A Method and System for Installing the Nest of an Unmanned Aerial Vehicle for Inspecting Transmission Lines

Abstract

The present invention discloses a method and system for arranging drone nests for power transmission line inspection. The method includes: identifying a number of inflection point positions according to the geographical data of the power transmission line; obtaining the terrain data within the preset search range of the inflection point positions, and determining a number of initial drone nest arrangement positions in combination with the preset safety distance requirements; further screening out candidate drone nest arrangement positions without inspection blind spots according to the position information of a number of inspection target points and the obstacle distribution range; calculating the visual coverage rate corresponding to each candidate drone nest arrangement position, generating an inspection path starting from the candidate drone nest arrangement position with the largest current visual coverage rate, and calculating the inspection coverage rate; when the inspection coverage rate is greater than or equal to the inspection coverage rate threshold, determining that the candidate drone nest arrangement position is the target drone nest arrangement position. The present invention can significantly improve the reliability of arranging drone nests at the inflection points of power transmission lines, and helps to improve the inspection coverage rate of drones for power transmission line inspection.

Description

Technical Field

[0001] The present invention relates to the technical field of drone nest planning, and in particular, to a method and system for arranging drone nests for power transmission line inspection. Background Art

[0002] In the research on the tower-to-tower scheduling strategy of drone nests for power transmission line inspection, the correlation between the arrangement position of the drone nest at the corner of the power transmission line and the inspection dead zone is a key issue. When the drone nest is located in the line turning area, due to the complexity of the terrain and the line orientation, the inspection path of the drone will be significantly affected. Specifically, there are usually large spatial variations in the line turning area, resulting in the difficulty for the drone to cover all areas during flight, thus forming inspection blind spots. Different arrangement positions of the drone nest will directly affect the range and distribution density of the inspection blind spots. Further analysis shows that the inspection path of the drone is restricted by the distribution of the inspection blind spots. Due to the existence of the inspection blind spots, the drone needs to adjust its inspection path within the limited flight time to cover as many areas as possible. However, the spatial distribution of the inspection blind spots is often irregular, making it difficult for the path planning algorithm to process efficiently, and thus unable to guarantee the overall inspection coverage rate. The existing selection of drone nest locations usually relies on expert experience and fails to fully consider the existence of inspection blind spots after the arrangement of the drone nest, resulting in a low inspection coverage rate of the drone for power transmission line inspection. Summary of the Invention

[0003] The present invention provides a method and system for arranging drone nests for power transmission line inspection to solve the technical problem that the existing drone nest location selection for power transmission line inspection cannot guarantee the overall inspection coverage rate.

[0004] To solve the above technical problem, in the first aspect of the embodiments of the present invention, a method for arranging drone nests for power transmission line inspection is provided, including:

[0005] Identifying several inflection point positions of the power transmission line according to the geographical data corresponding to the power transmission line;

[0006] Obtaining the terrain data within the preset search range of the inflection point positions, and determining several initial drone nest arrangement positions according to the terrain data and the preset safety distance requirement;

[0007] Selecting several candidate drone nest arrangement positions without inspection blind spots from several initial drone nest arrangement positions according to the position information of several preset inspection target points and the obstacle distribution range in the area where the power transmission line is located;

[0008] Calculating the visual coverage rate corresponding to each candidate drone nest arrangement position;

[0009] Based on the position information of several inspection target points, starting from the candidate location for the drone nest layout with the maximum current field of view coverage rate, a path planning algorithm is used to generate an inspection path, and the inspection coverage rate of the inspection path is calculated;

[0010] When the inspection coverage rate is greater than or equal to a preset inspection coverage rate threshold, it is determined that the candidate location for the drone nest layout with the maximum current field of view coverage rate is the target location for the drone nest layout.

[0011] As an optimal solution, the specific steps of identifying several inflection point positions of the transmission line according to the geographical data corresponding to the transmission line include:

[0012] According to the geographical data, obtain the set of longitude and latitude coordinate points within the transmission line corridor area;

[0013] Using the density clustering algorithm to perform clustering analysis on the set of longitude and latitude coordinate points with a preset search radius and the minimum number of coordinate points as clustering conditions, and obtain several inflection point positions.

[0014] As an optimal solution, the specific steps of obtaining the terrain data within the preset search range of the inflection point position and determining several initial locations for the drone nest layout according to the terrain data and the preset safety distance requirements include:

[0015] According to the set of longitude and latitude coordinate points, use the Kriging spatial interpolation algorithm to generate the terrain elevation surface data within the preset search range of the inflection point position; where the preset search range is specifically a three-dimensional cylindrical search range;

[0016] According to the terrain elevation surface data, calculate the maximum height difference value and the average slope value within the preset search range;

[0017] Obtain the remote sensing image data and terrain stability index within the preset search range, and calculate the normalized vegetation index within the preset search range according to the remote sensing image data;

[0018] According to the maximum height difference value, the average slope value, the normalized vegetation index and the terrain stability index, construct a spatial suitability scoring matrix, and use the multi-level fuzzy evaluation method to calculate the spatial suitability score of the inflection point position;

[0019] According to the obstacle distribution range, select several initial locations for the drone nest layout that meet the preset safety distance requirements from several inflection point positions where the spatial suitability score is greater than or equal to the spatial suitability score threshold;

[0020] Among them, the preset safety spacing requirements include: the distance between the location where the nest is arranged and any obstacle is greater than the preset safety distance threshold, the angle between the line connecting the location where the nest is arranged and its adjacent inflection point position and the horizontal plane is within the preset safety angle range, and the spacing between the location where the nest is arranged and its adjacent nest is greater than the preset safety spacing threshold.

[0021] As a preferred solution, the method of screening out several candidate nest arrangement positions without inspection blind spots from several initial nest arrangement positions according to the position information of several preset inspection target points and the obstacle distribution range in the area where the transmission line is located specifically includes:

[0022] Generate simulated inspection paths starting from each of the initial nest arrangement positions according to the position information of several inspection target points and the initial nest arrangement positions;

[0023] Obtain several flight trajectory sampling points on the simulated inspection paths according to the preset sampling distance;

[0024] Establish an observation frustum at each of the flight trajectory sampling points according to the preset apex angle parameter of the viewing cone and the observation distance threshold;

[0025] Use the ray detection method to obtain the intersection coordinate set of the boundary surface of the observation frustum and the obstacle distribution range, and determine the field of view occlusion area based on the intersection coordinate set;

[0026] Determine the field of view occlusion ratio corresponding to each of the simulated inspection paths according to the ratio between the number of inspection target points located in the field of view occlusion area corresponding to each of the simulated inspection paths and the total number of inspection target points;

[0027] Based on the initial nest arrangement positions corresponding to each of the simulated inspection paths, screen out several candidate nest arrangement positions without inspection blind spots from several initial nest arrangement positions according to the comparison result between the field of view occlusion ratio and the preset field of view occlusion ratio threshold; among them, the initial nest arrangement position corresponding to the simulated inspection path with the field of view occlusion ratio less than the field of view occlusion ratio threshold is the candidate nest arrangement position without inspection blind spots.

