A low-altitude airspace risk quantification method and device

Abstract

A low-altitude airspace risk quantification method and device are disclosed, and relate to the technical field of computers. A computing device pre-processes geographic information data to obtain first vector data including line network region data and land data, and then quantifies the risk of the first vector data according to the categories of the first vector data to obtain a first risk degree and a second risk degree, respectively. The first risk degree and the second risk degree are mapped to a three-dimensional grid, avoiding the problem of a large amount of data processing when processing all three-dimensional grids, reducing the amount of data processing, and improving the efficiency of risk quantification of low-altitude airspace. Moreover, the computing device only maps the first risk degree and the second risk degree to the three-dimensional grid, reducing the amount of data mapping, and further improving the efficiency of risk quantification of low-altitude airspace.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method and apparatus for quantifying low-altitude airspace risks. Background Technology

[0002] Low-altitude airspace typically refers to the airspace area relatively close to the ground or water surface, primarily used for general aviation, drone flights, and other activities. Risks in low-altitude airspace mainly stem from various geographical elements, such as terrain and buildings. To quantify the risks of low-altitude airspace, the airspace (terrain, buildings, etc.) can be divided into a three-dimensional grid, and then risk quantification can be performed using the three-dimensional grid as the basic unit. However, using a three-dimensional grid as a carrier of multi-source heterogeneous data (such as terrain, buildings, and wire networks) results in high data redundancy, leading to low computational efficiency and consequently, low efficiency in quantifying the risks of low-altitude airspace. Summary of the Invention

[0003] This application provides a method and apparatus for quantifying low-altitude airspace risks, which addresses the problem of high data redundancy when using three-dimensional meshes as carriers of multi-source heterogeneous data, resulting in low computational efficiency and consequently low efficiency in quantifying low-altitude airspace risks.

[0004] Firstly, this application provides a method for quantifying low-altitude airspace risk. This method can be applied to a computer system or to a computing device that supports the computer system in implementing the method, such as a server or terminal. In one possible example, the method includes: the computing device acquiring geographic information data indicating at least one type of land use within a first region, and then determining first vector data based on the at least one type of land use within the first region. The first vector data includes network area data and land parcel data. The computing device determines a first risk level of a first sub-region based on the category of the network area data, and determines a second risk level of a second sub-region based on the category of the land parcel data. Finally, the computing device maps the first risk level of the first sub-region and the second risk level of the second sub-region to a three-dimensional grid. The at least one type of land use includes transportation land, the network area data indicates the first sub-region where transportation land is located within the first region, the land parcel data indicates the second sub-region where land parcels are located within the first region, and the first and second sub-regions are adjacent.

[0005] In this application, the computing device preprocesses the geographic information data to obtain first vector data including network area data and land parcel data. Then, based on the category of the first vector data, risk quantification is performed to obtain a first risk level and a second risk level. The first and second risk levels are then mapped to a three-dimensional grid. This avoids the problem of excessive data processing when processing all three-dimensional grids, which carry redundant geographic information data, thus reducing the amount of data processed and improving the efficiency of risk quantification for low-altitude airspace. Furthermore, the computing device only maps the first and second risk levels to the three-dimensional grid, reducing the amount of data mapping and further improving the efficiency of risk quantification for low-altitude airspace.

[0006] In one possible scenario, the geographic information data is second vector data within a first region. The second vector data includes: zoning, grid lines, and point of interest (POI) and / or area of ​​interest (AOI) data within the first region.

[0007] In another possible scenario, the geographic information data includes raster data within a first region and second vector data.

[0008] In one possible example, the raster data described above records land use types, or population information, etc.

[0009] In one possible scenario, attribute information from land parcel data is used to indicate the category of the land parcel data. This attribute information includes one or more of the following: POI data, AOI data, and raster data.

[0010] In this application, since POI data, AOI data, and raster data show the geographic entities of the corresponding land parcel data, and these geographic entities characterize the category of the land parcel data, the computing device can directly use POI data, AOI data, and raster data to determine the category of the land parcel data, thereby accurately determining the category of the land parcel data and improving the accuracy of risk quantification for low-altitude airspace.

[0011] In one possible example, if the attribute information of land parcel data 1 includes: supermarket, two houses, vegetable market, etc., then the category of land parcel data 1 is: "residential community".

[0012] In one possible scenario, the aforementioned level of risk could be a risk value or a risk level.

[0013] In one possible implementation, there is a third mapping relationship between the category of network area data and the risk level, and a fourth mapping relationship between the category of land parcel data and the risk level.

[0014] In this application, the computing device directly determines the first risk level that matches the category of the network area data and the second risk level that matches the category of the land parcel data from the third and fourth mapping relationships, thereby achieving rapid and accurate determination of the risk level and accelerating the efficiency of risk quantification in low-altitude airspace.

[0015] In one possible implementation, the aforementioned low-altitude airspace risk quantification method further includes: a computing device outputting a three-dimensional mesh.

[0016] In this application, the computing device outputs a three-dimensional mesh for display, thereby enabling the visualization of the three-dimensional mesh.

[0017] In one possible example, the computing device outputs a 3D mesh to a front end. This front end can be a terminal or display device connected to the computing device.

[0018] In one possible implementation, the aforementioned low-altitude airspace risk quantification method further includes: a computing device outputting third vector data. The third vector data includes a first risk level, a second risk level, and the first vector data.

[0019] In this application, the computing device outputs and displays third vector data, which enables visualization of the third vector data and improves the interactivity between the computing device and the user.

[0020] In one possible example, the computing device outputs third vector data to the front end.

[0021] In one possible scenario, after the computing device outputs the third vector data to the terminal for display, it receives the modified data returned by the terminal, and then adjusts the third vector data according to the modified data to obtain the adjusted third vector data.

[0022] In one possible example, the modified data is used to indicate modifications to one or more of the following: a first risk value, a second risk value, a first sub-region, a second sub-region, vector data located in the first or second sub-region (such as POI data, AOI data, etc.), etc.

[0023] In one possible example, the computing device outputs adjusted third vector data to the terminal so that the user can confirm the third vector data again on the terminal.

[0024] In one possible implementation, the computing device determines first vector data based on at least one type of land use within a first region, including: the computing device determines at least one traffic land use based on at least one type of land use within the first region, and then constructs buffer areas for each of the at least one traffic land use to obtain at least one first sub-region, thereby dividing the first region based on each of the at least one first sub-region to obtain a second sub-region.

[0025] In this application, the computing device constructs a buffer zone for traffic land to obtain a first sub-region, thereby transforming linear traffic land into a first sub-region that is closer to the actual road. Then, the first region is further divided according to the first sub-region to obtain a second sub-region that is closer to the actual land parcel. As a result, the vector data obtained is closer to the real environment, and the three-dimensional grid obtained based on the aforementioned vector data is more accurate, thus improving the accuracy of low-altitude airspace risk quantification.

[0026] In one possible example, the computing device identifies traffic land from at least one type of land use within a first region, and then widens at least one traffic land use to create a buffer zone for that traffic land use, resulting in at least one first sub-region. Finally, the first region is divided using the at least one first sub-region as a boundary to obtain multiple second sub-regions.

[0027] Furthermore, the computing device 211 uses the first sub-region, vector data (POI data and / or AOI data, etc.) located in the first sub-region, the second sub-region, and vector data (POI data and / or AOI data, etc.) located in the second sub-region as vector data b.

[0028] In one possible scenario, since different first sub-regions have different levels, the computing device can divide the first region layer by layer from high level to low level according to the level of the first sub-region to obtain multiple second sub-regions.

[0029] In one possible implementation, the first sub-region mentioned above includes a target sub-region, which is obtained by constructing a buffer zone for the first traffic land. At least one traffic land includes the first traffic land, and there is a first mapping relationship between the width of the target sub-region and the grade of the first traffic land.

[0030] In this application, the computing device determines the width matching the first traffic land grade based on the first mapping relationship between the traffic land grade and the width, and then constructs a buffer area of ​​the aforementioned width for the first traffic land to obtain the target sub-region. This results in a target sub-region (transportation line) that is closer to the real situation, so the final vector data can be closer to the real environment. The three-dimensional grid obtained based on the aforementioned vector data is more accurate, which improves the accuracy of low-altitude airspace risk quantification.

[0031] In one possible example, the classification of transportation land includes: national highways, provincial highways, county roads, township roads, village roads, expressways, or special-purpose roads, etc.

[0032] In another possible example, the aforementioned transportation land use is classified into: Level 1 transportation lines, Level 2 transportation lines, Level 3 transportation lines, Level 4 transportation lines, Level 5 transportation lines, etc. For example, a Level 1 transportation line can be a Level 1 highway, a Level 1 waterway, or a Level 1 railway.

