Low altitude take-off and landing field obstacle adaptive analysis method and device

Abstract

The application provides a low-altitude take-off and landing field obstacle adaptive analysis method and device, and relates to the technical field of computers. The method comprises the following steps: obtaining obstacle information corresponding to a take-off and landing field; obtaining a first set according to the obstacle information and a preset screening condition, wherein the first obstacles in the first set comprise first candidate obstacles and first other obstacles; performing shielding analysis on the first other obstacles based on the first candidate obstacles to obtain a second set, wherein the shielding analysis is used to analyze whether the first obstacles close to a take-off point shield the first obstacles far from the take-off point, so as to determine the first obstacles not shielded as second candidate obstacles, and the second set comprises the first candidate obstacles and the second candidate obstacles; and obtaining a target analysis result according to the second set, wherein the target analysis result is used to indicate obstacles that need to be calibrated. In this way, the key obstacles that really need to be calibrated can be screened from a large number of obstacles, thereby reducing the number of obstacles to be calibrated.

Description

Technical Field

[0001] This application relates to the field of computer technology, and more specifically, to an adaptive analysis method and apparatus for obstacles in low-altitude take-off and landing fields. Background Technology

[0002] In low-altitude flight scenarios, obstacle analysis of the UAV takeoff and landing area is generally required for flight safety. Based on the analysis results, obstacles affecting flight safety are then practically calibrated, for example, by placing warning markers on top of obstacles. Currently, the common method is to determine if the height of an obstacle within a fixed range exceeds a preset fixed value. If it does, the obstacle is considered to require calibration, i.e., it is considered a critical obstacle. This method calibrates a large number of obstacles, making it impossible to filter out the truly critical obstacles that need calibration from among them. Summary of the Invention

[0003] This application provides an adaptive analysis method, apparatus, electronic device, and readable storage medium for obstacles in low-altitude take-off and landing fields. It can filter out the key obstacles that truly need to be calibrated from a large number of obstacles, thereby reducing the number of obstacles to be calibrated.

[0004] The embodiments of this application can be implemented as follows: In a first aspect, embodiments of this application provide an adaptive analysis method for obstacles in low-altitude takeoff and landing fields, the method comprising: Obtain obstacle information corresponding to the takeoff and landing field; Based on the obstacle information and preset screening conditions, a first set is obtained, wherein the first obstacle in the first set includes a first candidate obstacle and a first other obstacle, and the first candidate obstacle is the first obstacle that first generates clearance constraints; Based on the first candidate obstacle, an occlusion analysis is performed on the first other obstacles to obtain a second set. The occlusion analysis is used to analyze whether the first obstacle near the take-off and landing point occludes the first obstacle far from the take-off and landing point to determine the first obstacle that is not occluded as the second candidate obstacle. The second set includes the first candidate obstacle and the second candidate obstacle. Based on the second set, target analysis results are obtained, wherein the target analysis results are used to indicate the obstacles that need to be calibrated identified from the second set.

[0005] Secondly, embodiments of this application provide an adaptive analysis device for obstacles in low-altitude takeoff and landing fields, the device comprising: The information acquisition module is used to acquire obstacle information corresponding to the take-off and landing field; The filtering module is used to obtain a first set based on the obstacle information and preset filtering conditions, wherein the first obstacle in the first set includes a first candidate obstacle and a first other obstacle, and the first candidate obstacle is the first obstacle that first generates clearance constraints; The filtering module is also used to perform occlusion analysis on the first other obstacles based on the first candidate obstacles to obtain a second set, wherein the occlusion analysis is used to analyze whether the first obstacle near the take-off and landing point occludes the first obstacle far from the take-off and landing point to determine the first obstacle that is not occluded as the second candidate obstacle, and the second set includes the first candidate obstacle and the second candidate obstacle. An analysis module is used to obtain target analysis results based on the second set, wherein the target analysis results are used to indicate obstacles that need to be calibrated from the second set.

[0006] Thirdly, embodiments of this application provide an electronic device, including a processor and a memory, wherein the memory stores machine-executable instructions that can be executed by the processor, and the processor can execute the machine-executable instructions to implement the low-altitude take-off and landing field obstacle adaptive analysis method described in the foregoing embodiments.

[0007] Fourthly, embodiments of this application provide a readable storage medium storing a computer program thereon, which, when executed by a processor, implements the low-altitude take-off and landing field obstacle adaptive analysis method as described in the foregoing embodiments.

[0008] The adaptive analysis method, apparatus, electronic device, and readable storage medium for low-altitude take-off and landing field obstacles provided in this application embodiment first obtain obstacle information corresponding to the take-off and landing field; then, based on the obstacle information and preset screening conditions, a first set is obtained, in which obstacles are designated as first obstacles, including first candidate obstacles and first other obstacles, the first candidate obstacles being the first obstacles to generate clearance constraints; next, based on the first candidate obstacles, occlusion analysis is performed on the first other obstacles in the first set, to determine whether the first obstacles near the take-off and landing point occlude the first obstacles far from the take-off and landing point, thereby identifying the unoccluded first obstacles as second candidate obstacles, thus obtaining a second set including the first candidate obstacles and the second candidate obstacles; finally, based on the second set, a target analysis result is obtained, which is used to indicate the obstacles that need to be calibrated determined from the second set. In this way, obstacles to be calibrated can be screened based on the order in which they generate clearance constraints, reducing the number of unnecessary calibration objects. Attached Figure Description

[0009] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a schematic diagram showing the distribution of the three obstacles; Figure 2 A block diagram illustrating an electronic device provided in an embodiment of this application; Figure 3 One of the flowcharts for the adaptive analysis method for low-altitude take-off and landing field obstacles provided in the embodiments of this application; Figure 4 A second schematic flowchart illustrating the adaptive analysis method for low-altitude take-off and landing field obstacles provided in this application embodiment; Figure 5 This is a schematic diagram of the target operational envelope provided in the embodiments of this application; Figure 6 for Figure 4 A flowchart illustrating the sub-steps included in step S120; Figure 7 for Figure 3 A flowchart illustrating the sub-steps included in step S130; Figure 8 for Figure 3 A flowchart illustrating the sub-steps included in step S140; Figure 9 for Figure 8 A flowchart illustrating the sub-steps included in the neutron step S142; Figure 10 for Figure 3 A flowchart illustrating the sub-steps included in step S150; Figure 11 for Figure 10 A flowchart illustrating the sub-steps included in neutron step S151; Figure 12 for Figure 10 A flowchart illustrating the sub-steps included in neutron step S153; Figure 13 A block diagram of an adaptive analysis device for low-altitude take-off and landing field obstacles provided in an embodiment of this application.

[0011] Icons: 100 - Electronic device; 110 - Memory; 120 - Processor; 130 - Communication unit; 200 - Low-altitude take-off and landing field obstacle adaptive analysis device; 210 - Information acquisition module; 220 - Screening module; 230 - Analysis module. Detailed Implementation

[0012] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0013] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0014] It should be noted that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0015] Currently, a horizontal area is typically defined using the drone's take-off and landing point O as a baseline. Obstacles within this area with a height greater than a preset height are considered critical obstacles. Furthermore, to avoid omissions, the preset height is usually set relatively low. This results in a large number of obstacles requiring calibration.

[0016] The inventors of this application have discovered that the aforementioned method directly identifies obstacles with a height greater than a preset height within a horizontal area as obstacles to be calibrated. It does not determine which obstacle primarily restricts the drone's operational airspace, nor does it analyze whether there is obstruction between obstacles. This results in a large number of obstacles being identified that require calibration. For example, ... Figure 1 As shown, obstacle B1, which is closer to the take-off and landing point, has already formed the main constraint in this direction. Obstacles B2 and B3 behind it actually need to be determined in conjunction with occlusion analysis. However, using the existing method described above, obstacles B1, B2, and B3 would be directly marked, but in reality, obstacles B2 and B3 may not actually need to be marked.

[0017] In view of the above situation, this application provides an adaptive analysis method, device, electronic device and readable storage medium for obstacles in low-altitude take-off and landing fields. The method selects obstacles to be calibrated based on the order in which obstacles generate clearance constraints, thereby reducing the number of unnecessary calibration objects.

[0018] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0019] Please refer to Figure 2 , Figure 2 This is a block diagram of an electronic device 100 provided in an embodiment of this application. The electronic device 100 may be, but is not limited to, a computer, a server, etc. The electronic device 100 may include a memory 110, a processor 120, and a communication unit 130. The memory 110, processor 120, and communication unit 130 are electrically connected to each other directly or indirectly to achieve data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses or signal lines.

