Burial depth monitoring method of submarine cable, program product and electronic equipment

Abstract

The invention provides a submarine cable burial depth monitoring method, a program product and electronic equipment. The submarine cable burial depth monitoring method comprises the following steps: acquiring load data and temperature data of a to-be-measured space point in a first submarine cable in at least one load change process; obtaining a load characteristic value based on the load data; obtaining a temperature response characteristic value based on the temperature data; obtaining a feature set of the spatial point to be measured according to the load feature value and the temperature response feature value; and inputting the feature set into a burial depth monitoring model to obtain burial depth data of the to-be-measured spatial point. The burial depth monitoring model is used for reasoning the feature set to obtain the burial depth data of the to-be-measured space point, and the heat dissipation condition does not need to be calculated, so that errors caused by parameter estimation in the calculation are reduced, a large amount of grid calculation is avoided, calculation resources are saved, the burial depth measurement complexity of the submarine cable is reduced, and the calculation time consumption is reduced; and the burial depth measuring and calculating efficiency is improved. And the mode is lower in use threshold and convenient to operate.

Description

Technical Field

[0001] This application relates to the field of monitoring, and more specifically, to a method, program product, and electronic equipment for monitoring the burial depth of submarine cables. Background Technology

[0002] With the vigorous development of offshore energy development and marine engineering projects, a large number of submarine cables have been laid on the seabed. Ensuring the safety of submarine cables has become a key focus of subsequent maintenance. Monitoring the condition of submarine cables by measuring their burial depth and enabling early warning of problems can effectively protect them and reduce the risk of failure.

[0003] In traditional methods of calculating submarine cable burial depth, each calculation requires either a ship to scan the seabed or a person to dive to the bottom. However, manual operations are time-consuming, labor-intensive, and inefficient; furthermore, seabed operations are difficult and costly, and personnel diving to the bottom face significant risks.

[0004] In addition, some related technologies simulate the heat dissipation of submarine cables under current load and the soil covering them, then extract the differences in heat dissipation at different burial depths to establish a mapping relationship to solve for the burial depth. However, this method has high computational complexity and low efficiency in burial depth calculation. Summary of the Invention

[0005] The purpose of this application is to provide a method, program product, and electronic device for monitoring the burial depth of submarine cables, in order to improve at least one of the aforementioned technical problems.

[0006] In a first aspect, embodiments of this application provide a method for monitoring the burial depth of a submarine cable, comprising: acquiring load data and temperature data of a spatial point to be measured in a first submarine cable during at least one load change process; obtaining load characteristic values ​​based on the load data; obtaining temperature response characteristic values ​​based on the temperature data; obtaining a feature set of the spatial point to be measured based on the load characteristic values ​​and the temperature response characteristic values; and inputting the feature set into a burial depth monitoring model to obtain burial depth data of the spatial point to be measured.

[0007] In the implementation of the above embodiments, the burial depth data of the spatial points to be measured is obtained by inferring from the feature set using the burial depth monitoring model. This eliminates the need to calculate heat dissipation, thereby reducing errors introduced by parameter estimation in this part of the calculation, avoiding extensive grid calculations, saving computing resources, reducing the complexity of submarine cable burial depth calculation, reducing calculation time, and improving burial depth calculation efficiency. The changing characteristics of load data and temperature data show a trend correlation with different burial depths. The embodiments of this application construct at least one load characteristic value and temperature response characteristic value during the load change process for subsequent burial depth calculation. These load characteristic values ​​and temperature response characteristic values ​​fully reflect the cumulative characteristics of the load change process, which is beneficial to improving the accuracy of monitoring.

[0008] Optionally, in this embodiment, determining a load change process includes: acquiring real-time load data of the spatial point to be measured in the first submarine cable; determining the moment when the real-time load data exceeds a preset load threshold as the start moment, and determining the first moment after the start moment when the real-time load data does not exceed the preset load threshold as the end moment of the load change process, and taking the time period between the start moment and the end moment as a load change process; or, acquiring real-time load data of the spatial point to be measured in the first submarine cable; determining the maximum and minimum values ​​based on the real-time load data; taking the time period between two adjacent minimum values ​​as a load change process, or taking the time period between an adjacent minimum value and a maximum value as a load change process.

[0009] In the implementation of the above embodiments: the load increase during the load change process determined by the above method is sufficiently large and the duration is sufficiently long. This ensures that the temperature response characteristic values ​​formed by the temperature data at different burial depths during the load change process differ sufficiently, providing a good data foundation for subsequent burial depth data detection. Furthermore, in some embodiments, multiple methods for determining the load change process can be provided, offering greater flexibility.

[0010] Optionally, in this embodiment of the application, obtaining load characteristic values ​​based on load data includes: calculating the accumulated value of load data changing over time from the start time to the end time of the load change process, and using the accumulated value as the load characteristic value; wherein, the load data is a parameter of the electrical load characteristics borne by the first submarine cable; the load characteristic value is used to characterize the load distribution during the load change process.

[0011] In the implementation of the above embodiments: load characteristic values ​​are obtained based on load data. These load characteristic values ​​characterize the load distribution during load changes, providing a good data foundation for subsequent detection of burial depth data using the burial depth monitoring model, thus improving the accuracy of burial depth monitoring. Furthermore, an integral method is used to calculate the load characteristic values, making them highly representative and able to better reflect the changes and distribution of data within the analysis period. This approach also simplifies the feature extraction process, making it more intuitive and easier to understand, reducing computational complexity, avoiding the need for complex data transformations or high-dimensional feature extraction, and improving computational efficiency.

[0012] Optionally, in this embodiment, the temperature data includes current temperature data, initial temperature data at the start of the load change process, and temperature deformation data, wherein the temperature deformation data is a parameter characterizing the temperature characteristics during the load change process; obtaining temperature response feature values ​​based on the temperature data includes: calculating the integral of the difference between the current temperature data and the initial temperature data from the start to the end of the load change process to obtain a first temperature response feature value; obtaining a second temperature response feature value based on the temperature deformation data from the start to the end of the load change process; and using at least one of the first temperature response feature value and the second temperature response feature value as the temperature response feature value.

[0013] In the implementation of the above embodiments: temperature response feature values ​​are obtained based on temperature data. These feature values ​​reflect the temperature change characteristics during load changes. The temperature deformation data used to construct the second temperature response feature value can include multiple factors such as temperature amplitude, mean, and rate of change. Temperature amplitude reflects the maximum range of temperature fluctuations during load changes, but may ignore the persistence and trend of temperature changes. Combining it with the mean can analyze the degree of temperature deviation from normal; combining it with the rate of change can reveal the speed of extreme temperature changes. The mean reflects the average temperature state during load changes, but ignores temperature fluctuations. Combined with temperature amplitude, it can assess the central trend and extreme cases of temperature fluctuations; combined with the rate of change, it can analyze the long-term trend and short-term fluctuations of temperature changes. The rate of change can capture the trend of temperature changes, but cannot fully reflect the overall temperature level or fluctuation range. Combined with temperature amplitude, it can identify rapid and large-amplitude temperature changes; combined with the mean, it can analyze the speed at which the temperature deviates from the average level. By using multiple feature values ​​in combination, a more comprehensive temperature feature profile can be constructed, which helps to more accurately understand the changing patterns of the system or environment. This improves the accuracy of the burial depth monitoring model in detecting burial depth data based on these parameters, and the characteristic parameters have clear meanings, thus enhancing the interpretability of the burial depth monitoring model.

