as a set of differences between the temperature sequence H of the abnormal observation point X and the temperature sequence W of the comparison observation point Y at the same time;

[0013] S5: Set the temperature difference status evaluation threshold The abnormal status of the submarine cable is evaluated by analyzing the growth trend of the temperature difference.

[0014] Preferably, in step S2, a valid maximum value set D is obtained. * and the set of valid maximum positions F * , including the following steps:

[0015] Based on the data set A, a temperature maximum value set D and a fiber position point set F corresponding to the maximum temperature data are established;

[0016] Based on the trimmed mean, the mean μ and standard deviation σ of the data set A are calculated. Based on the mean μ and standard deviation σ of the data set A, the effective maximum value set D is obtained by screening. * and the set of valid maximum positions F * .

[0017] Preferably, we get the effective maximum value set D * and the set of valid maximum positions F * , specifically including the following steps:

[0018] Set the judgment threshold of the maximum point ε = 3σ, traverse the data set A containing temperature data, check each temperature value one by one, and add the data that satisfies the difference between the temperature data and the mean μ greater than the threshold ε to D. According to the maximum value elements in the set D, find the corresponding maximum value position in the position set L, and fill them into the set F one by one;

[0019] Select x consecutive time points from the current time t to obtain the corresponding x sets of temperature data, and add the maximum points that meet the conditions to the effective maximum data set D * , and obtain the corresponding effective maximum value location set F * .

[0020] Preferably, in step S2, a valid minimum value set S is obtained. * and the effective minimum position set R * , including the following steps:

[0021] Based on the temperature data set A * , establish a temperature minimum value set S and a fiber position point set R corresponding to the minimum temperature data;

[0022] Find the data set A based on the trimmed mean * The mean μ * and standard deviation σ * , based on the data set A * The mean μ * and standard deviation σ * Screen and obtain the effective minimum value set S * and the effective minimum position set R * .

[0023] Preferably, based on data set A * The mean μ * and standard deviation σ * Screen and obtain the effective minimum value set S * and the effective minimum position set R * , including the following steps:

[0024] Set the judgment threshold ε of the minimum point * =3σ * , traverse the data set A containing temperature data * , check each temperature value one by one, for each temperature value, the mean μ will be satisfied * With temperature data A * i The difference is greater than the threshold ε * The data is added to S, and according to the minimum value element in the set S, in the position set L *Find the corresponding minimum value positions and fill them into the set R one by one;

[0025] From the current t * Select x consecutive time points forward to obtain the corresponding x sets of temperature data, and add the minimum points that meet the conditions to the effective minimum data set S * , the corresponding effective minimum value location set is R * .

[0026] Preferably, in step S2, screening the effective peak interval set M and screening the effective depression interval set N specifically include:

[0027] Slide and filter to both sides with the position corresponding to the extreme point as the center;

[0028] The point that satisfies the preset condition that the difference between the point and the mean is less than the difference between the extreme point and the mean is defined as the endpoint of the peak interval; that is, the point on the left of the extreme point is the starting point of the peak interval, and the point on the right of the extreme point is the end point of the peak, and the peak interval is obtained;

[0029] The point that satisfies the preset condition that the difference between the mean and the point is less than the difference between the mean and the extreme point is defined as the endpoint of the concave interval; that is, the point on the left of the extreme point is the starting point of the concave interval, and the point on the right of the extreme point is the end point of the concave, and the concave interval is obtained;

[0030] The filtered peak interval set or concave interval set is processed and the same set elements are merged.

[0031] Preferably, the mean μ and standard deviation σ of the data set A are calculated based on the trimmed mean, and the mean μ and standard deviation σ of the data set A are calculated based on the trimmed mean. * The mean μ * and standard deviation σ * , specifically including the following steps:

[0032] Data set A or data set A * Sort all the data in by size to get data set B;

[0033] Remove a certain proportion of the maximum and minimum values ​​in the data set, and determine the proportion of removed data to be α. Then remove α / 2 proportions from both ends of the data set B, that is, remove nα / 2 data from both ends, and obtain data set C;

[0034] Find the mean of data set C;

[0035] Calculate the data set A or data set A based on the obtained mean * The standard deviation of .

[0036] Preferably, in step S3, the peak intervals of the peak interval set M and the depression interval set N that overlap by more than a preset threshold λ are added to the shallow buried abnormality peak interval set P. The specific steps of screening the peaks caused by the shallow buried abnormality are:

[0037] S31: establishing a peak interval set of shallow buried anomalies as P;

[0038] S32: Add the peak intervals that satisfy any of the following conditions to the set P:

[0039] The temperature peak interval and the temperature depression interval overlap more than the specified threshold λ;

[0040] The interval of the temperature peak and the interval of the temperature depression include each other.

[0041] Preferably, in step S5, a temperature difference state evaluation threshold is set By analyzing the growth trend of the temperature difference Z, the abnormal state of the submarine cable is evaluated, including:

[0042] If the temperature difference Z and the temperature difference state evaluation threshold If the preset condition is met, the evaluation result of the abnormal peak is deterioration.