[0020] The memory 110 is used to store programs or data. The memory 110 may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0021] The processor 120 is used to read / write data or programs stored in the memory 110 and execute corresponding functions. For example, the memory 110 stores a low-altitude takeoff and landing field obstacle adaptive analysis device 200, which includes at least one software function module that can be stored in the memory 110 in the form of software or firmware. The processor 120 executes various functional applications and data processing by running the software programs and modules stored in the memory 110, such as the low-altitude takeoff and landing field obstacle adaptive analysis device 200 in this embodiment, thereby realizing the low-altitude takeoff and landing field obstacle adaptive analysis method in this embodiment.

[0022] The communication unit 130 is used to establish a communication connection between the electronic device 100 and other communication terminals through the network, and to send and receive data through the network.

[0023] It should be understood that, Figure 2 The structure shown is only a schematic diagram of the electronic device 100. The electronic device 100 may also include components that are larger than... Figure 2 The more or fewer components shown, or having the same Figure 2 The different configurations shown. Figure 2 The components shown can be implemented using hardware, software, or a combination thereof.

[0024] Please refer to Figure 3 , Figure 3 This is one of the flowcharts illustrating the adaptive analysis method for obstacles in low-altitude take-off and landing fields provided in this application. The method can be applied to the aforementioned electronic equipment. This method can be applied to the fields of low-altitude UAV operation support, take-off and landing field clearance management, and obstacle identification and calibration. The specific flow of the adaptive analysis method for obstacles in low-altitude take-off and landing fields is described in detail below. In this embodiment, the method may include steps S110 and S130-S150.

[0025] Step S110: Obtain obstacle information corresponding to the take-off and landing field.

[0026] In this embodiment, obstacle data around the takeoff and landing field can be obtained, and this obstacle data can be uniformly converted and normalized to obtain obstacle information corresponding to the takeoff and landing field. The obstacle information is used to indicate the position, height, etc. of each obstacle, and the specific details can be determined according to actual needs. The takeoff and landing field is a low-altitude takeoff and landing field.

[0027] Step S120: Obtain the first set based on the obstacle information and preset screening conditions.

[0028] In this embodiment, the preset filtering conditions are used to select obstacles that create clearance constraints from the obstacles corresponding to the obstacle information as first obstacles, that is, to select obstacles that affect the flight safety of the UAV as first obstacles, thereby obtaining a first set. The obstacles in the first set can be referred to as first obstacles. The preset filtering conditions can be determined according to actual needs and are not specifically limited here.

[0029] The first obstacle in the first set is divided into two types: a first candidate obstacle and a first other obstacle. The first candidate obstacle is the first obstacle that first generates clearance constraints. The first obstacle that first generates clearance constraints is either the first obstacle with the smallest height among the first obstacles with the smallest horizontal distance to the take-off and landing points of the take-off and landing field, or the first obstacle with the smallest height among the first obstacles with the smallest horizontal distance to the take-off and landing points of the take-off and landing field in the second azimuth sector corresponding to the first obstacle. The second azimuth sector is the azimuth sector obtained by dividing the entire target operating envelope corresponding to the take-off and landing field by angle. The UAV takes off and lands at the take-off and landing points.

[0030] Optionally, the first candidate obstacle can be determined during the filtering process based on the preset filtering conditions, or it can be determined after all first obstacles have been filtered out. The timing of determining the first candidate obstacle can be determined according to actual needs and is not specifically limited here. The first candidate obstacle refers to the filtered candidate key obstacle, that is, the obstacle that may need to be marked.

[0031] Step S130: Perform occlusion analysis on the first other obstacles based on the first candidate obstacles to obtain a second set.

[0032] In this embodiment, based on the first candidate obstacle, occlusion analysis is performed on the first other obstacles in the first set, and the unoccluded first other obstacles identified through the occlusion analysis are used as second candidate obstacles. Specifically, the occlusion analysis is used to analyze whether a first obstacle near the take-off and landing point occludes a first obstacle far from the take-off and landing point, thereby identifying unoccluded first obstacles as second candidate obstacles. The second candidate obstacle refers to a candidate key obstacle identified from the first other obstacles, i.e., an obstacle that may need to be calibrated. Thus, a second set is obtained, which includes the first candidate obstacles and the second candidate obstacles. The first and second candidate obstacles in the second set can also be referred to as second obstacles. The specific occlusion analysis method can be determined based on actual needs and is not specifically limited here.

[0033] Step S140: Obtain the target analysis results based on the second set.

[0034] Given the second set, target analysis results can be obtained based on it. These target analysis results indicate the obstacles identified from the second set that need to be calibrated. The method for obtaining these target analysis results can be determined based on actual needs and is not specifically limited here. For example, each second obstacle in the second set can be directly used as an obstacle to be calibrated, i.e., the second set can be directly used as the target analysis result. Alternatively, other filtering rules can be used to filter the second set to select the obstacles that need to be calibrated, thus obtaining the target analysis results.

[0035] In this way, obstacles to be calibrated can be selected based on the order in which they generate clearance constraints, thereby reducing the number of unnecessary calibration objects.

[0036] In this embodiment, basic data of the takeoff and landing field can be obtained. This basic data may include the coordinates of the UAV's takeoff and landing point, the boundary of the takeoff and landing area, the direction of the arrival and departure flight paths, the altitude of the connecting flight paths, the operational level of the takeoff and landing field, the operational safety margin, the elevation of the surrounding terrain, and the type of UAV to be operated. The UAV type may include one or more of rotary-wing UAVs, fixed-wing UAVs, and compound-wing UAVs.

[0037] In this embodiment, obstacle data of the surrounding obstacles of the take-off and landing field can be obtained through at least one of the following methods: on-site surveying, lidar, oblique photography, remote sensing imagery, urban 3D model, building database, and manual investigation. The obstacle data may include: location, height, type, outline dimensions, data source, acquisition time, planar coordinate accuracy, and height measurement accuracy, forming an obstacle set. This set of obstacles includes obstacles around the take-off and landing field.

[0038] The take-off and landing field can include at least one take-off and landing unit, where any UAV can take off and land. The take-off and landing point of the UAV is the take-off and landing point of that take-off and landing unit. This solution can perform obstacle analysis and then calibration for a single take-off and landing unit.

[0039] The take-off and landing point of the UAV can be used as the take-off and landing point corresponding to the take-off and landing field. A local three-dimensional coordinate system can be established with this take-off and landing point as the origin. Then, the coordinates of the obstacles in the obstacle data can be transformed into this local three-dimensional coordinate system, and the horizontal distance of each obstacle relative to the origin can be calculated. Azimuth ,high and projection contour width , where i represents the i-th obstacle. This is how the obstacle information corresponding to the takeoff and landing field is obtained.

[0040] In the above processing, a three-dimensional rectangular coordinate system is established with the UAV's take-off and landing point as the origin: O-XYZ, where the origin O (0,0,0) is the take-off and landing point; the X and Y axes lie in the horizontal plane; and the Z axis represents the vertical height direction, with upward being positive. For any obstacle i, its spatial position and geometric parameters can be expressed as: ,in, and Let be the coordinates of the obstacle in the horizontal plane relative to the take-off and landing point. This represents the height of the highest point of the obstacle relative to the plane containing the takeoff and landing point. The horizontal distance from the obstacle to the takeoff and landing point is: The azimuth angle of the obstacle relative to the take-off and landing point is: Therefore, each obstacle can be further represented in polar coordinates: ,in, Indicates the horizontal distance to the obstacle. Indicates the direction of the obstacle. Indicates the height of the obstacle.

[0041] As one possible implementation, the first set can be obtained by analyzing the target operational envelope corresponding to the takeoff and landing field. The target operational envelope can be generated by other devices and sent to the electronic device, or it can be obtained through data analysis before the electronic device analyzes and obtains the first set.

[0042] Please refer to Figure 4 , Figure 4 This is a second schematic flowchart of the adaptive analysis method for low-altitude takeoff and landing field obstacles provided in this application embodiment. In this embodiment, the method may further include step S120 before step S130.

[0043] Step S120: Obtain the target operating envelope corresponding to the take-off and landing field.

[0044] During takeoff and landing, the trajectory of a drone typically changes from low to high altitude or vice versa. To ensure safe takeoff and landing, such as... Figure 5 As shown, this scheme abstracts the operational influence space around the take-off and landing field into a three-dimensional operational envelope with the take-off and landing point as the vertex and extending outward to a preset height (e.g., the altitude of the arrival and departure routes). This three-dimensional operational envelope is the target operational envelope corresponding to the take-off and landing field.

[0045] In this embodiment, when the take-off and landing field is not a mixed take-off and landing field for multi-configuration UAVs, but rather a take-off and landing field for single-configuration UAVs, the operational envelope corresponding to the single-configuration UAV can be generated based on its operational performance parameters, and this operational envelope can be used as the target operational envelope. The operational envelope region of a single-configuration UAV can be approximately represented as an inverted cone or quasi-inverted cone-shaped space extending outward from the take-off and landing point as its vertex.

