Intelligent management system for high-voltage transmission line

Through the intelligent management system of high-voltage transmission line, deep neural network and multiple mapping processing technology are used to judge the shape loss level of insulator porcelain in real time, solving the problem of indistinguishable deformation of insulator porcelain and realizing the distortion of insulator porcelain in the state of insulator porcelain Intelligent monitoring.

CN119273665AInactive Publication Date: 2025-01-07南京逸禾思新能源科技有限公司
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Patent Information

Application Number
CN202411415546.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Priority Date
2023-12-20
Filing Date
2024-10-11
Publication Date
2025-01-07
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

Insulator porcelain parts have deformation during long-term use, and subtle deformations are difficult to effectively and accurately distinguish, and it is impossible to judge their distorted state in real time.

Method used

Design an intelligent management system for high-voltage transmission lines, obtain the imaging images of insulator porcelain through a timing imaging mechanism, combine multiple mapping processing and deep neural network to analyze the pixel rows and sequence values ​​and the total number of pixel points of insulator porcelain, and calculate their shape The level of loss of standards is realized to realize real-time intelligent judgment.

Benefits of technology

It realizes accurate judgment of the shape of insulator porcelain, ensures that it is close to the reference profile curve, and completes real-time intelligent monitoring of the distorted state of insulator porcelain.

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Abstract

The invention relates to an intelligent management system for a high-voltage transmission line. The intelligent management system comprises a timing imaging mechanism, a multi-mapping mechanism, a first-stage processing mechanism, a second-stage processing mechanism and a third-stage processing mechanism. The intelligent management system for the high-voltage transmission line can be enhanced.
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Description

Technical Field

[0001] The present invention relates to the field of power transmission equipment, and in particular to an intelligent management system for high-voltage power transmission lines. Background Art

[0002] Insulators made of electrical ceramics. Electrical ceramics are baked from quartz, feldspar and clay. The surface of the porcelain insulator is usually covered with enamel to improve its mechanical strength, waterproof infiltration and increase surface smoothness. Among all types of insulators, porcelain insulators are the most commonly used. Low-voltage insulators refer to insulators used for low-voltage distribution lines and communication lines. High-voltage insulators refer to insulators used for high-voltage and ultra-high-voltage overhead transmission lines and substations. In order to meet the needs of different voltage levels, different numbers of single-piece insulators of the same type are usually used to form insulator strings or multi-section insulating pillars.

[0003] However, the environment in which insulator porcelain parts are located is relatively harsh and complex. During long-term use, insulator porcelain parts may be deformed. However, subtle deformations and the precision between different deformations are difficult to effectively and accurately distinguish, and it is impossible to complete real-time judgment of the deformation status of each insulator porcelain part in use. Summary of the invention

[0004] In order to solve the technical problems in the related fields, the present invention provides an intelligent management system for high-voltage transmission lines, which can simultaneously input the curvature values ​​on the reference contour curve corresponding to the insulator porcelain parts, the row sequence values ​​of each pixel row occupied by the insulator porcelain parts, the column sequence values ​​of each pixel column and the total number of pixel points occupied by the insulator porcelain parts into a deep neural network, and execute the deep neural network to obtain the shape deviation level of the insulator porcelain parts as output, wherein the lower the shape deviation level of the insulator porcelain parts, the closer the insulator porcelain parts are to the contour shape corresponding to the reference contour curve, thereby completing real-time intelligent judgment of the deformation state of the monitored insulator porcelain parts.

