A UAV grid inspection method and system with intelligent nest site selection

Abstract

The present invention provides a drone grid inspection method and system with intelligent nest site selection. The site selection of drone nests is carried out based on the cruising capability of drones and the total human complexity between poles and towers, which solves the problem of high complexity and high cost in the deployment of human drones, realizes low-cost and efficient drone deployment of single lines, two lines and multiple lines, ensures that all poles and towers can be included in the inspection range of the corresponding drone nests, improves the drone inspection efficiency and reduces the deployment cost.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) power inspection technology, and in particular to a UAV grid inspection method and system with intelligent nest site selection. Background Art

[0002] The statements in this section merely provide background art related to the present invention and do not necessarily constitute prior art.

[0003] Power transmission and distribution lines are crucial for power grids. Overhead transmission and distribution lines are widely distributed and exposed to the elements for extended periods of time. They are subject not only to normal mechanical and electrical loads, but also to interference from various external factors and the ever-changing nature of nature. These factors can cause aging, fatigue, oxidation, and corrosion of components along the lines. If not promptly identified and eliminated, these changes can lead to qualitative changes, resulting in various failures. Damage to transmission and distribution lines from atmospheric pollution, lightning strikes, strong winds, flooding, landslides, earthquakes, bird strikes, and external forces can also cause various line failures if preventive measures are not taken.

[0004] Therefore, inspection of power grid lines is particularly important. Traditional inspection methods have many hidden dangers, such as the inability to objectively and truly reflect the inspection results, difficulty in post-processing, difficulty in evaluation, and single reference value. The current new inspection method is to use drones for autonomous inspections, using drone nests as carriers for field deployment. Each drone nest is responsible for autonomous inspections within the radius covered by the drone flight.

[0005] The inventors discovered that the existing drone inspection method has the following problems:

[0006] (1) Existing power inspection methods mostly rely on drone parameters and human experience to deploy drone nests. The distribution of drone nests is messy, and it is impossible to maximize the inspection of towers by drone nests. As a result, the number of drone nests in the entire deployment area exceeds the actual demand, resulting in additional costs.

[0007] (2) In the existing drone inspection method, drone nests are mostly deployed according to a single line. In areas where multiple inspection lines exist at the same time, due to the large number of towers and the influence of different drone inspection capabilities, the existing manual deployment scheme cannot carry out unmanned inspections of multiple (at least two) parallel or intersecting lines at low cost and high efficiency;

[0008] (3) When the UAV lands on the landing platform at night or in low light conditions, the UAV camera cannot recognize the visual signs of the landing platform due to the dim light. The UAV starts landing after relying solely on RTK positioning. During the landing process, the RTK information acquisition frequency is low, and the UAV position and attitude adjustment is slow. In addition, due to the influence of environmental factors such as wind, the UAV is prone to deviate from the predetermined landing point, resulting in relatively low landing accuracy. Summary of the Invention

[0009] In order to address the shortcomings of the existing technology, the present invention provides a drone grid inspection method and system with intelligent site selection for drone nests, which solves the problems of high inspection complexity and high inspection cost caused by manual deployment of drone nests, realizes low-cost and efficient drone nest grid deployment and power inspection, ensures that all poles and towers can be included in the inspection range of the corresponding drone nest, and improves the efficiency of drone inspection.

[0010] In order to achieve the above object, the present invention adopts the following technical solutions:

[0011] The first aspect of the present invention provides a grid inspection method for drones with intelligent site selection for machine nests.

[0012] A drone grid inspection method with intelligent nest site selection is applied to a single transmission line or distribution line. The towers of each line are numbered in sequence according to their distance from the substation. The method includes the following steps:

[0013] When the total complexity of the first tower and the Nth tower is greater than twice the cruising capability of the drone, and when the total complexity of the first tower and each tower before the Nth tower is less than or equal to twice the cruising capability of the drone, a drone nest can be deployed between the first tower and the N-1th tower to simultaneously meet the inspection needs of each tower from the first tower to the N-1th tower, where N is a positive integer greater than 2;

[0014] When N-1 is an odd number, the coordinates of the N / 2th tower are taken as the coordinates of the first drone nest. When N-1 is an even number, the position of the (N-1) / 2th tower is taken as the position of the first drone nest.

[0015] When the total complexity of the first tower and the second tower is greater than twice the cruising capability of the UAV, the position of the first tower is taken as the position of the first UAV nest;

[0016] Take the Nth tower as the new first tower and continue the above site selection process for subsequent towers until all drone nest locations are obtained;

[0017] Conduct tower inspections based on all acquired gridded drone nest locations.

[0018] As an optional implementation, the total complexity of the first tower and the Nth tower is:

[0019] The inspection complexity is the time required for a drone to inspect a tower, which is the sum of the ratio of twice the distance between the first tower and the Nth tower, the flying speed of the drone, the inspection complexity of the first tower, and the inspection complexity of the Nth tower.

[0020] As an optional implementation, when the total complexity of the first tower and the second tower is less than or equal to twice the cruising capability of the drone, and the total complexity of the first tower and the third tower is less than or equal to twice the cruising capability of the drone, the first drone nest only meets the capability of inspecting the first tower and the second tower. In this case, the site selection interval of the first drone nest A1 (X1, Y1, Z1) is:

[0021]

[0022] Among them, T is the cruising capability of the drone, T1 is the inspection complexity of the first tower, T2 is the inspection complexity of the second tower, G x1 and G y1 is the horizontal coordinate of the first tower, G x2 and G y2 is the horizontal coordinate of the second tower, |A1-G2| is the distance between the first UAV nest and the second tower, and |A1-G1| is the distance between the first UAV nest and the first tower.

[0023] As an optional implementation method, when the heights of the towers are different, the ground point directly below the obtained coordinate Z value is used as the coordinate position of the drone nest.

[0024] The second aspect of the present invention provides a drone grid inspection system with intelligent nest site selection.

[0025] A drone grid inspection system with intelligent nest location selection is applied to a single transmission line or distribution line. The towers of each line are numbered in sequence according to their distance from the substation, including:

[0026] The machine nest layout judgment module is configured as follows:

[0027] When the total complexity of the first tower and the Nth tower is greater than twice the cruising capability of the drone, and when the total complexity of the first tower and each tower before the Nth tower is less than or equal to twice the cruising capability of the drone, a drone nest can be deployed between the first tower and the N-1th tower to simultaneously meet the inspection needs of each tower from the first tower to the N-1th tower, where N is a positive integer greater than 2;

[0028] The machine nest position determination module is configured as follows:

[0029] When N-1 is an odd number, the coordinates of the N / 2th tower are taken as the coordinates of the first drone nest. When N-1 is an even number, the position of the (N-1) / 2th tower is taken as the position of the first drone nest.

[0030] When the total complexity of the first tower and the second tower is greater than twice the cruising capability of the UAV, the position of the first tower is taken as the position of the first UAV nest;

[0031] Take the Nth tower as the new first tower and continue the above site selection process for subsequent towers until all drone nest locations are obtained;

[0032] The inspection module is configured to perform tower inspection based on all acquired gridded drone nest locations.

[0033] The third aspect of the present invention provides a drone grid inspection method with intelligent site selection for machine nests.

[0034] A drone grid inspection method with intelligent nest site selection is applied to two parallel transmission or distribution lines. The towers of each line are numbered in sequence according to their distance from the substation. The method includes the following steps:

[0035] When the total complexity of the first tower of the first line and the first tower of the second line is less than or equal to twice the cruising capability of the drone, the first tower of the first line and the first tower of the second line are included in the inspection range of the first drone's nest;

[0036] The two lines select any tower in turn and calculate the total complexity with the tower with the smallest number within the inspection range of the drone nest, until the total complexity of the selected tower and any tower within the inspection range of the drone nest is greater than twice the drone cruising capacity;

[0037] The maximum numbered tower and the minimum numbered tower of the two lines within the inspection range of the drone nest are connected by a cross straight line, and the intersection is the location of the drone nest within the inspection range;

[0038] The first towers of the two lines that were not included in the inspection range of the previous drone nest are used as the first towers of the new first line and the first tower of the second line. The above site selection process is continued for subsequent towers until the locations of all drone nests are obtained.

[0039] Conduct tower inspections based on all acquired gridded drone nest locations.

[0040] As an optional implementation, a second tower from any two lines forms a triangle with the first tower of the first line and the first tower of the second line. When the total complexity of the second tower, the first tower of the first line, and the total complexity of the second tower with the first tower of the second line are both less than twice the cruising capability of the drone, the first tower of the first line, the first tower of the second line, and the second tower are included in the inspection range of the first drone's nest.

