Cable condition detection methods, devices and computer equipment

Abstract

This disclosure relates to a method, apparatus, computer device, storage medium, and computer program product for cable condition detection. The method includes: acquiring operational status data corresponding to a target area of ​​the cable to be detected, wherein the target area is determined based on signal data during cable operation, the signal data including at least one of vibration data and temperature data; inputting the operational status data into a fault detection model, and outputting the status of the target area to be detected through the fault detection model, wherein the fault detection model is trained based on the correspondence between cable operational status data samples and their corresponding statuses. This method enables real-time detection of the status of the target area of ​​the cable without requiring on-site inspection by maintenance personnel.

Description

Technical Field

[0001] This disclosure relates to the field of power cable technology, and in particular to a method, apparatus, computer equipment, and storage medium for detecting the condition of cables. Background Technology

[0002] Submarine power cables, also known as submarine cables, are a crucial means of acquiring and transmitting new energy sources. They are widely used in scenarios such as island power supply and near-shore wind farm power output, and are electrical or optical cables laid in the ocean. Submarine cables are typically connected from the seabed to a platform or wind turbine via a rigid conduit. To ensure smooth installation, the inner diameter of the conduit needs to be larger than the outer diameter of the cable. Due to ocean currents and other factors, friction occurs between the cable and the conduit, causing cable wear. Typically, bend limiters or center clamps are added at the connection point to secure the cable. Therefore, if these protective devices become loose or malfunction, the cable cannot be secured, affecting its normal operation.

[0003] Traditional methods require maintenance personnel to physically inspect the protection devices of submarine cables in the relevant areas. However, this method is costly, inefficient, and makes it difficult to obtain timely information on the status of the protection devices, posing risks to the operation of submarine cables. Summary of the Invention

[0004] Therefore, it is necessary to provide a cable status detection method, device, computer equipment, storage medium, and computer program product that can detect the status of the target area of ​​the cable in real time without the need for on-site inspection by maintenance personnel, in order to address the above-mentioned technical problems.

[0005] In a first aspect, embodiments of this disclosure provide a method for detecting the state of a cable. The method includes:

[0006] The working status data corresponding to the target area to be detected of the cable is obtained, wherein the target area to be detected is determined based on the signal data of the cable during operation, and the signal data includes at least one of vibration data and temperature data;

[0007] The working status data is input into the fault detection model, and the fault detection model outputs the status of the target area to be detected. The fault detection model is trained based on the correspondence between cable working status data samples and status.

[0008] In one embodiment, the fault detection model is obtained by means of:

[0009] A set of cable operating status data samples is obtained, wherein the cable operating status data samples include operating status data of a target area of ​​the sampled cable marked with status labels, the target area is determined based on sample signal data during the operation of the sampled cable, and the sample signal data includes at least one of vibration data and temperature data;

[0010] An initial fault detection model is constructed, wherein training parameters are set in the initial fault detection model;

[0011] The cable operating status data sample is input into the initial fault detection model to obtain the output result;

[0012] Based on the difference between the output result and the labeled status, the initial fault detection model is iteratively adjusted until the difference meets the preset requirements, thus obtaining the fault detection model.

[0013] In one embodiment, the method for determining the target region to be detected includes:

[0014] Acquire vibration data of the cable during operation; if the vibration data exceeds a preset threshold, determine the cable region corresponding to the vibration data exceeding the preset threshold as the target region to be detected, wherein the preset threshold is determined based on the difference between the vibration data of the target region to be detected and non-target regions to be detected; or,

[0015] The method for determining the target region to be detected includes:

[0016] Acquire temperature data of the cable during operation; if the temperature data changes within a preset time period and meets preset conditions, determine the cable area corresponding to the temperature data that meets the preset conditions as the target area to be detected, wherein the preset conditions are determined based on the difference between the temperature data of the target area to be detected and the non-target areas to be detected.

[0017] In one embodiment, the method for determining the target region to be detected includes:

[0018] Obtain vibration and temperature data of the cable during operation;

[0019] If the vibration data is greater than a preset threshold, the cable area corresponding to the vibration data that is greater than the preset threshold is determined as the first candidate area;

[0020] If the temperature data changes within a preset time period and meet preset conditions, the cable area corresponding to the temperature data that meets the preset conditions is determined as the second candidate area.

[0021] The regions that are the same in the first candidate region and the second candidate region are identified as the target regions to be detected.

[0022] In one embodiment, the cable includes a submarine cable, and a submarine cable protection device is provided at the connection point between the submarine cable and the offshore power equipment. There is a correlation between the target area to be detected on the submarine cable and the area of ​​the submarine cable protection device. The operating status data includes the operating status data of the submarine cable protection device and the operating signal data of the target area to be detected obtained based on distributed optical fiber sensing monitoring. The acquisition of the operating status data corresponding to the target area to be detected on the cable includes:

[0023] Determine the target area to be detected for the submarine cable;

[0024] Acquire working signal data of the target area to be detected, wherein the working signal data includes at least one of vibration data, temperature data, and stress data;

[0025] Obtain the operating status data of the submarine cable protection device corresponding to the target area to be detected, wherein the operating status data includes at least one of the following: operating time data and maintenance data.

[0026] In one embodiment, the method for acquiring signal data during cable operation includes:

[0027] Acquire initial signal data during submarine cable operation;

[0028] Determine the data acquisition period corresponding to the initial signal data;

[0029] If there is marine vessel signal data in the first preset area during the data acquisition period, the initial signal data is filtered to obtain the signal data of the submarine cable during operation. The first preset area is determined by the area where the submarine cable is located and the target area to be detected.

[0030] Secondly, embodiments of this disclosure also provide a cable status detection device. The device includes:

[0031] The acquisition module is used to acquire the working status data corresponding to the target area to be detected of the cable, wherein the target area to be detected is determined based on the signal data of the cable during operation, and the signal data includes at least one of vibration data and temperature data;

[0032] The input module is used to input the working status data into the fault detection model, and the fault detection model outputs the status of the target area to be detected. The fault detection model is trained based on the correspondence between cable working status data samples and status.

[0033] In one embodiment, the fault detection model acquisition module includes:

[0034] The first acquisition submodule is used to acquire a set of cable operating status data samples, wherein the cable operating status data samples include operating status data of a target area of ​​the sampled cable marked with a status label, the target area is determined based on sample signal data during the operation of the sampled cable, and the sample signal data includes at least one of vibration data and temperature data;

[0035] A construction module is used to build an initial fault detection model, which includes training parameters.

