Flight path generation method, apparatus, device, medium, and program product

Abstract

This application provides a flight path generation method, apparatus, device, medium, and program product, relating to the field of electronic technology, for improving the accuracy of flight paths. The specific technical solution is as follows: Based on the grid point data of each grid point in the target flight area corresponding to the flight mission information, a first demand value is calculated for each grid point. The grid point data represents the distribution of objects contained within the grid point. Based on the comprehensive anomaly map and building white film data corresponding to the target flight area, high-anomaly grid points in the target flight area are identified, and the first demand value of the high-anomaly grid points is updated to zero, obtaining a second demand value for each grid point. The building white film data represents the three-dimensional data of each building in the target flight area. Based on the second demand value of each grid point, the comprehensive anomaly map, and the flight mission information, flight path information for the target area is generated. This application is applied to scenarios where flight paths are generated based on demand.

Description

Technical Field

[0001] This application relates to the field of electronic technology, and in particular to a method, apparatus, device, medium, and program product for generating flight paths. Background Technology

[0002] Currently, low-altitude flight is becoming increasingly important. The flight path information determined by low-altitude route flight technology directly determines the operational efficiency, flight safety, and airspace resource utilization of low-altitude aircraft.

[0003] In related technologies, meteorological information and airspace conflict information are first obtained. Then, based on the obtained meteorological information and airspace conflict information, a preset path with high safety and high flight efficiency is selected from the preset path template as the flight path information.

[0004] However, the above-mentioned scheme selects a flight path from a fixed preset path based on meteorological and airspace conflict information of the current flight environment. This makes the selected flight path unable to accurately adapt to the current complex flight environment, resulting in insufficient safety or low efficiency when the aircraft flies along the selected path. Therefore, the determined flight path in the related technology is not accurate enough. Summary of the Invention

[0005] This application provides a flight path generation method to improve the accuracy of flight paths.

[0006] In a first aspect, embodiments of this application provide a flight path generation method, the method comprising: calculating a first demand value for each grid point based on grid point data of each grid point in a target flight area corresponding to flight mission information, wherein the grid point data characterizes the distribution of objects contained in the grid point; determining high-anomaly grid points in the target flight area based on a comprehensive anomaly map and building white film data corresponding to the target flight area, and updating the first demand value of the high-anomaly grid point to zero to obtain a second demand value for each grid point, wherein the comprehensive anomaly map is used to indicate the anomaly value of each grid point in the target flight area as a flight node, the building white film data characterizes the three-dimensional data of each building in the target flight area, and the second demand value of each grid point characterizes the suitability of each grid point as a take-off and landing field; and generating flight path information for the target area based on the second demand value of each grid point, the comprehensive anomaly map, and the flight mission information.

[0007] The technical solution provided in this application brings at least the following beneficial effects: In this solution, grid point data, which characterizes the distribution of objects contained in the grid points, is used to obtain the required value characterizing the suitability of the grid points as take-off and landing sites, ensuring that site selection prioritizes coverage of densely demanded areas and improves flight efficiency; furthermore, by combining comprehensive risk maps and building white film data to eliminate high-risk grid points, flight paths are avoided in unreasonable areas, ensuring the safety of aircraft flight and achieving a dual balance between flight efficiency and safety, thus improving the accuracy of the generated flight paths.

[0008] One possible implementation, based on the comprehensive anomaly map and building white film data corresponding to the target flight area, determines the high anomaly grid points in the target flight area, including: based on the comprehensive anomaly map, determining grid points in the target flight area with anomaly values ​​greater than a first threshold as high anomaly grid points; based on the building white film data, determining the highest building height value corresponding to each grid point, and determining grid points in the target flight area with the highest building height value greater than a second threshold as high anomaly grid points.

[0009] Another possible implementation involves generating flight path information for the target area based on the second demand value of each grid point, the integrated anomaly map, and the flight mission information. This includes: determining takeoff and landing site location information based on the second demand value of each grid point; and generating flight path information for the target flight area based on the takeoff and landing site location information, the integrated anomaly map, and the flight mission information.

[0010] Another possible implementation is that the aforementioned takeoff and landing site location information includes the location information of a first takeoff and landing site and the location information of at least one second takeoff and landing site, wherein the first takeoff and landing site has a higher priority than the second takeoff and landing site; the determination of the takeoff and landing site location information based on the second demand value of each grid point includes: generating a demand scale grid map based on the second demand value of each grid point, which is used to indicate the matching degree of each grid point within the target flight area as a takeoff and landing site; calculating the total demand value corresponding to each grid point based on the demand scale grid map and the aircraft endurance data, wherein the total demand value corresponding to a grid point represents the sum of the second demand values ​​of all grid points within a circular area with the grid point as the center and the aircraft endurance data as the radius; determining the location information of the first takeoff and landing site based on the total demand value corresponding to each grid point; and determining the location information of the second takeoff and landing site based on the total demand value corresponding to each grid point and the location information of the first takeoff and landing site.

[0011] Another possible implementation involves generating flight path information for the target flight area based on the aforementioned takeoff and landing site location information, the aforementioned integrated anomaly map, and the aforementioned flight mission information. This includes: constructing an airway network topology map corresponding to the target flight area based on the aforementioned takeoff and landing site location information and the aforementioned integrated anomaly map; and determining the target flight path information corresponding to the aforementioned flight mission information based on the aforementioned airway network topology map and the aforementioned flight mission information. The aforementioned airway network topology map is a directed graph constructed with each takeoff and landing site as a node and the optimal airway segment between every two takeoff and landing sites as directed edges.

[0012] Another possible implementation method, the above method further includes: determining the outlier value of each grid point based on multidimensional outlier data; constructing the above comprehensive outlier map based on the outlier value of each grid point; wherein the above multidimensional outlier data includes at least one of the following: grid point data of each grid point, digital surface model (DSM) data, meteorological wind speed data, and electromagnetic communication data.

[0013] Secondly, embodiments of this application provide a flight path generation apparatus, comprising: a calculation module, a determination module, and a generation module; the calculation module is used to calculate a first demand value for each grid point based on grid point data of each grid point in a target flight area corresponding to flight mission information, wherein the grid point data represents the distribution of objects contained in the grid point; the determination module is used to determine high-anomaly grid points in the target flight area based on a comprehensive anomaly map and building white film data corresponding to the target flight area, and update the first demand value of the high-anomaly grid point to zero to obtain a second demand value for each grid point, wherein the comprehensive anomaly map is used to indicate the anomaly value of each grid point in the target flight area as a flight node, the building white film data represents the three-dimensional data of each building in the target flight area, and the second demand value of each grid point represents the adaptability of each grid point as a take-off and landing field; the generation module is used to generate flight path information for the target area based on the second demand value of each grid point obtained by the determination module, the comprehensive anomaly map, and the flight mission information.

[0014] One possible implementation is that the aforementioned determining module is specifically used to: based on the aforementioned comprehensive anomaly map, determine grid points in the aforementioned target flight area whose anomaly values ​​are greater than a first threshold as high anomaly grid points; based on the aforementioned building white film data, determine the highest building height value corresponding to each grid point, and determine grid points in the aforementioned target flight area whose highest building height value is greater than a second threshold as high anomaly grid points.