[0014] Optionally, in this embodiment of the application, obtaining the feature set of the space point to be measured based on the load characteristic value and the temperature response characteristic value includes: obtaining the ambient temperature parameter of the space point to be measured during at least one load change process; and obtaining the feature set of the space point to be measured based on the load characteristic value, the temperature response characteristic value and the ambient temperature parameter.

[0015] In the implementation of the above embodiments: when constructing the feature set, not only load characteristic values ​​and temperature response characteristic values ​​are used, but also environmental temperature parameters are considered. Incorporating environmental temperature parameters into the feature set ensures that they do not fluctuate due to changes in environmental temperature, thereby enabling a more accurate estimation of the absolute burial depth. When the feature set contains information across multiple dimensions, the model can more comprehensively understand the data, improving distortion issues and significantly enhancing the accuracy and interpretability of the burial depth monitoring model.

[0016] Optionally, in this embodiment, obtaining the feature set of the spatial point to be measured based on the load characteristic value, temperature response characteristic value, and ambient temperature parameter includes: concatenating the load characteristic value, temperature response characteristic value, and ambient temperature parameter to obtain the feature set of the spatial point to be measured; or, performing a function operation on each temperature response characteristic value with the load characteristic value to obtain the function operation result corresponding to the load change process; summing the function operation results of the same function operation corresponding to the load change process to obtain the function operation summation result; and concatenating the summation result of each function operation with the ambient temperature parameter to obtain the feature set of the spatial point to be measured.

[0017] In the implementation of the above embodiments: multiple methods are provided to construct the feature set. The feature set can be constructed by splicing load feature values, temperature response feature values ​​and ambient temperature parameters, or by first performing function operations on each temperature response feature value with the load feature value and then splicing them together. This improves the richness of the feature set, enables the model to understand the data more comprehensively, improves the distortion problem, and greatly improves the accuracy and interpretability of the burial depth monitoring model.

[0018] Optionally, in this embodiment of the application, before inputting the feature set into the burial depth monitoring model to obtain the burial depth data of the spatial point to be measured, the method further includes: acquiring historical load data and historical temperature data of the spatial sampling point of the second submarine cable during at least one load change process in a historical period, as well as the measured burial depth value of the spatial sampling point; obtaining historical load feature values ​​based on the historical load data; obtaining historical temperature response feature values ​​based on the historical temperature data; obtaining the historical feature set of the spatial sampling point according to the historical load feature values ​​and the historical temperature response feature values; and performing regression modeling using the historical feature set of the spatial sampling point and the measured burial depth value to obtain the burial depth monitoring model.

[0019] In the implementation of the above embodiments: by using the historical feature set of spatial sampling points and the measured burial depth value for regression modeling, the relationship between the historical feature set and the measured burial depth value is captured, and an accurate burial depth monitoring model is established. Then, the burial depth value of the spatial point to be measured can be monitored using the burial depth monitoring model, thereby improving the accuracy of burial depth monitoring.

[0020] In this embodiment of the application, the step of obtaining the measured burial depth value of the spatial sampling point includes: obtaining the latitude and longitude data and the measured burial depth value of the detection point for the second submarine cable using the measured burial depth method; determining the length coordinates of the detection point using the latitude and longitude data of the detection point; obtaining the length coordinates of the spatial sampling point; calculating the distance between the detection starting point and the spatial sampling starting point based on the latitude and longitude data of the detection starting point in the detection point and the spatial sampling starting point in the spatial sampling point, thereby aligning the length coordinates of the detection point and the length coordinates of the spatial sampling point; and performing interpolation calculation using the measured burial depth value of the detection point based on the aligned length coordinates of the detection point and the length coordinates of the spatial sampling point to obtain the measured burial depth value of the spatial sampling point.

[0021] In the implementation of the above embodiments: after aligning the length coordinates of the detection point and the length coordinates of the spatial sampling point, the measured burial depth value of the spatial sampling point is obtained by interpolation, which eliminates the need to spend a lot of time and resources to measure the burial depth value for each spatial sampling point separately, thereby improving the efficiency of obtaining the measured burial depth value.

[0022] Secondly, embodiments of this application also provide a device for monitoring the burial depth of a submarine cable, comprising: a data acquisition module for acquiring load data and temperature data of a spatial point to be measured in a first submarine cable during at least one load change process; a feature module for obtaining load feature values ​​based on the load data and temperature response feature values ​​based on the temperature data; a feature set module for obtaining a feature set of the spatial point to be measured based on the load feature values ​​and temperature response feature values; and a monitoring module for inputting the feature set into a burial depth monitoring model to obtain burial depth data of the spatial point to be measured.

[0023] Thirdly, embodiments of this application also provide a computer program product, including computer program instructions, which are executed by a processor to perform the method provided in the first aspect or any implementation thereof.

[0024] Fourthly, embodiments of this application also provide an electronic device, including: a processor and a memory, the memory storing computer program instructions, which are executed by the processor to perform the method provided in the first aspect or any implementation thereof.

[0025] Fifthly, embodiments of this application also provide a computer-readable storage medium storing computer program instructions, which, when executed by a processor, perform the method provided in the first aspect or any implementation thereof.

[0026] The method, apparatus, electronic equipment, and storage medium for monitoring the burial depth of submarine cables provided in this application utilize a burial depth monitoring model to infer the feature set and obtain the burial depth data of the spatial points to be measured. This eliminates the need to calculate heat dissipation, thereby reducing errors introduced by parameter estimation in this part of the calculation, avoiding extensive grid computation, saving computational resources, reducing the complexity of submarine cable burial depth measurement, reducing computation time, and improving the efficiency of burial depth measurement. Furthermore, this method has a lower barrier to entry and is easier to operate. Attached Figure Description

[0027] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 A flowchart illustrating a method for monitoring the burial depth of a submarine cable, provided as an embodiment of this application;

[0029] Figure 2 This is a schematic diagram of the burial depth monitoring system provided in the embodiments of this application;

[0030] Figure 3 A schematic diagram of the structure of the submarine cable burial depth monitoring device provided in the embodiments of this application;

[0031] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0032] The embodiments of the technical solution of this application will now be described in detail with reference to the accompanying drawings. These embodiments are only used to more clearly illustrate the technical solution of this application and are therefore merely examples, and should not be used to limit the scope of protection of this application.

[0033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this application.

