Line defect distribution diagram generation method and system

Abstract

The invention provides a line defect distribution diagram generation method and system, and relates to the related technical field of electric power inspection, and the method comprises the following steps: S1, obtaining tower information collected by an unmanned aerial vehicle during line inspection, and carrying out the defect detection of a tower according to the tower information, establishing a tower identity information database according to the detection result and the tower information; s2, performing defect data mining and analysis on the tower identity information database to obtain a defect summary table; s3, obtaining a GIS map containing a line obtained through the tower identity information database; and S4, extracting defect classifications and defect properties corresponding to the towers in the defect summary sheet, performing defect degree fusion on the defect data according to weight values corresponding to the defect classifications and the defect properties, and displaying a defect degree fusion result on the towers on the GIS map through marks with different colors to form a line defect distribution diagram.

Description

Technical Field

[0001] This invention relates to the field of power line inspection technology, specifically to a method and system for generating line defect distribution maps. Background Technology

[0002] To maintain the stable operation of power facilities, relevant personnel are arranged to conduct regular inspections of power poles and towers. However, manual inspection is very inconvenient when inspecting high-level nodes of power poles and towers and is prone to safety problems. Therefore, existing technologies also use drones to inspect power poles and towers.

[0003] When using drones to inspect power poles, the backend processing unit further processes the data uploaded by the drones. Based on the data uploaded by the drones, it analyzes the comprehensive information of the power poles and determines the current status of the power poles, i.e., it detects defects in the power poles. By integrating the above information, the power poles can be maintained. However, current technologies usually only perform simple statistics on the above information, which is not very intuitive for workers. When maintaining power poles, the impact on adjacent power poles cannot be directly considered. Therefore, the maintenance of the entire line is not comprehensive enough. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for generating line defect distribution maps. While using drones to inspect towers, the drones provide feedback on tower information and coordinates. Based on the tower coordinates, the tower information is combined with a GIS map, and the tower information is displayed intuitively through markings.

[0005] To solve the above-mentioned technical problems, the present invention adopts the following solution: A method for generating a line defect distribution map includes: S1. Obtain pole information collected by the drone during line inspection, perform defect detection on the poles based on the pole information, and establish a pole identity information database based on the detection results and pole information; S2. Perform defect data mining and analysis on the tower identity information database to obtain a defect summary table; S3. Obtain a GIS map containing the line from the pole and tower identity information database; S4. Extract the defect categories and defect properties corresponding to the towers in the defect summary table. Based on the weight values ​​corresponding to the defect categories and defect properties, fuse the defect data for defect severity. Display the results of defect severity fusion on the tower locations on the GIS map using different colored markers to form a line defect distribution map.

[0006] A further preferred technical solution is that when the drone inspects the line, it takes pictures of the towers in the order of their hierarchical structure, collects and analyzes the information of the pictures to obtain tower information, and performs defect detection on the pictures, marking the defect targets on the pictures with detection boxes to obtain the detection results.

[0007] A further preferred technical solution is that the process of performing defect data mining and analysis on the pole identification information database to obtain a defect summary table is as follows: The system extracts images of defective targets corresponding to poles from the pole identification information database, analyzes the defective targets in the images to obtain the component types and defect descriptions of the defective targets; By classifying the types of components, the defect categories corresponding to the defect targets are obtained; Call the defect nature determination conditions corresponding to the component type, analyze and determine the component defect description according to the defect nature determination conditions, and obtain the defect nature corresponding to the defect target; A defect summary table is constructed based on the component type, component defect description, defect classification, and defect nature corresponding to the defect targets in the tower.

[0008] A further preferred technical solution is that the process of constructing a defect summary table based on the component type, component defect description, defect classification, and defect nature corresponding to the defect target in the tower is as follows: the defect summary table is defined in advance, the towers are numbered sequentially according to the hierarchical order of the towers in the line, the number is used as the column header of each row, and then the component type, component defect description, defect classification, and defect nature corresponding to the defect target are stored in each row in sequence.