[0005] According to the technical solution of the present invention, the system comprises: A timing imaging mechanism is arranged opposite to the insulator porcelain part used for the high-voltage power transmission line, and is used to complete the imaging action of the environment where the insulator porcelain part is located at a set time interval, so as to obtain and output the corresponding working environment picture, and the imaging plane of the imaging lens of the timing imaging mechanism is parallel to the plane where the surface of the insulator porcelain part is located; A multi-mapping mechanism, connected to the timing imaging mechanism, for continuously performing gamma correction processing, statistical sorting filtering processing and bilinear interpolation processing on the received working environment picture to obtain and output a corresponding multi-mapping image; a primary processing mechanism connected to the multi-mapping mechanism, and used to obtain a plane coordinate system in the received multi-mapping image, wherein the plane coordinate system takes the pixel point at the lower left corner of the multi-mapping image as the origin, the bottommost pixel row as the positive direction of the horizontal coordinate system, and the leftmost pixel column as the positive direction of the vertical coordinate system; A secondary processing mechanism, connected to the primary processing mechanism, for parsing the row sequence values ​​of each pixel row and the column sequence values ​​of each pixel column occupied by the insulator porcelain parts in the multi-mapped image based on the plane coordinate system, wherein the bottommost pixel row is the pixel row with the smallest row sequence value and a value of 0, and the leftmost pixel column is the pixel column with the smallest column sequence value and a value of 0; The third-level processing mechanism is connected to the second-level processing mechanism, and is used to input the curvature value on the reference contour curve corresponding to the insulator porcelain component, the row sequence value of each pixel row occupied by the insulator porcelain component, the column sequence value of each pixel column, and the total number of pixel points occupied by the insulator porcelain component into the deep neural network at the same time, and execute the deep neural network to obtain the shape deviation level of the insulator porcelain component output by it, wherein the lower the shape deviation level of the insulator porcelain component, the closer the insulator porcelain component is to the contour shape corresponding to the reference contour curve; Among them, the curvature value on the reference contour curve corresponding to the insulator porcelain component, the row sequence value of each pixel row occupied by the insulator porcelain component, the column sequence value of each pixel column and the total number of pixel points occupied by the insulator porcelain component are simultaneously input into the deep neural network, and the deep neural network is executed to obtain the shape deviation level of the insulator porcelain component output by it, wherein the lower the shape deviation level of the insulator porcelain component, the closer the insulator porcelain component is to the contour shape corresponding to the reference contour curve, including: the deep neural network is a deep neural network after completing each learning and the total number of learning is positively correlated with the area of ​​the plane where the surface of the insulator porcelain component is located. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] The embodiments of the present invention will be described below with reference to the accompanying drawings.

[0007] Figure 1 The present invention is a structural block diagram of an intelligent management system for high-voltage power transmission lines according to an insulator porcelain implementation scheme of the present invention.

[0008] Figure 2 It is a structural block diagram of a high-voltage transmission line intelligent management system according to Embodiment B of the present invention.

[0009] Figure 3 It is a structural block diagram of an intelligent management system for high-voltage transmission lines according to implementation scheme C of the present invention. DETAILED DESCRIPTION

[0010] The implementation scheme of the high-voltage transmission line intelligent management system of the present invention will be described in detail below with reference to the accompanying drawings.

