A high-voltage cable intelligent inspection method and system based on data analysis

Abstract

The present invention relates to the field of high-voltage cable inspection technology, and is a method and system for intelligent inspection of high-voltage cables based on data analysis. The specific method includes: collecting terrain data and real-time water data of the water area where each inspection point of the high-voltage cable to be inspected is located; constructing a sediment deposition deep learning model to simulate and predict the cross-sectional sediment deposition amount of the high-voltage cable to be inspected; respectively calculating the heat dissipation performance threat factor, insulation performance threat factor and stability threat factor of the cross-sectional sediment deposition of the high-voltage cable to the working performance of the high-voltage cable; integrating and evaluating the overall performance threat value of the cross-sectional sediment deposition to the working performance of the high-voltage cable; extracting the overall performance threat value of each inspection point of the high-voltage cable to be inspected, and optimizing and executing the corresponding high-voltage cable maintenance plan. The present invention solves the problems in the prior art that underwater high-voltage cable inspection requires a lot of manpower, material and financial resources, the inspection cycle is not fixed, the inspection target is poor and the inspection efficiency is low.

Description

Technical Field

[0001] The present invention relates to the technical field of high-voltage cable inspection, and is a method and system for intelligent inspection of high-voltage cables based on data analysis. Background Art

[0002] In freshwater areas such as rivers and lakes, the distribution of underwater cables is very common; underwater high-voltage cables are mainly used to transmit electricity and data. Specifically, underwater high-voltage cables are usually used to transmit electricity on land to underwater facilities such as submarine oil fields, submarine power stations, and submarine cities to meet their power needs. In addition, underwater high-voltage cables are also used to transmit communication data, such as telephone, Internet, and television signals, connecting different regions or marine facilities to achieve information transmission and communication. However, due to factors such as turbidity in different river waters, drought and flood seasons, and riverbed topography, the deposition of silt around underwater cables will continue to change. When the amount of silt deposited is much greater than or much less than the laying standard of high-voltage cables, it will not only affect the electrical conductivity and mechanical strength of the cable, but may also increase the heat accumulation and breakdown risk of the insulation layer. Therefore, it is necessary to clean up the silt in time, inspect the cables regularly, and strengthen monitoring and maintenance work in areas with more silt accumulation to ensure the normal operation and safety of underwater cables; how to reduce the cost of high-voltage cable inspections while improving the coverage and accuracy of inspections is an important problem.

[0003] In the existing disclosed invention technologies, for example, the patent with application publication number CN113541047A discloses a remote automated inspection system for cables, including a cable and an automated inspection system arranged on the cable, wherein a high-pressure gas source is introduced into one end of the cable to drive the automated inspection system to move along the length direction of the cable and inspect the cable; the automated inspection system includes an inspection sleeve and a repair box installed on the inspection sleeve, wherein the repair box has a hollow cavity, one side of the repair box protrudes from the repair box, and an outlet cavity is provided at the bottom of one side of the repair box, and the outlet cavity is arranged on the side away from the forward direction of the inspection sleeve.

[0004] The above patent lacks quantitative consideration of the real-time complex environmental changes of the cable, has low inspection accuracy, and poor targeted inspection of high-voltage cables, and has the problems described in the background technology. Summary of the invention

[0005] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0006] The technical problem to be solved by the present invention is that in the prior art, the present invention solves the problem that the underwater high-voltage cable inspection requires a lot of manpower, material and financial resources, the inspection cycle is not fixed, the inspection targeting is poor and the inspection efficiency is low. A high-voltage cable intelligent inspection method and system based on data analysis is proposed.