[0034] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0035] Please see Figure 1 The illustrated diagram shows a flowchart of a method for monitoring the burial depth of a submarine cable according to an embodiment of this application. This method can be applied to electronic devices, which may include physical devices such as servers, PCs, tablets, or smartphones, or virtual devices such as virtual machines or containers. The electronic device can be a single device, a combination of multiple devices, or a cluster of numerous devices. The method for monitoring the burial depth of a submarine cable may include:

[0036] Step S110: Obtain load data and temperature data of the test point in the first submarine cable during at least one load change process.

[0037] Step S120: Obtain load characteristic values ​​based on load data; and obtain temperature response characteristic values ​​based on temperature data.

[0038] Step S130: Obtain the feature set of the spatial points to be measured based on the load characteristic value and the temperature response characteristic value.

[0039] Step S140: Input the feature set into the burial depth monitoring model to obtain the burial depth data of the spatial points to be measured.

[0040] In step S110, a submarinecable cable refers to a submarine cable laid on the seabed for at least power transmission, and may also be used for telecommunications transmission. The first submarinecable cable can be a submarinecable cable requiring depth monitoring. Depth refers to the depth to which the submarinecable cable is buried on the seabed. The spatial point to be monitored within the first submarinecable cable is the location or area within the first submarinecable cable where depth monitoring is required.

[0041] Load is a parameter describing the electrical load characteristics borne or generated by a measured spatial point in the first submarine cable. Load data is a parameter describing the electrical load characteristics borne by the first submarine cable, such as current or power. Load data can be acquired through power generation-related sensing equipment. It should be noted that the load data of all measured spatial points in the first submarine cable at the same time are generally consistent. However, in some cases, the load data of different measured spatial points in the entire submarine cable may be inconsistent, such as the collector lines of offshore wind farms, where the load values ​​differ due to the different number of wind turbines connected in series. In this case, it is necessary to calculate the burial depth of the submarine cable segments to ensure that the load of each segment is the same. For example, the sections of the first submarine cable with different load data can be segmented, and the load data of each segment can be calculated and the burial depth monitored separately to improve the accuracy of burial depth monitoring. The load change process refers to the period during which the electrical load borne or generated by the first submarine cable fluctuates. For example, the moment when the real-time load data exceeds a preset load threshold is determined as the start moment, and the first moment after the start moment when the real-time load data does not exceed the preset load threshold is determined as the end moment of the load change process, and the time period between the start moment and the end moment is taken as a load change process; or, real-time load data of the spatial point to be measured in the first submarine cable is obtained; the maximum and minimum values ​​are determined according to the real-time load data; the time period between two adjacent minimum values ​​is taken as a load change process, or the time period between an adjacent minimum value and a maximum value is taken as a load change process.

[0042] The temperature data of the space point to be measured can be the temperature value obtained by measuring the space point through a distributed optical fiber sensing electronic device; this temperature value is obtained in real time.

[0043] In step S120, load feature values ​​can be obtained by calculating or extracting features from the load data. These load feature values ​​are used to characterize the load distribution during load changes. The calculation or feature extraction methods can include, for example, integrating the load data during load changes or obtaining features based on load deformation data. Load deformation data refers to parameters obtained by calculating the load data to characterize a specific load characteristic during load changes, such as the mean, load amplitude, or median of the load data during load changes.

[0044] Furthermore, temperature response feature values ​​can be obtained by calculating or extracting features from temperature data. These feature values ​​are one of the attribute values ​​used to reflect burial depth data. For example, temperature response feature values ​​can be calculated by integrating temperature data or temperature deformation data during load changes. Temperature deformation data is a parameter characterizing the temperature properties during load changes, and it can be calculated from the temperature parameters during load changes. There can be one or more temperature response feature values. The calculation process for load feature values ​​and temperature response feature values ​​will be explained in detail later.

[0045] In step S130, the load characteristic value and the temperature response characteristic value can be spliced ​​or combined to obtain the feature set of the spatial point to be measured; alternatively, the feature set of the spatial point to be measured can be obtained by performing mathematical operations on the load characteristic value and the temperature response characteristic value, which will be explained in detail later.

[0046] In step S140, after obtaining the feature set of the spatial point to be measured, the burial depth monitoring model is used to infer the feature set to obtain the burial depth data of the spatial point to be measured. The burial depth data of the spatial point to be measured can be the burial depth data during the load change process. For example, if the load change process is 10 hours, then in step S110, it is necessary to collect the load data and temperature data at instantaneous moments of 10 hours. When calculating the burial depth data, the burial depth data of the spatial point to be measured during the load change process can be obtained. Generally speaking, the time scale of a single load change process is in the range of several hours to several days.

[0047] The generation of the burial depth monitoring model may include the following steps: acquiring historical load data and historical temperature data, as well as the measured burial depth values ​​of spatial sampling points; obtaining historical load characteristic values ​​based on historical load data; obtaining historical temperature response characteristic values ​​based on historical temperature data; obtaining the historical feature set of spatial sampling points based on the historical load characteristic values ​​and historical temperature response characteristic values; and performing regression modeling using the historical feature set of spatial sampling points and the measured burial depth values ​​to obtain the burial depth monitoring model. The above process will be explained in detail later.

[0048] In the above implementation process, the burial depth data of the spatial point to be measured is obtained by inferring from the feature set using the burial depth monitoring model. This eliminates the need to calculate heat dissipation, thereby reducing the error introduced by parameter estimation in this part of the calculation, avoiding a large amount of grid computation, saving computing resources, reducing the complexity of submarine cable burial depth calculation, reducing computation time, and improving the efficiency of burial depth calculation. The changing characteristics of load data and temperature data show a trend correlation with different burial depths. The embodiments of this application construct at least one load characteristic value and temperature response characteristic value during the load change process for subsequent burial depth calculation. These load characteristic values ​​and temperature response characteristic values ​​fully reflect the cumulative characteristics of the load change process, which is beneficial to improving the accuracy of monitoring.

[0049] Optionally, in the embodiments of this application, a load change process can be determined in any of the following ways:

[0050] The first method involves acquiring real-time load data of the test point within the first submarine cable. The moment when the real-time load data exceeds a preset load threshold is defined as the start time, and the first (or nth) moment after the start time when the real-time load data does not exceed the preset load threshold is defined as the end time of the load change process. The period between the start and end times is considered a single load change process. The preset load threshold can be set according to actual conditions. For example, the preset load threshold is considered to be a sufficiently large specific load value, where a load exceeding this threshold can cause a sufficiently large difference in temperature response at different burial depths, such as a current exceeding 200 amperes (A).

[0051] Optionally, the load change process can be identified by searching the real-time load data I(t) - a preset load threshold I. threshold The upper and lower zero points are obtained.

[0052] The second method involves acquiring real-time load data from the spatial points to be measured within the first submarine cable. After smoothing the real-time load data, the maxima and minima can be determined using the smoothed data curve, for example, by calculating the first derivative of the real-time load data. After determining the maxima and minima, the time interval between two adjacent minima is considered as a load change process, or the time interval between an adjacent minima and a maxima is considered as a load change process.