[0028] As a preferred solution, the method of generating simulated inspection paths starting from each of the initial nest arrangement positions according to the position information of several inspection target points and the initial nest arrangement positions specifically includes:

[0029] Generate an initial flight path point sequence starting from each of the initial nest arrangement positions according to the position information of several inspection target points and the initial nest arrangement positions by using the shortest path search algorithm;

[0030] Connect the initial flight path point sequence with a cubic Bezier curve according to the preset control point interval, curve smoothness, minimum turning radius, and maximum climbing angle to generate the simulated inspection path.

[0031] As a preferred solution, calculating the field of view coverage corresponding to each candidate nest layout position specifically includes:

[0032] Determine the inspection area of the transmission line according to each candidate nest layout position;

[0033] Divide the inspection area into several inspection grid units through a honeycomb grid with a preset size;

[0034] Determine the inspection target point density of each inspection grid unit according to the number of inspection target points in each inspection grid unit and the area of each inspection grid unit, and use the inspection grid units with an inspection target point density greater than the preset density threshold as the core monitoring areas;

[0035] Establish a polar coordinate scanning grid at the candidate nest layout position within the core monitoring area, and generate several observation sampling points on the polar coordinate scanning grid;

[0036] According to the obstacle distribution range, use the ray casting method to obtain the obstacle occlusion situation between the observation sampling points and each inspection grid unit within the core monitoring area, so as to screen out several effective observation points from several observation sampling points;

[0037] Determine the field of view coverage corresponding to each candidate nest layout position according to the ratio between the number of effective observation points and the number of observation sampling points.

[0038] As a preferred solution, generating an inspection path based on the position information of several inspection target points, starting from the candidate nest layout position with the largest current field of view coverage, using a path planning algorithm specifically includes:

[0039] Generate an initial inspection point sequence based on the position information of several inspection target points, starting from the candidate nest layout position with the largest current field of view coverage, using a path planning algorithm;

[0040] Adjust the positions of the inspection points in the initial inspection point sequence according to the preset flight altitude constraint and turning angle constraint to obtain an optimized inspection point sequence;

[0041] Connect the optimized inspection point sequence with a cubic spline curve according to the preset curvature constraint, control point interval, and turning angle constraint to generate the inspection path.

[0042] As a preferred solution, calculating the inspection coverage rate of the inspection path specifically includes:

[0043] Determining the inspection target area of the transmission line according to the position information of a plurality of the inspection target points;

[0044] Generating a plurality of three-dimensional inspection sampling points corresponding to the inspection path according to a preset sampling interval and height stratification information; wherein, the height stratification information is the height information of a plurality of height layers located above the center of the transmission line;

[0045] Determining the monitoring scan area corresponding to the inspection path according to the position information of each of the three-dimensional inspection sampling points and a preset monitoring scan width;

[0046] Obtaining the inspection coverage rate of the inspection path according to the ratio between the area of the overlapping area between the monitoring scan area and the inspection target area and the area of the inspection target area.

[0047] As a preferred solution, the method further includes:

[0048] When the inspection coverage rate is less than the inspection coverage rate threshold, eliminating the candidate drone nest layout position with the largest current field of view coverage rate;

[0049] Regenerating a new inspection path starting from the candidate drone nest layout position with the largest current field of view coverage rate, and calculating the inspection coverage rate of the new inspection path until the inspection coverage rate meets the inspection coverage rate threshold.

[0050] The second aspect of the embodiments of the present invention provides a drone nest layout system for inspecting transmission lines, including:

[0051] An inflection point position recognition module, configured to recognize a plurality of inflection point positions of the transmission line according to the geographical data corresponding to the transmission line;

[0052] An initial drone nest layout position determination module, configured to obtain the terrain data within a preset search range of the inflection point positions, and determine a plurality of initial drone nest layout positions according to the terrain data and a preset safety distance requirement;

[0053] A candidate drone nest layout position screening module, configured to screen out a plurality of candidate drone nest layout positions without inspection blind spots from the plurality of initial drone nest layout positions according to the position information of a preset plurality of inspection target points and the obstacle distribution range of the area where the transmission line is located;

[0054] A field of view coverage rate calculation module, configured to calculate the field of view coverage rate corresponding to each of the candidate drone nest layout positions;

[0055] An inspection coverage calculation module, configured to generate an inspection path by using a path planning algorithm starting from the candidate drone nest layout position with the largest current field of view coverage based on the position information of several inspection target points, and calculate the inspection coverage of the inspection path.

[0056] A target drone nest layout position determination module, configured to determine the candidate drone nest layout position with the largest current field of view coverage as the target drone nest layout position when the inspection coverage is greater than or equal to a preset inspection coverage threshold.

[0057] Compared with the prior art, the beneficial effect of the embodiment of the present invention is that in the process of screening the drone nest layout positions at the turning points of the transmission line, it is possible to eliminate the drone nest layout positions with inspection blind spots based on the obstacle distribution range in the area where the transmission line is located, and it is possible to determine the optimal drone nest layout position by combining the field of view coverage corresponding to the candidate drone nest layout positions and the inspection coverage of the inspection paths corresponding to each candidate drone nest layout position, so as to significantly improve the reliability of the drone nest layout at the turning points of the transmission line, which helps to improve the inspection coverage, inspection efficiency and inspection safety of the inspection drones for the transmission line. Description of the Drawings

[0058] Figure 1 is a schematic flowchart of the drone nest layout method for the inspection drone of the transmission line in the embodiment of the present invention;

[0059] Figure 2 is a schematic structural diagram of the drone nest layout system for the inspection drone of the transmission line in the embodiment of the present invention. Detailed Embodiments

[0060] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0061] Please refer to Figure 1 , the first aspect of the embodiment of the present invention provides a drone nest layout method for an inspection drone of a transmission line, including the following steps S1 to S6:

[0062] Step S1, identify several turning point positions of the transmission line according to the geographical data corresponding to the transmission line;

[0063] Step S2, obtain the terrain data within the preset search range of the turning point positions, and determine several initial drone nest layout positions according to the terrain data and the preset safety distance requirements;

[0064] Step S3, based on the position information of a plurality of preset inspection target points and the obstacle distribution range in the area where the transmission line is located, screen out a plurality of candidate drone nest deployment positions without inspection blind spots from the plurality of initial drone nest deployment positions;

[0065] Step S4, calculate the field of view coverage rate corresponding to each of the candidate drone nest deployment positions;

[0066] Step S5, based on the position information of the plurality of inspection target points, starting from the candidate drone nest deployment position with the largest current field of view coverage rate, generate an inspection path using a path planning algorithm, and calculate the inspection coverage rate of the inspection path;

[0067] Step S6, when the inspection coverage rate is greater than or equal to a preset inspection coverage rate threshold, determine the candidate drone nest deployment position with the largest current field of view coverage rate as the target drone nest deployment position.

[0068] Specifically, since the embodiments of the present invention are for deploying drone nests at the inflection point positions of transmission lines, and the different deployment positions of the drone nests will directly affect the range and distribution density of inspection blind spots. Therefore, in order to obtain the optimal drone nest deployment position, this embodiment first needs to identify a plurality of inflection point positions of the transmission line based on the geographical data corresponding to the transmission line; further, since the terrain conditions at different inflection point positions are usually different, and some inflection point positions may have poor geological stability. If a drone nest is deployed at this inflection point position, it may greatly affect the safety of drone takeoff, landing, or flight. Therefore, this embodiment determines a plurality of initial drone nest deployment positions based on the terrain data within a preset search range of the inflection point position and in combination with the safety distance requirements.

