Nest laying method and system for power transmission line inspection unmanned aerial vehicle

Abstract

The invention discloses a nest layout method and system for a power transmission line inspection unmanned aerial vehicle, and the method comprises the steps: recognizing a plurality of inflection point positions according to the geographic data of a power transmission line; obtaining topographic data in a preset search range of the inflection point position, and determining a plurality of initial nest layout positions in combination with a preset safety spacing requirement; according to the position information of the plurality of inspection target points and the obstacle distribution range, further screening out candidate nest layout positions without inspection blind areas; calculating a view coverage rate corresponding to each candidate nest layout position, generating an inspection path by taking the candidate nest layout position with the maximum current view coverage rate as a starting point, and calculating an inspection coverage rate; and when the routing inspection coverage rate is greater than or equal to a routing inspection coverage rate threshold value, determining that the candidate nest layout position is a target nest layout position. According to the method, the nest layout reliability at the inflection point of the power transmission line can be remarkably improved, and the inspection coverage rate of the power transmission line inspection unmanned aerial vehicle can be improved.

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 laying out a nest of a transmission line inspection drone. Background Art

[0002] In the study of the inter-tower scheduling strategy of the drone nest for power transmission line inspection, the correlation between the nest layout location at the corner of the transmission line and the inspection blind spot is a key issue. When the nest is located in the line turning area, the inspection path of the drone will be significantly affected due to the complexity of the terrain and line direction. Specifically, there are usually large spatial changes in the line turning area, which makes it difficult for the drone to cover all areas during the flight, thus forming an inspection blind spot. The different nest layout locations will directly affect the scope and distribution density of the inspection blind spot. Further analysis shows that the inspection path of the drone will be restricted by the distribution of the inspection blind spot. Due to the existence of the inspection blind spot, the drone needs to adjust the inspection path within the limited flight time to cover as many areas as possible. However, the spatial distribution of the inspection blind spot is often irregular, which makes it difficult for the path planning algorithm to process efficiently, and thus cannot guarantee the overall inspection coverage rate. The site selection of existing drone nests usually relies on expert experience, and fails to fully consider the existence of the inspection blind spot after the drone nest is deployed, resulting in a low inspection coverage rate of the transmission line inspection drone. Summary of the invention

[0003] The present invention provides a method and system for deploying a nest of a transmission line inspection UAV, so as to solve the technical problem in the prior art that the nest selection of the transmission line inspection UAV cannot guarantee the overall inspection coverage rate.

[0004] In order to solve the above technical problems, a first aspect of an embodiment of the present invention provides a method for deploying a nest of a transmission line inspection drone, comprising: 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, the candidate machine nest layout position with the largest current field of view coverage rate is determined as the target machine nest layout position.

[0005] As a preferred solution, the step of identifying several turning point positions 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.

[0006] As a preferred solution, the step of acquiring 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 preset safety spacing requirements 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.

[0007] As a preferred solution, 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, several candidate machine nest layout positions without inspection blind spots are screened out from several initial machine nest layout positions, specifically including: Generate a simulated inspection path starting from each of the initial machine nest layout positions according to the location information of the plurality of inspection target points and the initial machine nest layout positions; According to the 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.

[0008] As a preferred solution, generating a simulated inspection path starting from each of the initial machine nest layout positions according to the location 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.

[0009] As a preferred solution, the calculation 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.

[0010] As a preferred solution, based on the location information of several inspection target points, the inspection path is generated by using a path planning algorithm with the candidate nest layout position with 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.

[0011] As a preferred solution, 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.

[0012] As a preferred embodiment, 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.

[0013] A second aspect of an embodiment of the present invention provides a system for deploying a nest of a transmission line inspection drone, comprising: 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; The target machine nest layout position determination module is used to determine 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.