[0053] In the implementation of the above embodiments: the load increase during the load change process determined by the above method is sufficiently large and the duration is sufficiently long. This ensures that the temperature response characteristic values ​​at different burial depths differ sufficiently during the load change process, providing a good data foundation for subsequent burial depth data detection. Furthermore, it provides multiple methods for determining the load change process, offering greater flexibility.

[0054] Optionally, in this embodiment of the application, obtaining load characteristic values ​​based on load data includes: calculating the accumulated value of load data changing over time from the start time to the end time of the load change process, and using the accumulated value as the load characteristic value; wherein, the load data is a parameter of the electrical load characteristics borne by the first submarine cable; the load characteristic value is used to characterize the load distribution during the load change process.

[0055] For example, taking load data from a load change process as an example, the calculation of load characteristic values ​​can be as follows:

[0056]

[0057] Among them, t start t represents the start time of the load change process. end I(t) represents the end time of the load change process, and I(t) represents the real-time load data at time (t).

[0058] In one implementation, the load characteristic value can be calculated as the integral of real-time load data over the load change process. The integrand (real-time load data) can be replaced by any power of the original integrand or a standardized form based on the mean, median, standard deviation, etc.; alternatively, it can be a combination of one or more of the mean, median, and load amplitude of the load data during the load change process. The load amplitude is the difference between the maximum and minimum values ​​of the load data during the load change process.

[0059] In the implementation of the above embodiments: load characteristic values ​​are obtained based on load data. These load characteristic values ​​characterize the load distribution during load changes, providing a good data foundation for subsequent detection of burial depth data using the burial depth monitoring model, thus improving the accuracy of burial depth monitoring. Furthermore, an integral method is used to calculate the load characteristic values, making them highly representative and able to better reflect the changes and distribution of data within the analysis period. This approach also simplifies the feature extraction process, making it more intuitive and easier to understand, reducing computational complexity, avoiding the need for complex data transformations or high-dimensional feature extraction, and improving computational efficiency.

[0060] Optionally, in this embodiment, the temperature data includes current temperature data, initial temperature data at the start of the load change process, and temperature deformation data, whereby the temperature deformation data is a parameter characterizing the temperature properties during the load change process. Obtaining temperature response characteristic values ​​based on the temperature data includes the following steps:

[0061] The first temperature response characteristic value is obtained by integrating the difference between the current temperature data and the initial temperature data from the start to the end of the load change process. The current temperature data can be the temperature data at any point between the start and end of the load change process. The formula for calculating the first temperature response characteristic value is as follows:

[0062]

[0063] Among them, t start t represents the start time of the load change process. end T represents the end time of the load change process. i (t) represents the current temperature data, T i (t start This represents the initial temperature data at the start of the load change process. The current temperature data can be the temperature data at any point during the load change process.

[0064] As one implementation method, the first temperature response characteristic value, also known as the integral characteristic value, refers to the integral of the difference between the current temperature data (temperature value at any time) and the initial temperature value during the load change process. The integrand (the difference between the current temperature data and the initial temperature data) can be replaced by any power of the original integrand (the difference between the current temperature data and the initial temperature data) or a standardized form based on the mean, median, standard deviation, etc. In other words, the first temperature response characteristic value has multiple calculation methods.

[0065] The second temperature response characteristic value is obtained based on the temperature deformation data from the start to the end of the load change process. Temperature deformation data can include, for example, the temperature amplitude, mean, initial temperature, heating / cooling time, temperature change rate, and arithmetic combinations of these values ​​during the load change process. It can also be data obtained by arbitrary powers or deformations based on the mean, median, standard deviation, etc.

[0066] At least one of the first temperature response characteristic value and the second temperature response characteristic value is used as the temperature response characteristic value; that is, there can be one or more temperature response characteristic values. In an optional embodiment, the second temperature response characteristic value may include multiple values, and the second temperature response characteristic values ​​may be named separately. For example, the temperature amplitude, average value, initial temperature, heating / cooling time, temperature change rate, and arithmetic combinations of the above values ​​can all be used as the second temperature response characteristic value, respectively denoted as C_temp2(i), C_temp3(i), ..., C_tempn(i).

[0067] In the implementation of the above embodiments: temperature response feature values ​​are obtained based on temperature data. These feature values ​​reflect the temperature change characteristics during load changes. The temperature deformation data used to construct the second temperature response feature value can include multiple factors such as temperature amplitude, mean, and rate of change. Temperature amplitude reflects the maximum range of temperature fluctuations during load changes, but may ignore the persistence and trend of temperature changes. Combining it with the mean can analyze the degree of temperature deviation from normal; combining it with the rate of change can reveal the speed of extreme temperature changes. The mean reflects the average temperature state during load changes, but ignores temperature fluctuations. Combined with temperature amplitude, it can assess the central trend and extreme cases of temperature fluctuations; combined with the rate of change, it can analyze the long-term trend and short-term fluctuations of temperature changes. The rate of change can capture the trend of temperature changes, but cannot fully reflect the overall temperature level or fluctuation range. Combined with temperature amplitude, it can identify rapid and large-amplitude temperature changes; combined with the mean, it can analyze the speed at which the temperature deviates from the average level. By using multiple feature values ​​in combination, a more comprehensive temperature feature profile can be constructed, which helps to more accurately understand the changing patterns of the system or environment. This improves the accuracy of the burial depth monitoring model in detecting burial depth data based on these parameters, and the characteristic parameters have clear meanings, thus enhancing the interpretability of the burial depth monitoring model.

[0068] Optionally, in this embodiment, due to differences in ambient temperature, the temperature change under the same load conditions will also be different. Therefore, the model also needs to consider the influence of ambient temperature. In this embodiment, an ambient temperature parameter is introduced. The ambient temperature parameter reflects the ambient temperature around the submarine cable at that point, which can be obtained from a weather station or temperature measuring electronic equipment. Preferably, a distributed optical fiber temperature measuring device is used to obtain it; specifically, the submarine cable integrates temperature-sensing optical fiber, and the temperature of each point to be measured at each time can be measured by the distributed optical fiber temperature measuring device. By analyzing and processing the monitored temperature, the ambient temperature of the submarine cable at that point can be obtained. As one implementation method, the temperature measurement data at the time of no load or low load near the time of load change process can also be taken as (approximately considered) the ambient temperature.

[0069] Based on the load characteristic value and temperature response characteristic value, the feature set of the space point to be measured is obtained, including: obtaining the ambient temperature parameters of the space point to be measured during at least one load change process.

[0070] For example, the minimum temperature of the test point i within a preset period before the load change process can be obtained (the lowest temperature is measured when the conductor is not heating up). For example, the minimum temperature of the test point i within one month before the load change process can be used as the ambient temperature parameter.