[0007] In order to achieve the above object, the technical solution of a high-voltage cable intelligent inspection method based on data analysis of the present invention comprises the following steps:

[0008] S1: Collect terrain data and real-time water data of the waters where each inspection point of the high-voltage cable to be inspected is located;

[0009] S2: Construct a deep learning model for sediment deposition, pre-process the water area topography data and real-time water body data and input them into the deep learning model to simulate and predict the sediment deposition amount of the cross section of the high-voltage cable to be inspected;

[0010] S3: The predicted sediment deposition amount of the cross section of the high-voltage cable to be inspected is introduced into the heat dissipation performance threat strategy, the insulation performance threat strategy and the stability threat strategy, and the heat dissipation performance threat factor, the insulation performance threat factor and the stability threat factor of the sediment deposition on the cross section of the high-voltage cable to the working performance of the high-voltage cable are calculated respectively;

[0011] S4: The overall performance threat value of cross-section sediment to the working performance of high-voltage cables is evaluated by integrating the heat dissipation performance threat factor, insulation performance threat factor and stability threat factor;

[0012] S5: Extract the overall performance threat value of each inspection point of the high-voltage cable to be inspected, and optimize and execute the corresponding high-voltage cable maintenance plan.

[0013] Specifically, in S1, the terrain data of the waters where each inspection point of the high-voltage cable to be inspected is located includes: the tortuosity coefficient of the river where the high-voltage cable is located; the real-time water body data specifically includes: a data collection point is set every 50 meters in the waters upstream of the inspected high-voltage cable to collect average water depth data, average water flow velocity data, water level height data and average water body sand content data.

[0014] Specifically, S2 includes the following specific steps:

[0015] S21: Construct a deep learning model for sediment deposition, including: input layer, convolution layer, sampling layer, pooling layer, fully connected layer and output layer;

[0016] S22: extracting the water area topography data and the real-time water body data into a feature vector form to obtain a feature vector set of hydrological data, normalizing the data in the feature vector set of hydrological data, and inputting the processed data into an input layer of the historical data neural network;

[0017] The specific steps of data preprocessing include:

[0018] ;

[0019] in, is the normalized data, x is the specific value of the data, is the maximum value of the feature, is the minimum value of the characteristic;

[0020] S23: By Mean ,variance Normally distributed convolutional layer and convolutional layers Perform convolution calculation on the input water area terrain data and real-time water body data to extract their data features. The convolution layer and convolutional layers Set 10 The convolution kernel of

[0021] S24: The water area terrain data and real-time water body data obtained by convolution are sampled by the sampling layer, and the feature matrix is ​​used Function to activate;

[0022] S25: Used in the pooling layer The technology eliminates some extreme data, including The probability of technology is set to 0.5;

[0023] S26: The feature matrix is ​​compressed into a one-dimensional vector through the fully connected layer, and finally the cross-sectional sediment deposition of the high-voltage cable to be inspected corresponding to the water area terrain data and real-time water body data is output through the output layer, where Function to activate.

[0024] Specifically, in S3, the heat dissipation performance threat strategy is as follows:

[0025] ;

[0026] in, It is the threat factor of heat dissipation performance of the high-voltage cable to be inspected;

[0027] is the i-th inspection point on the high-voltage cable to be inspected, and I is the total number of inspection points;

[0028] C is the unit time length of temperature data collection at the inspection point;

[0029] is the surface temperature data of the high-voltage cable bushing collected at the ith inspection point when the temperature data collection time point is c;

[0030] is the median temperature of the high-voltage cable bushing surface temperature data collected at the i-th inspection point;

[0031] It is the standard temperature data of high voltage cable under working condition;

[0032] It is the difference between the predicted amount of sediment deposition in the cross section of the high-voltage cable to be inspected and the initial sediment thickness when the high-voltage cable is laid.

[0033] Specifically, the insulation performance threat strategy described in S3 is as follows:

[0034] ;

[0035] in, It is the threat factor of the insulation performance of the high-voltage cable to be inspected;

[0036] is the insulation resistance of the high-voltage cable insulation layer collected at the i-th inspection point; is the average insulation resistance of the high-voltage cable insulation layer; It is the factory insulation resistance of the high voltage cable insulation layer;

[0037] is the leakage current on the surface of the high-voltage cable collected at the i-th inspection point; The mean leakage current of the high voltage cable insulation layer; They are respectively the maximum value and minimum value of leakage current that the cable surface can withstand when the high-voltage cable is in working condition;

[0038] The threat influence coefficients of high-voltage cable insulation performance are insulation resistance and leakage current respectively.