[0046] When the take-off and landing site is a mixed take-off and landing site for multi-configuration UAVs, it can be achieved through... Figure 6 The target runtime envelope is obtained as shown. Please refer to... Figure 6 , Figure 6 for Figure 4 A flowchart illustrating the sub-steps included in step S120. In this embodiment, step S120 may include sub-steps S121 to S122.

[0047] Sub-step S121: For each type of UAV corresponding to the take-off and landing field, obtain the single-configuration operating envelope corresponding to that type of UAV based on the operating performance parameters of that type of UAV.

[0048] Sub-step S122: Based on the single-configuration operational envelopes corresponding to various configuration UAVs at the take-off and landing site, obtain the comprehensive operational envelope as the target operational envelope.

[0049] In this embodiment, based on the operational performance parameters of different UAV configurations corresponding to the takeoff and landing sites, a single-configuration operational envelope is established for each UAV configuration. The operational performance parameters include at least the climb and descent clearance angles. The radius of the operating envelope of a single configuration is related to... Proportional. This can be determined based on the elevation gain / loss angle. The altitude H corresponding to the approach and departure routes of the takeoff and landing field is used to obtain the corresponding single-configuration operating envelope. The radius of the operating envelope increases with the increase of altitude.

[0050] Optionally, the operational performance parameters may also include at least one of the following: minimum turning radius, approach and departure speed, navigation and positioning error, wind yaw error, and operational safety margin. For rotary-wing UAVs, the near-field operating space under vertical takeoff and landing, low-speed climb, and hovering and turning conditions should be the primary considerations. For fixed-wing UAVs, the maximum horizontal approach and departure distance, minimum turning radius, and stable climb / descent bank angle (i.e., climb / descent net bank angle) should be the primary considerations. For compound-wing UAVs, segmented operational envelopes can be established by combining the vertical takeoff and landing segment, the transition segment, and the fixed-wing cruise connection segment.

[0051] Having obtained the single-configuration operational envelopes corresponding to various UAV configurations at the takeoff and landing sites, a comprehensive operational envelope, serving as the target operational envelope, can be obtained based on these multiple single-configuration operational envelopes using a preset method. The specific method for determining the target operational envelope based on multiple single-configuration operational envelopes can be determined according to actual needs and is not specifically limited here.

[0052] Optionally, based on the single-configuration operational envelopes corresponding to each of the multi-configuration UAVs, a comprehensive operational envelope under the hybrid operational scenario can be formed by using envelope union, most unfavorable boundary extraction, maximum radius envelope, or risk-weighted fusion.

[0053] Alternatively, an initial integrated operational envelope can be obtained from multiple single-configuration operational envelopes using methods such as envelope union, intersection expansion, maximum radius envelope, most unfavorable boundary extraction, risk-weighted fusion, or hierarchical fusion based on operational level. Then, this initial integrated operational envelope is corrected by incorporating a risk correction amount determined based on at least one of the following: UAV operational risk level, navigation error, wind drift error, and safety margin, to obtain the integrated operational envelope. In this approach, the integrated operational envelope can be expressed as: ,in, Indicates the integrated envelope of mixed operation. This represents the single-configuration operational envelope of the m-th type of UAV. This represents the risk correction amount determined based on the UAV's operational risk level, navigation error, wind drift error, and safety margin. F represents the envelope union, intersection expansion, maximum radius envelope, most unfavorable boundary extraction, risk-weighted fusion, or hierarchical fusion function based on operational level.

[0054] The following example illustrates how to obtain the comprehensive runtime envelope.

[0055] Assuming the connection altitude of the UAV arrival and departure routes corresponding to the take-off and landing site is H, this altitude can be determined based on the UAV operating procedures, low-altitude route altitude, route access altitude, airspace management requirements, or take-off and landing site design standards.

[0056] Let the minimum climb / descent clearance angle (i.e., climb / descent clearance angle) required for the drone's takeoff or landing be: ,in, The specific angle can be determined based on the drone type, power performance, operating mode, and safety margin. Different bank angles can be set for different types of drones (i.e., drones with different configurations). 'm' indicates the type of drone, such as rotary-wing drone, fixed-wing drone, compound-wing drone, etc.

[0057] In the simplest axisymmetric case, the horizontal radius of extension R(z) corresponding to any height z can be expressed as: Where: 0 ≤ z ≤ H. Therefore, the operational envelope region of a UAV (i.e., the single-configuration operational envelope) can be expressed as: .

[0058] Alternatively, a safety margin can be considered, for example, by introducing a vertical safety margin. and horizontal safety margin In this case, the expansion radius can be corrected as follows: With the introduction of a horizontal safety margin, the expansion radius can be modified as follows: The corresponding safe operation envelope is: .

[0059] For mixed operation scenarios of multi-configuration UAVs, the above method can be used to establish operational envelopes for different UAV types: In this case, without considering safety margin, The mixed operational envelope can be taken as the union of the operational envelopes of various types of UAVs: Alternatively, to ensure higher security, the most conservative envelope can be used, i.e., the envelope with the largest radius can be used: ,Right now This envelope describes the spatial range that may affect the operational airspace during the takeoff or landing of a drone.

[0060] Optionally, the integrated operational envelope can be an inverted conical envelope, a quasi-inverted conical envelope, a segmented conical envelope, a curved envelope, a route buffer envelope, a corridor-type three-dimensional envelope, or a composite envelope formed by combining multiple local envelopes, which can be determined based on actual needs.

[0061] In this embodiment, the target running envelope can be combined with, through Figure 7 The first set is obtained as shown. Please refer to... Figure 7 , Figure 7 for Figure 3 A flowchart illustrating the sub-steps included in step S130. In this embodiment, step S130 may include sub-steps S131 to S134.

[0062] Sub-step S131: Select one height layer as the current height layer in order of height from low to high.

[0063] Sub-step S132: For each first orientation sector of the current height layer, search for whether there is an obstacle located on the arc corresponding to the search radius and satisfying the preset filtering conditions among the obstacles corresponding to the obstacle information, with the search radius gradually increasing.

[0064] Sub-step S133: If there is an obstacle, then the searched obstacle is taken as the first obstacle in the first initial set corresponding to the current height layer.

[0065] Sub-step S134: Obtain the first set based on the first initial set corresponding to each height layer.

[0066] In this embodiment, the target's operational envelope can be divided into multiple height layers in the height direction, and the horizontal 360-degree range of each height layer can be divided into multiple azimuth sectors as the first azimuth sector. The radius of the operational envelope of the target's operational envelope increases with increasing height. A height layer is selected as the current height layer in ascending order of height. The current height layer is the height layer that needs to be analyzed. The first set can be obtained through a circular wave diffusion search method based on height layers and azimuth sectors.

[0067] Within each first-azimuth sector of the current altitude layer, with the horizontal projection point of the take-off and landing point as the center, a circular wave pattern is gradually expanded from near to far to search for whether an obstacle corresponding to the obstacle information is located on the arc corresponding to the search radius and meets the preset filtering conditions. The center of the arc is the horizontal projection point of the take-off and landing point at the current altitude layer.

[0068] The preset screening conditions may include height constraints, distance constraints, and envelope penetration conditions. The height constraints are determined based on the current height of the high-rise building and the vertical safety margin. If the height of an obstacle satisfies this height constraint, it can be expressed as: The distance constraint is determined based on the operating envelope radius and horizontal safety margin obtained from the current height. If the horizontal distance to an obstacle satisfies this distance constraint, it can be expressed as: The envelope penetration condition indicates that the height of the obstacle is greater than the height of the envelope surface corresponding to the horizontal position of the obstacle.

[0069] If any obstacle in the obstacle information is located on the arc corresponding to the search radius and meets the preset filtering conditions, then that obstacle is saved as a first obstacle in the first initial set corresponding to the current height layer. The first initial set corresponding to the current height layer includes all the first obstacles found in the above manner.

[0070] In each second-azimuth sector obtained by dividing the target operating envelope, the obstacle that first meets the preset screening conditions and is found in the second-azimuth sector through circular wave search is the first candidate obstacle. There is only one first candidate obstacle in each second-azimuth sector of the target operating envelope. Each second-azimuth sector of the target operating envelope is a three-dimensional region, and the number of second-azimuth sectors and their corresponding angle ranges can be determined according to actual needs.

[0071] For example, suppose the height of the target's operational envelope is 50 meters, with a height layer of 10 meters, for a total of 6 height layers; the first azimuth sector of each height layer is the same, that is, the angle range corresponding to each first azimuth sector of each height layer is the same; suppose one of the first azimuth sectors of each height layer is 30°~60°, and one of the second azimuth sectors of the target's operational envelope (the height of this sector is 50 meters) is also 30°~60°. During the search process of circular wave diffusion from low to high height, the first obstacle found in this second azimuth sector (i.e., a three-dimensional fan-shaped area with a height of 50 meters and an angle range of 30°~60°) is the first candidate obstacle. That is, a first candidate obstacle is determined within the second azimuth sector of 30°~60° with a total height of 50 meters.