[0069] Furthermore, to ensure that the finally selected drone nest deployment position will not cause a large number of inspection blind spots during the inspection of the drone, this embodiment further screens the candidate drone nest deployment positions from the initial drone nest deployment positions based on the position information of a plurality of inspection target points and the obstacle distribution range in the area where the transmission line is located. It can be understood that the inspection routes starting from different drone nest deployment positions are different, and irregular obstacle distributions may be encountered during the inspection, resulting in irregular field of view occlusion areas. If too many inspection target points fall into this field of view occlusion area, it will form an inspection blind spot. Therefore, it is necessary to screen out a plurality of candidate drone nest deployment positions without inspection blind spots from the plurality of initial drone nest deployment positions, that is, the initial drone nest deployment positions with inspection blind spots are no longer considered.

[0070] Further, in order to improve the inspection safety and efficiency of the UAV, the location of the nest should be selected in a position with as wide a view as possible. For example, if the location of a certain nest is on the ridge line, the view of such a nest location is generally wide and there is no obvious obstruction. However, if the location of a certain nest is in the valley, the obstruction is relatively serious. Therefore, in this embodiment, the priorities of the current candidate nest locations are sorted by calculating the view coverage rates corresponding to the respective candidate nest locations. Starting from the candidate nest location with the largest current view coverage rate, a path planning algorithm is used to generate an inspection path to simulate the inspection situation of the UAV starting from this candidate nest location. By calculating the inspection coverage rate of this inspection path, it is determined whether it is greater than or equal to a preset inspection coverage rate threshold. If so, it indicates that the candidate nest location with the largest current view coverage rate meets the inspection requirements and is used as the target nest location.

[0071] The method for arranging the nest of the UAV for power transmission line inspection provided by the embodiment of the present invention can eliminate the nest locations with inspection blind spots based on the obstacle distribution range in the area where the power transmission line is located during the screening process of the nest locations at the turning points of the power transmission line, and can determine the optimal nest location by combining the view coverage rate corresponding to the candidate nest location and the inspection coverage rate of the inspection path corresponding to each candidate nest location, thereby significantly improving the reliability of arranging the nest at the turning points of the power transmission line and helping to improve the inspection coverage rate, inspection efficiency and inspection safety of the UAV for power transmission line inspection.

[0072] As a preferred solution, the step of identifying several turning point positions of the power transmission line according to the geographical data corresponding to the power transmission line specifically includes:

[0073] According to the geographical data, obtain the set of longitude and latitude coordinate points in the corridor area of the power transmission line;

[0074] Using the preset search radius and the minimum number of coordinate points as the clustering conditions, perform density clustering analysis on the set of longitude and latitude coordinate points to obtain several of the turning point positions.

[0075] It should be noted that the transmission line corridor area refers to the area between two parallel lines formed by extending a certain distance outward from the outer sides of the two conductors of the transmission line on the premise of considering the maximum wind deflection and safety distance. Based on geographical data, a set of longitude and latitude coordinate points within the transmission line corridor area can be obtained, and this set of longitude and latitude coordinate points is used to describe the boundary and position of the transmission line corridor area. Further, considering that the inflection point positions of the transmission line are usually the positions where the direction of the transmission line changes significantly. In the geographical space, points near these inflection points (such as poles, line nodes, etc.) often gather together to form a high-density area. Therefore, in this embodiment, the search radius and the minimum number of coordinate points are set as clustering conditions. For example, the search radius is set to 25 meters, the minimum number of coordinate points is set to 4, etc. This embodiment does not make specific limitations here, which can be set according to actual needs, so as to be able to perform clustering analysis on this set of longitude and latitude coordinate points using the density clustering algorithm, screen out several clusters containing the inflection point positions, and then the center point of each cluster can be regarded as the inflection point position, or further geometric analysis can be performed, such as the angle of direction change of the transmission line, etc., to determine the final inflection point position.

[0076] As a preferred solution, obtaining the terrain data within the preset search range of the inflection point position, and determining several initial drone nest layout positions according to the terrain data and the preset safety spacing requirements specifically includes:

[0077] Generating terrain elevation surface data within the preset search range of the inflection point position by using the Kriging spatial interpolation algorithm according to the set of longitude and latitude coordinate points; wherein, the preset search range is specifically a three-dimensional cylindrical search range;

[0078] Calculating the maximum height difference value and the average slope value within the preset search range according to the terrain elevation surface data;

[0079] Obtaining the remote sensing image data and the terrain stability index within the preset search range, and calculating the normalized vegetation index within the preset search range according to the remote sensing image data;

[0080] Constructing a spatial suitability scoring matrix according to the maximum height difference value, the average slope value, the normalized vegetation index and the terrain stability index, and calculating the spatial suitability score of the inflection point position by using the multi-level fuzzy evaluation method;

[0081] Selecting several of the initial drone nest layout positions that meet the preset safety spacing requirements from several inflection point positions whose spatial suitability scores are greater than or equal to the spatial suitability score threshold according to the obstacle distribution range;

[0082] Among them, the preset safety distance requirements include: the distance between the location where the aircraft nest is arranged and any obstacle is greater than the preset safety distance threshold, the angle between the line connecting the location where the aircraft nest is arranged and its adjacent inflection point location and the horizontal plane is within the preset safety angle range, and the distance between the location where the aircraft nest is arranged and its adjacent aircraft nest is greater than the preset safety distance threshold.

[0083] Further, in order to fully reflect the terrain features of each inflection point location, in this embodiment, the Kriging spatial interpolation algorithm is first used to generate terrain elevation surface data based on the set of longitude and latitude coordinate points within the preset search range of the inflection point location. It should be noted that the preset search range is specifically a three-dimensional cylindrical search range. Exemplarily, the radius of the three-dimensional cylindrical search range can be set to 80 meters, and its height range is from the ground to the inflection point location. Then, the maximum height difference value and the average slope value within the preset search range are calculated from the terrain elevation surface data within the preset search range as terrain feature parameters.

[0084] Further, the normalized vegetation index within the preset search range can be calculated based on the remote sensing image data within the preset search range. Specifically, the normalized vegetation index is a commonly used vegetation index for evaluating the health status and coverage of vegetation. It can be calculated by using the red light and near-infrared light bands in the remote sensing data: NDVI = (NIR - Red) / (NIR + Red), where NDVI represents the normalized vegetation index, NIR represents the reflectance of the near-infrared light band, and Red represents the reflectance of the red light band. Combining with the geological parameter database, the terrain stability indicators within the preset search range can be obtained, including the dip angle of the rock layer, the degree of weathering, etc. The normalized vegetation index and the terrain stability indicators are used as environmental feature parameters and safety feature parameters respectively.