[0014] Compared with the prior art, the beneficial effect of the embodiments of the present invention lies in that, in the process of screening the machine nest layout positions at the turning points of the transmission lines, the machine nest layout positions with inspection blind spots can be eliminated based on the obstacle distribution range in the area where the transmission lines are located, and the optimal machine nest layout position can be determined by combining the field of view coverage corresponding to the candidate machine nest layout positions and the inspection coverage of the inspection paths corresponding to each candidate machine nest layout position, thereby significantly improving the reliability of the machine nest layout at the turning points of the transmission lines, and helping to improve the inspection coverage, inspection efficiency and inspection safety of transmission line inspection drones. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flow chart of a method for deploying a nest of a transmission line inspection drone in an embodiment of the present invention; Figure 2 It is a structural schematic diagram of a nest deployment system for a transmission line inspection drone in an embodiment of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] See also Figure 1 According to a first aspect of an embodiment of the present invention, a method for deploying a nest of a transmission line inspection drone is provided, comprising the following steps S1 to S6: Step S1, identifying several turning point positions of the transmission line according to the geographical data corresponding to the transmission line; Step S2, obtaining terrain data within a preset search range of the inflection point position, and determining a number of initial machine nest layout positions according to the terrain data and preset safety spacing requirements; Step S3, based on 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; Step S4, calculating the field of view coverage corresponding to each of the candidate machine nest layout positions; Step S5, based on the position information of the plurality of inspection target points, taking the candidate nest layout position with the largest field of view coverage as the starting point, using a path planning algorithm to generate an inspection path, and calculating the inspection coverage of the inspection path; Step S6: When the inspection coverage rate is greater than or equal to a preset inspection coverage rate threshold, the candidate machine nest layout position with the largest current field of view coverage rate is determined as the target machine nest layout position.

[0018] Specifically, since the embodiment of the present invention is aimed at deploying drone nests at the turning points of the transmission line, and the different locations of the nests will directly affect the range and distribution density of the inspection blind spots, in order to obtain the optimal nest deployment location, this embodiment first needs to identify several turning points of the transmission line based on the geographic data corresponding to the transmission line; further, since the terrain conditions at different turning points are usually different, some turning points may have poor geological stability. If the nests are deployed at the turning points, it may greatly affect the safety of the drone take-off and landing or flight. Therefore, this embodiment determines several initial nest deployment locations based on the terrain data within the preset search range of the turning point position and combined with the safety spacing requirements.

[0019] Furthermore, in order to ensure that the final selected machine nest layout location will not cause a large number of inspection blind spots in the drone during the inspection process, this embodiment further screens the candidate machine nest layout locations from the initial machine nest layout location based on the location information of several inspection target points and the obstacle distribution range of the area where the transmission line is located. It can be understood that the inspection routes starting from different machine nest layout locations are different, and irregular obstacle distribution may be encountered during the inspection process, resulting in irregular field of view obstruction areas. If too many inspection target points fall into the field of view obstruction area, inspection blind spots will be formed. Therefore, it is necessary to screen out several candidate machine nest layout locations without inspection blind spots from several initial machine nest layout locations, that is, the initial machine nest layout positions with inspection blind spots are no longer considered.

[0020] Furthermore, in order to improve the inspection safety and efficiency of drones, the location of the machine nest needs to be selected in a location with a wide field of view as much as possible. For example, if a machine nest location is on a ridge line, the field of view of such a machine nest location is generally wide and without obvious obstruction, while if a machine nest location is in a valley, the obstruction is more serious. Therefore, this embodiment sorts the priorities of the current candidate machine nest locations by calculating the field of view coverage corresponding to each candidate machine nest location, and takes the candidate machine nest location with the largest current field of view coverage as the starting point, and uses a path planning algorithm to generate an inspection path to simulate the inspection situation of the drone starting from the candidate machine nest location. The inspection coverage of the inspection path is calculated to determine whether it is greater than or equal to a preset inspection coverage threshold. If so, it indicates that the candidate machine nest location with the largest current field of view coverage meets the inspection requirements, that is, it is used as the target machine nest location.

[0021] The method for deploying machine nests for transmission line inspection drones provided in an embodiment of the present invention can, during the screening process of machine nest deployment locations at the turning points of transmission lines, eliminate machine nest deployment locations with inspection blind spots based on the obstacle distribution range in the area where the transmission lines are located, and can determine the optimal machine nest deployment location based on the field of view coverage corresponding to the candidate machine nest deployment locations and the inspection coverage of the inspection paths corresponding to each candidate machine nest deployment location, thereby significantly improving the reliability of machine nest deployment at the turning points of transmission lines, and helping to improve the inspection coverage, inspection efficiency and inspection safety of transmission line inspection drones.