[0043] Preferably, the preset condition includes at least any one of the following conditions, wherein:

[0044] Condition 1: The temperature difference Z shows a trend of increasing over time, and the growth exceeds the temperature difference state assessment threshold

[0045] Condition 2: The year-on-year increase in temperature difference Z compared to previous years exceeds the temperature difference status assessment threshold

[0046] Condition 3: In the difference set between the temperature difference Z and the temperature difference of each month in the past year, there is an element greater than the temperature difference state assessment threshold Wherein, the temperature difference state evaluation threshold

[0047] The present invention provides a method for detecting and evaluating the insulation anomaly of a submarine cable based on optical fiber temperature measurement. The method collects the temperature data of the submarine cable based on optical fiber sensing technology, processes the real-time temperature data collected by the distributed temperature sensing equipment, classifies the temperature peaks into the abnormal depth and the insulation anomaly, searches for the abnormal depth peaks by analyzing the temperature data of the submarine cable and eliminates them, uses the difference method to eliminate the current information dependence, and uses the same period comparison method to eliminate the influence of seasonal factors on the temperature difference, and further performs trend analysis on the temperature peaks caused by the insulation anomaly, thereby realizing the evaluation of the abnormal insulation status of the submarine cable. This method for detecting and evaluating the insulation anomaly of a submarine cable based on optical fiber temperature measurement is aimed at submarine cables with complex working conditions in marine environments. It is a comprehensive detection method suitable for various environmental factors, with or without current, and solves the problem of monitoring the online insulation temperature anomaly of the submarine cable. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0049] Figure 1 The process of the method for detecting and evaluating the insulation anomaly of a submarine cable based on optical fiber temperature measurement according to the embodiment of the present invention is as follows Figure 1 ;

[0050] Figure 2 The process of the method for detecting and evaluating the insulation anomaly of a submarine cable based on optical fiber temperature measurement according to the embodiment of the present invention is as follows Figure 2 ;

[0051] Figure 3 It is an example interval, where the realized box is marked as an insulating abnormal peak and the dotted box is marked as a shallow buried abnormal peak;

[0052] Figure 4 This is the growth trend chart of the temperature difference series. DETAILED DESCRIPTION

[0053] To make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described in detail below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other implementation methods obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0054] In the description of the present invention, it is to be understood that the terms “center”, “lateral”, “length”, “width”, “height”, “up”, “down”, “front”, “back”, “left”, “right”, “vertical”, “horizontal”, “top”, “bottom”, “inside”, “outside”, “side”, etc., indicating orientations or positional relationships are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0055] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances.

[0056] In view of the problems mentioned in the background technology, the embodiment of the present invention provides a method for detecting insulation anomalies and evaluating the status of submarine cables based on optical fiber temperature measurement. Figure 1 As shown, the submarine cable insulation anomaly detection and status assessment method based on optical fiber temperature measurement includes the following steps:

[0057] S1: obtaining temperature data corresponding to a dynamic cable target position and a target time point based on a distributed optical fiber acoustic wave sensing device, wherein the temperature data at least includes historical temperature data of a first time period close to the dynamic cable target position and the current time t;

[0058] S2: Obtain the optical fiber temperature measurement value corresponding to the current time t to establish the temperature data set A and the location set L corresponding to the temperature data, and obtain the effective maximum value set D * and the set of valid maximum positions F * , filter the effective peak interval set M;

[0059] Search forward from the current time t to select the temperature data of the nearest second time period, obtain the date with the lowest temperature in the second time period, and obtain a time t within the date * The corresponding optical fiber temperature measurement value establishes the temperature data set A * The location set L corresponding to the temperature data * , and obtain the effective minimum value set S * and the effective minimum position set R * , and screen the effective concave interval set N;

[0060] S3: define the anomaly types as shallow buried anomaly and insulation anomaly, add the peak interval set M and the peak interval set N whose overlap exceeds the preset threshold λ to the shallow buried anomaly peak interval set P; the peak interval set M filters out the shallow buried anomaly peak interval set P to obtain the insulation anomaly peak set Q, and evaluate the status of the insulation anomaly peaks;

[0061] S4: Establish a temperature sequence H of the abnormal observation point X; establish a temperature sequence W of the comparison observation point Y for the abnormal observation point X; define a temperature difference sequence Z as a set of differences between the temperature sequence H of the abnormal observation point X and the temperature sequence W of the comparison observation point Y at the same time;

[0062] S5: Set the temperature difference status evaluation threshold The abnormal status of the submarine cable is evaluated by analyzing the growth trend of the temperature difference.

[0063] Through the above steps, the temperature data of the submarine cable is collected based on the optical fiber sensing technology. By analyzing the temperature data of the submarine cable and searching for abnormal temperature spikes, the causes of the abnormal temperature spikes are identified and screened to obtain abnormal insulation spikes. Further trend analysis of the temperature spikes caused by abnormal insulation is performed to achieve abnormal insulation status evaluation of the submarine cable. This submarine cable insulation abnormality detection and status evaluation method based on optical fiber temperature measurement is suitable for submarine cables with complex working conditions in marine environments. It is a comprehensive detection method applicable to various environmental factors, with or without current, and solves the problem of abnormal insulation temperature monitoring of submarine cables online.

[0064] like Figure 2-Figure 4 As shown, the submarine cable insulation anomaly detection and status assessment method based on optical fiber temperature measurement specifically includes the following steps:

[0065] S1: obtaining temperature data corresponding to a dynamic cable target position and a target time point based on a distributed optical fiber acoustic wave sensing device, wherein the temperature data at least includes historical temperature data of a first time period close to the dynamic cable target position and the current time t;

[0066] In this embodiment, the first time period is at least 9 months of historical temperature data close to the current time t. In actual submarine cable insulation anomaly detection and status assessment, distributed optical fiber temperature sensing equipment can be used to obtain temperature data corresponding to each position and each time point of the submarine cable; it is required to include at least 9 months of historical temperature data for analysis.