[0039] Specifically, the stability threat strategy described in S3 is as follows:

[0040] ;

[0041] in, It is the stability threat factor of the high-voltage cable to be inspected;

[0042] are the high-voltage cable position deviations collected at the i-1th, i-th and i-1th inspection points respectively; They are respectively the maximum position deviation and the minimum position deviation of the high-voltage cable to be inspected.

[0043] Specifically, the overall performance threat value of sediment deposition in the fusion assessment section to the working performance of the high-voltage cable described in S4 is as follows:

[0044] ;

[0045] in, It is the overall performance threat value of cross-section sediment deposition to the working performance of high-voltage cables;

[0046] are the threat ratios of heat dissipation threat factor, insulation performance threat factor and stability threat factor, among which, , are respectively the number of high-voltage cable heat dissipation fault points, the number of insulation fault points, and the number of stability fault points; the heat dissipation fault point judgment strategy includes: when When , the inspection point of the high-voltage cable is marked as a heat dissipation fault point; the judgment strategy of the insulation fault point includes: when hour, Indicates that they occur simultaneously, marking the inspection point of the high-voltage cable as an insulation fault point; the judgment strategy of the stability fault point includes: When the high voltage cable inspection point is marked as a stability fault point.

[0047] Specifically, S5 includes: extracting the overall performance threat value of each inspection point of the high-voltage cable to be inspected, comparing the overall performance threat value of the inspection point with the maximum performance threat value that the high-voltage cable can withstand. Make comparisons to determine the working status of the high-voltage cables at the inspection points;

[0048] when When the high-voltage cable is located, it is judged that the sediment deposited in the area where the high-voltage cable is located is within a controllable range and does not affect the normal operation of the high-voltage cable;

[0049] when When the high-voltage cable is located, it is determined that the sediment in the area where the high-voltage cable is located needs to be cleaned up, and the maintenance countdown of the high-voltage cable is estimated according to the inspection data collection time of the high-voltage cable. The maintenance countdown of each section of the high-voltage cable to be inspected is arranged in order and sent to the maintenance department management personnel to arrange the daily maintenance plan of the high-voltage cable;

[0050] when When the high-voltage cable is located, it is judged that the sediment deposited in the area where the high-voltage cable is located poses a major threat to the working performance of the high-voltage cable. The sediment data of this section of the high-voltage cable is sent to the management personnel of the maintenance department for emergency repair of the high-voltage cable, and the degree of damage to the high-voltage cable is confirmed based on various threat factors.

[0051] In addition, the present invention provides a high-voltage cable intelligent inspection system based on data analysis, which includes the following modules:

[0052] Inspection point data collection module, deep learning model building module, threat factor calculation module, overall performance threat value module and high-voltage cable maintenance plan optimization module;

[0053] The inspection point data collection module is used to collect terrain data and real-time water data of the water area where each inspection point of the high-voltage cable to be inspected is located;

[0054] The deep learning model construction module is used to construct a deep learning model for sediment deposition, pre-process the water area terrain data and real-time water body data and input them into the deep learning model, and simulate and predict the cross-section sediment deposition of the high-voltage cable to be inspected;

[0055] The threat factor calculation module is used to import the predicted cross-section sediment deposition amount of the high-voltage cable to be inspected into the heat dissipation performance threat strategy, the insulation performance threat strategy and the stability threat strategy, and respectively calculate the heat dissipation performance threat factor, the insulation performance threat factor and the stability threat factor of the cross-section sediment deposition of the high-voltage cable on the working performance of the high-voltage cable;

[0056] The overall performance threat value module evaluates the overall performance threat value of cross-section sediment to the working performance of the high-voltage cable based on the heat dissipation performance threat factor, the insulation performance threat factor and the stability threat factor;

[0057] The high-voltage cable maintenance plan optimization module is used to extract the overall performance threat value of each inspection point of the high-voltage cable to be inspected, and optimize and execute the corresponding high-voltage cable maintenance plan.