[0015] Another possible implementation, the above-mentioned generation module is specifically used to: determine the take-off and landing site location information based on the second demand value of each grid point; and generate the flight path information of the target flight area based on the take-off and landing site location information, the above-mentioned integrated anomaly map and the above-mentioned flight mission information.

[0016] Another possible implementation is that the aforementioned takeoff and landing site location information includes the location information of a first takeoff and landing site and the location information of at least one second takeoff and landing site, wherein the first takeoff and landing site has a higher priority than the second takeoff and landing site; the aforementioned determining module is specifically used to: generate a demand scale grid map based on the second demand value of each grid point, the demand scale grid map being used to indicate the matching degree of each grid point within the target flight area as a takeoff and landing site; calculate the total demand value corresponding to each grid point based on the demand scale grid map and the aircraft endurance data, the total demand value corresponding to a grid point representing the sum of the second demand values ​​of all grid points within a circular area centered on that grid point and with the aircraft endurance data as the radius; determine the location information of the first takeoff and landing site based on the total demand value corresponding to each grid point; and determine the location information of the second takeoff and landing site based on the total demand value corresponding to each grid point and the location information of the first takeoff and landing site.

[0017] Another possible implementation is that the above-mentioned generation module is specifically used to: construct the airway network topology map corresponding to the target flight area based on the above-mentioned take-off and landing site location information and the above-mentioned comprehensive anomaly map; determine the target flight path information corresponding to the above-mentioned flight mission information based on the above-mentioned airway network topology map and the above-mentioned flight mission information; wherein, the above-mentioned airway network topology map is a directed graph constructed with each take-off and landing site as a node and the optimal airway segment between every two take-off and landing sites as directed edges.

[0018] In another possible implementation, the above-mentioned device further includes: a construction module; the determination module is further configured to determine the outlier value of each grid point based on the multidimensional anomaly data; the construction module is configured to construct the above-mentioned comprehensive anomaly map based on the outlier value of each grid point determined by the determination module; wherein the multidimensional anomaly data includes at least one of the following: grid point data of each grid point, DSM data, meteorological wind speed data, and electromagnetic communication data.

[0019] Thirdly, this application provides an electronic device comprising: a processor and a memory; the memory stores a program or instructions executable on the processor, wherein the program or instructions, when executed by the processor, implement the method of the first aspect described above.

[0020] Fourthly, this application provides a readable storage medium on which a program or instructions are stored, which, when executed by a computer, implement the method of the first aspect described above.

[0021] Fifthly, this application provides a computer program product stored in a storage medium, which, when executed by a computer, implements the method described in the first aspect.

[0022] In a sixth aspect, embodiments of this application provide a chip including a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the method described in the first aspect.

[0023] The beneficial effects of the second to sixth aspects mentioned above are described in the corresponding description of the first aspect and will not be repeated here. Attached Figure Description

[0024] Figure 1 A schematic diagram of the network architecture for an application of a flight path generation method provided in this application embodiment;

[0025] Figure 2 A flowchart illustrating a flight path generation method provided in an embodiment of this application;

[0026] Figure 3 A flowchart illustrating another flight path generation method provided in this application embodiment;

[0027] Figure 4 A flowchart illustrating another flight path generation method provided in this application embodiment;

[0028] Figure 5 A flowchart illustrating another flight path generation method provided in this application embodiment;

[0029] Figure 6 A flowchart illustrating another flight path generation method provided in this application embodiment;

[0030] Figure 7 A schematic diagram of a route network topology provided in an embodiment of this application;

[0031] Figure 8 A schematic diagram of another airway network topology provided in an embodiment of this application;

[0032] Figure 9 This is a schematic diagram of the structure of a flight path generation device provided in an embodiment of this application;

[0033] Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0034] The flight path generation method, apparatus, equipment, medium, and program products provided in this application will now be described in detail with reference to the accompanying drawings.

[0035] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0036] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0037] The terms "at least one," "at least one of," etc., used in the specification and claims of this application refer to any one, any two, or a combination of two or more of the included items. For example, at least one of a, b, and c can mean: "a," "b," "c," "a and b," "a and c," "b and c," and "a, b, and c," where a, b, and c can be single or multiple. Similarly, "at least two" refers to two or more items, and its meaning is similar to that of "at least one."

[0038] In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0039] The embodiments of this application provide a flight path generation method, apparatus, device, medium, and program product that can be applied to scenarios where flight paths need to be generated.

[0040] Currently, low-altitude flight technology is becoming increasingly important. Low-altitude aircraft such as drones and light general aviation aircraft are widely used in various fields including low-altitude logistics, power line inspection, emergency rescue, urban air traffic, and geographic surveying. This places higher demands on the safety, real-time performance, and adaptability of low-altitude route planning. As a core technology for low-altitude flight safety management, the performance of low-altitude route planning directly determines the operational efficiency, flight safety, and airspace resource utilization of low-altitude aircraft, making it a key infrastructure supporting the large-scale development of the low-altitude economy.

[0041] In related technologies, low-altitude route planning technology is mainly divided into two categories: static planning and traditional dynamic planning. Static planning technology relies on preset airspace parameters, fixed path templates, and historical environmental data. It can only plan fixed routes and cannot respond to dynamic risk factors in the low-altitude environment, such as sudden weather disasters, temporary airspace control, aircraft swarm conflicts, and electromagnetic interference. In complex dynamic environments, it is prone to route failures and prominent safety hazards, making it difficult to meet the diverse and high-frequency operational needs of low-altitude aircraft. Thus, the determined flight paths in related technologies are not precise enough.

[0042] To address the aforementioned technical problems, embodiments of this application provide a flight path generation method, apparatus, device, medium, and program product. Based on grid point data of each grid point in a target flight area corresponding to flight mission information, a first demand value is calculated for each grid point. The grid point data characterizes the distribution of objects contained within the grid point. Based on the comprehensive anomaly map and building white film data corresponding to the target flight area, high-anomaly grid points in the target flight area are identified, and the first demand value of each high-anomaly grid point is updated to zero, resulting in a second demand value for each grid point. The comprehensive anomaly map indicates the anomaly value of each grid point as a flight node within the target flight area. The building white film data characterizes the three-dimensional data of each building in the target flight area. The second demand value of each grid point characterizes the suitability of each grid point as a takeoff and landing field. Based on the second demand value of each grid point, the comprehensive anomaly map, and the flight mission information, flight path information for the target area is generated. In this scheme, grid point data, which characterizes the distribution of objects contained in the grid points, is used to obtain the required value that characterizes the suitability of the grid points as take-off and landing sites. This ensures that site selection prioritizes coverage of densely demanded areas, thereby improving flight efficiency. Furthermore, by combining comprehensive risk maps and building white film data, high-risk grid points are eliminated to avoid selecting flight nodes in unreasonable areas, ensuring the safety of aircraft flight and achieving a dual balance between flight efficiency and safety. This improves the accuracy of the generated flight paths.