[0011] Figure 1 The structural block diagram of the intelligent management system of high-voltage transmission lines is shown according to the insulator porcelain implementation scheme of the present invention, and the system comprises: A timing imaging mechanism is arranged opposite to the insulator porcelain part used for the high-voltage power transmission line, and is used to complete the imaging action of the environment where the insulator porcelain part is located at a set time interval, so as to obtain and output the corresponding working environment picture, and the imaging plane of the imaging lens of the timing imaging mechanism is parallel to the plane where the surface of the insulator porcelain part is located; A multi-mapping mechanism, connected to the timing imaging mechanism, for continuously performing gamma correction processing, statistical sorting filtering processing and bilinear interpolation processing on the received working environment picture to obtain and output a corresponding multi-mapping image; Specifically, the multi-mapping mechanism is connected to the timing imaging mechanism and is used to continuously perform gamma correction processing, statistical sorting filtering processing and bilinear interpolation processing on the received working environment picture to obtain and output the corresponding multi-mapping image, including: a numerical simulation mode can be selected to realize the simulation and test of the data processing process of continuously performing gamma correction processing, statistical sorting filtering processing and bilinear interpolation processing on the received working environment picture to obtain and output the corresponding multi-mapping image; a primary processing mechanism connected to the multi-mapping mechanism, and used to obtain a plane coordinate system in the received multi-mapping image, wherein the plane coordinate system takes the pixel point at the lower left corner of the multi-mapping image as the origin, the bottommost pixel row as the positive direction of the horizontal coordinate system, and the leftmost pixel column as the positive direction of the vertical coordinate system; A secondary processing mechanism, connected to the primary processing mechanism, for parsing the row sequence values ​​of each pixel row and the column sequence values ​​of each pixel column occupied by the insulator porcelain parts in the multi-mapped image based on the plane coordinate system, wherein the bottommost pixel row is the pixel row with the smallest row sequence value and a value of 0, and the leftmost pixel column is the pixel column with the smallest column sequence value and a value of 0; The third-level processing mechanism is connected to the second-level processing mechanism, and is used to input the curvature value on the reference contour curve corresponding to the insulator porcelain component, the row sequence value of each pixel row occupied by the insulator porcelain component, the column sequence value of each pixel column, and the total number of pixel points occupied by the insulator porcelain component into the deep neural network at the same time, and execute the deep neural network to obtain the shape deviation level of the insulator porcelain component output by it, wherein the lower the shape deviation level of the insulator porcelain component, the closer the insulator porcelain component is to the contour shape corresponding to the reference contour curve; Wherein, the curvature value on the reference contour curve corresponding to the insulator porcelain part, the row sequence value of each pixel row occupied by the insulator porcelain part, the column sequence value of each pixel column and the total number of pixel points occupied by the insulator porcelain part are simultaneously input into the deep neural network, and the deep neural network is executed to obtain the shape deviation level of the insulator porcelain part output by it, wherein the lower the shape deviation level of the insulator porcelain part, the closer the insulator porcelain part is to the contour shape corresponding to the reference contour curve, including: the deep neural network is a deep neural network after completing each learning and the total number of learning is positively correlated with the area of ​​the plane where the surface of the insulator porcelain part is located; Among them, the curvature value on the reference contour curve corresponding to the insulator porcelain component, the row sequence value of each pixel row occupied by the insulator porcelain component, the column sequence value of each pixel column, and the total number of pixel points occupied by the insulator porcelain component are simultaneously input into the deep neural network, and the deep neural network is executed to obtain the shape deviation level of the insulator porcelain component output by it, wherein the lower the shape deviation level of the insulator porcelain component, the closer the insulator porcelain component is to the contour shape corresponding to the reference contour curve. It also includes: performing octal value conversion on the curvature value on the reference contour curve corresponding to the insulator porcelain component, the row sequence value of each pixel row occupied by the insulator porcelain component, the column sequence value of each pixel column, and the total number of pixel points occupied by the insulator porcelain component, and then simultaneously inputting them into the deep neural network.

[0012] Figure 2 It is a structural block diagram of a high-voltage transmission line intelligent management system according to Embodiment B of the present invention.

[0013] Compared with the insulator porcelain implementation scheme, the intelligent management system for high-voltage transmission lines shown in implementation scheme B of the present invention may include: A timing imaging mechanism is arranged opposite to the insulator porcelain part used for the high-voltage power transmission line, and is used to complete the imaging action of the environment where the insulator porcelain part is located at a set time interval, so as to obtain and output the corresponding working environment picture, and the imaging plane of the imaging lens of the timing imaging mechanism is parallel to the plane where the surface of the insulator porcelain part is located; A multi-mapping mechanism, connected to the timing imaging mechanism, for continuously performing gamma correction processing, statistical sorting filtering processing and bilinear interpolation processing on the received working environment picture to obtain and output a corresponding multi-mapping image; a primary processing mechanism connected to the multi-mapping mechanism, and used to obtain a plane coordinate system in the received multi-mapping image, wherein the plane coordinate system takes the pixel point at the lower left corner of the multi-mapping image as the origin, the bottommost pixel row as the positive direction of the horizontal coordinate system, and the leftmost pixel column as the positive direction of the vertical coordinate system; A secondary processing mechanism, connected to the primary processing mechanism, for parsing the row sequence values ​​of each pixel row and the column sequence values ​​of each pixel column occupied by the insulator porcelain parts in the multi-mapped image based on the plane coordinate system, wherein the bottommost pixel row is the pixel row with the smallest row sequence value and a value of 0, and the leftmost pixel column is the pixel column with the smallest column sequence value and a value of 0; The third-level processing mechanism is connected to the second-level processing mechanism, and is used to input the curvature value on the reference contour curve corresponding to the insulator porcelain component, the row sequence value of each pixel row occupied by the insulator porcelain component, the column sequence value of each pixel column, and the total number of pixel points occupied by the insulator porcelain component into the deep neural network at the same time, and execute the deep neural network to obtain the shape deviation level of the insulator porcelain component output by it, wherein the lower the shape deviation level of the insulator porcelain component, the closer the insulator porcelain component is to the contour shape corresponding to the reference contour curve; A user input device is arranged near the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism and is connected to the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism respectively; Among them, the user input device is arranged near the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism and is respectively connected to the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism, including: the user input device is used to respectively realize real-time configuration of the working parameters of the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism based on user input.