[0041] Continue to select the remaining second towers. When the total complexity of the remaining second tower and the first tower of the first line, the total complexity of the remaining second tower and the first tower of the second line, and the total complexity of the remaining second tower and the previously selected second tower are all less than twice the cruising capability of the drone, the first tower of the first line, the first tower of the second line, the second tower of the first line, and the second tower of the second line are all included in the inspection range of the first drone's nest.

[0042] As an optional implementation, the selected tower is the Nth tower of the first line, and the total complexity of the Nth tower and any tower within the determined inspection range of the first drone nest is greater than twice the drone cruising capacity;

[0043] Determine whether the total complexity of the Nth tower of the second line and any tower within the determined drone nest inspection range is greater than twice the drone cruising capability. If so, the Nth tower of the first line and the Nth tower of the second line are not included in the first drone nest inspection range; if not, include the Nth tower of the second line in the first drone nest inspection range, and continue to determine whether the N+1th tower of the second line can be included in the first drone nest inspection range, until the last tower of the second line that can be included in the first drone nest inspection range is found.

[0044] As an optional implementation method, the total complexity between two towers is: the sum of the ratio of twice the distance between the two and the drone's flight speed, the inspection complexity of one tower, and the inspection complexity of the other tower. The inspection complexity is the time required for the drone to inspect a tower.

[0045] As an optional implementation method, when the heights of the towers are different, the ground point directly below the obtained coordinate Z value is used as the coordinate position of the drone nest.

[0046] The fourth aspect of the present invention provides a drone grid inspection system with intelligent nest site selection.

[0047] A drone grid inspection system with intelligent nest location selection is applied to two parallel transmission lines or distribution lines. The towers of each line are numbered in sequence according to their distance from the substation, including:

[0048] The nest layout judgment module is configured to include the first tower of the first line and the first tower of the second line in the inspection range of the first drone nest when the total complexity of the first tower of the first line and the first tower of the second line is less than or equal to twice the cruising capability of the drone;

[0049] The two lines select any tower in turn and calculate the total complexity with the tower with the smallest number within the inspection range of the drone nest, until the total complexity of the selected tower and any tower within the inspection range of the drone nest is greater than twice the drone cruising capacity;

[0050] The nest location determination module is configured to: connect the largest numbered tower and the smallest numbered tower of two lines within the drone nest inspection range with a cross straight line, and the intersection is the nest location within the drone nest inspection range;

[0051] The first towers of the two lines that were not included in the inspection range of the previous drone nest are used as the first towers of the new first line and the first tower of the second line. The above site selection process is continued for subsequent towers until the locations of all drone nests are obtained.

[0052] The inspection module is configured to perform tower inspection based on all acquired gridded drone nest locations.

[0053] The fifth aspect of the present invention provides a drone grid inspection method with intelligent site selection for machine nests.

[0054] A drone grid inspection method with intelligent nest site selection is applied to two intersecting transmission lines or distribution lines. The towers of each line are numbered in sequence according to their distance from the substation. The method includes the following steps:

[0055] For each transmission line or distribution line, the single line site selection method is first performed until there are two lines with the smallest numbered towers that have not been assigned a drone nest, and the total complexity between them is less than or equal to twice the drone's cruising capability. In this case, these two towers can be included in the first intersection drone nest inspection range.

[0056] The two lines select any tower in turn and calculate the total complexity with the tower with the smallest number within the inspection range of the first intersection drone nest, until the total complexity of the selected tower and any tower within the inspection range of the first intersection drone nest is greater than twice the drone cruising capacity;

[0057] Within the first intersection drone nest inspection range, the total distance to each tower is minimized to obtain the nest position within the first intersection drone nest inspection range, or four towers with the farthest straight-line distances are selected and connected in pairs, and the intersection point is the drone nest position;

[0058] The first towers of the two lines that are not included in the previous first intersection drone nest inspection range are used as the new first tower of the first line and the first tower of the second line. The above site selection process is continued for subsequent towers until the nest locations within the inspection range of all intersection drone nests are obtained;

[0059] For the towers of the two lines after the last intersecting drone nest inspection range, the site selection method of a single line is implemented respectively;

[0060] Conduct tower inspections based on all acquired gridded drone nest locations.

[0061] As an optional implementation, the selected tower is the Nth tower of the first line, and the total complexity of the Nth tower and any tower within the determined first intersection drone nest inspection range is greater than twice the drone cruising capacity;

[0062] Determine whether the total complexity of the Nth tower of the second line and any tower within the determined drone nest inspection range is greater than twice the drone cruising capability. If so, the Nth tower of the first line and the Nth tower of the second line are not included in the first intersection drone nest inspection range; if not, include the Nth tower of the second line in the first intersection drone nest inspection range, and continue to determine whether the N+1th tower of the second line can be included in the first intersection drone nest inspection range, until the last tower of the second line that can be included in the first intersection drone nest inspection range is found.

[0063] As an optional implementation method, the total complexity between two towers is: the sum of the ratio of twice the distance between the two and the drone's flight speed, the inspection complexity of one tower, and the inspection complexity of the other tower. The inspection complexity is the time required for the drone to inspect a tower.

[0064] As an optional implementation method, when the heights of the towers are different, the ground point directly below the obtained coordinate Z value is used as the coordinate position of the drone nest.

[0065] The sixth aspect of the present invention provides a drone grid inspection system with intelligent nest site selection.

[0066] A drone grid inspection system with intelligent nest location selection is applied to two intersecting transmission lines or distribution lines. The towers of each line are numbered in sequence according to their distance from the substation, including:

[0067] The first single-line site selection module is configured as follows:

[0068] For each transmission line or distribution line, the single line site selection method is first performed until there are two lines with the smallest numbered towers that have not been assigned a drone nest, and the total complexity between them is less than or equal to twice the drone's cruising capability. In this case, these two towers can be included in the first intersection drone nest inspection range.

[0069] The cross-site selection module is configured as follows:

[0070] The two lines select any tower in turn and calculate the total complexity with the tower with the smallest number within the inspection range of the first intersection drone nest, until the total complexity of the selected tower and any tower within the inspection range of the first intersection drone nest is greater than twice the drone cruising capacity;

[0071] Within the first intersection drone nest inspection range, the total distance to each tower is minimized to obtain the nest position within the first intersection drone nest inspection range, or four towers with the farthest straight-line distances are selected and connected in pairs, and the intersection point is the drone nest position;

[0072] The first towers of the two lines that are not included in the previous first intersection drone nest inspection range are used as the new first tower of the first line and the first tower of the second line. The above site selection process is continued for subsequent towers until the nest locations within the inspection range of all intersection drone nests are obtained;

[0073] Single-line site selection module, configured as:

[0074] For the towers of the two lines after the last intersecting drone nest inspection range, the site selection method of a single line is implemented respectively;

[0075] The inspection module is configured to perform tower inspection based on all acquired gridded drone nest locations.

[0076] A seventh aspect of the present invention provides a drone grid inspection method with intelligent nest site selection, which is applied to at least three intersecting transmission lines or distribution lines, where the towers of each line are numbered in sequence according to their distance from the substation, and includes the following process:

[0077] When the total complexity of the minimum numbered towers of two adjacent lines is less than or equal to twice the UAV cruising capability, calculate the total complexity of the minimum numbered tower of the third line and the minimum numbered towers of the two adjacent lines to determine whether it is less than or equal to twice the UAV cruising capability. If so, continue to select the minimum numbered tower of the next adjacent line for total complexity calculation until a line with a tower greater than twice the UAV cruising capability is found or until the last line is found;

[0078] The total complexity is calculated for each tower in the direction of increasing number until the smallest tower number that has not been assigned a drone nest is found, and the largest tower number that is less than or equal to twice the drone's cruising capacity is found, and then the towers of each line within the inspection range of the drone nest are obtained.

[0079] As an optional implementation method, select four towers with the farthest straight-line distance within the inspection range of the drone nest and connect them in pairs, and the intersection is the location of the drone nest.

[0080] As an optional implementation method, multiple transmission and distribution lines include non-intersecting lines and intersecting lines, which are divided into two-by-two intersecting or two-by-two parallel lines for site selection based on the principle of line proximity;

[0081] The site selection method of two parallel lines is the site selection method adopted by the third aspect, and the site selection method of two intersecting lines is the site selection method adopted by the fifth aspect.