[0022] As a preferred solution, the step of identifying several turning point positions 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.

[0023] It is worth noting that the transmission line corridor area refers to the area between two parallel lines formed by the conductors on both sides of the transmission line extending outward for a certain distance under the premise of considering the maximum wind deviation and the safety distance. Based on the geographic data, the longitude and latitude coordinate point set in the transmission line corridor area can be obtained, and the longitude and latitude coordinate point set is used to describe the boundary and position of the transmission line corridor area. Further, considering that the inflection point position of the transmission line is usually the position where the direction of the transmission line changes significantly. In the geographic space, the points near these inflection points (such as towers, line nodes, etc.) tend to gather together to form a high-density area. Therefore, this embodiment sets the search radius and the minimum number of coordinate points 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 is not specifically limited here. This can be set according to actual needs, so that the density clustering algorithm can be used to perform cluster analysis on these longitude and latitude coordinate point sets, and several clusters containing the inflection point position can be screened out. Then the center point in each cluster can be regarded as the inflection point position, or further geometric analysis, such as the direction change angle of the transmission line, can be performed to determine the final inflection point position.

[0024] As a preferred solution, the step of acquiring 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 preset safety spacing requirements 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.

[0025] Furthermore, in order to fully reflect the terrain characteristics of each inflection point, the present embodiment first uses the Kriging spatial interpolation algorithm 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. It is worth noting 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. Then, the maximum height difference and average slope value within the preset search range are calculated from the terrain elevation surface data within the preset search range as terrain characteristic parameters.

[0026] Furthermore, based on the remote sensing image data within the preset search range, the normalized vegetation index within the preset search range can be calculated. Specifically, the normalized vegetation index is a commonly used vegetation index used to evaluate the health 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 reflectivity of the near-infrared light band, and Red represents the reflectivity of the red light band. Combined with the geological parameter database, the terrain stability index within the preset search range can be obtained, including the inclination of the rock layer, the degree of weathering, etc., and the normalized vegetation index and the terrain stability index are used as environmental characteristic parameters and safety characteristic parameters respectively.

[0027] Further, according to the maximum height difference, the average slope value, the normalized vegetation index and the terrain stability index, a spatial suitability scoring matrix is ​​constructed. It is worth noting that due to the different numerical ranges and dimensions of each index, fuzzy processing is required to convert them into dimensionless fuzzy values. The evaluation index system is set to include terrain layer, environmental layer and safety layer using a multi-level fuzzy evaluation method. The weight of each layer can be set separately. For example, the weight of the terrain layer is 0.4, the weight of the environmental 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 and the average slope value. The weight of the maximum height difference and the weight of the average slope value can be the same or different based on actual needs; for the environmental layer, the fuzzy value of the normalized vegetation index is used as the score of the environmental 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 inclination and the weathering degree. The weight of the rock layer inclination and the weight of the weathering degree can be the same or different based on actual needs. Therefore, based on the scores and weights of the terrain layer, environment layer, and safety layer, a spatial suitability scoring matrix can be constructed and the spatial suitability score of the inflection point can be further calculated. For example, assuming that the score of the terrain layer is 85 and the weight is 0.4; the score of the environment layer is 78 and the weight is 0.3; the score of the safety layer is 82 and the weight is 0.3, then the spatial suitability score of the current inflection point is 82.

[0028] Furthermore, by setting a spatial suitability score threshold to screen the inflection point positions, 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 if a nest is arranged at the inflection point position, there will be a greater safety hazard for the drone to take off and land at the nest. In order to further ensure the flight safety of the drone, this embodiment sets a safety distance requirement, including: the distance between the nest arrangement position and any obstacle is greater than a preset safety distance threshold (such as 15 meters), so as to avoid collision with obstacles during the take-off and landing of the drone; the angle between the line connecting the nest arrangement position and the adjacent inflection point position and the horizontal plane is within the preset safety angle range (such as greater than 75 degrees and less than 150 degrees), and the distance between the nest arrangement position and the adjacent nest is greater than the preset safety distance threshold (such as 200 meters), so as to ensure the safety of the drone take-off and landing and flight. As one of the optional embodiments, this embodiment can obtain the obstacle distribution range and the location information of the deployed machine nests in the area where the transmission line is located. Based on the obstacle distribution range and the location information of the deployed machine nests, the minimum distance constraint algorithm can be used to quickly obtain the inflection point position that meets the corresponding safety spacing requirements.