[0043] The flight path generation method, apparatus, equipment, medium, and program products provided in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0044] Figure 1 This illustration shows the network architecture of a flight path generation method provided in an embodiment of this application. For example... Figure 1 As shown, the network architecture includes a flight path generation device 101 and a terminal device 102. The flight path generation device 101 and the terminal device 102 are interconnected.

[0045] In some embodiments, the flight path generation device 101 may be a server, a computer, or a processor or processing unit within a server or computer. The server may be a single server or a server cluster comprising multiple servers. It should be noted that the embodiments of this application do not limit the specific device form of the flight path generation device 101. Figure 1 The example shown is a flight path generation device 101, which is a single server.

[0046] In some embodiments, the terminal device may be a mobile phone, tablet computer, laptop computer, handheld computer, in-vehicle electronic device, mobile internet device (MID), augmented reality (AR) / virtual reality (VR) device, robot, wearable device, personal computer (PC), ultra-mobile personal computer (UMPC), netbook, personal digital assistant (PDA), aircraft, etc., and the embodiments of this application do not specifically limit it. Figure 1 The example shown is a terminal device 102 for an aircraft.

[0047] In some embodiments, the flight path generation device 101 calculates a first demand value for each grid point based on grid point data of each grid point in the target flight area corresponding to the flight mission information. The grid point data represents the distribution of objects contained in the grid point. Based on the comprehensive anomaly map and building white film data corresponding to the target flight area, it identifies high-anomaly grid points in the target flight area and updates the first demand value of the high-anomaly grid points to zero, obtaining a second demand value for each grid point. The comprehensive anomaly map is used to indicate the anomaly value of each grid point in the target flight area as a flight node. The building white film data represents the three-dimensional data of each building in the target flight area. The second demand value of each grid point represents the adaptability of each grid point as a take-off and landing field. Based on the second demand value of each grid point, the comprehensive anomaly map, and the flight mission information, it generates flight path information for the target area and sends the flight path information to the terminal device 102. After receiving the flight path information sent by the flight path generation device 101, the terminal device 102 performs flight in the target area based on the flight path information.

[0048] It should be noted that the network architecture described in the embodiments of this application is for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and does not constitute a limitation on the technical solutions provided in the embodiments of this application. As network architectures evolve, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0049] See Figure 2 This is a flowchart illustrating a flight path generation method provided in an embodiment of this application. Figure 2 As shown, the flight path generation method provided in this application embodiment can be implemented by the above-mentioned flight path generation device, specifically including the following steps 201 to 203.

[0050] Step 201: The flight path generation device calculates the first required value for each grid point based on the grid point data of each grid point in the target flight area corresponding to the flight mission information.

[0051] In some embodiments, the above-described grid data characterizes the distribution of objects contained within the grid.

[0052] In some embodiments, the objects mentioned above include at least one of the following: commercial locations, industrial locations, inspection target points, and population gathering points. Of course, the objects mentioned above may also include other objects, which can be determined according to actual needs, and this application does not limit them.

[0053] In some embodiments, the aforementioned flight mission information includes at least one of the following: flight mission type, flight area location, and aircraft mission priority. Of course, the aforementioned flight mission information may also include other flight mission information, which can be determined according to actual needs, and this application does not limit this.

[0054] In some embodiments, the grid point refers to the smallest geographic spatial unit formed by uniformly dividing the target flight area according to a preset precision.

[0055] In some embodiments, the first demand value described above characterizes the initial demand for the corresponding grid point within the target flight area as a low-altitude operational take-off and landing site.

[0056] In some embodiments, the flight path generation device may calculate the first demand value for each grid point using the following formula 1.

[0057] ) (Formula 1)

[0058] in, The required value for each raster. This represents the number of commercial locations within the current grid. This represents the number of industrial locations within the current grid. This represents the number of specific targets within the current grid. , , This is a scenario-based adjustment factor. For example, in an urban logistics scenario, the commercial weight is increased. In routine inspection scenarios, specific target weights are introduced. For example, points of interest (POIs) for poles and pipelines; in emergency rescue scenarios, increase the population density correction factor. This is to ensure that site selection accurately covers areas with high demand.

[0059] Step 202: The flight path generation device determines the high-anomaly grid points in the target flight area based on the comprehensive anomaly map and building white film data corresponding to the target flight area, and updates the first demand value of the high-anomaly grid points to zero, thereby obtaining the second demand value of each grid point.

[0060] In some embodiments, the above-mentioned integrated anomaly map is used to indicate the anomaly value of each grid point in the target flight area as a flight node, the above-mentioned building white film data represents the three-dimensional data of each building in the target flight area, and the second demand value of each grid point represents the degree of adaptation of each grid point as a take-off and landing field.

[0061] In some embodiments, the above-mentioned comprehensive anomaly diagram may also be referred to as a comprehensive risk diagram.

[0062] In some embodiments, each grid point is used as an outlier to characterize the risk level of each grid point as a node in the flight path.

[0063] In some embodiments, the range of the outlier value for each grid point as a flight node can be from 0 to 1.

[0064] In some embodiments, the aforementioned high-anomaly grid point refers to a grid point that poses a greater risk when used as a flight path node.

[0065] In some embodiments, the flight path generation device can determine the outlier value of each grid point based on multidimensional outlier data, and then construct the above-mentioned comprehensive outlier map based on the outlier value of each grid point.

[0066] It should be noted that the specific implementation process of the flight path generation device, which determines the outlier value of each grid point based on multi-dimensional outlier data and then constructs the above-mentioned comprehensive outlier map based on the outlier value of each grid point, can be found in the relevant description in the following embodiments. To avoid repetition, this application will not elaborate on it here.

[0067] In some embodiments, combined with Figure 2 ,like Figure 3 As shown, step 202 above can be implemented through steps 202a and 202b.

[0068] Step 202a: Based on the comprehensive anomaly map, the flight path generation device identifies grid points in the target flight area with anomaly values ​​greater than the first threshold as high anomaly grid points.

[0069] In some embodiments, the first threshold can be a fixed value, such as 0.5. Of course, the first threshold can also be other values ​​preset by the system, which can be determined according to actual needs, and this application does not limit this.

[0070] In some embodiments, the flight path generation device can identify and parse the above-mentioned comprehensive anomaly map to obtain the anomaly value corresponding to each grid point, and then, based on the anomaly value corresponding to each grid point, filter out the grid points whose anomaly value is greater than the above-mentioned first threshold as the above-mentioned high anomaly grid points.

[0071] Step 202b: The flight path generation device determines the highest building height value corresponding to each grid point based on the building white film data, and identifies grid points in the target flight area where the highest building height value is greater than the second threshold as high-abnormal grid points. It also updates the first demand value of the high-abnormal grid points to zero to obtain the second demand value of each grid point.

[0072] In some embodiments, the highest building height value corresponding to each grid point refers to the height value of the tallest building among all the buildings contained in each grid point.

[0073] In some embodiments, the second threshold can be a fixed value, such as 100m. Of course, the second threshold can also be other values ​​preset by the system, which can be determined according to actual needs, and this application does not limit it.

[0074] It should be noted that the first requirement value and the second requirement value are the same for all grid points in the target flight area except for those with high anomalies.

