Low-altitude airspace earth subdivision grid data organization, query method and device

Abstract

The present invention discloses a method and device for organizing and querying low-altitude airspace earth subdivision grid data, which performs grid coding expression on static spatial geometric objects, unmanned aerial vehicles, and dynamic spatial geometric objects in the low-altitude airspace, and realizes the organization, query, and display of data in different spatio-temporal ranges of the low-altitude airspace. The method includes the following steps: constructing a spatio-temporal grid model for the low-altitude airspace, where the spatio-temporal grid model includes spatial grid coding, time subdivision coding, and a grid data association relationship table; obtaining low-altitude airspace data; based on the constructed spatio-temporal grid model, expressing various spatial geometric objects in the low-altitude airspace, generating a set of grid codes corresponding to each spatial geometric object, and storing them; according to the set of codes of the spatial geometric objects, using the low-altitude airspace spatio-temporal grid database to perform data query; outputting the data of the spatial geometric objects obtained by the query, and displaying them on the spatio-temporal grid map.

Description

Technical Field

[0001] The present invention relates to the technical field of geospatial information organization, and particularly relates to a method and device for organizing and querying low-altitude airspace earth dissection grid data. Background Art

[0002] The low-altitude airspace generally refers to the part below 1000 meters in the air, including near-ground buildings and facilities, which is an important national aviation strategic resource. Each country should attach importance to and formulate relevant policies for management. Traditional airspace identification is not applicable to low-altitude airspace identification in terms of spatial organization, multi-aircraft identification, dynamic display, etc., and cannot meet the existing needs. It is necessary to study a new low-altitude airspace identification mode to ensure sustainable development and service prospects and efficiently manage the relevant activities of general aviation in the low-altitude airspace.

[0003] There are various data superpositions in the low altitude, such as route, building, dynamic flying object data, etc. The current airspace information expression methods are difficult to cope with complex and dynamic airspace environments. Different information is expressed in different ways, and it is difficult to interoperate between various methods. Therefore, an airspace expression method is needed to integrally organize the data of various spatial geometric objects in the airspace.

[0004] The current basic spatial information expression technologies include: First, the vector map model. Vector data represents various spatial geometric objects with coordinates, can effectively express points, lines and planes, and can also express spatial entities after processing. Compared with raster, the expression of space can be continuous. However, when processing different types of data, the data structure of vector data is relatively complex, and the neighborhood and collision detection of unmanned aerial vehicles have high computational complexity. Second, the geometric map model, which mainly refers to expressing the real environment with graphic shapes and can easily express various spatial geometric objects in the airspace. However, when facing a large amount of obstacle route data in the airspace, the construction complexity is relatively large, and it is difficult to effectively express the low-altitude airspace. Third, the raster map model, which means dividing the space with equally sized grids to obtain grids of equal size, and then identifying them in a certain order to finally realize the expression of space and the identification of relevant data. Although raster data solves the problem of high computational complexity of vector data neighborhood and collision detection, it is difficult to select an appropriate scale when facing a large amount of data, and raster is generally a local map and cannot achieve global unity. Fourth, the topological map model. A topological map is an abstract map in cartography that maintains the correct relative position relationship between points and lines but does not necessarily maintain the correct graphic shape, area, distance, and direction. When facing a large amount of data, it is difficult to generate a topological map relying on a single algorithm and often requires manual intervention, consuming a large amount of manpower and material resources.

[0005] The current problem with the expression of low-altitude airspace is that, based on the existing global space-based grid index and multi-level subdivision three-dimensional grid model, there is no unified method for expressing static spatial geometric objects, dynamic spatial geometric objects, and unmanned aerial vehicles in low-altitude airspace. There is a need to establish a more targeted and accurate spatio-temporal expression method and spatio-temporal data query method. Currently, the airspace expression method environment is separated from the unmanned aerial vehicle itself, and it is impossible to use a unified method for expression. Different expression methods need to be combined.

[0006] How to reasonably express, organize, and query dynamic data in low-altitude airspace is an urgent problem to be solved currently. Summary of the Invention

[0007] In view of this, the present invention provides a method and device for organizing and querying low-altitude airspace earth subdivision grid data. By constructing a spatio-temporal grid model of low-altitude airspace, considering both the airspace environment and unmanned aerial vehicles, grid coding expressions are carried out for static spatial geometric objects, unmanned aerial vehicles, and dynamic spatial geometric objects in low-altitude airspace, realizing the organization, query, and display of data in different spatio-temporal ranges of low-altitude airspace.

[0008] To achieve the above object, the technical solution of the present invention for the method of organizing low-altitude airspace earth subdivision grid data includes the following steps:

[0009] Step 1: Construct a spatio-temporal grid model for low-altitude airspace. The spatio-temporal grid model includes a spatial grid coding, a time subdivision coding, and a grid data association relationship table.

[0010] Step 2: Obtain low-altitude airspace data.

[0011] Step 3: Based on the constructed spatio-temporal grid model, express various spatial geometric objects in low-altitude airspace, generate a set of grid codings corresponding to each spatial geometric object, and store them.

[0012] Further, in Step 1, the spatial grid coding uses a 3D grid subdivision coding model to subdivide low-altitude airspace, obtaining spatial grids and codings. The spatial grid coding is used to represent the three-dimensional spatial position information and subdivision levels of the subdivision grid body.

[0013] The time subdivision coding is a multi-scale time coding;

[0014] The grid data association relationship table associates data from different sources in low-altitude airspace. By using the spatio-temporal codings of each grid as indexes, the passage information of the grid is integrated.