[0082] The eighth aspect of the present invention provides a drone grid inspection system with intelligent nest site selection.

[0083] A drone grid inspection system with intelligent nest location selection is applied to at least three intersecting transmission lines or distribution lines. The towers of each line are numbered in sequence according to their distance from the substation, including:

[0084] The longitudinal site selection module is configured to: when the total complexity of the minimum-numbered towers of two adjacent lines is less than or equal to twice the drone cruising capability, calculate the total complexity of the minimum-numbered tower of the third line and the minimum-numbered towers of the two adjacent lines, and determine whether it is less than or equal to twice the drone cruising capability. If so, continue to select the minimum-numbered tower of the next adjacent line for total complexity calculation until a line with a tower greater than twice the drone cruising capability is found or until the last line is found;

[0085] The horizontal site selection module is configured to calculate the total complexity of each tower in the direction of increasing number until the smallest tower number that has not been assigned a drone nest is found and the largest tower number that is less than or equal to twice the drone's cruising capacity is found. Then, the towers of each line within the drone nest inspection range are obtained, and the tower locations are determined. The locations of all drone nests are determined in the same way.

[0086] The inspection module is configured to perform tower inspection based on all acquired gridded drone nest locations.

[0087] A ninth aspect of the present invention provides a method for precise landing of a UAV in a gridded machine nest.

[0088] A method for accurately landing a UAV on a gridded machine nest is provided, which is based on the above-mentioned UAV grid inspection method with intelligent nest site selection. The landing method includes:

[0089] During the landing process of the UAV, images of the landing platform are collected at set intervals;

[0090] The brightness and contrast of the image are detected to determine whether they meet the set threshold. If so, image recognition is performed directly; otherwise, image correction algorithm is used to perform image equalization processing;

[0091] Obtain the position of the landing platform visual mark relative to the UAV in the camera coordinate system, and then determine the position information of the UAV in the landing platform visual mark coordinate system;

[0092] Based on the error between the position information and the desired landing point, the drone position is adjusted taking into account external disturbances until the error is adjusted to within a set range, and the drone is controlled to land.

[0093] As an optional implementation, the landing platform is provided with a fill light device for increasing the brightness of the visual sign of the landing platform;

[0094] Before the drone returns to the landing platform and begins automatic landing, it first detects the brightness of the acquired visual sign image. If the image brightness is lower than the set first threshold, the fill light device is turned on;

[0095] After the fill light device is turned on, the brightness and contrast of the visual sign image are detected. When the brightness of the visual sign image is lower than a set second threshold, the brightness of the fill light device is adjusted by adjusting the PWM duty cycle of the control signal so that the image brightness is not lower than the set second threshold;

[0096] When the brightness signal adjustment value of the fill light device is greater than its adjustable upper limit or less than its adjustable lower limit, the brightness of the fill light device will no longer be adjusted; if the brightness of the image still does not meet the set threshold at this time, the image correction algorithm will perform image equalization processing.

[0097] As an optional implementation, before acquiring images of the landing platform, the drone camera can be calibrated. The specific process is as follows:

[0098] Determine the number of corner points on the calibration plate and the actual size of each chessboard grid;

[0099] Use the drone camera to take pictures of the calibration plate in different directions and angles to obtain a set of images;

[0100] Detect the feature points in the calibration plate image, obtain the pixel coordinates of the corner points of the calibration plate, and calculate the physical coordinate values ​​of the corner points of the calibration plate based on the actual size of the chessboard and the coordinates of the world coordinate system;

[0101] According to the relationship between the obtained physical coordinate values ​​of the corner points and the pixel coordinate values, the camera's intrinsic parameter matrix I and distortion parameter matrix D are calculated and obtained, and the intrinsic parameter matrix I and distortion parameter matrix D are optimized.

[0102] The tenth aspect of the present invention provides a computer-readable storage medium having a program stored thereon, characterized in that when the program is executed by a processor, the steps of the drone grid inspection method for intelligent site selection of machine nests as described in the first, third, fifth or seventh aspect of the present invention are implemented; or, when the program is executed by a processor, the steps of the drone precise landing method for grid machine nests as described in the ninth aspect of the present invention are implemented.

[0103] The eleventh aspect of the present invention provides an electronic device, comprising a memory, a processor, and a program stored in the memory and runnable on the processor, characterized in that when the processor executes the program, it implements the steps of the drone grid inspection method for intelligent site selection of machine nests as described in the first, third, fifth or seventh aspect of the present invention; or, when the program is executed by the processor, it implements the steps of the drone precise landing method for grid machine nests as described in the ninth aspect of the present invention.

[0104] Compared with the prior art, the present invention has the following beneficial effects:

[0105] (1) The present invention innovatively proposes a drone grid inspection method with intelligent nest site selection, which is applied to a single transmission line or distribution line. A mathematical model for the site selection of drone nests is constructed, and the pole towers are numbered from small to large. The total inspection complexity of the first pole tower and the subsequent numbered pole towers are compared in turn according to the inspection capability of the drone nest, thereby avoiding the blind and irregular site selection of drone nests. The nest site selection is judged and extended in sequence from small to large tower numbers, realizing the scalability and grid deployment of drone nest site selection, increasing the number of pole towers within the nest inspection range, reducing deployment costs, realizing the grid deployment of maximized drone nest inspections, and ensuring the sustainability of drone inspections within the inspection range.

[0106] (2) The present invention innovatively proposes a drone grid inspection method with intelligent nest site selection, which is applied to two or more lines. The total inspection complexity between each tower is calculated based on two or more lines starting from the smallest number, and the cruising capability of the drone is less than or equal to twice. This avoids the problem of a single nest patrolling only a single line, and realizes the comprehensive planning of all lines in the inspection area. Site selection strategies are designed for parallel lines, intersecting lines and intersection towers respectively, ensuring that all towers can be included in the inspection range of the corresponding drone nest, increasing the number of drone nest patrol towers and realizing the improvement of drone inspection efficiency.

[0107] (3) The present invention innovatively proposes a method for precise landing of UAVs on a gridded machine nest. By setting up a fill light device on the landing platform, the position of the visual mark of the machine nest landing platform can be clearly displayed through illumination at night or in low light conditions, thereby improving the positioning accuracy of the visual mark of the landing platform when the UAV lands. This solves the problem of being unable to obtain the position of the visual mark of the landing platform at night and having to rely only on RTK positioning, thereby improving the accuracy of UAV landing at night. BRIEF DESCRIPTION OF THE DRAWINGS

[0108] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0109] Figure 1 A flow chart of the drone grid inspection method for intelligent site selection of machine nests on a single line provided in Example 1 of the present invention.

[0110] Figure 2 Schematic diagram of drone nest deployment provided by Example 1 of the present invention Figure 1 .

[0111] Figure 3 Schematic diagram of drone nest deployment provided by Example 1 of the present invention Figure 2 .

[0112] Figure 4 Schematic diagram of drone nest deployment provided by Example 1 of the present invention Figure 3 .

[0113] Figure 5 Schematic diagram of drone nest deployment provided by Example 1 of the present invention Figure 4 .

[0114] Figure 6 This is a flow chart of the drone grid inspection method for intelligent site selection of machine nests on a single line as described in Examples 3 and 5 of the present invention.

[0115] Figure 7 Schematic diagram of drone nest deployment provided by Example 3 of the present invention Figure 1 .

[0116] Figure 8 Schematic diagram of drone nest deployment provided by Example 3 of the present invention Figure 2 .

[0117] Figure 9 This is a schematic diagram of the drone nest deployment provided in Example 5 of the present invention.

[0118] Figure 10This is a flow chart of the drone grid inspection method for intelligent site selection of machine nests on a single line as described in Example 7 of the present invention.

[0119] Figure 11 Schematic diagram of drone nest deployment provided in Example 7 of the present invention.

[0120] Figure 12 A schematic diagram of the process flow of the method for precise landing of a UAV in a gridded machine nest provided in Example 9 of the present invention. DETAILED DESCRIPTION

[0121] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0122] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0123] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0124] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0125] Assume that the three-dimensional coordinates of the drone nest An are (Xn, Yn, Zn), n = 0, 1, 2, 3, ···, n, and the drone nests are numbered A1, A2, A3, ···, An, where A0 defaults to the location of the substation drone nest. The power transmission and distribution line towers (hereinafter collectively referred to as towers) have different complexity. The complexity of the towers refers to the different time required for drone inspections of different types of towers. For example, the inspection paths and time consumption of straight towers, tension towers, corner towers, etc. are very different, so the inspection time consumption is also inconsistent. The concept of tower complexity is introduced here. The complexity of a single tower can be obtained through its three-dimensional point cloud model according to the tower type (tension tower, straight tower, corner tower, etc.). The complexity of the tower N is represented by time Tn. Its physical meaning represents the time Tn consumed by the drone to inspect the tower N (tower number, N = 0, 1, 2, 3, ...), in seconds s. The tower numbers are numbered in order from near to far from the substation.