[0071] In other methods, a low-pass filter can be used to remove high-frequency fluctuations caused by load changes, yielding an approximate ambient temperature for the current time as the ambient temperature parameter. For example, a distributed fiber optic temperature measurement device can be used to acquire the temperature of the measured spatial point at multiple time points within a preset period before the load change process, and this data can be used to construct corresponding temperature time-series data. Then, a low-pass filter can be used to remove high-frequency fluctuations caused by load changes, yielding an approximate ambient temperature for the current time as the ambient temperature parameter.

[0072] Based on load characteristic values, temperature response characteristic values, and ambient temperature parameters, a feature set of the spatial points to be measured is obtained. For example, the load characteristic values, temperature response characteristic values, and ambient temperature parameters can be concatenated or combined to obtain the feature set of the spatial points to be measured.

[0073] In the implementation of the above embodiments: when constructing the feature set, not only load characteristic values ​​and temperature response characteristic values ​​are used, but also environmental temperature parameters are considered. Incorporating environmental temperature parameters into the feature set ensures that they do not fluctuate due to changes in environmental temperature, thereby enabling a more accurate estimation of the absolute burial depth. When the feature set contains information across multiple dimensions, the model can more comprehensively understand the data, improving distortion issues and significantly enhancing the accuracy and interpretability of the burial depth monitoring model.

[0074] Optionally, in this embodiment of the application, the process of obtaining the feature set of the spatial points to be measured based on load characteristic values, temperature response characteristic values, and ambient temperature parameters can be carried out in any of the following ways:

[0075] The first method involves splicing together the load characteristic value, temperature response characteristic value, and ambient temperature parameter to obtain the feature set of the spatial point to be measured.

[0076] The second method involves performing a function operation on each temperature response characteristic value and the load characteristic value to obtain the function operation result corresponding to the load change process; for example, using C_load p The load characteristic value representing the p-th load change process is represented by C_temp. pk (i) represents the k-th temperature response characteristic value of the p-th load change process at the measured spatial point i. Then the result of the function operation can be expressed as f k (C_load p C_temp pk (i)). Among them, the function operations, such as multiplication, exponentiation, or division, are not limited in this application.

[0077] The results of the same function operations corresponding to the load change process are summed to obtain the sum of the function operations. The sum of each function operation is then combined with the ambient temperature parameter to obtain the feature set of the spatial point to be measured.

[0078] Based on the above embodiments, the feature set of the spatial point i to be measured can be expressed as:

[0079]

[0080] Where p represents the p-th load change process, p = 1 - n, and n can be set according to requirements. Furthermore, the function calculations (i.e., f1, f2, ..., f...) are performed between different temperature response characteristic values ​​and load characteristic values. k The calculation rules can be the same or different, depending on how the historical feature set is constructed when building the burial depth monitoring model. The number of n represents the number of load change processes that meet the defined requirements within the analysis time. In the above formula, k temperature response feature values ​​from C_temp(i) can be selected as needed, where k = 1, 2, ..., n. The load feature value in any p-th load change process... With temperature response characteristic value The two can be combined in any functional form, using This means that the results of the same function operations corresponding to N load change processes are summed to obtain the summation result of the function operations, i.e. Finally, the summation results of each function operation are concatenated with the ambient temperature parameters to obtain the feature set of the spatial points to be measured.

[0081] The third method involves iteratively calculating the load-temperature covariance eigenvalue Ci(Δt) within a time window (Δt time) using an iterative time step approach, and then concatenating it with the ambient temperature parameters to obtain a feature set. The calculation method for the load-temperature covariance eigenvalue Ci(Δt) is as follows:

[0082]

[0083] The load-temperature covariance eigenvalue is the product of the load data and the temperature difference data over time Δt within the time window t. max The summation of all time points t within the range is similar in form to the definition of covariance; (the load data and temperature difference data can be any power or standardized forms based on the mean, median, standard deviation, etc.)

[0084] When Δt takes different values, the corresponding Ci(Δt) is calculated. Based on different Δt values, the maximum value of Ci(Δt), the Δt corresponding to the maximum value of Ci(Δt), and the steepness of the rise / fall of Ci(Δt) as Δt changes can be calculated. One or more of these values ​​can be used as multiple feature values ​​reflecting the burial depth attribute of the i-th point, denoted as cov1(i), cov2(i), cov3(i). These multiple feature values, combined with environmental parameters, form the feature set of the i-th point in this time window as {cov1(i), cov2(i), cov3(i), ..., Tenv(i)}.

[0085] In the implementation of the above embodiments: multiple methods are provided to construct the feature set. The feature set can be constructed by splicing load feature values, temperature response feature values ​​and ambient temperature parameters, or by first performing function operations on each temperature response feature value with the load feature value and then splicing them together. This improves the richness of the feature set, enabling the model to understand the data more comprehensively, and greatly improving the accuracy and interpretability of the burial depth monitoring model.

[0086] Submarine cables generate self-heating due to load. According to heat transfer theory, heat transfer in submarine cables mainly occurs through conduction and convection. A shallower burial depth means the cable is closer to the seawater, making heat dissipation easier. In actual projects, it has been observed that as load data increases, locations with greater burial depth experience a larger temperature rise and a longer heating time compared to locations with shallower burial depth. Therefore, by comparing the temperature differences at different locations (the points to be measured) as load data changes, the relative burial depth can be determined, and a burial depth monitoring model can be used to quantify the burial depth of the monitored points. The process of obtaining the burial depth monitoring model is described below.

[0087] Historical load data and historical temperature data for at least one load variation process during a historical period were obtained from the spatial sampling points of the second submarine cable, along with the measured burial depth values ​​of the spatial sampling points. The measured burial depth values ​​of the spatial sampling points refer to the burial depth values ​​obtained through measurements using specialized equipment and techniques; this step will be explained in detail later.

[0088] The second submarine cable can be the same submarine cable as the first submarine cable, or it can be a different submarine cable. If the second submarine cable is the same submarine cable as the first submarine cable, the burial depth value of the same spatial point may change over time. Therefore, the spatial sampling points used in the process of generating the burial depth monitoring model can also be used as subsequent spatial points to be measured. That is, the spatial sampling points and the spatial points to be measured can be the same location or region, or they can be different locations or regions within the same submarine cable.

[0089] Historical load data, such as current and power, can be obtained through power generation-related sensing equipment. Historical temperature data can be obtained by measuring the spatial points under test using distributed fiber optic sensing equipment.

[0090] Historical load characteristic values ​​are obtained based on historical load data; and historical temperature response characteristic values ​​are obtained based on historical temperature data. The methods for obtaining historical load characteristic values ​​and historical temperature response characteristic values ​​are similar to those in step S120, which involves obtaining load characteristic values ​​based on load data and obtaining temperature response characteristic values ​​based on temperature data.

[0091] For example, historical load characteristic values ​​can be obtained by integrating historical load data / transformations of at least one load change process or by obtaining historical load deformation data. Similarly, temperature response characteristic values ​​can be obtained by integrating historical temperature data / transformations of at least one load change process or by obtaining historical temperature deformation data. Implementation methods are described above.