[0009] A further preferred technical solution is that S4 includes the following steps: S41. Extract the defect categories and defect properties corresponding to the towers from the defect summary table, and package the component types and defect properties corresponding to the towers according to the same defect category to form defect classification data; S42. Extract the same defect classification data corresponding to different towers, perform first defect degree fusion on the defect classification data according to the first weight value corresponding to the defect nature, and display the result of the first defect degree fusion on the tower on the GIS map through different color markers to form the first line defect distribution map. S43. Based on the first weight value corresponding to the defect nature and the second weight value corresponding to the defect classification, perform a second defect degree fusion on the defect data, and display the result of the second defect degree fusion on the towers on the GIS map through different color markers to form a second line defect distribution map. Different first-line defect distribution maps are generated based on different defect classifications. The first-line defect distribution map is used to show the degree of defect of the towers on the GIS map under the same defect classification. The second-line defect distribution map is used to show the degree of defect of the towers on the GIS map under the overall situation.

[0010] A further preferred technical solution is that the process of first defect degree fusion is as follows: obtain a preset base number for the current defect classification, extract all defect classification data in a tower that are the same as the current defect classification, obtain the first weight value corresponding to the defect nature in all defect classification data, sum all the first weight values, multiply the sum by the preset base number, and obtain the result of first defect degree fusion for a tower, and so on.

[0011] A further preferred technical solution is that the process of second defect degree fusion is as follows: obtain the first weight value corresponding to the defect nature and the second weight value corresponding to the defect classification, extract all defect data in a tower, multiply the first weight value corresponding to the defect nature and the second weight value corresponding to the defect classification in turn, sum the results of multiplication, and obtain the result of second defect degree fusion for a tower, and so on.

[0012] A further preferred technical solution is that the process of obtaining a GIS map containing the route through the pole identity information database is as follows: when the drone inspects the route, it takes pictures of the poles in the order of their hierarchical position. The information of the captured pictures is collected and analyzed to obtain the pole information. At the same time, the location information of the pole is obtained through the drone's own GPS positioning. Based on the location information of the pole, the pole is mapped onto the GIS map and connected to form the route.

[0013] A line defect distribution map generation system, applying the aforementioned line defect distribution map generation method, includes: Identity information database construction module: Obtains pole information collected by drones during line inspection, performs defect detection on poles based on pole information, and establishes a pole identity information database based on the detection results and pole information; Defect Summary Table Generation Module: Performs defect data mining and analysis on the tower identification information database to obtain a defect summary table; GIS map acquisition module: Acquires a GIS map containing the lines obtained from the pole and tower identity information database; Line defect distribution map generation module: Extracts the defect categories and defect properties corresponding to the towers in the defect summary table, performs defect degree fusion on the defect data according to the weight values ​​corresponding to the defect categories and defect properties, and displays the result of defect degree fusion on the towers on the GIS map through different color markers to form a line defect distribution map.

[0014] The beneficial effects of this invention are: This invention provides a method and system for generating line defect distribution maps. While using drones to inspect power poles, the drones also provide feedback on pole information and coordinates. The existing data statistics method is replaced with data classification and display. Based on the pole coordinates, the pole information is combined with a GIS map. Furthermore, defect data within the pole information is mined and analyzed, and the degree of defect is fused with different emphases based on the weight values ​​corresponding to defect classification and nature. Then, it is displayed on the GIS map using different color markers, allowing staff to intuitively grasp the current condition of the line, comprehensively assess the overall line situation, make regional judgments, and achieve the maintenance of power facilities. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating the method for generating a line defect distribution map in Embodiment 1 of the present invention. Figure 2 This is a schematic diagram of the defect distribution of the first line in Embodiment 1 of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the present invention or its application or use. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of the invention.

[0018] At the same time, it should be understood that, for ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn according to actual scale.

[0019] Furthermore, for clarity and brevity, descriptions of well-known structures, functions, and configurations may have been omitted. Those skilled in the art will recognize that various changes and modifications can be made to the examples described herein without departing from the spirit and scope of this disclosure.