[0036] The input submodule is used to input the cable operating status data sample into the initial fault detection model and obtain the output result;

[0037] The adjustment module is used to iteratively adjust the initial fault detection model based on the difference between the output result and the labeled status label until the difference meets the preset requirements, thereby obtaining the fault detection model.

[0038] In one embodiment, the module for determining the target region to be detected includes:

[0039] The second acquisition submodule is used to acquire vibration data of the cable during operation; if the vibration data is greater than a preset threshold, the cable area corresponding to the vibration data exceeding the preset threshold is determined as the target area to be detected, wherein the preset threshold is determined based on the difference between the vibration data of the target area to be detected and non-target areas to be detected; or...

[0040] The module for determining the target region to be detected includes:

[0041] The third acquisition submodule is used to acquire temperature data of the cable during operation; if the temperature data changes within a preset time period and meets preset conditions, the cable area corresponding to the temperature data that meets the preset conditions is determined as the target area to be detected, wherein the preset conditions are determined based on the difference between the temperature data of the target area to be detected and the non-target areas to be detected.

[0042] In one embodiment, the module for determining the target region to be detected includes:

[0043] The fourth acquisition submodule is used to acquire vibration data and temperature data of the cable during operation;

[0044] The first determining submodule is used to determine the cable area corresponding to the vibration data that is greater than the preset threshold as the first candidate area when the vibration data is greater than the preset threshold.

[0045] The second determination submodule is used to determine the cable area corresponding to the temperature data that meets the preset conditions as the second candidate area when the temperature data changes within a preset time period and meets the preset conditions.

[0046] The third determination submodule is used to determine the same region in the first candidate region and the second candidate region as the target region to be detected.

[0047] In one embodiment, the cable includes a submarine cable, and a submarine cable protection device is provided at the connection point between the submarine cable and the offshore power equipment. There is a correlation between the target area to be detected of the submarine cable and the area of ​​the submarine cable protection device. The operating status data includes the operating status data of the submarine cable protection device and the operating signal data of the target area to be detected obtained based on distributed optical fiber sensing monitoring. The module for acquiring the operating status data corresponding to the target area to be detected of the cable includes:

[0048] The fourth determination submodule is used to determine the target area to be detected for the submarine cable;

[0049] The fifth acquisition submodule is used to acquire the working signal data of the target area to be detected, wherein the working signal data includes at least one of vibration data, temperature data, and stress data;

[0050] The sixth acquisition submodule is used to acquire the operating status data of the submarine cable protection device corresponding to the target area to be detected. The operating status data includes at least one of the following: running time data and maintenance data.

[0051] In one embodiment, the signal data acquisition module for the cable during operation includes:

[0052] The seventh acquisition submodule is used to acquire the initial signal data during the operation of the submarine cable;

[0053] The fifth determining submodule is used to determine the data acquisition time period corresponding to the initial signal data;

[0054] The filtering module is used to filter the initial signal data to obtain the signal data of the submarine cable during operation when there is marine signal data in a first preset area during the data acquisition period. The first preset area is determined by the area where the submarine cable is located and the target area to be detected.

[0055] Thirdly, embodiments of this disclosure also provide a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method described in any one of the embodiments of this disclosure.

[0056] Fourthly, embodiments of this disclosure also provide a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps of the method described in any one of the embodiments of this disclosure.

[0057] Fifthly, embodiments of this disclosure also provide a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps of the method described in any one of the embodiments of this disclosure.

[0058] This embodiment of the invention acquires the working status data corresponding to the target area of ​​the cable to be detected, and inputs the working status data into a fault detection model. The fault detection model then outputs the status of the target area to be detected, realizing the detection of the status of the target area of ​​the cable without the need for on-site inspection by maintenance personnel. By predicting the status of the target area through the working status data of the target area to be detected, fault information can be obtained in a timely manner, ensuring the normal operation of the cable. Furthermore, this embodiment uses the signal data of the cable during operation to locate the target area to be detected, accurately determining the target area and focusing on the target area, effectively improving the efficiency of cable status detection and reducing maintenance costs. The fault detection model trained based on the correspondence between the cable working status data samples and the status ensures the accuracy of the fault detection model's output, improves the reliability of cable status detection, reduces the probability of cable operation risks caused by target area faults, and further ensures the normal operation of the cable. Attached Figure Description

[0059] Figure 1 This is a flowchart illustrating a cable status detection method in one embodiment;

[0060] Figure 2 This is a flowchart illustrating the method for obtaining a fault detection model in one embodiment;

[0061] Figure 3 This is a flowchart illustrating the method for determining the target region to be detected in one embodiment;

[0062] Figure 4 This is a flowchart illustrating the method for determining the target region to be detected in one embodiment;

[0063] Figure 5 This is a schematic diagram of a submarine cable protection device in one embodiment;

[0064] Figure 6 This is a schematic diagram of the working status data of the target area to be detected in one embodiment;

[0065] Figure 7This is a flowchart illustrating the method for acquiring signal data during cable operation in one embodiment;

[0066] Figure 8 This is a structural block diagram of a cable status detection device in one embodiment;

[0067] Figure 9 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0068] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the embodiments of this disclosure will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the embodiments of this disclosure and are not intended to limit the embodiments of this disclosure.