[0075] In this way, the flight path generation device can identify grid points with high anomaly levels in the target flight area as high-anomaly grid points based on the comprehensive anomaly map, and determine the maximum building height value at each grid point based on the building white film data. Then, based on the maximum building height value at each grid point, grid points with excessively high maximum building height values ​​are identified as high-anomaly grid points, thereby eliminating high-risk grid points, avoiding the selection of take-off and landing sites in unreasonable areas, ensuring the safety of aircraft flight, and achieving a dual balance between flight efficiency and safety. This improves the accuracy of the generated flight path.

[0076] Step 203: The flight path generation device generates flight path information for the target area based on the second demand value of each grid point, the integrated anomaly map, and the flight mission information.

[0077] In some embodiments, combined with Figure 2 ,like Figure 4 As shown, step 203 above can be implemented through steps 203a and 203b.

[0078] Step 203a: The flight path generation device determines the take-off and landing site location information based on the second demand value of each grid point.

[0079] In some embodiments, the aforementioned takeoff and landing site location information refers to the takeoff and landing positions adopted by the aircraft to complete the flight mission corresponding to the aforementioned flight mission information.

[0080] In some embodiments, the aforementioned takeoff and landing site location information includes the location information of a first takeoff and landing site and the location information of a second takeoff and landing site. The first takeoff and landing site has a higher priority than the second takeoff and landing site.

[0081] In some embodiments, the first take-off and landing field described above may also be referred to as the core take-off and landing field.

[0082] In some embodiments, the aforementioned second take-off and landing field may also be referred to as a secondary take-off and landing field.

[0083] In some embodiments, the aforementioned takeoff and landing site location information includes location information of a first takeoff and landing site and location information of at least one second takeoff and landing site, wherein the first takeoff and landing site has a higher priority than the second takeoff and landing site. For example, in combination with... Figure 4 ,like Figure 5 As shown, step 203a can be implemented through steps 203a1 to 203a4.

[0084] Step 203a1: The flight path generation device generates a demand scale raster map based on the second demand value of each grid point.

[0085] In some embodiments, the aforementioned demand scale grid map is used to indicate the matching degree of each grid point within the target flight area as a takeoff and landing field.

[0086] In some embodiments, the flight path generation device can normalize the grid point data using the following formula 2 to obtain the above-mentioned required scale grid map.

[0087] (Formula 2)

[0088] in, This represents the first 3% of the data after sorting in ascending order. This represents the first 97 percent of the values ​​after the data is sorted in ascending order, to avoid the influence of extreme values.

[0089] Step 203a2: The flight path generation device calculates the total demand value corresponding to each grid point based on the demand scale grid map and the aircraft endurance data.

[0090] In some embodiments, the total demand value corresponding to a grid point represents the sum of the second demand values ​​of all grid points within a circular area centered on that grid point and with the aircraft endurance data as the radius.

[0091] In some embodiments, the flight path generation device can calculate the total demand value within the range covered by the range of each point based on the demand scale grid map and the aircraft's endurance data, thereby obtaining a total demand grid.

[0092] Step 203a3: The flight path generation device determines the location information of the first take-off and landing field based on the total demand value corresponding to each grid point.

[0093] In some embodiments, the flight path generation device can determine the location information of the grid point with the largest corresponding demand sum as the location information of the first take-off and landing field.

[0094] Step 203a4: The flight path generation device determines the location information of the second take-off and landing field based on the total demand value corresponding to each grid point and the location information of the first take-off and landing field.

[0095] In some embodiments, after determining the core takeoff and landing field, the flight path generation device can select subsequent secondary takeoff and landing fields through iterative iteration. The specific logic is as follows: Extract all grids within the coverage radius R of the currently selected takeoff and landing field (such as the core takeoff and landing field), and multiply their demand scale values ​​by a uniform attenuation coefficient of 0.5 to simulate the "diminishing marginal coverage utility" effect. This assumes that the business demand in this area has been partially shared by existing sites. By reducing its residual score, the algorithm is forced to shift towards the demand "vacuum zone" in subsequent iterations. Then, based on the updated grid values, the "total demand grid" of the entire region is recalculated to ensure that the site selection view is always based on the global remaining demand. Continue iterating, and lock the maximum value point in the updated total grid as the next priority secondary takeoff and landing field until the preset number of sites is reached. This mechanism effectively avoids excessive site clustering and achieves Pareto optimal distribution of regional logistics or inspection coverage.

[0096] In this way, the flight path generation device can generate a demand scale grid map representing the matching degree of each grid point as a take-off and landing field based on the second demand value of each grid point. Then, based on the demand scale grid map and the aircraft's endurance data, it calculates the total demand value corresponding to each grid point. Based on the total demand value of each grid point, the most suitable location of the first take-off and landing field is determined. Subsequently, based on the location information of the first take-off and landing field, the most suitable locations of other secondary take-off and landing fields are determined, avoiding the selection of take-off and landing fields in unreasonable areas, ensuring the safety of aircraft flight, and achieving a dual balance between flight efficiency and safety. This improves the accuracy of the generated flight path.

[0097] Step 203b: The flight path generation device generates flight path information for the target flight area based on takeoff and landing site location information, integrated anomaly map and flight mission information.

[0098] In some embodiments, the flight path generation device can construct an airway network topology map corresponding to the target flight area based on takeoff and landing site location information and integrated anomaly map, and then generate flight path information for the target flight area based on the airway network topology map.

[0099] It should be noted that the flight path generation device can construct a route network topology map corresponding to the target flight area based on the take-off and landing site location information and the integrated anomaly map. The specific implementation process of generating flight path information of the target flight area based on the route network topology map can be found in the relevant description in the following embodiments. To avoid repetition, this application will not elaborate further here.

[0100] In this way, the flight path can be determined based on the second requirement value representing the adaptability of each grid point as a take-off and landing field in the target flight area, thus determining the most suitable location of the take-off and landing field. Then, based on the location information of the take-off and landing field, the flight path is generated, avoiding the selection of take-off and landing fields in unreasonable areas, ensuring the safety of aircraft flight, and achieving a dual balance between flight efficiency and safety. This improves the accuracy of the generated flight path.

[0101] In some embodiments, combined with Figure 4 ,like Figure 6 As shown, step 203b above can be implemented through steps 203b1 and 203b2.

[0102] Step 203b1: The flight path generation device constructs the airway network topology map corresponding to the target flight area based on the take-off and landing site location information and the integrated anomaly map.

[0103] In some embodiments, the above-described airway network topology represents a structured low-altitude airway network constructed with core take-off and landing fields and secondary take-off and landing fields selected within the target flight area as nodes and low-cost airway segments as directed edges.

[0104] Step 203b2: The flight path generation device determines the target flight path information corresponding to the flight mission information based on the airway network topology map and flight mission information.

[0105] The aforementioned air route network topology is a directed graph constructed with each take-off and landing field as a node and the optimal air route segment between every two take-off and landing fields as directed edges.

[0106] In some embodiments, the flight path generation device can group all take-off and landing sites into pairs and find the optimal route segment between them based on the A* algorithm. Specifically, the flight path generation device can use one take-off and landing site in each group as the starting point and the other as the ending point; and set the search direction of the A* algorithm to 10 directions: forward, backward, left, right, up, down, right forward, left forward, right backward, and left backward.

