Low-altitude target conflict identification method and device

Abstract

This application provides a method and apparatus for identifying low-altitude flight target conflicts. The method includes: obtaining a basic spatial grid scale based on target airspace characteristics and aircraft performance characteristics; dividing the target airspace into multi-level grids based on difference logic and the basic spatial grid scale to construct a distributed grid reference space; performing altitude reference conversion and time reference synchronization on flight data in the reference space and associating it with the reference space to obtain a target airspace index space; performing altitude layering on the target airspace index space; performing secondary fine-grained flight conflict detection within each index space layer according to a preset time period to determine whether there is a potential flight conflict risk; if so, acquiring real-time status data of the flight targets, predicting the minimum distance between flight targets, and determining whether there is an actual flight conflict risk. This application can support real-time conflict identification of large-scale aircraft, improving the flight efficiency and safety of low-altitude aircraft.

Description

Technical Field

[0001] This application relates to the field of data processing, specifically to a method and apparatus for identifying low-altitude flying target conflicts. Background Technology

[0002] With the rapid development and widespread application of low-altitude aircraft, the flight density in urban airspace has increased dramatically, reaching tens of thousands of flights per 100 square kilometers. These low-altitude aircraft include various configurations such as fixed-wing aircraft, helicopters, and multi-rotor aircraft, each with different flight characteristics and performance, posing unprecedented challenges to airspace management.

[0003] Traditional aircraft conflict identification methods are primarily designed for large and medium-sized aircraft such as commercial airliners, employing a point-to-point probabilistic cloud computing approach. However, this method suffers from significant limitations in computational frequency and efficiency, typically only handling conflict identification needs for 3-5 aircraft. Faced with the low-altitude, multi-configuration, and high-frequency flight environment of aircraft, traditional methods prove inadequate and fail to meet the demands of practical applications.

[0004] Specifically, the shortcomings of traditional methods are mainly reflected in the following aspects: First, their computation frequency is low, making them unsuitable for high-density, high-frequency low-altitude flight environments; second, traditional methods lack unified management methods for different types of aircraft, resulting in poor universality of identification; and finally, when faced with complex and ever-changing low-altitude flight environments, the computational efficiency and accuracy of traditional methods are difficult to guarantee.

[0005] Therefore, in order to effectively address the challenges brought about by the rapid development of low-altitude aircraft, there is an urgent need to establish a new method for identifying low-altitude flight target conflicts. Summary of the Invention

[0006] To address the problems in the prior art, this application provides a method and apparatus for identifying low-altitude flight target conflicts, which can support real-time conflict identification of large-scale aircraft and improve the flight efficiency and safety of low-altitude aircraft.

[0007] To solve at least one of the above problems, this application provides the following technical solution:

[0008] In a first aspect, this application provides a method for identifying conflicts involving low-altitude flying targets, including:

[0009] Based on the preset target airspace characteristics and preset aircraft performance characteristics, the corresponding basic scale of the spatial grid is determined. Based on the preset difference logic, the geographic coordinates of the target airspace range and the basic scale of the spatial grid, the target airspace is divided into multi-level grids to determine the corresponding spatial grids. The topological relationship between each spatial grid is constructed to determine the corresponding distributed grid reference space.

[0010] Receive flight data of low-altitude flying targets, perform altitude reference conversion and time reference synchronization on the flight data, determine the corresponding flight target reference data, associate the flight target reference data with the distributed grid reference space, and determine the corresponding target airspace index space;

[0011] The target airspace index space is height-stratified according to a preset altitude value to determine multiple corresponding index layers. Secondary fine-grained flight conflict detection is performed on each index layer according to a preset time period to determine whether there is a potential flight conflict risk. If so, real-time status data of flight targets in the potential flight conflict area is obtained. Flight trajectory prediction is performed on the real-time status data of flight targets according to a preset aircraft dynamics model to determine the minimum distance between corresponding flight targets. The existence of actual flight conflict risk is determined based on the minimum distance.

[0012] Furthermore, determining the corresponding basic scale of the spatial grid based on preset target airspace characteristics and preset aircraft performance characteristics includes:

[0013] Grid scale rules are constructed based on airspace type. The corresponding basic grid scale is determined according to the grid scale rules and the preset target airspace characteristics. The airspace type includes urban airspace, suburban airspace and mountainous airspace.

[0014] Based on the preset aircraft performance characteristics, a corresponding grid scale threshold is determined, and the basic grid scale is constrained based on the grid scale threshold to determine the corresponding basic spatial grid scale.

[0015] Furthermore, the step of dividing the target airspace into multi-level grids based on preset difference logic, the geographic coordinates of the target airspace range, and the basic scale of the spatial grid to determine the corresponding spatial grid includes:

[0016] The differential logic basis is set according to the minimum safe interval between flight targets, wherein the differential logic basis is the scale reduction ratio between control grid levels;

[0017] The latitude and longitude coordinates of the boundary vertex of the target airspace range are used as the initial range of the top-level grid, and the basic scale of the spatial grid is used as the minimum range. The initial range is divided into hierarchical progressive grids according to the difference logic cardinality to determine the corresponding spatial grids that conform to the minimum range.

[0018] Furthermore, the step of constructing the topological relationships between the spatial grids and determining the corresponding distributed grid reference space includes:

[0019] A unique code generation operation is performed on the spatial grid based on the space filling curve to determine the unique code corresponding to each spatial grid.

[0020] A graph database is constructed to record the unique codes and spatial relationships of each of the aforementioned spatial grids, thereby determining the corresponding distributed grid reference space.

[0021] Furthermore, the step of performing altitude reference conversion and time reference synchronization on the flight data to determine the corresponding flight target reference data includes:

[0022] An altitude reference transformation matrix is ​​constructed to perform standard data transformation on the heterogeneous altitude data in the flight data, and the corresponding unified altitude reference value is determined.

