Fault positioning method and system for submarine cable

Abstract

The invention provides a submarine cable fault positioning method and system, and relates to the technical field of cable fault positioning, and the method comprises the steps: transmitting a plurality of electric pulse signals with different frequencies to a submarine cable from high to low in an available frequency range, and obtaining electric pulse reflection signals reflected by each fault point, recording the electric pulse reflection signals with the lowest signal intensity, and screening out selectable reflection signals with the signal intensity exceeding a preset intensity threshold value from the electric pulse reflection signals; and determining the electric pulse signal with the highest corresponding emission frequency in the selectable reflection signals as a target electric pulse signal, inputting the electric pulse reflection signal reflected back at each fault point into the trained machine learning model for identification, obtaining the position information of each fault point, and displaying the position information in a three-dimensional image. By implementing the method, the optimal electric pulse signal with the maximum energy and the minimum attenuation influence can be screened out for fault detection, and intelligentization and high efficiency of submarine cable fault detection are realized.

Description

Technical Field

[0001] The present application relates to the technical field of cable fault location, and in particular to a method and system for locating a submarine cable fault. Background Art

[0002] Submarine cables, as key infrastructure connecting different continents and islands, undertake the task of transmitting international data and electricity. These cables are often exposed to complex marine environments and are prone to failure. Therefore, it is crucial to locate the cable fault point in a timely and accurate manner.

[0003] In related technologies, time domain reflectometry (TDR) is mainly used to locate the fault location of submarine cables. This method sends electrical pulses to the cable and analyzes the strength and time delay of the reflected signal to infer the specific location of the fault.

[0004] However, the electrical pulse signal will attenuate during long-distance transmission, which may significantly reduce the strength of the electrical pulse reflection signal reflected back to the sending end, affecting the accuracy of fault point location. Summary of the invention

[0005] The present application provides a submarine cable fault location method and system for addressing the problem that, during long-distance submarine cable fault detection, a single-frequency electrical pulse signal may lose its effectiveness due to attenuation, making it difficult to accurately identify the fault point.

[0006] In a first aspect, the present application provides a method for locating a fault of a submarine cable, which is applied to a cable fault locating system. The method comprises: In the available frequency range, multiple electric pulse signals of different frequencies are sequentially transmitted from high to low to the submarine cable, and electric pulse reflection signals reflected from each fault point under each frequency electric pulse signal are obtained. The available frequency range is determined according to the physical characteristics of the submarine cable and the environmental factors of the laying location; Record the electric pulse reflection signal with the lowest signal strength under each frequency of the electric pulse signal; Selecting a selectable reflection signal having a signal strength exceeding a preset strength threshold from a plurality of electrical pulse reflection signals having the lowest signal strength, wherein the selectable reflection signal corresponds to a transmission frequency of the electrical pulse signal one by one; Determine the electric pulse signal with the highest corresponding transmission frequency in the optional reflected signal as the target electric pulse signal; The electric pulse reflection signal of the target electric pulse signal reflected from each fault point is input into the trained machine learning model for identification to obtain the position information of each fault point; This location information is displayed in a three-dimensional image using advanced data visualization techniques.

[0007] Through the above embodiments, the cable fault location system transmits multiple frequency electrical pulse signals from high to low within the available frequency range, and screens out the optimal target signal with the maximum energy and the minimum attenuation effect by analyzing the strength of the reflected signal at each frequency. The screened target signal is input into the pre-trained machine learning model to identify the location information of each fault point. This method overcomes the problem that a single frequency signal is easy to attenuate and difficult to accurately locate, and realizes the intelligent and efficient detection of submarine cable faults.

[0008] In some embodiments, before the step of transmitting multiple electric pulse signals of different frequencies from high to low in the available frequency range to the submarine cable to obtain electric pulse reflection signals reflected from each fault point under each frequency electric pulse signal, the method further includes: Obtain the physical characteristics of the submarine cable and the environmental factors where it is laid, including temperature, salinity and water depth; Determine the upper limit and lower limit of the electric pulse signal emission according to the physical characteristic; Matching the optimal range of the electric pulse signal emission from a database according to the environmental factors; The available frequency range of the electrical pulse signal is determined according to the optimal range, the upper limit value and the lower limit value.