[0020] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0021] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0022] The present invention will now be described in detail with reference to the accompanying drawings and embodiments: Example 1 In this embodiment, to maintain the stable operation of power facilities, and considering that manual inspection is very inconvenient when inspecting high-level nodes of towers, a method of using drones to inspect towers is proposed. Although there are existing technical solutions for using drones to inspect towers, this application aims to process and visualize the data collected by drone inspections, so that the back-end processing terminal can take maintenance measures for power facilities through visualized drone inspection results to maintain the stable operation of power facilities.

[0023] When using drones to inspect power poles, the backend processing unit further processes the data uploaded by the drones. Based on the data uploaded by the drones, it analyzes the comprehensive information of the power poles and determines the current status of the power poles, i.e., it detects defects in the power poles. By integrating the above information, the power poles can be maintained. However, current technologies usually only perform simple statistics on the above information, which is not very intuitive for workers. When maintaining power poles, the impact on adjacent power poles cannot be directly considered. Therefore, the maintenance of the entire line is not comprehensive enough.

[0024] To address the aforementioned issues, this invention proposes a method for generating a line defect distribution map. This method involves using drones to inspect towers and simultaneously receiving tower information and coordinates from the drones. The tower information is then combined with a GIS map based on these coordinates, and the tower information is displayed intuitively through markings.

[0025] Specifically, such as Figure 1 As shown, the method for generating a line defect distribution map includes the following steps: S1. Obtain pole information collected by the drone during line inspection, perform defect detection on the poles based on the pole information, and establish a pole identity information database based on the detection results and pole information; S2. Perform defect data mining and analysis on the tower identity information database to obtain a defect summary table; S3. Obtain a GIS map containing the line from the pole and tower identity information database; S4. Extract the defect categories and defect properties corresponding to the towers in the defect summary table. Based on the weight values ​​corresponding to the defect categories and defect properties, fuse the defect data for defect severity. Display the results of defect severity fusion on the tower locations on the GIS map using different colored markers to form a line defect distribution map.

[0026] A further preferred technical solution is that when the drone inspects the line, it takes pictures of the towers in the order of their hierarchical structure, collects and analyzes the information of the pictures to obtain tower information, and performs defect detection on the pictures, marking the defect targets on the pictures with detection boxes to obtain the detection results.

[0027] A further preferred technical solution is that the process of performing defect data mining and analysis on the pole identification information database to obtain a defect summary table is as follows: The system extracts images of defective targets corresponding to poles from the pole identification information database, analyzes the defective targets in the images to obtain the component types and defect descriptions of the defective targets; By classifying the types of components, the defect categories corresponding to the defect targets are obtained; Call the defect nature determination conditions corresponding to the component type, analyze and determine the component defect description according to the defect nature determination conditions, and obtain the defect nature corresponding to the defect target; A defect summary table is constructed based on the component type, component defect description, defect classification, and defect nature corresponding to the defect targets in the tower.

[0028] A further preferred technical solution is that the process of constructing a defect summary table based on the component type, component defect description, defect classification, and defect nature corresponding to the defect target in the tower is as follows: the defect summary table is defined in advance, the towers are numbered sequentially according to the hierarchical order of the towers in the line, the number is used as the column header of each row, and then the component type, component defect description, defect classification, and defect nature corresponding to the defect target are stored in each row in sequence.

[0029] Specifically, the defect detection can be performed by an AI model or by manual judgment. Defect detection can determine whether there are defective targets in the captured image. If so, they are marked by a detection box. The AI ​​model can then be used as a target detection model. After the defective targets are marked, further data analysis can be performed on the defective targets in the detection box to determine the component type, component defect description, defect classification, and defect nature of the defective target, so as to construct a defect summary table.