[0014] Figure 3 It is a structural block diagram of an intelligent management system for high-voltage transmission lines according to implementation scheme C of the present invention.

[0015] Compared with the insulator porcelain implementation scheme, the intelligent management system for high-voltage transmission lines shown in implementation scheme C of the present invention may include: A timing imaging mechanism is arranged opposite to the insulator porcelain part used for the high-voltage power transmission line, and is used to complete the imaging action of the environment where the insulator porcelain part is located at a set time interval, so as to obtain and output the corresponding working environment picture, and the imaging plane of the imaging lens of the timing imaging mechanism is parallel to the plane where the surface of the insulator porcelain part is located; A multi-mapping mechanism, connected to the timing imaging mechanism, for continuously performing gamma correction processing, statistical sorting filtering processing and bilinear interpolation processing on the received working environment picture to obtain and output a corresponding multi-mapping image; a primary processing mechanism connected to the multi-mapping mechanism, and used to obtain a plane coordinate system in the received multi-mapping image, wherein the plane coordinate system takes the pixel point at the lower left corner of the multi-mapping image as the origin, the bottommost pixel row as the positive direction of the horizontal coordinate system, and the leftmost pixel column as the positive direction of the vertical coordinate system; A secondary processing mechanism, connected to the primary processing mechanism, for parsing the row sequence values ​​of each pixel row and the column sequence values ​​of each pixel column occupied by the insulator porcelain parts in the multi-mapped image based on the plane coordinate system, wherein the bottommost pixel row is the pixel row with the smallest row sequence value and a value of 0, and the leftmost pixel column is the pixel column with the smallest column sequence value and a value of 0; The third-level processing mechanism is connected to the second-level processing mechanism, and is used to input the curvature value on the reference contour curve corresponding to the insulator porcelain component, the row sequence value of each pixel row occupied by the insulator porcelain component, the column sequence value of each pixel column, and the total number of pixel points occupied by the insulator porcelain component into the deep neural network at the same time, and execute the deep neural network to obtain the shape deviation level of the insulator porcelain component output by it, wherein the lower the shape deviation level of the insulator porcelain component, the closer the insulator porcelain component is to the contour shape corresponding to the reference contour curve; a parallel data bus, arranged near the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism and connected to the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism respectively; Among them, the parallel data bus is arranged near the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism and is respectively connected to the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism, including: the parallel data bus is used to respectively realize the establishment of parallel data communication links between the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism.

[0016] Next, the specific structure of the intelligent management system for high-voltage power transmission lines of the present invention will be further described.

[0017] In the intelligent management system for high-voltage transmission lines according to various embodiments of the present invention: A data enhancement mechanism is used to perform image data processing on the output data of the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism to obtain output processing data corresponding to the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism respectively.

[0018] In the intelligent management system for high-voltage transmission lines according to various embodiments of the present invention: The data enhancement mechanism is used to perform image data processing on the output data of the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism to obtain the output processing data corresponding to the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism respectively, including: performing Lanczos interpolation on the output data of the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism to obtain the output processing data corresponding to the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism respectively.

[0019] In the intelligent management system for high-voltage transmission lines according to various embodiments of the present invention: The data enhancement mechanism is used to perform image data processing on the output data of the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism to obtain the output processing data corresponding to the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism respectively, including: performing median fuzzy processing on the output data of the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism to obtain the output processing data corresponding to the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism respectively.

[0020] In the intelligent management system for high-voltage transmission lines according to various embodiments of the present invention: The data enhancement mechanism is used to perform image data processing on the output data of the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism to obtain the output processing data corresponding to the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism respectively, including: performing spatial differential sharpening on the output data of the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism to obtain the output processing data corresponding to the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism respectively.

[0021] And in the high-voltage transmission line intelligent management system according to various embodiments of the present invention: Using a data enhancement mechanism to perform image data processing on the output data of the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism to obtain the output processing data corresponding to the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism respectively includes: performing logarithmic image enhancement on the output data of the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism to obtain the output processing data corresponding to the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism respectively.