[0033] For example, the first mapping relationship mentioned above could be: national highway - 20 meters wide, provincial highway - 15 meters wide, county road - 12 meters wide, township road - 8 meters wide, etc.; or, mapping relationship a could be: Class 1 highway - 40 meters wide, Class 1 waterway - 500 meters wide, Class 1 railway - 1.435 meters wide.

[0034] In one possible implementation, at least one type of land use further includes: pipeline land with different grades. The first vector data also includes pipeline data, which is used to indicate a third sub-region where the pipeline land is located within the first region. The third sub-region is obtained by constructing buffer zones of corresponding widths for different grades of pipeline land based on a second mapping relationship between the grade and width of the pipeline land.

[0035] In this application, the computing device constructs buffer areas of corresponding widths for different levels of pipeline land use based on the second mapping relationship between the level and width of the pipeline land use, thereby obtaining multiple third sub-regions. This results in a third sub-region (pipeline area) that is closer to the real situation, and the final vector data obtained is closer to the real environment. The three-dimensional mesh obtained based on the aforementioned vector data is more accurate, which improves the accuracy of low-altitude airspace risk quantification.

[0036] In one possible scenario, the land use classification for pipelines may include: Class 1 high-voltage lines (≥500 kV), Class 2 high-voltage lines (≥330 kV, <500 kV), Class 3 high-voltage lines (≥154 kV, <330 kV), Class 1 gas pipelines, Class 2 gas pipelines, etc.

[0037] Therefore, the above mapping relationship d can be: Level 1 high-voltage line - 17 meters, Level 2 high-voltage line - 12 meters, Level 3 high-voltage line - 10 meters, Level 1 gas pipeline - 5 meters, Level 2 gas pipeline - 3 meters, etc.

[0038] In one possible implementation, at least one type of land use further includes: no-fly zone and / or high-risk zone. The aforementioned low-altitude airspace risk quantification also includes: if the no-fly zone and / or high-risk zone completely overlap with the location of a first land parcel area, the computing device classifies the first land parcel data as a no-fly zone and / or a high-risk zone. The first land parcel data is used to indicate the first land parcel area located within a first region, and a second sub-region includes the first land parcel area. If the area of ​​overlap between the no-fly zone and / or high-risk zone and the second land parcel area is greater than or equal to a threshold, the computing device classifies the second land parcel data as a no-fly zone and / or a high-risk zone. The second land parcel data is used to indicate the second land parcel area located within a first region, and a second sub-region includes the second land parcel area. If the area of ​​overlap between the no-fly zone and / or high-risk zone and the second land parcel area is less than a threshold, the computing device classifies the third land parcel data as a no-fly zone and / or a high-risk zone. The third plot data is used to indicate the sub-plot area where the sub-plot in the first region is located. The third plot area includes the sub-plot area, and the second sub-region includes the third plot area.

[0039] In this application, the computing device corrects the category of the land parcel data corresponding to the second sub-region based on the positional relationship between the no-fly zone and / or high-risk land parcel and the second sub-region (first land parcel area, second land parcel area, and third land parcel area), thereby further refining the category of the land parcel data and improving the accuracy of risk quantification for low-altitude airspace.

[0040] In one possible example, the aforementioned no-fly zones include: government offices, closed management areas, nature reserves, and historical and cultural heritage protection areas.

[0041] In one possible scenario, the aforementioned high-risk land use designation is used to indicate land with a risk level greater than or equal to a threshold. This threshold could be 300 or a risk level of 3, etc.

[0042] In another possible scenario, the aforementioned high-risk land use includes teaching and research buildings, office buildings for foreign-related institutions, transformers, etc.

[0043] The aforementioned no-fly zone and / or high-risk zone are used to indicate that the location of the first plot of land completely overlaps with that of the no-fly zone and / or high-risk zone.

[0044] In one possible implementation, the computing device further modifies the category of the network area data corresponding to the first sub-region based on the positional relationship between the no-fly zone and / or high-risk zone and the first sub-region. The content of this implementation can be referenced from the above-described modification of the land parcel data category, and will not be repeated here.

[0045] Secondly, this application provides a low-altitude airspace risk quantification device. This low-altitude airspace risk quantification device is applied to a computer system or a computing device that supports the computer system in implementing the low-altitude airspace risk quantification device. The low-altitude airspace risk quantification device includes modules for executing the low-altitude airspace risk quantification method in the first aspect or any optional implementation of the first aspect. In one possible example, the low-altitude airspace risk quantification device includes:

[0046] The acquisition module is used to acquire geographic information data. The geographic information data is used to indicate at least one type of land use within a first area, including transportation land use.

[0047] The first determining module is used to determine first vector data based on at least one type of land use within a first region. The first vector data includes network area data and land parcel data. The network area data is used to indicate the first sub-region where traffic land is located within the first region, and the land parcel data is used to indicate the second sub-region where the land parcels are located within the first region. The first sub-region and the second sub-region are adjacent.

[0048] The second determining module is used to determine the first risk level of the first sub-region based on the category of the network area data, and to determine the second risk level of the second sub-region based on the category of the land parcel data.

[0049] The mapping module is used to map the first risk level of the first sub-region and the second risk level of the second sub-region to the 3D mesh.

[0050] For more detailed implementation information regarding the low-altitude airspace risk quantification device, please refer to the description of any of the implementation methods in the first aspect above, as well as the content of the specific implementation methods below, which will not be repeated here.

[0051] Thirdly, this application provides a chip. The chip includes: a control circuit and an interface circuit; the interface circuit is used to acquire geographic information data, and the control circuit is used to execute the method in the first aspect or any possible implementation of the first aspect based on the geographic information data.

[0052] Fourthly, this application provides a computing device cluster. The computing device cluster includes at least one computing device, which includes a memory and a processor. The memory stores computer instructions; when the processor executes the computer instructions, it implements the method described in the first aspect or any possible implementation of the first aspect.

[0053] Fifthly, this application provides a computer-readable storage medium. The storage medium stores a computer program or instructions that, when executed by a processing device, implement the method described in the first aspect or any possible implementation thereof.

[0054] Sixthly, this application provides a computer program product. The computer program product includes a computer program or instructions that, when executed by a processing device, implement the method described in the first aspect or any possible implementation thereof.

[0055] The beneficial effects of aspects two through six above can be referred to in the first aspect or any possible implementation of the first aspect, and will not be elaborated here. Based on the implementations provided in the above aspects, this application can also be further combined to provide more implementations. Attached Figure Description

[0056] Figure 1 A vector data schematic diagram provided for this application;

[0057] Figure 2 A schematic diagram of the structure of a computer system provided in this application;

[0058] Figure 3 A flowchart illustrating a low-altitude airspace risk quantification method provided in this application. Figure 1 ;

[0059] Figure 4 This application provides a schematic diagram of the projection relationship between a three-dimensional mesh and a second sub-region.

[0060] Figure 5 A flowchart illustrating a low-altitude airspace risk quantification method provided in this application. Figure 2 ;

[0061] Figure 6 A schematic diagram of the structure of a low-altitude airspace risk quantification device provided in this application Figure 1 ;

[0062] Figure 7 A schematic diagram of the structure of a low-altitude airspace risk quantification device provided in this application Figure 2 ;

[0063] Figure 8 A schematic diagram of the structure of a computing device provided in this application;

[0064] Figure 9 This application provides a schematic diagram of the structure of a computing device cluster;

[0065] Figure 10 This is a schematic diagram of the connection between computing devices provided in this application. Detailed Implementation

[0066] To facilitate understanding, the technical terms used in this application will be introduced first.

[0067] Low-altitude airspace is used not only for non-commercial transportation activities (such as agricultural plant protection, emergency rescue, and aviation sports), but also for commercial transportation scenarios, such as drone logistics delivery, urban air traffic (such as electric vertical take-off and landing (eVTOL) aircraft), and industrial inspection.

[0068] In Geographic Information Systems (GIS), vector data is a data format used to represent geospatial information. It describes the spatial location and shape of geographic entities using geometric objects such as point data, line data, and polygon data. The core of vector data is to accurately represent the location, shape, and attribute information of geographic objects by recording coordinates. For example... Figure 1 As shown, Figure 1 A vector data schematic diagram provided for this application.

[0069] POI data describes a specific point in a geographic location. Examples include a house, a shop, the entrance to a residential complex, or a bus stop. Each POI contains at least two basic pieces of information: category and latitude / longitude coordinates. For instance, the aforementioned categories can be divided into multiple levels, such as type-major category-minor category, or first-level category, second-level category, etc. Different data sources may have different classifications. In one possible scenario, POI data may also include a name.