[0067] S2: Obtain the optical fiber temperature measurement value corresponding to the current time t to establish the temperature data set A and the location set L corresponding to the temperature data, and obtain the effective maximum value set D * and the set of valid maximum positions F * , filter the effective peak interval set M.

[0068] Among them, the effective maximum value set D is obtained * and the set of valid maximum positions F * , including the following steps: establishing a temperature maximum value set D and a fiber position point set F corresponding to the maximum temperature data based on a data set A; calculating the mean μ and standard deviation σ of the data set A based on the trimmed mean, and screening the effective maximum value set D based on the mean μ and standard deviation σ of the data set A. * and the set of valid maximum positions F * .

[0069] Specifically, the optical fiber temperature measurement value at the current moment is selected to establish a temperature data set A = {A1, A2, ... A n}, define A i The temperature value of the optical fiber at the i-th position at the current time t is established, and the position set L corresponding to the temperature data is established as {L1, L2, ... L n}, define L i is the position of the optical fiber corresponding to the i-th temperature value at the current time t.

[0070] The trimmed mean is used to find the mean μ and standard deviation σ of the data set A. The specific steps are as follows:

[0071] S21: Sort all the data in data set A by size to obtain data set B, and define B = {B1, B2, ...B n};

[0072] S22: Remove a certain proportion of the maximum and minimum values ​​in the data set, and determine the proportion of removed data to be α, then remove α / 2 of the proportion from both ends of the data set B, that is, remove nα / 2 data from both ends, and obtain the data set C, and define C = {C1, C2, ... C n-nα};

[0073] S23: Find the mean of data set C, which is a more robust mean estimate μ of data set A, that is, μ = (C1 + C2 + ... + C n-nα ) / (n-nα);

[0074] S24: After obtaining the mean μ, the standard deviation σ of the data set A can be obtained, that is,

[0075]

[0076] This embodiment establishes a temperature maximum value set D, and defines D = {D1, D2, D3, ...}, D i is the temperature value of the ith maximum position of the optical fiber, and a set of optical fiber position points F corresponding to the maximum temperature data is established, and F = {F1, F2, F3, ...} is defined, F jis the optical fiber position corresponding to the jth maximum value.

[0077] Based on the mean μ and standard deviation σ of the data set A, the effective maximum value set D is screened according to the following steps: * :

[0078] S25: Set the judgment threshold ε=3σ of the maximum point, traverse the data set A containing temperature data, check each temperature value one by one, and for each temperature value, add the data that satisfies the difference between the temperature data and the mean μ greater than the threshold ε to D, and find the corresponding maximum position in the position set L according to the maximum element in the set D, and fill them into the set F one by one.

[0079] Specifically, it includes setting the judgment threshold of the maximum point ε = 3σ, traversing the data set A containing temperature data, checking each temperature value one by one, and for each temperature value, i The data whose difference from the mean μ is greater than the threshold ε is added to D, that is, A i -μ>ε, A i Fill them into set D one by one. According to the maximum value elements in set D, find the corresponding maximum value position in position set L and fill them into set F one by one.

[0080] S26: Select x consecutive time points from the current time t to obtain the corresponding x sets of temperature data, and add the maximum points that meet the conditions to the effective maximum data set D * , and obtain the corresponding effective maximum value location set F * .

[0081] In order to reduce the measurement jitter error, based on the maximum value position set F, the valid maximum value and its position set are screened out through the following conditions, that is, the continuous x groups of temperature data corresponding to the optical fiber position of the maximum value point meet the above maximum value judgment conditions.

[0082] The specific steps are as follows: Select x consecutive time points t, t-1, ... tx from the current time t to obtain the corresponding x sets of temperature data, and add the maximum points that meet the conditions to the effective maximum data set D * ={D * 1,D * 2,D * 3,...}, the corresponding effective maximum value position set can be obtained as F * ={F * 1,F * 2,F * 3,...}.

[0083] Define the temperature peak interval set as M = {M1, M2, M3, ...}, where M i is the i-th peak interval Mi =[a i ,b i ], a i is the starting position of the ith peak, b i is the end position of the ith peak. Based on the effective maximum temperature set D * and the set of valid maximum positions F * Filter the effective peak interval.

[0084] The screening of the effective peak interval set M includes the following steps:

[0085] S27: Sliding screening is performed on both sides with the position corresponding to the extreme point as the center;

[0086] S28: defining a point that satisfies a preset condition that the difference between the point and the mean is less than the difference between the extreme point and the mean as the endpoint of the peak interval; that is, the point on the left side of the extreme point is the starting point of the peak interval, and the point on the right side of the extreme point is the end point of the peak, thereby obtaining the peak interval;

[0087] S29: Process the filtered peak interval set and merge the same set elements.

[0088] In this embodiment, the extreme point D i * The corresponding position is the center and the sliding screening is performed on both sides, and a point is satisfied so that the difference between the point and the mean is less than the extreme point D. i * The point with a 10% difference from the mean is defined as the endpoint of the peak interval, that is, the point on the left of the extreme point is the starting point of the peak interval, and the point on the right of the extreme point is the end point of the peak. i Make A little on the right i Make The peak interval is M i =[a i ,b i ]; process the peak interval set finally screened out, merge the same set elements, and obtain the final peak set M.

[0089] Optionally, this embodiment selects the optical fiber temperature measurement value corresponding to the first moment of October 2023 to establish a temperature data set A = {A1, A2, ... A n}, establish the location set L corresponding to the temperature data = {L1, L2, ... L n}, the trimmed mean is used to obtain the mean μ=20.1, the standard deviation σ=1.001 of the data set, and the threshold ε=3.003.