[0009] Through the above embodiments, the cable fault location system determines the upper and lower limits of the frequency of the electric pulse signal emission according to the physical characteristics of the submarine cable and environmental factors, and matches the best emission frequency range from the database to ensure that the signal can achieve the best transmission effect in a specific submarine environment, making the emission of the electric pulse signal more accurate, thereby improving the accuracy and efficiency of fault point location.

[0010] In some embodiments, before the step of inputting the electric pulse reflection signal of the target electric pulse signal reflected from each fault point into the trained machine learning model for identification to obtain the position information of each fault point, the step further includes: Calculate the predicted location of the fault point based on the historical electrical pulse reflection signal of the submarine cable at the fault point; A data set is constructed based on the predicted location of the fault point and the corresponding actual location of the fault point. The data set includes a training set, a validation set, and a test set. The data set is used to train a machine learning model to identify the correspondence between the predicted fault point location and the corresponding actual fault point location, thereby obtaining a trained machine learning model. The trained machine learning model is used to output the corresponding fault point location information based on the input electrical pulse reflection signal.

[0011] Through the above embodiments, before using the machine learning model to identify the fault point location, the cable fault location system first calculates the predicted location of the fault point based on the historical electrical pulse reflection signal of the submarine cable at the fault point, and constructs a data set containing the predicted location and the actual location to train the machine learning model, so that the model can learn the characteristics of the fault point more accurately and improve the accuracy of the fault point prediction.

[0012] In some embodiments, after the step of inputting the electric pulse reflection signal of the target electric pulse signal reflected from each fault point into the trained machine learning model for identification to obtain the position information of each fault point, the step further includes: Using multiple sensors to collect data from the submarine cable to obtain multiple types of detection data; the sensors include acoustic sensors, electromagnetic sensors and light sensors; Determine the fault point prediction range corresponding to each type of detection data; The multiple fault point prediction ranges are merged to obtain a target fault point range that is shared by each of the fault point prediction ranges.

[0013] Through the above embodiments, the cable fault location system uses multiple sensors to collect data after determining the location information of the fault point, including acoustic, electromagnetic and light sensors. The fault point can be detected and analyzed from different angles and levels, thereby more accurately verifying the location information of the fault point and further improving the accuracy and reliability of fault location.

[0014] In some embodiments, after the step of fusing the multiple fault point prediction ranges to obtain a target fault point range shared by each of the fault point prediction ranges, the step further includes: Determine whether the location information of each fault point is within the corresponding target fault point range; If so, the position information is displayed in a three-dimensional image; If not, perform incremental training on the trained machine learning model.

[0015] Through the above embodiments, the cable fault location system can optimize the model through incremental learning, so that the accuracy of fault diagnosis can be continuously optimized after each fault analysis error.

[0016] In some embodiments, the step of displaying the location information in a three-dimensional image using advanced data visualization technology specifically includes: Obtaining the laying location information of the submarine cable; generating a three-dimensional model according to the laying position information; The location information is displayed in the three-dimensional model through advanced data visualization technology.

[0017] Through the above embodiments, the cable fault location system uses advanced data visualization technology to display the fault point location information in a three-dimensional image. It not only intuitively displays the fault location, but also displays the actual operating status and surrounding environment of the cable, helping operators to more intuitively understand the problem and make more effective maintenance decisions.

[0018] In some embodiments, after the step of displaying the location information in a three-dimensional image using advanced data visualization technology, the method further includes: Determine the cause of the fault according to the electric pulse reflection signal, and display the cause of the fault at the corresponding fault position of the three-dimensional image; A fault diagnosis report is generated based on the fault cause and the location information.

[0019] Through the above embodiments, the cable fault location system uses the electric pulse reflection signal to analyze the cause of the fault, and integrates the cause information into the corresponding position in the three-dimensional visualization scene for display, and automatically generates a diagnostic report containing the cause and location of the fault, helping operators to understand the problem more intuitively and make more effective maintenance decisions.