[0029] As a preferred solution, 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, several candidate machine nest layout positions without inspection blind spots are screened out from several initial machine nest layout positions, specifically including: Generate a simulated inspection path starting from each of the initial machine nest layout positions according to the location information of the plurality of inspection target points and the initial machine nest layout positions; According to the 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.

[0030] Specifically, in order to detect the visual field obstruction of the inspection path starting from different initial machine nest layout positions, this embodiment first generates a simulated inspection path starting from each initial machine nest layout position based on the position information of each inspection target point and the initial machine nest layout position. Further, in order to obtain the visual field of the drone in each simulated inspection path, this embodiment obtains several flight trajectory sampling points on the simulated inspection path according to a preset sampling distance. For example, the preset sampling distance can be set to 10 meters, and then an observation cone is established at each flight trajectory sampling point. For example, the cone vertex angle parameter can be set to 120 degrees, and the observation distance threshold can be set to 80 meters, so as to reasonably simulate the visual field of the drone.

[0031] Furthermore, a ray detection method is used to emit rays from the vertex of the observation cone to each point on the boundary surface, and the first intersection point of the ray and the obstacle range is recorded. When continuous intersection points appear, the boundary of a blocked area can be determined, so that the field of view blocked area can be obtained based on the intersection point coordinate set.

[0032] Furthermore, based on the ratio between the number of inspection target points that fall into the field of view obstruction area and the total number of inspection target points, the field of view obstruction ratio corresponding to each simulated inspection path can be calculated, and a number of candidate machine nest layout locations without inspection blind spots can be screened out based on the preset field of view obstruction ratio threshold. Exemplarily, the field of view obstruction ratio threshold can be set to 15%, which not only takes into account the static obstacle distribution, but also combines the dynamic observation characteristics of the actual flight process of the UAV to ensure the integrity of the field of view coverage of the inspection task.

[0033] As a preferred solution, generating a simulated inspection path starting from each of the initial machine nest layout positions according to the location 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.

[0034] Specifically, this embodiment takes each initial nest layout position as the starting point, and based on the position information of several inspection target points, uses the shortest path search algorithm to generate an initial flight path point sequence starting from each initial nest layout position. Since the initial flight path point sequence is a discrete path point, this embodiment further smoothly connects each path point in the initial flight path point sequence through a cubic Bezier curve. For example, the control point interval can be set to 50 meters, the curve smoothness can be set to 0.8, and 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.

[0035] 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 (eg, 300 meters), a transition point may be set between the two path points.

[0036] As a preferred solution, the calculation 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.

[0037] Specifically, the boundaries of the inspection area of ​​the transmission line can be constructed according to the layout positions of each candidate machine nest, so as to determine the corresponding inspection area, and then the inspection area is divided into a number of inspection grid units using a honeycomb grid of a preset size. By way of example, in order to ensure the regularity of the divided inspection grid units, the honeycomb grid in this embodiment is a regular hexagonal honeycomb grid with a side length of 50 meters.

[0038] 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 high inspection target point density is the key area of ​​the inspection task and is also the key to ensuring the inspection coverage rate, this embodiment uses the inspection grid unit with an inspection target point density greater than a preset density threshold as the core monitoring area. For example, the preset density threshold can be set to 80 inspection target points per square kilometer.