[0107] Among them, the cost of the current node in the A* algorithm The calculation formula is as follows: Formula 3:

[0108] (Formula 3)

[0109] in, The distance to the next node is calculated as follows: 1.414 for the right front, left front, right back, and left back directions, and 1 for the other directions. This represents the risk value of the node.

[0110] The cost of heuristic estimation in A* algorithm The calculation formulas are as follows: Formula 4 and Formula 5:

[0111] (Formula 4)

[0112] (Formula 5)

[0113] in Let be the coordinates of the two nodes. To randomly generate three numbers 0, 1, and 2, and simultaneously increase their weights. =2.5, which avoids local optima and improves computational efficiency. The cost f in the A* algorithm is calculated using the following formula (Formula 6):

[0114] (Formula 6)

[0115] in For linear measurement, it is obtained by calculating whether the current point lies on the straight line formed by the parent node and the next node; if it does, the value is 0, otherwise it is 1. This can effectively reduce the number of inflection points in the path.

[0116] In some embodiments, the flight path generation device may use the A* algorithm to calculate the shortest path on risk assessment data while recording the cost of each path. Then, the shortest path is optimized by removing redundant points on the uniform straight line to obtain the optimal route segment between all take-off and landing sites.

[0117] In this way, the flight path generation device can construct a route network topology based on the take-off and landing site location information and the comprehensive anomaly map, with each take-off and landing site as a node and the optimal route segment between every two take-off and landing sites as directed edges. Subsequently, based on the route network topology and flight mission information, the optimal flight path corresponding to the flight mission information can be determined.

[0118] In some embodiments, the flight path generation method provided in this application may further include the following steps 200a and 200b.

[0119] Step 200a: The flight path generation device determines the outlier value of each grid point based on multidimensional anomaly data.

[0120] In some embodiments, the aforementioned multidimensional anomaly data includes at least one of the following: grid point data for each grid point, DSM data, meteorological wind speed data, and electromagnetic communication data.

[0121] In some embodiments, the flight path generation device can determine the outlier values ​​of each grid point in different dimensions based on the aforementioned multidimensional anomaly data and the risk characteristics of different dimensions using differentiated spatial analysis algorithms.

[0122] Specifically, for discrete POI and population distribution data, the kernel density estimation (KDE) algorithm is used for continuous processing, and the calculation formula is shown in Formula 7 below:

[0123] (Formula 7)

[0124] Where h is the bandwidth (search radius) and K is the kernel function. The KDE algorithm transforms scattered population or commercial points into spatially continuous demand or risk heat fields.

[0125] In some embodiments, the flight path generation device uses a spatial topological association method for land cover classification data. Based on the type of land cover (e.g., water body, building) where the grid center point falls, a preset risk matrix is ​​directly retrieved and assigned a score. A majority filter is then used to eliminate jagged edges at classification edges, ensuring smooth risk transitions. For meteorological and electromagnetic communication data (continuous field): based on the physical characteristic of electromagnetic wave attenuation with distance, a weighted inverse distance squared interpolation model is used for modeling using the following formula 8:

[0126] (Formula 8)

[0127] in, To determine the comprehensive electromagnetic risk value of grid j, Given the power weights of interference source i, The Euclidean distance between the two is given. This formula uses an inverse square ratio of the distance for weighting, which not only conforms to the attenuation law of radio propagation but also effectively identifies local high-risk blind spots caused by dense high-voltage lines or base stations. Simultaneously, by incorporating communication quality data as gain correction, a communication stability base map is ultimately generated to constrain airway network planning.

[0128] Step 200b: The flight path generation device constructs a comprehensive anomaly map based on the anomaly value of each grid point.

[0129] The aforementioned multidimensional anomaly data includes at least one of the following: grid point data for each grid point, DSM data, meteorological wind speed data, and electromagnetic communication data.

[0130] In some embodiments, the flight path generation device may determine the risk level of each grid point based on the outlier value of each grid point and in conjunction with Table 1 below, and then construct a comprehensive anomaly map based on the risk level of each grid point.

[0131] Table 1

[0132]

[0133]

[0134] In this way, the flight path generation device can determine the outlier value of each grid point based on multi-dimensional anomaly data including meteorological wind speed data, electromagnetic communication data, etc., and construct the above-mentioned comprehensive anomaly map based on the outlier value of each grid point. In the subsequent process, high-risk grid points are removed by combining the comprehensive risk map and building white film data, avoiding the selection of take-off and landing sites in unreasonable areas, ensuring the safety of aircraft flight, and achieving a dual balance between flight efficiency and safety. This improves the accuracy of the generated flight path.

[0135] This application proposes a flight path generation method that integrates dynamic risk assessment, solving the problem of simultaneously considering flight safety and efficiency in low-altitude route planning. It calculates the takeoff and landing site locations and risk assessment maps of the target area by weighted fusion of multi-source risk assessment data, addressing the issue of lag in dynamic risk response. Simultaneously, the multi-source risk data supports setting different weights to calculate takeoff and landing site locations and risk assessment maps under various scenarios. Finally, it calculates the target's airway network data based on the takeoff and landing site locations and risk assessment maps. The method of this application is illustrated below through specific embodiments. The flight path method proposed in this application may include the following steps:

[0136] Step 1: Site selection for take-off and landing.

[0137] This step includes 9 sub-steps, namely sub-step 1 to sub-step 9.

[0138] Sub-step 1: Calculate the demand value based on population density, industrial and commercial POI data (i.e., the grid point data mentioned above). (That is, the first demand value mentioned above).

[0139] Sub-step 2: Introduce a risk assessment map (i.e., the aforementioned comprehensive anomaly map) for spatial filtering. The comprehensive risk map (i.e., the aforementioned comprehensive anomaly map) integrates multi-dimensional data such as ground value, geographical environment, meteorology, and electromagnetic communication to divide the airspace into tens of millions of grids, and assigns a normalized risk score (i.e., the aforementioned anomaly value) to each grid. Combined with the comprehensive risk map, grids with risk values ​​greater than 0.7 (i.e., the aforementioned first threshold) are filtered out to ensure that candidate points are not in high-risk areas.

[0140] Sub-step 3: Introduce building white film data for airworthiness constraints: Use the building white film height as a "hard rejection" condition. First, use the building height in the building white film as the value in the building grid height data. If the height within the grid is greater than the preset take-off and landing layer height, then the grid requirement value is directly set to 0. That is, the white film acts as a spatial mask to reject grids that do not meet the physical take-off and landing conditions due to excessive building height.

[0141] Sub-step 4: Normalize the rasterized data of industrial and commercial POIs, population density raster data, and building white film raster data to obtain the final demand scale raster map. Depending on the application scenario, these raster data are overlaid with different weights to obtain the demand scale raster map, reflecting the size of the demand for building a landing pad at each point.

[0142] Sub-step 5: Based on the demand scale grid and the drone's range, calculate the total demand value within the range covered by the range of each point to obtain the total demand grid (i.e., the total demand value mentioned above).