[0015] Furthermore, in the grid data association relation table, data from different sources corresponding to the same spatial grid are organized and associated. Whether the current spatio-temporal grid is passable is judged through relevant rules, and then relevant data are organized and associated with the spatio-temporal grid code as the primary key.

[0016] Furthermore, the low-altitude airspace data includes terrain GIS information, BIM building information, various sensor meteorological information, network information, and data from aircraft routes.

[0017] Furthermore, the spatial geometric objects include static spatial geometric objects, unmanned aerial vehicles, and dynamic spatial geometric objects.

[0018] Furthermore, for the expression of various spatial geometric objects in the low-altitude airspace, where the spatial geometric objects are static spatial geometric objects, a set of grid codes corresponding to various spatial geometric objects is generated, and the low-altitude airspace data of the spatial geometric objects is obtained. The following steps are used for the expression:

[0019] Step1: Convert the low-altitude airspace data of the spatial geometric object into the form of longitude, latitude, and altitude.

[0020] Step2: According to the coordinate information and accuracy requirements of the spatial geometric object, the spatial geometric object is meshed with the grid L that meets the accuracy requirements highest to obtain the code set C through meshing subdivision. The current maximum level L now = L highest .

[0021] The generation principle of the code set C is that when using spatio-temporal grids to express relevant spatial geometric objects in the low-altitude airspace, it should be ensured that the code set of the spatio-temporal grids can completely cover the spatial range and time range where the relevant spatial geometric objects in the low-altitude airspace are located.

[0022] Step3: Judge whether there are aggregable grids in the current set C. If so, aggregate the aggregable grids at the same level in C into grids at the L now -1 level. The current maximum level is L now -1.

[0023] Step4: Repeat Step2 to Step3 until there are no aggregable grids in C, and obtain the grid code set C for the expression of static spatial geometric objects in the low-altitude airspace static .

[0024] Furthermore, the method of grid aggregation is as follows:

[0025] S1: Input the code set C.

[0026] S2: Read the first subdivision code in the set C as the current processing code, and perform S3.

[0027] S3: Calculate the parent code of the currently processed code.

[0028] The method for calculating the parent code is to discard the corresponding number of digits at the current level to generate a new code.

[0029] If all the sub - codes included in the parent code of the currently processed code appear in the code set C, then proceed to S4; otherwise, jump to S5.

[0030] S4: Insert the aggregated parent code, and delete the sub - codes corresponding to this parent code, then jump to S5.

[0031] S5: If there are still unjudged codes, then continue to read the next dissection code as the currently processed code, and return to S3; if all codes have been judged, then jump to S6.

[0032] S6: If S4 has been executed in this round of the algorithm, then execute another round starting from S2; otherwise, directly jump to S7;

[0033] S7: Output the obtained dissection code set.

[0034] Furthermore, for expressing various spatial geometric objects in the low - altitude airspace, generating a grid code set corresponding to various spatial geometric objects. When the spatial geometric object is an unmanned aerial vehicle, the following steps are used for expression:

[0035] SS1: Select grids at a reasonable level, regard the unmanned aerial vehicle as a static spatial geometric object, and encode the position of the unmanned aerial vehicle at the current moment to obtain the position code set C of the unmanned aerial vehicle static .

[0036] SS2: According to the flight speed and flight direction of the unmanned aerial vehicle, and the level selected in SS1, calculate the buffer area set C buffer .

[0037] Among them, C buffer The calculation method is C buffer = C near ∪C front , C near is the neighborhood grid of the unmanned aerial vehicle, C front is a set composed of grids, α is a coefficient, v is the speed, t takes 1s, and d is the grid length in the forward direction of the unmanned aerial vehicle.

[0038] SS3: Aggregate the aggregable codes in C static , C buffer .

[0039] Repeat SS3 until C static, C buffer There is no aggregable encoding in it; finally, the grid encoding set C of the unmanned aerial vehicle is obtained. UAV , the grid encoding set C of the buffer area grid of the unmanned aerial vehicle UAV-buffer .

[0040] Furthermore, for various spatial geometric objects in the low-altitude airspace, a grid encoding set corresponding to each spatial geometric object is generated. When the spatial geometric object is a dynamic spatial geometric object, the following steps are used for expression:

[0041] SSS1: Obtain the static encoding set C of the dynamic spatial geometric object according to the expression rules of the static spatial geometric objects in the low-altitude airspace static .

[0042] SSS2: Express the time of the dynamic obstacle using the time grid of the smallest level to obtain the time set T.

[0043] SSS3: Aggregate the aggregable time encodings in T.

[0044] Repeat SSS3 until there is no aggregable encoding in T, and use the spatial geometric objects in T as the attributes of the spatial geometric objects in C static to obtain the final grid encoding set C of the dynamic spatial geometric object dynamic .

[0045] Furthermore, for the low-altitude airspace data, the above organization method is used to construct the grid encoding set corresponding to each spatial geometric object.

[0046] According to the encoding set of the spatial geometric object, use the low-altitude airspace spatio-temporal grid database to query data;

[0047] Output the spatial geometric object data obtained by the query and display it on the spatio-temporal grid map.

[0048] Another embodiment of the present invention also provides a low-altitude airspace earth subdivision grid data organization device, including a spatio-temporal grid model construction module, a low-altitude airspace data acquisition module, and an organization and storage module;

[0049] The spatio-temporal grid model construction module is used to construct a spatio-temporal grid model for the low-altitude airspace. The spatio-temporal grid model includes a spatial grid encoding, a time subdivision encoding, and a grid data association relationship table.