[0058] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned intelligent inspection method for high-voltage cables based on data analysis is implemented.

[0059] Compared with the prior art, the technical effects of the present invention are as follows:

[0060] 1. The present invention saves manpower and material costs by collecting hydrological elements and terrain data, and applies deep learning models for processing and analysis, making the inspection work more accurate and reliable.

[0061] 2. The present invention establishes a prediction model to quickly and accurately obtain the sediment deposition conditions at downstream points, thereby realizing real-time monitoring and early warning of sediment deposition of underwater cables. At the same time, it can predict the sediment deposition conditions in a wider range and more points in the water area, rather than being limited to a single point, and has high sustainability.

[0062] 3. The present invention realizes automated decision-making on high-voltage cable inspection and maintenance by extracting the overall performance threat value and optimizing the execution of the maintenance plan, which facilitates the targeted formulation of maintenance plans, improves the inspection efficiency and reduces the risk of errors caused by human intervention. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0064] Figure 1 A schematic diagram of a process of a high-voltage cable intelligent inspection method based on data analysis of the present invention;

[0065] Figure 2 It is a structural schematic diagram of a high-voltage cable intelligent inspection system based on data analysis of the present invention. DETAILED DESCRIPTION

[0066] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0067] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0068] Embodiment 1:

[0069] like Figure 1 As shown, a high-voltage cable intelligent inspection method based on data analysis in an embodiment of the present invention is as follows: Figure 1 As shown, the specific steps are as follows:

[0070] S1: Collect terrain data and real-time water data of the waters where each inspection point of the high-voltage cable to be inspected is located;

[0071] In S1, the terrain data of the waters where each inspection point of the high-voltage cable to be inspected is located includes: the tortuosity coefficient of the river where the high-voltage cable is located; the real-time water body data specifically includes: a data collection point is set every 50 meters in the waters upstream of the inspection high-voltage cable to collect average water depth data, average water flow velocity data, water level height data and average water body sand content data.

[0072] S2: Construct a deep learning model for sediment deposition, pre-process the water area topography data and real-time water body data and input them into the deep learning model to simulate and predict the sediment deposition amount of the cross section of the high-voltage cable to be inspected;

[0073] S2 includes the following specific steps:

[0074] S21: Construct a deep learning model for sediment deposition, including input layer, convolution layer, sampling layer, pooling layer, fully connected layer and output layer;

[0075] S22: extracting the water area topography data and the real-time water body data into a feature vector form to obtain a feature vector set of hydrological data, normalizing the data in the feature vector set of hydrological data, and inputting the processed data into an input layer of the historical data neural network;

[0076] The specific steps of data preprocessing include:

[0077] ;

[0078] in, is the normalized data, x is the specific value of the data, is the maximum value of the feature, is the minimum value of the characteristic;

[0079] S23: By Mean ,variance Normally distributed convolutional layer and convolutional layers Perform convolution calculation on the input water area terrain data and real-time water body data to extract their data features. The convolution layer and convolutional layers Set 10 The convolution kernel of

[0080] S24: The water area terrain data and real-time water body data obtained by convolution are sampled by the sampling layer, and the feature matrix is ​​used Function to activate;

[0081] S25: Used in the pooling layer The technology eliminates some extreme data, including The probability of technology is set to 0.5;

[0082] S26: The feature matrix is ​​compressed into a one-dimensional vector through the fully connected layer, and finally the cross-sectional sediment deposition of the high-voltage cable to be inspected corresponding to the water area terrain data and real-time water body data is output through the output layer, where Function to activate.

