High-voltage cable operation inspection method and system based on online monitoring and intelligent inspection

Abstract

The invention relates to the technical field of high-voltage cable operation inspection, and provides a high-voltage cable operation inspection method and system based on online monitoring and intelligent inspection. An online monitoring system based on an artificial neural network algorithm is used for diagnosing the cable operation fault probability, and when the cable operation fault probability does not exceed a threshold value, the cable operation fault probability is detected; according to the method, image information of a corresponding cable is obtained, image inspection is further carried out on the position of the cable suspected to have a fault, image detection is carried out through an image defect diagnosis algorithm, a predicted value of the operation fault probability of the cable based on the image is obtained, and when the predicted value does not reach a predicted threshold value, the cable is detected. Sending a confirmation operation inspection signal to a cable operation inspection unit corresponding to the position information so as to remind the cable operation inspection unit to inspect and confirm whether a fault occurs in a cable area corresponding to the position information; and when the fault cannot be completely eliminated, the fault detection precision is improved, unnecessary inspection operation is reduced, and manpower and material resources are saved.

Description

Technical Field

[0001] The present invention relates to the technical field of high-voltage cable operation and inspection, and in particular to a high-voltage cable operation and inspection method and system based on online monitoring and intelligent inspection. Background Art

[0002] The laying range of high-voltage cables is very wide. In the distribution network, high-voltage cables are operated in harsh environments such as high temperature, high pressure, and humidity for a long time. Their insulation is easily damaged, which in turn causes failures. Therefore, the maintenance, detection and protection of high-voltage cables have become key issues when using cables.

[0003] In order to ensure the stable operation of high-voltage cables, power companies have taken preventive measures such as daily inspections and tests in the early stage to promptly detect and eliminate cable defects and abnormalities and avoid cable failures. However, due to the huge area of ​​high-voltage cables, it is very difficult to inspect and repair high-voltage cables in various places, and it takes a huge amount of manpower and material resources to troubleshoot problems, and it is impossible to quickly detect and repair cables in a short time.

[0004] Later, with the rise of sensor technology, high-voltage cable online monitoring fault location technology was developed. High-voltage cable online monitoring fault location technology refers to the use of advanced monitoring technology and equipment to monitor high-voltage cables in real time in the power system, so as to timely detect cable faults and locate the fault location. This monitoring system usually includes components such as sensors, data acquisition equipment and data processing software, which can collect cable current, voltage, temperature and other parameters in real time, and judge whether the cable has faults through data analysis.

[0005] At present, with the development of artificial intelligence and robotics technology, many power companies have launched power inspection robots, which use the infrared, video, partial discharge and other sensors carried by the robots to monitor the operating status of equipment in real time, and upload the captured video images to the system background as soon as possible to achieve accurate assessment of the equipment status.

[0006] Although conventional manual inspections consume manpower and material resources, they can accurately detect the location and cause of abnormalities. Due to the distribution of sensors, online monitoring technology has poor positioning accuracy and can usually only locate the faulty area. Manual maintenance is also required, and sensors are often subject to false alarms and other problems due to environmental influences and fluctuations in measurement accuracy. Inspection robots are currently still in the concept and testing stage, and the overall deployment cost of inspection robots is too high. Summary of the invention

[0007] The object of the present invention is to provide a high-voltage cable operation and inspection method and system based on online monitoring and intelligent inspection to solve the above-mentioned technical problems.

[0008] The present invention provides a high-voltage cable operation and inspection method based on online monitoring and intelligent inspection, comprising the steps of:

[0009] S1, obtaining cable operating parameters monitored by a distributed online monitoring system for detecting cable operating parameters; the cable operating parameters include cable location information and corresponding cable current parameter values, cable voltage parameter values, cable temperature parameter values, ambient temperature parameter values, ambient humidity parameter values, and partial discharge parameter values;

[0010] S2, using a preset artificial neural network algorithm based on data-driven fault prediction technology, taking the cable operation parameter as input of the artificial neural network algorithm, and calculating a first prediction value of the cable operation fault probability;

[0011] S3, online monitoring and diagnosis, including:

[0012] S31, when the first prediction value is greater than a preset first threshold, sending a fault inspection signal to the cable inspection unit corresponding to the location information;