[0020] In a second aspect, the present application provides a cable fault location system, the cable fault location system comprising: one or more processors and a memory; The memory is coupled to the one or more processors, and the memory is used to store computer program code, which includes computer instructions. The one or more processors call the computer instructions so that the cable fault location system can implement a submarine cable fault location method provided in the above embodiment, which will not be repeated here.

[0021] In a third aspect, the present application provides a computer-readable storage medium, including instructions. When the instructions are executed on a cable fault location system, the cable fault location system can implement a submarine cable fault location method provided in the above embodiment, which will not be described in detail here.

[0022] In a fourth aspect, the present application provides a computer program product. When the computer program product runs on a cable fault location system, the cable fault location system can implement a submarine cable fault location method provided in the above embodiment, which will not be described in detail here.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. The cable fault location system transmits multiple frequency electrical pulse signals from high to low within the available frequency range, and screens out the optimal target signal with the maximum energy and the least attenuation effect by analyzing the strength of the reflected signal at each frequency. The screened target signal is input into the pre-trained machine learning model, and with the help of the model's intelligent feature extraction and classification capabilities, the location information of each fault point can be quickly and accurately identified. This method overcomes the problem that a single frequency signal is easily attenuated and difficult to accurately locate, and realizes the intelligent and efficient detection of submarine cable faults.

[0024] 2. The cable fault location system uses acoustic, electromagnetic, optical and other sensors to collect all-round data on submarine cables, making full use of different physical field information, and integrating the fault point prediction results of each sensor to finally determine the target fault point range with the highest confidence, thereby improving the accuracy and reliability of fault location.

[0025] 3. The cable fault location system seamlessly integrates the location information and cause analysis of the fault point, and presents it in a three-dimensional cable model scene using advanced data visualization technology, allowing managers to intuitively understand the global distribution and cause mechanism of the fault. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flow chart of a method for locating a fault of a submarine cable in an embodiment of the present application; Figure 2 is another flow chart of a method for locating a fault of a submarine cable in an embodiment of the present application; Figure 3 It is a schematic diagram of the structure of a physical device of a cable fault location system in an embodiment of the present application. DETAILED DESCRIPTION

[0027] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more listed items.

[0028] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0029] The key to locating submarine cable faults is to transmit detection signals to the cable and analyze the reflected echoes to determine the fault location. Traditional methods usually use fixed-frequency electric pulse signals for detection. However, due to the long laying distance of submarine cables, the signal will attenuate during the transmission process, resulting in a weak reflected signal and reduced positioning accuracy. In response to this, the present application provides a submarine cable fault location method and system for coping with the problem that a single-frequency electric pulse signal may lose its effectiveness due to attenuation during long-distance submarine cable fault detection, making it difficult to accurately identify the fault point.

[0030] For ease of understanding, the following is a flow chart of the method provided in this implementation. Figure 1 As shown, it is a flow chart of a method for locating a fault of a submarine cable in an embodiment of the present application.

[0031] S101. In an available frequency range, multiple electric pulse signals of different frequencies are transmitted to the submarine cable in sequence from high to low, and electric pulse reflection signals reflected from each fault point under each frequency electric pulse signal are obtained.

[0032] The cable fault location system matches the corresponding available frequency range by searching the system database based on the physical characteristics of the submarine cable (such as cable material, thickness, insulation performance, etc.) and laying environmental factors (such as seawater temperature, salinity, water pressure, etc.) uploaded by relevant technicians. The electrical pulse signal emitted in this available frequency range can both ensure the signal's penetration and control the attenuation to an acceptable level.

[0033] Furthermore, the cable fault location system controls the time domain reflectometer to transmit a series of electric pulse signals of different frequencies in the available frequency range in a descending order, and records the electric pulse reflection signals of each frequency electric pulse signal reflected from each fault point of the cable.