[0023] A three-level time synchronization network is deployed, and the flight data is sorted based on the time synchronization network to determine the corresponding unified time reference value. The three-level time synchronization network includes a first-level node, a second-level node, and a third-level node. The first-level node is used to connect to the Beidou atomic clock time source, the second-level node is used to synchronize the time difference through the fiber optic PTP protocol, and the third-level node is used to synchronize the time difference using NTP-over-4G.

[0024] By combining the unified altitude reference value and the unified time reference value, the corresponding flight target reference data is determined.

[0025] Furthermore, the target airspace index space is height-stratified according to preset altitude values ​​to determine multiple corresponding index layers. Secondary fine-grained flight conflict detection is performed on each index layer according to a preset time period to determine whether there are potential flight conflict risks, including:

[0026] The target spatial index space is divided into multiple sub-space index layers according to the height layer. The height layers include 120 meters, 300 meters, 600 meters, 1000 meters and 4000 meters. The 4000-meter height layer corresponds to the large-scale spatial index layer, the 300-meter, 600-meter and 1000-meter height layers correspond to the medium-scale spatial index layers, and the 120-meter height layer corresponds to the refined spatial index layer.

[0027] The time dimension is divided into multiple calculation cycles, and a fast conflict scan is performed on the large-scale spatial index layer within each calculation cycle to filter out potential conflict regions.

[0028] A detailed analysis of the potential conflict areas is conducted to determine whether there is a risk of potential flight conflict.

[0029] Further, the step of predicting the flight trajectory of the flight target based on the real-time state data of the flight target according to the preset aircraft dynamics model, and determining the minimum distance between the corresponding flight targets, includes:

[0030] A short-term trajectory prediction algorithm is constructed based on an aircraft dynamics model. The flight trajectory of the flight target is predicted according to the short-term trajectory prediction algorithm. Meteorological data and airspace constraints are introduced to constrain the flight trajectory after the flight trajectory prediction, and the corresponding predicted flight trajectory is determined.

[0031] The minimum distance between the flight targets is predicted based on the predicted flight trajectory, and the corresponding minimum horizontal distance and minimum vertical distance are determined.

[0032] Secondly, this application provides a low-altitude target conflict identification device, comprising:

[0033] The spatial reference construction module is used to determine the corresponding basic scale of the spatial grid based on the preset target airspace characteristics and preset aircraft performance characteristics, to perform multi-level grid division of the target airspace based on the preset difference logic, the geographic coordinates of the target airspace range and the basic scale of the spatial grid, to determine the corresponding spatial grid, to construct the topological relationship between the spatial grids, and to determine the corresponding distributed grid reference space. The historical form usage data includes at least one of historical form performance data and historical form structure change data.

[0034] The index space construction module is used to receive flight data of low-altitude flying targets, perform altitude reference conversion and time reference synchronization on the flight data, determine the corresponding flight target reference data, associate the flight target reference data with the distributed grid reference space, and determine the corresponding target airspace index space.

[0035] The flight conflict detection module is used to perform altitude layering of the target airspace index space according to a preset altitude value, determine multiple corresponding index layers, perform secondary fine-grained flight conflict detection on each index layer according to a preset time period, determine whether there is a potential flight conflict risk, if so, acquire real-time status data of flight targets in the potential flight conflict area, predict the flight trajectory of the real-time status data of the flight targets according to a preset aircraft dynamics model, determine the minimum distance between the corresponding flight targets, and determine whether there is an actual flight conflict risk based on the minimum distance.

[0036] Thirdly, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the low-altitude flight target conflict identification method.

[0037] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the low-altitude flight target conflict identification method.

[0038] Fifthly, this application provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the low-altitude flight target conflict identification method.

[0039] As can be seen from the above technical solution, this application provides a method and apparatus for identifying low-altitude flight target conflicts. It obtains a basic spatial grid scale based on target airspace characteristics and aircraft performance characteristics. The target airspace is then divided into multi-level grids based on difference logic and the basic spatial grid scale to construct a distributed grid reference space. Flight data in the reference space undergoes altitude reference conversion and time reference synchronization, and is associated with the reference space to obtain a target airspace index space. The target airspace index space is then hierarchically layered, and secondary fine-grained flight conflict detection is performed within each index space layer according to a preset time period to determine if there is a potential flight conflict risk. If so, real-time status data of the flight targets is acquired, the minimum distance between flight targets is predicted, and the existence of an actual flight conflict risk is determined. This enables real-time conflict identification of large-scale aircraft, improving the flight efficiency and safety of low-altitude aircraft. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of this application 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 some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is one of the flowcharts illustrating the low-altitude target conflict identification method in the embodiments of this application;

[0042] Figure 2 This is a structural diagram of the low-altitude flight target conflict identification device in the embodiments of this application;

[0043] Figure 3 This is a schematic diagram of the structure of the electronic device in the embodiments of this application.

[0044] Figure label:

[0045] Electronic device 9600, central processing unit 9100, memory 9140, communication module 9110, input unit 9120, audio processor 9130, display 9160, power supply 9170, buffer memory 9141, application / function storage unit 9142, data storage unit 9143, driver storage unit 9144, antenna 9111, speaker 9131, microphone 9132. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0047] The acquisition, storage, use, and processing of data in this application all comply with the relevant provisions of national laws and regulations.