[0090] In this embodiment, the distance interval [5982, 6678] is taken as an example to establish a temperature maximum value set D = {D1, D2, D3, ...}, and the maximum temperature data corresponds to a fiber position point set F = {F1, F2, F3, ...}. Based on the mean μ and standard deviation σ of the data set A, typical effective maximum values ​​are selected to form an effective maximum value set D * ={23.24,23.09,23.17}, the corresponding effective maximum value position set can be obtained as F * ={6032,6335,6611}.

[0091] Define the temperature peak interval set as M = {M1, M2, M3, ...}, based on the effective maximum temperature set D * and the set of valid maximum positions F * According to the effective peak screening rules, by screening and merging the same set elements, the effective peak interval set is obtained as M = {M1, M2, M3}, where M1 = [6022, 6042], M2 = [6311, 6348], and M3 = [6605, 6615].

[0092] The second time period in this embodiment is the most recent December to February. The temperature data of the most recent second time period is searched forward from the current time t to obtain the date with the lowest temperature in the second time period, and the temperature data of a time t within the date is obtained. * The corresponding optical fiber temperature measurement value establishes the temperature data set A * The location set L corresponding to the temperature data * , and obtain the effective minimum value set S * and the effective minimum position set R * , and screen the effective concave interval set N;

[0093] Among them, the effective minimum value set S is obtained * and the effective minimum position set R * , including the following steps:

[0094] Based on the temperature data set A * , establish the temperature minimum value set S and the optical fiber position point set R corresponding to the minimum temperature data; calculate the data set A based on the trimmed mean * The mean μ * and standard deviation σ * , based on the data set A * The mean μ * and standard deviation σ * Screen and obtain the effective minimum value set S * and the effective minimum position set R * .

[0095] Specifically, search forward from the current time t to select the temperature data from the most recent December to February, select the date with the lowest temperature from the most recent December to February based on the marine meteorological data, and select a time t within that date. * The corresponding optical fiber temperature data is defined as A * ={A * 1,A * 2,...A * n}, define A * i The optical fiber is at the current t * The temperature value of the i-th position at the moment, establish the position set L corresponding to the temperature data * ={L * 1,L * 2,...L * n}, define L * i The optical fiber is at the current t * The position corresponding to the i-th temperature value at the moment.

[0096] Refer to the above method of using the trimmed mean to find the mean μ and standard deviation σ of the data set A. * The mean μ * and standard deviation σ * ; I will not go into details here.

[0097] In this embodiment, based on data set A * The mean μ * and standard deviation σ * Screen and obtain the effective minimum value set S * and the effective minimum position set R * , including the following steps:

[0098] S210: Setting the judgment threshold ε of the minimum point * =3σ * , traverse the data set A containing temperature data * , check each temperature value one by one, for each temperature value, the mean μ will be satisfied * With temperature data A * i The difference is greater than the threshold ε * The data is added to S, and according to the minimum value element in the set S, in the position set L * Find the corresponding minimum value positions and fill them into the set R one by one;

[0099] Specifically, based on the data set A *Establish a temperature minimum set S, define S = {S1, S2, S3, ...}, S i is the temperature value of the i-th minimum position of the optical fiber, and the set of optical fiber position points corresponding to the minimum temperature data is established as R, and R = {R1, R2, R3, ...}, R j is the optical fiber position corresponding to the jth minimum value. Based on the data set A * The mean μ * and standard deviation σ * Follow the steps below to filter the effective minimum value set: Sub-step 1. Set the judgment threshold ε of the minimum value point * =3σ * , traverse the data set A containing temperature data * , check each temperature value one by one, for each temperature value, the mean μ will be satisfied * With temperature data A * i The difference is greater than the threshold ε * The data is added to S, that is, μ * -A * i >ε * , A * i Fill in the set S one by one, and according to the minimum value element in the set S, in the position set L * Find the corresponding minimum positions in and fill them into the set R one by one.

[0100] S211: From the current t * Select x consecutive time points forward to obtain the corresponding x sets of temperature data, and add the minimum points that meet the conditions to the effective minimum data set S * , the corresponding effective minimum value location set is R * .

[0101] In order to reduce the measurement jitter error, based on the minimum value position set R, the effective minimum value and its position set are screened out through the following conditions: the temperature data of the continuous x groups of the optical fiber position corresponding to each minimum value point meets the above minimum value judgment conditions. The specific steps are as follows: * Select x consecutive time points t forward in time * ,t * -1,...t * -x, get the corresponding x groups of temperature data, add the minimum points that meet the conditions to the data set S of effective minimum values * ={S * 1,S * 2,S * 3,...}, the corresponding effective minimum value position set can be obtained as R * = {R* 1,R * 2,R * 3,...}.

[0102] Define the temperature depression position set as N = {N1, N2, N3, ...}, where N i is the i-th concave interval N i =[c i ,d i ], c i is the starting position of the ith depression, d i is the end position of the i-th concavity. The effective minimum value set S * and the effective minimum position set R * Filter the effective concave interval.

[0103] The screening of the effective concave interval set N includes the following steps:

[0104] S212: Sliding screening is performed on both sides with the position corresponding to the extreme point as the center;

[0105] S213: defining a point that satisfies a preset condition that the difference between the mean and the point is less than the difference between the mean and the extreme point as the endpoint of the concave interval; that is, the point on the left side of the extreme point is the starting point of the concave interval, and the point on the right side of the extreme point is the end point of the concave interval, thereby obtaining the concave interval;

[0106] S214: Process the screened concave interval set and merge the same set elements.

