Method and device for determining buried state of submarine cable, storage medium and electronic equipment

Abstract

The present disclosure relates to a submarine cable burying state determination method and device, a storage medium and an electronic device, wherein the method comprises: determining real-time optical fiber temperature data of a submarine cable; determining real-time optical fiber temperature transient characteristics according to the real-time optical fiber temperature data; determining the burying state of the submarine cable according to the real-time optical fiber temperature transient characteristics and a preset depth threshold. The preset depth threshold is obtained by processing a historical optical fiber temperature transient characteristic set by a K-means clustering analysis method. The burying state of the submarine cable can be accurately estimated when the seabed surface temperature and the seabed depth temperature are the same or similar.

Description

Technical Field

[0001] This disclosure relates to the field of data processing technology, and more specifically, to a method, apparatus, storage medium, and electronic equipment for determining the burial status of submarine cables. Background Technology

[0002] Submarine cables are cables laid on the seabed for telecommunications transmission. To ensure the safety of submarine cables, the burial depth is usually set between two and three meters. However, due to ocean currents eroding the seabed and issues with the quality of cable laying, there may be cases where the burial depth is too shallow or the cables are exposed on the seabed surface, which can easily lead to corrosion and damage.

[0003] In related technologies, the burial status of submarine cables is typically monitored based on the temperature difference between the seabed surface temperature and the temperature at seabed depth. However, when using this method to estimate the burial status of submarine cables, if the seabed surface temperature and the temperature at seabed depth are the same or similar, the burial status of the submarine cables cannot be accurately estimated. Summary of the Invention

[0004] The purpose of this disclosure is to provide a method, apparatus, storage medium, and electronic device for determining the burial status of submarine cables in order to solve the aforementioned related problems.

[0005] To achieve the above objectives, in a first aspect, this disclosure provides a method for determining the burial status of a submarine cable, the method comprising the following steps:

[0006] Determine the real-time fiber optic temperature data of the submarine cable;

[0007] Based on the real-time fiber optic temperature data, determine the transient characteristics of the real-time fiber optic temperature;

[0008] The burial status of the submarine cable is determined based on the real-time transient characteristics of optical fiber temperature and the preset depth threshold.

[0009] The preset depth threshold is obtained by processing the historical fiber temperature transient feature set using the K-means clustering analysis method. The preset depth threshold is used to characterize the critical value between the deep burial state and the shallow burial state of the submarine cable. The historical fiber temperature transient feature set is used to characterize the feature set of fiber temperature rise amplitude and fiber temperature rise time corresponding to each depth data during the historical current step time period of the submarine cable.

[0010] Optionally, the method further includes:

[0011] Determine the first fiber temperature dataset of the submarine cable within a historical time period;

[0012] Based on the first optical fiber temperature dataset, the historical optical fiber temperature transient feature set is determined;

[0013] K-means clustering analysis was performed on the historical fiber temperature transient feature set to obtain the preset depth threshold.

[0014] Optionally, the step of performing K-means clustering analysis on the historical fiber temperature transient feature set to obtain the preset depth threshold includes:

[0015] The historical fiber temperature transient feature set was clustered using the K-means clustering method to obtain the cluster centers and the datasets within each cluster.

[0016] Based on the cluster centers and the dataset within the clusters, a first Euclidean distance is determined, where the first Euclidean distance is the farthest distance between the data within the clusters and the cluster centers.

[0017] The preset depth threshold is determined based on the first Euclidean distance.

[0018] Optionally, determining the historical fiber temperature transient feature set based on the first fiber temperature dataset includes:

[0019] The first fiber optic temperature dataset is smoothed using a locally weighted linear regression method to obtain the second fiber optic temperature dataset.

[0020] The second fiber temperature dataset is processed by the difference calculation method to obtain the historical fiber temperature transient feature set.

[0021] Optionally, determining the first fiber optic temperature dataset of the submarine cable within a historical time period includes:

[0022] Determine multiple depth data points of the submarine cable within a historical time period, as well as the time periods during which current steps occurred;

[0023] During the time period in which the current step occurs, the fiber temperature data corresponding to each depth data is extracted to obtain multiple fiber temperature data.

[0024] The first fiber optic temperature dataset is composed of multiple fiber optic temperature data.