[0050] The low-altitude airspace data acquisition module is used to acquire low-altitude airspace data.

[0051] An organization storage module is used to express various spatial geometric objects in the low-altitude airspace based on the constructed spatio-temporal grid model, generate a set of grid codes corresponding to each spatial geometric object, and store them.

[0052] Another embodiment of the present invention further provides a low-altitude airspace earth subdivision grid data query device, which is characterized by including a spatio-temporal grid model construction module, a low-altitude airspace data acquisition module, an organization storage module, and a data query and display module;

[0053] The spatio-temporal grid model construction module is used to construct a spatio-temporal grid model for the low-altitude airspace. The spatio-temporal grid model includes spatial grid codes, time subdivision codes, and a grid data association relationship table;

[0054] The low-altitude airspace data acquisition module is used to acquire low-altitude airspace data;

[0055] The organization storage module is used to express various spatial geometric objects in the low-altitude airspace based on the constructed spatio-temporal grid model, generate a set of grid codes corresponding to each spatial geometric object, and store them;

[0056] The data query and display module is used to perform data query according to the set of codes of the spatial geometric object, utilize the low-altitude airspace spatio-temporal grid database, output the spatial geometric object data obtained by the query, and display it on the spatio-temporal grid map.

[0057] Another embodiment of the present invention further provides a low-altitude airspace earth subdivision grid data organization device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. It is characterized in that when the processor executes the program, it implements the above-mentioned organization method for low-altitude airspace earth subdivision grid data.

[0058] Another embodiment of the present invention further provides a low-altitude airspace earth subdivision grid data query device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. It is characterized in that when the processor executes the program, it implements the above-mentioned query method for low-altitude airspace earth subdivision grid data.

[0059] Beneficial effects:

[0060] 1. The organization method for low-altitude airspace earth subdivision grid data provided by the present invention, based on the existing grid index based on the global space and the multi-level subdivision three-dimensional grid model, expresses multi-source low-altitude airspace earth subdivision grid data with a unified spatio-temporal grid, can effectively support various application scenarios in the low-altitude airspace, performs grid code expression for static spatial geometric objects, unmanned aerial vehicles, and dynamic spatial geometric objects in the low-altitude airspace, and realizes the organization of data in different spatio-temporal ranges in the low-altitude airspace.

[0061] 2. The low-altitude airspace Earth subdivision grid data organization method provided by the present invention uniformly expresses static spatial geometric objects, dynamic spatial geometric objects, and unmanned aerial vehicles in the low-altitude airspace, forming a more targeted and accurate spatio-temporal expression method and spatio-temporal data organization method for the low-altitude airspace.

[0062] 3. When the spatial geometric object of the low-altitude airspace Earth subdivision grid data organization method provided by the present invention is an unmanned aerial vehicle, the expression of the unmanned aerial vehicle in the low-altitude airspace requires a set composed of the corresponding three-dimensional position encoding of the unmanned aerial vehicle and the grid encoding where the buffer area is located.

[0063] 4. Since the high-level grids of the low-altitude airspace Earth subdivision grid data organization method provided by the present invention are obtained by subdividing the low-level grids, a certain number of high-level grids can be aggregated into the upper level, reducing the number of grid encodings. Therefore, when expressing the low-altitude airspace, it is necessary to aggregate the aggregable parts in the grid encoding to finally obtain a set of encodings at different levels. On the basis of accurate expression, the grid encoding is more concise. At the same time, the multiple aggregations of data greatly reduce the data volume, which can save hard disk space and improve data query efficiency.

[0064] 5. The grid data association information formed by the low-altitude airspace Earth subdivision grid data organization method provided by the present invention for the application scenarios of the low-altitude airspace, such as whether it can be passed through, signal strength, etc., is conducive to the rapid query and flexible application of data.

[0065] 6. The low-altitude airspace Earth subdivision grid data query method provided by the present invention, based on the existing grid index based on the global space and the multi-level subdivision three-dimensional grid model, expresses multi-source low-altitude airspace Earth subdivision grid data with a unified spatio-temporal grid, which can effectively support various application scenarios in the low-altitude airspace, encodes and expresses static spatial geometric objects, unmanned aerial vehicles, and dynamic spatial geometric objects in the low-altitude airspace, and realizes the query and display of data in different spatio-temporal ranges of the low-altitude airspace.

[0066] 7. Since the high-level grids of the low-altitude airspace Earth subdivision grid data query method provided by the present invention are obtained by subdividing the low-level grids, a certain number of high-level grids can be aggregated into the upper level, reducing the number of grid encodings. Therefore, when expressing the low-altitude airspace, it is necessary to aggregate the aggregable parts in the grid encoding to finally obtain a set of encodings at different levels. On the basis of accurate expression, the grid encoding is more concise. At the same time, the multiple aggregations of data greatly reduce the data volume, which can save hard disk space and improve data query efficiency.

[0067] 8. The method for querying low-altitude airspace earth subdivision grid data provided by the present invention uniformly expresses static spatial geometric objects, dynamic spatial geometric objects, and unmanned aerial vehicles in the low-altitude airspace, forming a spatio-temporal expression method and a spatio-temporal data query method for the low-altitude airspace that are more targeted and accurate.