[0070] AOI data is used to indicate geographic entities that are geographically located. Examples include a residential area, a university, an industrial park, a shopping mall, a hospital, or a tourist attraction. Each AOI also contains at least two or three basic pieces of information.

[0071] Line data is used to describe linear information in a geographic location. Examples include power lines (roads, railways, waterways, etc.) and pipelines (high-voltage lines, gas pipelines).

[0072] To quantify the risks of low-altitude airspace, the airspace is first divided into three-dimensional grids, each carrying multi-source heterogeneous data (such as vector data and spatial data). Risk quantification is then performed on each grid to obtain a corresponding risk value. However, because many grids carry identical multi-source heterogeneous data, data redundancy is high. This leads to redundant calculations when quantifying risks across all grids, resulting in low computational efficiency and consequently, low efficiency in risk quantification of low-altitude airspace.

[0073] Based on this, this application provides a method for quantifying low-altitude airspace risk, which is applied to a computing device. The computing device includes a server or terminal, etc. The method includes: the computing device acquiring geographic information data indicating at least one type of land use within a first region, and then determining first vector data based on the at least one type of land use within the first region. The first vector data includes network area data and land parcel data. The computing device determines a first risk level of a first sub-region based on the category of the network area data, and determines a second risk level of a second sub-region based on the category of the land parcel data. Finally, the computing device maps the first risk level of the first sub-region and the second risk level of the second sub-region to a three-dimensional grid. The at least one type of land use includes transportation land, the network area data indicates the first sub-region where transportation land is located within the first region, and the land parcel data indicates the second sub-region where land parcels are located within the first region; the first sub-region and the second sub-region are adjacent.

[0074] In this application, the computing device preprocesses the geographic information data to obtain first vector data including network area data and land parcel data. Then, based on the category of the first vector data, risk quantification is performed to obtain a first risk level and a second risk level. The first and second risk levels are then mapped to a three-dimensional grid. This avoids the problem of excessive data processing when processing all three-dimensional grids, which carry redundant geographic information data, thus reducing the amount of data processed and improving the efficiency of risk quantification for low-altitude airspace. Furthermore, the computing device only maps the first and second risk levels to the three-dimensional grid, reducing the amount of data mapping and further improving the efficiency of risk quantification for low-altitude airspace.

[0075] Next, the low-altitude airspace risk quantification method provided in this application will be described in detail with reference to the accompanying drawings.

[0076] The low-altitude airspace risk quantification method provided in this application can be applied to… Figure 2 The computer system shown, such as Figure 2 As shown, Figure 2 This application provides a schematic diagram of the structure of a computer system. The computer system includes a computing device cluster 210, which may include at least one computing device. For example, the computing device cluster 210 includes computing devices 211, 212, and 213. The computing devices 211, 212, and 213 can be connected via a channel 214. The channel 214 can be a wired channel or a wireless channel.

[0077] For detailed information on computing devices 211-213, please refer to the following: Figures 8-10 The specific wording is omitted here.

[0078] In one possible example, Figure 2 The computer system shown also includes multiple computing devices, such as computing device 220 and computing device 230.

[0079] The computing devices 220 and 230 can be connected to the computing device cluster 210 via a wired or wireless channel. The computing device 220 or 230 can be a terminal, such as a computing device, drone, smartphone, laptop, tablet, or personal desktop computer. In one possible example, the computing device 220 or 230 runs drone control software, flight control software, image transmission software, etc.

[0080] In another possible example, computing device 220 or computing device 230 is equipped with hardware or software for collecting geographic information data.

[0081] The aforementioned wired channels can include: Ethernet, fiber optic cables, cloud direct connects, and various peripheral component interconnect (PCIe) buses installed within the computer system to connect area A and area B. A cloud direct connect refers to a physical leased line that directly connects the local network to the data center or other regional networks of a cloud service provider, achieving a high-speed, low-latency, secure, and reliable connection.

[0082] The aforementioned wireless channels can include: the Internet, cloud connectivity, wireless fidelity (Wi-Fi), ultra-wideband (UWB) technology, and remote procedure call (RPC). Cloud connectivity refers to the technology and microservices that connect a local network to other cloud service providers or networks in other regions via the Internet. RPC is a technology used to enable communication between different computing devices or processes in a distributed system. RPC allows one program / microservice to call the procedure or method of another program / microservice as if calling a local procedure, hiding the details of the underlying communication.

[0083] In one possible scenario, the aforementioned computing device cluster 210 serves as the infrastructure supporting the operation of cloud services. Furthermore, virtualized instances such as containers, PODs, or virtual machines deployed within this infrastructure can be used to implement the low-altitude airspace risk quantification method provided in this application.

[0084] It should be noted that, Figure 2The illustrated computer system architecture is merely an example. The types or number of devices within the system can be configured according to actual needs, and this application embodiment does not limit this. For example, the computing device cluster 210 may also include more or fewer computing devices, and the computing devices in the computer system that are communicatively connected to the computing device cluster 210 may also be more or fewer.

[0085] Figure 3 The low-altitude airspace risk quantification method shown can be applied to Figure 2 The computer system shown. For example... Figure 3 As shown, Figure 3 A flowchart illustrating a low-altitude airspace risk quantification method provided in this application. Figure 1 , Figure 3 The method shown can be executed by any one or more computing devices included in the computer cluster 210. In this embodiment, the low-altitude airspace risk quantification method is illustrated using computing device 211 within the computer cluster 210 as an example. Figure 3 The low-altitude airspace risk quantification method shown includes the following steps S310-S350.

[0086] S310, computing device 211 acquires geographic information data.

[0087] The aforementioned geographic information data is used to indicate at least one type of land use within the first region.

[0088] The first region mentioned above is the area where low-altitude airspace risk quantification will be carried out. For example, the first region can be an administrative division, such as the area managed by a country, province, city, county, district, street, township, or village.

[0089] For example, the first area is a region defined by the user. Such as a rectangular area within xx city, or a polygonal area within xx district.

[0090] In one possible example, the first area includes at least one type of land use, i.e., land use planning within the first area. For example, the first area includes transportation land (such as highways, railways, waterways, etc.), pipeline land (high-voltage lines, gas pipelines, etc.), building land (residential buildings, office buildings, apartments, libraries, hospitals, etc.), no-fly zones (government offices, closed management areas, etc.), and high-risk land (hospitals, transformers, etc.).

[0091] In one possible scenario, the geographic information data is vector data a (second vector data) within the first region.

[0092] In one possible example, vector data 'a' includes: zoning, network, and POI data within a first region. This network includes the aforementioned transportation land, pipeline land, etc.

[0093] In another possible example, vector data a includes: zoning, grid lines, and AOI data within the first region.

[0094] In another possible example, vector data a includes: zoning, grid lines, POI data, and AOI data within the first region.

[0095] The zoning within the first region mentioned above is used to represent the boundaries of the area, such as the boundary of a district or street within a city. The network may include: road networks (roads, railways, waterways, etc.), pipelines (high-voltage lines, gas pipelines, etc.), etc. POI data includes: a building, a shop, a bus stop, etc., within the first region. AOI data includes: schools, hospitals, foreign-related venues, government office buildings, etc., within the first region.

[0096] In another possible scenario, the geographic information data includes raster data and vector data a within the first region.

[0097] In one possible example, the raster data could be raster data recording land use types, or raster data recording population information. Land use types could include: residential land, commercial land, government land, etc. Population information could be any one or more of the following: population density, population housing situation, etc.

[0098] In one possible implementation, computing device 211 acquires geographic information data, including: computing device 211 acquires geographic information data input by computing device 220.

[0099] In one possible example, computing device 211 provides an application programming interface (API), and computing device 220 calls the aforementioned API to send a request carrying geographic information data to computing device 211.

[0100] In another possible example, computing device 220 accesses computing device 211 via an API provided by computing device 211 to display a user interface on computing device 220. Furthermore, the user triggers control components on the user interface using various input devices (keyboard, mouse, touchscreen, etc.) connected to computing device 220. Based on these triggering operations, computing device 220 obtains geographic information data and then sends the geographic information data to computing device 211.

[0101] The aforementioned triggering operation can be: the user confirms the control component via the keyboard, such as by pressing the Enter key, to confirm the parameters configured by the user. These parameters may be the name or identifier of the first area, etc.

[0102] Alternatively, the triggering operation described above could be: a user clicking on the touchscreen, such as the user clicking the confirmation button displayed on the touchscreen to confirm the parameters configured by the user.

[0103] The above-mentioned triggering operations are merely examples provided in this application and should not be construed as limiting this application.

[0104] S320, computing device 211 determines first vector data based on at least one type of land use within the first area.