[0085] Furthermore, based on the maximum height difference value, the average slope value, the normalized vegetation index, and the terrain stability index, a spatial suitability scoring matrix is constructed. It should be noted that since the numerical ranges and dimensions of the various indicators are different, fuzzy processing is required to convert them into dimensionless fuzzy values. The multi-level fuzzy evaluation method is used to set the evaluation index system to include a terrain layer, an environment layer, and a safety layer, and the weights of each level can be set separately. For example, the weight of the terrain layer is 0.4, the weight of the environment layer is 0.3, and the weight of the safety layer is 0.3. For the terrain layer, the score of the terrain layer needs to be obtained based on the weighted sum of the fuzzy values of the maximum height difference value and the average slope value. The weights of the maximum height difference value and the average slope value can be the same or set to be different based on actual requirements; for the environment layer, the fuzzy value of the normalized vegetation index is used as the score of the environment layer; for the safety layer, the score of the safety layer needs to be obtained based on the weighted sum of the fuzzy values of the rock layer dip angle and the weathering degree. The weights of the rock layer dip angle and the weathering degree can be the same or set to be different based on actual requirements. Therefore, based on the scores and weight values of the terrain layer, the environment layer, and the safety layer respectively, a spatial suitability scoring matrix can be constructed and the spatial suitability score of the inflection point position can be further calculated. Exemplarily, assuming that the score of the terrain layer is 85 and the weight value is 0.4; the score of the environment layer is 78 and the weight value is 0.3; the score of the safety layer is 82 and the weight value is 0.3, then the spatial suitability score of the current inflection point position is 82.

[0086] Furthermore, the inflection point positions are screened by setting a spatial suitability score threshold. It can be understood that if the spatial suitability score of a certain inflection point position is less than the spatial suitability score threshold, it indicates that there will be a greater potential safety hazard for the UAV to take off and land at this inflection point position. In order to further ensure the flight safety of the UAV, a safety distance requirement is set in this embodiment, including: the distance between the UAV nest layout position and any obstacle is greater than a preset safety distance threshold (such as 15 meters), so as to avoid the UAV colliding with obstacles during takeoff and landing; the angle between the line connecting the UAV nest layout position and its adjacent inflection point position and the horizontal plane is within a preset safe angle range (such as greater than 75 degrees and less than 150 degrees), and the distance between the UAV nest layout position and its adjacent UAV nest is greater than a preset safety distance threshold (such as 200 meters), so as to ensure the safety of the UAV takeoff, landing and flight. As an optional embodiment, this embodiment can obtain the obstacle distribution range and the information of the already laid UAV nest positions in the area where the transmission line is located. Based on the obstacle distribution range and the information of the already laid UAV nest positions, the minimum distance constraint algorithm can be used to quickly obtain the inflection point positions that meet the corresponding safety distance requirements.

[0087] As a preferred solution, screening out several candidate drone nest deployment positions without inspection blind spots from several initial drone nest deployment positions according to the position information of several preset inspection target points and the obstacle distribution range of the area where the transmission line is located specifically includes:

[0088] Generating simulated inspection paths starting from each of the initial drone nest deployment positions according to the position information of several inspection target points and the initial drone nest deployment positions;

[0089] Obtaining several flight trajectory sampling points on the simulated inspection paths according to a preset sampling distance;

[0090] Establishing an observation frustum at each of the flight trajectory sampling points according to a preset apex angle parameter of the frustum and an observation distance threshold;

[0091] Using a ray detection method to obtain the intersection coordinate set of the boundary surface of the observation frustum and the obstacle distribution range, and determining the field-of-view occlusion area based on the intersection coordinate set;

[0092] Determining the field-of-view occlusion ratio corresponding to each of the simulated inspection paths according to the ratio between the number of inspection target points located in the field-of-view occlusion area corresponding to each of the simulated inspection paths and the total number of inspection target points;

[0093] Based on the initial drone nest deployment positions corresponding to each of the simulated inspection paths, screening out several candidate drone nest deployment positions without inspection blind spots from several initial drone nest deployment positions according to the comparison result between the field-of-view occlusion ratio and a preset field-of-view occlusion ratio threshold; wherein, the initial drone nest deployment position corresponding to the simulated inspection path with a field-of-view occlusion ratio less than the field-of-view occlusion ratio threshold is the candidate drone nest deployment position without inspection blind spots.

[0094] Specifically, in order to detect the field-of-view occlusion situation of the inspection paths starting from different initial drone nest deployment positions, in this embodiment, first, simulated inspection paths starting from each initial drone nest deployment position are generated according to the position information of each inspection target point and the initial drone nest deployment positions. Further, in order to obtain the field of view of the drone on each simulated inspection path, in this embodiment, several flight trajectory sampling points on the simulated inspection paths are obtained according to a preset sampling distance. Exemplarily, the preset sampling distance can be set to 10 meters, and then an observation frustum is established at each flight trajectory sampling point. Exemplarily, the apex angle parameter of the frustum can be set to 120 degrees, and the observation distance threshold can be set to 80 meters, so as to reasonably simulate the field of view of the drone.

[0095] Further, by using the ray detection method, rays are emitted from the vertex of the observation frustum to each point on the boundary surface, and the first intersection points of the rays with the obstacle range are recorded. When there are continuous intersection points, the boundary of an occlusion area can be determined, and thus the field of view occlusion area can be obtained based on the intersection point coordinate set.

[0096] Further, based on the ratio between the number of inspection target points falling within the field of view occlusion area and the total number of inspection target points, the field of view occlusion ratio corresponding to each simulated inspection path can be calculated, and several candidate drone nest layout positions without inspection blind spots can be screened out based on a preset field of view occlusion ratio threshold. Exemplarily, the field of view occlusion ratio threshold can be set to 15%, which not only considers the static obstacle distribution but also combines the dynamic observation characteristics during the actual flight of the drone, ensuring the integrity of the field of view coverage for the inspection task.

[0097] As a preferred solution, generating the simulated inspection paths starting from each of the initial drone nest layout positions according to the position information of the several inspection target points and the initial drone nest layout positions specifically includes:

[0098] According to the position information of the several inspection target points and the initial drone nest layout positions, an initial flight path point sequence starting from each of the initial drone nest layout positions is generated by using the shortest path search algorithm;

[0099] According to the preset control point interval, curve smoothness, minimum turning radius, and maximum climbing angle, the initial flight path point sequence is connected by a cubic Bézier curve to generate the simulated inspection path.

[0100] Specifically, in this embodiment, taking each initial drone nest layout position as the starting point, based on the position information of the several inspection target points, an initial flight path point sequence starting from each initial drone nest layout position is generated by using the shortest path search algorithm. Since the initial flight path point sequence consists of discrete path points, in this embodiment, the various path points in the initial flight path point sequence are further smoothly connected by a cubic Bézier curve. Exemplarily, the control point interval can be set to 50 meters, the curve smoothness can be set to 0.8. Considering the flight performance constraints of the drone, the minimum turning radius can be set to 25 meters, and the maximum climbing angle can be set to 35 degrees, thereby generating a continuous simulated inspection path.

[0101] Preferably, after determining the initial flight path point sequence, if the distance between any two path points in the initial flight path point sequence exceeds a preset distance threshold (such as 300 meters), a transition point can be set between these two path points.

[0102] As a preferred solution, calculating the field of view coverage rate corresponding to each of the candidate drone nest layout positions specifically includes:

[0103] Determine the inspection area of the transmission line according to each candidate location for the drone nest layout.

[0104] Divide the inspection area into a number of inspection grid units by a honeycomb grid of a preset size.

[0105] Determine the inspection target point density of each inspection grid unit according to the number of inspection target points in each inspection grid unit and the area of each inspection grid unit, and use the inspection grid units with the inspection target point density greater than a preset density threshold as the core monitoring areas.