[0022] In addition, in the intelligent management system of high-voltage transmission lines, the curvature value on the reference contour curve corresponding to the insulator porcelain component, the row sequence value of each pixel row occupied by the insulator porcelain component, the column sequence value of each pixel column and the total number of pixel points occupied by the insulator porcelain component are simultaneously input into the deep neural network, and the deep neural network is executed to obtain the shape deviation level of the insulator porcelain component output by it, wherein the lower the shape deviation level of the insulator porcelain component, the closer the insulator porcelain component is to the contour shape corresponding to the reference contour curve. It also includes: executing the deep neural network to obtain the shape deviation level of the insulator porcelain component represented by the octal value of its output.

[0023] The technical features of the present invention are: Firstly, a plane coordinate system is obtained in a multi-mapped image obtained after performing targeted enhancement processing on an imaging image corresponding to the environment where the insulator porcelain part is located, wherein the plane coordinate system takes the pixel point at the lower left corner of the multi-mapped image as the origin, the bottommost pixel row as the positive direction of the horizontal coordinate system, and the leftmost pixel column as the positive direction of the vertical coordinate system; Secondly: based on the plane coordinate system, the row sequence values ​​of each pixel row and the column sequence values ​​of each pixel column occupied by the insulator porcelain parts are analyzed in the multi-mapped image. The bottom pixel row is the pixel row with the smallest row sequence value and a value of 0, and the leftmost pixel column is the pixel column with the smallest column sequence value and a value of 0; Again: the curvature value on the reference contour curve corresponding to the insulator porcelain component, the row sequence value of each pixel row occupied by the insulator porcelain component, the column sequence value of each pixel column and the total number of pixel points occupied by the insulator porcelain component are simultaneously input into the deep neural network, and the deep neural network is executed to obtain the shape deviation level of the insulator porcelain component as its output, wherein the lower the shape deviation level of the insulator porcelain component, the closer the insulator porcelain component is to the contour shape corresponding to the reference contour curve, thereby completing real-time intelligent judgment of the deformation state of the monitored insulator porcelain component.

[0024] The intelligent management system for high-voltage transmission lines of the present invention solves the technical problem in the prior art that the deformation state of each insulator porcelain part is difficult to accurately distinguish. A deep neural network is used to intelligently judge the shape deviation level of the insulator porcelain part based on various basic data of the insulator porcelain part. The lower the shape deviation level of the insulator porcelain part, the closer the insulator porcelain part is to the contour shape corresponding to the reference contour curve, thereby completing real-time intelligent judgment of the deformation state of the monitored insulator porcelain part.

[0025] It will be appreciated by those skilled in the art that various modifications and changes may be made without departing from the scope and spirit of the present invention. It should therefore be understood that the above embodiments are for illustration only and are not intended to be limiting. Because the scope of the present invention is defined by the claims rather than the foregoing description, any changes and modifications that fall within the scope and boundaries of the claims or the equivalents of these scopes and boundaries are subject to the claims.

Claims

1. A high-voltage transmission line intelligent management system, characterized in that: The system comprises: A timing imaging mechanism is arranged opposite to the insulator porcelain part used for the high-voltage power transmission line, and is used to complete the imaging action of the environment where the insulator porcelain part is located at a set time interval, so as to obtain and output the corresponding working environment picture, and the imaging plane of the imaging lens of the timing imaging mechanism is parallel to the plane where the surface of the insulator porcelain part is located; A multi-mapping mechanism, connected to the timing imaging mechanism, for continuously performing gamma correction processing, statistical sorting filtering processing and bilinear interpolation processing on the received working environment picture to obtain and output a corresponding multi-mapping image; a primary processing mechanism connected to the multi-mapping mechanism, and used to obtain a plane coordinate system in the received multi-mapping image, wherein the plane coordinate system takes the pixel point at the lower left corner of the multi-mapping image as the origin, the bottommost pixel row as the positive direction of the horizontal coordinate system, and the leftmost pixel column as the positive direction of the vertical coordinate system; A secondary processing mechanism, connected to the primary processing mechanism, for parsing the row sequence values ​​of each pixel row and the column sequence values ​​of each pixel column occupied by the insulator porcelain parts in the multi-mapped image based on the plane coordinate system, wherein the bottommost pixel row is the pixel row with the smallest row sequence value and a value of 0, and the leftmost pixel column is the pixel column with the smallest column sequence value and a value of 0; The third-level processing mechanism is connected to the second-level processing mechanism, and is used to input the curvature value on the reference contour curve corresponding to the insulator porcelain component, the row sequence value of each pixel row occupied by the insulator porcelain component, the column sequence value of each pixel column, and the total number of pixel points occupied by the insulator porcelain component into the deep neural network at the same time, and execute the deep neural network to obtain the shape deviation level of the insulator porcelain component output by it, wherein the lower the shape deviation level of the insulator porcelain component, the closer the insulator porcelain component is to the contour shape corresponding to the reference contour curve; Among them, the curvature value on the reference contour curve corresponding to the insulator porcelain component, the row sequence value of each pixel row occupied by the insulator porcelain component, the column sequence value of each pixel column and the total number of pixel points occupied by the insulator porcelain component are simultaneously input into the deep neural network, and the deep neural network is executed to obtain the shape deviation level of the insulator porcelain component output by it, wherein the lower the shape deviation level of the insulator porcelain component, the closer the insulator porcelain component is to the contour shape corresponding to the reference contour curve, including: the deep neural network is a deep neural network after completing each learning and the total number of learning is positively correlated with the area of ​​the plane where the surface of the insulator porcelain component is located.

