An intelligent on-line monitoring system for high voltage cables

Abstract

The application relates to the technical field of cable monitoring, and discloses an intelligent online monitoring system for a high-voltage cable, which comprises a positioning and collecting unit configured with a first sensor for collecting cable terminal monitoring data in the high-voltage cable at a point and a second sensor for collecting intermediate joint monitoring data in the high-voltage cable at a point; a whole-line collecting unit configured with a camera for collecting position information and image information of the high-voltage cable; and a data operation and inspection unit configured with an artificial neural network model based on a data-driven fault prediction technology, wherein the collecting data of the positioning and collecting unit and the whole-line collecting unit are taken as inputs of the artificial neural network model, a prediction value of a cable operation fault probability is calculated, and when the prediction value is greater than a preset threshold value, a fault operation and inspection signal is initiated to a cable operation and inspection unit corresponding to the position information of the high-voltage cable in the whole-line collecting unit.

Description

Technical Field

[0001] This invention relates to the technical field of cable monitoring, and more specifically to an intelligent online monitoring system for high-voltage cables. Background Technology

[0002] With the continuous development of the power grid and the need for urban power grid transformation, the usage of power cable lines in my country is increasing year by year. Among them, cross-linked polyethylene (XLPE) power cables have advantages such as simple manufacturing process, excellent electrical and heat resistance, large transmission capacity, lightweight structure, easy bending, simple accessory joints, convenient installation, no limitation by height difference, and no risk of oil leakage or fire. Therefore, XLPE power cables are widely welcomed by users. However, practical experience shows that cable line faults are a major cause of power grid accidents and significant economic losses.

[0003] Therefore, accurately and promptly grasping the operating status of cable lines is of great significance for the proper handling of potential faults and ensuring the stable operation of the power grid. However, currently, the only detection devices and methods available for cable lines during operation are those used after a fault occurs. These devices and methods primarily target partial discharge detection of cables within a limited area around high-voltage cable accessories. For example, professionals rely on external testing instruments (oscilloscopes, spectrum analyzers, etc.) to observe and analyze the data on-site, or retrieve the collected data to a laboratory for further analysis. This method is inconvenient for locating high-voltage cable faults and cannot quickly determine their location.

[0004] However, to address the aforementioned technical issues, current high-voltage cable insulation fault location technology is employed. This technology is based on an offline pulse method. When a cable fault occurs, power is first cut off, then the cable line is disconnected from the system. Pulse signals are then used to determine the fault location. Based on the cable line's parameter model, pulse signals are applied to the cable on-site, and the distance to the fault point is calculated from the propagation time and velocity of voltage and current traveling waves within the cable. While this high-voltage cable insulation fault location technology can calculate the fault location by measuring the propagation time and velocity of voltage and current traveling waves, it requires shutting down the high-voltage cable interface after a fault occurs, transporting the fault measurement equipment to the site, setting up the equipment, and wiring before measurements can begin. This makes long-term, real-time online monitoring of high-voltage cables impossible. Summary of the Invention

[0005] The present invention aims to provide an intelligent online monitoring system for high-voltage cables, which can monitor the status of high-voltage cables in real time and quickly obtain the location of fault points.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: 1) An intelligent online monitoring system for high-voltage cables, comprising: The positioning and acquisition unit is configured to have a first sensor for collecting monitoring data of cable terminals inside the high-voltage cable at a point location, and a second sensor for collecting monitoring data of intermediate joints inside the high-voltage cable at a point location. The first sensor and the second sensor work together to collect data in real time. The full-line acquisition unit is configured with a camera that can acquire the location information and image information of the high-voltage cable along the entire line, and performs real-time acquisition synchronously with the first and second sensors in the positioning acquisition unit. The data operation and maintenance unit is configured with a preset artificial neural network model based on data-driven fault prediction technology. The data collected by the positioning acquisition unit and the full-line acquisition unit are used as the input of the artificial neural network model to calculate the predicted value of the cable operation fault probability. When the predicted value is greater than a preset threshold, a fault operation and maintenance signal is sent to the cable operation and maintenance unit corresponding to the location information of the high-voltage cable in the full-line acquisition unit.