[0126] This embodiment also introduces the concept of a drone inspection radius. Given a fixed drone flight speed V, the drone's cruising capacity (T) in seconds, and the drone inspection speed (V) in meters per second, it can be calculated that the drone can take off from its nest, complete an inspection of a remote tower, and return to its nest just in time. This is defined as the inspection radius Dn of the drone nest An, where n = 0, 1, 2, 3, ...

[0127] In this embodiment, it should be pointed out that the inspection radius Dn of the drone nest An is based on the distance from the drone nest to the pole tower with the maximum inspection time. For example, for a single power tower N, the time required to complete an inspection is T = Sn / V + Tn + Sn / V, which includes three parts: the time Sn / V for the drone to go from the nest to the pole tower N, the time Tn for inspecting the target object of the pole tower N, and the time Sn / V for the drone to return to the nest after the inspection is completed.

[0128] However, it is possible that there is a tower M closer to tower N. Although the time it takes for the drone to go from the nest to tower M and the time it takes for the drone to return from tower M to the nest are shorter, the complexity of tower M is much higher than that of tower N, making it impossible for the drone to complete the inspection task of tower M in a single time. In this case, the inspection radius of the drone's nest cannot be the distance to tower N, but may have to be the radius within tower M.

[0129] Example 1:

[0130] like Figure 1 As shown, embodiment 1 of the present invention provides a method for autonomous inspection and site selection of drone nests for a single power transmission and distribution line, including the following process:

[0131] S1: If the tower out of a substation is a single line and there are no other line towers around, in this case, the location of the drone nest is based on the substation and deployed in sequence along the tower line. It is necessary to consider the complexity Tn and three-dimensional coordinates (Gn, Gn, Gn) of each tower. The location of the drone nest A1 is as follows: Figure 2 As shown, select tower G1 (GX1, GY1, GZ1), whose complexity is T1, as a point on the circle with inspection radius, and then find tower G2 (GX2, GY2, GZ2), whose complexity is T2, and find the total inspection complexity of the two towers G1 and G2, which is recorded as T G1G2 , the distance between G1 and G2 is S G1G2 ,but:

[0132]

[0133] Total complexity:

[0134]

[0135] If T G1G2 >2*T, that is, the total inspection complexity of the two towers G1 and G2 is recorded as T G1G2 > 2 times the UAV inspection capacity T, indicating that it is not possible to deploy a UAV nest between the two towers G1 and G2 to meet the inspection capacity of G1 and G2 respectively, then jump to S3; if T G1G2 If ≤2*T, it means that a drone nest can be deployed between the G1 and G2 towers, meeting the inspection capacity of G1 and G2 towers respectively, and jump to S2;

[0136] S2: Already know T G1G2 ≤2*T, that is, the total inspection complexity of the two towers G1 and G2 is recorded as T G1G2 ≤2 times the drone inspection capacity T indicates that a drone nest can be deployed between the G1 and G2 towers to meet the inspection capacity of G1 and G2 towers respectively. It is also necessary to determine whether the drone nest meets the autonomous inspection capacity of G3 tower. The judgment is based on the total complexity of calculating the two towers G1 and G3, which is recorded as T G1G3 , the distance between G1 and G3 is S G1G3 ,but:

[0137]

[0138] Total complexity:

[0139]

[0140] If T G1G3 ≤2*T, that is, the total inspection complexity of the two towers G1 and G3 is recorded as T G1G3 A drone inspection capacity T of ≤2 indicates that a drone nest can be deployed between the G1 and G3 towers to meet the inspection capacity of the three towers G1, G2, and G3. If it is not met, it is assumed that drone nest A1 can only meet the inspection capacity of the two towers G1 and G2.

[0141] Then, the location range of the drone nest A1 (X1, Y1, Z1) is:

[0142]

[0143] Among them, formula (3) ensures that the drone nest site A1 is on the line between tower G1 and tower G2. According to the principle of the shortest straight line between two points, the drone nest is located on a straight line from the inspection route between the two towers G1 and G2, ensuring the shortest inspection flight distance. Figure 2The location of drone nest 1 is shown as follows. If the conditions are met, jump to S4;

[0144] S3: According to S1, the deployed drone nest A1 cannot meet the inspection capacity of both G1 and G2 towers at the same time. Here, the G1 tower is prioritized. According to the shortest inspection path principle, the drone nest A1 is deployed at G1 to achieve the shortest inspection path. Figure 3 As shown, jump to S5;

[0145] S4: According to S2, the deployed drone nest A1 meets the inspection capabilities of the three towers G1, G2, and G3 respectively; referring to the method of S1, find the towers Gn (GXn, GYn, GZn) in turn, whose complexity is Tn, and calculate the total inspection complexity of the two towers G1 and Gn, recorded as T G1Gn , the distance between G1 and Gn is S G1Gn ,but:

[0146]

[0147] Total complexity:

[0148]

[0149] If T G1Gn ≤2*T, repeat S4 until T G1Gn >2*T, then it can be concluded that the drone nest A1 meets the capabilities of inspecting the n-1 towers G1, G2,...Gn-1 respectively.

[0150] like Figure 4 As shown in the figure, the site selection is carried out in two cases according to the number of n-1 towers G1, G2, ..., Gn-1:

[0151] (1) When n-1 is an odd number, the coordinates of the tower Gn / 2 (GX n / 2, GY n / 2, GZ n / 2) are taken as the coordinates of the drone nest A1.

[0152] (2) When n-1 is an even number, the midpoint of the three-dimensional coordinates of the tower Gn-1 / 2 and Gn+1 / 2 is taken as the coordinate of the drone nest A1.

[0153] After the classification of the above two situations, the inspection distance of the middle tower can be guaranteed to be the shortest. It should be pointed out here that when the towers Gn are not at the same level, the ground point directly below the Z value is calculated as the coordinate position of the drone nest to prevent the calculated coordinate height from not being on the ground and jumping to S5;

[0154] S5: UAV nest A1 has been deployed, and the next step is to select the location of UAV nest A2. Assuming that it is known that UAV nest A1 already includes tower Gn-1, the next step is to inspect towers Gn, Gn+1, etc., that is, the method for selecting the location of UAV nest A2. The site selection idea is consistent with the above steps. Tower Gn is regarded as G1 in S1. Starting from Gn, the total complexity of judging Gn to Gn+1 is recorded as T GnGn+1 , the total complexity from Gn+1 to Gn+2 is recorded as T Gn+1Gn+2 By repeating the above steps, the position of the drone nest A2 or other drone nests An can be derived and calculated.

[0155] When encountering a corner tower, Figure 5 As shown in the figure, the site selection method is the same as the above method, which can be regarded as changing to another straight pole tower line and reselecting the site;

[0156] Conduct tower inspections based on all acquired gridded drone nest locations.

[0157] Example 2:

[0158] Embodiment 2 of the present invention provides a drone grid inspection system with intelligent nest location selection, which is applied to a single transmission line or distribution line. The towers of each line are numbered in sequence according to their distance from the substation, including:

[0159] The machine nest layout judgment module is configured as follows:

[0160] When the total complexity of the first tower and the Nth tower is greater than twice the cruising capability of the drone, and when the total complexity of the first tower and each tower before the Nth tower is less than or equal to twice the cruising capability of the drone, a drone nest can be deployed between the first tower and the N-1th tower to simultaneously meet the inspection needs of the first tower...the N-1th tower, where N is a positive integer greater than 2;

[0161] The machine nest position determination module is configured as follows:

[0162] When N-1 is an odd number, the coordinates of the N / 2th tower are taken as the coordinates of the first drone nest. When N-1 is an even number, the position of the (N-1) / 2th tower is taken as the position of the first drone nest.

[0163] When the total complexity of the first tower and the second tower is greater than twice the cruising capability of the UAV, the position of the first tower is taken as the position of the first UAV nest;

[0164] Take the Nth tower as the new first tower and continue the above site selection process for subsequent towers until all drone nest locations are obtained;

[0165] The inspection module is configured to perform tower inspection based on all acquired gridded drone nest locations.