[0092] Based on historical load characteristic values ​​and historical temperature response characteristic values, a historical feature set for the spatial sampling points is obtained. It should be noted that the method used to construct the feature set in step S130 of the submarine cable burial depth monitoring method should be consistent with the method used to construct the historical feature set during the construction of the burial depth monitoring model. For example, if the historical feature set is obtained by concatenating historical load characteristic values ​​and historical temperature response characteristic values ​​during the construction of the burial depth monitoring model, then the same method should be used when conducting burial depth monitoring of the spatial points to be measured. Similarly, if historical ambient temperature parameters are introduced when constructing the historical feature set, then ambient temperature parameters should also be introduced when constructing the feature set.

[0093] A burial depth monitoring model is obtained by using historical feature sets and measured burial depth values ​​from spatial sampling points for regression modeling. Each spatial sampling point provides its corresponding historical feature set and measured burial depth value, which can be considered as a label in machine learning. For example, a suitable regression model is selected based on monitoring requirements; regression methods include decision tree regression, random forest regression, or Bagging regression. For instance, a linear regression model or a multinomial regression model can be established, representing the burial depth as a linear or multinomial function of each feature value in the feature set, and then optimization algorithms such as least squares or gradient descent are used to determine the coefficients of the linear or multinomial function. Alternatively, regression models such as decision tree regression, random forest regression, or Bagging ensemble regression can be used, with model construction and training based on machine learning algorithms. Using the determined regression method, a regression model is constructed based on the historical feature set and measured burial depth values, and model parameters are determined, completing the establishment of the burial depth monitoring model. Furthermore, the performance of the burial depth monitoring model can be validated and evaluated after its generation to ensure that the model meets performance requirements.

[0094] In the implementation of the above embodiments: by using the historical feature set of spatial sampling points and the measured burial depth value for regression modeling, the relationship between the historical feature set and the measured burial depth value is captured, and an accurate burial depth monitoring model is established. Then, the burial depth value of the spatial point to be measured can be monitored using the burial depth monitoring model, thereby improving the accuracy of burial depth monitoring.

[0095] Optionally, in this embodiment of the application, the step of obtaining the measured burial depth value of the spatial sampling point includes:

[0096] The latitude and longitude data and measured burial depth values ​​of the detection points for the second submarine cable are obtained using the measured burial depth method. The detection points are the locations or areas within the second submarine cable where the latitude and longitude data and measured burial depth values ​​are obtained using this method. The measured burial depth method includes scanning burial depth or initial point-marking burial depth, where scanning burial depth can be detected through shallow seismic profiling, side-scan sonar, or multibeam sonar.

[0097] Using the latitude and longitude data of the detection points, the length coordinates of the detection points are determined. For example, the distance between two detection points can be calculated from the latitude and longitude data. The latitude and longitude of the measured burial depth measurement points are converted into length coordinates, which can be regarded as one-dimensional coordinates. For example, the length coordinates of the detection points can be "0 meters, 52.5 meters, 78.8 meters, 128.2 meters...", where each value represents the location of a detection point. Furthermore, during the initial laying or manual detection using shallow seismic profiling, side-scan sonar, or multibeam sonar, the length and latitude / longitude coordinates corresponding to each location of the second submarine cable can be recorded simultaneously, thus directly establishing the correlation between the length and latitude / longitude coordinates in the early stages.

[0098] Interpretatively, when the distance between two detection points is sufficiently short, high accuracy is generally achievable, making the distance between the two detection points equivalent to the length of the second submarine cable between them. However, since the second submarine cable extends not only along latitude and longitude but also along altitude, further consideration of the absolute height of the detection points allows for the calculation of the specific extension direction of the second submarine cable in three-dimensional space. Based on the absolute height and latitude / longitude coordinates, a high-precision three-dimensional route map of the cable is fitted and verified, thereby determining the high-precision length coordinates of each detection point. This enables a high-precision match between the measured burial depth value and the length coordinates of the temperature detection points in the distributed optical fiber. The absolute height of the detection points can be calculated using technologies such as sonar scanning and GPS positioning. For example, GPS positioning can determine the absolute height of the probe vessel, and combined with sonar scanning and DAS detection, the height difference between the detection point and the probe vessel can be determined, thus calculating the absolute height of the detection point. Alternatively, during the cable laying phase, latitude, longitude, and absolute height can be marked at the detection points of the cable using seabed detectors or manually.

[0099] Obtain the length coordinates of spatial sampling points. For example, consider the second submarine cable integrating distributed optical fibers. The length coordinates of the corresponding spatial sampling points of the distributed optical fibers can be obtained through DTS (Distributed Fiber Optic Sensing) devices. For instance, the length coordinates of spatial sampling points can be "0 meters, 1 meter, 2 meters…50 meters…129 meters," where each value represents the location of a spatial sampling point. Specifically, based on the sampling frequency and refractive index of the fiber optic sensing device, combined with OTDR (Optical Time Domain Analysis), the step size between spatial sampling points is determined, thereby determining the length coordinates of the spatial sampling points. The latitude and longitude data of the spatial sampling starting point can be the latitude and longitude data of the location of the distributed optical fiber sensing device.

[0100] Based on the latitude and longitude data of the detection starting point in the detection points and the latitude and longitude data of the spatial sampling starting point in the spatial sampling points, the distance between the detection starting point and the spatial sampling starting point is calculated, thereby aligning the length coordinates of the detection points and the length coordinates of the spatial sampling points. Alignment refers to the process of unifying the reference system of the length coordinates of the detection points and the length coordinates of the spatial sampling points.

[0101] Both the detection points and spatial sampling points are distributed along the second submarine cable, and the length coordinates of both are calculated from 0 meters. However, the starting point of the detection at the detection point and the starting point of the spatial sampling at the spatial sampling point may not be the same point (i.e., the origins of their length coordinate systems are different). Therefore, it is necessary to align the length coordinates of the detection points and the spatial sampling points. Alignment refers to the process of unifying the reference systems of the length coordinates of the detection points and the spatial sampling points. For example, due to the varying depths of the seawater, some points along the second submarine cable may not be detected. Therefore, the starting point of the detection at the detection point (0 meters) is the first point detected; the starting point of the spatial sampling at the spatial sampling point (0 meters) is the first spatial sampling point in the distributed optical fiber. Because the starting points are different, even if the length coordinates of the detection points and the spatial sampling points are the same before alignment, they represent different spatial locations in practice.

[0102] The alignment process can be as follows: Based on the latitude and longitude data of the detection starting point in the detection points and the latitude and longitude data of the spatial sampling starting point in the spatial sampling points, determine the distance between the detection starting point and the spatial sampling starting point. Add this distance to the length coordinate of each detection point to obtain the aligned length coordinate of the detection point. In this way, the aligned length coordinates of the detection points and the length coordinates of the spatial sampling points are aligned, meaning that both are based on the spatial sampling starting point in the spatial sampling points. The aligned length coordinates of the detection points are consistent with the length coordinates of the spatial sampling points, representing the same location or region. The latitude and longitude data of the spatial sampling starting point in the spatial sampling points can be the latitude and longitude data of the location of the distributed fiber optic sensing device.