[0048] Considering the difficulty in guaranteeing the computational efficiency and accuracy of traditional methods when facing complex and ever-changing low-altitude flight environments, this application provides a method and apparatus for identifying low-altitude flight target conflicts. Based on target airspace characteristics and aircraft performance characteristics, a basic spatial grid scale is obtained. The target airspace is then divided into multi-level grids according to difference logic and the basic spatial grid scale to construct a distributed grid reference space. Flight data in the reference space undergoes altitude reference transformation and time reference synchronization, and is associated with the reference space to obtain a target airspace index space. The target airspace index space is then hierarchically layered, and secondary fine-grained flight conflict detection is performed within each index space layer according to a preset time period to determine the existence of potential flight conflict risks. If so, real-time status data of the flight targets is acquired, the minimum distance between flight targets is predicted, and the existence of actual flight conflict risks is determined. This enables real-time conflict identification of large-scale aircraft, improving the flight efficiency and safety of low-altitude aircraft.

[0049] To support real-time conflict identification for large-scale aircraft and improve the flight efficiency and safety of low-altitude aircraft, this application provides an embodiment of a low-altitude target conflict identification method, see [link to embodiment]. Figure 1 The low-altitude flight target conflict identification method specifically includes the following:

[0050] Step S101: Determine the corresponding basic scale of the spatial grid based on the preset target airspace characteristics and preset aircraft performance characteristics; divide the target airspace into multi-level grids based on the preset difference logic, the geographic coordinates of the target airspace range and the basic scale of the spatial grid; determine the corresponding spatial grids; construct the topological relationship between each spatial grid; and determine the corresponding distributed grid reference space.

[0051] Optionally, in this embodiment, the purpose of this step is to establish a distributed spatial grid benchmark, laying the foundation for subsequent hierarchical calculations and conflict detection.

[0052] Traditional methods typically employ fixed-scale mesh generation, which cannot adapt to the dynamic requirements of different airspace characteristics (such as high-density urban flight and low-density mountain flight) and aircraft performance (such as high-speed fixed-wing vs. low-speed multirotor). This step dynamically adjusts the mesh scale to avoid over-subdivision in low-density airspace (wasting computational resources) or excessively large meshes in high-density airspace (reducing collision detection accuracy), ensuring that the mesh scale matches the aircraft's motion characteristics, reducing unnecessary computation, and improving real-time performance.

[0053] Optionally, in this embodiment, the airspace feature is the type feature to which the airspace belongs, including urban airspace, suburban airspace, and mountainous airspace. A corresponding grid scale rule is constructed based on the airspace type, as illustrated by the following example:

[0054] The urban airspace adopts a high-density grid (basic scale ≤ 500m);

[0055] Suburban airspace uses a medium-density grid (basic scale 500m to 2km);

[0056] Low-density grids (basic scale ≥ 2km) are used for airspace in mountainous areas;

[0057] The grid scale is dynamically adjusted based on airspace control levels (such as no-fly zones and restricted areas), increasing the grid density in controlled areas by 50%.

[0058] Specifically, aircraft performance characteristics are the flight attribute characteristics of an aircraft. A preset grid scale threshold is established based on attributes such as the aircraft's maximum speed and maneuver radius.

[0059] For high-speed fixed-wing aircraft (speed > 200 km / h), the grid size is ≥ 1 km;

[0060] For low-speed multirotor aircraft (speed < 50 km / h), the grid size is ≤ 300 m.

[0061] For mixed flight airspace, the minimum grid size is determined based on the performance of the aircraft with the lowest speed.

[0062] A specific example is provided for illustration:

[0063] Example 1: Assume a grid division of multi-rotor UAVs in urban airspace.

[0064] Airspace characteristics type: urban airspace; basic scale: 300m (high density requirements);

[0065] Aircraft: Logistics drones (speed ≤ 50km / h, maneuver radius ≤ 100m) → minimum grid size ≥ 100m;

[0066] The integrated airspace characteristics and aircraft performance characteristics are combined using a 200m×200m grid to ensure coverage of UAV maneuverability while avoiding excessive subdivision.

[0067] Example 2: Assume a grid division of fixed-wing aircraft in mountainous airspace.

[0068] Airspace characteristics type: mountainous airspace; basic scale: 2km (low density requirement);

[0069] Aircraft: General aviation fixed-wing aircraft (speed ≥ 200 km / h, maneuver radius ≥ 1 km), → grid scale ≥ 1 km;

[0070] The integrated airspace feature type and aircraft performance characteristics are combined using a 1.5km×1.5km grid to balance computational efficiency and conflict detection requirements.

[0071] Example 3: Hypothetical dynamic adjustment of mixed airspace. The airspace at the boundary between urban and suburban areas includes both UAVs and general aviation aircraft. Initial grid scale: 1 km (suburban baseline).

[0072] When a drone swarm is detected entering, the grid is subdivided to 500m.

[0073] When general aviation aircraft pass by, the grid is temporarily merged to 1km to reduce the computational load.

[0074] It is understood that in this embodiment, the basic grid scale is dynamically determined based on the airspace feature type and aircraft performance characteristics. This design can improve computational efficiency, reduce system resource consumption, and lay a solid foundation for supporting real-time conflict identification of large-scale aircraft in the future.

[0075] Optionally, in this embodiment, after obtaining the basic scale of the spatial grid through the above steps, the target spatial domain is divided into different levels of grids using a hierarchical approach. A top-down method is adopted, starting from the largest grid and progressively subdividing it to form a grid tree structure. Each grid is assigned a unique code for easy and rapid indexing. Simultaneously, topological relationships between grids are established to support correlation analysis between adjacent grids.

[0076] Specifically, this embodiment uses dynamic layering driven by n-difference logic.

[0077] First, define the cardinality n (n≥2) of the difference logic to control the scale reduction ratio between grid levels, preferably n=2 or n=3; based on the minimum safe separation and maximum maneuverability of the aircraft, dynamically adjust the difference logic parameters of different altitude levels to ensure that the grid scale matches the flight safety requirements.

[0078] Then, input the latitude and longitude coordinates of the boundary vertices of the target airspace, generate the minimum bounding rectangle (MBR) of the airspace through the convex hull algorithm, and use the MBR as the initial range of the top-level grid (maximum grid), that is, the level 0 grid, and divide it into n×n subgrids (e.g., 4 subgrids) according to the cardinality n of the difference logic.