[0068] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 It is a schematic diagram of the encoding length for each level;

[0070] Figure 2 It is a schematic diagram of time encoding;

[0071] Figure 3 It is an example diagram of the modeling result of a cylindrical static spatial geometric object;

[0072] Figure 4 It is a schematic diagram of modeling an unmanned aerial vehicle based on the spatio-temporal grid model;

[0073] Figure 5 It is a schematic diagram of modeling an unmanned aerial vehicle and its buffer area based on the spatio-temporal grid model

[0074] Figure 6 A schematic diagram of the neighborhood of the airspace grid;

[0075] Figure 7 It is a flowchart of the method for organizing low-altitude airspace earth subdivision grid data;

[0076] Figure 8 It is a flowchart of the method for querying low-altitude airspace earth subdivision grid data. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0077] The following describes the present invention in detail with reference to the accompanying drawings and by way of examples. It can be understood that the specific embodiments described herein are only for explaining the relevant invention and not for limiting the invention. Additionally, it should be noted that only parts related to the relevant invention are shown in the drawings for the convenience of description.

[0078] It should be noted that, without conflict, the embodiments in this application and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the drawings and in conjunction with the embodiments.

[0079] Embodiment 1:

[0080] The embodiment of the present invention provides a method for organizing low-altitude airspace earth subdivision grid data, and its process is as follows Figure 7As shown in the figure, it includes steps 1 to 3:

[0081] Step 1: Construct a spatio-temporal grid model for the low-altitude airspace. The spatio-temporal grid model includes a spatial grid code, a time division code, and a grid data association relationship table;

[0082] The airspace grid is a three-dimensional spatial subdivision grid system developed for the application of unmanned aerial vehicles in the low-altitude airspace based on the three-dimensional geospatial stereo subdivision grid GeoSOT-3D. It is a subset of the GeoSOT-3D grid in the application of the low-altitude airspace environment, generally with a height range from the ground surface to 20 kilometers above the ground. The low-altitude airspace is generally from the ground surface to 1000 meters above the ground. The construction of the spatio-temporal grid model for the low-altitude airspace mainly includes spatial subdivision code design, time division code design, and grid data association.

[0083] The spatial coding is based on the 3D grid subdivision coding model. The 3D grid subdivision coding model is used to subdivide the low-altitude airspace to obtain spatial grids and codes; the spatial grid code is used to represent the three-dimensional spatial position information and subdivision levels of the subdivision grid body. The present invention gives a specific embodiment: Combining the actual application requirements of unmanned aerial vehicles, the height dimension is restricted within the range from the ground surface to 8192m above the ground, and the height is the geodetic height of the three-dimensional point. The airspace is divided into 10 levels and encoded according to certain coding rules. Finally, the highest level is 10 and the longest code is 26 bits. The minimum grid size is about 0.25m×0.25m×0.25m. The coding lengths of each level of the spatio-temporal grid are as Figure 1 shown. Since most of the flight heights of civilian unmanned aerial vehicles are below 2000 meters, and the highest plateau in the world, the Qinghai-Tibet Plateau, is about 5000 meters. Combining the actual flight of unmanned aerial vehicles and grid division, the height of the spatio-temporal grid model is set at 8192 meters, and generally 1000 meters or below is adopted for general applications.

[0084] The time coding is based on the multi-scale time coding theory. Combining the actual requirements of the flight duration of unmanned aerial vehicles, the day is the largest scale and the second is the smallest scale. 0 is used to represent the time of the previous day and 1 is used to represent the time of the next day at the front. The time dimension is restricted within 48 hours before and after the current day. This range is divided to obtain the longest 18-bit code. The time coding schematic diagram is as Figure 2 shown.

[0085] Grid data association is to associate data from different sources in the low-altitude airspace. By using each grid code as an index, the passing information of the grid is integrated. Thus, the relevant data information in the low altitude is represented to realize data display and applications for different services.

[0086] The specific process is as follows: Use the grid encoding as the key to mark relevant attributes. First, obtain the spatio-temporal grid encoding of the task airspace according to the above method, that is, the spatial encoding + temporal encoding of the grid. Then, organize and associate data from different sources, such as GIS information from terrain, building information from BIM, meteorological and network information from various sensors, flight route data from aircraft, etc. Determine whether the current spatio-temporal grid is passable through relevant rules, and then organize and associate relevant information with the spatio-temporal grid encoding as the primary key. An example is shown in the following table.

[0087] Table 1 Data Organization and Association Table

[0088]

[0089] Step 2: Obtain low-altitude airspace data.

[0090] Step 3: Based on the constructed spatio-temporal grid model, express various spatial geometric objects in the low-altitude airspace, generate a set of grid encodings corresponding to each spatial geometric object, and store them.

[0091] Based on the constructed spatio-temporal grid model, express various spatial geometric objects in the low-altitude airspace, generate a set of grid encodings corresponding to various spatial geometric objects, and store them. The spatial geometric objects mainly include: static spatial geometric objects, unmanned aerial vehicles, and dynamic spatial geometric objects.

[0092] The static spatial geometric objects in the low-altitude airspace mainly include terrain spatial geometric objects such as mountains, hills, and basins, artificial buildings with certain shapes such as buildings and bridges, and some small objects on the ground surface such as street lamps, green belts, and trees. The positions, heights, and shapes of these spatial geometric objects do not change with time within a certain period, so they can be expressed by one or a group of spatio-temporal grids. For the static spatial geometric objects in the low-altitude airspace, select an appropriate grid level according to the accuracy requirements to express the spatial geometric objects in the low-altitude airspace. This grid or this group of grids can completely cover the range of this spatial geometric object, and all the grids in this group of grids do not intersect.