[0106] Establish a polar coordinate scanning grid at the candidate location for the drone nest layout within the core monitoring area, and generate a number of observation sampling points on the polar coordinate scanning grid.

[0107] According to the obstacle distribution range, use the ray casting method to obtain the obstacle occlusion situation between the observation sampling points in the core monitoring area and each inspection grid unit, so as to screen out a number of effective observation points from a number of the observation sampling points.

[0108] Determine the field of view coverage rate corresponding to each candidate location for the drone nest layout according to the ratio between the number of effective observation points and the number of observation sampling points.

[0109] Specifically, according to each candidate location for the drone nest layout, the boundary of the inspection area of the transmission line can be constructed to determine the corresponding inspection area, and then the inspection area is divided into a number of inspection grid units by a honeycomb grid of a preset size. Exemplarily, in order to ensure the regularity of the divided inspection grid units, the honeycomb grid in this embodiment is a regular hexagon honeycomb grid with a side length of 50 meters.

[0110] Furthermore, based on the number of inspection target points in each inspection grid unit and the area of each inspection grid unit, the corresponding inspection target point density can be obtained. Considering that the area with a higher inspection target point density is the key area of the inspection task and also the key to ensuring the inspection coverage rate, in this embodiment, the inspection grid units with the inspection target point density greater than a preset density threshold are used as the core monitoring areas. Exemplarily, the preset density threshold can be set to 80 inspection target points per square kilometer.

[0111] Furthermore, a polar coordinate scanning grid is established for each candidate drone nest layout position within the core monitoring area, and a number of observation sampling points are generated on the polar coordinate scanning grid. Exemplarily, the observation sampling points in this embodiment are evenly generated. Specifically, the azimuth angle interval is 5 degrees, and the sampling interval is 10 meters in the direction of each azimuth angle to generate the observation sampling points. To analyze the line-of-sight accessibility of each candidate drone nest layout position, the ray transmission method is adopted in this embodiment. Detection rays are emitted from each observation sampling point to the inspection target points within the core monitoring area, and the intersection situations of the detection rays with obstacles are recorded. If a detection ray does not intersect with an obstacle, it indicates that the line of sight of this observation sampling point is good and it is a valid observation point; if a detection ray intersects with any obstacle, it indicates that the line of sight of this observation sampling point is blocked and it is an invalid observation point. Based on this analysis method, a number of valid observation points can be screened out from the number of observation sampling points, and then according to the ratio between the number of valid observation points and the number of observation sampling points, the field of view coverage rate corresponding to each candidate drone nest layout position can be determined. The higher the proportion of valid observation points, the better the line of sight of this candidate drone nest layout position. It is worth noting that the terrain with large undulations has a significant impact on the field of view coverage rate. The field of view coverage rate of the candidate drone nest layout positions located in valleys is generally lower than 70%, while the field of view coverage rate of the candidate drone nest layout positions at higher altitudes mostly exceeds 80%. This fully reflects the important impact of the selection of drone nest layout positions at different line inflection points on the inspection effect.

[0112] As a preferred solution, based on the position information of the several inspection target points, starting from the candidate drone nest layout position with the largest current field of view coverage rate, a path planning algorithm is used to generate an inspection path, which specifically includes:

[0113] Based on the position information of the several inspection target points, starting from the candidate drone nest layout position with the largest current field of view coverage rate, an initial inspection point sequence is generated by using a path planning algorithm;

[0114] According to the preset flight height constraint and turning angle constraint, the positions of the inspection points in the initial inspection point sequence are adjusted to obtain an optimized inspection point sequence;

[0115] According to the preset curvature constraint, control point interval and the turning angle constraint, a cubic spline curve is used to connect the optimized inspection point sequence to generate the inspection path.

[0116] Specifically, in order to simulate the inspection path of the UAV starting from the candidate nest deployment position with the largest current field of view coverage rate, based on the position information of several inspection target points in this embodiment, taking the candidate nest deployment position with the largest current field of view coverage rate as the starting point, the path planning algorithm is used to generate an initial sequence of inspection points. During the path planning process, the shortest path can be used as the optimization goal, combined with the preset flight altitude constraint and turning angle constraint, to generate the optimal initial sequence of inspection points, so as to improve the inspection efficiency as much as possible. Further, according to the flight altitude constraint and turning angle constraint, the positions of the inspection points in the initial sequence of inspection points are adjusted. For example, if the flight altitude of a certain inspection point exceeds the flight altitude constraint, it is lowered to meet the flight altitude constraint; if the turning angle between the two flight path segments formed by any three consecutive inspection points exceeds the turning angle constraint, the positions of these three consecutive inspection points can be adjusted so that the turning angle between the two flight path segments meets the turning angle constraint. This embodiment will not elaborate too much here, so as to obtain an optimized sequence of inspection points that can not only ensure the inspection efficiency but also conform to the flight performance of the UAV.

[0117] Further, since the inspection points in the optimized sequence of inspection points are discrete, in this embodiment, the cubic spline curve is used to smoothly connect the optimized sequence of inspection points, so as to generate a continuous inspection path. Exemplarily, during the process of smoothly connecting the inspection points, the minimum curvature radius can be limited to 60 meters, the maximum turning angle can be set to 45 degrees, and the control point interval can be set to 50 meters. These parameters can all be set according to actual needs, and this embodiment does not make specific limitations here.

[0118] As a preferred solution, calculating the inspection coverage rate of the inspection path specifically includes:

[0119] According to the position information of several inspection target points, determine the inspection target area of the transmission line;

[0120] According to the preset sampling interval and height stratification information, generate several three-dimensional inspection sampling points corresponding to the inspection path; wherein, the height stratification information is the height information of several height layers above the center of the transmission line;

[0121] According to the position information of each three-dimensional inspection sampling point and the preset monitoring scan width, determine the monitoring scan area corresponding to the inspection path;

[0122] According to the ratio of the overlapping area between the monitoring scan area and the inspection target area to the area of the inspection target area, obtain the inspection coverage rate of the inspection path.

[0123] Specifically, to determine the inspection coverage rate of the current inspection path, in this embodiment, it is first necessary to determine the inspection target area of the transmission line according to the position information of several inspection target points.

[0124] Furthermore, considering that during the inspection process of the drone along the inspection path, the monitored scanning range it covers is definitely not only the area where the inspection path is located. Therefore, in this embodiment, several height layers are set above the center of the transmission line in the vertical direction. For example, height layers are set at 30 meters, 45 meters, and 60 meters above the center of the transmission line, and based on a preset sampling interval (such as 15 meters), several three-dimensional inspection sampling points are generated along the inspection path and at each height layer. This multi-layer sampling method shows good adaptability in practical applications.

[0125] Furthermore, when constructing the monitored scanning area in this embodiment, the field-of-view angle characteristics of the sensors installed on the drone are considered, and the monitored scanning width is preset. For example, the monitored scanning width is set to 120 meters. In an alternative embodiment, the scanning range can be expanded to both sides centered on the three-dimensional inspection sampling points, so that the width of the entire monitored scanning range is always 120 meters, thereby forming the monitored scanning area corresponding to the current inspection path.