[0030] In real-world scenarios, power poles have various component types, including tension clamps, conductors, and markers. Different maintenance strategies are required for defects in different component types. This application considers component types and their corresponding defect descriptions. By analyzing the component types and defect descriptions, the nature of the defect corresponding to the current component type can be determined. This determination process involves calling the defect nature judgment conditions corresponding to the component type for analysis and judgment, determining whether the defect nature is a general defect, a serious defect, or a critical defect. The defect nature judgment conditions are standard conditions and will not be elaborated here. This invention allows the determination of the defect severity of the component type corresponding to the defect target on the power pole. By integrating the defect severity of all component types on the power pole, the overall defect level of the current power pole can be obtained. The different overall defect levels corresponding to different power poles are displayed on a GIS map using different color markers, allowing staff to intuitively see the defects of the power pole and providing decision support for defect elimination and power pole optimization.

[0031] In real-world scenarios, after analyzing the types of defective components, eight typical application scenarios were identified. These components were then categorized into eight major types, as described in this embodiment: hardware, poles, insulators, auxiliary facilities, conductors, channel environment, foundations, and grounding devices. Furthermore, defect analysis of different component types within the same defect category can be mutually influential. Therefore, this invention proposes two types of line defect distribution maps: one integrates the defect severity of all component types on the pole, visually representing the overall defect severity of the pole; the other pre-classifies component types on the pole for different application scenarios, integrating the defect severity of component types belonging to the same defect category on the pole, visually representing the predominant defect severity of the pole in different application scenarios, providing customers with diverse options suitable for various application scenarios.

[0032] Based on the above principles, this embodiment proposes to obtain a defect summary table by performing defect mining and analysis on the pole information collected by the UAV during line inspection, as shown in Table 1 below. The defect summary table stores the defect targets in each pole, with each row representing a defect target. The defect target is represented by the corresponding component type, component defect description, defect classification, and defect nature. Here, X refers to longitude and Y refers to latitude.

[0033] Table 1 Defect Summary Table A further preferred technical solution is that S4 includes the following steps: S41. Extract the defect categories and defect properties corresponding to the towers from the defect summary table, and package the component types and defect properties corresponding to the towers according to the same defect category to form defect classification data; S42. Extract the same defect classification data corresponding to different towers, perform first defect degree fusion on the defect classification data according to the first weight value corresponding to the defect nature, and display the result of the first defect degree fusion on the tower on the GIS map through different color markers to form the first line defect distribution map. S43. Based on the first weight value corresponding to the defect nature and the second weight value corresponding to the defect classification, perform a second defect degree fusion on the defect data, and display the result of the second defect degree fusion on the towers on the GIS map through different color markers to form a second line defect distribution map. Different first-line defect distribution maps are generated based on different defect classifications. The first-line defect distribution map is used to show the degree of defect of the towers on the GIS map under the same defect classification. The second-line defect distribution map is used to show the degree of defect of the towers on the GIS map under the overall situation.

[0034] A further preferred technical solution is that the process of first defect degree fusion is as follows: obtain a preset base number for the current defect classification, extract all defect classification data in a tower that are the same as the current defect classification, obtain the first weight value corresponding to the defect nature in all defect classification data, sum all the first weight values, multiply the sum by the preset base number, and obtain the result of first defect degree fusion for a tower, and so on.

[0035] A further preferred technical solution is that the process of second defect degree fusion is as follows: obtain the first weight value corresponding to the defect nature and the second weight value corresponding to the defect classification, extract all defect data in a tower, multiply the first weight value corresponding to the defect nature and the second weight value corresponding to the defect classification in turn, sum the results of multiplication, and obtain the result of second defect degree fusion for a tower, and so on.

[0036] Specifically, the nature of the defect includes general defects, serious defects, and critical defects. Different first weight values can be set in advance for different defect natures under different defect classifications. At this time, the first weight values of the same defect nature under different defect classifications can be the same or different. For example, generally, general defects are set as q1, serious defects are set as q2, and critical defects are set as q3, and q1 < q2 < q3. The first defect degree fusion is performed on the defect classification data according to the first weight value corresponding to the defect nature. Since the same defect classification is targeted at this time, the same base number a can be set. The first defect degree fusion is to add up the first weight values of the defect targets corresponding to the same defect classification, and multiply the sum by the same base number a. For example: in an application scenario with a base number of a, Tower A has two defect targets, which respectively correspond to general defects and serious defects. Then, through the first defect degree fusion, a*(q1 + q2) is obtained; Tower B has two defect targets, which respectively correspond to general defects and critical defects. Then, through the first defect degree fusion, a*(q1 + q3) is obtained. It can be seen that a*(q1 + q2) < a*(q1 + q3). Then, the different degrees of defects of Tower A and Tower B in this application scenario can be displayed on the GIS map through different color markings.