[0069] In one embodiment, such as Figure 1 As shown, a method for detecting the status of a cable is provided. The method includes:

[0070] Step S110: Obtain the working status data corresponding to the target area to be detected of the cable, wherein the target area to be detected is determined based on the signal data of the cable during operation, and the signal data includes at least one of vibration data and temperature data;

[0071] In this embodiment of the disclosure, when it is necessary to detect the state of a cable, the operating state data corresponding to the target area to be detected of the cable is acquired. The cable may include, but is not limited to, optical cables, electrical cables, etc. The applicant has found that different areas of the cable have different probabilities of loss or failure. In this embodiment, the target area to be detected is determined based on the signal data of the cable during operation, and the state of the target area to be detected is predicted. The determined target area to be detected includes areas of the cable prone to failure. Specifically, the signal data includes at least one of vibration data and temperature data, and may also include, but is not limited to, stress data. During the operation of the cable, there are differences in vibration data and / or temperature data between different areas. In one example, the signal characteristics of areas prone to failure can be determined based on the differences in signal data between different areas of the cable. The target area to be detected of the cable is determined based on the determined signal characteristics and the signal data of the cable during operation. In one example, after acquiring the initial signal data of the cable during operation, filtering processing is performed to obtain the signal data of the cable during operation. In one example, signal data collected by distributed sensing fiber optic monitoring technology during cable operation can be converted into a time-domain spectral signal. Image processing algorithms can then be used to determine target feature signals from this time-domain spectral signal, thereby obtaining the target region to be detected corresponding to these feature signals. The image processing algorithm can include, but is not limited to, image recognition algorithms and edge detection algorithms. In some possible implementations, the target region to be detected can be determined using a single signal data source, such as vibration data or temperature data. Preferably, the applicant has found that, based on the differences in state characteristics between the target region to be detected and other non-target regions, a combination of multiple signal data sources, such as vibration data and temperature data, can be used for cross-validation to determine the target region to be detected. This can significantly improve the accuracy of pinpointing the location of the target region, thereby reducing the amount of subsequent data analysis and processing, and lowering false alarms and misjudgments. In one example, different cable devices may be installed in certain areas of the cable, such as submarine cable protection devices and cable connection devices. The working status data of the target area to be tested can include signal data of the target area to be tested during cable operation (such as temperature data, vibration data, stress data, etc.); it can also include working data of the cable device corresponding to the target area to be tested, such as the parameters of the cable device, the working time of the cable device, and the maintenance frequency of the cable device.

[0072] Step S120: Input the working status data into the fault detection model, and output the status of the target area to be detected through the fault detection model. The fault detection model is trained based on the correspondence between cable working status data samples and status.

[0073] In this embodiment, the acquired operating status data is input into a fault detection model, which then outputs the status of the target area to be detected. The fault detection model is trained based on the correspondence between cable operating status data samples and their corresponding statuses. The type of status output by the model can be determined according to the actual application scenario. In one example, the model output status type can be set to normal and abnormal. A normal status indicates a low fault risk in the target area, while an abnormal status indicates a high fault risk. In another example, the status can be further refined, setting the model output status type to a fault risk value. Different fault risk values ​​correspond to different risks of faults in the target area; for example, a higher output risk value indicates a higher probability of faults in the target area. The cable operating status data samples can be determined based on cable sample operation data. In some examples, the data type in the cable operating status data sample is the same as the data type of the operating status data of the target area to be detected. The correspondence between the cable operating status data sample and the status can be determined according to the actual application scenario. For example, when sampling, if the target area of ​​the sampled cable is working normally, the status corresponding to the sampled cable operating status data sample can be set to normal; if the target area of ​​the sampled cable fails, the status corresponding to the sampled cable operating status data sample can be abnormal.

[0074] This embodiment of the invention acquires the working status data corresponding to the target area of ​​the cable to be inspected, and inputs the working status data into a fault detection model. The fault detection model outputs the status of the target area to be inspected, realizing the detection of the status of the target area of ​​the cable without the need for on-site inspection by maintenance personnel. By predicting the status of the target area through the working status data of the target area to be inspected, fault information can be obtained in a timely manner, ensuring the normal operation of the cable. In addition, this embodiment uses signal data collected during cable operation to locate the target area to be inspected, accurately determining the target area to be inspected, allowing for focused attention on the target area, effectively improving the efficiency of cable status detection and reducing maintenance costs. The fault detection model trained based on the correspondence between cable working status data samples and statuses ensures the accuracy of the fault detection model's output, improves the reliability of cable status detection, reduces the probability of cable operation risks caused by target area faults, and further ensures the normal operation of the cable. In some examples, the signal data collected during cable operation includes temperature data and / or vibration data and / or stress data at different locations of the cable collected based on distributed optical fiber sensing technology.

[0075] In one embodiment, such as Figure 2 As shown, the method for obtaining the fault detection model includes:

[0076] Step S210: Obtain a set of cable operating status data samples, wherein the cable operating status data samples include operating status data of the target area of ​​the sampling cable marked with status labels, the target area of ​​the sampling cable is determined based on the sample signal data during the operation of the sampling cable, and the sample signal data includes at least one of vibration data and temperature data;

[0077] Step S220: Construct an initial fault detection model, wherein training parameters are set in the initial fault detection model;

[0078] Step S230: Input the cable operating status data sample into the initial fault detection model to obtain the output result;

[0079] Step S240: Based on the difference between the output result and the labeled status label, the initial fault detection model is iteratively adjusted until the difference meets the preset requirements, thereby obtaining the fault detection model.

[0080] Among them, the cable operating status data sample is obtained through long-term collection and accumulation of actual sample data reflecting the cable under the influence of various environmental factors in various actual working scenarios. It is the data that can most accurately describe the operating status of the target area of ​​the sampled cable. Using this data to train the fault detection model can greatly improve the judgment accuracy of the fault detection model. It overcomes the problem that fault detection models obtained by manual setting and simulation methods cannot be used, and has great engineering and economic value and significance for monitoring the operating status of the target area of ​​the cable.

[0081] In this embodiment, a fault detection model is trained based on the correspondence between cable operating status data samples and their states. Specifically, a set of cable operating status data samples is obtained. These samples include operating status data of the target area of ​​the sampled cable labeled with status tags. The sampled cable can be determined according to the actual application scenario. In one example, the sampled cable may include, but is not limited to, the cable to be detected, other cables of the same type as the cable to be detected, or different cables at multiple locations in the same sea area. The status tags are used to indicate the operating status of the target area of ​​the sampled cable. In some specific examples, these can be divided into abnormal status and normal status. There can be one or more sampled cables. Based on the sample signal data of the sampled cable during operation, the target area of ​​the sampled cable can be determined. The sample signal data of the sampled cable during operation includes at least one of vibration data and temperature data. The sample signal data may also include, but is not limited to, stress data. In one example, the sample signal data of the sampled cable during operation can be converted into a time-domain spectral signal. An image processing algorithm is used to determine the target feature signal from the time-domain spectral signal, thereby obtaining the target area of ​​the sampled cable corresponding to the target feature signal. The image processing algorithm may include, but is not limited to, image recognition algorithms and edge detection algorithms. In some possible implementations, the target area of ​​the sampling cable can be determined using a single sample signal data, such as vibration data or temperature data. Alternatively, the target area can be determined by combining multiple signal data, such as vibration data and temperature data, and cross-validating them. In one example, a portion of the cable may correspond to different cable devices, such as submarine cable protection devices or cable connection devices. The operational status data of the target area of ​​the sampling cable can include signal data of the target area during cable operation (such as temperature data, vibration data, stress data, etc.); it can also include operational data of the cable devices corresponding to the target area, such as cable device parameters, cable device operating time, and cable device maintenance frequency.