[0013] S32, when the first prediction value is greater than the second threshold value and less than the first threshold value, acquiring image information of the cable corresponding to the position information;

[0014] S4, performing fault diagnosis on the image information by using an image defect diagnosis algorithm according to the image information of the cable corresponding to the position information, to obtain an image defect diagnosis result; the image defect diagnosis result includes a second predicted value of the operating fault probability of the cable corresponding to the position information obtained based on image analysis;

[0015] S5, image detection and diagnosis, including:

[0016] S51, when the second prediction value is greater than a preset third threshold, initiating a fault inspection signal to the cable inspection unit corresponding to the location information;

[0017] S52, when the second prediction value is greater than the fourth threshold and less than the third threshold, a confirmation inspection signal is initiated to the cable inspection unit corresponding to the location information to remind the cable inspection unit to conduct an inspection and confirm whether a fault occurs in the cable area corresponding to the location information.

[0018] Furthermore, in step S32, the step of obtaining the image information of the cable corresponding to the position information includes:

[0019] When a distributed camera is installed in the area of ​​the cable corresponding to the position information, obtaining image information of the corresponding distributed camera;

[0020] When the area of ​​the cable corresponding to the location information is not equipped with a distributed camera, requesting to deploy an automatic inspection robot to the area of ​​the cable corresponding to the location information to obtain image information of the cable corresponding to the location information;

[0021] When the area of ​​the cable corresponding to the location information has no distributed cameras installed and an automatic inspection robot cannot be deployed, a manual inspection request signal is initiated to the cable inspection unit corresponding to the location information to obtain image information of the cable corresponding to the location information.

[0022] Furthermore, after the step S31, the step S51 and the step S52, the method further includes obtaining the inspection result of the cable inspection unit;

[0023] The method further comprises the steps of:

[0024] S61, using the cable operation parameter and the inspection result as correction training samples of the artificial neural network algorithm, and performing correction training on the artificial neural network algorithm;

[0025] S62, using the image information of the cable corresponding to the position information and the inspection result as correction training samples of the image defect diagnosis algorithm, and performing correction training on the image defect diagnosis algorithm.

[0026] Furthermore, the method further comprises the steps of:

[0027] S63, receiving fault reporting information actively sent by the cable operation and inspection unit; the fault reporting information includes the time of fault occurrence, the location information of the cable where the fault occurs, and specific fault data;

[0028] S641, obtaining cable operating parameters of the area where the cable is located corresponding to the location information of the cable where the fault occurs at the time when the fault occurs, using the cable operating parameters and the specific fault data as correction training samples of the artificial neural network algorithm, and performing correction training on the artificial neural network algorithm;

[0029] S642, obtaining the image information of the cable corresponding to the position information of the cable where the fault occurred at the time when the fault occurred, using the image information and the inspection result as correction training samples of the image defect diagnosis algorithm respectively, and performing correction training on the image defect diagnosis algorithm.

[0030] Furthermore, in the step S641, the cable operation parameters of the area where the cable is located corresponding to the location information of the cable where the fault occurs are obtained from a preset time period before the fault occurs to a time period after the fault is eliminated, and the cable operation parameters and the specific fault data are used as correction training samples of the artificial neural network algorithm to perform correction training on the artificial neural network algorithm;

[0031] S642, obtaining the image information of the cable corresponding to the location information of the cable where the fault occurred in a preset time period before the fault occurred to a time period after the fault is eliminated, using the image information and the inspection result as correction training samples of the image defect diagnosis algorithm, respectively, to perform correction training on the image defect diagnosis algorithm.

[0032] Furthermore, after step S1, the method further includes the following steps:

[0033] When any parameter value of the cable operation parameters exceeds a preset safety threshold, a fault inspection signal is sent to the cable inspection unit corresponding to the location information;

[0034] When all parameter values ​​of the cable operation parameters do not exceed the preset safety threshold, the process proceeds to step S2.

[0035] Furthermore, the method further comprises the steps of:

[0036] Deploy automatic inspection robots or initiate manual inspections according to the preset inspection cycle plan;

[0037] Receive the fault reporting information reported by the automatic inspection robot or manual inspection, and enter step S63; the inspection fault reporting information includes two fault results: fault occurrence and no fault occurrence.