[0083] S3: The predicted sediment deposition amount of the cross section of the high-voltage cable to be inspected is introduced into the heat dissipation performance threat strategy, the insulation performance threat strategy and the stability threat strategy, and the heat dissipation performance threat factor, the insulation performance threat factor and the stability threat factor of the sediment deposition on the cross section of the high-voltage cable to the working performance of the high-voltage cable are calculated respectively;

[0084] In S3, the heat dissipation performance threat strategy is as follows:

[0085] ;

[0086] in, It is the threat factor of heat dissipation performance of the high-voltage cable to be inspected;

[0087] is the i-th inspection point on the high-voltage cable to be inspected, and I is the total number of inspection points;

[0088] C is the unit time length of temperature data collection at the inspection point;

[0089] is the surface temperature data of the high-voltage cable bushing collected at the ith inspection point when the temperature data collection time point is c;

[0090] is the median temperature of the high-voltage cable bushing surface temperature data collected at the i-th inspection point;

[0091] It is the standard temperature data of high voltage cable under working condition;

[0092] It is the difference between the predicted amount of sediment deposition in the cross section of the high-voltage cable to be inspected and the initial sediment thickness when the high-voltage cable is laid.

[0093] The insulation performance threat strategies described in S3 are as follows:

[0094] ;

[0095] in, It is the threat factor of the insulation performance of the high-voltage cable to be inspected;

[0096] is the insulation resistance of the high-voltage cable insulation layer collected at the i-th inspection point; is the average insulation resistance of the high-voltage cable insulation layer; It is the factory insulation resistance of the high voltage cable insulation layer;

[0097] is the leakage current on the surface of the high-voltage cable collected at the i-th inspection point; The mean leakage current of the high voltage cable insulation layer; They are respectively the maximum value and minimum value of leakage current that the cable surface can withstand when the high-voltage cable is in working condition;

[0098] The threat influence coefficients of high-voltage cable insulation performance are insulation resistance and leakage current respectively.

[0099] The stability threat strategies described in S3 are as follows:

[0100] ;

[0101] in, It is the stability threat factor of the high-voltage cable to be inspected;

[0102] are the high-voltage cable position deviations collected at the i-1th, i-th and i-1th inspection points respectively; They are respectively the maximum position deviation and the minimum position deviation of the high-voltage cable to be inspected.

[0103] S4: The overall performance threat value of cross-section sediment to the working performance of high-voltage cables is evaluated by integrating the heat dissipation performance threat factor, insulation performance threat factor and stability threat factor;

[0104] The overall performance threat value of sediment deposition in the integrated assessment section to the working performance of the high-voltage cable described in S4 is as follows:

[0105] ;

[0106] in, It is the overall performance threat value of cross-section sediment deposition to the working performance of high-voltage cables;

[0107] are the threat ratios of heat dissipation threat factor, insulation performance threat factor and stability threat factor, among which, , are respectively the number of high-voltage cable heat dissipation fault points, the number of insulation fault points, and the number of stability fault points; the heat dissipation fault point judgment strategy includes: when When , the inspection point of the high-voltage cable is marked as a heat dissipation fault point; the judgment strategy of the insulation fault point includes: when hour, Indicates that they occur simultaneously, marking the inspection point of the high-voltage cable as an insulation fault point; the judgment strategy of the stability fault point includes: When the high voltage cable inspection point is marked as a stability fault point.

[0108] S5: Extract the overall performance threat value of each inspection point of the high-voltage cable to be inspected, and optimize and execute the corresponding high-voltage cable maintenance plan.

[0109] S5 includes: extracting the overall performance threat value of each inspection point of the high-voltage cable to be inspected, comparing the overall performance threat value of the inspection point with the maximum performance threat value that the high-voltage cable can withstand. Make comparisons to determine the working status of the high-voltage cables at the inspection points;

[0110] when When the high-voltage cable is located, it is judged that the sediment deposited in the area where the high-voltage cable is located is within a controllable range and does not affect the normal operation of the high-voltage cable;

[0111] when When the high-voltage cable is located, it is determined that the sediment in the area where the high-voltage cable is located needs to be cleaned up, and the maintenance countdown of the high-voltage cable is estimated according to the inspection data collection time of the high-voltage cable. The maintenance countdown of each section of the high-voltage cable to be inspected is arranged in order and sent to the maintenance department management personnel to arrange the daily maintenance plan of the high-voltage cable;

[0112] when When the high-voltage cable is located, it is judged that the sediment deposited in the area where the high-voltage cable is located poses a major threat to the working performance of the high-voltage cable. The sediment data of this section of the high-voltage cable is sent to the management personnel of the maintenance department for emergency repair of the high-voltage cable, and the degree of damage to the high-voltage cable is confirmed based on various threat factors.