[0082] In the data sample, the operating status data of the target area of ​​the sampling cable corresponds to status labels. These status labels are determined based on the status of the target area corresponding to the operating status data of the sampling cable's target area. In one example, the status labels can be set to include normal status labels and abnormal status labels. A normal status indicates a low risk of failure in the target area, while an abnormal status indicates a high risk of failure. In another example, the status labels can be further refined, setting them as fault risk value labels. Different fault risk value labels correspond to different risks of failure in the target area; for example, the higher the risk value of the label, the higher the probability of a fault in the target area. In one example, the target area of ​​the sampling cable corresponds to a cable device, and the status labels can be determined based on the status of the cable device.

[0083] An initial fault detection model is constructed, with training parameters set. Cable operating state data samples are input into the initial fault detection model to obtain the output results. Typically, the output results of an untrained initial fault detection model have low accuracy and differ from the corresponding state labels. Based on the differences between the output results and the labeled state labels, the initial fault detection model is iteratively adjusted until the difference between the model's output results and the labeled labels meets preset requirements, thus obtaining the fault detection model. These preset requirements can be set according to the actual application scenario. For example, a preset requirement could be that the model's output accuracy is greater than a preset accuracy threshold. When the model's output accuracy exceeds this threshold, the model's accuracy is high and meets the preset requirements. When the difference between the output results and the labeled state labels meets the preset requirements, the model's output results can be considered relatively accurate and meet the prediction needs.

[0084] In one example, considering the interpretability of the model's inference results and the need for subsequent model iterations and upgrades, the fault detection model can be trained using a data-driven machine learning method. Furthermore, due to limitations in model deployment and inference time, and to balance model output accuracy and operational efficiency while avoiding the risk of overfitting due to a limited sample size, the fault detection model can be trained using a gradient decision tree model. In this case, the initial fault detection model is constructed using a gradient decision tree algorithm. Specifically, an initial set of cable operating status data samples is obtained. The data in the initial set undergoes preprocessing and feature engineering, and is then divided into training and testing sets. An initial fault detection model is constructed, containing basic model parameters, i.e., training parameters, i.e., initial model parameters. During training, the model parameters are adjusted using the log-likelihood loss function and gradient descent to achieve the highest classification accuracy on the testing set as the training objective. The training set is then input into the initial fault detection model for training until the resulting model meets the requirements, thus obtaining the detection model. In the model training process, firstly, a weak learner is initialized. Based on the training and the corresponding state label set, a weak learner and initial residual values ​​are obtained, and this learner is added to the strong learner model. The model is then iteratively trained to build the m-th classification tree (a total of M classification trees): the negative gradient (residual) of the loss function is calculated on the m-th tree for all training samples in the training set. The residuals obtained in the previous step are used as new label values ​​for the samples. The m-th regression tree is obtained by fitting the training samples and new label values ​​using a decision tree, and the corresponding leaf node regions of this tree are also obtained. The best fit value is calculated for the leaf node regions determined by the m-th tree. The m-th tree is added to the strong learner, updating it to obtain the strong learner. The learners obtained through the iterative training are integrated to obtain the trained strong learner model. The model is then tested on the test set. If the model output accuracy does not meet the preset requirements, the iterative adjustment process continues according to the above steps. If it meets the preset requirements, it is determined to be a fault detection model.

[0085] Logarithmic loss, also known as logistic regression loss or cross-entropy loss, is defined in probability estimation. It is commonly used in logistic regression and neural networks, as well as some variants of the expectation-maximization algorithm, and can be used to evaluate the probability output of a classifier. Gradient descent is a common first-order optimization method and one of the simplest and most classic methods for solving unconstrained optimization problems.

[0086] In this embodiment, the target area of ​​the sampling cable is determined based on the sample signal data of the sampling cable's operation, and a fault detection model is trained based on the working status data of the target area of ​​the sampling cable and the corresponding status label. The resulting fault detection model has an output accuracy that meets the requirements, enabling accurate prediction of the cable's status and real-time fault judgment of the target area of ​​the cable to be detected. This reduces labor costs and ensures the normal operation of the cable.

[0087] In one embodiment, the method for determining the target region to be detected includes:

[0088] Acquire vibration data of the cable during operation; if the vibration data exceeds a preset threshold, determine the cable region corresponding to the vibration data exceeding the preset threshold as the target region to be detected, wherein the preset threshold is determined based on the difference between the vibration data of the target region to be detected and non-target regions to be detected; or,

[0089] The method for determining the target region to be detected includes:

[0090] Acquire temperature data of the cable during operation; if the temperature data changes within a preset time period and meets preset conditions, determine the cable area corresponding to the temperature data that meets the preset conditions as the target area to be detected, wherein the preset conditions are determined based on the difference between the temperature data of the target area to be detected and the non-target areas to be detected.

[0091] In this embodiment of the disclosure, when determining the target area to be detected, it can be determined based on vibration data or temperature data at different locations during cable operation. When using vibration data to determine the target area, in this embodiment, the target area includes areas prone to failure. Typically, areas prone to failure experience greater vibration than other areas, thus increasing the likelihood of wear and tear. Therefore, this characteristic of the vibration data can be used to determine the target area. Specifically, vibration data during cable operation is acquired, and vibration data exceeding a preset threshold is identified as the target area to be detected. The preset threshold is determined based on the difference between the vibration data of the target area and non-target areas, and can be predetermined according to the actual application scenario. For example, it can be analyzed and set based on the magnitude of the sampled vibration data corresponding to the faulty cable area. In some possible implementations, depending on the application scenario, the method for determining the target area to be detected can be adjusted based on other characteristics of the area prone to failure. In some application scenarios, the applicant's research and analysis have found that when the cable is a submarine cable, wear or failure usually occurs at the connection between the submarine cable and the offshore power equipment. Cable devices are typically installed at the connection point. In this case, the vibration of the cable area prone to failure is continuous and stable. However, the vibration of other cable areas located in the sea is affected by various factors, resulting in discontinuous vibration with a large range of variation. In this case, the target area to be detected can be determined based on the fluctuation pattern of the vibration data. For example, through image recognition, image comparison, and other methods, the area corresponding to the vibration data that conforms to a preset variation pattern can be identified as the target area to be detected. In one example, for the sampling cable or the cable to be monitored, after obtaining the corresponding vibration signal during cable operation, the corresponding vibration signal can be converted into a time-domain spectrum signal to obtain the corresponding vibration data.