[0039] Furthermore, a polar coordinate scanning grid is established for each candidate machine nest layout location in 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 generated uniformly, specifically, with an azimuth interval of 5 degrees and a sampling interval of 10 meters in each azimuth direction. In order to analyze the line of sight accessibility of each candidate machine nest layout location, this embodiment adopts a ray transmission method to emit detection rays from each observation sampling point to the inspection target point in the core monitoring area, and record the intersection of each detection ray with the obstacle. If the detection ray does not intersect with the obstacle, it indicates that the line of sight of the observation sampling point is good and it is a valid observation point; if the detection ray intersects with any obstacle, it indicates that the line of sight of the observation sampling point is blocked and it is an invalid observation point. Based on this analysis method, it is possible to select several valid observation points from several observation sampling points, and then determine the field of view coverage corresponding to each candidate nest layout location based on the ratio between the number of valid observation points and the number of observation sampling points. The higher the proportion of valid observation points, the better the line of sight of the candidate nest layout location. It is worth noting that the undulating terrain has a significant impact on the field of view coverage. The field of view coverage of candidate nest layout locations in valleys is generally less than 70%, while the field of view coverage of candidate nest layout locations at higher altitudes is mostly over 80%, which fully reflects the important influence of the selection of nest layout locations at different line turning points on the inspection effect.

[0040] As a preferred solution, based on the location information of several inspection target points, the inspection path is generated by using a path planning algorithm with the candidate nest layout position with 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.

[0041] Specifically, in order to simulate the inspection path of the UAV starting from the candidate nest layout position with the largest current field of view coverage, this embodiment is based on the position information of several inspection target points, takes the candidate nest layout position with the largest current field of view coverage as the starting point, and uses the path planning algorithm to generate the initial inspection point sequence. In the path planning process, the shortest path can be used as the optimization goal, combined with the preset flight height constraint and steering angle constraint, to generate the optimal initial inspection point sequence, so as to improve the inspection efficiency as much as possible. Further, according to the flight height constraint and steering angle constraint, the position of each inspection point in the initial inspection point sequence is adjusted. For example, if the flight height of a certain inspection point exceeds the flight height constraint, it is reduced to meet the flight height constraint; if the steering angle between two flight path segments formed by any three consecutive inspection points exceeds the steering angle constraint, the positions of the three consecutive inspection points can be adjusted so that the steering angle between the two flight path segments meets the steering angle constraint. This embodiment will not be repeated here, so as to obtain an optimized inspection point sequence that can ensure the inspection efficiency and meet the flight performance of the UAV.

[0042] Furthermore, since each inspection point in the optimized inspection point sequence is discrete, the present embodiment uses a cubic spline curve to smoothly connect the optimized inspection point sequence, thereby generating a continuous inspection path. By way of example, in the process of smoothly connecting the inspection points, the minimum radius of curvature 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 be set according to actual needs, and the present embodiment does not make specific limitations here.

[0043] As a preferred solution, 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.

[0044] Specifically, in order to determine the inspection coverage rate of the current inspection path, this embodiment first needs to determine the inspection target area of ​​the transmission line according to the position information of a number of inspection target points.

[0045] Furthermore, considering that the monitoring and scanning range covered by the drone during the inspection along the inspection path is certainly not limited to the area where the inspection path is located, the present embodiment sets several height layers 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, respectively, 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.

[0046] Furthermore, this embodiment takes into account the field of view angle characteristics of the sensor carried by the drone when constructing the monitoring scanning area, and pre-sets the monitoring scanning width. For example, the monitoring scanning width is set to 120 meters. In an optional embodiment, the three-dimensional inspection sampling point can be used as the center to expand the scanning range to both sides, so that the width of the entire monitoring scanning range is always 120 meters, thereby forming a monitoring scanning area corresponding to the current inspection path.

[0047] Furthermore, the area of ​​the overlapping area between the monitoring scanning area and the inspection target area is determined. The area of ​​the overlapping area indicates the area of ​​the inspection target area that can be covered by the current inspection path. Then, the ratio between the area of ​​the overlapping area and the area of ​​the inspection target area is calculated to obtain the inspection coverage rate of the inspection path.

[0048] As a preferred embodiment, 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.

[0049] Exemplarily, the inspection coverage rate threshold in this embodiment can be set to 85%, or it can be set to other values ​​according to the actual inspection requirements. This embodiment is not specifically limited here. When the calculated inspection coverage rate is less than the inspection coverage rate threshold, it indicates that the candidate machine nest layout position with the largest current field of view coverage rate does not meet the inspection requirements, so the candidate machine nest layout position is eliminated and not considered. At this time, the candidate machine nest layout position with the largest field of view coverage rate is the candidate machine nest layout position that originally ranked second in field of view coverage rate. With this candidate machine nest layout position as the starting point, the path planning algorithm is re-adopted to generate a new inspection path, and the inspection coverage rate of the new inspection path is calculated until the inspection coverage rate meets the inspection coverage rate threshold.