[0007] This invention uses a first sensor in the positioning and acquisition unit to collect monitoring data of cable terminals within the high-voltage cable at specific locations and transmits it to the data operation and maintenance unit in real time. Simultaneously, a second sensor in the positioning and acquisition unit collects monitoring data of intermediate joints within the high-voltage cable at specific locations and transmits it to the data operation and maintenance unit in real time. At the same time, a camera in the full-line acquisition unit collects the location information and image information of the high-voltage cable along the entire line and transmits it to the data operation and maintenance center platform in real time.

[0008] The data operation and maintenance unit synchronously receives monitoring data from the first sensor, including cable terminal monitoring data, intermediate joint monitoring data, and location and image information of the high-voltage cable. This data is then transmitted to an artificial neural network model. The model performs big data calculations on the operation of the high-voltage cable, predicting the probability of cable malfunctions online. Only when the predicted value exceeds a preset threshold is a fault operation and maintenance signal sent to the cable operation and maintenance center platform corresponding to the location information of the high-voltage cable in the full-line acquisition unit. This allows for real-time online monitoring of the high-voltage cable's status, quickly identifying the location of the fault point and alerting the cable operation and maintenance center platform to conduct inspections to confirm whether a fault has occurred in the cable area corresponding to the location information. This reduces unnecessary inspection operations, saves manpower and resources, and improves the accuracy of fault detection.

[0009] 2) According to 1), an intelligent online monitoring system for high-voltage cables is provided, wherein a first sensor is provided to collect monitoring data of cable terminals inside the high-voltage cable at point locations. The first sensor is at least three sets of temperature sensors, which are respectively set on the surface of the cable joint, the installation environment around the cable, and the cable core. The first set of temperature sensors corresponds to the surface of the cable joint. The first set of temperature sensors has three temperature sensors, which are respectively installed on the surface of the cable joint of the three phases A, B, and C of the high-voltage cable. The second set of temperature sensors corresponds to the cable installation environment and consists of one temperature sensor. The third set of temperature sensors corresponds to the cable core wire. The third set of temperature sensors has three temperature sensors, which are respectively installed on the core wire of the joint of the three phases A, B, and C of the high-voltage cable. The data is transmitted to the data operation and inspection unit in real time through the three sets of temperature sensors and is detected in real time through an artificial neural network model.

[0010] 3) An intelligent online monitoring system for high-voltage cables as described in 2), wherein three sets of temperature sensors transmit data to a data transmission and inspection unit in real time, and real-time detection is performed through an artificial neural network model, including: The artificial neural network model has preset temperature thresholds for the connector surface, ambient temperature, and core temperature. The first set of temperature sensors collects the surface temperature of the cable joints of phases A, B, and C of the high-voltage cable. The second set of temperature sensors collects the ambient temperature at the cable installation location. The third set of temperature sensors collects the core temperature of the conductors at the joints of phases A, B, and C of the high-voltage cable. The temperature difference between the joint surface, the ambient temperature, and the core temperature are calculated using the subtraction method within the artificial neural network model. The temperature information is then obtained by determining whether the temperature difference conforms to the temperature range of the high-voltage cable joint surface, the ambient temperature range of the high-voltage cable, and the temperature range of the high-voltage cable core, and is uploaded to the data operation and maintenance center platform.

[0011] 4) According to the intelligent online monitoring system for high-voltage cables described in 3), the temperature difference is used to determine whether it conforms to the temperature range of the high-voltage cable joint surface, the ambient temperature range of the high-voltage cable, and the core temperature range of the high-voltage cable. Specifically, the temperature range of the joint surface, the ambient temperature range of the high-voltage cable, and the core temperature range of the high-voltage cable are all less than 5°C; and: a change of more than 5°C in the temperature range of the joint surface, the ambient temperature range of the high-voltage cable, and the core temperature range of the high-voltage cable within 1 hour is considered abnormal; an increase of 5°C in the ambient temperature range of the high-voltage cable and the core temperature range of the high-voltage cable within 1 hour is considered abnormal.

[0012] 5) According to the intelligent online monitoring system for high-voltage cables described in 3), the temperature information results are uploaded to the data operation and maintenance center platform. This includes comparing the temperature difference of the joint surface, the ambient temperature difference, and the core temperature difference with the joint surface temperature range, the ambient temperature range, and the core temperature range of the high-voltage cable every 10 minutes to determine whether the cable surface temperature and the cable core temperature are within the normal temperature range. When an abnormality is detected more than twice consecutively, the data upload interval is automatically shortened to once every 1-600 seconds to the data operation and maintenance center platform.