[0143] Sub-step 6: Select the point with the largest total demand value as the core take-off and landing field (i.e., the first take-off and landing field mentioned above).

[0144] Sub-step 7: After determining the core take-off and landing field, the subsequent secondary take-off and landing fields (i.e., the second take-off and landing field mentioned above) are selected through iterative iteration. The specific logic is as follows: Extract all grids within the coverage radius R of the currently selected take-off and landing field (such as the core take-off and landing field), and multiply their demand scale values ​​by a uniform attenuation coefficient of 0.5 to simulate the "diminishing marginal coverage utility" effect. It is assumed that the business demand in this area has been partially shared by existing sites. By reducing its residual score, the algorithm is forced to shift towards the demand "vacuum zone" in subsequent iterations. Then, based on the updated grid values, the "total demand grid" of the entire region is recalculated to ensure that the site selection view is always based on the global remaining demand. Continue iterating, and lock the maximum value point in the updated total grid as the next priority secondary take-off and landing field until the preset number of sites is reached. This mechanism effectively avoids excessive site clustering and achieves Pareto optimal distribution of regional logistics or inspection coverage.

[0145] Sub-step 8: Adjust the scenario weights to obtain the site selection results for the core and secondary take-off and landing fields in region A, meeting the site selection needs of multiple scenarios such as logistics, inspection, and emergency response. Example of scenario adaptability: For logistics and distribution scenarios: Adjust the weights of Commercial POIs... Setting the value to 0.8 will prioritize site selection in commercial districts and densely populated residential areas. For urban inspection scenarios (such as power and river inspections): the inspection target (such as high-voltage towers and water quality monitoring stations) will be used as a specific POI input, and the demand weight of the grid in which it is located will be assigned. Increased to the highest level. At this point, the takeoff and landing sites will automatically be distributed in a "chain" shape along the inspection route, serving as energy relay stations and data unloading points for the inspection drones, thus solving the pain points of long inspection paths and high endurance requirements; for emergency rescue scenarios: reduce the population density weight. Prioritize selecting open fenced areas with convenient transportation and extremely low risk to ensure that relief supplies can be delivered quickly.

[0146] Step 2: Quantitative risk assessment.

[0147] This step includes sub-steps 10 through 14.

[0148] Sub-step 10: The flight path generation device performs a comprehensive score on the ground value based on a risk level of 0 (minimum risk) and 1 (maximum risk). Normalization. The risk level classification and score for ground value is low risk. Medium risk and high risk .

[0149] Sub-step 11: Within the height range of 0-300m, divide the area into 6 floors according to a floor height of 50m.

[0150] Sub-step 12: For DSM data, set the vertical safety distance to 20m. Locations with a floor height greater than the DSM value plus 20m are designated as flyable zones, and those with a floor height less than 20m are designated as no-fly zones. The risk value for flyable zones is calculated in the following steps.

[0151] Sub-step 13, land cover classification, population density heat map, and meteorological wind speed risk level division intervals are shown in Table 1 above.

[0152] Sub-step 14: Through the differentiated representation and fusion mechanism of heterogeneous risk elements, differentiated spatial analysis algorithms are used for rasterization representation of risk characteristics in different dimensions: For discrete POI and population distribution data (point-like): KDE algorithm is used for continuous processing; for land cover classification data (area-like): spatial topological association method is used. According to the land cover type (such as water body, building) where the grid center point falls, the preset risk matrix is ​​directly retrieved for scoring, and MajorityFilter is used to eliminate the jagged effect of classification edges to ensure the smoothness of risk transition; For meteorological and electromagnetic communication data (continuous field): based on the physical characteristics of electromagnetic wave attenuation with distance, this application uses a weighted inverse distance squared interpolation model for modeling: on the basis of rasterization of each element, combined with the risk level classification table, the risk map of each element is obtained. Then, for specific scenarios, comprehensive risk output is achieved by adjusting the weights of each element (such as the autonomous configuration of the weights of terrain, population, meteorology, communication quality, etc.) and the weighted superposition mechanism.

[0153] Step 3: Route network planning.

[0154] This step includes sub-steps 15 through 23.

[0155] Sub-step 15: Group all take-off and landing sites into pairs and find the optimal path based on the A* algorithm.

[0156] Sub-step 16: Use one of the take-off and landing sites in each group as the starting point and the other as the ending point.

[0157] Sub-step 17: Set the search directions of the A* algorithm to 10 directions: front, back, left, right, up, down, right front, left front, right back, and left back.

[0158] Sub-step 17: Use the A* algorithm to calculate the shortest path on the risk assessment data and record the cost of each path. .

[0159] Sub-step 18: Optimize the shortest path using the Douglas-Peucker algorithm, removing redundant points on the uniform straight line to obtain the optimal route segment between all takeoff and landing fields, such as... Figure 7 As shown.

[0160] Sub-step 19: Based on Dijkstra's algorithm, determine the route segment and its corresponding cost. Calculate the shortest path between every two takeoff and landing airports, remove other route segments to form a route network, such as... Figure 8 As shown.

[0161] Sub-step 20: Implementation of Multi-Constraint Dynamic Route Planning Based on the Route Network. Specifically, firstly, task path calculation is performed to obtain the starting and target points of the flight mission. Using Dijkstra's algorithm, the sequence of route segments with the lowest comprehensive cost score (the sum of distance cost and risk cost) is retrieved from the constructed structured route network topology to form the basic mission route. Next, dynamic risk replanning is performed. The route environment is monitored in real time. If the dynamic risk value (such as sudden weather changes or electromagnetic interference) of certain segments in the route network exceeds a preset safety threshold during mission execution, the cost value of that segment is automatically updated, and incremental path search is initiated within the route network to adjust the route in real time to avoid high-risk areas. Then, flight performance smoothing is performed. For the corners in the basic route, combined with the kinematic constraints of the specific UAV (such as maximum turning radius and climb rate), the inflection points of the route are smoothed to ensure that the generated route conforms to the actual physical execution capabilities of the aircraft. Finally, the planning results are output. The final generated route not only avoids both static and dynamic high-risk areas but also achieves an optimal balance between efficiency and safety within the route network.

[0162] It should be noted that all the above embodiments are merely specific implementations of this application and are not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

[0163] Thus, the flight path generation method proposed in this application calculates the location of takeoff and landing sites by integrating dynamic risk assessment and using data from industrial and commercial POIs, population density, and building white film data as a basis. It quantifies flight risks using DSM data, land cover classification, population density heatmaps, electromagnetic communication data, and meteorological wind speed data. Random numbers are used to avoid local optima in the A* algorithm while improving computational efficiency, and straight-line measures are added to reduce the number of inflection points in the flight path to obtain the optimal route segment. The Dijkstra algorithm is used to filter the airway network within the route segment. This method balances flight safety and operational efficiency, meets the compliance and practicality requirements of low-altitude flight, and supports setting different weights for multi-source risk data to calculate takeoff and landing site locations and risk assessment maps under different scenarios, adapting to various scenarios such as low-altitude logistics, routine inspections, and emergency rescue.