[0105] In one possible scenario, the first vector data (vector data b) includes network area data and land parcel data. The network area data is used to indicate a first sub-region in which traffic land is located within the first region, and the at least one type of land use mentioned above includes traffic land. The land parcel data is used to indicate a second sub-region in which the land parcels in the first region are located, and the first sub-region is adjacent to the second sub-region.

[0106] In one possible implementation, the computing device 211 determines vector data b based on at least one type of land use within a first region, including: the computing device 211 determines at least one traffic land use from at least one type of land use within the first region, and then constructs buffer areas for each of the at least one traffic land use to obtain at least one first sub-region, thereby dividing the first region based on the at least one first sub-region to obtain multiple second sub-regions.

[0107] In one possible example, computing device 211 identifies traffic land from at least one type of land use within a first region, and then widens at least one traffic land use to create a buffer zone for the at least one traffic land use, resulting in at least one first sub-region. Finally, the first region is divided using the at least one first sub-region as a boundary to obtain multiple second sub-regions.

[0108] Furthermore, the computing device 211 uses the first sub-region, the vector data (POI data and / or AOI data, etc.) in the first sub-region, the second sub-region, and the vector data (POI data and / or AOI data, etc.) in the second sub-region as vector data b.

[0109] For example, computing device 211 selects transportation land from at least one type of land use within a first region, and then widens at least one transportation land to construct a real transportation route, that is, widens the linear transportation land into a strip (area)-shaped first sub-region, thereby constructing a buffer zone for at least one transportation land and obtaining at least one first sub-region. Computing device 211 divides the first region using spatial difference, with at least one first sub-region as the boundary, to obtain multiple second sub-regions.

[0110] For example, computing device 211 filters out transportation land from at least one type of land use in a first area, including: computing device 211 determines transportation land from linear land use based on the attributes of linear land use, such as roads, railways, rivers, high-voltage lines, gas pipelines, etc.

[0111] For example, different traffic land uses have different grades. The computing device 211 can construct buffer zones of different widths for different grades of traffic land uses based on the mapping relationship between the grade of the traffic land use and the width of the network area, thereby obtaining the corresponding first sub-region. For instance, if at least one traffic land use includes traffic land use 1 (first traffic land use), the computing device 211 constructs a buffer zone of a first width for the first traffic land use, obtaining the target sub-region. The first width of the target sub-region satisfies a first mapping relationship (mapping relationship a) with the grade of traffic land use 1, and the aforementioned at least one first sub-region includes the target sub-region.

[0112] The aforementioned transportation land use categories may include: national highways, provincial highways, county roads, township roads, village roads, expressways, or special-purpose roads, etc.

[0113] Alternatively, the aforementioned transportation land use can be categorized into: Level 1 transportation lines, Level 2 transportation lines, Level 3 transportation lines, Level 4 transportation lines, Level 5 transportation lines, etc. For example, a Level 1 transportation line can be a Level 1 highway, a Level 1 waterway, or a Level 1 railway.

[0114] The above mapping relationship a can be: national highway - 20 meters wide, provincial highway - 15 meters wide, county road - 12 meters wide, township road - 8 meters wide, etc.; or, mapping relationship a can be: Class 1 highway - 40 meters wide, Class 1 waterway - 500 meters wide, Class 1 railway - 1.435 meters wide.

[0115] In another possible example, the difference between this example and the example above is that, since different first sub-regions have different levels, the computing device 211 can divide the first region layer by layer from high level to low level according to the level of the first sub-region to obtain multiple second sub-regions.

[0116] The grade of the first sub-region is consistent with the grade of its corresponding transportation land, such as the grade of the target sub-region being consistent with the grade of transportation land 1.

[0117] For details on this example, please refer to the following: Figure 5 The specific wording is omitted here.

[0118] S330 and computing device 211 determine the first risk level of the first sub-region based on the category of the network area data.

[0119] The first level of risk can be a risk value or a risk grade. This risk grade can include Level 1 risk, Level 2 risk, etc. The following explanation uses the risk value as an example to illustrate the risk level.

[0120] In one possible scenario, the category of the network area data can be determined based on the attribute information 'a' of the transportation land corresponding to the network area data. This attribute information 'a' indicates the category of the network area data, and the geographic information data includes the aforementioned attribute information 'a'.

[0121] In one possible example, if the attribute information 'a' of the traffic land corresponding to the network area data is: Level 1 road, then the category of the network area is Level 1 road.

[0122] In another possible example, if the attribute information 'a' of the transportation land corresponding to the network area data is: road, and it is a national highway, then the category of the network area is a national highway.

[0123] In one possible implementation, the computing device 211 determines a first risk value for a first sub-region based on the category of the network area data, including: the computing device 211 determines a first risk value that matches the category of the network area data from the mapping relationship b (third mapping relationship) between the categories of the network area data and risk values.

[0124] For a detailed description of mapping relationship b, please refer to Table 1 below, which will not be elaborated here.

[0125] S340, computing device 211 determines the second risk level of the second sub-region based on the category of land parcel data.

[0126] In one possible scenario, attribute information b of the land parcel data is used to indicate the category of the land parcel data. Attribute information b includes one or more of the following: POI data, AOI data, and raster data.

[0127] In one possible example, since the POI and AOI data in attribute information b are vector data, they contain location information. The POI and AOI data in the parcel data indicate that the location information points to the POI and AOI data within the second sub-region.

[0128] The raster data representation of the parcel data is the raster data located in the same or corresponding position as the second sub-region.

[0129] Regarding the attribute information b used to indicate the category of the land parcel data, two possible examples are provided below.

[0130] In one possible example, computing device 211 inputs attribute information b into an artificial intelligence model and outputs the category of the land parcel data. This artificial intelligence model is used to determine the category of the land parcel data based on the attribute information.

[0131] The aforementioned artificial intelligence models may include machine learning modules and deep learning models. For example, machine learning models may be any of the following: latent dirichlet allocation (LDA), latent semantic analysis (LSA), tree models (decision trees, random forests, gradient boosting trees, etc.), etc. Deep learning models may be any of the following: deep structured semantic models (DSSM), convolutional deep structured semantic models (CDSSM), enhanced sequential inference models, BERT (bidirectional encoder representations from transformers), bilateral multi-perspective matching (BIMPM) models, etc.

[0132] For details regarding the categories of the land parcel data output by the computing device 211 when it inputs attribute information b into the artificial intelligence model, please refer to the following description, which will not be repeated here.

[0133] In another possible example, computing device 211 uses a statistical algorithm to process attribute information b to obtain the category of land parcel data.

[0134] The statistical algorithm mentioned above can be any of the following: term frequency–inverse document frequency (TF-IDF), co-occurrence, BM25 (best matching 25), pointwise mutual information (PMI), etc.

[0135] For example, the computing device 211 uses TF-IDF to determine the similarity between the POI data, AOI data and the preset category label in the attribute information b, and then uses the preset category label with the highest similarity as the category of the land parcel data.

[0136] In one possible implementation, the computing device 211 determines a second risk value for a land parcel based on the category of the land parcel data, including: the computing device 211 determines a second risk value that matches the category of the land parcel data from the mapping relationship c (fourth mapping relationship) between the categories of the land parcel data and the risk values.

[0137] For a detailed description of the mapping relationship c, please refer to Table 1 below, which will not be elaborated here.

[0138] like Figure 3 As shown, the above-mentioned low-altitude airspace risk quantification method also includes the following step S350.

[0139] S350, computing device 211 maps the first risk level of the first sub-region and the second risk level of the second sub-region to the three-dimensional mesh.

[0140] In one possible scenario, the projected area of ​​the 3D mesh is greater than or equal to the area of ​​the first region. Figure 3 Only the projection surface of the 3D mesh is shown.

[0141] In one possible implementation, the computing device 211 maps a first risk value of a first sub-region and a second risk value of a second sub-region to a three-dimensional mesh, including: the computing device 211 maps the first risk value and the second risk value to the three-dimensional mesh according to the positional relationship between the first sub-region, the second sub-region and the three-dimensional mesh.

[0142] In one possible example, such as Figure 4 As shown, Figure 4 This is a schematic diagram illustrating the projection relationship between a three-dimensional mesh and a second sub-region, as provided in this application. Figure 4 As shown in Figure a, if the second sub-region falling into the three-dimensional mesh A is only the second sub-region 1, then the computing device 211 maps the risk value of the second sub-region 1 to the three-dimensional mesh.

[0143] like Figure 4 As shown in Figure b, if the second sub-region falling into the three-dimensional mesh B includes at least two second sub-regions, such as second sub-region 2 and second sub-region 3, then the computing device 211 maps the risk value of the second sub-region 2 or the second sub-region 3 to the three-dimensional mesh B.