[0166] The working method of the system is the same as the drone grid inspection method for intelligent nest site selection provided in Example 1, and will not be repeated here.

[0167] Example 3:

[0168] like Figure 6 As shown, embodiment 3 of the present invention provides a method for autonomous inspection and site selection of drone nests of two power transmission and distribution lines, including the following process:

[0169] S1: Determine whether the two transmission and distribution line towers are non-intersecting lines (e.g., parallel lines) based on their 3D coordinates. If they are non-intersecting lines, first determine whether the first tower of the two tower lines meets the patrol capability T of a single drone nest to cover both lines simultaneously. The judgment is based on the numbering of the two tower lines starting from the substation from near to far, 1, 2, 3, ..., n. The total inspection complexity of the two towers G1 and g1 is recorded as T G1g1 , the distance between G1 and g1 is S G1g1 ,but:

[0170]

[0171] Total complexity:

[0172]

[0173] If T G1g1 ≤2*T, indicating that the first towers TG1 and g1 of the two tower lines meet the cruising capability of a single drone nest T, jump to S2, if T G1g1 >2*T, indicating that the first towers TG1 and g1 of the two tower lines cannot meet the cruising capability T of a single drone nest, and jump to S3;

[0174] S2: The first towers G1 and g1 meet the cruising capability T of a single drone nest. Then select the second tower G2 in the two routes to form a triangle with the previous G1 and g1. Determine whether these three towers meet the inspection range of the same drone nest. The judgment is based on calculating the total complexity T of G1 and G2. G1G2 , and the total complexity of g1 and G2 is T g1G2 , if T G1G2 ≤2*T and T g1G2 ≤2*T, then it is considered that towers G1, G2, and g1 are within the inspection range of the same drone nest. At this time, the second tower g2 in another line is judged according to the previous method, and the calculation complexity T of the previous three towers is respectively G1g2 、T G2g2and T g1g2 , determine whether the g2 tower is within the same drone nest inspection range as the first three towers. If the complexity of the three towers all meets the condition of less than 2*T, then it can be considered that the four towers G1, g1, G2 and g2 meet the same drone nest inspection range, and jump to S4:

[0175] If T G1G2 ≤2*T and T g1G2 ≤2*T has one not satisfied, it can be considered that tower G2 and tower TG1, g1 are not within the inspection range of the same drone nest, which is not relevant to the calculation of T G1G2 and T g1G2 The complexity of is consistent with the method described above, so I will not go into details.

[0176] S3: It is known that the two tower lines are parallel and the first towers TG1 and g1 cannot meet the cruising capacity T of a single drone nest. Then, the site selection method of a single tower line is used first. When the total complexity of the tower with the smallest number that is not assigned to a drone nest and the tower with the smallest number of the other line is ≤2*T, the towers are planned for the same drone nest according to the judgment method of the two tower lines in the second step, such as Figure 7 As shown;

[0177] S4: According to the above steps, the two lines select one tower in turn and perform complexity sum calculation with the tower with the smallest number in the inspection range of the aforementioned drone nest (here, the newly added towers of the two lines only need to be compared with the tower with the smallest number in the inspection range, because the newly added towers with the smallest number are the towers with the largest span and distance. If the comparison range with the tower with the smallest number is met, then the inspection range of the newly added towers with the largest and smallest numbers must be met. For example, the newly added tower G3 only needs to be compared with G1 and g1 for distance, and does not need to be compared with G2 and g2; similarly, g3 only needs to be compared with G1 and g1 for distance, and does not need to be compared with G2 and g2), until it is greater than 2*T condition, it is considered that the subsequent towers are beyond the inspection range of the same drone nest. Here, the maximum number and the minimum number in the calculation range of the previous two lines are connected by a cross straight line, and the intersection is considered to be the site selection point of this drone nest. Figure 8 As shown, for example, if the four towers G1, G2, g1, and g2 are within the same inspection radius, connect G1 and g2, and g1 and G2 with straight lines, and the intersection point is the drone site selection point corresponding to these four towers;

[0178] Conduct tower inspections based on all acquired gridded drone nest locations.

[0179] Example 4:

[0180] Embodiment 4 of the present invention provides a drone grid inspection system with intelligent nest site selection, which is applied to two parallel transmission lines or distribution lines. The towers of each line are numbered in sequence according to their distance from the substation, including:

[0181] The nest layout judgment module is configured to include the first tower of the first line and the first tower of the second line in the inspection range of the first drone nest when the total complexity of the first tower of the first line and the first tower of the second line is less than or equal to twice the cruising capability of the drone;

[0182] The two lines select any tower in turn to calculate the total complexity with the towers within the determined drone nest inspection range, until the total complexity of the selected tower and any tower within the determined drone nest inspection range is greater than twice the drone cruising capacity;

[0183] The nest location determination module is configured to: connect the largest numbered tower and the smallest numbered tower of two lines within the drone nest inspection range with a cross straight line, and the intersection is the nest location within the drone nest inspection range;

[0184] The first towers of the two lines that were not included in the inspection range of the previous drone nest are used as the first towers of the new first line and the first tower of the second line. The above site selection process is continued for subsequent towers until the locations of all drone nests are obtained.

[0185] The inspection module is configured to perform tower inspection based on all acquired gridded drone nest locations.

[0186] The working method of the system is the same as the drone grid inspection method for intelligent nest site selection provided in Example 1, and will not be repeated here.

[0187] Example 5:

[0188] Embodiment 5 of the present invention provides a method for autonomous inspection and site selection of drone nests for two intersecting power transmission and distribution lines, including the following process:

[0189] S1: Determine whether the two transmission and distribution lines are crossed based on the three-dimensional coordinates of the towers of the two transmission and distribution lines;

[0190] S2: It is known that the two tower lines are cross-routed and both lines start from the substation. Since the two lines are far apart, the sites are first planned and selected for each line separately. The method is the same as the autonomous inspection and site selection of drone nests for a single transmission and distribution line described in Example 1.

[0191] When the distances between the two lines to the minimum numbered towers assigned to the drone nests are gradually approaching, and the total complexity of the minimum numbered towers that are not assigned to the drone nests on these two lines is ≤ 2*T, it is considered that the two towers are included in the inspection range of the same drone nest, and then jump to S3;

[0192] like Figure 9 As shown in the figure, for example, towers G3, G3, and G4 are within the inspection range of the same drone nest. The nest location is selected by connecting G3 and G4, with tower G3 as the perpendicular intersection of the line connecting G3 and G4. This point is used as the drone nest location for towers G3, G3, and G4. This ensures that the sum of the distances from this point to all towers is the shortest path.

[0193] S3: According to the above steps, the two lines select one tower in turn and perform the complexity sum calculation with the tower with the smallest number in the inspection range of the drone nest mentioned above (here, the newly added towers of the two lines only need to be compared with the tower with the smallest number in the inspection range, because the newly added towers with the smallest number are the towers with the largest span and distance. If the comparison range with the tower with the smallest number is met, then the inspection range of the newly added tower with the largest and smallest number must be met. For example, if a new tower G3 is added, it only needs to be compared with G1 and g1 in distance, and no longer needs to be compared with G1 and g1. G2 and g2 are compared; similarly, g3 only needs to be compared with G1 and g1 in distance, and does not need to be compared with G2 and g2 again) until it is greater than the 2*T condition, and the subsequent tower is considered to be beyond the inspection range of the same drone nest. Here, the maximum number and the minimum number within the calculation range of the previous two lines are connected by a cross straight line, and the intersection is considered to be the site selection point of this drone nest. For example, if the four towers G1, G2, g1, and g2 are within the same inspection radius, G1 and g2, g1 and G2 are connected by straight lines, and the intersection is the drone site selection point corresponding to these four towers;

[0194] Conduct tower inspections based on all acquired gridded drone nest locations.

[0195] Example 6:

[0196] Embodiment 6 of the present invention provides a drone grid inspection system with intelligent nest site selection, which is applied to two intersecting transmission lines or distribution lines. The towers of each line are numbered in sequence according to their distance from the substation, including:

[0197] The first single-line site selection module is configured as follows:

[0198] For each transmission line or distribution line, the single line site selection method is first performed until there are two lines with the smallest numbered towers that have not been assigned a drone nest, and the total complexity between them is less than or equal to twice the drone's cruising capability. In this case, these two towers can be included in the first intersection drone nest inspection range.