[0038] Furthermore, the step S1 also includes: performing validity analysis on the cable operating parameters and filtering out redundant data to obtain available cable operating parameters for subsequent steps; wherein the validity analysis includes filtering out error data, and the redundant data filtering includes filtering out identified redundant data.

[0039] The present invention also provides a high-voltage cable operation and inspection system based on online monitoring and intelligent inspection, including a distributed online monitoring system for detecting cable operation parameters, a monitoring background service end, and one or two of a distributed camera and an automatic inspection robot; characterized in that:

[0040] The distributed online monitoring system is used to detect the cable operating parameters of the high-voltage cable;

[0041] The distributed camera is used to collect image data of the high-voltage cable;

[0042] The automatic inspection robot is used to inspect high-voltage cables and obtain image data of high-voltage cables; the monitoring background server 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 steps of the high-voltage cable operation and inspection method based on online monitoring and intelligent inspection as described in any one of the above items are implemented.

[0043] Furthermore, it also includes multiple distributed data nodes, each of which is matched with the distributed online monitoring system and distributed cameras, and is used to receive data from the online monitoring system and cameras in the corresponding area, and transmit it to the monitoring background server.

[0044] The high-voltage cable inspection method and system based on online monitoring and intelligent inspection provided by the present invention first perform big data calculation on the operation status of the high-voltage cable through an online monitoring big data model, and online monitor and diagnose the first predicted value of the cable operation failure probability. Only when the first predicted value is greater than a preset first threshold value, a fault inspection signal is initiated to the cable inspection unit corresponding to the position information; when the first predicted value is greater than a second threshold value and less than the first threshold value, the image information of the cable corresponding to the position information is obtained, and further by performing image inspection on the cable position suspected of having a fault, image detection is performed through image information and image defect diagnosis algorithm to obtain a second predicted value of the operation failure probability of the cable based on the image, and when the second predicted value is greater than a preset third threshold value, a fault inspection signal is initiated to the cable inspection unit corresponding to the position information; only when the second predicted value is greater than a fourth threshold value and less than the third threshold value, a confirmation inspection signal is initiated to the cable inspection unit corresponding to the position information to remind the cable inspection unit to conduct an inspection to confirm whether a fault occurs in the cable area corresponding to the position information; unnecessary inspection operations are reduced, manpower and material resources are saved, and the accuracy of fault detection is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 The figure is a flow chart of a high-voltage cable operation and inspection method based on online monitoring and intelligent inspection in one embodiment of the present invention.

[0046] Figure 2 It is a schematic diagram of the hardware structure of a system running a high-voltage cable operation and inspection method based on online monitoring and intelligent inspection in one embodiment of the present invention.

[0047] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0048] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0049] In the subsequent description, the suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present invention, and have no specific meanings. Therefore, "module", "component" or "unit" can be used in a mixed manner.

[0050] The present invention will now be described in further detail with reference to the accompanying drawings.

[0051] Please combine Figure 1 and Figure 2 , Figure 1 FIG. 1 is a flow chart of a high-voltage cable operation and inspection method based on online monitoring and intelligent inspection in one embodiment of the present invention. Figure 2 This is a schematic diagram of the framework structure of the high-voltage cable operation and inspection system based on online monitoring and intelligent inspection.

[0052] The high-voltage cable operation and inspection system 100 based on online monitoring and intelligent inspection includes a distributed online monitoring system 10 for detecting cable operation parameters and a monitoring background server 40. In this embodiment, it also includes a distributed camera 20 and at least one automatic inspection robot 30. It is understandable that the distributed camera 20 and at least one automatic inspection robot 30 can also be selected separately.

[0053] The distributed online monitoring system 10 may include an ammeter, a voltmeter, a temperature sensor, a humidity sensor, a partial discharge sensor, etc. for detecting cable current parameter values ​​(such as intermediate joint ground loop current), cable voltage parameter values ​​(such as intermediate joint voltage), cable temperature parameter values, ambient temperature parameter values, ambient humidity parameter values, and partial discharge parameter values. It may also include a liquid level sensor for sensing the water level in the tunnel, a smoke sensor for detecting smoke in the tunnel, an infrared thermal imaging camera for detecting infrared thermal imaging, etc. The distributed online monitoring system 10 is used to detect the cable operating parameters of the high-voltage cable, and those skilled in the art may set and select it as needed.