[0144] If the second sub-region falling into the three-dimensional mesh B includes at least two second sub-regions, such as second sub-region 2 and second sub-region 3, then the computing device 211 maps the maximum risk value of the second sub-region 2 and the second sub-region 3 to the three-dimensional mesh.

[0145] For example, if the second sub-region 2 and the second sub-region 3 are high-risk areas or no-fly zones, the computing device 211 maps the maximum risk value of the second sub-region 2 and the second sub-region 3 to a three-dimensional mesh.

[0146] If the second sub-region falling into the three-dimensional grid B includes at least two second sub-regions, such as second sub-region 2 and second sub-region 3, then the computing device 211 projects the risk values ​​of the second sub-region 2 and the second sub-region 3 onto the three-dimensional grid B by weighted summation or weighted average based on the areas of the second sub-region 2 and the second sub-region 3 projected onto the three-dimensional grid B respectively.

[0147] For example, if the areas of the second sub-region 2 and the second sub-region 3 projected onto the three-dimensional mesh B are respectively half of the projected area of ​​the three-dimensional mesh B, then the weights of the second sub-region 2 and the second sub-region 3 are both 0.5. The computing device 211 multiplies the risk value of the second sub-region 2 by 0.5 and multiplies the risk value of the second sub-region 3 by 0.5, and then adds them together, and then projects the summed risk value onto the three-dimensional mesh B.

[0148] For example, if the areas of the second sub-region 2 and the second sub-region 3 projected onto the three-dimensional mesh B are respectively 1 / 3 of the projected area of ​​the three-dimensional mesh B, then the weights of the second sub-region 2 and the second sub-region 3 are both 1 / 3. The computing device 211 multiplies the risk value of the second sub-region 2 by 1 / 3 and multiplies the risk value of the second sub-region 3 by 1 / 3 and adds them together. Then, the risk value after addition is divided by the weight and the resulting risk value is mapped to the three-dimensional mesh B.

[0149] It is worth noting that the above description only uses the relationship between the second sub-region and the 3D mesh as an example. In other embodiments of the application, the first risk value of the first sub-region can also be mapped to the 3D mesh based on the relationship between the first sub-region and the 3D mesh, or the risk value can be mapped to the 3D mesh based on the relationship between the first and second sub-regions and the 3D mesh, as well as the first and second risk values. For details of the aforementioned scenarios, please refer to the above. Figure 4 The content shown will not be repeated here.

[0150] In one possible embodiment, computing device 211 outputs the aforementioned three-dimensional mesh to a front end. This front end may be computing device 220 (terminal), or a display device connected to computing device 211.

[0151] Furthermore, the terminal or a display device connected to the computing device 211 can display the three-dimensional mesh. For example, the display device displays the three-dimensional mesh on a display interface.

[0152] In one possible embodiment, computing device 211 outputs third vector data (vector data c) to a front end. Vector data c includes: a first risk value, a second risk value, and vector data. The front end may be computing device 220, or a display device connected to computing device 211.

[0153] For example, computing device 220 displays vector data c before displaying the three-dimensional mesh.

[0154] For example, computing device 220 displays a three-dimensional mesh and vector data c on the same display interface.

[0155] Regarding the above-mentioned computing device 211 inputting attribute information b into the artificial intelligence model and outputting the category of land parcel data, the following are four possible examples.

[0156] Example 1: The aforementioned artificial intelligence model is a text matching model, such as the BIMPM model. The computing device 211 inputs the POI data from attribute information b and preset category labels into the BIMPM model to obtain the similarity between the POI data and the preset category labels. Then, it uses the preset category label corresponding to any of the top N similarities as the category of the land parcel data. The aforementioned arrangement is in descending order of similarity.

[0157] Alternatively, computing device 211 may use a preset category label corresponding to any of the last M similarities as the category of the land parcel data. The aforementioned arrangement is an ascending order of similarity.

[0158] For example, the POI data for the aforementioned land parcels includes: supermarket and two houses. Preset category labels can be found in the major and minor categories in Table 1 below, and will not be elaborated upon here. The computing device 211 inputs "supermarket" and "two houses" along with the preset category labels into the BIMPM model, obtaining a similarity of 80% between "supermarket" and "two houses" and a residential community, and a similarity of 60% between "supermarket" and "two houses" and a mixed-use area. Therefore, the computing device 211 can select the preset list label corresponding to the highest similarity as the category of the land parcel data, namely "residential community".

[0159] Example 2: The computing device 211 inputs the AOI data and preset category labels from attribute information b into the BIMPM model to obtain the similarity between the AOI data and the preset category labels. Then, it takes the preset category label corresponding to any of the top N similarities as the category of the land parcel data. The aforementioned arrangement is a descending order of similarity.

[0160] For details of Example 2, please refer to the description in Example 1 above, which will not be repeated here.

[0161] Example 3: The computing device 211 inputs the AOI data and POI data in attribute information b, along with the preset category labels, into the BIMPM model to obtain the similarity between the AOI data and POI data and the preset category labels. Then, it takes the preset category label corresponding to any of the first N similarities as the category of the land parcel data.

[0162] For details on Example 3, please refer to the descriptions in Examples 1 and 2 above, which will not be repeated here.

[0163] Example 4: The aforementioned artificial intelligence model is BERT, etc. The computing device 211 inputs the raster data (used to record land use type, or population information) in attribute information b and preset category labels into BERT, and outputs the category of the land parcel data.

[0164] The descriptions of mapping relationships b and c are shown in Table 1 below, and will not be repeated here.

[0165] In one possible example, the risk values ​​in mapping relationships b and c range from 1 to 1000.

[0166] The above values ​​are merely examples and should not be construed as limiting this application. In other embodiments of this application, the risk values ​​in mapping relationship b and mapping relationship c can be any values ​​set by the user, or any default values.

[0167] In one possible scenario, the above mapping relationships b and c can be configured by the user, such as when computing device 211 receives mapping relationships b and c sent by computing device 220, i.e., Table 1 below.

[0168] Alternatively, the above mapping relationships b and c are the default mapping relationships used by computing device 211.

[0169] Table 1

[0170]

[0171] It is worth noting that the above content is merely an example and should not be construed as limiting this application. Other embodiments of this application may include more or fewer major-minor-risk values. Furthermore, the risk value can be set to any numerical value, for example, road-urban arterial road-risk value: 100.

[0172] In one possible embodiment, the at least one type of land use mentioned above further includes pipeline land with different grades. The computing device 211 constructs buffer regions of corresponding widths for pipeline land of different grades based on a second mapping relationship (mapping relationship d) between the grade and width of the pipeline land, thus obtaining a third sub-region. Then, the third sub-region and information indicating the category of the third sub-region are used as pipeline data, and the pipeline data is then superimposed on the first vector data.

[0173] In one possible example, the classification of pipeline land use could include: Class 1 high-voltage lines (≥500 kV), Class 2 high-voltage lines (≥330 kV, <500 kV), Class 3 high-voltage lines (≥154 kV, <330 kV), Class 1 gas pipelines, Class 2 gas pipelines, etc. Information indicating the category of the third sub-area includes: high-voltage lines, gas pipelines, etc.

[0174] Therefore, the above mapping relationship d can be: Level 1 high-voltage line - 17 meters, Level 2 high-voltage line - 12 meters, Level 3 high-voltage line - 10 meters, Level 1 gas pipeline - 5 meters, Level 2 gas pipeline - 3 meters, etc.

[0175] In one possible implementation, the computing device 211 overlays pipeline data onto the first vector data, including: the computing device 211 overlays the pipeline data onto the first vector data according to the position of the third sub-region in the first region.

[0176] In one possible example, if the third sub-region falls into the second region (such as a school), the computing device 211 uses the third sub-region to divide the second region into second region 1 and second region 2, and thus the original second region is divided into second region 1, second region 2 and third sub-region.

[0177] In one possible embodiment, the aforementioned geographic information data further includes: no-fly zones and / or high-risk zones. The low-altitude airspace risk quantification method provided in this embodiment also includes:

[0178] If a no-fly zone and / or high-risk zone completely overlaps with the location of the first land parcel area (land parcel area a), then the computing device 211 classifies the first land parcel data (land parcel data a) as a no-fly zone and / or a high-risk zone. Land parcel data a indicates the land parcel area a within the first region, and the second sub-region includes said land parcel area a.

[0179] The aforementioned no-fly zone and / or high-risk zone are used to indicate that the location of plot area a completely overlaps with that of plot area a, and that plot area a can fall entirely within the no-fly zone and / or high-risk zone.

[0180] If the area of ​​the no-fly zone and / or high-risk zone overlapping with the location of the second plot area (plot area b) is greater than or equal to threshold a, then the computing device 211 classifies the second plot data (plot data b) as a no-fly zone and / or a high-risk zone. Plot data b is used to indicate the plot area b where the second plot is located in the first region, and the second sub-region includes plot area b.