[0072] For example, suppose the target's operational envelope has a height of 50 meters, with a height layer of 10 meters, for a total of 6 height layers; the first azimuth sector of each height layer is different, that is, the angle range corresponding to the first azimuth sector of different layers is different. For example, in the 0-meter height layer, a first azimuth sector has a range of 0° to 20°, and in the 10-meter height layer, a first azimuth sector has a range of 0° to 25°; and suppose one of the second azimuth sectors of the target's operational envelope (the height of this sector is the target's operational envelope height of 50 meters) has a range of 0° to 60°. Based on the search results of circular wave diffusion search from low to high height, the first obstacle found in this second azimuth sector is taken as a first candidate obstacle.

[0073] Thus, by using the first obstacle that meets the preset screening conditions in a second-direction sector as the first candidate obstacle, the first obstacle with the smallest height among the first obstacles with the smallest horizontal distance from the take-off and landing point in each second-direction sector can be identified during the search process.

[0074] Alternatively, the target running envelope can be discretized into several dryness layers in the height direction as follows: ,in, H represents the height of the target's running envelope. The step size for each altitude layer can be determined based on the accuracy of the obstacle database, the safety requirements for UAV operation, and computational efficiency. For each altitude layer, multiple first-azimuth sectors can be evenly divided. The first-azimuth sectors of different altitude layers can be the same; for example, altitude layers 1 and 2 each have a first-azimuth sector of 30°~60°. Above, the radius of the target's running envelope is: The circular wave scan set constructed at this height level can be shown below: ,in, This represents the current diffusion radius of the circular wave (i.e., the search radius mentioned earlier). The search radius can be increased uniformly according to a preset value. That is, in the above method, the height layer step size, azimuth step size, and search radius step size are all fixed values; that is, the height layer is divided at equal intervals, the first azimuth sector is divided at equal angles, and the search radius increases according to a fixed value.

[0075] Optionally, the height layer step size, the azimuth sector step size of the first azimuth sector, and the search radius step size can be pre-set based on at least one of the obstacle density, constraint contribution changes, and height dispersion corresponding to the obstacle information. This allows for adaptive adjustment of the height layer step size, the azimuth sector angle of the first azimuth sector, and the circular wave diffusion radius step size. The constraint contribution represents the degree to which obstacles restrict the target's operational envelope. A finer search granularity is used in areas with dense obstacles or significant risk changes, while a larger search step size is used in areas with sparse obstacles. This reduces computational load and improves engineering processing efficiency while ensuring that no critical obstacles are missed.

[0076] For example, when obstacles are dense in a certain area, the constraint contribution varies greatly, or the height difference is significant, the height step size, angle step size, and radius diffusion step size for that area can be reduced to improve the accuracy of key obstacle identification. When obstacles are sparse in a certain area or the constraint contribution varies little, the search step size can be increased to reduce the computational load. The height layer step size and azimuth step size can be expressed as: , ,in, This indicates the height step size used in the k-th region or the k-th round of search. This indicates the corresponding azimuth step size. Indicates the local obstacle density. This represents the local maximum constraint contribution or the rate of change of constraint contribution. Indicates the degree of dispersion of obstacle height. Indicates the rate of change of the occlusion angle. Indicates the degree of dispersion in directional distribution. and This is the step size adjustment function.

[0077] Optionally, in addition to equal-spaced division, the height layer can also be divided using non-equal-spaced division or adaptive division based on obstacle height distribution. The first azimuth sector can be divided using equal angles, or using non-equal-angled division, sector division based on flight path direction densification, sector division based on obstacle density densification, or sector division based on risk level adjustment, etc.

[0078] Alternatively, as a possible implementation, after obtaining the first initial set corresponding to a height layer, the next height layer can be determined based on the minimum height of the obstacles in the first initial set. For example, if the current height layer is 20 meters, and the minimum height of the first obstacle in the first initial set corresponding to the current height layer is 50 meters, then 50 meters can be taken as the next height layer, and the search will not be performed within the height range of 20 to 50 meters. This can speed up the search process.

[0079] Optionally, as a possible implementation, when a first obstacle is identified in a first azimuth sector of the current height layer, the next first azimuth sector to be analyzed can be determined based on the angle information of the first obstacle. For example, if a first azimuth sector of the current height layer is 0°~10°, and the original plan was to set the next first azimuth sector to be searched to be 10°~20°, when an obstacle is found in this first azimuth sector, assuming that the obstacle occupies an angle range of 5°~15°, then the next first azimuth sector can be modified to 15°~25°, and the 10°~15° angle range will no longer be searched. This can speed up the search process.

[0080] If the first azimuth sectors of multiple height layers are different, the second azimuth sectors can be divided for the entire target operating envelope. Then, based on the second azimuth sectors obtained in this way, the first candidate obstacle corresponding to each second azimuth sector obtained by dividing the entire target operating envelope can be determined.

[0081] Alternatively, other methods can be used to replace the circular wave diffusion search described above, such as radial scan search, sector progressive search, polar coordinate grid search, distance-first search, envelope boundary advancement search, or nearest constraint obstacle search based on a spatial index structure. The spatial index structure can be a raster index, quadtree, octree, R-tree, KD-tree, or other data structures that can improve the efficiency of obstacle spatial retrieval.

[0082] Having obtained the first initial set for each height layer, the same obstacle appearing repeatedly in adjacent height layers and adjacent first azimuth sectors of the same height layer can be merged to obtain the first set. This avoids redundant calibration.

[0083] After obtaining the first set, a masking zone can be constructed in the direction away from the take-off and landing point, based on the currently identified candidate obstacles. Then, masking analysis is performed using these masking zones to obtain the second set. The masking zone constructed for a single obstacle can be analogous to setting a light beam at the take-off and landing point. The boundary of the target's operational envelope is opaque. When the light beam emits light, the shadow area formed by the obstacle under the light beam's illumination is defined as follows: the side of the shadow area closer to the take-off and landing point represents the obstacle, and the side farther away represents the boundary of the target's operational envelope. This shadow area is the masking zone corresponding to that obstacle.

[0084] As one possible implementation method, it can be achieved through Figure 8 The second set is obtained as shown. Please refer to... Figure 8 , Figure 8 for Figure 3 A flowchart illustrating the sub-steps included in step S140. In this embodiment, step S140 may include sub-steps S141 to S143.

[0085] Sub-step S141: According to the order of horizontal distance between the obstacle and the take-off and landing point from near to far, select one of the first other obstacles that has not been analyzed as the target obstacle to be analyzed.

[0086] Sub-step S142: Determine whether the target obstacle to be analyzed is occluded by the current set of candidate obstacles.

[0087] Sub-step S143: If the target obstacle to be analyzed is not occluded by the current candidate obstacle set, then the target obstacle to be analyzed is added to the current candidate obstacle set; if the target obstacle to be analyzed is occluded by the current obstacle set, then the target obstacle to be analyzed is not added to the current candidate obstacle set; and the analysis continues until all other obstacles in the first set have been analyzed, thus obtaining the second set.

[0088] In this embodiment, based on the order of horizontal distance between obstacles and the take-off and landing points from closest to farthest, a first other obstacle that has not undergone occlusion analysis is selected as the target obstacle to be analyzed. The target obstacle to be analyzed represents the first other obstacle currently undergoing occlusion analysis. Then, it is determined whether the target obstacle to be analyzed is occluded by the current set of candidate obstacles; the specific method can be determined based on actual needs.

[0089] If the target obstacle to be analyzed is occluded by the current set of candidate obstacles, then the target obstacle to be analyzed will not be considered as a second candidate obstacle; that is, the target obstacle to be analyzed does not create new clearance constraints, and the current set of candidate obstacles will not be updated. In other words, if the target obstacle to be analyzed is occluded by the current set of obstacles, then the target obstacle to be analyzed will not be added to the current set of candidate obstacles. Then, according to the order of horizontal distance between the obstacle and the take-off and landing point from near to far, a first other obstacle that has not undergone occlusion analysis will be selected as the target obstacle to be analyzed.

[0090] If the target obstacle to be analyzed is not occluded by the current set of candidate obstacles, then the target obstacle to be analyzed is regarded as a second candidate obstacle, that is, the target obstacle to be analyzed forms a new clearance constraint, and the target obstacle to be analyzed is added to the current set of candidate obstacles. Then, according to the order of horizontal distance between the obstacle and the take-off and landing point from near to far, a first other obstacle that has not been occluded is regarded as the target obstacle to be analyzed.