[0093] Store the multi-source data of the low-altitude airspace based on the correlation between the spatio-temporal grid encoding and the grid data.

[0094] The above-mentioned association method has several characteristics: (1) The structure is not fixed. One spatio-temporal grid code can be associated with multiple attributes and different amounts of data. (2) Each attribute of the relevant information has a different structure, which is relatively complex and the method is complicated. (3) The quantity is large. There are many spatial geometric objects in the airspace. After spatio-temporal grid expression and attribute recording, there is a large amount of data. Therefore, a relational database is not suitable for the storage and application of the low-altitude spatio-temporal map model. In order to store the low-altitude airspace spatio-temporal grid model and effectively meet the above requirements, the MongoDB database is used for data storage to generate a spatio-temporal grid database.

[0095] Specifically, for example, when storing, use the spatio-temporal code of a certain position grid as the key, calculate the passability of the spatio-temporal grid code from the terrain GIS information, BIM building information, various sensor meteorological and network information, and aircraft route data at this position, and then record it in the form of an attribute. Other required information is recorded as other attribute information. The document structure is as follows:

[0096] {

[0097] "AirSpaceCode":code,

[0098] "LonCode":loncode,

[0099] "LatCode":latcode,

[0100] "HeightCode":heightcode,

[0101] "TCode":timecode,

[0102] "Attribute1":[Attribute11,Attribute12,...,Attribute1i],

[0103] "Attribute2":[Attribute21,Attribute22,...,Attribute2j],

[0104] "Attribute3":[Attribute31,Attribute32,...,Attribute3k],

[0105] }

[0106] Among them, AirSpaceCode represents the low-altitude airspace spatio-temporal grid code corresponding to the document, and the remaining codes represent the decomposed codes of the grid spatio-temporal grid code in the longitude, latitude, and altitude dimensions. TCode represents the time attribute. Attr1, Attr2, and Attr3 represent different types of data, while Attribute11, Attribute12,..., Attribute1i, Attribute21, Attribute22,..., Attribute2j, Attribute31, Attribute32,..., Attribute3k represent the values of different attributes of different data.

[0107] In addition, the spatio-temporal grid database manages the encoded data through grids and grid codes, realizes functions such as data indexing and data index update, provides services for the grid-based rapid query and retrieval of multi-source heterogeneous data, supports business applications such as grid-based basic calculation and analysis, and solves the problem that it is currently impossible to reasonably express dynamic data in the low-altitude airspace.

[0108] Embodiment 2:

[0109] In step 3 of the above-mentioned Embodiment 1, based on the constructed spatio-temporal grid model, various spatial geometric objects in the low-altitude airspace are expressed, and a set of grid codes corresponding to each spatial geometric object is generated.

[0110] Spatial geometric objects include static spatial geometric objects, unmanned aerial vehicles, and dynamic spatial geometric objects. There are different data formats for static spatial geometric objects in the low-altitude airspace, and different methods are required for expression.

[0111] For static spatial geometric objects in the low-altitude airspace that can be simply transformed to obtain longitude, latitude, and altitude, such as digital elevation model (DEM) data, the specific expression method is as follows:

[0112] Step1: Select the corresponding method to convert the data into the form of longitude, latitude, and altitude.

[0113] The conversion method for common BIM data such as ground models and buildings is:

[0114] Step101: Obtain the spatial range of the building in the BIM platform, and then calculate the relevant information of the approximate spatial center point.

[0115] Step102: After obtaining the spatial range and the spatial center point, use the coordinate conversion method provided in BIM to convert the building coordinates into longitude, latitude, and altitude.

[0116] The conversion method for irregular triangulated network TIN data is: use linear interpolation or natural neighbor interpolation to obtain the specified height value of each pixel, thereby obtaining longitude and latitude height data.

[0117] Step 2: According to the coordinate information and accuracy requirements of the low-altitude airspace obstacles, the obstacles are mapped with a grid L that meets the accuracy requirements. highest Gridding is performed to obtain the coding set C, the current maximum level L now =L highest .

[0118] The principle of generating the coding set C is: when using the space-time grid to express the relevant spatial geometric objects in the low-altitude airspace, it should be ensured that the coding set of the space-time grid can completely contain the spatial range and time range of the relevant spatial geometric objects in the low-altitude airspace. In other words, the spatial range and time orientation of the relevant spatial geometric objects in the low-altitude airspace are a subset of the coding set of the space-time grid coding used. Generally speaking, it is allowed to express the space-time boundaries of the relevant spatial geometric objects in the low-altitude airspace with redundant space-time grids, but it is not allowed that the space-time grid coding set lacks some information about the relevant spatial geometric objects in the low-altitude airspace. It is guaranteed that the low-altitude airspace can be accurately expressed.

[0119] Determine the subdivision level in advance: the space-time grid is obtained by dividing space and time from small to large. Each division will improve the expression accuracy of the space-time grid, and the number of space-time grids will increase accordingly. When constructing the low-altitude airspace space-time grid model, set different subdivision levels and divide it to that level. This ensures the accurate expression of the low-altitude airspace and controls the amount of data, which is conducive to the accurate and efficient application of unmanned aerial vehicles.

[0120] Step 3: Determine whether there are meshes that can be aggregated in the current set C. If so, aggregate the meshes in C that can be aggregated to the previous level into L now -1 level grid, current maximum level L now =L now -1.

[0121] Step 4: Repeat Step 2 until there are no aggregated grids in C, and obtain the grid code set C expressing the static obstacles in the low-altitude airspace. static .