[0025] Optionally, determining the burial status of the submarine cable based on the real-time transient characteristics of the optical fiber temperature and the preset depth threshold includes:

[0026] Based on the real-time fiber temperature transient characteristics and the objective function, real-time clustering data points are determined, wherein the objective function is the clustering result of clustering analysis of the historical fiber temperature transient characteristic set using the K-means clustering method;

[0027] The second Euclidean distance is determined based on the real-time clustering data points and the cluster centers;

[0028] The burial status of the submarine cable is determined based on the second Euclidean distance and the preset depth threshold.

[0029] Optionally, determining the burial status of the submarine cable based on the second Euclidean distance and the preset depth threshold includes:

[0030] When the second Euclidean distance is greater than the preset depth threshold, the burial state of the submarine cable is determined to be shallow burial.

[0031] When the second Euclidean distance is less than the preset depth threshold, the burial state of the submarine cable is determined to be deep burial.

[0032] Secondly, this disclosure provides a device for determining the burial status of submarine cables, the device comprising a first determining module, a second determining module and a third determining module;

[0033] The first determining module is used to determine the real-time fiber optic temperature data of the submarine cable;

[0034] The second determining module is used to determine the transient characteristics of real-time fiber temperature based on the real-time fiber temperature data.

[0035] The third determining module is used to determine the burial status of the submarine cable based on the real-time transient characteristics of the optical fiber temperature and a preset depth threshold.

[0036] The preset depth threshold is obtained by processing the historical fiber temperature transient feature set using the K-means clustering analysis method. The depth threshold is used to characterize the critical value between the deep burial state and the shallow burial state of the submarine cable. The fiber temperature transient feature set is used to characterize the feature set of fiber temperature rise amplitude and fiber temperature rise time corresponding to each depth data during the historical current step time period of the submarine cable.

[0037] Optionally, the device further includes:

[0038] The fourth determining module is used to determine the first optical fiber temperature dataset of the submarine cable within a historical time period.

[0039] The fifth determining module is used to determine the historical fiber temperature transient feature set based on the first fiber temperature dataset;

[0040] The first analysis module is used to perform K-means clustering analysis on the historical fiber temperature transient feature set to obtain the preset depth threshold.

[0041] Optionally, the first analysis module includes:

[0042] The second analysis module is used to perform cluster analysis on the historical fiber temperature transient feature set using the K-means clustering method to obtain the cluster centers and the dataset within each cluster.

[0043] The sixth determining module is used to determine a first Euclidean distance based on the cluster center and the dataset within the cluster, wherein the first Euclidean distance is the farthest distance between the data within the cluster and the cluster center;

[0044] The seventh determining module is used to determine the preset depth threshold based on the first Euclidean distance.

[0045] Optionally, the fifth determining module includes:

[0046] The processing module is used to smooth the first fiber optic temperature dataset using a locally weighted linear regression method to obtain the second fiber optic temperature dataset.

[0047] The calculation module is used to process the second optical fiber temperature dataset using a difference calculation method to obtain the historical optical fiber temperature transient feature set.

[0048] Optionally, the fourth determining module includes:

[0049] The first sub-determination module is used to determine multiple depth data of the submarine cable within a historical time period and the time period in which the current step occurred;

[0050] The data extraction module is used to extract the fiber temperature data corresponding to each depth data during the time period in which the current step occurs, so as to obtain multiple fiber temperature data.

[0051] The data integration module is used to combine multiple optical fiber temperature data into the first optical fiber temperature dataset.

[0052] Optionally, the third determining module includes:

[0053] The second sub-determination module is used to determine real-time clustering data points based on the real-time fiber temperature transient characteristics and the objective function, wherein the objective function is the clustering result of clustering analysis of the historical fiber temperature transient characteristic set by the K-means clustering method;

[0054] The third sub-determination module is used to determine the second Euclidean distance based on the real-time clustering data points and the cluster centers;

[0055] The fourth sub-determination module is used to determine the burial status of the submarine cable based on the second Euclidean distance and the preset depth threshold.

[0056] Optionally, the fourth sub-determining module is used to:

[0057] When the second Euclidean distance is greater than the preset depth threshold, the burial state of the submarine cable is determined to be shallow burial.

[0058] When the second Euclidean distance is less than the preset depth threshold, the burial state of the submarine cable is determined to be deep burial.

[0059] Thirdly, this disclosure provides a non-transitory computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of any of the methods provided in the first aspect of this disclosure.