[0091] After all other obstacles have undergone occlusion analysis, the current set of candidate obstacles obtained at this point is taken as the second set. That is, the second set is the set of candidate obstacles obtained after the occlusion analysis is completed. The initial set of candidate obstacles includes the first candidate obstacles; that is, the obstacles in the initial set of candidate obstacles are the aforementioned first candidate obstacles. The obstacles in the second set can be referred to as second obstacles.

[0092] The following example illustrates how to obtain the second set.

[0093] Assume the first candidate obstacles are A and B, and the first other obstacles in the first set are C, D, and E, wherein the horizontal distance between the first other obstacles C, D, and E and the take-off and landing point increases sequentially.

[0094] First, since C is closest to the takeoff and landing point among C, D, and E, C is selected as the target obstacle to be analyzed. At this point, the current candidate obstacle set includes A and B. Next, it is determined whether C is occluded by A or B in the current candidate obstacle set. Assuming it is not occluded, C is added to the current candidate obstacle set. Since D and E have not yet undergone occlusion analysis, the process continues.

[0095] Since D is closer to the takeoff and landing point than E, D is selected as the target obstacle to be analyzed. The current candidate obstacle set includes A, B, and C. It is then determined whether D is occluded by A, B, or C in the current candidate obstacle set. Assuming D is occluded, it is not added to the current candidate obstacle set. Since E has not yet undergone occlusion analysis, the process continues.

[0096] Let E be the target obstacle to be analyzed. The current candidate obstacle set includes A, B, and C. Determine if E is occluded by A, B, or C in the current candidate obstacle set. Assuming it is not occluded, add E to the current candidate obstacle set, which now includes A, B, C, and E. Since all other obstacles in the first set have undergone occlusion analysis, the occlusion analysis is complete and can be stopped. The current candidate obstacle set is then designated as the second set, which includes A, B, C, and E.

[0097] One possible implementation method is through Figure 9 The method shown determines whether the target obstacle to be analyzed is occluded by the current set of candidate obstacles. Please refer to [reference needed]. Figure 9 , Figure 9 for Figure 8 A flowchart illustrating the sub-steps included in neutron step S142. In this embodiment, sub-step S142 may include sub-steps S1421 to S1423.

[0098] Sub-step S1421: Obtain the occlusion area corresponding to the current candidate obstacle set, and determine whether the target obstacle to be analyzed is within the occlusion area.

[0099] Sub-step S1422: Determine whether the envelope penetration depth corresponding to the target obstacle to be analyzed is not greater than the envelope penetration depth corresponding to each obstacle in the current candidate obstacle set.

[0100] Sub-step S1423: If the target obstacle to be analyzed is within the occlusion area and the corresponding envelope penetration depth is not greater than the envelope penetration depth of each obstacle in the current candidate obstacle set, then it is determined that the target obstacle to be analyzed is occluded; otherwise, it is determined that the target obstacle to be analyzed is not occluded.

[0101] In this embodiment, the occlusion zone corresponding to the current candidate obstacle set can be determined based on the height of each obstacle in the current candidate obstacle set, the horizontal distance between the obstacle and the take-off and landing point, and the width of the projected profile. Where a safety margin is not considered in the establishment of the target running envelope, the occlusion zone can also be determined in conjunction with the safety margin. The occlusion zone of an obstacle can be an occlusion cone or an occlusion sector, etc. When the current candidate obstacle set includes multiple obstacle zones, the height of the occlusion zone corresponding to the current candidate obstacle set can be the lowest height among all obstacles in the current candidate obstacle set. Next, it is determined whether the azimuth, height, and projected profile of the target obstacle to be analyzed fall within the constraint range represented by the occlusion zone (i.e., whether it falls within the aforementioned shadow area). If it falls within, it means that the target obstacle to be analyzed is within the occlusion zone; otherwise, it means that the target obstacle to be analyzed is not within the occlusion zone.

[0102] The envelope penetration depth corresponding to an obstacle is represented by the obstacle's height minus the height of the obstacle on the running envelope surface corresponding to the target running envelope. It is also determined whether the envelope penetration depth corresponding to the target obstacle to be analyzed is not greater than the envelope penetration depths corresponding to each obstacle in the current candidate obstacle set; that is, the envelope penetration depth corresponding to each obstacle in the current candidate obstacle set is compared with the envelope penetration depth corresponding to the target obstacle to be analyzed.

[0103] If the target obstacle to be analyzed is within the occlusion area, and its corresponding envelope penetration depth is not greater than the envelope penetration depth of each obstacle in the current candidate obstacle set (i.e., the envelope penetration depth of each obstacle in the current candidate obstacle set is greater than the envelope penetration depth of the obstacle to be analyzed), then it can be determined that the target obstacle is occluded by the current candidate obstacle set. If the target obstacle to be analyzed is not within the occlusion area, and / or, the envelope penetration depth of the target obstacle is greater than the envelope penetration depth of each obstacle in the current candidate obstacle set, then it can be determined that the target obstacle is not occluded.

[0104] In the aforementioned masking analysis, based on the identified near-range candidate obstacles, a masking zone is constructed in the direction away from the take-off and landing point according to their height, horizontal distance, and projected profile width. Then, it is determined whether the far-range obstacle falls within the constraints of this masking zone in terms of azimuth, height, and projected profile, and whether its penetration depth into the target's operational envelope is not greater than that of the near-range candidate obstacle. If the far-range obstacle falls completely within the masking range corresponding to the masking zone, and its penetration depth into the overall operational envelope is not greater than that of the near-range candidate obstacle, then it is determined that the far-range obstacle does not create new clearance constraints for the target's operational envelope and can be eliminated as a redundant obstacle. For far-range obstacles, as long as they do not create new clearance constraint boundaries, do not increase the envelope penetration depth, and do not expand the affected azimuth range, they can be identified as redundant obstacles.

[0105] For ease of description, the following example uses a nearby candidate obstacle to illustrate how occlusion analysis is performed. The occlusion determination in occlusion analysis can be expressed by the following formula: , Indicates the azimuth angle of the nearby obstacle i. Indicates the azimuth angle of a distant obstacle j. The occlusion angle is determined by the width of the projected profile of obstacle i and the safety margin. Indicates the height of obstacle i. Indicates the height of obstacle j. This represents the horizontal distance of obstacle i relative to the take-off and landing point. This represents the horizontal distance of obstacle j relative to the takeoff and landing point. Indicates the tolerance for height or slope. This represents the penetration depth of obstacle i into the target's operational envelope. This represents the penetration depth of obstacle j into the target's operational envelope. Under the condition that the above formula is satisfied, a distant obstacle j is determined to be occluded and classified as a redundant obstacle. When the current set of candidate obstacles includes multiple near-range candidate obstacles, these multiple near-range candidate obstacles can be treated as a single near-range candidate obstacle, and the occlusion determination is still performed using the above formula.

[0106] In occlusion analysis based on occlusion regions, the occlusion region can be an occlusion cone, an occlusion sector, an occlusion polygon, a line-of-sight occlusion model, a clearance constraint slope comparison model, a projected contour overlap model, etc.

[0107] In the case of obtaining the second set, one possible implementation is through... Figure 10 As shown, the target analysis results are determined from the second set based on the constraint contribution. Please refer to... Figure 10 , Figure 10for Figure 3 A flowchart illustrating the sub-steps included in step S150. In this embodiment, step S150 may include sub-steps S151 to S153.

[0108] Sub-step S151: For each second obstacle in the second set, obtain the constraint contribution of that second obstacle.

[0109] In this embodiment, obstacles in the second set are referred to as second obstacles. For each second obstacle, its constraint contribution is analyzed based on a pre-set method. The constraint contribution represents the degree to which the second obstacle restricts the target's operational envelope. The specific calculation method for the constraint contribution of an obstacle can be determined according to actual needs and is not specifically limited here.

[0110] Optionally, it can be achieved through Figure 11 The constraint contribution of a second obstacle is obtained as shown. Please refer to... Figure 11 , Figure 11 for Figure 10 A flowchart illustrating the sub-steps included in neutron step S151. In this embodiment, sub-step S151 may include sub-steps S1511 to S1512.

[0111] Sub-step S1511: Obtain the constraint contribution parameters of the second obstacle.

[0112] Sub-step S1512: Obtain the constraint contribution degree of the second obstacle based on the constraint contribution parameters of the second obstacle.

[0113] Optionally, constraint contribution parameters of the second obstacle can be obtained first. These parameters include limitations describing the airspace constraints formed by the second obstacle. The constraint contribution parameters may include at least one of the following: the penetration depth of the second obstacle on the target's operational envelope, the width of the obstruction angle, the horizontal distance attenuation factor, and the influence weight. The horizontal distance attenuation factor increases with the horizontal distance; the influence weight is determined based on the number of UAV types affected by the second obstacle, with a larger number of UAV types affected by a single second obstacle resulting in a larger influence weight.