[0037] Based on the above principle, the process of the second defect degree fusion can be obtained. Different second weight values are set for different defect classifications. Substituting the defect classification into the calculation process, the defect degree of the tower under the overall situation can be obtained. Specifically, as Figure 2 shown, Figure 2 It belongs to the first line defect distribution map, which shows the defect degree of the tower when the defect classification is fittings; the second line defect distribution map, which shows the defect degree of the tower under the overall situation. Through different color markings and explaining the color markings below, the staff can intuitively see the defects of the tower, providing decision support for defect elimination and assisting in optimizing the tower. Moreover, the overweight defect degrees of the tower in different application scenarios can be visually displayed, providing diverse choices for customers and generating maintenance strategies.

[0038] In one embodiment, the process of obtaining the GIS map containing the line through the tower identity information database is as follows: When the drone inspects the line, it takes pictures of the towers in sequence according to the upper and lower levels of the towers in the line, collects and analyzes the information of the taken pictures to obtain the tower information. At the same time, the position information of the tower is obtained through the GPS positioning of the drone itself. The tower is mapped onto the GIS map according to the position information of the tower and connected to form a line.

[0039] In one embodiment, when using drones to inspect power poles, the backend processing unit receives images taken by the drones, along with the corresponding time and location information. The images can be marked using the time and location information. Simultaneously, by analyzing the images, information about the status of the power poles can be collected. The backend processing unit obtains the current power pole information and uses this information to maintain the power facilities.

[0040] Based on this, in existing technologies, since drones have their own positioning devices, namely GPS positioning, when the drone uploads images to the backend processing terminal, it also uploads the location of the drone when the image was taken. This location information is the longitude X and latitude Y obtained through GPS, that is, the longitude and latitude point (X, Y).

[0041] When using drones to inspect power poles, the position of the power pole is usually marked using latitude and longitude points (X, Y). Once the position of the power pole is marked, it can be substituted into a GIS map, i.e., marked on the GIS map. However, after substituted into the GIS map, only the position of the power pole itself can be obtained. Since the latitude and longitude points (X, Y) are point data, they cannot reflect direction. Therefore, the route cannot be directly formed on the GIS map. Staff need to manually import the route or connect the lines in order to display the drone inspection route on the GIS map. This operation is quite cumbersome.

[0042] To address the aforementioned issues, this invention proposes a preprocessing design scheme for latitude and longitude coordinates. This preprocessing involves modifying the latitude and longitude coordinates into line segments based on the associated coordinates and inspection results before mapping the tower information onto the GIS map. These line segments are composed of the latitude and longitude coordinates of the towers above and below, establishing a "tower-latitude and longitude line segment" relationship. When mapping these line segments onto the GIS map according to this relationship, several line segments can be directly connected on the GIS map to form a line. This eliminates the need for further judgment on the connections between latitude and longitude coordinates, thus improving the efficiency of generating geographical wiring diagrams for power facilities.

[0043] Furthermore, this invention uses the GPS positioning uploaded when the drone takes pictures as the location of the pole. In actual operation, in order to collect accurate location information, the drone can be operated to hover directly above the pole, and the GPS positioning uploaded by the drone when it takes pictures directly above the pole can be used as the latitude and longitude of the pole.

[0044] A further preferred technical solution is that the process of obtaining a GIS map containing the route through the pole and tower identity information database is as follows: The corresponding pole and tower information is associated according to the hierarchical order of the poles and towers in the route to construct a pole and tower identity information database; the latitude and longitude coordinates corresponding to the poles in the pole and tower information are modified into latitude and longitude line segments according to the association relationship between the pole and tower information in the pole and tower identity information database, and the pole and tower identity information database is updated; the latitude and longitude line segments corresponding to the pole and tower numbers in the updated pole and tower identity information database are mapped onto the GIS map, and several latitude and longitude line segments are connected on the GIS map to form a route, thus obtaining a GIS map containing the route.