[0113] Embodiment 2:

[0114] like Figure 2 As shown, a high-voltage cable intelligent inspection system based on data analysis according to an embodiment of the present invention is as follows: Figure 2 As shown, it includes the following modules:

[0115] Inspection point data collection module, deep learning model building module, threat factor calculation module, overall performance threat value module and high-voltage cable maintenance plan optimization module;

[0116] The inspection point data collection module is used to collect terrain data and real-time water data of the water area where each inspection point of the high-voltage cable to be inspected is located;

[0117] The deep learning model construction module is used to construct a deep learning model for sediment deposition, pre-process the water area terrain data and real-time water body data and input them into the deep learning model, and simulate and predict the cross-section sediment deposition of the high-voltage cable to be inspected;

[0118] The threat factor calculation module is used to import the predicted cross-section sediment deposition amount of the high-voltage cable to be inspected into the heat dissipation performance threat strategy, the insulation performance threat strategy and the stability threat strategy, and respectively calculate the heat dissipation performance threat factor, the insulation performance threat factor and the stability threat factor of the cross-section sediment deposition of the high-voltage cable on the working performance of the high-voltage cable;

[0119] The overall performance threat value module evaluates the overall performance threat value of cross-section sediment to the working performance of the high-voltage cable based on the heat dissipation performance threat factor, the insulation performance threat factor and the stability threat factor;

[0120] The high-voltage cable maintenance plan optimization module is used to extract the overall performance threat value of each inspection point of the high-voltage cable to be inspected, and optimize and execute the corresponding high-voltage cable maintenance plan.

[0121] Embodiment three:

[0122] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0123] The processor executes the above-mentioned intelligent inspection method for high-voltage cables based on data analysis by calling the computer program stored in the memory.

[0124] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement a high-voltage cable intelligent inspection method based on data analysis provided by the above method embodiment. The electronic device may also include other components for realizing the functions of the device, for example, the electronic device may also have components such as a wired or wireless network interface and an input and output interface to input and output data. This embodiment will not be described in detail here.

[0125] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center through a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0126] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0127] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only one, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0128] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0129] In summary, compared with the prior art, the technical effects of the present invention are as follows:

[0130] 1. The present invention saves manpower and material costs by collecting hydrological elements and terrain data, and applies deep learning models for processing and analysis, making the inspection work more accurate and reliable.

[0131] 2. The present invention establishes a prediction model to quickly and accurately obtain the sediment deposition conditions at downstream points, thereby realizing real-time monitoring and early warning of sediment deposition of underwater cables. At the same time, it can predict the sediment deposition conditions in a wider range and more points in the water area, rather than being limited to a single point, and has high sustainability.

[0132] 3. The present invention realizes automated decision-making on high-voltage cable inspection and maintenance by extracting the overall performance threat value and optimizing the execution of the maintenance plan, which facilitates the targeted formulation of maintenance plans, improves the inspection efficiency and reduces the risk of errors caused by human intervention.

[0133] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A high-voltage cable intelligent inspection method based on data analysis, characterized in that: The method comprises the following specific steps: S1: Collect terrain data and real-time water data of the waters where each inspection point of the high-voltage cable to be inspected is located; S2: Construct a deep learning model for sediment deposition, pre-process the water area topography data and real-time water body data and input them into the deep learning model to simulate and predict the sediment deposition amount of the cross section of the high-voltage cable to be inspected; S3: The predicted sediment deposition amount of the cross section of the high-voltage cable to be inspected is introduced into the heat dissipation performance threat strategy, the insulation performance threat strategy and the stability threat strategy, and the heat dissipation performance threat factor, the insulation performance threat factor and the stability threat factor of the sediment deposition on the cross section of the high-voltage cable to the working performance of the high-voltage cable are calculated respectively; S4: The overall performance threat value of cross-section sediment to the working performance of high-voltage cables is evaluated by integrating the heat dissipation performance threat factor, insulation performance threat factor and stability threat factor; S5: Extract the overall performance threat value of each inspection point of the high-voltage cable to be inspected, and optimize and execute the corresponding high-voltage cable maintenance plan.