[0164] It should be noted that the various method embodiments described above, or the various possible implementations of the various method embodiments, can be executed individually, or, provided there is no conflict, they can be combined with each other. The specific implementation can be determined according to actual usage requirements, and this application embodiment does not impose any limitations on this. It can be seen that the above mainly introduces the solutions provided by the embodiments of this application from a methodological perspective. To achieve the above functions, this application embodiment provides corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the modules and algorithm steps of the various examples described in the embodiments disclosed herein, this application embodiment can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0165] This application embodiment can divide the flight path generation device into functional modules according to the above method example. For example, each function can be divided into its own functional module, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. Optionally, the module division in this application embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0166] In some embodiments, this application also provides a flight path generation apparatus. The flight path generation apparatus may include one or more functional modules for implementing the flight path generation method of the above method embodiments.

[0167] For example, Figure 9This is a schematic diagram of a flight path generation device provided in an embodiment of this application. Figure 9 As shown, the flight path generation device 900 includes: a calculation module 901, a determination module 902, and a generation module 903.

[0168] The calculation module 901 is used to calculate the first demand value of each grid point based on the grid point data of each grid point in the target flight area corresponding to the flight mission information. The grid point data represents the distribution of objects contained in the grid point. The determination module 902 is used to determine the high-anomaly grid points in the target flight area based on the comprehensive anomaly map and building white film data corresponding to the target flight area, and update the first demand value of the high-anomaly grid point to zero to obtain the second demand value of each grid point. The comprehensive anomaly map is used to indicate the anomaly value of each grid point in the target flight area as a flight node. The building white film data represents the three-dimensional data of each building in the target flight area. The second demand value of each grid point represents the adaptability of each grid point as a take-off and landing field. The generation module 903 is used to generate the flight path information of the target area based on the second demand value of each grid point, the comprehensive anomaly map, and the flight mission information.

[0169] One possible implementation is that the determining module 902 is specifically used to: determine grid points in the target flight area with anomalies greater than a first threshold as high anomaly grid points based on the comprehensive anomaly map; determine the highest building height value corresponding to each grid point based on the building white film data, and determine grid points in the target flight area with the highest building height value greater than a second threshold as high anomaly grid points.

[0170] Another possible implementation, the aforementioned generation module 903, is specifically used to: determine the take-off and landing site location information based on the second demand value of each grid point; and generate the flight path information of the target flight area based on the take-off and landing site location information, the aforementioned integrated anomaly map, and the aforementioned flight mission information.

[0171] Another possible implementation is that the aforementioned takeoff and landing site location information includes the location information of a first takeoff and landing site and the location information of at least one second takeoff and landing site, wherein the first takeoff and landing site has a higher priority than the second takeoff and landing site; the aforementioned determining module is specifically used to: generate a demand scale grid map based on the second demand value of each grid point, the demand scale grid map being used to indicate the matching degree of each grid point within the target flight area as a takeoff and landing site; calculate the total demand value corresponding to each grid point based on the demand scale grid map and the aircraft endurance data, the total demand value corresponding to a grid point representing the sum of the second demand values ​​of all grid points within a circular area centered on that grid point and with the aircraft endurance data as the radius; determine the location information of the first takeoff and landing site based on the total demand value corresponding to each grid point; and determine the location information of the second takeoff and landing site based on the total demand value corresponding to each grid point and the location information of the first takeoff and landing site.

[0172] Another possible implementation is that the aforementioned generation module 903 is specifically used to: construct a route network topology map corresponding to the aforementioned target flight area based on the aforementioned take-off and landing site location information and the aforementioned integrated anomaly map; and determine the target flight path information corresponding to the aforementioned flight mission information based on the aforementioned route network topology map and the aforementioned flight mission information; wherein the aforementioned route network topology map is a directed graph constructed with each take-off and landing site as a node and the optimal route segment between every two take-off and landing sites as directed edges.

[0173] In another possible implementation, the above-mentioned device further includes: a construction module; the determination module is further configured to determine the outlier value of each grid point based on the multidimensional anomaly data; the construction module is configured to construct the above-mentioned comprehensive anomaly map based on the outlier value of each grid point determined by the determination module; wherein the multidimensional anomaly data includes at least one of the following: grid point data of each grid point, DSM data, meteorological wind speed data, and electromagnetic communication data.

[0174] The flight path generation device provided in this application utilizes grid point data, which characterizes the distribution of objects contained in the grid points, to obtain a demand value characterizing the suitability of the grid points as take-off and landing sites. This ensures that site selection prioritizes coverage of densely demanded areas, improving flight efficiency. Furthermore, by combining comprehensive risk maps and building white film data, high-risk grid points are eliminated, preventing take-off and landing sites from being located in unreasonable areas and ensuring the safety of aircraft flight. This achieves a dual balance between flight efficiency and safety, thereby improving the accuracy of the generated flight path.

[0175] It should be noted that the flight path generation device can implement all the processes implemented in the above method embodiments and achieve the same beneficial effects. To avoid repetition, it will not be described again here.

[0176] In the case where the functions of the integrated modules described above are implemented in hardware, this application provides a possible structural schematic diagram of the electronic device involved in the above embodiments. For example... Figure 10 As shown, the electronic device 90 includes: a processor 92, a communication interface 93, and a bus 94. Optionally, the electronic device 90 may also include a memory 91.

[0177] Processor 92 may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 92 may be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 92 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0178] Communication interface 93 is used to connect with other devices via a communication network. This communication network can be Ethernet, wireless access network, wireless local area network (WLAN), etc.

[0179] The memory 91 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto.

[0180] As one possible implementation, the memory 91 can exist independently of the processor 92. The memory 91 can be connected to the processor 92 via a bus 94 and is used to store instructions or program code. When the processor 92 calls and executes the instructions or program code stored in the memory 91, it can implement the flight path generation method provided in the embodiments of this application.

[0181] In another possible implementation, memory 91 can also be integrated with processor 92.

[0182] Bus 94 can be an Extended Industry Standard Architecture (EISA) bus, etc. Bus 94 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 10 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0183] Through the above description of the implementation methods, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the service calling device can be divided into different functional modules to complete all or part of the functions described above.

[0184] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-described flight path generation method embodiments and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0185] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0186] This application also provides a readable storage medium storing a program or instructions that, when executed by a computer, implement the flight path generation method provided in the above embodiments. It is understood that all or part of the processes in the above method embodiments can be executed by computer instructions instructing related hardware; the readable storage medium can be any of the foregoing embodiments or memory; the readable storage medium can also be an external storage device of the service invocation device, such as a pluggable hard drive, SmartMedia Card (SMC), Secure Digital (SD) card, flash card, etc., equipped on the service invocation device. Further, the readable storage medium can include both internal storage units of the service invocation device and external storage devices. The readable storage medium is used to store the computer program and other programs and data required by the service invocation device. The readable storage medium can also be used to temporarily store data that has been output or will be output.

[0187] This application also provides a computer program product, which is stored in a storage medium and, when executed by a computer, implements the flight path generation method provided in the above embodiments.