[0126] Furthermore, determine the overlapping area between the monitored scanning area and the inspection target area. This overlapping area indicates the area of the inspection target area that can be covered by the current inspection path. Then, calculate the ratio between the overlapping area and the area of the inspection target area to obtain the inspection coverage rate of the inspection path.

[0127] As a preferred solution, the method further includes:

[0128] When the inspection coverage rate is less than the inspection coverage rate threshold, eliminate the candidate drone base layout position with the largest current field-of-view coverage rate;

[0129] Regenerate a new inspection path starting from the candidate drone base layout position with the largest current field-of-view coverage rate, and calculate the inspection coverage rate of the new inspection path until the inspection coverage rate meets the inspection coverage rate threshold.

[0130] Exemplarily, the inspection coverage rate threshold in this embodiment can be set to 85%, or can be set to other values according to actual inspection requirements. This embodiment does not make specific limitations here. When the calculated inspection coverage rate is less than the inspection coverage rate threshold, it indicates that the candidate airframe layout position with the largest current field of view coverage does not meet the inspection requirements. Therefore, this candidate airframe layout position is excluded and not considered. At this time, the candidate airframe layout position with the largest field of view coverage is the candidate airframe layout position that originally ranked second in terms of field of view coverage. Starting from this candidate airframe layout position, the path planning algorithm is used again to generate a new inspection path, and the inspection coverage rate of this new inspection path is calculated until the inspection coverage rate meets the inspection coverage rate threshold.

[0131] As an optional embodiment, in the process of planning the inspection path in this embodiment, for the inflection point positions in the inspection path, first, according to the set of longitude and latitude coordinate points in the transmission line corridor area, the line direction vectors on both sides of the inflection point position are calculated through the adjacent longitude and latitude coordinate points at the inflection point position, so as to identify the directions of the transmission lines on both sides of the inflection point position. Then, several auxiliary waypoints are added near the inflection point position in the directions of the transmission lines on both sides of the inflection point position. For example, 6 auxiliary waypoints can be added respectively in the two directions, so that the UAV can pass through the inflection point smoothly and effectively cover the transmission lines on both sides when passing through the inflection point position.

[0132] Please refer to Figure 2 , the second aspect of the embodiment of the present invention provides a system for laying out airframes of an unmanned aerial vehicle for inspecting transmission lines, including:

[0133] An inflection point position recognition module 101, configured to recognize several inflection point positions of the transmission line according to the geographical data corresponding to the transmission line;

[0134] An initial airframe layout position determination module 102, configured to obtain the terrain data within the preset search range of the inflection point position, and determine several initial airframe layout positions according to the terrain data and the preset safety distance requirements;

[0135] A candidate airframe layout position screening module 103, configured to screen out several candidate airframe layout positions without inspection blind spots from several initial airframe layout positions according to the position information of several preset inspection target points and the obstacle distribution range in the area where the transmission line is located;

[0136] A field of view coverage rate calculation module 104, configured to calculate the field of view coverage rate corresponding to each candidate airframe layout position;

[0137] The inspection coverage calculation module 105 is configured to generate an inspection path starting from the candidate drone nest deployment location with the maximum current field of view coverage based on the location information of a plurality of the inspection target points, and calculate the inspection coverage of the inspection path;

[0138] The target drone nest deployment location determination module 106 is configured to determine the candidate drone nest deployment location with the maximum current field of view coverage as the target drone nest deployment location when the inspection coverage is greater than or equal to a preset inspection coverage threshold.

[0139] As an optimal solution, the inflection point position recognition module 101 is configured to identify a plurality of inflection point positions of the transmission line according to the geographic data corresponding to the transmission line, specifically including:

[0140] Obtain a set of longitude and latitude coordinate points within the transmission line corridor area according to the geographic data;

[0141] Perform clustering analysis on the set of longitude and latitude coordinate points by using a density clustering algorithm with a preset search radius and a minimum number of coordinate points as clustering conditions to obtain a plurality of the inflection point positions.

[0142] As an optimal solution, the initial drone nest deployment location determination module 102 is configured to obtain terrain data within a preset search range of the inflection point positions, and determine a plurality of initial drone nest deployment locations according to the terrain data and the preset safety distance requirement, specifically including:

[0143] Generate terrain elevation surface data within a preset search range of the inflection point positions by using a Kriging spatial interpolation algorithm according to the set of longitude and latitude coordinate points; wherein, the preset search range is specifically a three-dimensional cylindrical search range;

[0144] Calculate the maximum height difference value and the average slope value within the preset search range according to the terrain elevation surface data;

[0145] Obtain remote sensing image data and terrain stability indicators within the preset search range, and calculate the normalized vegetation index within the preset search range according to the remote sensing image data;

[0146] Construct a spatial suitability scoring matrix according to the maximum height difference value, the average slope value, the normalized vegetation index and the terrain stability indicators, and calculate the spatial suitability score of the inflection point positions by using a multi-level fuzzy evaluation method;

[0147] Select a plurality of the initial drone nest deployment locations that meet the preset safety distance requirement from a plurality of inflection point positions with a spatial suitability score greater than or equal to a spatial suitability score threshold according to the obstacle distribution range;

[0148] Among them, the preset safety distance requirements include: the distance between the position where the aircraft nest is arranged and any obstacle is greater than the preset safety distance threshold, the angle between the line connecting the position where the aircraft nest is arranged and its adjacent inflection point position and the horizontal plane is within the preset safety angle range, and the distance between the position where the aircraft nest is arranged and its adjacent aircraft nest is greater than the preset safety distance threshold.

[0149] As a preferred solution, the candidate aircraft nest layout position screening module 103 is used to screen out several candidate aircraft nest layout positions without inspection blind spots from several initial aircraft nest layout positions according to the position information of several preset inspection target points and the obstacle distribution range in the area where the transmission line is located. Specifically, it includes:

[0150] Generate simulated inspection paths starting from each of the initial aircraft nest layout positions according to the position information of several inspection target points and the initial aircraft nest layout positions;

[0151] Obtain several flight trajectory sampling points on the simulated inspection path according to the preset sampling distance;

[0152] Establish an observation frustum at each flight trajectory sampling point according to the preset apex angle parameter of the viewing cone and the observation distance threshold;

[0153] Use the ray detection method to obtain the intersection coordinate set of the boundary surface of the observation frustum and the obstacle distribution range, and determine the field of view occlusion area based on the intersection coordinate set;

[0154] Determine the field of view occlusion ratio corresponding to each simulated inspection path according to the ratio between the number of inspection target points located in the field of view occlusion area corresponding to each simulated inspection path and the total number of inspection target points;

[0155] Based on the initial aircraft nest layout positions corresponding to each simulated inspection path, screen out several candidate aircraft nest layout positions without inspection blind spots from several initial aircraft nest layout positions according to the comparison result between the field of view occlusion ratio and the preset field of view occlusion ratio threshold; among them, the initial aircraft nest layout position corresponding to the simulated inspection path with the field of view occlusion ratio less than the field of view occlusion ratio threshold is the candidate aircraft nest layout position without inspection blind spots.

[0156] As a preferred solution, the candidate aircraft nest layout position screening module 103 is used to generate simulated inspection paths starting from each of the initial aircraft nest layout positions according to the position information of several inspection target points and the initial aircraft nest layout positions. Specifically, it includes:

[0157] According to the position information of several of the inspection target points and the initial drone nest layout positions, use the shortest path search algorithm to generate an initial flight path point sequence starting from each of the initial drone nest layout positions;

[0158] According to the preset control point interval, curve smoothness, minimum turning radius, and maximum climbing angle, connect the initial flight path point sequence through a cubic Bezier curve to generate the simulated inspection path.