2. A high-voltage cable intelligent inspection method based on data analysis according to claim 1, characterized in that: In S1, the terrain data of the waters where each inspection point of the high-voltage cable to be inspected is located includes: the tortuosity coefficient of the river where the high-voltage cable is located; the real-time water body data specifically includes: a data collection point is set every 50 meters in the waters upstream of the inspection high-voltage cable to collect average water depth data, average water flow velocity data, water level height data and average water body sand content data.

3. A high-voltage cable intelligent inspection method based on data analysis according to claim 2, characterized in that: S2 includes the following specific steps: S21: Construct a deep learning model for sediment deposition, including: input layer, convolution layer, sampling layer, pooling layer, fully connected layer and output layer; S22: extracting the water area topography data and the real-time water body data into a feature vector form to obtain a feature vector set of hydrological data, normalizing the data in the feature vector set of hydrological data, and inputting the processed data into an input layer of the historical data neural network; The specific steps of data preprocessing include: ； in, is the normalized data, x is the specific value of the data, is the maximum value of the feature, is the minimum value of the feature; S23: By Mean ,variance Normally distributed convolutional layer and convolutional layers Perform convolution calculation on the input water area terrain data and real-time water body data to extract their data features. The convolution layer and convolutional layers Set 10 The convolution kernel of S24: The water area terrain data and real-time water body data obtained by convolution are sampled by the sampling layer, and the feature matrix is ​​used Function to activate; S25: Used in the pooling layer The technology eliminates some extreme data, including The probability of technology is set to 0.5; S26: The feature matrix is ​​compressed into a one-dimensional vector through the fully connected layer, and finally the cross-sectional sediment deposition of the high-voltage cable to be inspected corresponding to the water area terrain data and real-time water body data is output through the output layer, where Function to activate.

4. A high-voltage cable intelligent inspection method based on data analysis according to claim 3, characterized in that: In S3, the heat dissipation performance threat strategy is as follows: ； in, It is the threat factor of heat dissipation performance of the high-voltage cable to be inspected; is the i-th inspection point on the high-voltage cable to be inspected, and I is the total number of inspection points; C is the unit time length of temperature data collection at the inspection point; is the surface temperature data of the high-voltage cable bushing collected at the ith inspection point when the temperature data collection time point is c; is the median temperature of the high-voltage cable bushing surface temperature data collected at the i-th inspection point; It is the standard temperature data of high voltage cable under working condition; It is the difference between the predicted amount of sediment deposition in the cross section of the high-voltage cable to be inspected and the initial sediment thickness when the high-voltage cable is laid.

5. A high-voltage cable intelligent inspection method based on data analysis according to claim 4, characterized in that: The insulation performance threat strategies described in S3 are as follows: ； in, It is the threat factor of the insulation performance of the high-voltage cable to be inspected; is the insulation resistance of the high-voltage cable insulation layer collected at the i-th inspection point; is the average insulation resistance of the high-voltage cable insulation layer; It is the factory insulation resistance of the high voltage cable insulation layer; is the leakage current on the surface of the high-voltage cable collected at the i-th inspection point; The mean leakage current of the high voltage cable insulation layer; They are respectively the maximum value and minimum value of leakage current that the cable surface can withstand when the high-voltage cable is in working condition; The threat influence coefficients of high-voltage cable insulation performance are insulation resistance and leakage current respectively.

6. A high-voltage cable intelligent inspection method based on data analysis according to claim 5, characterized in that: The stability threat strategies described in S3 are as follows: ； in, It is the stability threat factor of the high-voltage cable to be inspected; are the high-voltage cable position deviations collected at the i-1th, i-th and i-1th inspection points respectively; They are respectively the maximum position deviation and the minimum position deviation of the high-voltage cable to be inspected.