[0045] A further preferred technical solution involves constructing a pole / tower identity information database as follows: A table is predefined, and the corresponding pole / tower information is stored sequentially in each row according to the hierarchical order of the poles / towers in the line. The pole / tower name in the information serves as the column header for each row, and the latitude and longitude coordinates in the information are stored in other columns. Specifically, the pole / tower information includes pole / tower name, latitude and longitude coordinates, photographing time, voltage level, line name, turning angle, altitude, pole / tower type, span, total number of defects, tree obstructions, defect information, guy wire insulation, ancillary facilities, number of guy wires, license plate status, conductor type, number of circuits, conductor joints, pole / tower material, crossing area, guy wire model, insulator type, geographical environment, three-wire connection, high and low voltage on the same pole, and conductor model. The latitude and longitude coordinates consist of longitude and latitude.

[0046] A further preferred technical solution involves associating tower information based on the hierarchical order of towers along the line. This process involves adding a column to the table, defined as the "superior tower name." The superior tower name corresponding to the tower name in each row is found according to the hierarchical order of the towers along the line and entered into the column. The purpose of adding the superior tower name is to establish a relationship between tower information and its superior, thereby preprocessing the table based on this relationship.

[0047] A further preferred technical solution is that the process of updating the pole identification information database is as follows: In each row of the pole identity information database, find the name and latitude / longitude coordinates of the superior pole and use the latitude / longitude coordinates as the end coordinate point; locate the row containing the superior pole based on its name and extract the corresponding latitude / longitude coordinates, using the latitude / longitude coordinates as the start coordinate point. Modify the definition of latitude and longitude coordinates in each row of the table to the end coordinates; and add a column to the table, which is defined as the starting coordinates. Then connect the starting coordinates and the ending coordinates in each row to form a latitude and longitude line segment to update the tower identity information database.

[0048] Example 2 A line defect distribution map generation system, applying the aforementioned line defect distribution map generation method, includes: Identity information database construction module: Obtains pole information collected by drones during line inspection, performs defect detection on poles based on pole information, and establishes a pole identity information database based on the detection results and pole information; Defect Summary Table Generation Module: Performs defect data mining and analysis on the tower identification information database to obtain a defect summary table; GIS map acquisition module: Acquires a GIS map containing the lines obtained from the pole and tower identity information database; Line defect distribution map generation module: Extracts the defect categories and defect properties corresponding to the towers in the defect summary table, performs defect degree fusion on the defect data according to the weight values ​​corresponding to the defect categories and defect properties, and displays the result of defect degree fusion on the towers on the GIS map through different color markers to form a line defect distribution map.

[0049] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Based on the technical essence of the present invention, any simple modifications, equivalent substitutions, and improvements made to the above embodiments within the spirit and principles of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for generating a line defect distribution map, characterized in that, include: S1. Obtain pole information collected by the drone during line inspection, perform defect detection on the poles based on the pole information, and establish a pole identity information database based on the detection results and pole information; S2. Perform defect data mining and analysis on the tower identity information database to obtain a defect summary table; S3. Obtain a GIS map containing the line from the pole and tower identity information database; S4. Extract the defect categories and defect properties corresponding to the towers in the defect summary table. Based on the weight values ​​corresponding to the defect categories and defect properties, fuse the defect data for defect severity. Display the results of defect severity fusion on the tower locations on the GIS map using different colored markers to form a line defect distribution map.

2. The method for generating a line defect distribution map according to claim 1, characterized in that, When inspecting power lines, drones take pictures of the towers in sequence according to their hierarchical order. The drones collect and analyze the information from the pictures to obtain tower information, and perform defect detection on the pictures, marking the defect targets on the pictures with detection boxes to obtain the detection results.