[0079] Next, the above division process is repeated for each sub-grid until the grid scale reaches the preset minimum basic scale (basic scale of spatial grid). A buffer boundary (e.g., 50m) is reserved during grid division to avoid frequent grid switching caused by aircraft flying across grids. In the vertical direction, grids are independently divided according to altitude layers (120m, 300m, etc.) to ensure that the two-dimensional planar scale of the grid is dynamically correlated with the altitude layer.

[0080] At this point, spatial grids at different levels of the target airspace were obtained. A unique code was generated for each grid using a Z-order curve to ensure locality and fast retrieval. A multi-level grid index table was established to support rapid location of the target grid and its neighboring grids via latitude and longitude coordinates or grid codes. Each grid recorded its 26 neighboring grids (8 horizontal neighbors and 9 vertical neighbors above and below). The spatial relationships between grids were stored using a directed graph, with edge weights representing the distance to the grid center points, establishing topological relationships between grids and supporting efficient range queries. At this stage, the target airspace was successfully decomposed into a distributed grid reference space, laying the foundation for subsequent rapid conflict identification of large-scale flying targets.

[0081] Step S102: Receive flight data of low-altitude flying targets, perform altitude reference conversion and time reference synchronization on the flight data, determine the corresponding flight target reference data, associate the flight target reference data with the distributed grid reference space, and determine the corresponding target airspace index space.

[0082] Optionally, in this embodiment, the purpose of this step is to establish a unified spatiotemporal reference framework, convert flight data using different spatiotemporal references into a unified standard spatiotemporal reference, and realize the position description and time synchronization of multiple entities such as civil aviation, general aviation, and drones under the same framework.

[0083] A data receiving interface cluster is established to receive heterogeneous flight data through multiple communication links such as ADS-B, 4G / 5G, and BeiDou short message. The received raw flight data packets are parsed to extract key fields including: aircraft identification code, latitude and longitude coordinates, altitude value, timestamp, velocity vector, etc.

[0084] Regarding altitude references, a data transformation matrix is ​​used to uniformly convert the altitude references used by different aircraft (such as geometric altitude, barometric altitude, etc.) into a standard reference. The system receives altitude data reported by different aircraft and automatically identifies their altitude reference type, including but not limited to:

[0085] Barometric altimeter (QNH / QFE): from barometric altimeters on civil and general aviation aircraft;

[0086] GPS geometric altitude: from the GNSS module of the drone;

[0087] Radar Altitude (AGL): Radio altimeter from the helicopter;

[0088] Hybrid data: Some aircraft provide multiple altitude sources simultaneously.

[0089] Different conversion models are used depending on the type of height source:

[0090] 1. Convert barometric altitude to geometric altitude

[0091]

[0092] in:

[0093] H baro The barometric altitude reported by the aircraft;

[0094] P obs Corrected sea level pressure provided by the current weather station;

[0095] P std Standard sea level pressure (1013.25 hPa);

[0096] ρ is the air density (dynamically calculated based on temperature and humidity);

[0097] g is the acceleration due to gravity (adjusted with latitude).

[0098] Suppose a general aviation aircraft reports a pressure altitude of 500m, and the current weather station measures P... obs =1005hPa, calculated geometric height correction value +8.2m, final H geo =508.2m.

[0099] 2. Convert GPS ellipsoidal height to geodetic height (MSL)

[0100] Earth height H MSL =H ellipsoid -N

[0101] in:

[0102] H ellipsoid Ellipsoidal height provided for the GNSS module;

[0103] N is the elevation anomaly value calculated by calling the localized geoid model (EGM2008);

[0104] Suppose a drone's GPS reports an ellipsoidal height of 300m and a regional elevation anomaly N = 25m, then the corrected height H... MSL

[0105] =275m.

[0106] 3. Convert radar altitude (AGL) to geometric altitude

[0107] H geo =H ground +H AGL

[0108] in:

[0109] H AGL The height above the ground as measured by a radio altimeter;

[0110] H ground The ground elevation of the current location obtained from the digital elevation model (DEM);

[0111] Assuming the helicopter is flying in a mountainous area, with radar measuring its altitude at 150m and the DEM showing a ground elevation of 1000m, then H geo =1150m.

[0112] At the aforementioned altitude reference, meteorological data (temperature, air pressure, humidity) is accessed in real time to dynamically adjust the air density ρ and gravity compensation coefficient. For aircraft providing altitude data from multiple sources simultaneously, Kalman filtering is used to fuse the data and reduce errors from individual sensors.

[0113] The implementation of the height reference is illustrated with specific examples and application scenarios:

[0114] Suppose that a city's airspace simultaneously contains civilian airliners (barometric altitude reference), logistics drones (GPS altitude), and emergency rescue helicopters (radar altitude).

[0115] Civilian passenger plane reports: Hbaro = 800m, current weather station Pobs = 1008hPa;

[0116] Drone reported: GPS ellipsoid height Hellipsoid = 820m;

[0117] The helicopter reported: Radar altitude HAGL = 200m, DEM shows ground elevation 600m.

[0118] After height conversion:

[0119] The altitude of a civil airliner is 1237.5m.

[0120] Drone: HMSL = 790m;

[0121] Helicopter: Hgeo = 600 + 200 = 800m;

[0122] After conversion, the height was unified to the geometric height, and the test found no conflict between the civil airliner (1237.5m), the drone (790m), and the helicopter (800m).

[0123] Specifically, regarding the time reference, a unified time coordinate system is established to address the time synchronization issues among different devices. This step proposes a hierarchical collaborative method for unifying the time reference, achieving microsecond-level time alignment through a three-tiered architecture of "global time synchronization - regional synchronization - terminal compensation".