[0060] Fourthly, this disclosure provides an electronic device, comprising:

[0061] A memory on which computer programs are stored;

[0062] A processor for executing the computer program in the memory to implement the steps of the method according to any of the first aspects of this disclosure.

[0063] The above technical solution first determines the real-time fiber optic temperature data of the submarine cable. Then, based on this real-time temperature data, the transient characteristics of the real-time fiber optic temperature are determined. Next, based on these transient characteristics and a preset depth threshold, the burial status of the submarine cable is determined. The preset depth threshold is obtained by processing historical fiber optic temperature transient characteristic sets using K-means clustering analysis. This allows for accurate estimation of the burial status of the submarine cable when the seabed surface temperature and seabed depth temperature are the same or similar.

[0064] Other features and advantages of this disclosure will be described in detail in the following detailed description section. Attached Figure Description

[0065] The accompanying drawings are provided to further illustrate the present disclosure and form part of the specification. They are used together with the following detailed description to explain the present disclosure, but do not constitute a limitation thereof. In the drawings:

[0066] Figure 1 This is a schematic diagram illustrating a method for determining the burial status of a submarine cable according to an exemplary embodiment of this disclosure.

[0067] Figure 2 This is a schematic diagram illustrating the calculation of historical fiber temperature transient feature sets according to an exemplary embodiment of this disclosure.

[0068] Figure 3 This is a flowchart illustrating a method for determining the burial status of a submarine cable according to an exemplary embodiment of the present disclosure.

[0069] Figure 4 This is a schematic diagram of a device for determining the burial status of a submarine cable according to an exemplary embodiment of the present disclosure.

[0070] Figure 5 This is a block diagram illustrating an electronic device according to an exemplary embodiment. Detailed Implementation

[0071] The specific embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit this disclosure.

[0072] It should be noted that all actions involving the acquisition of signals, information, or data in this disclosure are carried out in compliance with the relevant data protection laws and policies of the country where the location is situated, and with authorization from the owner of the relevant device.

[0073] Submarine cables are cables laid on the seabed for telecommunications transmission. To ensure the safety of submarine cables, the burial depth is usually set between two and three meters. However, due to ocean currents eroding the seabed and issues with the quality of cable laying, the burial depth may be too shallow or the cables may be exposed on the seabed surface. This can lead to corrosion from seawater, damage from ship anchors, and being dragged by fishing nets, causing corrosion to the submarine cables.

[0074] In related technologies, the burial status of submarine cables is typically monitored based on the temperature difference between the seabed surface temperature and the temperature at seabed depth. For example, in summer, the seabed surface temperature is higher than the temperature at seabed depth, while in winter, the seabed surface temperature is lower than the temperature at seabed depth. Therefore, during summer and winter, the burial status of submarine cables can be calculated based on the temperature difference between the seabed surface temperature and the temperature at seabed depth.

[0075] However, when using this method to estimate the burial status of submarine cables, if the surface temperature of the seabed and the temperature at the depth of the seabed are the same or similar, the burial status of the submarine cables cannot be accurately estimated.

[0076] In view of this, this disclosure provides a method, apparatus, storage medium and electronic equipment for determining the burial status of submarine cables, in order to solve the problem in the related art that the burial status of submarine cables cannot be accurately estimated when the surface temperature of the seabed and the temperature at the depth of the seabed are the same or similar.

[0077] like Figure 1 As shown, Figure 1 This is a schematic diagram illustrating a method for determining the burial status of a submarine cable according to an exemplary embodiment of this disclosure, with reference to... Figure 1 The method includes:

[0078] S101: Determine the real-time fiber optic temperature data of the submarine cable;

[0079] S102: Determine the transient characteristics of real-time fiber optic temperature based on real-time fiber optic temperature data;

[0080] S103: Determine the burial status of the submarine cable based on the real-time transient characteristics of optical fiber temperature and the preset depth threshold.

[0081] The preset depth threshold is obtained by processing the historical fiber temperature transient feature set using the K-means clustering analysis method. The preset depth threshold is used to characterize the critical value between deep burial and shallow burial states of the submarine cable. The historical fiber temperature transient feature set is used to characterize the fiber temperature rise amplitude and fiber temperature rise time corresponding to each depth data during the historical current step time period of the submarine cable.