[0013] 6) According to 1), an intelligent online monitoring system for high-voltage cables is provided, wherein a second sensor is provided to collect monitoring data of intermediate joints in high-voltage cables at points. The second sensor is a current sensor, which is set on the grounding wire of the cable joint. The current sensor transmits the data to the data operation and inspection unit in real time and performs real-time detection through an artificial neural network model.

[0014] 7) An intelligent online monitoring system for high-voltage cables according to claim 6, wherein a current sensor transmits data to a data transmission and inspection unit in real time, and real-time detection is performed through an artificial neural network model, including: The artificial neural network model has a preset grounding current threshold. The grounding current value on the grounding wire of the cable joint is collected by the current sensor. The grounding current difference is calculated by subtraction in the artificial neural network model. The current information result is obtained by checking whether the grounding current difference meets the grounding current difference range of the high-voltage cable and then uploaded to the data operation and maintenance center platform.

[0015] 8) An intelligent online monitoring system for high-voltage cables according to 7), wherein: The grounding current difference range of the high-voltage cable is 0-200A, and the current information results are uploaded to the data operation and maintenance center platform at intervals of 1-8 hours by the current sensor; when the current sensor detects an abnormal value in the current information results more than once in a row, the upload interval is shortened to once every 1600 seconds.

[0016] 9) An intelligent online monitoring system for high-voltage cables as described in 1), comprising cameras for collecting location and image information of the high-voltage cables along the entire line, including multiple distributed cameras, each camera being matched with a data node in an artificial neural network model to receive image data of the corresponding area and upload it to the data operation and maintenance center platform.

[0017] 10) An intelligent online monitoring system for high-voltage cables as described in 9), wherein each camera is matched with a data node in an artificial neural network model to receive image data of the corresponding area and upload it to the data operation and maintenance center platform, including: Based on the image information of the cable corresponding to the location information, an image defect diagnosis algorithm is used to diagnose the image information and obtain the image defect diagnosis result; the image defect diagnosis result includes the predicted value of the operating failure probability of the cable corresponding to the location information obtained based on image analysis; When the predicted value is greater than the threshold, a confirmation operation and maintenance signal is sent to the data operation and maintenance center platform corresponding to the location information to remind the data operation and maintenance center platform to conduct an inspection and confirm whether a fault has occurred in the cable area corresponding to the location information.

[0018] Compared with the prior art, the present invention also has the following technical effects: This invention not only solves the problem of online monitoring and management of key components in urban power grids, primarily based on cables, but also provides design, dispatching, and operation and maintenance departments with decision-making basis for line optimization design, operational status assessment, maximum dynamic current carrying capacity control, and defect repair. Furthermore, it establishes a cable operation defect diagnosis and analysis model to determine the health status of high-voltage cables, achieving full-lifecycle management of cables and further realizing intelligent cable operation management goals such as intelligent analysis, safety early warning, and remaining life assessment. Practical experience has proven that this invention has technical advantages such as reliable performance, convenient installation, and strong compatibility. Attached Figure Description

[0019] Figure 1 This is a framework diagram of an intelligent online monitoring system for high-voltage cables according to the present invention. Detailed Implementation

[0020] The following detailed description illustrates the specific implementation method: See the example. Figure 1 As shown in the figure, an intelligent online monitoring system for high-voltage cables in this embodiment includes a positioning and acquisition unit, a full-line acquisition unit, and a data operation and inspection unit. The positioning and acquisition unit is configured with a first sensor that collects monitoring data of cable terminals within the high-voltage cable at specific locations, and a second sensor that collects monitoring data of intermediate joints within the high-voltage cable at specific locations. The first and second sensors work together synchronously to collect data in real time. The full-line acquisition unit is configured with a camera that collects the location information and image information of the high-voltage cable along its entire length, and works together synchronously with the first and second sensors in the positioning and acquisition unit to collect data in real time.

[0021] The data operation and maintenance unit is configured with a pre-set artificial neural network model based on data-driven fault prediction technology. It takes the data collected by the location acquisition unit and the full-line acquisition unit as the input of the artificial neural network model, calculates the predicted value of the cable operation fault probability, and sends a fault operation and maintenance signal to the cable operation and maintenance unit corresponding to the location information of the high-voltage cable in the full-line acquisition unit when the predicted value is greater than the preset threshold.