[0054] Furthermore, the distributed online monitoring system 10 also includes a plurality of distributed data nodes 50, each of which is matched with the distributed online monitoring system 10 and the distributed camera 20, and is used to receive data from the online monitoring system and the camera in the corresponding area, and transmit it to the monitoring background server 40. The distributed camera 10 is used to collect image data of the high-voltage cable.

[0055] The automatic inspection robot 30 can also be equipped with a camera and any suitable sensor in the distributed online monitoring system. The automatic inspection robot can be track-type, walking-type, or flying-type to adapt to inspection and detection in different geographical environments such as tunnels and the wild. The automatic inspection robot 30 is used to inspect high-voltage cables and obtain image data of high-voltage cables. The automatic inspection robot 30 can also be equipped with other sensors to collect other high-voltage power supply operation parameters.

[0056] The monitoring background server 40 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 steps of the high-voltage cable operation and inspection method based on online monitoring and intelligent inspection as described in any one of the embodiments are implemented.

[0057] Exemplarily, the computer program of the computer-readable storage medium includes computer program code, which may be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0058] Specifically, in a preferred embodiment, the high-voltage cable operation and inspection method based on online monitoring and intelligent inspection comprises the following steps:

[0059] S1, obtain the cable operating parameters monitored by the distributed online monitoring system 10 for detecting cable operating parameters; the cable operating parameters include the location information of the cable and the corresponding cable current parameter values, cable voltage parameter values, cable temperature parameter values, ambient temperature parameter values, ambient humidity parameter values, and partial discharge parameter values.

[0060] Furthermore, the step S1 also includes: performing validity analysis on the cable operating parameters and filtering out redundant data to obtain available cable operating parameters for subsequent steps; wherein the validity analysis includes filtering out error data, and the redundant data filtering includes filtering out identified redundant data.

[0061] Furthermore, after step S1, the method further includes the following steps:

[0062] When any parameter value of the cable operation parameters exceeds a preset safety threshold, a fault inspection signal is sent to the cable inspection unit corresponding to the location information;

[0063] When all parameter values ​​of the cable operation parameters do not exceed the preset safety threshold, the process proceeds to step S2.

[0064] S2, using a preset artificial neural network algorithm based on data-driven fault prediction technology, taking the cable operation parameters as input of the artificial neural network algorithm, and calculating a first prediction value of the cable operation fault probability.

[0065] Specifically, technicians in this field can choose algorithms such as artificial neural networks (ANN) or fuzzy systems, and use existing fault data and normal operation data to train the big data algorithm model, where the cable operation parameters are used as input and the fault results are used as output; then use forward propagation and back propagation to optimize the parameters and obtain the final prediction model; which can be used to predict the probability of cable operation failure.

[0066] Furthermore, the high-voltage cable operation and inspection method based on online monitoring and intelligent inspection in this embodiment further includes the steps of:

[0067] Deploy automatic inspection robots or initiate manual inspections according to the preset inspection cycle plan;

[0068] Receiving the fault report information reported by the automatic inspection robot or the manual inspection; the inspection fault report information includes two fault results: a fault occurs and a fault does not occur;

[0069] Entering the following steps of using the fault reporting information reported by the automatic inspection robot or manual inspection to perform correction training, specifically includes:

[0070] S63, receiving fault reporting information actively sent by the cable operation and inspection unit; the fault reporting information includes the time of fault occurrence, the location information of the cable where the fault occurs, and specific fault data;

[0071] S641, obtaining the cable operating parameters of the cable area corresponding to the position information of the cable where the fault occurred at the time when the fault occurred, using the cable operating parameters and the specific fault data as correction training samples of the artificial neural network algorithm (wherein, the cable operating parameters are used as the input of the artificial neural network algorithm, and the fault result corresponding to the specific fault data (i.e., the probability of the fault occurring is 100%) is used as the output of the artificial neural network algorithm), and performing correction training on the artificial neural network algorithm.