[0199] The cross-site selection module is configured as follows:

[0200] The two lines select any tower in turn and calculate the total complexity with the towers within the first intersection drone nest inspection range, until the total complexity of the selected tower and any tower within the first intersection drone nest inspection range is greater than twice the drone cruising capacity;

[0201] Within the first intersection drone nest inspection range, the total distance to each tower is minimized to obtain the nest position within the first intersection drone nest inspection range, or four towers with the farthest straight-line distances are selected and connected in pairs, and the intersection point is the drone nest position;

[0202] The first towers of the two lines that are not included in the previous first intersection drone nest inspection range are used as the new first tower of the first line and the first tower of the second line. The above site selection process is continued for subsequent towers until the nest locations within the inspection range of all intersection drone nests are obtained;

[0203] The first single-line site selection module is configured as follows:

[0204] For the towers of the two lines after the last intersecting drone nest inspection range, the site selection method of a single line is implemented respectively;

[0205] The inspection module is configured to perform tower inspection based on all acquired gridded drone nest locations.

[0206] The working method of the system is the same as that provided in Example 5 and will not be described again here.

[0207] Example 7:

[0208] like Figure 10 and Figure 11 As shown, embodiment 7 of the present invention provides a method for autonomous inspection and site selection of drone nests for multiple (three or more) power transmission and distribution lines, including the following process:

[0209] S1: According to the previous principle of numbering towers from small to large, each transmission and distribution line is numbered 1, 2, 3, 4, ..., n from the nearest to the substation;

[0210] Determine whether the total complexity of the smallest numbered towers of two adjacent lines satisfies ≤2*T. If so, prioritize the complexity calculation for the smallest numbered tower adjacent to the third line. If >2*T, then perform complexity calculations one by one in the direction of increasing numbers between the two adjacent lines. When the smallest numbered tower with no unassigned nest and the largest numbered tower ≤2*T are found, it is considered that the towers between them are within the inspection range of a drone nest.

[0211] S2: A more specific site selection method is to find four towers with the longest straight-line distance within the range selected in the first step, connect them two by two, and the intersection is the final site selection location of the drone nest within the range;

[0212] S3: If multiple (three or more) power transmission and distribution lines include both parallel lines and crossing lines, they can be processed by the line site selection method of two-by-two crossing or two-by-two parallel lines based on the principle of line proximity. The specific method can refer to the autonomous inspection site selection of drone nests for a single power transmission and distribution line described in Example 1 and the autonomous inspection site selection of drone nests for two power transmission and distribution lines described in Examples 3 and 5, which will not be repeated here;

[0213] Conduct tower inspections based on all acquired gridded drone nest locations.

[0214] Example 8:

[0215] Embodiment 8 of the present invention provides a drone grid inspection system with intelligent nest site selection, which is applied to at least three intersecting transmission lines or distribution lines, where the towers of each line are numbered in sequence according to their distance from the substation, including:

[0216] The longitudinal site selection module is configured to: when the total complexity of the minimum-numbered towers of two adjacent lines is less than or equal to twice the drone cruising capability, calculate the total complexity of the minimum-numbered tower of the third line and the minimum-numbered towers of the two adjacent lines, and determine whether it is less than or equal to twice the drone cruising capability. If so, continue to select the minimum-numbered tower of the next adjacent line for total complexity calculation until a line with a tower greater than twice the drone cruising capability is found or until the last line is found;

[0217] The horizontal site selection module is configured to calculate the total complexity of each tower in the direction of increasing number until the smallest tower number that has not been assigned a drone nest is found and the largest tower number that is less than or equal to twice the drone's cruising capacity is found, thereby obtaining the towers of each line within the drone nest inspection range;

[0218] The inspection module is configured to perform tower inspection based on all acquired gridded drone nest locations.

[0219] The working method of the system is the same as the drone grid inspection method for intelligent nest site selection provided in Example 7, and will not be repeated here.

[0220] Example 9:

[0221] Embodiment 9 of the present invention provides a method for precise landing of a UAV in a gridded drone nest, comprising the following steps:

[0222] (1) During the landing process, the UAV collects images of the landing platform at set intervals;

[0223] In this embodiment, combined with Figure 2 and Figure 3 A fill light device is set on the landing platform. The fill light device is a fill light. A backlight source with uniform light is selected. A fill light scheme is adopted to illuminate the bottom of the visual sign on the landing platform. According to the size of the drone landing platform, the fill light source is installed and fixed on the bottom of the visual sign on the landing platform. The light source line is made by punching a hole in the landing platform and connecting the line to the controller to facilitate the opening and closing of the light source.

[0224] When the drone returns to the airspace above the landing platform and begins automatic landing, the precise landing process begins. The image brightness of the visual landmark is first checked. If the image brightness falls below the first threshold (the fill light activation threshold), the landing platform fill light is activated. After the fill light is activated, the image brightness and contrast are checked. If the image brightness falls below the second threshold (the low image brightness threshold, set based on the actual project recognition requirements), the fill light brightness is adjusted. A fill light brightness adjustment signal is sent from the landing platform to adjust the image brightness by adjusting the PWM duty cycle of the fill light. A brightness check is then performed. If the adjusted fill light brightness signal value falls below the lower limit or exceeds the upper limit, the fill light brightness is no longer adjusted. If the image brightness still does not meet the set threshold (the image equalization threshold), the image correction algorithm performs image equalization. The lower and upper limits of the fill light brightness signal adjustment value are determined by the selected fill light model.

[0225] According to the landing control process, the time is calculated and the time interval for drone camera image acquisition is designed. During the landing process, images of the landing platform's visual landmarks are continuously collected to continuously adjust the drone's position and guide the drone to land.

[0226] (2) Detect the brightness and contrast of the image to determine whether it meets the set threshold. If so, perform image recognition directly; otherwise, use the image correction algorithm to perform image equalization processing;

[0227] In this embodiment, after obtaining the image of the landing platform visual sign collected by the drone, the brightness and contrast of the image are detected. When the set threshold is met, recognition detection can be directly performed; when the set threshold is not met, the collected image is equalized.

[0228] (3) Obtain the position of the landing platform visual mark relative to the UAV in the camera coordinate system, and then determine the position information of the UAV in the landing platform visual mark coordinate system;

[0229] In this embodiment, before acquiring an image, the drone camera is first calibrated using Zhang Dingyou's camera calibration method to obtain camera parameters. The specific calibration method is as follows:

[0230] 1) Prepare a camera calibration plate, determine the number of corner points on the calibration plate and the actual size of each checkerboard grid;

[0231] 2) Use the drone camera to take pictures of the calibration plate at different directions and angles to obtain a set of images;

[0232] 3) Detect the feature points in the calibration plate image, obtain the pixel coordinates of the corner points of the calibration plate, and calculate the physical coordinate values ​​of the corner points of the calibration plate based on the actual size of the chessboard and the coordinates of the world coordinate system;

[0233] 4) Based on the relationship between the obtained corner point physical coordinate values ​​and the corner point pixel coordinate values, calculate the camera's intrinsic parameter matrix I and distortion parameter matrix D;

[0234] 5) Use OpenCV to optimize the camera intrinsic parameters I and distortion parameters D.

[0235] (4) Based on the error between the coordinate position information of the UAV in the visual mark coordinate system of the landing platform and the position of the expected landing point, the UAV position is adjusted taking into account external disturbances until the error is adjusted to within the set range, and the UAV is controlled to land.

[0236] Example 10:

[0237] Embodiment 10 of the present invention provides a computer-readable storage medium having a program stored thereon. When the program is executed by a processor, the steps of the drone grid inspection method for intelligent site selection of machine nests as described in Embodiment 1, Embodiment 3, Embodiment 5 or Embodiment 7 of the present invention are implemented; or, when the program is executed by a processor, the steps of the drone precise landing method for grid machine nests as described in Embodiment 9 are implemented.

[0238] Example 11:

[0239] Embodiment 11 of the present invention provides an electronic device, comprising a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps of the drone grid inspection method for intelligent site selection of machine nests as described in Embodiment 1, Embodiment 3, Embodiment 5, or Embodiment 7 of the present invention are implemented; or, when the program is executed by the processor, the steps of the drone precise landing method for grid machine nests as described in Embodiment 9 are implemented.

[0240] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage and optical storage, etc.) containing computer-usable program code.