[0034] For example, the optimal detection frequency range for a submarine optical cable is 10MHz~100MHz. Within this range, the system will transmit a series of electrical pulse signals of 100MHz, 90MHz, 80MHz... until 10MHz in steps of 10MHz.

[0035] It is understandable that the higher the frequency of the electric pulse signal, the more serious the signal attenuation during its transmission process. For long-distance submarine cables, it may be difficult to receive the electric pulse reflection signal from the fault point; and the lower the frequency of the electric pulse signal, the lower the resolution of the received electric pulse reflection signal, making it difficult to ensure the accuracy of the fault point prediction.

[0036] S102, recording the electric pulse reflection signal with the lowest signal strength under each frequency of the electric pulse signal.

[0037] After the cable fault location system transmits an electric pulse signal of a certain frequency, it receives the signal reflected from various locations of the submarine cable in real time, and determines the waveform segment corresponding to each fault point based on the reflected signal, and then analyzes the waveform segment through a spectrum analyzer or signal analyzer to obtain the signal strength corresponding to the waveform segment. Similarly, if multiple fault points are detected based on the reflected signal corresponding to the electric pulse signal at this frequency, the signal strength of the reflected waveform corresponding to each fault point is analyzed, and the minimum signal strength of the fault point under the electric pulse reflected signal of this frequency is recorded.

[0038] Similarly, by adjusting the transmission frequency of the electric pulse signal, the minimum signal strength of the fault point corresponding to the electric pulse signal at different frequencies can be obtained.

[0039] S103 , selecting optional reflection signals whose signal strength exceeds a preset strength threshold from a plurality of electrical pulse reflection signals with the lowest signal strength.

[0040] After obtaining the minimum signal strength of the fault point corresponding to the electric pulse signal at different frequencies in step S102, the cable fault location system selects the minimum signal strength whose signal strength exceeds a preset strength threshold from all the minimum signal strengths.

[0041] For example, in one embodiment, the cable fault location system finally obtains the following minimum reflection signal records: 100MHz (2ms delay, 0.1μV), 90MHz (2.1ms delay, 0.2μV), 80MHz (2.2ms delay, 6μV)...20MHz (5ms delay, 8μV), 10MHz (8ms delay, 3μV). Based on previous detection experience, engineers set the preset strength threshold to 5μV in the cable fault location system. The system automatically compares the records with the threshold and selects the 20MHz and 80MHz frequencies, whose weakest reflection signal strengths meet the conditions. Then the reflection signals corresponding to 80MHz (2.2ms delay, 6μV) and 10MHz (8ms delay, 3μV) are marked as optional reflection signals.

[0042] S104: Determine the electrical pulse signal with the highest corresponding transmission frequency in the optional reflected signals as the target electrical pulse signal.

[0043] After screening in step S103, the cable fault location system obtains a set of optional reflection signals, which correspond to different detection frequencies, but all meet the minimum strength requirements. Under the premise of meeting the strength conditions, the higher the frequency of the detection signal, the better the positioning accuracy. Therefore, the cable fault location system screens out the electrical pulse signal with the highest transmission frequency from the optional reflection signals and determines it as the target electrical pulse signal.

[0044] For example, in one embodiment, the system selects the following three optional reflection signals: 40MHz (3ms delay, 7μV), 30MHz (4ms delay, 10μV) and 20MHz (5ms delay, 8μV). Since the electric pulse signal transmission frequency corresponding to the reflection signal (3ms delay, 7μV) is the highest at 40MHz, the system will determine the 40MHz corresponding to the reflection signal (3ms delay, 7μV) as the target electric pulse signal, and predict the location information of each fault point at this transmission frequency.

[0045] S105. Input the electric pulse reflection signal of the target electric pulse signal reflected from each fault point into the trained machine learning model for identification, so as to obtain the location information of each fault point.

[0046] The cable fault location system provides the reflected signal corresponding to the target electrical pulse signal with the highest transmission frequency selected in the previous step as input to a trained machine learning model to obtain the location information of each fault point.