[0082] The above technical solution first determines the real-time fiber optic temperature data of the submarine cable. Then, based on this real-time temperature data, the transient characteristics of the real-time fiber optic temperature are determined. Next, based on these transient characteristics and a preset depth threshold, the burial status of the submarine cable is determined. The preset depth threshold is obtained by processing historical fiber optic temperature transient characteristic sets using K-means clustering analysis. This allows for accurate estimation of the burial status of the submarine cable when the seabed surface temperature and seabed depth temperature are the same or similar.

[0083] To enable those skilled in the art to better understand the methods for determining the burial status of submarine cables provided in this disclosure, detailed examples of the above methods are provided below.

[0084] For example, during operation, a current step phenomenon may occur in a submarine cable. A current step can be a change in the operating current of the submarine cable from low to high within one hour. The low current can be 10% of the rated current carrying capacity of the submarine cable, and the high current can be 70% of the rated current carrying capacity. This disclosure does not specifically limit the specific current step.

[0085] Therefore, real-time fiber optic temperature data can be obtained by collecting temperature data during the period when the submarine cable experiences a current step. This temperature data can be collected using distributed fiber optic sensors. The period when the current step occurs can be a period of stable low current for more than four hours or stable high current for more than four hours, ensuring that the acquired real-time fiber optic temperature data is obtained when the submarine cable is operating in a stable state.

[0086] For example, real-time fiber optic temperature data can be locally regressed using the least squares method and a first-order polynomial model in locally weighted linear regression to obtain the transient characteristics of real-time fiber optic temperature. These transient characteristics can be defined as the magnitude and duration of the temperature rise during the period of a current step in the submarine cable.

[0087] For example, the real-time transient characteristics of fiber optic temperature are compared with a preset depth threshold to determine the current burial status of the submarine cable. The burial status of the submarine cable can include deep burial and shallow burial, but this embodiment does not specifically limit this. The preset depth threshold can be used to characterize the critical value between deep and shallow burial of the submarine cable, and can be obtained by processing the historical transient characteristic set of fiber optic temperature using the K-means clustering method.

[0088] The preset depth threshold can be obtained through the following steps, among possible methods:

[0089] Determine the first fiber temperature dataset of the submarine cable within a historical time period;

[0090] Based on the first fiber temperature dataset, determine the historical fiber temperature transient feature set;

[0091] K-means clustering analysis was performed on the historical fiber temperature transient feature set to obtain a preset depth threshold.

[0092] It should be understood that during the course of operation, the depth of the submarine cable may change due to the fluctuation of seawater. Therefore, the first fiber temperature dataset can be a collection of multiple first fiber temperature data, and each first fiber temperature data can correspond to a depth of the submarine cable on the seabed.

[0093] Based on the temperature differences between the seabed surface and deep layers during summer and winter, this historical time period can be used to represent the operating periods of submarine cables in summer or winter over the years. A first fiber optic temperature dataset can be determined for each operating period during summer or winter. Then, based on this dataset, a historical fiber optic temperature transient feature set is determined. Finally, the historical fiber optic temperature transient feature set is processed using K-means clustering to obtain a preset depth threshold. The historical fiber optic temperature transient feature set can include multiple historical fiber optic temperature transient features, and each feature corresponds to a specific first fiber optic temperature dataset.

[0094] In one possible manner, based on the first fiber temperature dataset, a set of historical fiber temperature transient features is determined, including:

[0095] The first fiber optic temperature dataset was smoothed using a locally weighted linear regression method to obtain the second fiber optic temperature dataset.

[0096] The second fiber temperature dataset is processed using a difference calculation method to obtain a historical fiber temperature transient feature set.

[0097] It should be understood that by smoothing the first fiber optic temperature dataset using a locally weighted linear regression method, the smoothing window can be set to a value of 5 to obtain the second fiber optic temperature dataset. Setting the smoothing window to a value of 5 can eliminate the jitter error of the collected first fiber optic temperature dataset. Then, the second fiber optic temperature dataset is processed using a difference calculation method to obtain the historical temperature transient feature set. The locally weighted linear regression method can be either the least squares method or a first-order polynomial model method; this embodiment does not specifically limit its application.