[0241] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0242] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0243] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0244] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware through a computer program. The program can be stored in a computer-readable storage medium, and when executed, the program can include the processes in the above-described method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0245] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A drone grid inspection method with intelligent nest site selection, characterized by: Applied to a single transmission line or distribution line, the towers of each line are numbered in sequence according to their distance from the substation, including the following process: The location of the drone nest is selected starting from the substation and deployed in sequence along the tower line; When the total complexity of the first tower and the Nth tower is greater than twice the cruising capability of the drone, and when the total complexity of the first tower and each tower before the Nth tower is less than or equal to twice the cruising capability of the drone, a drone nest can be deployed between the first tower and the N-1th tower to simultaneously meet the inspection needs of each tower from the first tower to the N-1th tower, where N is a positive integer greater than 2; When N-1 is an odd number, the coordinates of the N / 2th tower are taken as the coordinates of the first drone nest. When N-1 is an even number, the position of the (N-1) / 2th tower is taken as the position of the first drone nest. When the total complexity of the first tower and the second tower is greater than twice the cruising capability of the UAV, the position of the first tower is taken as the position of the first UAV nest; Assuming that drone nest A1 already includes tower Gn-1, the next step is to inspect towers Gn, Gn+1, etc.; with tower N as the new first tower, the above site selection process is continued for subsequent towers until all drone nest locations are obtained. When encountering a corner tower, it is treated as changing to another straight tower line and re-selecting the site; Conduct tower inspection based on all acquired gridded drone nest locations; The nest site selection is based on the tower number from small to large, and the extension is determined in sequence, so as to achieve the scalability and grid deployment of the drone nest site selection; The total complexity of the first tower and the Nth tower is: The inspection complexity is the time required for a drone to inspect a tower, which is the sum of the ratio of twice the distance between the first tower and the Nth tower, the flying speed of the drone, the inspection complexity of the first tower, and the inspection complexity of the Nth tower.

2. The drone grid inspection method for intelligent nest site selection according to claim 1, characterized in that: When the total complexity of the first and second towers is less than or equal to twice the cruising capability of the drone, and the total complexity of the first and third towers is less than or equal to twice the cruising capability of the drone, the first drone nest only meets the capability of inspecting the first and second towers. In this case, the site selection interval of the first drone nest A1 (X1, Y1, Z1) is: Among them, T is the cruising capability of the drone, T1 is the inspection complexity of the first tower, T2 is the inspection complexity of the second tower, G x1 and G y1 is the horizontal coordinate of the first tower, G x2 and G y2 is the horizontal coordinate of the second tower, |A1-G2| is the distance between the first UAV nest and the second tower, and |A1-G1| is the distance between the first UAV nest and the first tower.

3. The UAV grid inspection method for intelligent nest site selection according to any one of claims 1-2, characterized in that: When the heights of the towers are different, the ground point directly below the obtained coordinate Z value is used as the coordinate position of the drone nest.

4. A drone grid inspection system with intelligent nest location selection, characterized by: Applicable to a single transmission line or distribution line, the towers of each line are numbered in sequence according to the distance from the substation, including: The location of the drone nest is selected starting from the substation and deployed in sequence along the tower line; The machine nest layout judgment module is configured as follows: When the total complexity of the first tower and the Nth tower is greater than twice the cruising capability of the drone, and when the total complexity of the first tower and each tower before the Nth tower is less than or equal to twice the cruising capability of the drone, a drone nest can be deployed between the first tower and the N-1th tower to simultaneously meet the inspection needs of each tower from the first tower to the N-1th tower, where N is a positive integer greater than 2; The machine nest position determination module is configured as follows: When N-1 is an odd number, the coordinates of the N / 2th tower are taken as the coordinates of the first drone nest. When N-1 is an even number, the position of the (N-1) / 2th tower is taken as the position of the first drone nest. When the total complexity of the first tower and the second tower is greater than twice the cruising capability of the UAV, the position of the first tower is taken as the position of the first UAV nest; Assuming that drone nest A1 already includes tower Gn-1, the next step is to inspect towers Gn, Gn+1, etc.; with tower N as the new first tower, the above site selection process is continued for subsequent towers until all drone nest locations are obtained. When encountering a corner tower, it is treated as changing to another straight tower line and re-selecting the site; The total complexity of the first tower and the Nth tower is: The inspection complexity is the time required for a drone to inspect a tower, which is the sum of the ratio of twice the distance between the first tower and the Nth tower, the drone's flight speed, the inspection complexity of the first tower, and the inspection complexity of the Nth tower. The inspection module is configured to: perform tower inspection based on all acquired gridded drone nest locations; The location of the drone nest is determined by the tower number from small to large, and the extension is determined in sequence to achieve the scalability and grid deployment of the drone nest location.

5. A drone grid inspection method with intelligent nest site selection, characterized by: Applied to two parallel transmission or distribution lines, the towers of each line are numbered in sequence according to their distance from the substation, including the following process: When the first tower of the first line and the first tower of the second line cannot meet the cruising capability of a single drone nest, the site selection method for a single tower line is first used; When the total complexity of the first tower of the first line and the first tower of the second line is less than or equal to twice the cruising capability of the drone, the first tower of the first line and the first tower of the second line are included in the inspection range of the first drone's nest; The two lines select any tower in turn and calculate the total complexity with each tower with the smallest number within the determined drone nest inspection range, until the total complexity of the selected tower and any tower within the determined drone nest inspection range is greater than twice the drone cruising capacity; The maximum numbered tower and the minimum numbered tower of the two lines within the inspection range of the drone nest are connected by a cross straight line, and the intersection is the location of the drone nest within the inspection range; The first towers of the two lines that were not included in the inspection range of the previous drone nest are used as the first towers of the new first line and the first tower of the second line. The above site selection process is continued for subsequent towers until the locations of all drone nests are obtained. Conduct tower inspection based on all acquired gridded drone nest locations; The total complexity between two towers is the sum of the ratio of twice the distance between the first tower and the Nth tower and the drone's flight speed, the inspection complexity of one tower, and the inspection complexity of the other tower. The inspection complexity is the time required for a drone to inspect a tower.

6. The drone grid inspection method for intelligent nest site selection according to claim 5, characterized in that: A second tower from any two lines forms a triangle with the first tower of the first line and the first tower of the second line. When the total complexity of the second tower, the first tower of the first line, and the first tower of the second line are both less than twice the cruising capability of the drone, the first tower of the first line, the first tower of the second line, and the second tower are included in the inspection range of the first drone's nest. Continue to select the remaining second towers. When the total complexity of the remaining second tower and the first tower of the first line, the total complexity of the remaining second tower and the first tower of the second line, and the total complexity of the remaining second tower and the previously selected second tower are all less than twice the cruising capability of the drone, the first tower of the first line, the first tower of the second line, the second tower of the first line, and the second tower of the second line are all included in the inspection range of the first drone's nest.

7. The drone grid inspection method for intelligent nest site selection according to claim 5, characterized in that: The selected tower is the Nth tower of the first line, and the total complexity of the Nth tower and any tower within the inspection range of the first drone nest is greater than twice the drone cruising capacity; Determine whether the total complexity of the Nth tower of the second line and any tower within the determined drone nest inspection range is greater than twice the drone cruising capability. If so, the Nth tower of the first line and the Nth tower of the second line are not included in the first drone nest inspection range; if not, include the Nth tower of the second line in the first drone nest inspection range, and continue to determine whether the N+1th tower of the second line can be included in the first drone nest inspection range, until the last tower of the second line that can be included in the first drone nest inspection range is found.

8. The drone grid inspection method for intelligent nest site selection according to claim 5, characterized in that: When the heights of the towers are different, the ground point directly below the obtained coordinate Z value is used as the coordinate position of the drone nest.

9. A drone grid inspection system with intelligent nest location selection, characterized by: Applicable to two parallel transmission lines or distribution lines, the towers of each line are numbered in sequence according to the distance from the substation, including: The machine nest layout judgment module is configured to: when the first tower of the first line and the first tower of the second line cannot meet the cruising capability of a single UAV machine nest, first select a site according to the single tower line site selection method; When the total complexity of the first tower of the first line and the first tower of the second line is less than or equal to twice the cruising capability of the drone, the first tower of the first line and the first tower of the second line are included in the inspection range of the first drone's nest; The two lines select any tower in turn and calculate the total complexity with each tower with the smallest number within the determined drone nest inspection range, until the total complexity of the selected tower and any tower within the determined drone nest inspection range is greater than twice the drone cruising capacity; The total inspection complexity between two towers is the sum of the ratio of twice the distance between the first tower and the Nth tower, the speed of the drone, the inspection complexity of one tower, and the inspection complexity of the other tower. The inspection complexity is the time required for the drone to inspect a tower. The nest location determination module is configured to: connect the largest numbered tower and the smallest numbered tower of two lines within the drone nest inspection range with a cross straight line, and the intersection is the nest location within the drone nest inspection range; The first towers of the two lines that were not included in the inspection range of the previous drone nest are used as the first towers of the new first line and the first tower of the second line. The above site selection process is continued for subsequent towers until the locations of all drone nests are obtained. The inspection module is configured to perform tower inspection based on all acquired gridded drone nest locations.