[0047] Among them, the machine learning model learns the characteristics of the reflected signal through a large amount of training data, and establishes a mapping relationship between the signal characteristics and the fault location. When a new reflected signal is input, the model can quickly identify the characteristic pattern of the signal based on the learned experience, and give the corresponding fault point location prediction result. Optionally, the cable fault location system can choose to use a convolutional neural network (CNN) as a machine learning model, input the reflected signal samples of the known fault point location into the CNN, extract the local and overall characteristics of the signal through operations such as convolution and pooling, and map the characteristics to the corresponding fault point location coordinates using a fully connected layer at the end of the network. Repeated training and optimization enable CNN to learn the relationship between the reflected signal and the position as accurately as possible. When the reflection data of the target electrical pulse signal is input into the trained CNN, the network can quickly calculate and output the location prediction results of each fault point.

[0048] S106. Display the location information in a three-dimensional image using advanced data visualization technology.

[0049] The cable fault location system obtains the geographical location, direction, depth and other information of the target submarine cable and builds a three-dimensional model in the computer. Then, the spatial coordinates of each fault point identified by the machine learning model are mapped to the corresponding position in the three-dimensional cable model. Visual elements such as color markings and geometric figures can be used to prominently identify the fault point on the model, and text labels can be used to indicate attribute information such as the fault type and severity, which are not limited here.

[0050] In the above embodiment, the cable fault location system determines the upper and lower limits of the frequency of the electric pulse signal emission according to the physical characteristics of the submarine cable and environmental factors, and matches the best emission frequency range from the database to ensure that the signal can achieve the best transmission effect in a specific submarine environment, making the emission of the electric pulse signal more accurate and improving the accuracy and efficiency of fault point location.

[0051] The following is a more detailed description of the process of the method provided by this embodiment. Figure 2 As shown, it is another flow chart of a method for locating a fault of a submarine cable in an embodiment of the present application.

[0052] S201. Obtain the physical characteristics of the submarine cable and the environmental factors at the laying site.

[0053] The cable fault location system obtains the data uploaded by relevant technicians The cable fault location system can access the cable design and construction data database, and detect various physical parameter indicators of the submarine cable, including the cable's conductor material, insulation material, shielding structure, resistivity, capacitance, inductance, etc. It can also access the submarine observation network to obtain real-time environmental monitoring data at various locations along the cable.

[0054] S202: Determine the upper limit and lower limit of the electric pulse signal emission according to the physical characteristics.

[0055] According to the characteristic impedance and laying length of the cable, the maximum effective transmission distance of the electric pulse signal is calculated. Beyond this distance, the signal strength will be attenuated to a level that is difficult to distinguish. The upper and lower limits of the frequency of the electric pulse signal are determined by comprehensively considering the compressive strength of the cable insulation layer and the complex interference of the seabed environment. Of course, the upper and lower limits can also be directly set by relevant technical personnel.

[0056] S203, matching the optimal range of electric pulse signal emission from the database according to environmental factors.

[0057] After determining the theoretical upper and lower limits of the frequency of the electric pulse signal, the cable fault location system needs to further combine the actual environmental factors of the sea area where the cable is laid to select the best transmission frequency range from the upper and lower limits. Specifically, the system obtains the monitoring data of temperature, salinity, water depth, etc. along the submarine cable and compares and analyzes it with the system's environmental factor database. Through big data mining and pattern recognition, the similarities and differences between the current sea conditions and typical historical states are determined. And based on correlation analysis, a batch of reference states that best match the current environment are quickly locked. Then, the system queries the optimal electric pulse signal transmission parameters corresponding to these reference states to obtain the optimal electric pulse signal transmission frequency range. This frequency range is usually narrower than the previously calculated permissible range of physical properties, and can more finely adapt to the transmission characteristics of specific sea areas.

[0058] S204. Determine the available frequency range of the electrical pulse signal according to the optimal range, the upper limit value and the lower limit value.