3. The method for generating a line defect distribution map according to claim 2, characterized in that, The process of performing defect data mining and analysis on the pole identification information database to obtain a defect summary table is as follows: The system extracts images of poles with defects from the pole identification information database, analyzes the defective targets in the images to obtain the component types and defect descriptions of the defective targets; By classifying the types of components, the defect categories corresponding to the defect targets are obtained; Call the defect nature determination conditions corresponding to the component type, analyze and determine the component defect description according to the defect nature determination conditions, and obtain the defect nature corresponding to the defect target; A defect summary table is constructed based on the component type, component defect description, defect classification, and defect nature corresponding to the defect targets in the tower.

4. The method for generating a line defect distribution map according to claim 3, characterized in that, The process of constructing a defect summary table based on the component type, component defect description, defect classification, and defect nature corresponding to the defect target in the tower is as follows: the defect summary table is defined in advance, the towers are numbered sequentially according to the hierarchical order of the towers in the line, the number is used as the column header of each row, and then the component type, component defect description, defect classification, and defect nature corresponding to the defect target are stored in each row in sequence.

5. The method for generating a line defect distribution map according to claim 3, characterized in that, S4 includes the following steps: S41. Extract the defect categories and defect properties corresponding to the towers from the defect summary table, and package the component types and defect properties corresponding to the towers according to the same defect category to form defect classification data; S42. Extract the same defect classification data corresponding to different towers, perform first defect degree fusion on the defect classification data according to the first weight value corresponding to the defect nature, and display the result of the first defect degree fusion on the tower on the GIS map through different color markers to form the first line defect distribution map. S43. Based on the first weight value corresponding to the defect nature and the second weight value corresponding to the defect classification, perform a second defect degree fusion on the defect data, and display the result of the second defect degree fusion on the towers on the GIS map through different color markers to form a second line defect distribution map. Different first-line defect distribution maps are generated based on different defect classifications. The first-line defect distribution map is used to show the degree of defect of the towers on the GIS map under the same defect classification. The second-line defect distribution map is used to show the degree of defect of the towers on the GIS map under the overall situation.

6. The method for generating a line defect distribution map according to claim 5, characterized in that, The first defect degree fusion process is as follows: obtain the preset base number of the current defect classification, extract all defect classification data of a pole with the same defect classification as the current defect classification, obtain the first weight value corresponding to the defect nature in all defect classification data, sum all the first weight values, multiply the sum by the preset base number, and obtain the result of the first defect degree fusion of a pole, and so on.

7. The method for generating a line defect distribution map according to claim 5, characterized in that, The second defect degree fusion process is as follows: obtain the first weight value corresponding to the defect nature and the second weight value corresponding to the defect classification, extract all defect data in a tower, multiply the first weight value corresponding to the defect nature and the second weight value corresponding to the defect classification in turn, sum the results of the multiplication, and obtain the second defect degree fusion result of a tower, and so on.

8. The method for generating a line defect distribution map according to claim 2, characterized in that, The process of obtaining a GIS map containing the route from the pole and tower identity information database is as follows: When the drone inspects the route, it takes pictures of the poles and towers in the order of their hierarchical structure. The drone collects and analyzes the information from the pictures to obtain the pole and tower information. At the same time, the drone obtains the location information of the poles and towers through its own GPS positioning. Based on the location information of the poles and towers, the drone maps the poles and towers onto the GIS map and connects them to form the route.

9. A system for generating line defect distribution maps, characterized in that, The method for generating a line defect distribution map as described in any one of claims 1-8 includes: Identity information database construction module: Obtains pole information collected by drones during line inspection, performs defect detection on poles based on pole information, and establishes a pole identity information database based on the detection results and pole information; Defect Summary Table Generation Module: Performs defect data mining and analysis on the tower identification information database to obtain a defect summary table; GIS map acquisition module: Acquires a GIS map containing the lines obtained from the pole and tower identity information database; Line defect distribution map generation module: Extracts the defect categories and defect properties corresponding to the towers in the defect summary table, performs defect degree fusion on the defect data according to the weight values ​​corresponding to the defect categories and defect properties, and displays the result of defect degree fusion on the towers on the GIS map through different color markers to form a line defect distribution map.

Images