[0072] Furthermore, in step S641, the cable operation parameters of the area where the cable is located corresponding to the location information of the cable where the fault occurs are obtained from a preset time period before the fault occurs to a time period after the fault is eliminated, and the cable operation parameters and the specific fault data are used as correction training samples of the artificial neural network algorithm, and the artificial neural network algorithm is corrected and trained to optimize parameters. In this way, more data samples of faults can be obtained to improve the accuracy of the model.

[0073] S3, online monitoring and diagnosis, including:

[0074] S31, when the first prediction value is greater than a preset first threshold, sending a fault inspection signal to the cable inspection unit corresponding to the location information;

[0075] S32: When the first prediction value is greater than the second threshold value and less than the first threshold value, acquiring image information of the cable corresponding to the position information.

[0076] Furthermore, in step S32, the step of obtaining the image information of the cable corresponding to the position information includes:

[0077] When a distributed camera 20 is installed in the area of ​​the cable corresponding to the position information, image information of the corresponding distributed camera 20 is obtained;

[0078] When the area of ​​the cable corresponding to the location information is not equipped with a distributed camera, requesting to deploy an automatic inspection robot 30 to the area of ​​the cable corresponding to the location information to obtain image information of the cable corresponding to the location information;

[0079] When the area of ​​the cable corresponding to the location information has no distributed cameras installed and an automatic inspection robot cannot be deployed, a manual inspection request signal is initiated to the cable inspection unit corresponding to the location information to obtain image information of the cable corresponding to the location information.

[0080] S4. According to the image information of the cable corresponding to the position information, fault diagnosis is performed on the image information by using an image defect diagnosis algorithm to obtain an image defect diagnosis result; the image defect diagnosis result includes a second predicted value of the operating fault probability of the cable corresponding to the position information obtained based on image analysis.

[0081] Specifically, those skilled in the art may select an image defect diagnosis algorithm based on a cluster learning algorithm, and train the model algorithm using data of fault-free images and faulty images to obtain a final diagnosis model; and then calculate and predict the fault probability.

[0082] Furthermore, the high-voltage cable operation and inspection method based on online monitoring and intelligent inspection in this embodiment further includes the steps of:

[0083] Deploy automatic inspection robots or initiate manual inspections according to the preset inspection cycle plan;

[0084] Receiving the fault report information reported by the automatic inspection robot or the manual inspection; the inspection fault report information includes two fault results: a fault occurs and a fault does not occur;

[0085] Entering the following steps of using the fault reporting information reported by the automatic inspection robot or manual inspection to perform correction training, specifically includes:

[0086] S63, receiving fault reporting information actively sent by the cable operation and inspection unit; the fault reporting information includes the time of fault occurrence, the location information of the cable where the fault occurs, and specific fault data;

[0087] S642, obtaining the image information of the cable corresponding to the position information of the cable where the fault occurred at the time when the fault occurred, using the image information and the inspection result as correction training samples of the image defect diagnosis algorithm (wherein the image information is used as the input of the image defect diagnosis algorithm, and the inspection result (i.e., the probability of a fault occurring is 100%) is used as the output of the image defect diagnosis algorithm), and performing correction training on the image defect diagnosis algorithm.

[0088] Furthermore, in S642, the image information of the cable corresponding to the position information of the cable where the fault occurred is obtained in the preset time period before the fault occurred and the time period after the fault is eliminated, and the image information and the inspection result are used as correction training samples of the image defect diagnosis algorithm, and the image defect diagnosis algorithm is corrected and trained. In this way, more data samples of the fault can be obtained to improve the accuracy of the model.

[0089] S5, image detection and diagnosis, including:

[0090] S51, when the second prediction value is greater than a preset third threshold, initiating a fault inspection signal to the cable inspection unit corresponding to the location information;

[0091] S52, when the second prediction value is greater than the fourth threshold value and less than the third threshold value, a confirmation inspection signal is sent to the cable inspection unit corresponding to the location information to remind the cable inspection unit to conduct an inspection to confirm whether a fault occurs in the cable area corresponding to the location information. At this time, the probability of occurrence has yet to be determined, and the cable inspection unit can conduct an inspection to confirm whether a fault occurs in the cable area corresponding to the location information.