[0124] Specifically, deploy a global high-precision time synchronization network.

[0125] Deploy a BeiDou / GPS dual-mode timing server with a rubidium atomic clock, supporting PTP (IEEE 1588v2) and NTP protocols; prioritize satellite timing signals, and switch to local atomic clock timekeeping mode when satellite signals fail.

[0126] For example:

[0127] Deploy BeiDou / GPS dual-mode timing base stations, setting up one Class I reference station for every 100 square kilometers. Each base station outputs a 1PPS pulse signal (rising edge accuracy ±30ns) and an IRIG-B code time message (containing UTC time and leap second information), and then distributes the time signal to secondary nodes via fiber optic PTP.

[0128] Specifically, device-level time synchronization is implemented.

[0129] Each secondary node is connected to at least two primary nodes. The BMC (Best Master Clock) algorithm is used to select the optimal time source. The airborne equipment receives the IRIG-B code through a dedicated fiber optic network. The Flight Management System (FMS) uses an FPGA to implement the clock discipline circuit and hardware timestamps the PTP messages to avoid operating system scheduling delays.

[0130] Preferably, time synchronization is achieved using the IEEE 1588v2 precision protocol, with ±200ns synchronization achieved through Wi-Fi 6 air interface timestamps.

[0131] Specifically, time alignment at the data processing layer.

[0132] High-priority airborne equipment receives time signals via PTP-over-Aviation-Ethernet with a synchronization error of <10μs, and the data logger writes precise timestamps.

[0133] The low-power device adopts the lightweight NTP protocol and synchronizes via 4G / 5G networks with an error of <10ms. Within the cluster, microsecond-level relative synchronization is achieved through TDMA time slot allocation.

[0134] The above method achieves the unification of time reference values. For example, a city's low-altitude airspace control system needs to synchronize the time of 500 drones and 50 general aviation aircraft.

[0135] Primary node deployment: BeiDou timing servers are installed at three air traffic control radar stations to output time signals via fiber optic PTP.

[0136] Secondary node networking: PTP switches are deployed in 10 key areas to form a dual-ring redundant topology with a maximum path delay of <50μs.

[0137] Terminal synchronization is achieved as follows:

[0138] General aviation aircraft: receive PTP signals via airborne AFDX network with a time error of <20μs;

[0139] Drone swarm: The lead drone synchronizes via 4G-NTP (error ±5ms), while member drones synchronize within the swarm via LoRa-TDMA (±0.2ms).

[0140] The aforementioned unified spatiotemporal reference framework will transform flight data using different spatiotemporal references into a unified standard spatiotemporal reference, laying the foundation for subsequent flight conflict detection.

[0141] Step S103: The target airspace index space is height-stratified according to a preset altitude value to determine multiple corresponding index layers. Secondary fine-grained flight conflict detection is performed on each index layer according to a preset time period to determine whether there is a potential flight conflict risk. If so, real-time status data of flight targets in the potential flight conflict area is obtained. Flight trajectory prediction is performed on the real-time status data of the flight targets according to a preset aircraft dynamics model to determine the minimum distance between the corresponding flight targets. The existence of an actual flight conflict risk is determined based on the minimum distance.

[0142] Optionally, in this embodiment, the purpose of this step is to perform fast traversal calculations to discover potential conflicts based on a unified spatiotemporal reference.

[0143] Specifically, in terms of time, the time dimension is divided into multiple computation cycles, with periods of 15 seconds, 1 minute, 15 minutes, and 30 minutes, respectively. An independent task queue is allocated for parallel computation in each cycle. For each computation cycle time slice, in terms of space, it is traversed in layers according to different heights, such as 120 meters, 300 meters, 600 meters, 1000 meters, and 4000 meters.

[0144] Understandably, short cycles (15 seconds) are used for real-time conflict early warning, prioritizing the calculation of highly dynamic targets (such as drones); medium cycles (1 minute) are used for conflict scanning of conventional aircraft (such as helicopters and general aviation aircraft); and long cycles (more than 15 minutes) are used for airspace situation analysis (such as traffic hotspot prediction).

[0145] Specifically, in the spatial dimension, the grid is divided according to altitude levels, employing a "coarse screening + fine calculation" strategy to reduce computational load. The airspace grid is divided into multiple subspaces according to altitude levels (120 meters, 300 meters, 600 meters, 1000 meters, 4000 meters). Since the spatial grid is constructed from top to bottom and from large to small, the grid size corresponding to altitude levels of 4000 meters and above is large-scale, while the grid size corresponding to altitude levels of 120 meters and below is small-scale, and the rest are medium-scale. First, in the large-scale grid (4000-meter altitude level), rapid collision detection is performed based on the aircraft's position and velocity vectors to filter out potential conflict areas. For the filtered areas, local calculations are triggered in the finer grid (the next scale) until the smallest conflict area is found, reducing unnecessary computation.

[0146] Preferably, the fast collision detection adopts a spatial index-based fast detection method. In the index space, the R-tree index is used to index the aircraft position, quickly query the nearby targets, and use the direction-velocity vector method to predict the conflict. The relative velocity vector of the two aircraft is calculated. If the intersection angle is less than the threshold and the distance decreases, it is marked as a potential conflict.

[0147] Specifically, this applies when a potential conflict has been detected.

[0148] Multi-source heterogeneous sensors are used to collect aircraft status data in potential conflict areas in real time. This status data includes, but is not limited to, position, velocity, heading, and acceleration. Data preprocessing includes Kalman filtering for noise reduction and spatiotemporal alignment to ensure the reliability of the input data.

[0149] Based on real-time aircraft status data, a dynamic model is used to predict future trajectories and trigger conflict prediction. Preferably, a point mass model is used for fixed-wing aircraft and a maneuver constraint model is used for multi-rotor aircraft. Historical trajectory data is trained using an LSTM neural network to optimize short-term prediction accuracy. Real-time meteorological data (wind field, turbulence) and airspace restrictions (no-fly zones, flight paths) are introduced to constrain the prediction data to obtain the final predicted flight trajectory.