[0092] When using temperature data to determine the target area to be detected, in this embodiment, the target area to be detected includes areas prone to failure. The applicant has found that areas with complex temperature variations have a higher probability of failure than areas with stable temperatures. Therefore, this characteristic of temperature data can be used to determine the target area to be detected. Specifically, temperature data of the cable during operation is acquired, and the cable area corresponding to the temperature data that changes within a preset time period and meets preset conditions is identified as the target area to be detected. The preset conditions are determined based on the difference in temperature data between the target area and non-target areas, and can be determined according to the actual application scenario. For example, they can be obtained by analyzing and setting the variation pattern of the sampled temperature data of the faulty cable area of ​​a submarine cable. In some possible implementations, depending on the application scenario, the method for determining the target area to be detected can be adjusted based on other characteristics of the faulty area. In some application scenarios, the applicant has found that when the cable is a submarine cable, wear or failure often occurs at the connection point between the submarine cable and the offshore power equipment. In this case, the cable area prone to failure is exposed, and the corresponding temperature data changes conform to air temperature changes. In this case, the cable area whose temperature data changes conform to the air temperature changes of the corresponding region can be identified as the target area to be detected. In one example, edge detection and other algorithms can be used to determine the target area to be detected. In another example, for a sampling cable or a cable to be monitored, after obtaining the temperature signal corresponding to the cable's operation, the corresponding temperature signal is converted into a time-domain spectrum signal to obtain the corresponding temperature data.

[0093] This embodiment of the invention can determine the target area to be detected based on the signal characteristics of different areas of the cable, using vibration data or temperature data, and perform real-time status monitoring of the target area to be detected. This reduces the amount of data processing and improves data processing efficiency, thereby enabling timely acquisition of fault information and ensuring the normal operation of the cable. Based on vibration data or temperature data, the data characteristics of different areas of the cable can be combined to accurately identify areas prone to faults, thereby effectively improving the accuracy and effectiveness of fault detection and reducing the risk of cable operation.

[0094] In one embodiment, such as Figure 3 As shown, the method for determining the target area to be detected includes:

[0095] Step S310: Obtain vibration data and temperature data of the cable during operation;

[0096] Step S320: If the vibration data is greater than a preset threshold, determine the cable area corresponding to the vibration data that is greater than the preset threshold as the first candidate area;

[0097] Step S330: If the temperature data changes within a preset time period and meet preset conditions, determine the cable area corresponding to the temperature data that meets the preset conditions as the second candidate area.

[0098] Step S340: Determine the regions that are the same in the first candidate region and the second candidate region as the target regions to be detected.

[0099] In this embodiment, the target area to be detected is obtained through cross-validation of vibration data and temperature data during cable operation. Specifically, vibration data and temperature data during cable operation are acquired. If the vibration data exceeds a preset threshold, the cable area corresponding to the vibration data exceeding the preset threshold is determined as a first candidate area. If the temperature data changes within a preset time period and meets preset conditions, the cable area corresponding to the temperature data meeting the preset conditions is determined as a second candidate area. Areas identical to the first and second candidate areas are identified as the target area to be detected. The implementation method for determining the first candidate area based on vibration data can refer to the description of determining the target area to be detected based on vibration data in the above embodiments; the implementation method for determining the second candidate area based on temperature data can refer to the description of determining the target area to be detected based on temperature data in the above embodiments, and will not be repeated here. When using vibration data and temperature data to determine the target area to be detected, such as... Figure 4 As shown, vibration and temperature signals of the cable during operation are acquired, the acquired signals are converted into time-domain spectrum signals to obtain vibration data and temperature data, a first candidate region is determined based on the vibration data, a second candidate region is determined based on the temperature data, and the target region to be detected is determined based on the first and second candidate regions.

[0100] In this embodiment, the target area to be detected is obtained by cross-validation of vibration data and temperature data, which further improves the accuracy of the determined target area. The target area to be detected that needs to be monitored for faults can be determined quickly and efficiently based on the cable operation signal data, thereby enabling the detection of the status of the target area to be detected. Since the accuracy of the obtained target area to be detected is high, cable fault information can be obtained in a timely manner, reducing the risk of cable operation and improving the reliability of cable operation.

[0101] Specifically, in one embodiment, the cable includes a submarine cable, and a submarine cable protection device is provided at the connection point between the submarine cable and the offshore power equipment. The submarine cable protection device can be a bend limiter or a center clamp, etc. There is a correlation between the target area to be detected of the submarine cable and the area of ​​the submarine cable protection device. The operating status data includes the operating signal data of the target area to be detected and the operating status data of the submarine cable protection device. Acquiring the operating status data corresponding to the target area to be detected of the cable includes:

[0102] Determine the target area to be detected for the submarine cable;

[0103] Acquire working signal data of the target area to be detected, wherein the working signal data includes at least one of vibration data, temperature data, and stress data;

[0104] Obtain the operating status data of the submarine cable protection device corresponding to the target area to be detected, wherein the operating status data includes at least one of the following: operating time data and maintenance data.