[0022] The first sensor in the positioning and acquisition unit collects monitoring data of the cable terminals inside the high-voltage cable at specific locations and sends it to the data operation and maintenance unit in real time. Simultaneously, the second sensor in the positioning and acquisition unit collects monitoring data of the intermediate joints inside the high-voltage cable at specific locations and sends it to the data operation and maintenance unit in real time. At the same time, the camera in the full-line acquisition unit collects the location information and image information of the high-voltage cable along the entire line and sends it to the data operation and maintenance center platform in real time.

[0023] The data operation and maintenance unit synchronously receives monitoring data from the first sensor, including cable terminal monitoring data, intermediate joint monitoring data, and location and image information of the high-voltage cable. This data is then transmitted to an artificial neural network model. The model performs big data calculations on the operation of the high-voltage cable, predicting the probability of cable malfunctions online. Only when the predicted value exceeds a preset threshold is a fault operation and maintenance signal sent to the cable operation and maintenance center platform corresponding to the location information of the high-voltage cable in the entire data acquisition unit. This allows for real-time online monitoring of the high-voltage cable's status, enabling rapid identification of fault locations and alerting the cable operation and maintenance center platform to conduct inspections to confirm whether a fault has occurred in the corresponding cable area. This reduces unnecessary inspection operations, saves manpower and resources, and improves the accuracy of fault detection.

[0024] Furthermore, the system includes a first sensor for collecting monitoring data from cable terminals within the high-voltage cable at specific locations. This first sensor comprises at least three sets of temperature sensors, respectively positioned on the surface of the cable joint, in the surrounding installation environment, and on the cable core. Specifically, the first set of temperature sensors corresponds to the surface of the cable joint, consisting of three sensors positioned on the surface of the cable joints for phases A, B, and C of the high-voltage cable. The second set of temperature sensors corresponds to the installation environment and consists of a single sensor. The third set of temperature sensors corresponds to the cable core, consisting of three sensors positioned on the core of the joints for phases A, B, and C of the high-voltage cable. The data is transmitted in real-time to the data monitoring unit via these three sets of temperature sensors and monitored using an artificial neural network model.

[0025] More specifically, three sets of temperature sensors transmit data to the data processing unit in real time, and real-time detection is performed using an artificial neural network model, including: The artificial neural network model has preset temperature thresholds for the connector surface, ambient temperature, and core temperature. The first set of temperature sensors collects the surface temperature of the cable joints of phases A, B, and C of the high-voltage cable. The second set of temperature sensors collects the ambient temperature at the cable installation location. The third set of temperature sensors collects the core temperature of the conductors at the joints of phases A, B, and C of the high-voltage cable. The temperature difference between the joint surface, the ambient temperature, and the core temperature are calculated using the subtraction method within the artificial neural network model. The temperature information is then obtained by determining whether the temperature difference conforms to the temperature range of the high-voltage cable joint surface, the ambient temperature range of the high-voltage cable, and the temperature range of the high-voltage cable core, and is uploaded to the data operation and maintenance center platform.

[0026] Meanwhile, the temperature difference is assessed based on whether it conforms to the temperature range of the high-voltage cable joint surface, the ambient temperature range, and the core temperature range. Specifically, the temperature range of the joint surface, the ambient temperature range, and the core temperature range are all less than 5°C. Furthermore, a change of more than 5°C within one hour is considered abnormal; and an increase of 5°C within one hour is also considered abnormal.

[0027] The temperature information results are uploaded to the data operation and maintenance center platform. This includes comparing the temperature difference of the joint surface, the ambient temperature, and the core temperature every 10 minutes with the temperature range of the high-voltage cable joint surface, the ambient temperature range of the high-voltage cable, and the temperature range of the high-voltage cable core, respectively, to determine whether the cable surface temperature and cable core temperature are within the normal temperature range. When an abnormality is detected more than twice in a row, the data will be automatically uploaded to the data operation and maintenance center platform every 1-600 seconds.

[0028] Furthermore, it has a second sensor that collects monitoring data of intermediate joints inside high-voltage cables at specific locations. The second sensor is a current sensor, which is installed on the grounding wire of the cable joint. The current sensor transmits the data to the data operation and inspection unit in real time, and performs real-time detection through an artificial neural network model.

[0029] The data is transmitted in real time to the data processing and inspection unit via a current sensor, and real-time detection is performed using an artificial neural network model, including: The artificial neural network model has a preset grounding current threshold. The grounding current value on the grounding wire of the cable joint is collected by the current sensor. The grounding current difference is calculated by subtraction in the artificial neural network model. The current information result is obtained by checking whether the grounding current difference meets the grounding current difference range of the high-voltage cable and then uploaded to the data operation and maintenance center platform.