[0059] The cable fault location system compares and merges the two groups of ranges determined in steps S202 and S203, and determines the intersection of the two groups of ranges as the available frequency range. Of course, if the two groups of ranges have no intersection, they can also be directly set by relevant technical personnel, which is not limited here.

[0060] In the above embodiment, the cable fault location system determines the upper and lower limits of the frequency of the electric pulse signal emission according to the physical characteristics of the submarine cable and environmental factors, and matches the best emission frequency range from the database to ensure that the signal can achieve the best transmission effect in a specific submarine environment, making the emission of the electric pulse signal more accurate and improving the accuracy and efficiency of fault point location.

[0061] S205. Determine a target electrical pulse signal within an available frequency range.

[0062] This step has been introduced in steps S101 to S104 and will not be repeated here.

[0063] S206: Use multiple sensors to collect data from the submarine cable to obtain multiple types of detection data.

[0064] The cable fault location system uses various types of sensors to conduct all-round monitoring and data collection on the operating status of submarine cables. These sensors can include acoustic sensors, electromagnetic sensors, and optical sensors. Among them, acoustic sensors can capture sound wave signals in the environment around the cable to analyze whether the cable has abnormal vibration or collision; electromagnetic sensors can detect changes in the current and magnetic field inside the cable to determine whether there are electrical faults such as short circuits and grounding; optical sensors can use optical fiber technology to monitor the temperature, stress and other physical parameters of the cable.

[0065] S207: Determine the fault point prediction range corresponding to each type of detection data.

[0066] After obtaining submarine cable monitoring data from various types of sensors, the cable fault location system analyzes and processes these data to determine the approximate location range of the cable fault point reflected by various types of data.

[0067] Specifically, for acoustic sensor data, the system estimates the distance from the sound source (i.e., fault point) to the sensor by analyzing the time difference and attenuation law of the sound wave signal, and then determines the approximate area of ​​the fault point based on the sensor's layout information. For example, if the abnormal sound is determined to occur at a distance of about 1.5 kilometers from a certain sensor, and the cable segment where the sensor is located is 10 kilometers long, the initial locked fault range is the interval between 1 / 6 and 1 / 5 of this cable segment.

[0068] Similarly, electromagnetic sensor data can reflect the magnitude of the fault current and the resistance value between the short-circuit location and the sensor, and optical fiber sensor data can infer the distance between the fault point and the sensor based on the temperature change and stress distribution of the fault point. The cable fault location system uses a combination of cable physical parameter models and machine learning algorithms to model and analyze each type of data, and ultimately obtains a set of high-confidence fault point area prediction results.

[0069] S208: Merge multiple fault point prediction ranges to obtain a target fault point range that is shared by each fault point prediction range.

[0070] After obtaining the predicted range of fault points corresponding to each type of detection data, the system conducts confidence assessment on the prediction results of various sensor data, and calculates the credibility of each prediction range based on factors such as data quality and model accuracy. Then, these prediction ranges are mapped and aligned in space, and their intersection area (i.e., the possible range of fault points supported by multiple data) is found and determined as the target fault point range.

[0071] In the above embodiment, the cable fault location system uses multiple sensors to collect data after determining the location information of the fault point, including acoustic, electromagnetic and light sensors. The fault point can be detected and analyzed from different angles and levels, thereby more accurately verifying the location information of the fault point and further improving the accuracy and reliability of fault location.

[0072] S209: Whether the location information of each fault point is within the corresponding target fault point range.

[0073] After completing the multi-sensor data fusion and obtaining the target prediction range of each fault point, the cable fault location system compares and verifies these ranges with the precise location information of each fault point identified by the machine learning model in the previous step.

[0074] Specifically, the system spatially matches the location coordinates of each fault point with its corresponding target prediction range to determine whether the location falls within the prediction range. If the location information is within the target range, the prediction result is considered reasonable, and the system can automatically trigger a fault alarm, generate a fault report, and add the location information to the 3D visualization model to intuitively display the specific distribution of the fault point.

[0075] On the contrary, if the location information of a certain fault point is not within its target prediction range, the prediction result is considered abnormal. The system triggers an abnormal alarm, prompting engineers to conduct further analysis and judgment.