[0181] For example, the threshold 'a' mentioned above can be 10,000 square meters.

[0182] If the ratio of the area of ​​the no-fly zone and / or high-risk zone overlapping with the second plot area (plot area b) to the area of ​​the second plot area is greater than or equal to the threshold b, then the computing device 211 will classify the plot data b as a no-fly zone and / or a high-risk zone.

[0183] For example, the threshold b mentioned above can be 80%.

[0184] If the area overlapping the no-fly zone and / or high-risk zone with the third plot area (plot area c) is less than threshold a, then the computing device 211 divides plot area c into sub-plot area a and sub-plot area b. Sub-plot area a is the portion of plot area c that overlaps with the no-fly zone and / or the high-risk zone. Sub-plot area a and sub-plot area b together constitute plot area c. The second sub-region includes the third plot area.

[0185] The computing device 211 classifies the third land parcel data (land parcel data c) as a no-fly zone and / or a high-risk zone, and the land parcel data c is used to indicate the sub-land parcel area a in which the sub-land parcel in the first zone is located.

[0186] The category of the fourth plot data corresponding to sub-plot area b is determined based on the attribute information of the fourth plot data. This data information is also the vector data (such as POI data, AOI data, etc.) indicating the location within sub-plot area b.

[0187] It is worth noting that when the data type of high-risk land is point data, the computing device 211 needs to construct a buffer area for the location corresponding to the point data, forming a region, and then determine the overlap between this region and the aforementioned land area. For example, a transformer can be point data.

[0188] It is worth noting that the above description only illustrates the correction of the land parcel data category. In other embodiments of this application, the computing device 211 may also correct the category of the network area data based on the relationship between the location of the first sub-region and the no-fly zone and / or high-risk land. The content of correcting the category of the network area data can be referred to the above description of correcting the category of the land parcel data, and will not be repeated here.

[0189] exist Figure 3 Based on the content shown, the following embodiments further illustrate the method for quantifying low-altitude airspace risks in detail. Figure 5 As shown, Figure 5 A flowchart illustrating a low-altitude airspace risk quantification method provided in this application. Figure 2 . Figure 5 The content shown includes the following steps S510-S550.

[0190] S510, computing device 211 constructs buffer zones for different levels of traffic land use according to mapping relationship a, and obtains at least one first sub-region.

[0191] The description of mapping relationship 'a' can be found in Table 1 above, and will not be repeated here.

[0192] In one possible implementation, the computing device 211 performs hierarchical processing on traffic land of different levels according to the mapping relationship a to obtain at least one first sub-region, including: the computing device 211 performs hierarchical construction of buffer regions on national highway level traffic land 1 and county road level traffic land 2 according to the mapping relationship a to obtain first sub-region 1 corresponding to traffic land 1 and first sub-region 2 corresponding to traffic land 2.

[0193] In one possible example, computing device 211 determines, based on mapping relationship a, to widen the national highway-level traffic land 1 to 20 meters, resulting in a first sub-region 1. And, based on mapping relationship a, determines to widen the county road-level traffic land 1 to 12 meters, resulting in a first sub-region 2.

[0194] S520, computing device 211 divides the first region according to the hierarchy of multiple first sub-regions to obtain multiple second sub-regions.

[0195] In one possible implementation, the computing device 211 divides the first region into multiple second sub-regions according to the levels of multiple first sub-regions, including: the computing device 211 divides the first region into multiple second sub-regions from high to low according to the levels of multiple first sub-regions.

[0196] In one possible example, computing device 211 first divides the first region using the first sub-region 1 at the national highway level to obtain at least two second sub-regions, and then further divides the aforementioned at least two second sub-regions using the first sub-region 2 at the county road level to obtain multiple second sub-regions.

[0197] The above division method can be any of the following: spatial difference, parallel finite difference, etc.

[0198] S530, computing device 211 overlays pipeline data onto vector data b.

[0199] The vector data b includes at least one first sub-region and multiple second sub-regions, as well as vector data whose location is indicated by location information within the first and second sub-regions.

[0200] In one possible implementation, the computing device 211 superimposes pipeline data onto vector data b, including: the computing device 211 spatially stacks the pipeline data and vector data b according to the position of the third sub-region in the first region.

[0201] For example, the third sub-region is on a different layer than the first and second sub-regions.

[0202] For details on determining pipeline data, please refer to the content in Table 1 above; further details will not be provided here.

[0203] S540, computing device 211 corrects the category of land parcel data in vector data b based on no-fly zones and / or high-risk zones in geographic information data.

[0204] For details on S540, please refer to the description of the categories of plots a-c in Table 1 above, which will not be repeated here.

[0205] In one possible scenario, computing device 211 will also correct the category of the network area data in vector data b based on no-fly zones and / or high-risk zones in the geographic information data.

[0206] S550, computing device 211 determines the first risk value of the first sub-region and the second risk value of the second sub-region based on the category of vector data b.

[0207] For details on S550, please refer to the descriptions of S330 and S340 above, which will not be repeated here.

[0208] Then, the computing device 211 maps the first risk value of the first sub-region and the second risk value of the second sub-region to the three-dimensional mesh.

[0209] It is worth noting that, Figure 5 The risk values ​​shown are displayed in a labeled form. In other embodiments of this application, different risk values ​​can also be represented by different fills (colors, patterns, etc.) of each sub-region.

[0210] In one possible scenario, computing device 211 will also determine a third risk value for the third sub-region based on the category of pipeline data.

[0211] Then, the computing device 211 maps the first risk value of the first sub-region, the second risk value of the second sub-region, and the third risk value of the third sub-region to the three-dimensional mesh.

[0212] In one possible embodiment, the aforementioned geographic information data further includes: the boundaries of each zone in the first region. Furthermore, multiple computing devices in the computing device cluster 210 can perform risk quantification on the low-altitude airspace corresponding to each zone in parallel. For the description of each computing device performing risk quantification on the low-altitude airspace corresponding to a zone, refer to the above... Figures 2-4 The content shown will not be repeated here.

[0213] In one possible scenario, in addition to being applied to the quantification of risks in UAV flight areas to support UAV route planning (logistics delivery, agricultural monitoring, environmental inspection, etc.), this application can also be used in fields such as satellite navigation and urban planning.

[0214] It is understood that, in order to achieve the functions in the above embodiments, the computing device 211 includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, based on the units and method steps of the various examples described in conjunction with the embodiments disclosed in this application, this application 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 scenario and design constraints of the technical solution.

[0215] The above text combines Figures 3 to 5 This paper details the method for quantifying low-altitude airspace risk provided in this application. The following section will combine... Figure 6 Describe the low-altitude airspace risk quantification device provided in this application. Figure 6 A schematic diagram of the structure of a low-altitude airspace risk quantification device provided in this application Figure 1 The low-altitude airspace risk quantification device 600 can be used to implement the function of the computing device 211 in the above method embodiments, and therefore can also achieve the beneficial effects of the above method embodiments.

[0216] like Figure 6 As shown, the low-altitude airspace risk quantification device 600 includes an acquisition module 610, a first determination module 620, a second determination module 630, and a mapping module 640. This low-altitude airspace risk quantification device 600 is used to implement the above-mentioned... Figures 3 to 5 The corresponding method embodiment describes the function of the computing device 211. In one possible example, the specific process by which the low-altitude airspace risk quantification device 600 implements the aforementioned low-altitude airspace risk quantification method includes the following steps:

[0217] Acquisition module 610 is used to acquire geographic information data. The geographic information data is used to indicate at least one type of land use within a first area, including transportation land use. First determination module 620 is used to determine first vector data based on at least one type of land use within the first area. The first vector data includes network area data and parcel data. The network area data is used to indicate a first sub-area where transportation land use is located within the first area, and the parcel data is used to indicate a second sub-area where parcels are located within the first area. The first and second sub-areas are adjacent. Second determination module 630 is used to determine a first risk level of the first sub-area based on the category of the network area data, and a second risk level of the second sub-area based on the category of the parcel data. Mapping module 640 is used to map the first risk level of the first sub-area and the second risk level of the second sub-area to a three-dimensional grid.

[0218] To further achieve the above Figures 3 to 5 The method embodiment shown illustrates the functionality. This application also provides a low-altitude airspace risk quantification device, such as… Figure 7 As shown, Figure 7 A schematic diagram of the structure of a low-altitude airspace risk quantification device provided in this application Figure 2 The low-altitude airspace risk quantification device 600 also includes: a correction module 650, a first output module 660, and a second output module 670.