[0098] Indicatively, refer to Figure 2 The historical fiber temperature transient feature set can be represented by the following formula:

[0099] F k =[ΔT,Δt]

[0100] ΔT=T H -T L

[0101] Δt=t H -t L

[0102] Among them, F k For the historical transient temperature feature set of optical fibers, T L T represents the average temperature at which the submarine cable operates stably under low current. H The average temperature of the submarine cable during stable operation at high current, t H For T H -0.1(T H -T L The corresponding time, t L For T H +0.1(T H -T L The corresponding time is ΔT, where ΔT is the temperature rise rate of the optical fiber and Δt is the temperature rise time of the optical fiber. The first optical fiber temperature dataset collected during the time period of the current step is processed using a locally weighted linear regression method to obtain a historical transient feature set of optical fiber temperature, which can improve the accuracy of judging the burial status of submarine cables.

[0103] In one possible approach, K-means clustering analysis is performed on the historical fiber temperature transient feature set to obtain a preset depth threshold, including:

[0104] The historical transient temperature feature set of optical fibers was clustered using the K-means clustering method to obtain the cluster centers and the datasets within each cluster.

[0105] Based on the cluster centers and the dataset within each cluster, determine the first Euclidean distance, which is the farthest distance between the data within each cluster and the cluster center.

[0106] Determine the preset depth threshold based on the first Euclidean distance.

[0107] It should be understood that when processing the historical fiber temperature transient feature set using the K-means clustering method, cluster centers and cluster results within each cluster can be obtained. The K-means clustering method clusters the historical fiber temperature transient feature set based on feature similarity, with the goal of clustering the historical fiber temperature transient feature set into a single set.

[0108] Schematic, the first Euclidean distance can be expressed by the following formula:

[0109]

[0110] Where, d max Let (x, y) be the first Euclidean distance, and (x, y) be the cluster center. k ,y k The data represents data within a cluster; and each historical fiber temperature transient feature corresponds to data within a single cluster. The preset depth threshold can be expressed by the following formula:

[0111]

[0112] in, The depth threshold for the submarine cable is denoted as 'a'. The value of 'a' can be 1.1, but this embodiment does not impose a specific limitation on it.

[0113] Among possible methods, determining the first fiber optic temperature dataset of the submarine cable over a historical time period includes:

[0114] Determine multiple depth data points of the submarine cable within a historical time period, as well as the time periods during which current steps occurred;

[0115] During the time period in which the current step occurs, the fiber temperature data corresponding to each depth data is extracted to obtain multiple fiber temperature data.

[0116] The first fiber optic temperature dataset is composed of multiple fiber optic temperature data.

[0117] It should be understood that the multiple depth data can be different depth data acquired during the operation of the submarine cable in summer or winter over the years. A current step can be the phenomenon of the submarine cable's operating current changing from low to high within one hour. The low current can be 10% of the submarine cable's rated current carrying capacity, and the high current can be 70% of the rated current carrying capacity. This disclosure does not specifically limit the specific current step. The time period in which the current step occurs can be a period of more than four hours of stable operation at low current and more than four hours of stable operation at high current, ensuring that the acquired multiple fiber optic temperature data are obtained after the submarine cable has been operating stably.

[0118] In one possible manner, the burial status of the submarine cable is determined based on the real-time transient characteristics of the optical fiber temperature and the preset depth threshold, including:

[0119] Based on the real-time transient characteristics of fiber optic temperature and the objective function, real-time clustering data points are determined. The objective function is the clustering result of the historical fiber optic temperature transient characteristic set through cluster analysis using the K-means clustering method.

[0120] The second Euclidean distance is determined based on the real-time clustering data points and cluster centers;

[0121] The burial status of the submarine cable is determined based on the second Euclidean distance and the preset depth threshold.

[0122] It should be understood that when processing the historical transient temperature feature set of optical fibers using the K-means clustering method, the resulting clustering also includes an objective function. Inputting the real-time transient temperature features of the optical fibers into the objective function yields real-time clustered data points. Calculating the real-time clustered data points and cluster centers yields the second Euclidean distance. Comparing this second Euclidean distance with a preset depth threshold provides the real-time burial status of the submarine cable.

[0123] Schematic, the second Euclidean distance can be expressed by the following formula:

[0124]

[0125] Where, d m For the second Euclidean distance, (x m ,y m (x, y) represents the real-time clustering data points, and (x, y) represents the cluster centers.