[0076] S210, obtaining the laying location information of the submarine cable and generating a three-dimensional model.

[0077] The laying location information of submarine cables usually includes the starting and ending coordinates of the cables, the turning point coordinates of the laying path, the buried depth and altitude of the cables in different sea areas, etc. This information can come from the measurement records when the cables are laid, or can be detected and mapped in real time through underwater robots, sonar and other equipment, which is not limited here.

[0078] After obtaining the complete laying position information, the system generates a corresponding three-dimensional model in the computer based on the laying position information.

[0079] S211. Display location information in a three-dimensional model using advanced data visualization technology.

[0080] Specifically, the system converts the format and coordinates the fault data from different sources (such as cable core temperature, oil pressure, impedance, etc.) so that it can be seamlessly connected with the geographic space framework in the 3D model. Then, the system uses visual graphic language to map the fault information into the 3D scene according to the type and characteristics of the data.

[0081] In the above embodiment, the cable fault location system uses advanced data visualization technology to display the fault point location information in a three-dimensional image. It not only intuitively displays the fault location, but also displays the actual operating status of the cable and the surrounding environment, helping operators to more intuitively understand the problem and make more effective maintenance decisions.

[0082] S212. Perform incremental training on the trained machine learning model.

[0083] After each fault location practice is completed, the cable fault location system continuously updates and retrains the machine learning model used to predict the fault point location through incremental training, constantly absorbing new sample data and diagnostic results to improve the accuracy of model prediction.

[0084] The cable fault location system of the embodiment of the present invention is applied to electronic equipment. Figure 3 A schematic diagram of the architecture of an electronic device suitable for implementing an embodiment of the present invention is shown.

[0085] It should be noted that Figure 3 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0086] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be completed by instructions (computer programs), or by controlling related hardware through instructions (computer programs), and the instructions can be stored in a computer-readable storage medium and loaded and executed by a processor. The electronic device of this embodiment includes a storage medium and a processor, wherein a plurality of instructions are stored in the storage medium, and the instructions can be loaded by the processor to execute any step of the method provided in the embodiment of the present invention.

[0087] Specifically, the storage medium and the processor are electrically connected directly or indirectly to realize data transmission or interaction. For example, these elements can be electrically connected to each other through one or more signal lines. The storage medium stores computer execution instructions for implementing the data access control method, including at least one software function module that can be stored in the storage medium in the form of software or firmware. The processor executes various functional applications and data processing by running the software program and module stored in the storage medium. The storage medium can be, but is not limited to, random access storage medium (Random Access Memory, referred to as: RAM), read-only storage medium (Read Only Memory, referred to as: ROM), programmable read-only storage medium (Programmable Read-Only Memory, referred to as: PROM), erasable read-only storage medium (Erasable Programmable Read-Only Memory, referred to as: EPROM), electrically erasable read-only storage medium (Electric Erasable Programmable Read-Only Memory, referred to as: EEPROM), etc. Among them, the storage medium is used to store programs, and the processor executes the program after receiving the execution instruction.

[0088] Furthermore, the software programs and modules in the above-mentioned storage medium may also include an operating system, which may include various software components and / or drivers for managing system tasks (such as memory management, storage device control, power management, etc.), and may communicate with various hardware or software components to provide an operating environment for other software components. The processor may be an integrated circuit chip having signal processing capabilities. The above-mentioned processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc., which may implement or execute the various methods, steps, and logic flow diagrams disclosed in this embodiment. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0089] Since the instructions stored in the storage medium can execute the steps in any method provided in the embodiments of the present invention, the beneficial effects of any method provided in the embodiments of the present invention can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0090] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for locating a fault of a submarine cable, applied to a cable fault locating system, characterized in that: The method comprises: In the available frequency range, multiple electric pulse signals of different frequencies are sequentially transmitted from high to low to the submarine cable, and electric pulse reflection signals reflected from each fault point under each frequency electric pulse signal are obtained, wherein the available frequency range is determined according to the physical characteristics of the submarine cable and the environmental factors at the laying location; Record the electric pulse reflection signal with the lowest signal strength under each frequency of the electric pulse signal; Screening out optional reflection signals with signal strength exceeding a preset strength threshold from the plurality of electrical pulse reflection signals with the lowest signal strengths, wherein the optional reflection signals correspond one to one with the transmission frequency of the electrical pulse signal; Determine the electric pulse signal with the highest corresponding transmission frequency in the optional reflected signals as the target electric pulse signal; Inputting the electric pulse reflection signal reflected from each fault point into the trained machine learning model for identification to obtain the position information of each fault point; The location information is displayed in a three-dimensional image using advanced data visualization techniques.