[0030] Meanwhile, the grounding current difference range of the high-voltage cable is 0-200A, and the current information results are uploaded to the data operation and maintenance center platform at intervals of 1-8 hours by the current sensor; when the current sensor detects an abnormal value in the current information results more than once in a row, the upload time is shortened to once every 1600 seconds.

[0031] Furthermore, it features cameras that collect location and image information of the entire high-voltage cable, including multiple distributed cameras. Each camera is matched with a data node in an artificial neural network model to receive image data of the corresponding area and upload it to the data operation and maintenance center platform.

[0032] Each camera is matched with a data node in the artificial neural network model to receive image data of the corresponding area and upload it to the data operation and maintenance center platform, including: Based on the image information of the cable corresponding to the location information, the image defect diagnosis algorithm is used to diagnose the fault in the image information and obtain the image defect diagnosis result. The image defect diagnosis result includes the predicted value of the operating fault probability of the cable corresponding to the location information obtained based on image analysis. When the predicted value is greater than the threshold, a confirmation operation and maintenance signal is sent to the data operation and maintenance center platform corresponding to the location information to remind the data operation and maintenance center platform to conduct inspection and confirm whether a fault has occurred in the cable area corresponding to the location information.

[0033] This embodiment not only solves the problem of online monitoring and management of key components of urban power grids, primarily cables, but also provides design, dispatching, and operation and maintenance departments with decision-making basis for line optimization design, operational status assessment, maximum dynamic current carrying capacity control, and defect repair. Furthermore, it establishes a cable operation defect diagnosis and analysis model to determine the health status of high-voltage cables, achieving full-lifecycle management of cables and further realizing intelligent cable operation management goals such as intelligent analysis, safety early warning, and remaining life assessment. Practical experience has proven that this embodiment has technical advantages such as reliable performance, convenient installation, and strong compatibility.

[0034] The above are merely embodiments of this solution. Commonly known technical solutions and / or characteristics are not described in detail here. It should be noted that those skilled in the art can make various modifications and improvements without departing from the technical solution of this solution. These modifications and improvements should also be considered within the scope of protection of this solution, and will not affect the effectiveness of the implementation of this solution or the practicality of the patent. The scope of protection claimed in this application shall be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. An intelligent online monitoring system for high-voltage cables, characterized in that, include: The positioning and acquisition unit is configured to have a first sensor for collecting monitoring data of cable terminals inside the high-voltage cable at a point location, and a second sensor for collecting monitoring data of intermediate joints inside the high-voltage cable at a point location. The first sensor and the second sensor work together to collect data in real time. The full-line acquisition unit is configured with a camera that can acquire the location information and image information of the high-voltage cable along the entire line, and performs real-time acquisition synchronously with the first and second sensors in the positioning acquisition unit. The data operation and maintenance unit is configured with a preset artificial neural network model based on data-driven fault prediction technology. The data collected by the positioning acquisition unit and the full-line acquisition unit are used as inputs to the artificial neural network model to calculate the predicted value of the cable operation fault probability. When the predicted value is greater than a preset threshold, a fault operation and maintenance signal is sent to the cable operation and maintenance unit corresponding to the location information of the high-voltage cable in the full-line acquisition unit.

2. The intelligent online monitoring system for high-voltage cables according to claim 1, characterized in that: The first sensor is a point-based sensor that collects monitoring data of the cable terminal inside the high-voltage cable. The first sensor consists of at least three temperature sensors, which are respectively installed on the surface of the cable joint, in the installation environment around the cable, and in the cable core. The first set of temperature sensors corresponds to the surface of the cable joint. The first set of temperature sensors has three temperature sensors, which are respectively installed on the surface of the cable joint of the three phases A, B, and C of the high-voltage cable. The second set of temperature sensors corresponds to the cable installation environment and consists of one temperature sensor. The third set of temperature sensors corresponds to the cable core wire. The third set of temperature sensors has three temperature sensors, which are respectively installed on the core wire of the joint of the three phases A, B, and C of the high-voltage cable. The data is transmitted to the data operation and inspection unit in real time through the three sets of temperature sensors and is detected in real time through an artificial neural network model.