[0126] In possible ways, the burial status of the submarine cable is determined based on the second Euclidean distance and a preset depth threshold, including:

[0127] When the second Euclidean distance is greater than the preset depth threshold, the burial state of the submarine cable is determined to be shallow burial.

[0128] When the second Euclidean distance is less than the preset depth threshold, the burial state of the submarine cable is determined to be deep burial.

[0129] It should be understood that the current burial status of the submarine cable can be determined by the relationship between the second Euclidean distance and the preset depth threshold. When the second Euclidean distance is greater than the preset depth threshold, the burial status of the submarine cable can be determined to be shallow burial. When the second Euclidean distance is less than the preset depth threshold, the burial status of the submarine cable can be determined to be deep burial. This disclosure does not specifically limit the burial status of the submarine cable.

[0130] Figure 3 This is a flowchart illustrating a method for determining the burial status of a submarine cable according to an exemplary embodiment of the present disclosure, as shown below. Figure 3 As shown, the method includes the following steps.

[0131] S301: Determine the historical fiber temperature dataset for submarine cables.

[0132] S302: Determine the historical transient feature set of fiber temperature.

[0133] S303: Clustering of historical fiber temperature transient feature sets.

[0134] S304: Determine the cluster center of submarine cables and the preset depth threshold.

[0135] S305: Determine the real-time fiber optic temperature data of the submarine cable.

[0136] S306: Determine the real-time transient characteristics of fiber optic temperature in submarine cables.

[0137] S307: Determine the burial status of the submarine cable.

[0138] The specific implementation methods for each of the above steps have been described in detail above and will not be repeated here. It should also be understood that, for the sake of simplicity, the above method embodiments are described as a series of actions; however, those skilled in the art should understand that this disclosure is not limited to the order of actions described above. Furthermore, those skilled in the art should also understand that the embodiments described above are preferred embodiments, and the steps involved are not necessarily essential to this disclosure.

[0139] By processing the historical fiber temperature transient feature set using the K-means clustering method, a preset depth threshold is obtained. This preset depth threshold is then compared with the second Euclidean distance, which can accurately estimate the burial status of submarine cables when the surface temperature and the deep temperature of the seabed are the same or similar.

[0140] Based on the same concept, this disclosure provides a device 400 for determining the burial status of submarine cables, such as... Figure 4 As shown, Figure 4 This is a schematic diagram of a device 400 for determining the burial status of a submarine cable according to an exemplary embodiment of the present disclosure, with reference to... Figure 4 The device 400 includes a first determining module 401, a second determining module 402, and a third determining module 403;

[0141] The first determining module 401 is used to determine the real-time fiber optic temperature data of the submarine cable.

[0142] The second determining module 402 is used to determine the transient characteristics of real-time fiber temperature based on the real-time fiber temperature data.

[0143] The third determining module 403 is used to determine the burial status of the submarine cable based on the real-time transient characteristics of the optical fiber temperature and the preset depth threshold.

[0144] The preset depth threshold is obtained by processing the historical fiber temperature transient feature set using the K-means clustering analysis method. The depth threshold is used to characterize the critical value between the deep burial state and the shallow burial state of the submarine cable. The fiber temperature transient feature set is used to characterize the feature set of fiber temperature rise amplitude and fiber temperature rise time corresponding to each depth data during the historical current step time period of the submarine cable.

[0145] Optionally, the device 400 further includes:

[0146] The fourth determining module is used to determine the first optical fiber temperature dataset of the submarine cable within a historical time period.

[0147] The fifth determining module is used to determine the historical fiber temperature transient feature set based on the first fiber temperature dataset;

[0148] The first analysis module is used to perform K-means clustering analysis on the historical fiber temperature transient feature set to obtain the preset depth threshold.

[0149] Optionally, the first analysis module includes:

[0150] The second analysis module is used to perform cluster analysis on the historical fiber temperature transient feature set using the K-means clustering method to obtain the cluster centers and the dataset within each cluster.

[0151] The sixth determining module is used to determine a first Euclidean distance based on the cluster center and the dataset within the cluster, wherein the first Euclidean distance is the farthest distance between the data within the cluster and the cluster center;

[0152] The seventh determining module is used to determine the preset depth threshold based on the first Euclidean distance.

[0153] Optionally, the fifth determining module includes:

[0154] The processing module is used to smooth the first fiber optic temperature dataset using a locally weighted linear regression method to obtain the second fiber optic temperature dataset.