[0107] In this embodiment, the minimum point The corresponding position is the center and the sliding screening is performed on both sides to meet a point that makes the difference between the mean and the point smaller than the difference between the mean and the extreme point. The point of the 10% condition of the difference is defined as the endpoint of the concave interval, that is, the point on the left of the extreme point is the starting point of the concave interval, and the point on the right of the extreme point is the end point of the concave interval. i Make A little on the right i Make The concave interval is N i =[c i ,d i ]; Sub-step 2. Process the finally screened concave interval set, merge the same set elements, and obtain the final concave set N.

[0108] Optionally, this embodiment searches forward from the current moment to select the temperature data from the most recent December to February, selects the optical fiber temperature data corresponding to the first moment of February 2023 according to the marine meteorological data, and defines the optical fiber temperature data set as A* ={A * 1,A * 2,...A * n}, establish the location set L corresponding to the temperature data * ={L * 1,L * 2,...L * n}, use the same method as step 2 to find the data set A * The mean μ * =5.9 and standard deviation σ * =0.979, we can get the threshold ε * =2.937.

[0109] Based on data set A * Establish a temperature minimum value set S = {S1, S2, S3, ...}, establish a minimum temperature data corresponding to the optical fiber position point set R = {R1, R2, R3, ...}, based on the data set A * The mean μ * and standard deviation σ * Screen out typical effective minimum values ​​to form an effective minimum value set S * ={1.52,1.23}, the corresponding effective minimum value position set can be obtained as R * ={6030,6340};

[0110] Define the temperature depression position set as N = {N1, N2, N3, ...}, based on the effective minimum temperature set S * and the effective minimum position set R * According to the screening rules of effective concavities, by screening and merging the same set elements, the effective concavity interval set is obtained as N = {N1, N2}, where N1 = [6022, 6040], N2 = [6311, 6345].

[0111] S3: Define the anomaly types as shallow buried anomalies and insulation anomalies, add the peak interval set M and the peak interval set N whose overlap exceeds the preset threshold λ to the shallow buried anomaly peak interval set P; the peak interval set M filters out the shallow buried anomaly peak interval set P to obtain the insulation anomaly peak set Q, and evaluate the status of the insulation anomaly peak.

[0112] Among them, the peak intervals whose overlap with the peak interval set M and the depression interval set N exceeds the preset threshold λ are added to the shallow buried abnormal peak interval set P. The specific steps of screening the peaks caused by shallow buried abnormalities are:

[0113] S31: establishing a peak interval set of shallow buried anomalies as P;

[0114] S32: Add the peak intervals that satisfy any of the following conditions to the set P:

[0115] The temperature peak interval and the temperature depression interval overlap more than the specified threshold λ;

[0116] The interval of the temperature peak and the interval of the temperature depression include each other.

[0117] Specifically, in this embodiment, the abnormal types of submarine cable temperature spikes are divided into shallow burial anomalies and insulation anomalies. The shallow burial anomaly spike is caused by the shallow burial of the submarine cable. The seabed temperature in summer is lower than the seawater temperature, and the opposite is true in winter. Therefore, the temperature data characteristics of the shallow burial anomaly are that it shows a peak trend in summer and a concave trend in winter.

[0118] The peak intervals of the peak set M and the depression set N that overlap more than the specified threshold λ (80%) are added to the shallow buried anomaly peak interval set P. The difference set of the peak interval set and the shallow buried anomaly peak interval set is the insulation anomaly peak set. According to steps S31 and S32, the peaks caused by shallow buried anomalies are filtered.

[0119] Specifically, the peak interval set of shallow buried anomalies is established as P. Specifically, define P i ={P1,P2,P3...}, where P i is the peak interval P of the i-th shallow buried anomaly i =[a i ,b i ].

[0120] The set of known effective temperature peaks is M = {M1, M2, M3, ...}, where M i =[a i ,b i ], the temperature effective depression set is N = {N1, N2, N3, ...}, where N j =[c j ,d j ], add the peak intervals that meet any of the following conditions into the peak interval set P of shallow buried anomalies:

[0121] Condition 1: The temperature peak interval and the temperature depression interval overlap more than the specified threshold λ (80%), that is, a i <c j <b i <d j And (b i -c j ) / (b i -a i )>=λor c j <a i <d j <b i And (dj -a i ) / (d j -c j )>=λ;

[0122] Condition 2: The interval of the temperature peak and the interval of the temperature depression are mutually inclusive, that is, a i <c j <d j <b i or c j <a i <b i <d j .

[0123] Establish the peak interval set Q caused by insulation anomaly, defined as Q = {Q1, Q2, Q3, ...}, where Q i is the peak interval Q of the i-th insulation anomaly i =[a i ,b i ], after filtering out the peaks caused by shallow burial anomalies from all the peak intervals M, the remaining abnormal peaks are caused by insulation anomalies, that is, Q=MP is the abnormal peak caused by insulation anomalies, and it is necessary to evaluate and analyze the peak state caused by insulation anomalies.