[0105] In this embodiment of the disclosure, the cable includes a submarine cable, which is laid in the sea. A submarine cable protection device is provided at the connection between the submarine cable and the offshore power equipment. The offshore power equipment may include, but is not limited to, cable platforms, wind turbines, etc. Figure 5This is a schematic diagram illustrating a submarine cable protection device according to an exemplary embodiment. Typically, submarine cables are connected to offshore power equipment from the seabed via a conduit. To ensure smooth conduit installation, the inner diameter of the conduit is usually larger than the outer diameter of the submarine cable. To reduce wear and tear, and potential malfunctions caused by friction between the conduit and the cable due to ocean currents, a submarine cable protection device is installed to secure the cable. This device may include, but is not limited to, bend limiters and center clamps. The area of ​​the submarine cable protection device corresponds to a cable area prone to failure. There is a correlation between the target area of ​​the submarine cable to be detected and the area of ​​the submarine cable protection device. The target area of ​​the submarine cable to be detected can be determined based on the characteristics of the signal data from the area of ​​the submarine cable protection device. The operating status of the submarine cable protection device affects the status of the corresponding area. In this embodiment, the operating status data of the target area to be detected includes the operating signal data of the target area and the operating status data of the submarine cable protection device. Specifically, the target area of ​​the submarine cable to be detected is determined based on the signal data of the submarine cable during operation. In some possible implementations, the applicant's research and analysis revealed that, affected by the noise of offshore power equipment and ocean current scouring, the vibration data characteristics of the submarine cable's protection device area are continuous, regular, and have large amplitude vibrations. Based on these characteristics, the target area to be tested can be determined according to the submarine cable's vibration data. The protection device area of ​​the submarine cable is exposed on the water, while other areas are laid in the sea, where the seawater temperature is relatively stable. Therefore, the temperature data variation pattern of the protection device area conforms to the air temperature variation pattern of the corresponding region, while the temperature in other areas is relatively stable. Based on these characteristics, the target area to be tested can be determined according to the submarine cable's temperature data. Furthermore, the target area to be tested can be obtained by cross-validating vibration and temperature data. The working signal data of the target area to be tested is acquired, including at least one of vibration data, temperature data, and stress data. The operating status data of the submarine cable protection device corresponding to the target area to be tested is also acquired, including at least one of runtime data and maintenance data. The runtime data may include the actual runtime of the submarine cable protection device, and the maintenance data may include the frequency and methods of maintenance of the submarine cable protection device. In one example, the target area to be detected may include multiple sub-regions, and different sub-regions may correspond to different submarine cable protection devices. In one possible implementation, the state of the target area to be detected may be affected by the characteristics of the submarine cable protection device itself, the operation and maintenance characteristics of the submarine cable protection device, and the characteristics of the operating environment. Therefore, as... Figure 6As shown, the operational status data of the target area to be detected can be divided into operational signal data of the target area and operational status data of the submarine cable protection device. The operational status data of the submarine cable protection device includes its own characteristic data and its maintenance characteristic data. The operational signal data of the target area to be detected may include, but is not limited to, vibration data, temperature data, stress data, monitoring equipment type, and submarine cable structure. The characteristic data of the submarine cable protection device itself may include, but is not limited to, the effective lifespan of the protection device and the actual operating time of the protection device. The maintenance characteristic data of the submarine cable protection device may include, but is not limited to, the frequency of maintenance and maintenance methods. The operational signal data of the target area to be detected is obtained based on distributed optical fiber sensing monitoring.

[0106] This embodiment can be applied to the condition detection of submarine cables. By combining the characteristics of the submarine cable, the protection device area of ​​the submarine cable can be monitored in real time, realizing the monitoring of the protection device area with high failure incidence. It can detect abnormalities in a timely manner and ensure the normal operation of the submarine cable. By combining multi-dimensional feature data of the operation data of the protection device and the submarine cable, the accuracy of condition detection is improved, thereby timely and accurately detecting and eliminating faults, wear and other conditions, and further reducing the operational risks of the submarine cable.

[0107] In one embodiment, such as Figure 7 As shown, the method for acquiring signal data during cable operation includes:

[0108] Step S710: Obtain initial signal data during submarine cable operation;

[0109] Step S720: Determine the data acquisition period corresponding to the initial signal data;

[0110] Step S730: If there is ship signal data in the first preset area during the data acquisition period, the initial signal data is filtered to obtain the signal data of the submarine cable during operation. The first preset area is determined by the area where the submarine cable is located and the target area to be detected.

[0111] In this embodiment of the disclosure, the applicant further discovered that during the operation of the submarine cable, the signal data of the submarine cable is significantly affected by the passing of a sea vessel in the corresponding area. Therefore, in order to improve the accuracy of the target area to be detected, the initial signal data of the submarine cable operation can be filtered. Specifically, the initial signal data of the submarine cable operation is acquired, and the data acquisition period corresponding to the initial signal data is determined. The initial signal data can be collected by monitoring technology based on distributed sensing optical fiber. The data acquisition period can be a preset duration around the time when the initial signal data is detected. The preset duration can be a small duration determined according to the actual application scenario or a duration within a certain time interval. It is determined whether there is sea vessel signal data in a first preset area within the data acquisition period. The first preset area is determined based on the area where the submarine cable is located and the target area to be detected. For example, the first preset area can be determined based on the influence range of the signal data of the submarine cable when a sea vessel passes by in the actual application scenario. Typically, when a sea vessel travels within the first preset area, it will affect the signal data of the submarine cable. In some possible implementations, the ship signal data may include, but is not limited to, ship position data and ship visual signal data, which can be acquired through visual sensors, GPS positioning devices, etc. When ship signal data exists in the first preset area during the data acquisition period, it can be considered that the initial signal data is disturbed by the passing of the ship, and the initial signal needs to be processed to ensure the accuracy of the determination of the target area to be detected later. In this embodiment, the initial signal is filtered. After filtering, the filtered data is obtained, eliminating the influence of the ship passing disturbance, and the filtered data is determined to be the signal data of the submarine cable in operation. In one example, the filtering process may include, but is not limited to, high-frequency filtering, amplitude limiting filtering, etc. For example, when the noise disturbance signal generated by the passing of the ship includes high-frequency signals, the corresponding initial signal data will include high-frequency interference signals when the ship passes. At this time, the interference signals can be filtered out by high-frequency filtering of the initial signal data; when the noise disturbance signal generated by the passing of the ship includes high-amplitude signals, the corresponding initial signal data will include high-amplitude interference signals when the ship passes. At this time, the interference signals can be filtered out by amplitude limiting filtering of the initial signal data.

[0112] In this embodiment of the disclosure, when determining the target area to be detected of the submarine cable, the initial signal data that may be interfered with is filtered in conjunction with the ship's signal data to eliminate the influence of the ship's movement on the cable's operating signal data. This improves the accuracy of determining the target area to be detected and enables accurate status detection of the target area to be detected, thereby timely obtaining abnormal states caused by risks such as faults and wear, timely detection of abnormal situations, and ensuring the normal operation of the submarine cable.