[0155] The calculation module is used to process the second optical fiber temperature dataset using a difference calculation method to obtain the historical optical fiber temperature transient feature set.

[0156] Optionally, the fourth determining module includes:

[0157] The first sub-determination module is used to determine multiple depth data of the submarine cable within a historical time period and the time period in which the current step occurred;

[0158] The data extraction module is used to extract the fiber temperature data corresponding to each depth data during the time period in which the current step occurs, so as to obtain multiple fiber temperature data.

[0159] The data integration module is used to combine multiple optical fiber temperature data into the first optical fiber temperature dataset.

[0160] Optionally, the third determining module 403 includes:

[0161] The second sub-determination module is used to determine real-time clustering data points based on the real-time fiber temperature transient characteristics and the objective function, wherein the objective function is the clustering result of clustering analysis of the historical fiber temperature transient characteristic set by the K-means clustering method;

[0162] The third sub-determination module is used to determine the second Euclidean distance based on the real-time clustering data points and the cluster centers;

[0163] The fourth sub-determination module is used to determine the burial status of the submarine cable based on the second Euclidean distance and the preset depth threshold.

[0164] Optionally, the fourth sub-determining module is used to:

[0165] When the second Euclidean distance is greater than the preset depth threshold, the burial state of the submarine cable is determined to be shallow burial.

[0166] When the second Euclidean distance is less than the preset depth threshold, the burial state of the submarine cable is determined to be deep burial.

[0167] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0168] Based on the same concept, this disclosure provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for determining any of the above-described submarine cable burial states.

[0169] Based on the same concept, this disclosure provides an electronic device, including:

[0170] A memory on which computer programs are stored;

[0171] A processor is configured to execute the computer program in the memory to implement the steps of the method for determining the burial status of any of the above-described submarine cables.

[0172] Figure 5 This is a block diagram illustrating an electronic device 500 according to an exemplary embodiment. For example... Figure 5 As shown, the electronic device 500 may include a processor 501 and a memory 502. The electronic device 500 may also include one or more of a multimedia component 503, an input / output (I / O) interface 404, and a communication component 505.

[0173] The processor 501 controls the overall operation of the electronic device 500 to complete all or part of the steps in the method for determining the burial status of the submarine cable. The memory 502 stores various types of data to support the operation of the electronic device 500. This data may include, for example, instructions for any application or method operating on the electronic device 500, and application-related data such as contact data, sent and received messages, pictures, audio, video, etc. The memory 502 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. Multimedia component 503 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in memory 502 or transmitted via communication component 505. The audio component also includes at least one speaker for outputting audio signals. I / O interface 504 provides an interface between processor 501 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual or physical buttons. Communication component 505 is used for wired or wireless communication between the electronic device 500 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IoT, eMTC, or other 5G technologies, or combinations thereof, is not limited here. Therefore, the corresponding communication component 505 may include: a Wi-Fi module, a Bluetooth module, an NFC module, etc.

[0174] In an exemplary embodiment, the electronic device 500 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method for determining the burial status of submarine cables.

[0175] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the method for determining the burial status of a submarine cable described above. For example, the computer-readable storage medium may be the memory 502 including the program instructions described above, which may be executed by the processor 501 of the electronic device 500 to complete the method for determining the burial status of a submarine cable described above.

[0176] The preferred embodiments of this disclosure have been described in detail above with reference to the accompanying drawings. However, this disclosure is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this disclosure, various simple modifications can be made to the technical solutions of this disclosure, and these simple modifications all fall within the protection scope of this disclosure.

[0177] Furthermore, various different embodiments of this disclosure can be combined in any way, as long as they do not violate the spirit of this disclosure, they should also be regarded as the content disclosed in this disclosure.

Claims

1. A method for determining the burial status of a submarine cable, characterized in that, The method includes: Determine the real-time fiber optic temperature data of the submarine cable; Based on the real-time fiber optic temperature data, determine the transient characteristics of the real-time fiber optic temperature; The burial status of the submarine cable is determined based on the real-time transient characteristics of optical fiber temperature and the preset depth threshold. The preset depth threshold is obtained by processing the historical fiber temperature transient feature set using the K-means clustering analysis method. The preset depth threshold is used to characterize the critical value between the deep burial state and the shallow burial state of the submarine cable. The historical fiber temperature transient feature set is used to characterize the feature set of fiber temperature rise amplitude and fiber temperature rise time corresponding to each depth data during the historical current step time period of the submarine cable. The step of determining the transient characteristics of real-time fiber optic temperature based on the real-time fiber optic temperature data includes: The real-time fiber optic temperature data is subjected to local regression processing using the least squares method and a first-order polynomial model in locally weighted linear regression to obtain the transient characteristics of the real-time fiber optic temperature.