2. The intelligent management system for high-voltage transmission lines according to claim 1, characterized in that: The curvature value on the reference contour curve corresponding to the insulator porcelain component, the row sequence value of each pixel row occupied by the insulator porcelain component, the column sequence value of each pixel column, and the total number of pixel points occupied by the insulator porcelain component are simultaneously input into the deep neural network, and the deep neural network is executed to obtain the shape deviation level of the insulator porcelain component as its output, wherein the lower the shape deviation level of the insulator porcelain component, the closer the insulator porcelain component is to the contour shape corresponding to the reference contour curve. The method also includes: converting the curvature value on the reference contour curve corresponding to the insulator porcelain component, the row sequence value of each pixel row occupied by the insulator porcelain component, the column sequence value of each pixel column, and the total number of pixel points occupied by the insulator porcelain component into octal values ​​respectively and then simultaneously inputting them into the deep neural network.

3. The intelligent management system for high-voltage transmission lines according to claim 2, characterized in that: The system further comprises: A user input device is arranged near the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism and is connected to the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism respectively; Among them, the user input device is arranged near the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism and is respectively connected to the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism, including: the user input device is used to respectively realize real-time configuration of the working parameters of the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism based on user input.

4. The intelligent management system for high-voltage transmission lines according to claim 2, characterized in that: The system further comprises: a parallel data bus, arranged near the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism and connected to the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism respectively; Among them, the parallel data bus is arranged near the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism and is respectively connected to the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism, including: the parallel data bus is used to respectively realize the establishment of parallel data communication links between the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism.

5. The intelligent management system for high-voltage transmission lines according to any one of claims 2 to 4, characterized in that: A data enhancement mechanism is used to perform image data processing on the output data of the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism to obtain output processing data corresponding to the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism respectively.

6. The intelligent management system for high-voltage transmission lines according to claim 5, characterized in that: The data enhancement mechanism is used to perform image data processing on the output data of the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism to obtain the output processing data corresponding to the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism respectively, including: performing Lanczos interpolation on the output data of the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism to obtain the output processing data corresponding to the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism respectively.

7. The intelligent management system for high-voltage transmission lines according to claim 5, characterized in that: The data enhancement mechanism is used to perform image data processing on the output data of the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism to obtain the output processing data corresponding to the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism respectively, including: performing median fuzzy processing on the output data of the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism to obtain the output processing data corresponding to the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism respectively.

8. The intelligent management system for high-voltage transmission lines according to claim 5, characterized in that: The data enhancement mechanism is used to perform image data processing on the output data of the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism to obtain the output processing data corresponding to the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism respectively, including: performing spatial differential sharpening on the output data of the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism to obtain the output processing data corresponding to the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism respectively.

9. The intelligent management system for high-voltage transmission lines according to claim 5, characterized in that: Using a data enhancement mechanism to perform image data processing on the output data of the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism to obtain the output processing data corresponding to the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism respectively includes: performing logarithmic image enhancement on the output data of the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism to obtain the output processing data corresponding to the multi-mapping mechanism, the primary processing mechanism, the secondary processing mechanism and the tertiary processing mechanism respectively.