[0124] In this embodiment, the peak intervals of the peak set M and the depression set N that overlap more than the specified threshold λ (80%) are added to the shallow buried abnormal peak interval set P. The difference set of the peak interval set and the shallow buried abnormal peak interval set is the insulation abnormal peak set. As mentioned above, the interval overlap degree of M1 and N1 is 90%, and the interval overlap degree of M2 and N2 is 92%. It can be obtained that the shallow buried abnormal peak interval set P = {P1, P2}, where P1 = [6022, 6042], P2 = [6311, 6348];

[0125] After filtering out the peaks caused by shallow burial anomalies from all the peak intervals M, the remaining abnormal peaks are caused by insulation anomalies, that is, Q = MP, and Q = {Q1} can be obtained, where Q1 = [6605, 6615] is the abnormal peak interval caused by insulation anomalies. It is necessary to evaluate and analyze the peak state caused by insulation anomalies;

[0126] S4: Establish a temperature sequence H of the abnormal observation point X; establish a temperature sequence W of the comparison observation point Y for the abnormal observation point X; define a temperature difference sequence Z as a set of differences between the temperature sequence H of the abnormal observation point X and the temperature sequence W of the comparison observation point Y at the same time;

[0127] Specifically, the abnormal observation point X is defined as the insulation abnormality peak interval Q i =[a i ,b i], and establish the temperature sequence H = {H1, H2, H3...} of the abnormal observation point X. The temperature sequence H takes a month as a period, where H i Represents the average temperature value of the i-th month.

[0128] Follow the steps below to calculate the temperature sequence H of the abnormal observation point X i :

[0129] S41: Starting from the first data of each month, select the temperature measurement values ​​corresponding to n consecutive time points and establish H i Its own time detection sequence H i· ={H i1 ,H i2 ,...H in};

[0130] S 42: Take the average value of n detection values ​​as H i The temperature of H i =(H i1 +H i2 +...+H in ) / n;

[0131] In this embodiment, a temperature sequence W = {W1, W2, W3, ...} is established for the abnormal observation point X to compare the observation point Y. The temperature sequence W has a monthly period, where W i Represents the average temperature value of the i-th month.

[0132] Follow the steps below to calculate and compare the temperature series W of observation point Y:

[0133] S43: Define abnormal peak interval Q i =[a i ,b i ]'s starting position a i Select m consecutive fiber position points that are not extreme points to form a temperature stable spatial sequence U = {U1, U2, ...U m}, where U j Represents the jth position selected that is not an extreme point, and the corresponding temperature data sequence is W i· = {W i1 ,W i2 ,...W im}, where W ij Represents the temperature data corresponding to the jth position in the i-th month, i=1,2,3...; j=1,2,...m;

[0134] S44: Establish W ij Its own time detection sequence W ij· = {W ij1,W ij2 ,...W ijn}, where W ijk Represents the temperature data corresponding to the kth moment at the jth position in the i-th month, i = 1, 2, 3...; j = 1, 2, ...m; k = 1, 2, ...n, the selected time point is the same as the time point selected for the abnormal observation point, and the temperature measurement value corresponding to the n time points is selected;

[0135] S45: Detection sequence W i·· Averaging in time and space, we get the temperature sequence W = {W1, W2, W3, ...} of the comparison observation point Y, that is,

[0136]

[0137] In this embodiment, the abnormal observation point X is defined as the optical fiber position corresponding to the highest temperature value in the insulation abnormality peak interval Q1 = [6605, 6615], and X = 6611 is obtained. The temperature sequence H = {H1, H2, H3...} of the abnormal observation point X is established. The temperature sequence H has a monthly period, where H i represents the average temperature value of the i-th month, and the temperature sequence H of the abnormal observation point X is calculated according to steps S41 and S42. i , including: starting from the first data of each month, select the temperature measurement values ​​corresponding to 9 consecutive time points, establish H i Its own time detection sequence H i· ={H i1 ,H i2 ,...H i9}; Take the average of the 9 test values ​​as H i The temperature of H i =(H i1 +H i2 +...+H i9 ) / 9.

[0138] For the abnormal observation point X, a temperature sequence W = {W1, W2, W3, ...} is established for the comparison observation point Y. The temperature sequence W has a monthly period, where W i represents the average temperature value of the i-th month, and the temperature sequence W of the observation point Y is calculated and compared according to steps S43 to S45, including:

[0139] Define the abnormal peak interval Q i =[a i ,b i ]'s starting position a i Select 40 consecutive fiber position points that are not extreme points to form a temperature stable spatial sequence U = {U1, U2, ... U 40}, where Uj Represents the jth position selected that is not an extreme point, and the corresponding temperature data sequence is W i = {W i1 ,W i2 ,...W i40}, where W ij Represents the temperature data corresponding to the jth position in the i-th month, i=1,2,3...; j=1,2,...40;

[0140] Build W ij Its own time detection sequence W ij ·={W ij1 ,W ij2 ,...W ij9}, where W ijk Represents the temperature data corresponding to the kth moment at the jth position in the i-th month, i=1,2,3...; j=1,2,...40; k=1,2,...9, the selected time point is the same as the time point selected for the abnormal observation point, and the temperature measurement values ​​corresponding to 9 time points are selected;

[0141] The detection sequence W i·· Averaging in time and space, we get the temperature sequence W = {W1, W2, W3, ...} of the comparison observation point Y, that is,

[0142]

[0143] Define the temperature difference sequence Z = {Z1, Z2, Z3...} as the difference set between the abnormal observation sequence H and the comparison observation sequence W at the same time, where Z l is the temperature difference in the first month. Calculate Z according to the following steps: l :Temperature difference Z = abnormal observed temperature sequence - contrast observed temperature sequence, that is, Z l =H l -W l ; The following data can be obtained in Table 1:

[0144] Table 1 Insulation abnormality peak detection value (unit ℃)