2. The method according to claim 1, characterized in that, The method further includes: Determine the first fiber temperature dataset of the submarine cable within a historical time period; Based on the first optical fiber temperature dataset, the historical optical fiber temperature transient feature set is determined; K-means clustering analysis was performed on the historical fiber temperature transient feature set to obtain the preset depth threshold.

3. The method according to claim 2, characterized in that, The step of performing K-means clustering analysis on the historical fiber temperature transient feature set to obtain the preset depth threshold includes: The historical fiber temperature transient feature set was clustered using the K-means clustering method to obtain the cluster centers and the datasets within each cluster. Based on the cluster centers and the dataset within the clusters, a first Euclidean distance is determined, where the first Euclidean distance is the farthest distance between the data within the clusters and the cluster centers. The preset depth threshold is determined based on the first Euclidean distance.

4. The method according to claim 2 or 3, characterized in that, The step of determining the historical fiber temperature transient feature set based on the first fiber temperature dataset includes: The first fiber optic temperature dataset is smoothed using a locally weighted linear regression method to obtain the second fiber optic temperature dataset. The second fiber temperature dataset is processed by the difference calculation method to obtain the historical fiber temperature transient feature set.

5. The method according to claim 2 or 3, characterized in that, The first optical fiber temperature dataset for determining the submarine cable within a historical time period includes: Determine multiple depth data points of the submarine cable within a historical time period, as well as the time periods during which current steps occurred; During the time period in which the current step occurs, the fiber temperature data corresponding to each depth data is extracted to obtain multiple fiber temperature data. The first fiber optic temperature dataset is composed of multiple fiber optic temperature data.

6. The method according to any one of claims 1-3, characterized in that, The step of determining the burial status of the submarine cable based on the real-time transient characteristics of the optical fiber temperature and the preset depth threshold includes: Based on the real-time fiber temperature transient characteristics and the objective function, real-time clustering data points are determined, wherein the objective function is the clustering result of the historical fiber temperature transient characteristic set by the K-means clustering method. The second Euclidean distance is determined based on the real-time clustering data points and cluster centers; wherein, the cluster centers are obtained by performing clustering analysis on the historical fiber temperature transient feature set using the K-means clustering method. The burial status of the submarine cable is determined based on the second Euclidean distance and the preset depth threshold.

7. The method according to claim 6, characterized in that, Determining the burial status of the submarine cable based on the second Euclidean distance and the preset depth threshold includes: When the second Euclidean distance is greater than the preset depth threshold, the burial state of the submarine cable is determined to be shallow burial. When the second Euclidean distance is less than the preset depth threshold, the burial state of the submarine cable is determined to be deep burial.

8. A device for determining the burial status of submarine cables, characterized in that, The device includes a first determining module, a second determining module, and a third determining module; The first determining module is used to determine the real-time fiber optic temperature data of the submarine cable; The second determining module is used to determine the transient characteristics of real-time fiber temperature based on the real-time fiber temperature data. The third determining module is used to determine the burial status of the submarine cable based on the real-time transient characteristics of the optical fiber temperature and a preset depth threshold. The preset depth threshold is obtained by processing the historical fiber temperature transient feature set using the K-means clustering analysis method. The depth threshold is used to characterize the critical value between the deep burial state and the shallow burial state of the submarine cable. The fiber temperature transient feature set is used to characterize the feature set of fiber temperature rise amplitude and fiber temperature rise time corresponding to each depth data during the historical current step time period of the submarine cable. The second determining module is specifically used to perform local regression processing on the real-time fiber optic temperature data using the least squares method and a first-order polynomial model in locally weighted linear modeling, to obtain the transient characteristics of the real-time fiber optic temperature.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method described in any one of claims 1-7.

10. An electronic device, characterized in that, include: A memory on which computer programs are stored; A processor for executing the computer program in the memory to implement the steps of the method according to any one of claims 1-7.

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