7. A high-voltage cable intelligent inspection method based on data analysis according to claim 6, characterized in that: The overall performance threat value of sediment deposition in the integrated assessment section to the working performance of the high-voltage cable described in S4 is as follows: ； in, It is the overall performance threat value of cross-section sediment deposition to the working performance of high-voltage cables; are the threat ratios of heat dissipation threat factor, insulation performance threat factor and stability threat factor, among which, , are respectively the number of high-voltage cable heat dissipation fault points, the number of insulation fault points, and the number of stability fault points; the heat dissipation fault point judgment strategy includes: when When , the inspection point of the high-voltage cable is marked as a heat dissipation fault point; the judgment strategy of the insulation fault point includes: when hour, Indicates that they occur simultaneously, marking the inspection point of the high-voltage cable as an insulation fault point; the judgment strategy of the stability fault point includes: When the high voltage cable inspection point is marked as a stability fault point.

8. A high-voltage cable intelligent inspection method based on data analysis according to claim 7, characterized in that: S5 includes: Extract the overall performance threat value of each inspection point of the high-voltage cable to be inspected, and compare the overall performance threat value of the inspection point with the maximum performance threat value that the high-voltage cable can withstand. Make comparisons to determine the working status of the high-voltage cables at the inspection points; when When the high-voltage cable is located, it is judged that the sediment deposited in the area where the high-voltage cable is located is within a controllable range and does not affect the normal operation of the high-voltage cable; when When the high-voltage cable is located, it is determined that the sediment in the area where the high-voltage cable is located needs to be cleaned up, and the maintenance countdown of the high-voltage cable is estimated according to the inspection data collection time of the high-voltage cable. The maintenance countdown of each section of the high-voltage cable to be inspected is arranged in order and sent to the maintenance department management personnel to arrange the daily maintenance plan of the high-voltage cable; when When the high-voltage cable is located, it is judged that the sediment deposited in the area where the high-voltage cable is located poses a major threat to the working performance of the high-voltage cable. The sediment data of this section of the high-voltage cable is sent to the management personnel of the maintenance department for emergency repair of the high-voltage cable, and the degree of damage to the high-voltage cable is confirmed based on various threat factors.

9. A high-voltage cable intelligent inspection system based on data analysis, which is implemented based on a high-voltage cable intelligent inspection method based on data analysis as claimed in any one of claims 1 to 8, characterized in that: The system includes the following modules: Inspection point data collection module, deep learning model building module, threat factor calculation module, overall performance threat value module and high-voltage cable maintenance plan optimization module; The inspection point data collection module is used to collect terrain data and real-time water data of the water area where each inspection point of the high-voltage cable to be inspected is located; The deep learning model construction module is used to construct a deep learning model for sediment deposition, pre-process the water area terrain data and real-time water body data and input them into the deep learning model, and simulate and predict the cross-section sediment deposition of the high-voltage cable to be inspected; The threat factor calculation module is used to import the predicted cross-section sediment deposition amount of the high-voltage cable to be inspected into the heat dissipation performance threat strategy, the insulation performance threat strategy and the stability threat strategy, and respectively calculate the heat dissipation performance threat factor, the insulation performance threat factor and the stability threat factor of the cross-section sediment deposition of the high-voltage cable on the working performance of the high-voltage cable; The overall performance threat value module evaluates the overall performance threat value of cross-section sediment to the working performance of the high-voltage cable based on the heat dissipation performance threat factor, the insulation performance threat factor and the stability threat factor; The high-voltage cable maintenance plan optimization module is used to extract the overall performance threat value of each inspection point of the high-voltage cable to be inspected, and optimize and execute the corresponding high-voltage cable maintenance plan.

10. An electronic device, characterized in that: include: A memory for storing instructions; A processor is used to execute the instruction so that the device performs the operation of implementing a high-voltage cable intelligent inspection method based on data analysis as described in any one of claims 1-8.

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