10. A drone grid inspection method with intelligent nest location selection, characterized by: Applied to two intersecting transmission or distribution lines, the towers of each line are numbered in sequence according to their distance from the substation, including the following process: For each transmission line or distribution line, the single line site selection method is first performed until there are two lines with the smallest numbered towers that have not been assigned a drone nest, and the total complexity between them is less than or equal to twice the drone's cruising capability. In this case, these two towers can be included in the first intersection drone nest inspection range. The two lines select any tower in turn and calculate the total complexity with the tower with the smallest number within the inspection range of the first intersection drone nest, until the total complexity of the selected tower and any tower within the inspection range of the first intersection drone nest is greater than twice the drone cruising capacity; Within the first intersection drone nest inspection range, the total distance to each tower is minimized to obtain the nest position within the first intersection drone nest inspection range, or four towers with the farthest straight-line distances are selected and connected in pairs, and the intersection point is the drone nest position; The first towers of the two lines that are not included in the previous first intersection drone nest inspection range are used as the new first tower of the first line and the first tower of the second line. The above site selection process is continued for subsequent towers until the nest locations within the inspection range of all intersection drone nests are obtained; For the towers of the two lines after the last intersecting drone nest inspection range, the site selection method of a single line is implemented respectively; Conduct tower inspection based on all acquired gridded drone nest locations; The total complexity between two towers is the sum of the ratio of twice the distance between the first tower and the Nth tower and the drone's flight speed, the inspection complexity of one tower, and the inspection complexity of the other tower. The inspection complexity is the time required for a drone to inspect a tower.

11. The drone grid inspection method for intelligent nest location selection according to claim 10, characterized in that: The selected tower is the Nth tower of the first line, and the total complexity of the Nth tower and any tower within the determined first intersection drone nest inspection range is greater than twice the drone cruising capacity; Determine whether the total complexity of the Nth tower of the second line and any tower within the determined drone nest inspection range is greater than twice the drone cruising capability. If so, the Nth tower of the first line and the Nth tower of the second line are not included in the first intersection drone nest inspection range; if not, include the Nth tower of the second line in the first intersection drone nest inspection range, and continue to determine whether the N+1th tower of the second line can be included in the first intersection drone nest inspection range, until the last tower of the second line that can be included in the first intersection drone nest inspection range is found.

12. The drone grid inspection method for intelligent nest site selection according to claim 10, characterized in that: When the heights of the towers are different, the ground point directly below the obtained coordinate Z value is used as the coordinate position of the drone nest.

13. A drone grid inspection system with intelligent nest location selection, characterized by: Applicable to two intersecting transmission lines or distribution lines, the towers of each line are numbered in sequence according to the distance from the substation, including: The first single-line site selection module is configured as follows: For each transmission line or distribution line, the single line site selection method is first performed until there are two lines with the smallest numbered towers that have not been assigned a drone nest, and the total complexity between them is less than or equal to twice the drone's cruising capability. In this case, these two towers can be included in the first intersection drone nest inspection range. The cross-site selection module is configured as follows: The two lines select any tower in turn and calculate the total complexity with the tower with the smallest number within the inspection range of the first intersection drone nest, until the total complexity of the selected tower and any tower within the inspection range of the first intersection drone nest is greater than twice the drone cruising capacity; Within the first intersection drone nest inspection range, the total distance to each tower is minimized to obtain the nest position within the first intersection drone nest inspection range, or four towers with the farthest straight-line distances are selected and connected in pairs, and the intersection point is the drone nest position; The first towers of the two lines that are not included in the previous first intersection drone nest inspection range are used as the new first tower of the first line and the first tower of the second line. The above site selection process is continued for subsequent towers until the nest locations within the inspection range of all intersection drone nests are obtained; The total inspection complexity between two towers is the sum of the ratio of twice the distance between the first tower and the Nth tower, the speed of the drone, the inspection complexity of one tower, and the inspection complexity of the other tower. The inspection complexity is the time required for the drone to inspect a tower. Single-line site selection module, configured as: For the towers of the two lines after the last intersecting drone nest inspection range, the site selection method of a single line is implemented respectively; The inspection module is configured to perform tower inspection based on all acquired gridded drone nest locations.

14. A drone grid inspection method with intelligent nest site selection, characterized by: Applicable to at least three intersecting transmission or distribution lines, with the towers of each line numbered sequentially according to their distance from the substation, including the following process: When the total complexity of the minimum numbered towers of two adjacent lines is less than or equal to twice the UAV cruising capability, calculate the total complexity of the minimum numbered tower of the third line and the minimum numbered towers of the two adjacent lines to determine whether it is less than or equal to twice the UAV cruising capability. If so, continue to select the minimum numbered tower of the next adjacent line for total complexity calculation until a line with a tower greater than twice the UAV cruising capability is found or until the last line is found; The total complexity is calculated for each tower in the direction of increasing number until the smallest tower number that has not been assigned a drone nest is found, and the largest tower number that is less than or equal to twice the drone's cruising capacity is found. Then, the towers of each line within the drone nest inspection range are obtained, and the tower positions are determined. The positions of all drone nests are determined in the same way as above. Conduct tower inspection based on all acquired gridded drone nest locations; The total complexity between two towers is the sum of the ratio of twice the distance between the first tower and the Nth tower and the drone's flight speed, the inspection complexity of one tower, and the inspection complexity of the other tower. The inspection complexity is the time required for a drone to inspect a tower.

15. The drone grid inspection method for intelligent nest site selection according to claim 14, characterized in that: Select the four towers with the farthest straight-line distance within the inspection range of the drone nest and connect them in pairs. The intersection is the location of the drone nest.

16. The drone grid inspection method for intelligent nest site selection according to claim 14, characterized in that: Multiple transmission and distribution lines include non-intersecting lines and intersecting lines. They are divided into two-way intersecting or two-way parallel lines for site selection based on the principle of line proximity. The site selection method using two parallel lines is the drone grid inspection method for intelligent site selection of the machine nest as described in any one of claims 5-8, and the site selection method using two intersecting lines is the drone grid inspection method for intelligent site selection of the machine nest as described in any one of claims 10-12.

17. A drone grid inspection system with intelligent nest location selection, characterized by: Applicable to at least three intersecting transmission lines or distribution lines, with the towers of each line numbered in sequence according to their distance from the substation, including: The longitudinal site selection module is configured to: when the total complexity of the minimum-numbered towers of two adjacent lines is less than or equal to twice the drone cruising capability, calculate the total complexity of the minimum-numbered tower of the third line and the minimum-numbered towers of the two adjacent lines, and determine whether it is less than or equal to twice the drone cruising capability. If so, continue to select the minimum-numbered tower of the next adjacent line for total complexity calculation until a line with a tower greater than twice the drone cruising capability is found or until the last line is found; The horizontal site selection module is configured to calculate the total complexity of each tower in the direction of increasing number until the smallest tower number that has not been assigned a drone nest is found and the largest tower number that is less than or equal to twice the drone's cruising capacity is found. Then, the towers of each line within the drone nest inspection range are obtained, and the tower locations are determined. The locations of all drone nests are determined in the same way. The total inspection complexity between two towers is the sum of the ratio of twice the distance between the first tower and the Nth tower, the speed of the drone, the inspection complexity of one tower, and the inspection complexity of the other tower. The inspection complexity is the time required for the drone to inspect a tower. The inspection module is configured to perform tower inspection based on all acquired gridded drone nest locations.

18. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the steps in the drone grid inspection method for intelligent site selection of machine nests as described in any one of claims 1-3, 5-8, 10-12, and 14-16 are implemented.

19. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps in the drone grid inspection method for intelligent site selection of machine nests as described in any one of claims 1-3, 5-8, 10-12, and 14-16 are implemented.

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