3. The intelligent online monitoring system for high-voltage cables according to claim 2, characterized in that: Three sets of temperature sensors transmit data to the data processing and inspection unit in real time, and the data is detected in real time using an artificial neural network model, including: The artificial neural network model has preset temperature thresholds for the connector surface, ambient temperature, and core temperature. The first set of temperature sensors collects the surface temperature of the cable joints of phases A, B, and C of the high-voltage cable. The second set of temperature sensors collects the ambient temperature at the cable installation location. The third set of temperature sensors collects the core temperature of the conductors at the joints of phases A, B, and C of the high-voltage cable. The temperature difference between the joint surface, the ambient temperature, and the core temperature are calculated using the subtraction method within the artificial neural network model. The temperature information is then obtained by determining whether the temperature difference conforms to the temperature range of the high-voltage cable joint surface, the ambient temperature range of the high-voltage cable, and the temperature range of the high-voltage cable core, and is uploaded to the data operation and maintenance center platform.

4. The intelligent online monitoring system for high-voltage cables according to claim 3, characterized in that: The temperature difference is assessed based on whether it conforms to the temperature range of the high-voltage cable joint surface, the ambient temperature range, and the core temperature range. Specifically, the temperature range of the joint surface, the ambient temperature range, and the core temperature range are all less than 5°C. Furthermore, a change of more than 5°C within one hour is considered abnormal. An increase of 5°C within one hour in the ambient temperature range and the core temperature range is also considered abnormal.

5. The intelligent online monitoring system for high-voltage cables according to claim 3, characterized in that: The temperature information results are uploaded to the data operation and maintenance center platform. This includes comparing the temperature difference of the joint surface, the ambient temperature, and the core temperature every 10 minutes with the temperature range of the high-voltage cable joint surface, the ambient temperature range of the high-voltage cable, and the temperature range of the high-voltage cable core, respectively, to determine whether the cable surface temperature and cable core temperature are within the normal temperature range. When an abnormality is detected more than twice in a row, the data will be automatically uploaded to the data operation and maintenance center platform every 1-600 seconds.

6. The intelligent online monitoring system for high-voltage cables according to claim 1, characterized in that: It includes a second sensor that collects monitoring data of intermediate joints inside high-voltage cables at specific locations. The second sensor is a current sensor, which is installed on the grounding wire of the cable joint. The current sensor transmits the data to the data operation and inspection unit in real time, and performs real-time detection through an artificial neural network model.

7. The intelligent online monitoring system for high-voltage cables according to claim 6, characterized in that: The data is transmitted in real time to the data processing and inspection unit via a current sensor, and real-time detection is performed using an artificial neural network model, including: The artificial neural network model has a preset grounding current threshold. The grounding current value on the grounding wire of the cable joint is collected by the current sensor. The grounding current difference is calculated by subtraction in the artificial neural network model. The current information result is obtained by checking whether the grounding current difference meets the grounding current difference range of the high-voltage cable and then uploaded to the data operation and maintenance center platform.

8. The intelligent online monitoring system for high-voltage cables according to claim 7, characterized in that: The grounding current difference range of the high-voltage cable is 0-200A, and the current information results are uploaded to the data operation and maintenance center platform at intervals of 1-8 hours by the current sensor; when the current sensor detects an abnormal value in the current information results more than once in a row, the upload interval is shortened to once every 1600 seconds.

9. The intelligent online monitoring system for high-voltage cables according to claim 1, characterized in that: It includes cameras that can collect location and image information of the high-voltage cable along the entire line. These cameras are distributed and each is matched with a data node in an artificial neural network model to receive image data of the corresponding area and upload it to the data operation and maintenance center platform.

10. The intelligent online monitoring system for high-voltage cables according to claim 1, characterized in that: Each camera is matched with a data node in the artificial neural network model to receive image data of the corresponding area and upload it to the data operation and maintenance center platform, including: Based on the image information of the cable corresponding to the location information, an image defect diagnosis algorithm is used to diagnose the image information and obtain the image defect diagnosis result; the image defect diagnosis result includes the predicted value of the operating failure probability of the cable corresponding to the location information obtained based on image analysis; When the predicted value is greater than the threshold, a confirmation operation and maintenance signal is sent to the data operation and maintenance center platform corresponding to the location information to remind the data operation and maintenance center platform to conduct an inspection and confirm whether a fault has occurred in the cable area corresponding to the location information.

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