[0114] Next, the constraint contribution degree of the second obstacle can be calculated based on its constraint contribution parameters. The specific calculation method can be determined according to actual needs. The larger the value of the constraint contribution parameter, the greater the constraint contribution degree.

[0115] As one possible implementation, the constraint contribution parameters include: the penetration depth of the second obstacle into the target's operating envelope, the occlusion angle width of the second obstacle, the horizontal distance attenuation factor of the second obstacle, and the influence weight of the second obstacle. Then, based on a pre-set formula, the constraint contribution of the second obstacle is obtained through a weighted summation. This method can be expressed by the following formula: ,in, This represents the constraint contribution of the i-th second obstacle. This indicates the penetration depth (i.e., the height of the obstacle minus the height of the corresponding operating envelope). This indicates the width of the blocking angle formed by the obstacle within the corresponding azimuth sector. Indicates the horizontal distance attenuation factor. Indicates the influence weight; to The corresponding weighting coefficients can be set based on the take-off and landing field operation level, UAV type, site safety requirements, or engineering experience.

[0116] Optionally, in addition to obtaining the constraint contribution using the weighted summation model described above, the constraint contribution can also be calculated using a weighted product model, a hierarchical scoring model, a fuzzy evaluation model, a rule-based discrimination model, or a machine learning regression model. The evaluation factors for constraint contribution (i.e., constraint contribution parameters) may include, in addition to the aforementioned penetration depth, azimuth obstruction angle width, horizontal distance attenuation factor, and influence weight, obstacle type, obstacle height change risk, operating frequency, wind environment impact, and data reliability. Among these, obstacle height change risk represents the probability of an obstacle increasing in height, and operating frequency indicates the frequency of use of routes near the obstacle.

[0117] Sub-step S152: Based on the constraint contribution of each second obstacle, obtain multiple classification sets.

[0118] In this embodiment, given that the constraint contribution of each second obstacle in the second set is available, the second obstacles in the second set can be classified according to their constraint contribution and corresponding thresholds, thereby obtaining multiple classification sets. The specific classification method can be determined based on actual needs. For example, a contribution threshold can be set, and second obstacles with constraint contribution greater than or equal to the threshold can be classified into one classification set, while second obstacles with constraint contribution less than the threshold can be classified into another classification set, thus obtaining two classification sets. Alternatively, two contribution thresholds can be set: a first contribution threshold... Second contribution threshold The second obstacles in the second set are divided into: obstacles that must be labeled, candidate verification obstacles, and ordinary archive obstacles; when the constraint contribution of a second obstacle... Greater than or equal to the first contribution threshold When the second obstacle is identified as a mandatory obstacle, the constraint contribution of a second obstacle is determined. Greater than or equal to the second contribution threshold And less than the first contribution threshold When the second obstacle is identified as a candidate verification obstacle, the constraint contribution of a second obstacle is considered. Less than the second contribution threshold At that time, it was treated as an ordinary obstacle.

[0119] Sub-step S153: Obtain the target analysis result based on the multiple classification sets.

[0120] After obtaining the multiple classification sets, optionally, the classification set with the highest constraint contribution can be used as the target analysis result, or fewer classification sets than the multiple classification sets can be selected in descending order of constraint contribution as the target analysis set. For example, in the above example, the obstacles are divided into obstacles that must be labeled, candidate obstacles for verification, and ordinary obstacles, so the two classification sets of obstacles that must be labeled and candidate obstacles for verification can be used as the target analysis result. Alternatively, other methods can be used to obtain the target analysis result based on the classification sets.

[0121] As one possible implementation method, it can be achieved through Figure 12 The method shown obtains the target analysis results based on the multiple classification sets. Please refer to... Figure 12 , Figure 12 for Figure 10 A flowchart illustrating the sub-steps included in neutron step S153. In this embodiment, sub-step S153 may include sub-steps S1531 to S1533.

[0122] Sub-step S1531: For each obstacle to be reviewed in the multiple classification sets, the review priority of the obstacle to be reviewed is calculated based on the constraint contribution degree and data credibility corresponding to the obstacle to be reviewed.

[0123] Sub-step S1532: Sort the obstacles to be reviewed according to their review priority to obtain the sorting results.

[0124] Sub-step S1533: Based on the multiple classification sets and the verification results, the target analysis results are obtained.

[0125] In this embodiment, obstacles requiring on-site verification can first be identified from the classification set as obstacles to be verified. The specific method can be determined based on actual needs. For example, to avoid omitting obstacles that actually need to be labeled, the classification set containing the largest constraint contribution can be excluded, and then all obstacles in the remaining classification sets can be considered as obstacles to be verified. Alternatively, as mentioned above, in the case of being divided into three categories, all obstacles in the candidate verification obstacle classification set can be considered as obstacles to be verified.

[0126] After identifying the obstacles to be reviewed, the review priority of each obstacle can be calculated based on its constraint contribution and data reliability. The data reliability of an obstacle can be obtained from its relevant descriptive information, and the specific value can be determined based on actual needs.

[0127] Optionally, data reliability can be determined based on at least one of the following: data source reliability, data timeliness, planar coordinate accuracy, and height measurement accuracy. The data may originate from field surveying, lidar, oblique photography, remote sensing imagery, urban 3D models, building databases, or manual surveys, etc.

[0128] As one possible approach, data reliability can be calculated based on the following formula, taking into account factors such as data source reliability, data timeliness, planar coordinate accuracy, and height accuracy: ,in, This indicates the data credibility of the i-th obstacle to be reviewed. Indicates the reliability of the data source. This indicates the timeliness of the data (the more recent the collection time, the higher the timeliness). Indicates the precision of planar coordinates. This indicates the accuracy of height measurement (i.e., the accuracy of height measurement).

[0129] Optionally, the data credibility can be calculated not only based on the reliability of the data source, the timeliness of the data, the accuracy of the planar coordinates, and the accuracy of the height, but also by combining the data update frequency, sensor accuracy, manual verification records, or the consistency of multi-source data. In addition to the product model mentioned above, the data credibility can also be calculated using a weighted summation model, a grade assignment model, or a confidence interval model.

[0130] The review priority of an obstacle to be reviewed can be calculated based on its constraint contribution and data reliability. The review priority is directly proportional to the constraint contribution and inversely proportional to the data reliability. Then, the obstacles to be reviewed are sorted according to their review priority from highest to lowest, resulting in a ranking. This ranking indicates that the on-site review order of the obstacles should be based on their review priority from highest to lowest. This allows obstacles with high constraint contribution but low data reliability to be prioritized for on-site review.

[0131] Optionally, the review priority can be calculated using the following formula: ,in, This indicates the review priority of the i-th obstacle to be reviewed. The larger the value, the greater the impact of the obstacle on the operational clearance, but the less reliable the data is, and the more important it is to prioritize on-site verification.

[0132] Next, staff can conduct on-site verification based on the sorting results to determine whether each obstacle to be verified needs to be calibrated, thereby obtaining the verification results. Then, based on the multiple classification sets and the verification results, the target analysis results are obtained. For example, if the second set is divided into two classification sets: one requiring calibration and the other requiring verification, on-site verification is performed on the obstacles in the set requiring verification to obtain verification results. Then, the obstacles requiring calibration included in the verification results can be merged with the obstacles in the set requiring calibration to obtain the target analysis results.

[0133] Optionally, the target analysis results may include the number, type, coordinates, horizontal distance, azimuth, height, height layer, azimuth sector, penetration depth, constraint contribution, data credibility, and review priority of the obstacles to be calibrated.

[0134] Optionally, obstacles in the target analysis results can be verified on-site, their coordinates precisely measured, their altitude remeasured, and identified and numbered to generate a calibration result table. The calibration result table may include: a UAV take-off and landing site obstacle calibration result table, an obstacle distribution map, a clearance constraint diagram, a 3D model layer, a GIS data layer, an on-site verification checklist, a risk classification checklist, or data interface files suitable for the UAV operation management platform, etc.

[0135] Optionally, feedback updates can be performed to obtain target analysis results that match the actual situation. Feedback updates can take the form of periodic updates, event-triggered updates, updates after on-site verification, synchronous updates from multiple sources, or updates after changes in operational risks. Updated content can include obstacle coordinates, height, type, status, data reliability, constraint contribution, verification priority, and the set of key obstacles (i.e., the set of obstacles to be calibrated). This makes the above method applicable not only to obstacle screening and calibration during the initial construction phase of takeoff and landing sites, but also to continuous airspace management under dynamic obstacle changes in urban low-altitude operating environments.

[0136] The current method of directly identifying obstacles with a height higher than a preset height within a horizontal area as key obstacles that need to be marked, while enabling the investigation and assessment of obstacles around the take-off and landing field, still has the following objective drawbacks.