[0122] The grid aggregation method is as follows:

[0123] S1: a set of input segmentation codes;

[0124] S2: read the first segmentation code in the set and proceed to the next step;

[0125] S3: Calculate the parent code of this code. The method for calculating the parent code is to discard the corresponding number of digits at the current level to generate a new code. If all the sub-codes included in its parent subdivision code appear in this set of subdivision codes, then proceed to S4; otherwise, jump to S5;

[0126] S4: Insert the aggregated parent code and delete the sub-codes corresponding to this parent code, then jump to S5;

[0127] S5: If there are still unjudged codes, then continue to read the next subdivision code and perform the judgment in S3; if all codes have been judged, then jump to S6;

[0128] S6: If S4 has been executed in this round of the algorithm, then go to S2 to execute another round; otherwise, directly jump to S7;

[0129] S7: Output the obtained set of subdivision codes.

[0130] Since the high-level grids are obtained by subdividing the low-level grids, a certain number of high-level grids can be aggregated into the upper level, reducing the number of grid codes. Therefore, when expressing the low-altitude airspace, it is necessary to aggregate the aggregable parts in the grid codes to finally obtain a set of codes at different levels. On the basis of accurate expression, the grid codes are more concise. At the same time, the multiple aggregations of data greatly reduce the data volume, which can save hard disk space and improve the data query efficiency.

[0131] Through the above two algorithms, model the static spatial geometric objects in the airspace. An example of the modeling result of a cylindrical static spatial geometric object is as Figure 3 shown.

[0132] In step 3, when the spatial geometric object is an unmanned aerial vehicle, the following steps are used for expression: The expression of an unmanned aerial vehicle in the low-altitude airspace requires a set composed of the corresponding three-dimensional position code of the unmanned aerial vehicle and the grid codes of the buffer areas. The specific process is as follows:

[0133] SS1: Select grids at a reasonable level, regard the unmanned aerial vehicle as a static spatial geometric object, and code the position of the unmanned aerial vehicle at the current moment to obtain the set C of the position codes of the unmanned aerial vehicle static .

[0134] SS2: According to the flight speed and flight direction of the unmanned aerial vehicle and the level selected in Step1, calculate the set C of buffer areas buffer . Among them, the calculation method of C buffer is C buffer = C near ∪C front , C nearIt is the twenty-six neighborhood grid of the unmanned aerial vehicle (it can also be a six-neighborhood or ten-neighborhood grid), C front is a set composed of grids, α is a coefficient, v is the speed, t takes 1 s, and d is the grid length in the forward direction of the unmanned aerial vehicle.

[0135] SS3: Aggregate the aggregable encodings in C static and C buffer . Repeat SS3 until there are no aggregable encodings in C static and C buffer ; finally obtain the unmanned aerial vehicle grid encoding set C UAV , and the unmanned aerial vehicle buffer area grid encoding set C UAV-buffer .

[0136] Figure 4 is the modeling of the unmanned aerial vehicle based on the spatio-temporal grid model. Figure 5 is the modeling of the unmanned aerial vehicle and its buffer area based on the spatio-temporal grid model.

[0137] According to different needs, the definition of the neighborhood is also different. Generally, it includes the six-neighborhood grid in the six directions of front, back, left, right, up, and down of the current grid, the ten-neighborhood including the upper and lower and the diagonal neighborhoods in the front, back, left, right of the plane, and the twenty-six-neighborhood including the upper, lower, left, right, front, back and their diagonals. According to other requirements, the definition range of the neighborhood can also be extended, such as Figure 6 shown Figure 6 in which (a) is the six-neighborhood range, (b) is the ten-neighborhood range, and (c) is the twenty-six-neighborhood range.

[0138] The dynamic spatial geometric objects in the low-altitude airspace mainly include objects such as UAVs and hot air balloons. The shapes of these spatial geometric objects will not change with time within a certain period, while their positions and altitudes will change with time. Therefore, such spatial geometric objects can be expressed by a combination of one or a group of three-dimensional airspace grids and a time encoding. For the dynamic spatial geometric objects in the low-altitude airspace, the three-dimensional airspace grid can be used as the basic unit, and an appropriate grid level can be selected according to the accuracy requirements to express the dynamic spatial geometric objects therein. A set of three-dimensional airspace grids and a time encoding are generated at each time instant. The specific process is as follows:

[0139] SSS1: Obtain the static encoding set C static of this dynamic spatial geometric object according to the expression rules of the static spatial geometric objects in the low-altitude airspace.

[0140] SSS2: Express the time of the dynamic obstacle with the time grid of the smallest level to obtain the time set T.

[0141] SSS3: Aggregate the aggregable time encodings in T. Repeat SSS3 until there are no aggregable encodings in T, and use the spatial geometric objects in T as the attributes of the spatial geometric objects in C static to obtain the final grid encoding set C of the dynamic spatial geometric objects dynamic .

[0142] Embodiment 3

[0143] The embodiment of the present invention also provides a method for querying low-altitude airspace earth dissection grid data. Based on the organization method provided in the above embodiment, a grid encoding set corresponding to each spatial geometric object is constructed; its process is as Figure 8 shown, and steps 4 to 5 are also included after steps 1 to 3

[0144] Step 4: According to the encoding sets of static spatial geometric objects, unmanned aerial vehicles, and dynamic spatial geometric objects, use the low-altitude airspace spatio-temporal grid database to perform data query

[0145] Step 5: Output the spatial geometric object data obtained by the query and display it on the spatio-temporal grid map

[0146] For the above method, the present invention has conducted experiments. From the experimental results of terrain and building modeling, because after the spatio-temporal grid model is generated, multiple aggregation operations will be performed, and each aggregation can reduce the data. Therefore, after multiple aggregations, the data volume will decrease significantly, which can save hard disk space and improve data query efficiency

[0147] Embodiment 4

[0148] The embodiment of the present invention also provides a low-altitude airspace earth dissection grid data organization device, including a spatio-temporal grid model construction module, a low-altitude airspace data acquisition module, and an organization and storage module

[0149] The spatio-temporal grid model construction module is used to construct a spatio-temporal grid model for the low-altitude airspace. The spatio-temporal grid model includes a spatial grid encoding, a time dissection encoding, and a grid data association relationship table

[0150] The low-altitude airspace data acquisition module is used to acquire low-altitude airspace data

[0151] The organization and storage module is used to express various spatial geometric objects in the low-altitude airspace based on the constructed spatio-temporal grid model, generate a grid encoding set corresponding to each spatial geometric object, and store it

[0152] In this embodiment, the implementation methods of each module can adopt the methods disclosed in Embodiments 1 and 2

[0153] Embodiment 5:

[0154] An embodiment of the present invention further provides a device for querying low-altitude airspace earth subdivision grid data, including a spatio-temporal grid model construction module, a low-altitude airspace data acquisition module, an organization storage module, and a data query and display module.

[0155] The spatio-temporal grid model construction module is used to construct a spatio-temporal grid model for the low-altitude airspace. The spatio-temporal grid model includes a spatial grid code, a time subdivision code, and a grid data association relationship table.

[0156] The low-altitude airspace data acquisition module is used to acquire low-altitude airspace data.

[0157] The organization storage module is used to express various spatial geometric objects in the low-altitude airspace based on the constructed spatio-temporal grid model, generate a set of grid codes corresponding to each spatial geometric object, and store them.

[0158] The data query and display module is used to perform data query according to the set of codes of the spatial geometric object, utilize the low-altitude airspace spatio-temporal grid database, output the spatial geometric object data obtained by the query, and display it on the spatio-temporal grid map.

[0159] In this embodiment, the implementation methods of each module can adopt the methods disclosed in Embodiments 1 and 2.

[0160] Embodiment 6:

[0161] A low-altitude airspace earth subdivision grid data organization device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. The feature is that when the processor executes the program, it implements the low-altitude airspace earth subdivision grid data organization method provided in Embodiment 1.

[0162] Embodiment 7:

[0163] A low-altitude airspace earth subdivision grid data query device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. The feature is that when the processor executes the program, it implements the low-altitude airspace earth subdivision grid data query method provided in Embodiment 3.

[0164] In summary, the above are only the preferred embodiments of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for organizing low-altitude airspace Earth subdivision grid data, characterized in that, It includes the following steps: Step 1: Construct a spatio-temporal grid model for the low-altitude airspace. The spatio-temporal grid model includes a spatial grid code, a time division code, and a grid data association relationship table; Step 2: Obtain low-altitude airspace data; Step 3: Based on the constructed spatio-temporal grid model, express various spatial geometric objects in the low-altitude airspace, generate a set of grid codes corresponding to each spatial geometric object, and store them; The spatial geometric objects include static spatial geometric objects, unmanned aerial vehicles, and dynamic spatial geometric objects; When expressing various spatial geometric objects in the low-altitude airspace, where the spatial geometric object is a static spatial geometric object, generating a set of grid codes corresponding to various spatial geometric objects, obtaining the low-altitude airspace data of the spatial geometric object, the following steps are used for expression: Step1: Convert the low-altitude airspace data of the spatial geometric object into the form of longitude, latitude, and altitude; Step 2: According to the coordinate information and accuracy requirements of the spatial geometric object, the spatial geometric object is meshed with a grid L at a level that meets the accuracy requirements highest to obtain a coding set C through grid-based meshing. The current maximum level L now = L highest ; Step 3: Determine whether there are aggregable grids in the current set C. If so, aggregate the aggregable grids at the same level in C into grid L now Grids at level -1, with the current maximum level being L now -1; Step4: Repeat Steps 2 to 3 until there are no aggregable grids in C, and obtain the set C of grid encodings representing the static spatial geometric objects in the low-altitude airspace static1 ; When expressing various spatial geometric objects in the low-altitude airspace, generating a set of grid codes corresponding to various spatial geometric objects, where the spatial geometric object is an unmanned aerial vehicle, the following steps are used for expression: SS1: Select a reasonable level of grid, treat the unmanned aerial vehicle as a static spatial geometric object, encode the position of the unmanned aerial vehicle at the current moment, and obtain the position encoding set C of the unmanned aerial vehicle static2 ; SS2: Calculate the buffer area set C based on the flight speed and flight direction of the unmanned aerial vehicle and the layer selected in SS1 buffer ; SS3: Aggregate the encodings that can be aggregated in C static2、 C buffer Aggregate the encodings that can be aggregated in C Repeat SS3 until C static2、 C buffer has no aggregable codes; finally obtain the unmanned aerial vehicle grid code set C UAV , the unmanned aerial vehicle buffer area grid code set C UAV-buffer ; When expressing various spatial geometric objects in the low-altitude airspace, generating a set of grid codes corresponding to various spatial geometric objects, where the spatial geometric object is a dynamic spatial geometric object, the following steps are used for expression: SSS1: Obtain the static coding set C of the dynamic spatial geometric object according to the expression rules of the static spatial geometric object in the low-altitude airspace static3 ; SSS2: Express the time of the dynamic obstacle with the time grid of the smallest level to obtain the time set T; SSS3: Aggregate the time codes that can be aggregated in T; Repeat SSS3 until there is no aggregable encoding in T, and use the spatial geometric objects in T as C static3 For the attributes of the spatial geometric objects in static3 , obtain the final mesh encoding set C of the dynamic spatial geometric objects dynamic .

2. The method for organizing low-altitude airspace earth subdivision grid data according to claim 1, characterized in that In the said Step 1, the spatial grid code uses a 3D grid division coding model to divide the low-altitude airspace, obtaining spatial grids and codes; the spatial grid code is used to represent the three-dimensional spatial position information and division levels of the divided grid body; The time division code is a multi-scale time code; The grid data association relationship table is to associate different sources of data in the low-altitude airspace. By using the spatio-temporal codes of each grid as indexes, the passage information of the grid is integrated.

3. The method for organizing low-altitude airspace earth subdivision grid data according to claim 1 or 2, characterized in that, In the grid data association relationship table, organize and associate different sources of data corresponding to the same spatial grid. Judge whether the current spatio-temporal grid is passable through relevant rules, and then use the spatio-temporal grid code as the primary key to organize and associate relevant data.

4. The method for organizing low-altitude airspace earth dissection grid data according to claim 1, wherein The low-altitude airspace data includes terrain GIS information, BIM building information, various sensor meteorological information, network information, and aircraft route data.

5. The method for organizing low-altitude airspace earth division grid data according to claim 1, characterized in that The generation principle of the coding set C is: when using spatio-temporal grids to express relevant spatial geometric objects in the low-altitude airspace, it should be ensured that the coding set of the spatio-temporal grids can completely include the spatial range and time range where the relevant spatial geometric objects in the low-altitude airspace are located.

6. The method for organizing low-altitude airspace earth dissection grid data according to claim 1, characterized in that, The method of grid aggregation is as follows: S1: Input the coding set C; S2: Read the first division code in the set C as the currently processed code, and perform S3; S3: Calculate the parent code of the currently processed code; The method for calculating the parent code is to discard the corresponding digits at the current level to generate a new code; If all the sub-codes included in the parent code of the currently processed code appear in the coding set C, then proceed to S4; otherwise, jump to S5; S4: Insert the aggregated parent code, delete the sub-codes corresponding to this parent code, and jump to S5; S5: If there are still unjudged codes, then continue to read the next dissection code as the currently processed code, and return to S3; if all codes have been judged, then jump to S6; S6: If S4 has been executed in this round of the algorithm, then go to S2 and execute another round; otherwise, directly jump to S7; S7: Output the obtained set of dissection codes.

7. The method for organizing low-altitude airspace Earth dissection grid data according to claim 1, characterized in that, C buffer The calculation method is C buffer = C near ∪ C front , C near is the neighborhood grid of the unmanned aerial vehicle, and C front is a set composed of grids, α is a coefficient, v is the speed, t takes 1 s, and d is the grid length in the forward direction of the unmanned aerial vehicle.

8. Method for querying low-altitude airspace earth dissection grid data, characterized in that, For the low-altitude airspace Earth dissection grid data, adopt the method described in any one of claims 1 to 7 to construct the grid coding sets corresponding to each spatial geometric object; According to the coding sets of the spatial geometric objects, use the low-altitude airspace spatio-temporal grid database to perform data queries; Output the spatial geometric object data obtained from the query and display it on the spatio-temporal grid map.

9. A device for organizing low-altitude airspace earth dissection grid data applied to the method described in claim 1, characterized in that, It includes a spatio-temporal grid model construction module, a low-altitude airspace data acquisition module, and an organization and storage module; The spatio-temporal grid model construction module is used to construct a spatio-temporal grid model for the low-altitude airspace. The spatio-temporal grid model includes spatial grid codes, time dissection codes, and a grid data association relationship table; The low-altitude airspace data acquisition module is used to acquire low-altitude airspace data; The organization and storage module is used to, based on the constructed spatio-temporal grid model, express various spatial geometric objects in the low-altitude airspace, generate the grid coding sets corresponding to each spatial geometric object, and store them.

10. A low-altitude airspace Earth dissection grid data query device applied to the method according to claim 8, characterized in that, It includes a spatio-temporal grid model construction module, a low-altitude airspace data acquisition module, an organization and storage module, a data query and display module; The spatio-temporal grid model construction module is used to construct a spatio-temporal grid model for the low-altitude airspace. The spatio-temporal grid model includes spatial grid codes, time dissection codes, and a grid data association relationship table; The low-altitude airspace data acquisition module is used to acquire low-altitude airspace data; The organization and storage module is used to, based on the constructed spatio-temporal grid model, express various spatial geometric objects in the low-altitude airspace, generate the grid coding sets corresponding to each spatial geometric object, and store them; The data query and display module is used to, according to the coding sets of the spatial geometric objects, use the low-altitude airspace spatio-temporal grid database to perform data queries, output the spatial geometric object data obtained from the query, and display it on the spatio-temporal grid map.

11. Low-altitude airspace Earth subdivision grid data organization device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method for organizing low-altitude airspace Earth dissection grid data described in any one of claims 1 to 7.

12. Low-altitude airspace earth subdivision grid data query device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method for querying low-altitude airspace Earth dissection grid data described in claim 8.

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