[0103] Based on the aligned length coordinates of the detection points and the length coordinates of the spatial sampling points, interpolation calculations are performed using the measured burial depth values ​​of the detection points (interpolation calculations can include linear interpolation, spline interpolation, Lagrange interpolation, etc.) to obtain the measured burial depth values ​​of the spatial sampling points. For example, for each spatial sampling point, the two nearest detection points on either side are found (i.e., detection points whose length coordinates are less than and greater than the sampling point coordinates), and the measured burial depth value of the spatial sampling point is calculated using the interpolation calculation formula based on the two nearest detection points on either side.

[0104] Through the above interpolation process, the measured burial depth values ​​of all spatial sampling points in the overlapping area of ​​the length coordinates of the detection points and the spatial sampling points in the second submarine cable can be calculated. During regression modeling, spatial sampling points with already calculated measured burial depth values ​​and their historical feature sets can be selected for regression modeling to obtain the burial depth monitoring model.

[0105] In the implementation of the above embodiments: after aligning the length coordinates of the detection point and the length coordinates of the spatial sampling point, the measured burial depth value of the spatial sampling point is obtained by interpolation, which eliminates the need to spend a lot of time and resources to measure the burial depth value for each spatial sampling point separately, thereby improving the efficiency of obtaining the measured burial depth value.

[0106] Please see Figure 2 The diagram shows a framework schematic of the burial depth detection system provided in an embodiment of this application.

[0107] In an optional embodiment, the burial depth detection system can be built on a submarine cable monitoring platform, and the burial depth calculation function can be implemented through an embedded burial depth calculation service. The operation of the burial depth detection system can be divided into two steps: first, the initialization process is completed, and only after the initialization is completed can the burial depth calculation function be implemented.

[0108] The initialization process involves first acquiring historical baseline data and measured burial depth values ​​of at least one spatial sampling point of the second submarine cable during at least one load change process over a historical period. The historical baseline data includes historical load data and historical temperature data. This data is then forwarded to the burial depth calculation service via the internal communication of the submarine cable monitoring platform.

[0109] After receiving the raw data, the burial depth calculation service performs feature extraction and regression modeling steps. For example, it obtains historical load feature values ​​based on historical load data and historical temperature response feature values ​​based on historical temperature data. Based on the historical load feature values ​​and historical temperature response feature values, it obtains the historical feature set of the spatial sampling points. Then, by aligning the length coordinates of the detection points and the spatial sampling points, it obtains the measured burial depth values ​​of the spatial sampling points, and then performs regression modeling to obtain the burial depth monitoring model.

[0110] In the burial depth calculation function, the data of the burial depth to be calculated is received, the above-mentioned submarine cable burial depth monitoring method is executed, the burial depth is calculated, and the burial depth data of the spatial point to be measured is obtained.

[0111] Based on the burial depth value calculated by the burial depth calculation service, the data is transmitted through the internal communication of the submarine cable monitoring platform, which can realize subsequent burial depth alarm services and visualization functions. For example, the alarm service includes burial depth threshold alarm and burial depth change rate alarm, and the visualization displays the real-time submarine cable burial depth curve and historical burial depth map. The above alarm information and visualization content can be displayed to users through the client.

[0112] Please see Figure 3 The diagram shown is a structural schematic of a submarine cable burial depth monitoring device provided in an embodiment of this application; this embodiment of the application provides a submarine cable burial depth monitoring device 200, comprising:

[0113] The data acquisition module 210 acquires load data and temperature data of the test point in the first submarine cable during at least one load change process.

[0114] Feature module 220 is used to obtain load feature values ​​based on load data and temperature response feature values ​​based on temperature data;

[0115] The feature set module 230 is used to obtain the feature set of the spatial points to be measured based on the load characteristic value and the temperature response characteristic value.

[0116] The monitoring module 240 is used to input the feature set into the burial depth monitoring model to obtain the burial depth data of the spatial point to be measured.

[0117] Optionally, in this embodiment of the application, the submarine cable burial depth monitoring device 200, wherein the determination of a load change process includes: acquiring real-time load data of the spatial point to be measured in the first submarine cable; determining the moment when the real-time load data exceeds a preset load threshold as the start moment, and determining the first moment after the start moment when the real-time load data does not exceed the preset load threshold as the end moment of the load change process, and taking the time period between the start moment and the end moment as a load change process; or, acquiring real-time load data of the spatial point to be measured in the first submarine cable; determining the maximum and minimum values ​​based on the real-time load data; taking the time period between two adjacent minimum values ​​as a load change process, or taking the time period between an adjacent minimum value and a maximum value as a load change process.

[0118] Optionally, in this embodiment of the application, the submarine cable burial depth monitoring device 200 and the feature module 220 are specifically used to calculate the accumulated value of the load data changing over time from the start time to the end time of the load change process, and use the accumulated value as the load feature value; wherein, the load data is a parameter of the electrical load characteristics borne by the first submarine cable; the load feature value is used to characterize the load distribution during the load change process.

[0119] Optionally, in this embodiment, the submarine cable burial depth monitoring device 200 includes temperature data such as current temperature data, initial temperature data at the start of the load change process, and temperature deformation data, where temperature deformation data is a parameter characterizing the temperature characteristics during the load change process. The feature module 220 is specifically used to calculate the integral of the difference between the current temperature data and the initial temperature data from the start to the end of the load change process to obtain a first temperature response feature value; and to obtain a second temperature response feature value based on the temperature deformation data from the start to the end of the load change process; and to use at least one of the first and second temperature response feature values ​​as the temperature response feature value. Optionally, in this embodiment, the submarine cable burial depth monitoring device 200 includes a feature set module 230, which is specifically used to acquire the ambient temperature parameters of the target spatial point during at least one load change process; and to obtain a feature set of the target spatial point based on the load feature value, the temperature response feature value, and the ambient temperature parameters.

[0120] Optionally, in the embodiments of this application, the feature set module 230 of the submarine cable burial depth monitoring device 200 is further used to splice the load feature value, temperature response feature value and ambient temperature parameter to obtain the feature set of the spatial point to be measured; or, to perform a function operation on each temperature response feature value and the load feature value respectively to obtain the function operation result; and to splice the function operation result with the ambient temperature parameter to obtain the feature set of the spatial point to be measured.

[0121] Optionally, in this embodiment, the submarine cable burial depth monitoring device 200 further includes a model building module, used to acquire historical load data and historical temperature data of the spatial sampling points of the second submarine cable during at least one load change process in a historical period, as well as the measured burial depth value of the spatial sampling points; obtain historical load characteristic values ​​based on historical load data; obtain historical temperature response characteristic values ​​based on historical temperature data; obtain a historical feature set of the spatial sampling points based on the historical load characteristic values ​​and historical temperature response characteristic values; and perform regression modeling using the historical feature set of the spatial sampling points and the measured burial depth value to obtain a burial depth monitoring model.