[0092] Furthermore, in a preferred embodiment, after the step S31, the step S51 and the step S52, it further includes obtaining the inspection result of the cable inspection unit;

[0093] S61, using the cable operation parameter and the inspection result as correction training samples of the artificial neural network algorithm, and performing correction training on the artificial neural network algorithm;

[0094] S62, using the image information of the cable corresponding to the position information and the inspection result as correction training samples of the image defect diagnosis algorithm, and performing correction training on the image defect diagnosis algorithm.

[0095] Similarly, the operating parameter data corresponding to the final inspection result can also be used as a data sample for correcting the training, so as to use more data samples of failures to improve the accuracy of the model.

[0096] The high-voltage cable inspection method and system based on online monitoring and intelligent inspection provided by the present invention first perform big data calculation on the operation status of the high-voltage cable through an online monitoring big data model, and online monitor and diagnose the first predicted value of the cable operation failure probability. Only when the first predicted value is greater than a preset first threshold value, a fault inspection signal is initiated to the cable inspection unit corresponding to the position information; when the first predicted value is greater than a second threshold value and less than the first threshold value, the image information of the cable corresponding to the position information is obtained, and further by performing image inspection on the cable position suspected of having a fault, image detection is performed through image information and image defect diagnosis algorithm to obtain a second predicted value of the operation failure probability of the cable based on the image, and when the second predicted value is greater than a preset third threshold value, a fault inspection signal is initiated to the cable inspection unit corresponding to the position information; only when the second predicted value is greater than a fourth threshold value and less than the third threshold value, a confirmation inspection signal is initiated to the cable inspection unit corresponding to the position information to remind the cable inspection unit to conduct an inspection to confirm whether a fault occurs in the cable area corresponding to the position information; unnecessary inspection operations are reduced, manpower and material resources are saved, and the accuracy of fault detection is improved.

[0097] In the description of this specification, the description with reference to the terms "an embodiment", "another embodiment", "other embodiments", or "first embodiment to Xth embodiment" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, method steps or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0098] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0099] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A high-voltage cable operation and inspection method based on online monitoring and intelligent inspection, characterized in that: Includes steps: S1, obtaining cable operating parameters monitored by a distributed online monitoring system for detecting cable operating parameters; the cable operating parameters include cable location information and corresponding cable current parameter values, cable voltage parameter values, cable temperature parameter values, ambient temperature parameter values, ambient humidity parameter values, and partial discharge parameter values; S2, using a preset artificial neural network algorithm based on data-driven fault prediction technology, taking the cable operation parameter as input of the artificial neural network algorithm, and calculating a first prediction value of the cable operation fault probability; S3, online monitoring and diagnosis, including: S31, when the first prediction value is greater than a preset first threshold, sending a fault inspection signal to the cable inspection unit corresponding to the location information; S32, when the first prediction value is greater than the second threshold value and less than the first threshold value, acquiring image information of the cable corresponding to the position information; S4, performing fault diagnosis on the image information according to the image information of the cable corresponding to the position information by using an image recognition and defect diagnosis algorithm to obtain an image defect diagnosis result; the image defect diagnosis result includes a second predicted value of the operating fault probability of the cable corresponding to the position information obtained based on image analysis; S5, image detection and diagnosis, including: S51, when the second prediction value is greater than a preset third threshold, initiating a fault inspection signal to the cable inspection unit corresponding to the location information; S52, when the second prediction value is greater than the fourth threshold and less than the third threshold, a confirmation inspection signal is initiated to the cable inspection unit corresponding to the location information to remind the cable inspection unit to conduct an inspection and confirm whether a fault occurs in the cable area corresponding to the location information.

2. The high-voltage cable operation and inspection method based on online monitoring and intelligent inspection according to claim 1 is characterized in that: In the step S32, the step of obtaining the image information of the cable corresponding to the position information includes: When a distributed camera is installed in the area of ​​the cable corresponding to the position information, obtaining image information of the corresponding distributed camera; When the area of ​​the cable corresponding to the location information is not equipped with a distributed camera, requesting to deploy an automatic inspection robot to the area of ​​the cable corresponding to the location information to obtain image information of the cable corresponding to the location information; When the area of ​​the cable corresponding to the location information has no distributed cameras installed and an automatic inspection robot cannot be deployed, a manual inspection request signal is initiated to the cable inspection unit corresponding to the location information to obtain image information of the cable corresponding to the location information.