[0137] 1. Too many objects to calibrate, resulting in a large workload. Any obstacle located within the designated horizontal area and with a height greater than the preset height is included in the calibration, leading to the calibration of a large number of non-critical obstacles, increasing surveying, verification, and maintenance costs.

[0138] 2. Lack of a critical obstacle screening mechanism. Existing methods do not further determine whether an obstacle truly constitutes a primary operational constraint in a particular direction. Therefore, it is difficult to identify truly critical obstacles that require focused identification from a large number of obstacles.

[0139] 3. Insufficient consideration of the occlusion relationship between obstacles: In the same or similar orientations, if nearby obstacles already constitute a major constraint on the UAV's entry and exit airspace, distant obstacles may not further increase the actual operational limitations. Existing technologies typically lack the ability to determine the occlusion relationship between obstacles in front and behind, which can easily lead to duplicate and redundant calibrations.

[0140] 4. Insufficient adaptation to the three-dimensional operational characteristics of UAVs: UAV takeoff and landing processes exhibit significant altitude changes, with different altitude levels corresponding to varying operational risks and obstacle constraints. Existing methods do not adequately incorporate the three-dimensional approach and departure characteristics of UAVs from low to high or high to low.

[0141] 5. Difficulty in adapting to mixed operation of multi-configuration UAVs: Different configurations of UAVs, such as rotorcraft, fixed-wing, and compound-wing, have different climb performance, descent performance, and approach / departure requirements. Existing fixed rules are often difficult to uniformly adapt to mixed operation scenarios of multi-configuration UAVs, which may lead to overly conservative or inaccurate evaluation results.

[0142] 6. Low efficiency in urban low-altitude environments with dense obstacles: Around urban take-off and landing sites, there are numerous and complexly distributed obstacles such as buildings, poles, trees, and billboards. Existing methods tend to generate large obstacle lists, and subsequent screening relies on manual experience, resulting in low efficiency, high subjectivity, and hindering engineering implementation and automated processing.

[0143] This solution addresses the challenges of numerous, complex, and diverse obstacles around urban low-altitude take-off and landing fields, as well as inconsistent clearance requirements for mixed-configuration UAVs. It constructs an adaptive three-dimensional operational envelope for multi-configuration UAVs and identifies obstacles that primarily constrain UAV arrival and departure through height hierarchies, first-azimuth sector division, and circular wave diffusion search within this envelope. Simultaneously, it introduces obstacle constraint contribution calculation and occlusion constraint judgment mechanisms to determine whether obstacles create new constraints on the overall operational envelope. This eliminates redundant obstacles that are occluded by preceding obstacles or do not create new clearance constraints, ultimately forming a set of obstacles requiring on-site verification and calibration. This solution can identify key obstacles that truly constrain the clearance of mixed-configuration UAV operations from a large number of obstacles and eliminate redundant obstacles that do not create new clearance constraints, thereby reducing the number of objects requiring on-site calibration and improving the efficiency of take-off and landing field obstacle calibration and the accuracy of clearance assessment.

[0144] The adaptive obstacle analysis method for low-altitude take-off and landing fields provided in this embodiment does not indiscriminately screen obstacles around the take-off and landing field within a horizontal region based on a preset altitude. Instead, it establishes a single-configuration operational envelope based on the operational performance of different configurations such as rotary-wing UAVs, fixed-wing UAVs, and compound-wing UAVs. A comprehensive operational envelope is then formed through envelope union, most unfavorable boundary extraction, maximum radius envelope, or risk-weighted fusion. Therefore, this solution can adapt to mixed operation scenarios of multi-configuration UAVs, avoiding the problems of overly conservative assessments or missed screenings caused by using a single fixed airspace rule.

[0145] This solution also identifies obstacles that first constrain the UAV's airspace in each direction through height stratification, first-azimuth sector division, and circular wave diffusion search. Compared to listing all obstacles within the survey area as calibration objects, this solution can select key obstacles based on the order in which they create airspace constraints, reducing the number of unnecessary calibration objects and decreasing the workload of on-site surveying and verification.

[0146] This scheme also utilizes obstacle constraint contribution, quantifying the actual constraint effect of obstacles based on their penetration depth, azimuth occlusion angle width, horizontal distance attenuation factor, and influence weight within the target's operational envelope. Therefore, this scheme can distinguish between obstacles that must be calibrated, candidate verification obstacles, and ordinary archived obstacles, avoiding rough judgments based solely on whether they are within the investigation range or exceed a certain height limit, thus improving the accuracy and interpretability of key obstacle selection.

[0147] This solution also uses the occlusion zone to determine whether distant obstacles create new airspace constraints. For distant obstacles that are completely occluded by nearby candidate obstacles and do not increase the penetration depth of the target's operational envelope or the constraint slope, this solution classifies them as redundant obstacles and does not consider them as candidate obstacles that may need to be calibrated. This method can reduce redundant calibration and is particularly suitable for scenarios with densely distributed obstacles such as buildings, poles, trees, and billboards in urban low-altitude environments.

[0148] This scheme can adaptively adjust the height layer step size, azimuth step size, and circular wave diffusion radius step size based on the local obstacle density, the rate of change of constraint contribution, and the degree of height dispersion. A finer search granularity is used in areas with dense obstacles or significant risk changes, while a larger search step size is used in areas with sparse obstacles or continuous areas without new constraints. This reduces computational load and improves engineering processing efficiency while ensuring that no critical obstacles are missed.

[0149] This plan also assesses the credibility of obstacle data from different sources and determines the priority of on-site verification based on constraint contribution. For obstacles with high constraint contribution but low data credibility, on-site verification is prioritized, which helps reduce airspace safety risks caused by insufficient data timeliness, coordinate errors, or height errors.

[0150] This solution employs a technical approach that combines "multi-configuration operational envelope fusion, hierarchical circular wave diffusion search, constraint contribution calculation, masking redundancy elimination, data credibility verification, and feedback updates." This approach enables the automatic identification of key obstacles that truly affect the airspace clearance of mixed UAV operations from a large number of obstacles, thereby improving the accuracy, automation, and engineering applicability of takeoff and landing site airspace assessments.

[0151] To perform the corresponding steps in the above embodiments and various possible methods, an implementation of a low-altitude takeoff and landing field obstacle adaptive analysis device 200 is given below. Optionally, the low-altitude takeoff and landing field obstacle adaptive analysis device 200 can adopt the above-described... Figure 1 The device structure of the electronic device is shown. Further, please refer to... Figure 13 , Figure 13This is a block diagram of the low-altitude takeoff and landing field obstacle adaptive analysis device 200 provided in this embodiment. It should be noted that the basic principle and technical effects of the low-altitude takeoff and landing field obstacle adaptive analysis device 200 provided in this embodiment are the same as those in the above embodiments. For the sake of brevity, any parts not mentioned in this embodiment can be referred to the corresponding content in the above embodiments. In this embodiment, the low-altitude takeoff and landing field obstacle adaptive analysis device 200 may include: an information acquisition module 210, a filtering module 220, and an analysis module 230.

[0152] The information acquisition module 210 is used to acquire obstacle information corresponding to the take-off and landing field.

[0153] The filtering module 220 is used to obtain a first set based on the obstacle information and preset filtering conditions. The first set includes first candidate obstacles and first other obstacles, wherein the first candidate obstacle is the first obstacle that first generates clearance constraints. The filtering module 220 is further configured to perform occlusion analysis on the first other obstacles based on the first candidate obstacles to obtain a second set. The occlusion analysis is used to analyze whether a first obstacle near the take-off and landing point occludes a first obstacle far from the take-off and landing point to determine that the unoccluded first obstacle is used as a second candidate obstacle. The second set includes both the first candidate obstacle and the second candidate obstacle.

[0154] The analysis module 230 is used to obtain target analysis results based on the second set. The target analysis results are used to indicate the obstacles that need to be calibrated, identified from the second set.

[0155] In this embodiment, the information acquisition module 210 is further configured to obtain the target operational envelope corresponding to the takeoff and landing field. The target operational envelope is used to obtain the first set.

[0156] Optionally, the above modules can be stored in the form of software or firmware. Figure 2 The memory 110 shown is either stored in or embedded in the operating system (OS) of the electronic device 100, and can be used by... Figure 2 The processor 120 executes the program. Meanwhile, the data and program code required to execute the above modules can be stored in the memory 110.

[0157] This application also provides a readable storage medium storing a computer program thereon, which, when executed by a processor, implements the aforementioned adaptive analysis method for low-altitude take-off and landing field obstacles.