[0188] It should be noted that, in this document, 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 that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0189] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0190] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A flight path generation method characterized by, include: Based on the grid point data of each grid point in the target flight area corresponding to the flight mission information, the first demand value of each grid point is calculated, wherein the grid point data represents the distribution of objects contained in the grid point; Based on the comprehensive anomaly map and building white film data corresponding to the target flight area, high anomaly grid points in the target flight area are identified, and the first demand value of the high anomaly grid points is updated to zero to obtain the second demand value of each grid point. The comprehensive anomaly map is used to indicate the anomaly value of each grid point as a flight node in the target flight area. The building white film data represents the three-dimensional data of each building in the target flight area. The second demand value of each grid point represents the degree of adaptability of each grid point as a take-off and landing field. Based on the second demand value of each grid point, the integrated anomaly map, and the flight mission information, flight path information for the target area is generated.

2. The flight path generating method according to claim 1, characterized by, The step of determining high-anomaly grid points in the target flight area based on the integrated anomaly map and building white film data corresponding to the target flight area includes: Based on the comprehensive anomaly map, grid points in the target flight area with anomaly values ​​greater than the first threshold are identified as high anomaly grid points; Based on the building white film data, the highest building height value corresponding to each grid point is determined, and grid points in the target flight area whose highest building height value is greater than the second threshold are identified as high anomaly grid points.

3. The flight path generating method according to claim 1, characterized by, The generation of flight path information for the target area based on the second demand value of each grid point, the integrated anomaly map, and the flight mission information includes: Based on the second demand value of each grid point, the takeoff and landing site location information is determined; Based on the takeoff and landing site location information, the integrated anomaly map, and the flight mission information, flight path information for the target flight area is generated.

4. The flight path generating method according to claim 3, characterized by, The takeoff and landing site location information includes the location information of a first takeoff and landing site and the location information of at least one second takeoff and landing site, wherein the first takeoff and landing site has a higher priority than the second takeoff and landing site; determining the takeoff and landing site location information based on the second demand value of each grid point includes: Based on the second demand value of each grid point, a demand scale grid map is generated, which is used to indicate the matching degree of each grid point in the target flight area as a take-off and landing field. Based on the demand scale grid map and the aircraft endurance data, the total demand value corresponding to each grid point is calculated. The total demand value corresponding to a grid point represents the sum of the second demand values ​​of all grid points in a circular area with the grid point as the center and the aircraft endurance data as the radius. Based on the total demand value corresponding to each grid point, the location information of the first take-off and landing field is determined; Based on the total demand value corresponding to each grid point and the location information of the first take-off and landing field, the location information of the second take-off and landing field is determined.

5. The flight path generating method according to claim 3, characterized by, The step of generating flight path information for the target flight area based on the takeoff and landing site location information, the integrated anomaly map, and the flight mission information includes: Based on the takeoff and landing site location information and the integrated anomaly map, a route network topology map corresponding to the target flight area is constructed; Based on the airway network topology map and the flight mission information, the target flight path information corresponding to the flight mission information is determined; The route network topology graph is a directed graph constructed with each take-off and landing field as a node and the optimal route segment between every two take-off and landing fields as directed edges.

6. The flight path generating method according to any one of claims 1 to 5, characterized by, The method further includes: Based on multidimensional anomaly data, determine the outlier value of each grid point; The comprehensive anomaly map is constructed based on the outlier values ​​of each grid point; The multidimensional anomaly data includes at least one of the following: grid point data for each grid point, digital surface model (DSM) data, meteorological wind speed data, and electromagnetic communication data.

7. An air route generating apparatus characterized by comprising: include: Calculation module, determination module, and generation module; The calculation module is used to calculate the first demand value for each grid point based on the grid point data of each grid point in the target flight area corresponding to the flight mission information. The grid point data represents the distribution of objects contained in the grid point. The determining module is used to determine high-anomaly grid points in the target flight area based on the comprehensive anomaly map and building white film data corresponding to the target flight area, and update the first demand value of the high-anomaly grid points to zero to obtain the second demand value of each grid point. The comprehensive anomaly map is used to indicate the anomaly value of each grid point in the target flight area as a flight node. The building white film data represents the three-dimensional data of each building in the target flight area. The second demand value of each grid point represents the degree of adaptability of each grid point as a take-off and landing field. The generation module is used to generate flight path information for the target area based on the second demand value of each grid point obtained by the determining module, the integrated anomaly map, and the flight mission information.

8. The flight path generating apparatus according to claim 7, characterized by The determining module is specifically used for: Based on the comprehensive anomaly map, grid points in the target flight area with anomaly values ​​greater than the first threshold are identified as high anomaly grid points; Based on the building white film data, the highest building height value corresponding to each grid point is determined, and grid points in the target flight area whose highest building height value is greater than the second threshold are identified as high anomaly grid points.

9. The flight path generating apparatus according to claim 7, wherein The generation module is specifically used for: Based on the second demand value of each grid point, the takeoff and landing site location information is determined; Based on the takeoff and landing site location information, the integrated anomaly map, and the flight mission information, flight path information for the target flight area is generated.

10. The flight path generation device according to claim 9, characterized in that, The takeoff and landing site location information includes the location information of a first takeoff and landing site and the location information of at least one second takeoff and landing site, wherein the first takeoff and landing site has a higher priority than the second takeoff and landing site; the determining module is specifically used for: Based on the second demand value of each grid point, a demand scale grid map is generated, which is used to indicate the matching degree of each grid point in the target flight area as a take-off and landing field. Based on the demand scale grid map and the aircraft endurance data, the total demand value corresponding to each grid point is calculated. The total demand value corresponding to a grid point represents the sum of the second demand values ​​of all grid points in a circular area with the grid point as the center and the aircraft endurance data as the radius. Based on the total demand value corresponding to each grid point, the location information of the first take-off and landing field is determined; Based on the total demand value corresponding to each grid point and the location information of the first take-off and landing field, the location information of the second take-off and landing field is determined.

11. The flight path generation device according to claim 9, characterized in that, The generation module is specifically used for: Based on the takeoff and landing site location information and the integrated anomaly map, a route network topology map corresponding to the target flight area is constructed; Based on the airway network topology map and the flight mission information, the target flight path information corresponding to the flight mission information is determined; The route network topology graph is a directed graph constructed with each take-off and landing field as a node and the optimal route segment between every two take-off and landing fields as directed edges.

12. The flight path generation apparatus according to any one of claims 7 to 11, characterized in that, The device further includes: a construction module; The determining module is also used to determine the outlier value of each grid point based on multidimensional outlier data; The construction module is used to construct the comprehensive anomaly map based on the anomaly value of each grid point determined by the determining module; The multidimensional anomaly data includes at least one of the following: grid point data for each grid point, digital surface model (DSM) data, meteorological wind speed data, and electromagnetic communication data.

13. An electronic device, characterized in that, It includes a processor and a memory, the memory storing programs or instructions that can run on the processor, the programs or instructions being executed by the processor to implement the flight path generation method as described in any one of claims 1 to 6.

14. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a computer, implement the flight path generation method as described in any one of claims 1 to 6.

15. A computer program product, characterized in that, The computer program product is stored in a storage medium, and when executed by a computer, the computer program product implements the flight path generation method as described in any one of claims 1 to 6.

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