[0159] As a preferred solution, the field of view coverage calculation module 104 is used to calculate the field of view coverage corresponding to each of the candidate drone nest layout positions, specifically including:

[0160] According to each of the candidate drone nest layout positions, determine the inspection area of the power transmission line;

[0161] Divide the inspection area into several inspection grid units through honeycomb grids of a preset size;

[0162] According to the number of inspection target points in each of the inspection grid units and the area of each of the inspection grid units, determine the inspection target point density of each of the inspection grid units, and use the inspection grid units with an inspection target point density greater than a preset density threshold as the core monitoring areas;

[0163] Establish a polar coordinate scanning grid at the candidate drone nest layout positions within the core monitoring area, and generate several observation sampling points on the polar coordinate scanning grid;

[0164] According to the obstacle distribution range, use the ray casting method to obtain the obstacle occlusion situation between the observation sampling points in the core monitoring area and each of the inspection grid units, so as to screen out several effective observation points from several of the observation sampling points;

[0165] Determine the field of view coverage corresponding to each of the candidate drone nest layout positions according to the ratio between the number of effective observation points and the number of observation sampling points.

[0166] As a preferred solution, the inspection coverage calculation module 105 is used to generate an inspection path based on the position information of several of the inspection target points, starting from the candidate drone nest layout position with the largest current field of view coverage, using a path planning algorithm, specifically including:

[0167] Based on the position information of several of the inspection target points, starting from the candidate drone nest layout position with the largest current field of view coverage, use a path planning algorithm to generate an initial inspection point sequence;

[0168] Adjust the positions of the inspection points in the initial inspection point sequence according to the preset flight altitude constraint and steering angle constraint to obtain an optimized inspection point sequence;

[0169] Connect the optimized inspection point sequence using a cubic spline curve according to the preset curvature constraint, control point interval, and the steering angle constraint to generate the inspection path.

[0170] As a preferred solution, the inspection coverage calculation module 105 is used to calculate the inspection coverage of the inspection path, specifically including:

[0171] Determine the inspection target area of the transmission line according to the position information of several of the inspection target points;

[0172] Generate several three-dimensional inspection sampling points corresponding to the inspection path according to the preset sampling interval and height stratification information; wherein, the height stratification information is the height information of several height layers above the center of the transmission line;

[0173] Determine the monitoring scan area corresponding to the inspection path according to the position information of each of the three-dimensional inspection sampling points and the preset monitoring scan width;

[0174] Obtain the inspection coverage of the inspection path according to the ratio between the overlapping area between the monitoring scan area and the inspection target area and the area of the inspection target area.

[0175] As a preferred solution, the target nest layout position determination module 106 is further used for:

[0176] When the inspection coverage is less than the inspection coverage threshold, eliminate the candidate nest layout position with the largest current field of view coverage;

[0177] The inspection coverage calculation module 105 is further used for:

[0178] Regenerate a new inspection path starting from the candidate nest layout position with the largest current field of view coverage and calculate the inspection coverage of the new inspection path until the inspection coverage meets the inspection coverage threshold.

[0179] In the process of screening the installation positions of the drone nests at the turning points of the transmission line, the drone nest installation system provided by the embodiment of the present invention can eliminate the installation positions of the drone nests with inspection blind spots based on the obstacle distribution range in the area where the transmission line is located, and can determine the optimal installation position of the drone nest by combining the field of view coverage rate corresponding to the candidate drone nest installation position and the inspection coverage rate of the inspection path corresponding to each candidate drone nest installation position. Therefore, the reliability of the drone nest installation at the turning points of the transmission line can be significantly improved, which helps to improve the inspection coverage rate, inspection efficiency and inspection safety of the transmission line inspection drones.

[0180] The above is the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements are also regarded as the protection scope of the present invention.

Claims

1. A method for deploying a nest of a transmission line inspection drone, characterized in that: include: Identifying several turning point locations of the transmission line according to geographic data corresponding to the transmission line; Acquire terrain data within a preset search range of the inflection point position, and determine a number of initial machine nest layout positions according to the terrain data and preset safety spacing requirements; According to the location information of a plurality of preset inspection target points and the obstacle distribution range of the area where the transmission line is located, a plurality of candidate machine nest layout positions without inspection blind spots are screened out from the plurality of initial machine nest layout positions; Calculating the field of view coverage corresponding to each of the candidate machine nest layout positions; Based on the position information of several inspection target points, taking the candidate machine nest layout position with the largest field of view coverage as the starting point, a path planning algorithm is used to generate an inspection path, and the inspection coverage of the inspection path is calculated; When the inspection coverage rate is greater than or equal to a preset inspection coverage rate threshold, determining the candidate machine nest layout position with the largest current field of view coverage rate as the target machine nest layout position; Among them, the method of screening out several candidate machine nest layout positions without inspection blind spots from several initial machine nest layout positions according to the location information of several preset inspection target points and the obstacle distribution range of the area where the transmission line is located specifically includes: Generate a simulated inspection path starting from each of the initial machine nest layout positions according to the location information of the inspection target points and the initial machine nest layout positions; According to a preset sampling distance, a plurality of flight trajectory sampling points on the simulated inspection path are obtained; According to the preset cone vertex angle parameters and the observation distance threshold, an observation cone is established at each of the flight trajectory sampling points; Using a ray detection method to obtain a set of intersection coordinates between a boundary surface of the observation cone and the obstacle distribution range, and determining a field of view obstruction area based on the set of intersection coordinates; Determine the field of view obstruction ratio corresponding to each of the simulated inspection paths according to the ratio between the number of inspection target points located in the field of view obstruction area corresponding to each of the simulated inspection paths and the total number of inspection target points; Based on the initial machine nest layout positions corresponding to each of the simulated inspection paths, and according to the comparison result between the field of view obstruction ratio and the preset field of view obstruction ratio threshold, a number of candidate machine nest layout positions without inspection blind spots are screened out from the several initial machine nest layout positions; wherein, the initial machine nest layout positions corresponding to the simulated inspection paths whose field of view obstruction ratio is less than the field of view obstruction ratio threshold are candidate machine nest layout positions without inspection blind spots.

2. The method for deploying a nest of a transmission line inspection drone according to claim 1, characterized in that: The step of identifying a plurality of inflection points of the transmission line according to the geographical data corresponding to the transmission line specifically includes: According to the geographic data, a set of longitude and latitude coordinate points in the transmission line corridor area is obtained; Taking the preset search radius and the minimum number of coordinate points as clustering conditions, a density clustering algorithm is used to perform cluster analysis on the longitude and latitude coordinate point set to obtain a plurality of inflection point positions.