[0219] The correction module 650 is configured to classify the first plot data as no-fly and / or high-risk land if the no-fly zone and / or high-risk land completely overlap with the location of the first plot area. The first plot data indicates the first plot area within a first region, and the second sub-region includes the first plot area. If the area of ​​overlap between the no-fly zone and / or high-risk land and the second plot area is greater than or equal to a threshold, the second plot data is classified as no-fly and / or high-risk land. The second plot data indicates the second plot area within a first region, and the second sub-region includes the second plot area. If the area of ​​overlap between the no-fly zone and / or high-risk land and the third plot area is less than a threshold, the third plot data is classified as no-fly and / or high-risk land. The third plot data indicates the sub-plot area within a first region, and the third plot area includes the sub-plot area; the second sub-region includes the third plot area. The first output module 660 is used to output a three-dimensional mesh. The second output module 670 is used to output third vector data; the third vector data includes a first risk level, a second risk level, and the first vector data.

[0220] For more information on the functions of the acquisition module 610, the first determination module 620, the second determination module 630, and the mapping module 640, please refer to the description of the low-altitude airspace risk quantification method above, which will not be repeated here.

[0221] The acquisition module 610, the first determination module 620, the second determination module 630, and the mapping module 640 can all be implemented in software or in hardware. For example, the implementation of the acquisition module 610 will be described below. Similarly, the implementation of the first determination module 620, the second determination module 630, and the mapping module 640 can refer to the implementation of the acquisition module 610.

[0222] As an example of a software functional unit, module 610 may include code running on a computing instance. The computing instance may include at least one of a physical host (computing device), a virtual machine, and a container. Furthermore, the aforementioned computing instance may be one or more. For example, module 610 may include code running on multiple hosts / virtual machines / containers.

[0223] It should be noted that the multiple hosts / virtual machines / containers used to run this code can be distributed within the same region or in different regions. Furthermore, the multiple hosts / virtual machines / containers used to run this code can be distributed within the same availability zone (AZ) or in different AZs, each AZ comprising one or more geographically proximate data centers. Typically, a region can include multiple AZs.

[0224] Similarly, multiple hosts / virtual machines / containers used to run this code can be distributed within the same VPC or across multiple VPCs. Typically, a VPC is set up within a region. Communication between two VPCs within the same region, as well as between VPCs in different regions, requires a communication gateway to be set up within each VPC to enable interconnection between VPCs.

[0225] As an example of a hardware functional unit, the acquisition module 610 may include at least one computing device, such as a server. Alternatively, the acquisition module 610 may also be a device implemented using an application-specific integrated circuit (ASIC) or a programmable logic device (PLD). The PLD may be implemented using a complex programmable logical device (CPLD), a field-programmable gate array (FPGA), generic array logic (GAL), or any combination thereof.

[0226] The multiple computing devices included in the acquisition module 610 can be distributed in the same region or in different regions. Similarly, the multiple computing devices included in the acquisition module 610 can be distributed in the same Availability Zone (AZ) or in different AZs. Likewise, the multiple computing devices included in the acquisition module 610 can be distributed in the same VPC or in multiple VPCs. These multiple computing devices can be any combination of computing devices such as servers, ASICs, PLDs, CPLDs, FPGAs, and GALs.

[0227] It should be noted that, in other embodiments, the acquisition module 610 can be used to execute any step in the low-altitude airspace risk quantification method, the first determination module 620 can be used to execute any step in the low-altitude airspace risk quantification method, the second determination module 630 can be used to execute any step in the low-altitude airspace risk quantification method, and the mapping module 640 can be used to execute any step in the low-altitude airspace risk quantification method. The steps implemented by the acquisition module 610, the first determination module 620, the second determination module 630, and the mapping module 640 can be specified as needed. The acquisition module 610, the first determination module 620, the second determination module 630, and the mapping module 640 respectively implement different steps in the low-altitude airspace risk quantification method to realize all the functions of the computing device 211.

[0228] It is worth noting that the computing device 211 in the aforementioned embodiment can correspond to the low-altitude airspace risk quantification device 600, and can correspond to the method for executing the embodiments of this application. Figures 3 to 5 The corresponding entities, and the operation and / or function of each module in the low-altitude airspace risk quantification device 600, are respectively for the purpose of achieving... Figures 3 to 5 The corresponding processes of each method in the corresponding embodiments are not described in detail here for the sake of brevity.

[0229] in addition, Figure 6 or Figure 7 The low-altitude airspace risk quantification device 600 shown can also be implemented using a communication device, which can refer to the computing device 211 in the aforementioned embodiment. When the communication device is a chip or chip system applied to a processing device, the low-altitude airspace risk quantification device 600 can also be implemented using a chip or chip system.

[0230] This application embodiment also provides a chip system, which includes a control circuit and an interface circuit. The interface circuit is used to acquire geographic information data, and the control circuit is used to implement the functions of the computing device 211 in the above method based on the geographic information data.

[0231] In one possible design, the chip system also includes a memory for storing program instructions and / or data. This chip system can be composed of chips or may include chips and other discrete components.

[0232] This application also provides a computing device, please refer to... Figure 8 , Figure 8 This application provides a schematic diagram of the structure of a computing device. The computing device 800 includes a bus 802, a processor 804, a memory 806, and a communication interface 808. The processor 804, memory 806, and communication interface 808 are interconnected via the bus 802. The computing device 800 can be a server or a terminal device. It should be understood that this application does not limit the number of processors and memories in the computing device 800. For example, the computing device 800 can be the aforementioned computing device 211.

[0233] The 802 bus can be a PCIe bus or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be divided into address buses, data buses, control buses, etc. For ease of representation, Figure 8 The bus 802 may be represented by a single line, but this does not mean that there is only one bus or one type of bus. The bus 802 may include a path for transmitting information between various components of the computing device 800 (e.g., processor 804, memory 806, communication interface 808).

[0234] Processor 804 may include any one or more processors such as a central processing unit (CPU), a graphics processing unit (GPU), a neural network processing unit (NPU), an FPGA, a microprocessor (MP), or a digital signal processing unit (DSP).

[0235] The memory 806 may include volatile memory, such as random access memory (RAM). The processor 804 may also include non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid state drive (SSD).

[0236] The memory 806 stores executable program code, and the processor 804 executes this executable program code to implement the functions of the aforementioned acquisition module 610, first determination module 620, second determination module 630, and mapping module 640, thereby realizing the low-altitude airspace risk quantification method. That is, the memory 806 stores instructions for executing the low-altitude airspace risk quantification method.

[0237] The communication interface 808 uses transceiver modules, such as, but not limited to, network interface cards and transceivers, to enable communication between the computing device 800 and other devices or communication networks. The computing device 800 can be a computer (e.g., a server) in a cloud data center, a computer in an edge data center, or a terminal.

[0238] This application also provides a computing device cluster. The computing device cluster includes at least one computing device, which can be a server, such as a central server, an edge server, or a local server in a local data center. In some embodiments, the computing device can also be a terminal device such as a desktop computer, a laptop computer, or a smartphone. For example, the computing device cluster can be the aforementioned computing device cluster 210.

[0239] like Figure 9 As shown, Figure 9 This application provides a schematic diagram of a computing device cluster. The computing device cluster includes at least one computing device 800. The memory 806 of one or more computing devices 800 in the computing device cluster may store the same instructions for executing low-altitude airspace risk quantification methods.

[0240] In some possible implementations, the memory 806 of one or more computing devices 800 in the computing device cluster may also store partial instructions for executing the low-altitude airspace risk quantification method. In other words, a combination of one or more computing devices 800 can jointly execute instructions for executing the low-altitude airspace risk quantification method.

[0241] It should be noted that the memory 806 in different computing devices 800 within the computing device cluster can store different instructions, each used to execute a portion of the functions of the low-altitude airspace risk quantification method. That is, the instructions stored in the memory 806 of different computing devices 800 can implement the functions of one or more modules among the acquisition module 610, the first determination module 620, the second determination module 630, and the mapping module 640.

[0242] In some possible implementations, one or more computing devices in a computing device cluster can be connected via a network. This network can be a wide area network (WAN) or a local area network (LAN). Figure 10 One possible implementation is shown. For example... Figure 10 As shown, Figure 10 This application provides a schematic diagram of a connection between computing devices, where two computing devices 800A and 800B are connected via a network. Specifically, they are connected to the network through communication interfaces in each computing device. In this possible implementation, the memory 806 in computing device 800A stores instructions for executing the functions of the acquisition module 610 and the first determination module 620. Simultaneously, the memory 806 in computing device 800B stores instructions for executing the functions of the second determination module 630 and the mapping module 640.

[0243] It should be understood that Figure 10 The functions of the computing device 800A shown can also be performed by multiple computing devices 800. Similarly, the functions of the computing device 800B can also be performed by multiple computing devices 800.