[0158] In summary, the adaptive analysis method, apparatus, electronic device, and readable storage medium for low-altitude takeoff and landing field obstacles provided in this application first obtain obstacle information corresponding to the takeoff and landing field. Then, based on the obstacle information and preset filtering conditions, a first set is obtained, where the obstacles in the first set are first obstacles, including first candidate obstacles and first other obstacles. The first candidate obstacles are the first obstacles that first generate clearance constraints. Next, based on the first candidate obstacles, occlusion analysis is performed on the first other obstacles in the first set to determine whether the first obstacles near the takeoff and landing point occlude first obstacles far from the takeoff and landing point, thereby identifying unoccluded first obstacles as second candidate obstacles, thus obtaining a second set including the first candidate obstacles and the second candidate obstacles. Finally, based on the second set, a target analysis result is obtained, which is used to indicate the obstacles that need to be calibrated from the second set. In this way, obstacles to be calibrated can be filtered based on the order in which they generate clearance constraints, reducing the number of unnecessary calibration targets.

[0159] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0160] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0161] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0162] The above description is merely an optional embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. An adaptive analysis method for obstacles in low-altitude takeoff and landing fields, characterized in that, The method includes: Obtain obstacle information corresponding to the takeoff and landing field; Based on the obstacle information and preset screening conditions, a first set is obtained, wherein the first obstacle in the first set includes a first candidate obstacle and a first other obstacle, and the first candidate obstacle is the first obstacle that first generates clearance constraints; Based on the first candidate obstacle, an occlusion analysis is performed on the first other obstacles to obtain a second set. The occlusion analysis is used to analyze whether the first obstacle near the take-off and landing point occludes the first obstacle far from the take-off and landing point to determine the first obstacle that is not occluded as the second candidate obstacle. The second set includes the first candidate obstacle and the second candidate obstacle. Based on the second set, target analysis results are obtained, wherein the target analysis results are used to indicate the obstacles that need to be calibrated identified from the second set.

2. The method according to claim 1, characterized in that, The preset screening conditions include height constraints, distance constraints, and envelope penetration conditions. The height constraints are determined based on the current height of the high-rise building and the vertical safety margin. The distance constraints are determined based on the operating envelope radius obtained from the current height of the high-rise building and the horizontal safety margin. The first set obtained based on the obstacle information and the preset screening conditions includes: According to the order of altitude from low to high, one altitude layer is taken as the current altitude layer. The altitude layer is obtained by dividing the target operating envelope corresponding to the take-off and landing field in the altitude direction. The operating envelope radius of the target operating envelope increases with the increase of altitude. For each first-position sector of the current height layer, in a manner with a gradually increasing search radius, search for whether there is an obstacle located on the arc corresponding to the search radius and satisfying the preset filtering conditions among the obstacles corresponding to the obstacle information, wherein the center of the arc is the horizontal projection point of the take-off and landing point in the current height layer; If so, the obstacle found will be used as the first obstacle in the first initial set corresponding to the current height layer. In this case, the obstacle that first meets the preset screening conditions in each second directional sector obtained by dividing the target running envelope is the first candidate obstacle. The first set is obtained based on the first initial set corresponding to each height layer.

3. The method according to claim 2, characterized in that, The step of obtaining a first set based on the obstacle information and preset filtering conditions further includes: after obtaining a first initial set corresponding to a height layer, determining the next height layer based on the minimum height corresponding to the obstacles in the first initial set; and / or, if a first obstacle is determined from a first azimuth sector of the current height layer, determining the next first azimuth sector to be analyzed based on the angle information of the first obstacle; and / or The method further includes: setting the height layer step size, the azimuth sector step size of the first azimuth sector, and the search radius step size of different regions in advance based on at least one of the obstacle density, constraint contribution change, and height dispersion corresponding to the obstacle information, wherein the constraint contribution represents the degree of restriction of the obstacle on the target running envelope.

4. The method according to claim 2, characterized in that, The method further includes: obtaining the target operational envelope corresponding to the takeoff and landing field; Obtaining the target operational envelope corresponding to the takeoff and landing field includes: For various configurations of UAVs corresponding to the take-off and landing field, the single-configuration operating envelope corresponding to the UAV configuration is obtained based on the operating performance parameters of the UAV configuration, wherein the operating performance parameters include the climb and descent clearance angle. Based on the single-configuration operational envelopes corresponding to various configurations of UAVs at the take-off and landing site, a comprehensive operational envelope is obtained as the target operational envelope.

5. The method according to claim 1, characterized in that, The occlusion analysis based on the first candidate obstacle and the first other obstacles yields a second set, including: Based on the order of horizontal distance between the obstacle and the take-off and landing point from near to far, the first other obstacle that has not been analyzed is selected as the target obstacle to be analyzed. Determine whether the target obstacle to be analyzed is occluded by the current set of candidate obstacles, wherein the initial set of current candidate obstacles includes the first candidate obstacle; If the target obstacle to be analyzed is not occluded by the current candidate obstacle set, then the target obstacle to be analyzed is added to the current candidate obstacle set; if the target obstacle to be analyzed is occluded by the current obstacle set, then the target obstacle to be analyzed is not added to the current candidate obstacle set; and the analysis continues until all other obstacles in the first set have been analyzed, thus obtaining the second set; wherein, the second set is the current candidate obstacle set obtained when the analysis is completed.

6. The method according to claim 5, characterized in that, The step of determining whether the target obstacle to be analyzed is occluded by the current set of candidate obstacles includes: Obtain the occlusion area corresponding to the current set of candidate obstacles, and determine whether the target obstacle to be analyzed is within the occlusion area, wherein the occlusion area is determined based on the height of each obstacle in the current set of candidate obstacles, the horizontal distance between the obstacle and the take-off and landing point, and the width of the projected outline. Determine whether the envelope penetration depth corresponding to the target obstacle to be analyzed is not greater than the envelope penetration depth corresponding to each obstacle in the current candidate obstacle set; If the target obstacle to be analyzed is within the occlusion area and the corresponding envelope penetration depth is not greater than the envelope penetration depth of each obstacle in the current candidate obstacle set, then the target obstacle to be analyzed is determined to be occluded; otherwise, the target obstacle to be analyzed is determined to be unoccluded.

7. The method according to any one of claims 1-6, characterized in that, The obstacles in the second set are the second obstacles. The first set is obtained based on the target running envelope corresponding to the takeoff and landing field. The step of obtaining the target analysis results based on the second set includes: For each second obstacle in the second set, the constraint contribution of the second obstacle is obtained, wherein the constraint contribution represents the degree to which the second obstacle restricts the target running envelope; Based on the constraint contribution of each second obstacle, multiple classification sets are obtained; The target analysis results are obtained based on the multiple classification sets.

8. The method according to claim 7, characterized in that, The step of obtaining the constraint contribution of each second obstacle in the second set includes: Obtain the constraint contribution parameters of the second obstacle, wherein the constraint contribution parameters include at least one of the penetration depth, occlusion angle width, horizontal distance attenuation factor and influence weight of the second obstacle on the target running envelope, wherein the horizontal distance attenuation factor increases with the horizontal distance, and the influence weight is determined according to the number of UAV types affected by the second obstacle, wherein the larger the number of UAV types affected by a second obstacle, the larger the corresponding influence weight. The constraint contribution degree of the second obstacle is obtained based on the constraint contribution parameters of the second obstacle, wherein the larger the value of the parameter in the constraint contribution parameters, the greater the constraint contribution degree.

9. The method according to claim 7, characterized in that, The step of obtaining the target analysis result based on the multiple classification sets includes: For each obstacle to be reviewed in the multiple classification sets, the review priority of the obstacle to be reviewed is calculated based on the constraint contribution degree and data credibility corresponding to the obstacle to be reviewed. The data credibility is obtained based on at least one of the data source reliability, data timeliness, plane coordinate accuracy and height measurement accuracy. The review priority is directly proportional to the constraint contribution degree and inversely proportional to the data credibility. The obstacles to be reviewed are sorted according to their review priority, and the sorting result is used to indicate that the on-site review order of the obstacles to be reviewed is in descending order of review priority. Based on the multiple classification sets and the verification results, the target analysis results are obtained.

10. An adaptive analysis device for obstacles in low-altitude takeoff and landing fields, characterized in that, The device includes: The information acquisition module is used to acquire obstacle information corresponding to the take-off and landing field; The filtering module is used to obtain a first set based on the obstacle information and preset filtering conditions, wherein the first obstacle in the first set includes a first candidate obstacle and a first other obstacle, and the first candidate obstacle is the first obstacle that first generates clearance constraints; The filtering module is also used to perform occlusion analysis on the first other obstacles based on the first candidate obstacles to obtain a second set, wherein the occlusion analysis is used to analyze whether the first obstacle near the take-off and landing point occludes the first obstacle far from the take-off and landing point to determine the first obstacle that is not occluded as the second candidate obstacle, and the second set includes the first candidate obstacle and the second candidate obstacle. An analysis module is used to obtain target analysis results based on the second set, wherein the target analysis results are used to indicate obstacles that need to be calibrated from the second set.

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