3. The method for deploying a nest of a transmission line inspection drone according to claim 2, characterized in that: The obtaining of terrain data within a preset search range of the inflection point position, and determining a plurality of initial machine nest layout positions according to the terrain data and a preset safety spacing requirement, specifically includes: According to the set of longitude and latitude coordinate points, a Kriging spatial interpolation algorithm is used to generate terrain elevation surface data within a preset search range of the inflection point position; wherein the preset search range is specifically a three-dimensional cylindrical search range; Calculate the maximum height difference and average slope value within the preset search range according to the terrain elevation surface data; Acquire remote sensing image data and terrain stability index within the preset search range, and calculate the normalized vegetation index within the preset search range based on the remote sensing image data; According to the maximum height difference, the average slope value, the normalized difference vegetation index and the terrain stability index, a spatial suitability scoring matrix is ​​constructed, and a multi-level fuzzy evaluation method is used to calculate the spatial suitability score of the inflection point position; According to the obstacle distribution range, a plurality of initial machine nest layout positions that meet the preset safety spacing requirement are selected from a plurality of inflection point positions whose space suitability scores are greater than or equal to a space suitability score threshold; Among them, the preset safety distance requirements include: the distance between the machine nest layout position and any obstacle is greater than the preset safety distance threshold, the angle between the line connecting the machine nest layout position and its adjacent inflection point position and the horizontal plane is within the preset safety angle range, and the distance between the machine nest layout position and its adjacent machine nest is greater than the preset safety distance threshold.

4. The method for deploying a nest of a transmission line inspection drone according to claim 1, characterized in that: The generating of a simulated inspection path starting from each of the initial machine nest layout positions according to the position information of the plurality of inspection target points and the initial machine nest layout positions specifically includes: According to the position information of the plurality of inspection target points and the initial machine nest layout positions, a shortest path search algorithm is used to generate an initial flight path point sequence starting from each of the initial machine nest layout positions; According to the preset control point interval, curve smoothness, minimum turning radius and maximum climbing angle, the initial flight path point sequence is connected by a cubic Bezier curve to generate the simulated inspection path.

5. The method for deploying a nest of a transmission line inspection drone according to claim 1, characterized in that: The calculating of the field of view coverage corresponding to each of the candidate machine nest layout positions specifically includes: Determining the inspection area of ​​the transmission line according to the layout positions of the candidate machine nests; Dividing the inspection area into a plurality of inspection grid units by using honeycomb grids of preset sizes; Determine the inspection target point density of each inspection grid unit according to the number of inspection target points in each inspection grid unit and the area of ​​each inspection grid unit, and take the inspection grid unit whose inspection target point density is greater than a preset density threshold as the core monitoring area; Establishing a polar coordinate scanning grid at the candidate machine nest layout positions within the core monitoring area, and generating a plurality of observation sampling points on the polar coordinate scanning grid; According to the obstacle distribution range, a ray projection method is used to obtain the obstacle occlusion between the observation sampling point and each inspection grid unit in the core monitoring area, so as to select a number of valid observation points from a number of observation sampling points; According to the ratio between the number of the effective observation points and the number of the observation sampling points, the field of view coverage corresponding to each of the candidate machine nest layout positions is determined.

6. The method for deploying a nest of a transmission line inspection drone according to claim 1, characterized in that: Based on the location information of the plurality of inspection target points, the inspection path is generated by using a path planning algorithm with the candidate machine nest layout position having the largest field of view coverage as the starting point, specifically including: Based on the position information of several inspection target points, taking the candidate nest layout position with the largest field of view coverage as the starting point, a path planning algorithm is used to generate an initial inspection point sequence; According to the preset flight height constraint and steering angle constraint, the position of each inspection point in the initial inspection point sequence is adjusted to obtain an optimized inspection point sequence; According to the preset curvature constraint, control point interval and the steering angle constraint, the optimized inspection point sequence is connected by using a cubic spline curve to generate the inspection path.

7. The method for deploying a nest of a transmission line inspection drone according to claim 1, characterized in that: The calculating the inspection coverage rate of the inspection path specifically includes: Determining the inspection target area of ​​the transmission line according to the position information of the plurality of inspection target points; According to the preset sampling interval and height layer information, a plurality of three-dimensional inspection sampling points corresponding to the inspection path are generated; wherein the height layer information is the height information of a plurality of height layers located above the center of the transmission line; Determine the monitoring scanning area corresponding to the inspection path according to the position information of each of the three-dimensional inspection sampling points and the preset monitoring scanning width; The inspection coverage rate of the inspection path is obtained according to the ratio of the area of ​​the overlapping region between the monitoring scanning area and the inspection target area to the area of ​​the inspection target area.

8. The method for deploying a nest of a transmission line inspection drone according to any one of claims 1 to 7, characterized in that: The method further comprises: When the inspection coverage rate is less than the inspection coverage rate threshold, the candidate machine nest layout position with the largest current field of view coverage rate is eliminated; A new inspection path is generated again with the candidate machine nest layout position with the largest current field of view coverage as a starting point, and the inspection coverage of the new inspection path is calculated until the inspection coverage meets the inspection coverage threshold.

9. A system for deploying drone nests for power transmission line inspection, characterized in that: include: An inflection point position identification module, used to identify several inflection point positions of the transmission line according to geographical data corresponding to the transmission line; An initial machine nest layout position determination module is used to obtain terrain data within a preset search range of the inflection point position, and determine a number of initial machine nest layout positions according to the terrain data and preset safety spacing requirements; A candidate machine nest layout location screening module is used to screen out a number of candidate machine nest layout locations without inspection blind spots from a number of the initial machine nest layout locations according to the location information of a number of preset inspection target points and the obstacle distribution range of the area where the transmission line is located; A visual field coverage calculation module, used to calculate the visual field coverage corresponding to each candidate machine nest layout position; An inspection coverage calculation module is used to generate an inspection path using a path planning algorithm based on the location information of a number of inspection target points, taking the candidate machine nest layout position with the largest current field of view coverage as a starting point, and calculate the inspection coverage of the inspection path; A target machine nest layout position determination module, used for determining the candidate machine nest layout position with the largest current field of view coverage as the target machine nest layout position when the inspection coverage rate is greater than or equal to a preset inspection coverage rate threshold; The candidate machine nest layout location screening module is used to screen out several candidate machine nest layout locations without inspection blind spots from several initial machine nest layout locations according to the location information of several preset inspection target points and the obstacle distribution range of the area where the transmission line is located, specifically including: Generate a simulated inspection path starting from each of the initial machine nest layout positions according to the location information of the inspection target points and the initial machine nest layout positions; According to a preset sampling distance, a plurality of flight trajectory sampling points on the simulated inspection path are obtained; According to the preset cone vertex angle parameters and the observation distance threshold, an observation cone is established at each of the flight trajectory sampling points; Using a ray detection method to obtain a set of intersection coordinates between a boundary surface of the observation cone and the obstacle distribution range, and determining a field of view obstruction area based on the set of intersection coordinates; Determine the field of view obstruction ratio corresponding to each of the simulated inspection paths according to the ratio between the number of inspection target points located in the field of view obstruction area corresponding to each of the simulated inspection paths and the total number of inspection target points; Based on the initial machine nest layout positions corresponding to each of the simulated inspection paths, and according to the comparison result between the field of view obstruction ratio and the preset field of view obstruction ratio threshold, a number of candidate machine nest layout positions without inspection blind spots are screened out from the several initial machine nest layout positions; wherein, the initial machine nest layout positions corresponding to the simulated inspection paths whose field of view obstruction ratio is less than the field of view obstruction ratio threshold are candidate machine nest layout positions without inspection blind spots.

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