[0113] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the accompanying drawings may include multiple steps or stages, which are not necessarily completed at the same time, but may be executed at different times, and the execution order of these steps or stages is not necessarily sequential, but may be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0114] Based on the same inventive concept, this disclosure also provides a cable state detection device for implementing the cable state detection method described above. The solution provided by this device is similar to the implementation described in the above method; therefore, the specific limitations in one or more cable state detection device embodiments provided below can be found in the limitations of the cable state detection method described above, and will not be repeated here.

[0115] In one embodiment, such as Figure 8 As shown, a cable status detection device 800 is provided, comprising:

[0116] The acquisition module 810 is used to acquire the working status data corresponding to the target area to be detected of the cable, wherein the target area to be detected is determined based on the signal data of the cable during operation, and the signal data includes at least one of vibration data and temperature data;

[0117] The input module 820 is used to input the working status data into the fault detection model, and output the status of the target area to be detected through the fault detection model. The fault detection model is trained based on the correspondence between cable working status data samples and status.

[0118] In one embodiment, the fault detection model acquisition module includes:

[0119] The first acquisition submodule is used to acquire a set of cable operating status data samples, wherein the cable operating status data samples include operating status data of a target area of ​​the sampled cable marked with a status label, the target area is determined based on sample signal data during the operation of the sampled cable, and the sample signal data includes at least one of vibration data and temperature data;

[0120] A construction module is used to build an initial fault detection model, which includes training parameters.

[0121] The input submodule is used to input the cable operating status data sample into the initial fault detection model and obtain the output result;

[0122] The adjustment module is used to iteratively adjust the initial fault detection model based on the difference between the output result and the labeled status label until the difference meets the preset requirements, thereby obtaining the fault detection model.

[0123] In one embodiment, the module for determining the target region to be detected includes:

[0124] The second acquisition submodule is used to acquire vibration data of the cable during operation; if the vibration data is greater than a preset threshold, the cable area corresponding to the vibration data exceeding the preset threshold is determined as the target area to be detected, wherein the preset threshold is determined based on the difference between the vibration data of the target area to be detected and non-target areas to be detected; or...

[0125] The module for determining the target region to be detected includes:

[0126] The third acquisition submodule is used to acquire temperature data of the cable during operation; if the temperature data changes within a preset time period and meets preset conditions, the cable area corresponding to the temperature data that meets the preset conditions is determined as the target area to be detected, wherein the preset conditions are determined based on the difference between the temperature data of the target area to be detected and the non-target areas to be detected.

[0127] In one embodiment, the module for determining the target region to be detected includes:

[0128] The fourth acquisition submodule is used to acquire vibration data and temperature data of the cable during operation;

[0129] The first determining submodule is used to determine the cable area corresponding to the vibration data that is greater than the preset threshold as the first candidate area when the vibration data is greater than the preset threshold.

[0130] The second determination submodule is used to determine the cable area corresponding to the temperature data that meets the preset conditions as the second candidate area when the temperature data changes within a preset time period and meets the preset conditions.

[0131] The third determination submodule is used to determine the same region in the first candidate region and the second candidate region as the target region to be detected.

[0132] In one embodiment, the cable includes a submarine cable, and a submarine cable protection device is installed at the connection point between the submarine cable and the offshore power equipment. There is a correlation between the target area to be detected on the submarine cable and the area of ​​the submarine cable protection device. The operating status data includes the operating status data of the submarine cable protection device and the operating signal data of the target area to be detected obtained based on distributed optical fiber sensing monitoring. The applicant's research and analysis have found that in the marine environment, influenced by a combination of factors such as tides, ocean currents, and offshore power equipment, the temperature and vibration data signals obtained at the connection point between the submarine cable and the offshore power equipment, i.e., the location where the submarine cable protection device is installed, are significantly different from other parts of the submarine cable. Therefore, the location of the submarine cable protection device can be identified based on the temperature and vibration data signals, and targeted status monitoring can be performed. The acquisition module includes:

[0133] The fourth determination submodule is used to determine the target area to be detected for the submarine cable;

[0134] The fifth acquisition submodule is used to acquire the working signal data of the target area to be detected, wherein the working signal data includes at least one of vibration data, temperature data, and stress data;

[0135] The sixth acquisition submodule is used to acquire the operating status data of the submarine cable protection device corresponding to the target area to be detected. The operating status data includes at least one of the following: running time data and maintenance data.

[0136] In one embodiment, the signal data acquisition module for the cable during operation includes:

[0137] The seventh acquisition submodule is used to acquire the initial signal data during the operation of the submarine cable;

[0138] The fifth determining submodule is used to determine the data acquisition time period corresponding to the initial signal data;

[0139] A filtering module is used to filter the initial signal data to obtain the signal data of the submarine cable during operation when ship signal data exists in a first preset area during the data acquisition period. The first preset area is determined by the area where the submarine cable is located and the target area to be detected. Each module in the cable status detection device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0140] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 9As shown, the computer device includes a processor, memory, and network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores signal data during cable operation, operational status data of the target area to be detected, and other data relevant to this embodiment. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a cable status detection method.

[0141] Those skilled in the art will understand that Figure 9 The structures shown are merely block diagrams of some structures related to the embodiments of this disclosure and do not constitute a limitation on the computer devices on which the embodiments of this disclosure are applied. Specific computer devices may include more or fewer components than those shown in the figures, or combine certain components, or have different component arrangements.

[0142] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0143] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0144] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0145] It should be noted that the ship location data, cable data and other data (including but not limited to data used for analysis, stored data, and displayed data) involved in the embodiments of this disclosure are all information and data authorized by the user or fully authorized by all parties.

[0146] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this disclosure can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this disclosure may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this disclosure may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0147] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0148] The above-described embodiments are merely illustrative of several implementation methods of the present disclosure, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the patent for the embodiments of the present disclosure. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the embodiments of the present disclosure, and these all fall within the protection scope of the embodiments of the present disclosure. Therefore, the protection scope of the embodiments of the present disclosure should be determined by the appended claims.