[0145] time Mean variance Abnormal observation point temperature Compare the temperature of the observation point Temperature difference Z 2022.07.01 14.3 0.987 15.23 14.14119486 1.08880514 2022.08.01 14.5 1.102 15.54111111 14.74597432 0.79513679 2022.09.01 17.5 0.956 18.69222222 17.72072585 0.97149637 2022.10.01 18.6 1.061 19.74 18.64045784 1.09954216 2022.11.01 16.6 1.007 19.14666667 16.89759352 2.24907315 2022.12.01 13.4 1.025 15.83111111 13.77972641 2.0513847 2023.01.01 9.2 0.994 12.78555556 9.152183138 3.633372422 2023.02.01 5.9 0.979 9.577777778 5.828598548 3.74917923 2023.03.01 3.8 0.994 7.531111111 3.973595757 3.557515354 2023.04.01 4.4 1.008 7.014444444 4.525276382 2.489168062 2023.05.01 6.3 1.016 8.202222222 6.196063652 2.00615857 2023.06.01 8.6 1.100 10.62333333 8.681289782 1.942043548 2023.07.01 11.5 1.049 14.00333333 11.75288666 2.25044667 2023.08.01 16.2 0.991 17.78555556 16.48372976 1.3018258 2023.09.01 18.5 0.985 21.7 18.89083752 2.80916248 2023.10.01 20.1 1.001 23.03111111 19.83255165 3.19855946

[0146] S5: Set the temperature difference status evaluation threshold The abnormal status of the submarine cable is evaluated by analyzing the growth trend of the temperature difference.

[0147] Among them, set the temperature difference state evaluation threshold By analyzing the growth trend of the temperature difference Z, the abnormal state of the submarine cable is evaluated, including:

[0148] If the temperature difference Z and the temperature difference state evaluation threshold If the preset condition is met, the evaluation result of the abnormal peak is deterioration.

[0149] Specifically, the jitter error can be calculated as σ, and the state evaluation threshold of the temperature difference is defined as The method for evaluating the state of the abnormal peak state includes a preset condition. If the temperature difference sequence meets the preset condition, the evaluation result of the state of the abnormal peak is judged to be deteriorated.

[0150] The preset conditions in this embodiment include at least any one of conditions one to three, wherein:

[0151] Condition 1: The temperature difference Z shows a trend of increasing over time, and the growth exceeds the temperature difference state assessment threshold That is, the temperature difference Z = {Z1, Z2, Z3...} shows a trend of increasing over time, and the growth exceeds the threshold That is, Z1<Z2<Z3...<Z l ,

[0152] Condition 2: The year-on-year increase in temperature difference Z compared to previous years exceeds the temperature difference status assessment threshold That is, the temperature difference Z={Z1,Z2,Z3...} has increased by more than the threshold compared with previous years That is Z l-12r <Z l ,

[0153] Condition 3: In the set of differences between the temperature difference Z and the temperature difference of each month in the past year, there is an element greater than the temperature difference state assessment threshold That is, the temperature difference Z l In the set of differences between the temperature difference of each month in the past year, there is an element greater than the threshold That is, establish a difference set G = {G l,l-(l-1) ,G l,l-(l-2) ,...G l,l-(l-11)}, where G l,i =Z l -Z i ,

[0154] In this embodiment, the jitter error obtained above is σ=1.001, and the state evaluation threshold of the temperature difference is defined as It can be analyzed from the above chart that, excluding the influence of seasonal factors, the temperature difference on October 1, 2023 is 2.10℃ higher than the temperature difference on October 1, 2022, exceeding the threshold, so the status assessment result of the insulation abnormality spike is deterioration.

[0155] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for detecting insulation anomalies and evaluating the state of a submarine cable based on optical fiber temperature measurement, characterized in that: The steps include: S1: acquiring temperature data corresponding to a dynamic cable target position and a target time point based on a distributed optical fiber acoustic wave sensing device, wherein the temperature data at least includes historical temperature data of a first time period close to the dynamic cable target position and the current time t; S2: Obtain the optical fiber temperature measurement value corresponding to the current time t to establish the temperature data set A and the location set L corresponding to the temperature data, and obtain the effective maximum value set D * and the set of valid maximum positions F * , filter the effective peak interval set M; Search forward from the current time t to select the temperature data of the nearest second time period, obtain the date with the lowest temperature in the second time period, and obtain a time t within the date * The corresponding optical fiber temperature measurement value establishes the temperature data set A * The location set L corresponding to the temperature data * , and obtain the effective minimum value set S * and the effective minimum position set R * , and screen the effective concave interval set N; S3: define the anomaly types as shallow buried anomaly and insulation anomaly, add the peak interval set M and the peak interval set N whose overlap exceeds the preset threshold λ to the shallow buried anomaly peak interval set P; the peak interval set M filters out the shallow buried anomaly peak interval set P to obtain the insulation anomaly peak set Q, and evaluate the status of the insulation anomaly peaks; S4: Establish a temperature sequence H of the abnormal observation point X; establish a temperature sequence W of the comparison observation point Y for the abnormal observation point X; define a temperature difference sequence Z as a set of differences between the temperature sequence H of the abnormal observation point X and the temperature sequence W of the comparison observation point Y at the same time; S5: Set the temperature difference status evaluation threshold The abnormal status of the submarine cable is evaluated by analyzing the growth trend of the temperature difference.

2. The method for detecting insulation anomalies and evaluating the state of submarine cables based on optical fiber temperature measurement according to claim 1 is characterized in that: In step S2, the effective maximum value set D is obtained * and the set of valid maximum positions F * , including the following steps: Based on the data set A, a temperature maximum value set D and a fiber position point set F corresponding to the maximum temperature data are established; Based on the trimmed mean, the mean μ and standard deviation σ of the data set A are calculated. Based on the mean μ and standard deviation σ of the data set A, the effective maximum value set D is obtained by screening. * and the set of valid maximum positions F * .