[0122] Optionally, in this embodiment of the application, the submarine cable burial depth monitoring device 200 includes the following steps for obtaining the measured burial depth value of a spatial sampling point: obtaining latitude and longitude data and measured burial depth value of a detection point for the second submarine cable using a measured burial depth method; determining the length coordinates of the detection point using the latitude and longitude data of the detection point; obtaining the length coordinates of the spatial sampling point; calculating the distance between the detection starting point and the spatial sampling starting point based on the latitude and longitude data of the detection starting point in the detection point and the spatial sampling starting point in the spatial sampling point, thereby aligning the length coordinates of the detection point and the length coordinates of the spatial sampling point; and performing interpolation calculation using the measured burial depth value of the detection point based on the aligned length coordinates of the detection point and the length coordinates of the spatial sampling point to obtain the measured burial depth value of the spatial sampling point.

[0123] It should be understood that this device corresponds to the aforementioned embodiment of the method for monitoring the burial depth of submarine cables, and is capable of performing the various steps involved in the aforementioned method embodiment. The specific functions of this device can be found in the description above; to avoid repetition, detailed descriptions are appropriately omitted here. The device includes at least one software functional module that can be stored in memory or embedded in the device's operating system (OS) in the form of software or firmware.

[0124] Please see Figure 4 The diagram shows a structural schematic of an electronic device provided in an embodiment of this application. An electronic device 300 provided in this application includes a processor 310 and a memory 320. The memory 320 stores machine-readable instructions executable by the processor 310. When the machine-readable instructions are executed by the processor 310, the method described above is performed.

[0125] This application also provides a storage medium storing a computer program, which is executed by a processor to perform the above-described method.

[0126] The storage medium can be implemented by any type of volatile or non-volatile storage electronic 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 Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0127] It should be understood that the disclosed apparatus and methods can also be implemented in other ways, given the several embodiments provided in this application. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0128] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0129] The above description is only an optional implementation of the embodiments of this application, but the protection scope of the embodiments of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the embodiments of this application should be covered within the protection scope of the embodiments of this application.

Claims

1. A method of monitoring the burial depth of a submarine cable, characterized in that, The method comprises: obtaining load data and temperature data of a to-be-tested spatial point in a first submarine cable during at least one load change process; obtaining a load characteristic value based on the load data; obtaining a temperature response characteristic value based on the temperature data; obtaining a feature set of the to-be-tested spatial point according to the load characteristic value and the temperature response characteristic value; inputting the feature set into a burial depth monitoring model to obtain burial depth data of the to-be-tested spatial point.

2. The method of claim 1, wherein, A determination method of a load change process comprises: obtaining real-time load data of a to-be-tested spatial point in the first submarine cable; determining a start time as a time when the real-time load data exceeds a preset load threshold, determining an end time of the load change process as a time when the real-time load data does not exceed the preset load threshold after the start time, and determining a time period between the start time and the end time as the load change process; or, obtaining the real-time load data of the to-be-tested spatial point in the first submarine cable; determining a maximum value and a minimum value according to the real-time load data; determining a time period between two adjacent minimum values as the load change process, or determining a time period between an adjacent minimum value and a maximum value as the load change process.

3. The method of claim 1, wherein, The load characteristic value is obtained based on the load data, comprising: calculating an accumulated value of the load data changing with time from the start time to the end time of the load change process, and taking the accumulated value as the load characteristic value; wherein the load data is a parameter of a power load characteristic borne by the first submarine cable; and the load characteristic value is used to represent a load distribution in the load change process.

4. The method of claim 1, wherein, The temperature data comprises current temperature data, initial temperature data at the start time of the load change process, and temperature deformation data, which is a parameter representing temperature characteristics in the load change process; The temperature response characteristic value is obtained based on the temperature data, comprising: calculating an integral of a difference between the current temperature data and the initial temperature data from the start time to the end time of the load change process to obtain a first temperature response characteristic value; obtaining a second temperature response characteristic value according to the temperature deformation data from the start time to the end time of the load change process; taking at least one of the first temperature response characteristic value and the second temperature response characteristic value as the temperature response characteristic value.

5. The method according to any one of claims 1-4, characterized in that, The feature set of the to-be-tested spatial point is obtained according to the load characteristic value and the temperature response characteristic value, comprising: obtaining an environmental temperature parameter of the to-be-tested spatial point in at least one load change process; obtaining the feature set of the to-be-tested spatial point based on the load characteristic value, the temperature response characteristic value, and the environmental temperature parameter.

6. The method of claim 5, wherein, The feature set of the to-be-tested spatial point is obtained based on the load characteristic value, the temperature response characteristic value, and the environmental temperature parameter, comprising: splicing the load characteristic value, the temperature response characteristic value, and the environmental temperature parameter to obtain the feature set of the to-be-tested spatial point; Or, each of the temperature response characteristic values is respectively subjected to function operation with the load characteristic value to obtain a function operation result corresponding to the load change process; function operation results of the same function operation corresponding to the load change process are respectively accumulated and summed to obtain function operation sum results, and each of the function operation sum results is spliced with the environmental temperature parameter to obtain the feature set of the to-be-measured space point.

7. The method of claim 1, wherein, Before the feature set is input into the burial depth monitoring model to obtain the burial depth data of the to-be-measured space point, the method further comprises: obtaining historical load data and historical temperature data of a spatial sampling point of a second submarine cable in at least one load change process of a historical period, and a measured burial depth value of the spatial sampling point; obtaining a historical load characteristic value based on the historical load data, and a historical temperature response characteristic value based on the historical temperature data; obtaining a historical feature set of the spatial sampling point according to the historical load characteristic value and the historical temperature response characteristic value; obtaining the burial depth monitoring model by regression modeling based on the historical feature set of the spatial sampling point and the measured burial depth value.

8. The method of claim 7, wherein, Wherein, the step of obtaining the measured burial depth value of the spatial sampling point comprises: obtaining longitude and latitude data and a measured burial depth value of a detection point of the second submarine cable by using a measured burial depth method; determining a length coordinate of the detection point by using the longitude and latitude data of the detection point; obtaining a length coordinate of the spatial sampling point; calculating a distance between a detection starting point in the detection point and a spatial sampling starting point in the spatial sampling point based on the longitude and latitude data of the detection starting point and the longitude and latitude data of the spatial sampling starting point, so as to align the length coordinate of the detection point and the length coordinate of the spatial sampling point; obtaining the measured burial depth value of the spatial sampling point by interpolation calculation based on the aligned length coordinate of the detection point and the length coordinate of the spatial sampling point and the measured burial depth value of the detection point.

9. A computer program product, characterised in that, comprise computer program instructions, which are executed by a processor to perform the method of any one of claims 1 to 8.

10. An electronic device, comprising: comprise: a processor and a memory, wherein the memory stores computer program instructions, and the computer program instructions are executed by the processor to perform the method of any one of claims 1 to 8.

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