Claims

1. A method for detecting the condition of a cable, wherein the cable includes a submarine cable, and a submarine cable protection device is provided at the connection point between the submarine cable and offshore power equipment, characterized in that... The method includes: The process involves acquiring operational status data corresponding to the target area of ​​the cable under test. This operational status data includes operational status data of the submarine cable protection device and operational signal data of the target area obtained based on distributed optical fiber sensing monitoring. The acquisition of operational status data corresponding to the target area of ​​the cable under test includes: The method for determining the target area to be tested for the cable includes: acquiring vibration data and temperature data during cable operation; determining the cable area corresponding to the vibration data exceeding a preset threshold as a first candidate area; determining the cable area corresponding to the temperature data meeting the preset conditions as a second candidate area when the temperature data changes within a preset time period according to preset conditions; determining the same area in the first and second candidate areas as the target area to be tested; the first candidate area is further determined based on the area corresponding to vibration data whose changes conform to a preset change pattern, including cable areas with continuous and stable vibration; the preset conditions include cable areas where temperature data changes conform to the temperature changes of the corresponding region. Acquire working signal data of the target area to be detected, wherein the working signal data includes at least one of vibration data, temperature data, and stress data; Obtain the operational status data of the submarine cable protection device corresponding to the target area to be detected, wherein the operational status data includes at least one of runtime data and maintenance data; The working status data is input into the fault detection model, and the fault detection model outputs the status of the target area to be detected. The fault detection model is trained based on the correspondence between cable working status data samples and status.

2. The method according to claim 1, characterized in that, The methods for obtaining the fault detection model include: A set of cable operating status data samples is obtained, wherein the cable operating status data samples include operating status data of a target area of ​​the sampled cable marked with status labels, the target area is determined based on sample signal data during the operation of the sampled cable, and the sample signal data includes at least one of vibration data and temperature data; An initial fault detection model is constructed, wherein training parameters are set in the initial fault detection model; The cable operating status data sample is input into the initial fault detection model to obtain the output result; Based on the difference between the output result and the labeled status, the initial fault detection model is iteratively adjusted until the difference meets the preset requirements, thus obtaining the fault detection model.

3. The method according to claim 1, characterized in that, The method for determining the target region to be detected includes: Acquire vibration data of the cable during operation; if the vibration data exceeds a preset threshold, determine the cable region corresponding to the vibration data exceeding the preset threshold as the target region to be detected, wherein the preset threshold is determined based on the difference between the vibration data of the target region to be detected and non-target regions to be detected; or, The method for determining the target region to be detected includes: Acquire temperature data of the cable during operation; if the temperature data changes within a preset time period and meets preset conditions, determine the cable area corresponding to the temperature data that meets the preset conditions as the target area to be detected, wherein the preset conditions are determined based on the difference between the temperature data of the target area to be detected and the non-target areas to be detected.

4. The method according to claim 1, characterized in that, The methods for acquiring signal data during cable operation include: Acquire initial signal data during submarine cable operation; Determine the data acquisition period corresponding to the initial signal data; If there is marine vessel signal data in the first preset area during the data acquisition period, the initial signal data is filtered to obtain the signal data of the submarine cable during operation. The first preset area is determined by the area where the submarine cable is located and the target area to be detected.

5. A cable condition detection device, characterized in that, The cable includes a submarine cable, and a submarine cable protection device is installed at the connection point between the submarine cable and the offshore power equipment. The device includes: The acquisition module is used to acquire the working status data corresponding to the target area of ​​the cable to be tested. The working status data includes the operating status data of the submarine cable protection device and the working signal data of the target area to be tested acquired based on distributed optical fiber sensing monitoring. The acquisition module is also used to: determine the target area of ​​the cable to be tested; the target area of ​​the submarine cable to be tested corresponds to the area of ​​the submarine cable protection device; acquire the working signal data of the target area to be tested, the working signal data including at least one of vibration data, temperature data, and stress data; acquire the operating status data of the submarine cable protection device corresponding to the target area to be tested, the operating status data including at least one of runtime data and maintenance data; the method of determining the target area to be tested includes: acquiring the vibration data and temperature data of the cable during operation. If the vibration data exceeds a preset threshold, the cable region corresponding to the vibration data exceeding the preset threshold is identified as the first candidate region. If the temperature data changes within a preset time period and meets preset conditions, the cable region corresponding to the temperature data meeting the preset conditions is identified as the second candidate region. Regions that are identical in the first and second candidate regions are identified as the target region to be detected. The first candidate region is also determined based on regions corresponding to vibration data whose changes conform to a preset change pattern. Regions corresponding to vibration data with a preset change pattern include cable regions where the vibration is continuous and stable. The preset conditions include cable regions where the temperature data changes in accordance with the temperature changes in the corresponding region. The input module is used to input the working status data into the fault detection model, and the fault detection model outputs the status of the target area to be detected. The fault detection model is trained based on the correspondence between cable working status data samples and status.

6. The apparatus according to claim 5, characterized in that: The fault detection model acquisition module includes: The first acquisition submodule is used to acquire a set of cable operating status data samples, wherein the cable operating status data samples include operating status data of a target area of ​​the sampled cable marked with a status label, the target area is determined based on sample signal data during the operation of the sampled cable, and the sample signal data includes at least one of vibration data and temperature data; A construction module is used to build an initial fault detection model, which includes training parameters. The input submodule is used to input the cable operating status data sample into the initial fault detection model and obtain the output result; The adjustment module is used to iteratively adjust the initial fault detection model based on the difference between the output result and the labeled status label until the difference meets the preset requirements, thereby obtaining the fault detection model.

7. The apparatus according to claim 5, characterized in that: The module for determining the target region to be detected includes: The second acquisition submodule is used to acquire vibration data of the cable during operation; if the vibration data is greater than a preset threshold, the cable area corresponding to the vibration data exceeding the preset threshold is determined as the target area to be detected, wherein the preset threshold is determined based on the difference between the vibration data of the target area to be detected and non-target areas to be detected; or... The module for determining the target region to be detected includes: The third acquisition submodule is used to acquire temperature data of the cable during operation; if the temperature data changes within a preset time period and meets preset conditions, the cable area corresponding to the temperature data that meets the preset conditions is determined as the target area to be detected, wherein the preset conditions are determined based on the difference between the temperature data of the target area to be detected and the non-target areas to be detected.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the cable status detection method according to any one of claims 1 to 4.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the cable status detection method according to any one of claims 1 to 4.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the cable status detection method according to any one of claims 1 to 4.

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