[0150] For example, when a logistics drone is flying in strong crosswind conditions, the system combines its dynamic model (maximum wind resistance of 15 m / s) and real-time wind field data to predict its future trajectory (it may reach point A or point B in 15 seconds).

[0151] Next, based on the aforementioned baseline trajectory, we will comprehensively consider uncertainties such as the aircraft's maneuverability and weather conditions to calculate the position confidence interval and probability of conflict of the baseline trajectory, and assess the uncertainty of the above trajectory prediction.

[0152] For example, at a certain moment, the trajectory prediction model gives the predicted position of the aircraft as point A (X0, Y0, Z0) and its velocity as (V). x0 Vy0 V z0 Uncertainty quantification, on the other hand, analyzes the possible range of position deviations caused by various uncertain factors, based on this position and velocity.

[0153] Specifically, uncertainty quantification is achieved through Monte Carlo simulation. Sensor accuracy errors, model errors, environmental disturbances, and the predicted trajectory data output from the aforementioned trajectory prediction modeling are input into the Monte Carlo simulation to obtain the predicted trajectory position confidence interval and the probability conflict risk map. The position confidence interval is used to predict the possible deviation range around the predicted position; the probability conflict risk map is used to display the probability distribution of collisions between aircraft.

[0154] Monte Carlo simulations were used to simulate 1000 flight trajectories to obtain the position confidence interval (deviation range) for the predicted trajectory. This position confidence interval directly reflects the reliability of the trajectory prediction. If the position confidence interval (deviation range) is too large, it indicates high uncertainty in the trajectory prediction, requiring improvements to certain aspects of the trajectory prediction modeling. For example, if the uncertainty of meteorological data is found to cause the position confidence interval to be too large, then it is advisable to consider introducing a more accurate meteorological data source or improving the way meteorological data is processed in the trajectory prediction model.

[0155] In practical applications, trajectory prediction modeling and uncertainty quantification are iterative processes. First, preliminary trajectory prediction results are obtained through trajectory prediction modeling. Then, Monte Carlo simulation is performed. Based on the results of uncertainty quantification, the shortcomings of the trajectory prediction model are identified, and the trajectory prediction model is improved. Then, uncertainty quantification is performed again. This process is repeated until a near-accurate flight prediction trajectory and trajectory deviation range are obtained.

[0156] For example, the initially constructed trajectory prediction model may not have fully considered the impact of turbulence on multi-rotor UAVs. During uncertainty quantification, it was found that due to the uncertainty of turbulence, the position confidence interval was too large, leading to a high risk of probabilistic collisions. Therefore, the trajectory prediction model was improved by adding a correction term for the impact of turbulence, and uncertainty quantification was performed again to verify the improvement effect.

[0157] It is understandable that the flight prediction trajectories and trajectory deviation ranges obtained above are the flight prediction trajectories and trajectory deviation ranges of flight targets within the potential conflict area. Therefore, the minimum distance between flight targets can be calculated based on these prediction trajectories and trajectory deviation ranges. The minimum horizontal distance and minimum vertical distance are calculated based on the coordinates of the predicted trajectory points and the deviation range, and the distance range is compared with the distance threshold to obtain the conflict probability. A probability conflict risk map is generated. Through risk prediction, the areas and probabilities of high conflict risk between flight targets can be seen intuitively, and measures can be taken to provide early warning and manual intervention to reduce the probability of real-time conflict.

[0158] This example demonstrates how this embodiment constructs a benchmark index space based on a distributed grid benchmark and a spatiotemporal benchmark, performs hierarchical parallel traversal calculations within the benchmark index space to determine potential conflict areas, and calculates the minimum distance between aircraft within the potential conflict areas based on a trajectory prediction model and uncertainty quantification, thereby achieving real-time conflict identification between aircraft and improving the flight efficiency and safety of low-altitude aircraft.

[0159] As described above, the low-altitude flight target conflict identification method provided in this application can obtain the basic scale of the spatial grid based on the characteristics of the target airspace and the performance characteristics of the aircraft. It then divides the target airspace into multi-level grids based on difference logic and the basic scale of the spatial grid to construct a distributed grid reference space. The method performs altitude reference conversion and time reference synchronization on the flight data in the reference space and associates it with the reference space to obtain the target airspace index space. The target airspace index space is then hierarchically layered, and secondary fine-grained flight conflict detection is performed within each index space layer according to a preset time period to determine whether there is a potential flight conflict risk. If so, real-time status data of the flight targets is obtained, the minimum distance between flight targets is predicted, and the existence of an actual flight conflict risk is determined. This method can support real-time conflict identification of large-scale aircraft and improve the flight efficiency and safety of low-altitude aircraft.

[0160] In one embodiment of the low-altitude target conflict identification method of this application, see [link to relevant documentation]. Figure 2 It can also specifically include the following:

[0161] Step S201: Construct grid scale rules based on airspace type, and determine the corresponding basic grid scale according to the grid scale rules and preset target airspace characteristics. The airspace type includes urban airspace, suburban airspace and mountainous airspace.

[0162] Step S202: Determine the corresponding grid scale threshold based on the preset aircraft performance characteristics, constrain the basic grid scale based on the grid scale threshold, and determine the corresponding basic spatial grid scale.

[0163] Optionally, in this embodiment, traditional methods typically employ fixed-scale mesh generation, which cannot adapt to the dynamic requirements of different airspace characteristics (such as high-density urban flight and low-density mountain flight) and aircraft performance (such as high-speed fixed-wing vs. low-speed multi-rotor). This step dynamically adjusts the mesh scale to avoid excessive subdivision in low-density airspace (wasting computational power) or excessively large meshes in high-density airspace (reducing collision detection accuracy), ensuring that the mesh scale matches the aircraft's motion characteristics, reducing invalid computations, and improving real-time performance.