[0244] This application also provides a computer program product containing instructions. This computer program product may be software or program products containing instructions, capable of running on a computing device or stored on any usable medium. When the computer program product is run on at least one computing device, it causes the at least one computing device to perform the aforementioned low-altitude airspace risk quantification method.

[0245] This application also provides a computer-readable storage medium. The computer-readable storage medium can be any available medium capable of being stored by a computing device, or a data storage device such as a data center containing one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive). The computer-readable storage medium includes instructions that direct the computing device to perform a low-altitude airspace risk quantification method.

[0246] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described in the embodiments of this application are performed entirely or partially. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a network device, a user equipment, or other programmable device. The computer program or instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, the computer program or instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; it can also be an optical medium, such as a digital video disc (DVD); or it can be a semiconductor medium, such as an SSD.

[0247] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for quantifying low-altitude airspace risk, characterized in that, The method includes: Acquire geographic information data; the geographic information data is used to indicate at least one type of land use within a first area, the at least one type of land use including transportation land; Based on at least one type of land use within the first region, first vector data is determined; the first vector data includes network area data and land parcel data, wherein the network area data is used to indicate the first sub-region where the traffic land is located within the first region, and the land parcel data is used to indicate the second sub-region where the land parcel is located within the first region, wherein the first sub-region is adjacent to the second sub-region; Based on the category of the network area data, a first risk level of the first sub-region is determined, and based on the category of the land parcel data, a second risk level of the second sub-region is determined; Map the first risk level of the first sub-region and the second risk level of the second sub-region to a 3D mesh.

2. The method according to claim 1, characterized in that, The step of determining the first vector data based on at least one type of land use within the first area includes: Based on at least one type of land use within the first area, determine at least one transportation land use site; Buffer zones are constructed for each of the at least one traffic land use areas to obtain at least one first sub-region; The first region is divided according to the at least one first sub-region to obtain a second sub-region.

3. The method according to claim 2, characterized in that, The at least one first sub-region includes a target sub-region, which is obtained by constructing a buffer zone for the first traffic land. The at least one traffic land includes the first traffic land. The width of the target sub-region has a first mapping relationship with the grade of the first traffic land.

4. The method according to any one of claims 1 to 3, characterized in that, The at least one type of land use further includes: pipeline land with different grades; the first vector data also includes pipeline data, which is used to indicate the third sub-region where the pipeline land is located in the first region, and the third sub-region is obtained by constructing buffer areas of corresponding widths for pipeline land of different grades according to the second mapping relationship between the grade and width of the pipeline land.

5. The method according to any one of claims 1 to 4, characterized in that, The at least one type of land use further includes: no-fly zones and / or high-risk zones; the method further includes: If the no-fly zone and / or the high-risk zone completely overlap with the location of the first plot area, then the category of the first plot data is designated as the no-fly zone and / or high-risk zone. The first plot data is used to indicate the first plot area where the first plot is located in the first area, and the second sub-region includes the first plot area. If the area of ​​the no-fly zone and / or the high-risk zone that overlaps with the location of the second plot area is greater than or equal to a threshold, then the category of the second plot data is taken as the no-fly zone and / or the high-risk zone. The second plot data is used to indicate the second plot area where the second plot is located in the first area. The second sub-region includes the second plot area. If the area of ​​overlap between the no-fly zone and / or the high-risk zone and the third plot area is less than a threshold, then the category of the third plot data is used as the no-fly zone and / or the high-risk zone. The third plot data is used to indicate the sub-plot area where the sub-plot in the first area is located. The third plot area includes the sub-plot area, and the second sub-region includes the third plot area.

6. The method according to any one of claims 1 to 5, characterized in that, There is a third mapping relationship between the category and risk level of the network area data, and a fourth mapping relationship between the category and risk level of the land parcel data.

7. The method according to any one of claims 1 to 6, characterized in that, The geographic information data is the second vector data within the first area, or the geographic information data includes raster data within the first area and the second vector data; The second vector data includes: zoning, grid lines, and point of interest (POI) data and / or area of ​​interest (AOI) data within the first region.

8. The method according to claim 7, characterized in that, The attribute information of the land parcel data is used to indicate the category of the land parcel data; the attribute information includes any one or more of the following: the POI data, the AOI data, and the raster data.

9. The method according to any one of claims 1 to 8, characterized in that, The method further includes: Output the three-dimensional mesh.

10. The method according to any one of claims 1 to 9, characterized in that, The method further includes: Output third vector data; the third vector data includes the first risk level, the second risk level, and the first vector data.

11. A low-altitude airspace risk quantification device, characterized in that, The device includes: An acquisition module is used to acquire geographic information data; the geographic information data is used to indicate at least one type of land use within a first area, the at least one type of land use including transportation land. A first determining module is used to determine first vector data based on at least one type of land use within the first area; the first vector data includes network area data and land parcel data, wherein the network area data is used to indicate a first sub-area where the traffic land is located within the first area, and the land parcel data is used to indicate a second sub-area where the land parcel is located within the first area, wherein the first sub-area is adjacent to the second sub-area; The second determining module is used to determine the first risk level of the first sub-region based on the category of the network area data, and to determine the second risk level of the second sub-region based on the category of the land parcel data. The mapping module is used to map the first risk level of the first sub-region and the second risk level of the second sub-region to a three-dimensional mesh.

12. The apparatus according to claim 11, characterized in that, The first determining module is specifically used to determine at least one traffic land use based on at least one type of land use in the first area, construct buffer areas for the at least one traffic land use to obtain at least one first sub-area, and divide the first area according to the at least one first sub-area to obtain a second sub-area.

13. The apparatus according to claim 12, characterized in that, The at least one first sub-region includes a target sub-region, which is obtained by constructing a buffer zone for the first traffic land. The at least one traffic land includes the first traffic land. The width of the target sub-region has a first mapping relationship with the grade of the first traffic land.

14. The apparatus according to any one of claims 11 to 13, characterized in that, The at least one type of land use further includes: pipeline land with different grades; the first vector data also includes pipeline data, which is used to indicate the third sub-region where the pipeline land is located in the first region, and the third sub-region is obtained by constructing buffer areas of corresponding widths for pipeline land of different grades according to the second mapping relationship between the grade and width of the pipeline land.

15. The apparatus according to any one of claims 11 to 14, characterized in that, The at least one type of land use further includes: no-fly zones and / or high-risk zones; the device further includes: The correction module is configured to: if the no-fly zone and / or the high-risk zone completely overlap with the location of the first plot area, classify the first plot data as a no-fly zone and / or a high-risk zone, where the first plot data indicates the first plot area in the first region, and the second sub-region includes the first plot area; if the area of ​​overlap between the no-fly zone and / or the high-risk zone and the second plot area is greater than or equal to a threshold, classify the second plot data as the no-fly zone and / or the high-risk zone, where the second plot data indicates the second plot area in the first region, and the second sub-region includes the second plot area; if the area of ​​overlap between the no-fly zone and / or the high-risk zone and the third plot area is less than a threshold, classify the third plot data as the no-fly zone and / or the high-risk zone, where the third plot data indicates the sub-plot area in the first region, and the third plot area includes the sub-plot area, and the second sub-region includes the third plot area.

16. The apparatus according to any one of claims 11 to 15, characterized in that, There is a third mapping relationship between the category and risk level of the network area data, and a fourth mapping relationship between the category and risk level of the land parcel data.

17. The apparatus according to any one of claims 11 to 16, characterized in that, The geographic information data is the second vector data within the first area, or the geographic information data includes raster data within the first area and the second vector data; The second vector data includes: zoning, grid lines, and point of interest (POI) data and / or area of ​​interest (AOI) data within the first region.

18. The apparatus according to claim 17, characterized in that, The attribute information of the land parcel data is used to indicate the category of the land parcel data; the attribute information includes any one or more of the following: the POI data, the AOI data, and the raster data.

19. The apparatus according to any one of claims 11 to 18, characterized in that, The device further includes: The first output module is used to output the three-dimensional mesh.

20. The apparatus according to any one of claims 11 to 19, characterized in that, The device further includes: The second output module is used to output third vector data; the third vector data includes the first risk level, the second risk level, and the first vector data.

21. A computing device cluster, characterized in that, It includes at least one computing device, each computing device including a processor and memory; The processor of the at least one computing device is configured to execute instructions stored in the memory of the at least one computing device to cause the cluster of computing devices to perform the method as described in any one of claims 1 to 10.

22. A computer-readable storage medium, characterized in that, The storage medium stores a computer program or instructions, which, when executed by a computing device, implement the method of any one of claims 1 to 10.

23. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by a computing device, the method of any one of claims 1 to 10 is implemented.

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