[0050] As one of the optional embodiments, in the process of planning the inspection path, this embodiment first calculates the line direction vectors on both sides of the inflection point position based on the set of longitude and latitude coordinate points in the transmission line corridor area, through the adjacent longitude and latitude coordinate points at the inflection point position, thereby identifying the direction of the transmission lines on both sides of the inflection point position, and then adds several auxiliary waypoints near the inflection point position in the direction of the transmission lines on both sides of the inflection point position. For example, 6 auxiliary waypoints can be added in both directions, so that the drone can pass through the inflection point smoothly and effectively cover the transmission lines on both sides when passing the inflection point position.

[0051] See also Figure 2 A second aspect of an embodiment of the present invention provides a system for deploying a nest of a transmission line inspection drone, comprising: The inflection point position identification module 101 is used to identify several inflection point positions of the transmission line according to the geographical data corresponding to the transmission line; The initial machine nest layout position determination module 102 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; The candidate machine nest layout location screening module 103 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 104 is used to calculate the visual field coverage corresponding to each candidate machine nest layout position; The inspection coverage calculation module 105 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; The target machine nest layout position determination module 106 is used to determine 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.

[0052] As a preferred solution, the inflection point position identification module 101 is used to identify several inflection point positions of the transmission line according to the geographical data corresponding to the transmission line, specifically including: 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.

[0053] As a preferred solution, the initial machine nest layout position determination module 102 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, specifically including: 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.

[0054] As a preferred solution, the candidate nest layout location screening module 103 is used to screen out several candidate nest layout locations without inspection blind spots from several initial 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 plurality of inspection target points and the initial machine nest layout positions; According to the 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.

[0055] As a preferred solution, the candidate machine nest layout location screening module 103 is used to generate a simulated inspection path starting from each of the initial machine nest layout locations according to the location information of the inspection target points and the initial machine nest layout location, specifically including: 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.

[0056] As a preferred solution, the visual field coverage calculation module 104 is used to calculate the visual field coverage corresponding to each of the candidate machine nest layout positions, specifically including: 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.

[0057] As a preferred solution, the inspection coverage calculation module 105 is used to generate an inspection path based on the location information of several inspection target points, taking the candidate nest layout position with the largest field of view coverage as the starting point, and using a path planning algorithm, 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.

[0058] As a preferred solution, the inspection coverage calculation module 105 is used to calculate the inspection coverage of the inspection path, specifically including: 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.

[0059] As a preferred solution, the target machine nest layout position determination module 106 is further used to: 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; The inspection coverage calculation module 105 is also used for: 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.

[0060] The machine nest layout system for power transmission line inspection drones provided by an embodiment of the present invention can, during the screening process of machine nest layout positions at the turning points of transmission lines, eliminate machine nest layout positions with inspection blind spots based on the obstacle distribution range in the area where the transmission lines are located, and can determine the optimal machine nest layout position based on the field of view coverage corresponding to the candidate machine nest layout positions and the inspection coverage of the inspection paths corresponding to each candidate machine nest layout position, thereby significantly improving the reliability of machine nest layout at the turning points of transmission lines, and helping to improve the inspection coverage, inspection efficiency and inspection safety of transmission line inspection drones.

[0061] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection 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, the candidate machine nest layout position with the largest current field of view coverage rate is determined as the target machine nest layout position.

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 method of selecting a plurality of candidate machine nest layout positions without inspection blind spots from a plurality of the initial machine nest layout positions 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 specifically includes: Generate a simulated inspection path starting from each of the initial machine nest layout positions according to the location information of the plurality of inspection target points and the initial machine nest layout positions; According to the 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.

5. The method for deploying a nest of a transmission line inspection drone according to claim 4, 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.

6. 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.

7. 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.

8. 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.

9. The method for deploying a nest of a transmission line inspection drone according to any one of claims 1 to 8, 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.

10. 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; The target machine nest layout position determination module is used to determine 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.

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