[0164] Optionally, in this embodiment, the airspace feature is the type feature to which the airspace belongs, including urban airspace, suburban airspace, and mountainous airspace. A corresponding grid scale rule is constructed based on the airspace type, as illustrated by the following example:

[0165] The urban airspace adopts a high-density grid (basic scale ≤ 500m);

[0166] Suburban airspace uses a medium-density grid (basic scale 500m to 2km);

[0167] Low-density grids (basic scale ≥ 2km) are used for airspace in mountainous areas;

[0168] The grid scale is dynamically adjusted based on airspace control levels (such as no-fly zones and restricted areas), increasing the grid density in controlled areas by 50%.

[0169] Specifically, aircraft performance characteristics are the flight attribute characteristics of an aircraft. A preset grid scale threshold is established based on attributes such as the aircraft's maximum speed and maneuver radius.

[0170] For high-speed fixed-wing aircraft (speed > 200 km / h), the grid size is ≥ 1 km;

[0171] For low-speed multirotor aircraft (speed < 50 km / h), the grid size is ≤ 300 m.

[0172] For mixed flight airspace, the minimum grid size is determined based on the performance of the aircraft with the lowest speed.

[0173] A specific example is provided for illustration:

[0174] Example 1: Assume a grid division of multi-rotor UAVs in urban airspace.

[0175] Airspace characteristics type: urban airspace; basic scale: 300m (high density requirements);

[0176] Aircraft: Logistics drones (speed ≤ 50km / h, maneuver radius ≤ 100m) → minimum grid size ≥ 100m;

[0177] The integrated airspace characteristics and aircraft performance characteristics are combined using a 200m×200m grid to ensure coverage of UAV maneuverability while avoiding excessive subdivision.

[0178] Example 2: Assume a grid division of fixed-wing aircraft in mountainous airspace.

[0179] Airspace characteristics type: mountainous airspace; basic scale: 2km (low density requirement);

[0180] Aircraft: General aviation fixed-wing aircraft (speed ≥ 200 km / h, maneuver radius ≥ 1 km), → grid scale ≥ 1 km;

[0181] The integrated airspace feature type and aircraft performance characteristics are combined using a 1.5km×1.5km grid to balance computational efficiency and conflict detection requirements.

[0182] Example 3: Hypothetical dynamic adjustment of mixed airspace. The airspace at the boundary between urban and suburban areas includes both UAVs and general aviation aircraft. Initial grid scale: 1 km (suburban baseline).

[0183] When a drone swarm is detected entering, the grid is subdivided to 500m.

[0184] When general aviation aircraft pass by, the grid is temporarily merged to 1km to reduce the computational load.

[0185] Through step S202, this embodiment dynamically determines the basic grid scale based on airspace feature type and aircraft performance characteristics, which can improve computational efficiency, reduce system resource consumption, and lay a solid foundation for supporting real-time conflict identification of large-scale aircraft in the future.

[0186] In one embodiment of the low-altitude flight target conflict identification method of this application, it may further include the following:

[0187] Step S301: Set the differential logic basis according to the minimum safe interval between flight targets, wherein the differential logic basis is the scale reduction ratio between control grid levels;

[0188] Step S302: Using the latitude and longitude coordinates of the boundary vertex of the target airspace as the initial range of the top-level grid, and the basic scale of the spatial grid as the minimum range, the initial range is divided into hierarchical progressive grids according to the difference logic cardinality to determine the corresponding spatial grids that conform to the minimum range.

[0189] Optionally, in this embodiment, dynamic layering driven by n-difference logic is applied.

[0190] First, define the cardinality n (n≥2) of the difference logic to control the scale reduction ratio between grid levels, preferably n=2 or n=3; based on the minimum safe separation and maximum maneuverability of the aircraft, dynamically adjust the difference logic parameters of different altitude levels to ensure that the grid scale matches the flight safety requirements.

[0191] Then, input the latitude and longitude coordinates of the boundary vertices of the target airspace, generate the minimum bounding rectangle (MBR) of the airspace through the convex hull algorithm, and use the MBR as the initial range of the top-level grid (maximum grid), that is, the level 0 grid, and divide it into n×n subgrids (e.g., 4 subgrids) according to the cardinality n of the difference logic.

[0192] Next, the above subgrid division process is repeated for each subgrid until the grid scale reaches the preset minimum basic scale (basic scale of the spatial grid). A buffer boundary (e.g., 50m) is reserved during grid division to avoid frequent grid switching caused by aircraft flying across grids. In the vertical direction, grids are independently divided according to altitude layers (120m, 300m, etc.) to ensure that the two-dimensional planar scale of the grid is dynamically correlated with the altitude layer. At this point, spatial grids at different levels of the target airspace are obtained.

[0193] Through step S302, this embodiment obtains spatial grids of different levels in the target spatial domain based on dynamic hierarchical layering driven by n-difference logic, laying a data foundation for the subsequent construction of the benchmark index space.

[0194] In one embodiment of the low-altitude flight target conflict identification method of this application, it may further include the following:

[0195] Step S401: Perform a unique code generation operation on the spatial grid according to the space filling curve to determine the unique code corresponding to each spatial grid;

[0196] Step S402: Construct a graph database to record the unique codes and spatial relationships of each spatial grid with its adjacent grids, and determine the corresponding distributed grid reference space.

[0197] Optionally, in this embodiment, a space-filling curve (Z-order curve) is used to generate a unique code for each grid, ensuring the locality of the code and fast retrieval, and establishing a multi-level grid index table to support the rapid location of the target grid and its adjacent grids by latitude and longitude coordinates or grid codes.

[0198] For each grid cell, its 26 neighboring grids are recorded (including 8 horizontal neighbors and 9 vertical neighbors above and below). A directed graph is used to store the spatial relationships between grid cells, with edge weights representing the distance to the grid center points. This establishes the topological relationships between grid cells, supporting efficient range queries. At this point, the target spatial domain is successfully decomposed into a distributed grid baseline space.

[0199] Through step S402, this embodiment successfully obtained the distributed grid reference space, laying the spatial foundation for the subsequent construction of the reference index space.

[0200] In one embodiment of the low-altitude flight target conflict identification method of this application, it may further include the following:

[0201] Step S501: Construct an altitude reference transformation matrix to perform standard data transformation on the heterogeneous altitude data in the flight data, and determine the corresponding unified altitude reference value;

[0202] Step S502: Deploy a three-level time synchronization network, sort the flight data based on the time synchronization network, and determine the corresponding unified time reference value. The three-level time synchronization network includes a first-level node, a second-level node, and a third-level node. The first-level node is used to connect to the Beidou atomic clock time source. The second-level node is used to synchronize the time difference through the fiber optic PTP protocol. The third-level node is used to synchronize the time difference using NTP-over-4G.

[0203] Step S503: Combine the unified altitude reference value and the unified time reference value to determine the corresponding flight target reference data.

[0204] Optionally, in this embodiment, a data receiving interface cluster is established to receive heterogeneous flight data through multiple communication links such as ADS-B, 4G / 5G, and BeiDou short message. The received raw flight data packets are parsed to extract key fields including: aircraft identification code, latitude and longitude coordinates, altitude value, timestamp, velocity vector, etc.

[0205] Regarding altitude references, a data transformation matrix is ​​used to uniformly convert the altitude references used by different aircraft (such as geometric altitude, barometric altitude, etc.) into a standard reference. The system receives altitude data reported by different aircraft and automatically identifies their altitude reference type, including but not limited to:

[0206] Barometric altimeter (QNH / QFE): from barometric altimeters on civil and general aviation aircraft;

[0207] GPS geometric altitude: from the GNSS module of the drone;

[0208] Radar Altitude (AGL): Radio altimeter from the helicopter;

[0209] Hybrid data: Some aircraft provide multiple altitude sources simultaneously.

[0210] Different conversion models are used depending on the type of height source:

[0211] 1. Convert barometric altitude to geometric altitude

[0212]

[0213] in:

[0214] Hbaro is the barometric altitude reported by the aircraft;

[0215] Pobs provides corrected sea level pressure for the current weather station;

[0216] Pstd is the standard sea level pressure (1013.25 hPa);

[0217] ρ is the air density (dynamically calculated based on temperature and humidity);

[0218] g is the acceleration due to gravity (adjusted with latitude).

[0219] Suppose a general aviation aircraft reports a barometric altitude of 500m, the current weather station measures Pobs = 1005hPa, the calculated geometric altitude correction value is +8.2m, and the final Hgeo = 508.2m.

[0220] 2. Convert GPS ellipsoidal height to geodetic height (MSL)

[0221] Earth height HMSL = Helipsoid - N

[0222] in:

[0223] Hellipsoid provides the ellipsoidal height for the GNSS module;

[0224] N is the elevation anomaly value calculated by calling the localized geoid model (EGM2008);

[0225] Assuming a drone's GPS reports an ellipsoidal height of 300m and a regional elevation anomaly N = 25m, then the corrected HMSL (Height of the UAV) is...

[0226] =275m.

[0227] 3. Convert radar altitude (AGL) to geometric altitude

[0228] Hgeo = Hground + HAGL

[0229] in:

[0230] HAGL stands for altitude above ground as measured by a radio altimeter.

[0231] Hground is the ground elevation of the current location obtained from the digital elevation model (DEM);

[0232] Assuming the helicopter is flying in the mountains, the radar measures its altitude at 150m, and the DEM shows the ground elevation at 1000m, then Hgeo = 1150m.

[0233] At the aforementioned altitude reference, meteorological data (temperature, air pressure, humidity) is accessed in real time to dynamically adjust the air density ρ and gravity compensation coefficient. For aircraft providing altitude data from multiple sources simultaneously, Kalman filtering is used to fuse the data and reduce errors from individual sensors.

[0234] The implementation of the height reference is illustrated with specific examples and application scenarios:

[0235] Suppose that a city's airspace simultaneously contains civilian airliners (barometric altitude reference), logistics drones (GPS altitude), and emergency rescue helicopters (radar altitude).

[0236] Civilian passenger plane reports: Hbaro = 800m, current weather station Pobs = 1008hPa;

[0237] Drone reported: GPS ellipsoid height Hellipsoid = 820m;

[0238] The helicopter reported: Radar altitude HAGL = 200m, DEM shows ground elevation 600m.

[0239] After height conversion:

[0240] The altitude of a civil airliner is 1237.5m.

[0241] Drone: HMSL = 790m;

[0242] Helicopter: Hgeo = 600 + 200 = 800m;

[0243] After conversion, the height was unified to the geometric height, and the test found no conflict between the civil airliner (1237.5m), the drone (790m), and the helicopter (800m).

[0244] Specifically, regarding the time reference, a unified time coordinate system is established to address the time synchronization issues among different devices. This step proposes a hierarchical collaborative method for unifying the time reference, achieving microsecond-level time alignment through a three-tiered architecture of "global time synchronization - regional synchronization - terminal compensation".

[0245] Specifically, deploy a global high-precision time synchronization network.

[0246] Deploy a BeiDou / GPS dual-mode timing server with a rubidium atomic clock, supporting PTP (IEEE 1588v2) and NTP protocols; prioritize satellite timing signals, and switch to local atomic clock timekeeping mode when satellite signals fail.

[0247] For example:

[0248] