A complex low-altitude airspace digital expression and multi-dimensional data space construction method and system

Abstract

This invention discloses a method and system for digitally representing complex low-altitude airspace and constructing a multi-dimensional data space, applicable to the field of unmanned aerial vehicle (UAV) management technology. The method includes the following steps: collecting three-dimensional geographic data of the target low-altitude airspace and constructing a geographic information model; dividing the target low-altitude airspace into multiple sub-regions using an adaptive complexity scheme, assigning a corresponding identifier to each sub-region; performing static and dynamic data binding on the sub-regions; and constructing a digital twin of the target low-altitude airspace by combining the geographic information model, identifiers, static data, and dynamic data of all sub-regions, updating it based on real-time data, and using it for UAV path planning and risk warning. This invention can accurately describe available airspace in complex urban environments, respond to emergencies through real-time data-driven updates, improve safety, and provide data support for large-scale, multi-UAV collaborative path planning and conflict prediction.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) management technology, and more specifically to a method and system for digital representation of complex low-altitude airspace and construction of multi-dimensional data space. Background Technology

[0002] With the rapid development of drones, low-altitude airspace, especially ultra-low-altitude and urban airspace, has become extremely congested and complex. Traditional airspace management methods have the following limitations: dividing airspace using two-dimensional polygons or simple three-dimensional cubes cannot accurately describe complex urban canyons, indoor spaces, dynamic no-fly zones, etc.; airspace structure, geographic information, and real-time dynamic data (such as the positions of other drones and meteorological data) are separated, resulting in incomplete drone path decision-making information; and pre-planned drone flight paths are difficult to adjust to real-time changing environmental conditions. Therefore, how to provide a digital representation of complex low-altitude airspace and a method and system for constructing multi-dimensional data spaces is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0003] In view of this, the present invention provides a method and system for digital representation of complex low-altitude airspace and construction of multi-dimensional data space, which divides the target low-altitude airspace into multiple sub-regions and constructs a spatiotemporally unified multi-dimensional data space on this basis, providing data support for the safe, efficient and autonomous operation of UAVs.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] A method for digitally representing complex low-altitude airspace and constructing a multi-dimensional data space includes the following steps:

[0006] S1. Collect three-dimensional geographic data and airspace rule data of the target low-altitude airspace, and construct a geographic information model of the target low-altitude airspace.

[0007] S2. Adaptive complexity scheme is used to divide the target low-altitude airspace into multiple sub-regions, and each sub-region is assigned a corresponding unique identifier code.

[0008] S3. Perform static data binding on the sub-region based on the sub-region's 3D geographic data and spatial rule data;

[0009] S4. Dynamically bind data for sub-regions based on meteorological data, electromagnetic environment data, and dynamic risk data within the sub-regions;

[0010] S5. Construct a digital twin of the target low-altitude airspace by combining the geographic information models, identifiers, static data, and dynamic data of all sub-regions, and update it based on real-time data.

[0011] Optionally, in S2, the target low-altitude airspace is divided into multiple sub-regions as follows:

[0012] Based on building information, the target low-altitude airspace is divided into multiple initial sub-regions. The airspace complexity index of each initial sub-region is calculated. If the airspace complexity index is greater than a set threshold, it is used as a subdivision condition. If the initial sub-region meets the subdivision condition, the corresponding initial sub-region is divided into multiple sub-regions. The airspace complexity index of all sub-regions is calculated to judge the subdivision condition and divide the sub-regions. This process continues until all sub-regions no longer meet the subdivision condition. After the iterative division is completed, the sub-regions are merged based on the information and spatial connectivity of the sub-regions to complete the division of the target low-altitude airspace. The final sub-region is taken as the division result.

[0013] Optionally, the iterative stopping condition for target low-altitude airspace delineation may also include:

[0014] If the volume of a sub-region is smaller than the set minimum volume, the corresponding sub-region will not be divided; if all flight attributes within a sub-region are non-flyable, the corresponding sub-region will not be divided; when the number of sub-region division iterations reaches the maximum value, the division of all sub-regions will stop.

[0015] Optionally, the spatial complexity index is calculated as follows:

[0016]

[0017] In the formula, To account for the overall airspace complexity, For the first i A complexity metric, for The weights and complexity metrics include physical environment complexity, airspace rule complexity, and historical traffic data.

[0018] Optionally, the unique identifier code consists of the three-dimensional geographic coordinates of the corresponding sub-region, the division level, and the attribute status timestamp.

[0019] Optionally, the static data of the sub-region includes: geographic semantic attributes used to represent the geographic environmental characteristics of the sub-region, and airspace rule status used to represent flightability and flight constraints; the dynamic data includes: meteorological data and electromagnetic environment data used to represent environmental factors, and dynamic risk data used to represent real-time risks; the dynamic risk data is calculated by weighting the UAV collision risk, dynamic environmental risk and geographic environmental risk.

[0020] Optionally, S5 specifically involves: constructing a digital twin of the target low-altitude airspace by combining the geographic information models, identifiers, static data, and dynamic data of all sub-regions; receiving the real-time location information of all UAVs and all dynamic data; standardizing the format of all data; locating the corresponding sub-region based on the geographic information at the time of data collection; updating the dynamic data and the real-time location of the UAVs; and providing a visual status response.

[0021] A system for digitally representing complex low-altitude airspace and constructing a multi-dimensional data space, executing the aforementioned method for digitally representing complex low-altitude airspace and constructing a multi-dimensional data space, includes:

[0022] The data acquisition module is used to collect and calculate three-dimensional geographic data, airspace rule data, meteorological data, electromagnetic environment data, and dynamic risk data of the target low-altitude airspace;

[0023] The model building module is used to build a geographic information model of the target low-altitude airspace;

[0024] The region division module is used to divide the target low-altitude airspace into sub-regions;

[0025] The real-time update module is used to map real-time data to the corresponding geographic information model.

[0026] As can be seen from the above technical solutions, compared with the prior art, the present invention provides a method and system for digital representation of complex low-altitude airspace and construction of multi-dimensional data space, which has the following beneficial effects: The present invention can accurately describe the available airspace in complex urban environments. Through real-time data-driven updates, airspace management changes from static management to dynamic management, thereby responding to emergencies and greatly improving the safety of UAV flight. It also provides data support for large-scale, multi-UAV collaborative path planning and conflict prediction. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0028] Figure 1 This is a flowchart of the method for digital representation of complex low-altitude airspace and construction of multi-dimensional data space according to the present invention;

[0029] Figure 2 This is a flowchart illustrating the target low-altitude airspace delineation process of the present invention. Detailed Implementation

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] This invention discloses a method for digitally representing complex low-altitude airspace and constructing a multi-dimensional data space, such as... Figure 1 As shown, it includes the following steps:

[0032] S1. Collect three-dimensional geographic data and airspace rule data of the target low-altitude airspace, and construct a geographic information model of the target low-altitude airspace.

[0033] S2. Adaptive complexity scheme is used to divide the target low-altitude airspace into multiple sub-regions, and each sub-region is assigned a corresponding unique identifier code.

[0034] S3. Perform static data binding on the sub-region based on the sub-region's 3D geographic data and spatial rule data;

[0035] S4. Dynamically bind data for sub-regions based on meteorological data, electromagnetic environment data, and dynamic risk data within the sub-regions;

[0036] S5. Construct a digital twin of the target low-altitude airspace by combining the geographic information models, identifiers, static data, and dynamic data of all sub-regions, and update it based on real-time data.

[0037] Furthermore, such as Figure 2 As shown, S2 divides the target low-altitude airspace into multiple sub-regions as follows:

[0038] Based on building information, the target low-altitude airspace is divided into multiple initial sub-regions. The airspace complexity index of each initial sub-region is calculated. If the airspace complexity index is greater than a set threshold, it is used as a subdivision condition. If the initial sub-region meets the subdivision condition, the corresponding initial sub-region is divided into multiple sub-regions. The airspace complexity index of all sub-regions is calculated to judge the subdivision condition and divide the sub-regions. This process continues until all sub-regions no longer meet the subdivision condition. After the iterative division is completed, the sub-regions are merged based on the information and spatial connectivity of the sub-regions to complete the division of the target low-altitude airspace. The final sub-region is taken as the division result.

[0039] In this embodiment of the invention, after sub-region division, adjacent sub-regions with similar attributes are checked and merged into a larger sub-region. The merging condition is that multiple adjacent sub-regions have completely identical multidimensional state attributes and can form a larger regular cube. For example, 100 adjacent sub-regions (e.g., 5m³) with completely identical attributes over an open area can be merged into one large sub-region. When planning its path, the UAV does not need to traverse 100 small sub-regions, but only needs to consider one large sub-region, thus improving computational efficiency.

[0040] Furthermore, the iterative stopping conditions for target low-altitude airspace delineation also include:

[0041] If the volume of a sub-region is smaller than the set minimum volume, the corresponding sub-region will not be divided; if all flight attributes within a sub-region are non-flyable, the corresponding sub-region will not be divided; when the number of sub-region division iterations reaches the maximum value, the division of all sub-regions will stop.

[0042] In this embodiment of the invention, the minimum volume can be set based on the geographic semantic attributes of the sub-regions. For example, the area near buildings requires the highest precision, so the minimum volume is set to 1m³. The area above roads and trees requires medium precision, so the minimum volume is set to 5m³. The area in open scenes such as lawns, water bodies, and farmland does not require high precision, so the minimum volume is set to 10m³.

[0043] In this embodiment of the invention, after the construction is completed, if the 3D geographic data and airspace rule data change, a local subdivision strategy is adopted to adjust the sub-region division. New 3D geographic data and airspace rule data are received, the sub-regions affected by the data changes are identified, and only all affected sub-regions are re-divided. Static and dynamic data binding is then performed on the newly divided sub-regions. For example, when the airspace rule within a certain range is adjusted to prohibit drone flights, all sub-regions including this range are re-divided. The previous sub-region may have both allowed and prohibited flight attributes, while the new sub-region's attribute is only prohibited drone flights or other conditions, avoiding overly complex airspace rules for the same sub-region.

[0044] Furthermore, the spatial complexity index is calculated as follows:

[0045]

[0046] In the formula, To account for the overall airspace complexity, For the first i A complexity metric, for The weights and complexity metrics include physical environment complexity, airspace rule complexity, and historical traffic data.

[0047] In this embodiment of the invention, the calculation of physical environment complexity, airspace rule complexity, and historical traffic data is as follows: Physical environment complexity is represented by building density. The building density of all sub-regions is normalized, and the processed value is used as the physical environment complexity value. Airspace rule complexity is assigned a value by the number of airspace rules. The more airspace attributes (i.e., allow flight, prohibit flight, restricted flight altitude, restricted flight speed, etc.) in a region, the more complex the rules (i.e., allow certain aircraft types to fly, require flight permits, etc.), and the higher the assigned score. The assigned score is determined based on the overall situation and experience of all sub-regions. Historical traffic data is represented by historical UAV traffic data. Similarly, the historical UAV traffic data of all sub-regions is normalized and used as the value of the calculation index. In this embodiment, the threshold for the spatial complexity index is set to 0.3. When the spatial complexity index is greater than 0.3, the sub-regions need to be further subdivided. In addition, the number of subdivisions is also determined based on the complexity. If the spatial complexity index is between 0.3 and 0.5, it is further subdivided into 2 sub-regions. If the spatial complexity index is between 0.5 and 0.8, it is further subdivided into 4 sub-regions. If the spatial complexity index is greater than 0.8, it is further subdivided into 8 sub-regions.

[0048] Furthermore, the unique identifier code consists of the three-dimensional geographic coordinates of the corresponding sub-region, the hierarchical division, and the attribute status timestamp.

[0049] In this embodiment of the invention, each sub-region has a unique identifier code, ensuring that each sub-region can be accurately indexed. This embodiment introduces three-dimensional geographic coordinates, hierarchical division, and timestamps as identifier codes. When performing path planning, it can accurately locate sub-regions and their surrounding areas, construct an efficient spatial index, and achieve near real-time queries. Spatial hierarchy codes allow the system to operate on the airspace at different levels of detail. Timestamps support status traceability: the status of sub-region 1 at 3 PM yesterday can be queried, ensuring that the path planner and the drone use the same airspace status snapshot at the same time, avoiding decision-making errors due to data update delays.

[0050] Furthermore, the static data for the sub-region includes: geographic semantic attributes used to represent the geographic environmental characteristics of the sub-region, and airspace rule status used to represent flightability and flight constraints; the dynamic data includes: meteorological data and electromagnetic environment data used to represent environmental factors, and dynamic risk data used to represent real-time risks; the dynamic risk data is calculated by weighting the UAV collision risk, dynamic environmental risk and geographic environmental risk.

[0051] In this embodiment of the invention, flyable conditions include no-fly zones, altitude-restricted zones, speed-restricted zones, qualification-required zones, and nighttime no-fly zones. Flight constraints include maximum permissible speed, minimum / maximum altitude restrictions, required operator qualification levels, and permitted flight time periods. Meteorological data includes wind speed, wind direction, temperature, humidity, precipitation probability, and visibility. Electromagnetic environment data includes wireless communication signal strength and electromagnetic interference levels. Dynamic risk data includes drone collision risk calculated based on a weighted average of surrounding drone density, relative speed, and distance; dynamic environmental risk is based on a comprehensive assessment of meteorological conditions and the electromagnetic environment; and geographical environmental risk is calculated based on a weighted average of building density and building distance. Airspace rule status and geographical environment status are low-frequency updated data (static data), updated only when the airspace rule database changes, control instructions change, regulations change, temporary no-fly zones occur, or geographical information changes. Meteorological data, electromagnetic environment data, and dynamic risk data are high-frequency updated data (dynamic data), updated as soon as sensors, signal receivers, and other devices receive new data.

[0052] In this embodiment of the invention, geographic semantic attributes include: land type attributes, obstacle relationship attributes, and terrain relationship attributes, specifically:

[0053] Land type attributes refer to the land use type corresponding to the airspace, including high-density building areas, low-density building areas, farmland, parks, and water areas; topographic relationship attributes include elevation values ​​and topographic elevation differences; obstacle relationship attributes are used to describe the spatial relationship between sub-regions and facilities such as buildings, such as the side of a building, the airspace above a building, and the area below a building.

[0054] In this embodiment, land type attributes are identified by machine learning algorithms from the collected images, obstacle relationships are determined based on the relative positions of sub-regions and obstacles in the 3D model, and terrain relationship attributes are calculated based on the 3D geographic coordinates of the sub-regions.

[0055] In addition, geographic semantic attributes can also include environmentally sensitive attributes, such as sensitive facilities (government agencies, military areas, schools and hospitals), ecological protection areas (nature reserves, water source protection areas, ecological red line areas), and cultural heritage areas (the airspace above cultural relics and historical sites, and the protection area for historical buildings).

[0056] Furthermore, S5 specifically involves: constructing a digital twin of the target low-altitude airspace by combining geographic information models, identifiers, static data, and dynamic data of all sub-regions; receiving real-time location information of all UAVs and all dynamic data; standardizing the format of all data; locating the corresponding sub-region based on the geographic information at the time of data collection; updating the dynamic data and the real-time location of the UAVs; and providing a visualized status response.

[0057] and Figure 1Corresponding to the method described above, this invention also discloses a system for digital representation of complex low-altitude airspace and construction of multi-dimensional data space, which includes:

[0058] The data acquisition module is used to collect and calculate three-dimensional geographic data, airspace rule data, meteorological data, electromagnetic environment data, and dynamic risk data of the target low-altitude airspace;

[0059] The model building module is used to build a geographic information model of the target low-altitude airspace;

[0060] The region division module is used to divide the target low-altitude airspace into sub-regions;

[0061] The real-time update module is used to map real-time data to the corresponding geographic information model.

[0062] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0063] Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for digitally representing complex low-altitude airspace and constructing a multi-dimensional data space, characterized in that, Includes the following steps: S1. Collect three-dimensional geographic data and airspace rule data of the target low-altitude airspace, and construct a geographic information model of the target low-altitude airspace. S2. An adaptive complexity scheme is adopted to divide the target low-altitude airspace into multiple sub-regions, and each sub-region is assigned a corresponding unique identifier code. The unique identifier code consists of the three-dimensional geographic coordinates of the corresponding sub-region, the division level, and the attribute status timestamp. S3. Perform static data binding on the sub-region based on the sub-region's 3D geographic data and spatial rule data; S4. Dynamically bind data for sub-regions based on meteorological data, electromagnetic environment data, and dynamic risk data within the sub-regions; S5. Construct a digital twin of the target low-altitude airspace by combining the geographic information models, identifiers, static data, and dynamic data of all sub-regions, and update it based on real-time data; S2 divides the target low-altitude airspace into multiple sub-regions as follows: Based on building information, the target low-altitude airspace is divided into multiple initial sub-regions. The airspace complexity index of each initial sub-region is calculated. If the airspace complexity index is greater than a set threshold, it is used as a subdivision condition. If the initial sub-region meets the subdivision condition, the corresponding initial sub-region is divided into multiple sub-regions. The airspace complexity index of all sub-regions is calculated to judge the subdivision condition and divide the sub-regions. This process continues until all sub-regions no longer meet the subdivision condition. After the iterative division is completed, the sub-regions are merged based on the information and spatial connectivity of the sub-regions to complete the division of the target low-altitude airspace. The final sub-region is taken as the division result. The static data for the sub-region includes: geographic semantic attributes representing the geographic environmental characteristics of the sub-region, and airspace rule status representing flight availability and flight constraints; the dynamic data includes: meteorological data and electromagnetic environment data representing environmental factors, and dynamic risk data representing real-time risks; the dynamic risk data is calculated by weighting the UAV collision risk, dynamic environmental risk, and geographic environmental risk. Specifically, S5 involves: constructing a digital twin of the target low-altitude airspace by combining geographic information models, identifiers, static data, and dynamic data from all sub-regions; receiving real-time location information and dynamic data from all UAVs; standardizing the format of all data; locating the corresponding sub-region based on the geographic information at the time of data collection; updating the dynamic data and the real-time location of the UAVs; and providing a visual status response.

2. The method for digital representation of complex low-altitude airspace and construction of multi-dimensional data space according to claim 1, characterized in that, The iterative stopping conditions for target low-altitude airspace delineation also include: If the volume of a sub-region is smaller than the set minimum volume, the corresponding sub-region will not be divided; if all flight attributes within a sub-region are non-flyable, the corresponding sub-region will not be divided; when the number of sub-region division iterations reaches the maximum value, the division of all sub-regions will stop.

3. The method for digital representation of complex low-altitude airspace and construction of multi-dimensional data space according to claim 1, characterized in that, The spatial complexity index is calculated as follows: In the formula, To account for the overall airspace complexity, For the first i A complexity metric, for The weights and complexity metrics include physical environment complexity, airspace rule complexity, and historical traffic data.

4. A system for digital representation of complex low-altitude airspace and construction of multi-dimensional data space, characterized in that, The method for digital representation of complex low-altitude airspace and construction of multidimensional data space as described in any one of claims 1-3 includes: The data acquisition module is used to collect and calculate three-dimensional geographic data, airspace rule data, meteorological data, electromagnetic environment data, and dynamic risk data of the target low-altitude airspace; The model building module is used to build a geographic information model of the target low-altitude airspace; The region division module is used to divide the target low-altitude airspace into sub-regions; The real-time update module is used to map real-time data to the corresponding geographic information model.

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