2. The method according to claim 1, characterized in that: Before the step of transmitting a plurality of electric pulse signals of different frequencies from high to low in the available frequency range to the submarine cable to obtain electric pulse reflection signals reflected from each fault point under each frequency electric pulse signal, the method further includes: Obtaining the physical characteristics of the submarine cable and the environmental factors where it is laid, including temperature, salinity and water depth; Determining the upper limit and lower limit of the electric pulse signal emission according to the physical characteristics; Matching the optimal range of the electric pulse signal emission from a database according to the environmental factors; The available frequency range of the electrical pulse signal is determined according to the optimal range, the upper limit value and the lower limit value.

3. The method according to claim 1, characterized in that Before the step of inputting the electric pulse reflection signal reflected by the target electric pulse signal at each fault point into the trained machine learning model for identification to obtain the position information of each fault point, the step further includes: Calculate the predicted location of the fault point based on the historical electrical pulse reflection signal of the submarine cable at the fault point; Constructing a data set based on the predicted fault point location and the corresponding actual fault point location, the data set includes a training set, a validation set and a test set; The data set is used to train a machine learning model, and the corresponding relationship between the predicted fault point location and the corresponding actual fault point location is identified to obtain a trained machine learning model. The trained machine learning model is used to output corresponding fault point location information based on the input electrical pulse reflection signal.

4. The method according to claim 1, characterized in that: After the step of inputting the electric pulse reflection signal reflected by the target electric pulse signal at each fault point into the trained machine learning model for identification to obtain the position information of each fault point, the method further includes: Using multiple sensors to collect data from the submarine cable to obtain multiple types of detection data; the sensors include acoustic sensors, electromagnetic sensors and light sensors; Determine the fault point prediction range corresponding to each type of detection data; A plurality of the fault point prediction ranges are merged to obtain a target fault point range that is shared by each of the fault point prediction ranges.

5. The method according to claim 4, characterized in that After the step of fusing the plurality of fault point prediction ranges to obtain a target fault point range shared by each of the fault point prediction ranges, the method further includes: Determine whether the location information of each fault point is within the corresponding target fault point range; If so, displaying the position information in a three-dimensional image; If not, perform incremental training on the trained machine learning model.

6. The method according to claim 1, characterized in that The step of displaying the position information in a three-dimensional image by using advanced data visualization technology specifically includes: Obtaining the laying location information of the submarine cable; generating a three-dimensional model according to the laying position information; The location information is displayed in the three-dimensional model through advanced data visualization technology.

7. The method according to claim 1, characterized in that After the step of displaying the position information in a three-dimensional image by using advanced data visualization technology, the method further includes: Determining a fault cause according to the electrical pulse reflection signal, wherein the fault cause is displayed at a corresponding fault position of the three-dimensional image; A fault diagnosis report is generated according to the fault cause and the location information.

8. A cable fault location system, characterized in that: The cable fault location system includes: one or more processors and a memory; The memory is coupled to the one or more processors, and the memory is used to store computer program codes, wherein the computer program codes include computer instructions, and the one or more processors call the computer instructions to enable the cable fault location system to execute the method according to any one of claims 1 to 7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instruction is executed on a cable fault locating system, the cable fault locating system is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product runs on a cable fault location system, the cable fault location system is enabled to perform the method according to any one of claims 1 to 7.

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