3. The high-voltage cable operation and inspection method based on online monitoring and intelligent inspection according to claim 1 is characterized in that: After the step S31, the step S51 and the step S52, the process further includes obtaining the inspection results actively uploaded by the cable inspection unit; The method further comprises the steps of: S61, using the cable operation parameter and the inspection result as correction training samples of the artificial neural network algorithm, and performing correction training on the artificial neural network algorithm; S62, using the image information of the cable corresponding to the position information and the inspection result as correction training samples of the image defect diagnosis algorithm, and performing correction training on the image defect diagnosis algorithm.

4. The high-voltage cable operation and inspection method based on online monitoring and intelligent inspection according to claim 1 is characterized in that: Also includes the steps: S63, receiving fault reporting information actively sent by the cable operation and inspection unit; the fault reporting information includes the time of fault occurrence, the location information of the cable where the fault occurs, and specific fault data; S641, obtaining cable operating parameters of the area where the cable is located corresponding to the location information of the cable where the fault occurs at the time when the fault occurs, using the cable operating parameters and the specific fault data as correction training samples of the artificial neural network algorithm, and performing correction training on the artificial neural network algorithm; S642, obtaining the image information of the cable corresponding to the position information of the cable where the fault occurred at the time when the fault occurred, using the image information and the specific fault data as correction training samples of the image defect diagnosis algorithm respectively, and performing correction training on the image defect diagnosis algorithm.

5. The high-voltage cable operation and inspection method based on online monitoring and intelligent inspection according to claim 4 is characterized in that: In the step S641, the cable operation parameters of the area where the cable is located corresponding to the location information of the cable where the fault occurs are obtained from a preset time period before the time when the fault occurs to a time period after the fault is eliminated, and the cable operation parameters and the specific fault data are used as correction training samples of the artificial neural network algorithm to perform correction training on the artificial neural network algorithm; S642, obtaining the image information of the cable corresponding to the location information of the cable where the fault occurred in a preset time period before the fault occurred to a time period after the fault is eliminated, using the image information and the specific fault data as correction training samples of the image defect diagnosis algorithm, and performing correction training on the image defect diagnosis algorithm.

6. The high-voltage cable operation and inspection method based on online monitoring and intelligent inspection according to any one of claims 1 to 5, characterized in that: After step S1, the method further includes the following steps: When any parameter value of the cable operation parameters exceeds a preset safety threshold, a fault inspection signal is sent to the cable inspection unit corresponding to the location information; When all parameter values ​​of the cable operation parameters do not exceed the preset safety threshold, the process proceeds to step S2.

7. The high-voltage cable operation and inspection method based on online monitoring and intelligent inspection according to claim 4 or 5, characterized in that: Also includes the steps: Deploy automatic inspection robots or initiate manual inspections according to the preset inspection cycle plan; Receive the fault reporting information reported by the automatic inspection robot or the manual inspection, and enter step S63; the inspection fault reporting information includes two fault results: a fault occurs and a fault does not occur.

8. The high-voltage cable operation and inspection method based on online monitoring and intelligent inspection according to claim 1 is characterized in that: The step S1 also includes: performing validity analysis on the cable operating parameters and filtering out redundant data to obtain usable cable operating parameters for subsequent steps; wherein the validity analysis includes filtering out error data, and the redundant data filtering includes filtering out identified redundant data.

9. A high-voltage cable operation and inspection system based on online monitoring and intelligent inspection, comprising a distributed online monitoring system for detecting cable operation parameters, a monitoring background service end, and one or two of a distributed camera and an automatic inspection robot; characterized in that: The distributed online monitoring system is used to detect the cable operating parameters of the high-voltage cable; The distributed camera is used to collect image data of the high-voltage cable; The automatic inspection robot is used to inspect the high-voltage cable and obtain image data of the high-voltage cable; The monitoring background server 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 steps of the high-voltage cable operation and inspection method based on online monitoring and intelligent inspection as described in any one of the above items are implemented.

10. The high-voltage cable operation and inspection system based on online monitoring and intelligent inspection according to claim 9 is characterized in that: It also includes multiple distributed data nodes, each of which is matched with the distributed online monitoring system and distributed cameras, and is used to receive data from the online monitoring system and cameras in the corresponding area, and transmit it to the monitoring background server.

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