3. The method for detecting insulation anomalies and evaluating the state of submarine cables based on optical fiber temperature measurement according to claim 2 is characterized in that: Get the effective maximum value set D * and the set of valid maximum positions F * , specifically including the following steps: Set the judgment threshold of the maximum point ε = 3σ, traverse the data set A containing temperature data, check each temperature value one by one, and add the data that satisfies the difference between the temperature data and the mean μ greater than the threshold ε to D. According to the maximum value elements in the set D, find the corresponding maximum value position in the position set L, and fill them into the set F one by one; Select x consecutive time points from the current time t to obtain the corresponding x sets of temperature data, and add the maximum points that meet the conditions to the effective maximum data set D * , and obtain the corresponding effective maximum value location set F * .

4. The method for detecting insulation anomalies and evaluating the state of submarine cables based on optical fiber temperature measurement according to claim 1 is characterized in that: In step S2, the effective minimum value set S is obtained * and the effective minimum position set R * , including the following steps: Based on the temperature data set A * , establish a temperature minimum value set S and a fiber position point set R corresponding to the minimum temperature data; Find the data set A based on the trimmed mean * The mean μ * and standard deviation σ * , based on the data set A * The mean μ * and standard deviation σ * Screen and obtain the effective minimum value set S * and the effective minimum position set R * .

5. The method for detecting insulation anomalies and evaluating the state of submarine cables based on optical fiber temperature measurement according to claim 4 is characterized in that: Based on data set A * The mean μ * and standard deviation σ * Screen and obtain the effective minimum value set S * and the effective minimum position set R * , including the following steps: Set the judgment threshold ε of the minimum point * =3σ * , traverse the data set A containing temperature data * , check each temperature value one by one, for each temperature value, the mean μ will be satisfied * With temperature data A * i The difference is greater than the threshold ε * The data is added to S, and according to the minimum value element in the set S, in the position set L * Find the corresponding minimum value positions and fill them into the set R one by one; From the current t * Select x consecutive time points forward to obtain the corresponding x sets of temperature data, and add the minimum points that meet the conditions to the effective minimum data set S * , the corresponding effective minimum value location set is R * .

6. The method for detecting insulation anomalies and evaluating the state of submarine cables based on optical fiber temperature measurement according to any one of claims 1 to 5, characterized in that: In step S2, the effective peak interval set M and the effective depression interval set N are selected, which specifically includes: Slide and filter to both sides with the position corresponding to the extreme point as the center; The point that satisfies the preset condition that the difference between the point and the mean is less than the difference between the extreme point and the mean is defined as the endpoint of the peak interval; that is, the point on the left of the extreme point is the starting point of the peak interval, and the point on the right of the extreme point is the end point of the peak, and the peak interval is obtained; The point that satisfies the preset condition that the difference between the mean and the point is less than the difference between the mean and the extreme point is defined as the endpoint of the concave interval; that is, the point on the left of the extreme point is the starting point of the concave interval, and the point on the right of the extreme point is the end point of the concave, and the concave interval is obtained; The filtered peak interval set or concave interval set is processed and the same set elements are merged.

7. The method for detecting insulation anomalies and evaluating the state of submarine cables based on optical fiber temperature measurement according to claim 2 or 4, characterized in that: Find the mean μ and standard deviation σ of data set A based on the trimmed mean, and find the mean μ and standard deviation σ of data set A based on the trimmed mean. * The mean μ * and standard deviation σ * , specifically including the following steps: Data set A or data set A * Sort all the data in by size to get data set B; Remove a certain proportion of the maximum and minimum values ​​in the data set, and determine the proportion of removed data to be α. Then remove α / 2 proportions from both ends of the data set B, that is, remove nα / 2 data from both ends, and obtain data set C; Find the mean of data set C; Calculate the data set A or data set A based on the obtained mean * The standard deviation of .

8. The method for detecting insulation anomalies and evaluating the state of submarine cables based on optical fiber temperature measurement according to any one of claims 1 to 5, characterized in that: In step S3, the peak intervals of the peak interval set M and the depression interval set N that overlap by more than a preset threshold λ are added to the shallow buried abnormal peak interval set P. The specific steps of screening the peaks caused by the shallow buried abnormality are: S31: establishing a peak interval set of shallow buried anomalies as P; S32: Add the peak intervals that satisfy any of the following conditions to the set P: The temperature peak interval and the temperature depression interval overlap more than the specified threshold λ; The interval of the temperature peak and the interval of the temperature depression include each other.

9. The method for detecting insulation anomalies and evaluating the state of submarine cables based on optical fiber temperature measurement according to any one of claims 1 to 5, characterized in that: In step S5, the temperature difference state evaluation threshold is set By analyzing the growth trend of the temperature difference Z, the abnormal state of the submarine cable is evaluated, including: If the temperature difference Z and the temperature difference state evaluation threshold If the preset condition is met, the evaluation result of the abnormal peak is deterioration.

10. The method for detecting insulation anomalies and evaluating the state of submarine cables based on optical fiber temperature measurement according to claim 9, characterized in that: The preset conditions include at least any one of the following conditions, wherein: Condition 1: The temperature difference Z shows a trend of increasing over time, and the growth exceeds the temperature difference state assessment threshold Condition 2: The year-on-year increase in temperature difference Z compared to previous years exceeds the temperature difference status assessment threshold Condition 3: In the difference set between the temperature difference Z and the temperature difference of each month in the past year, there is an element greater than the temperature difference state assessment threshold Wherein, the temperature difference state evaluation threshold

Images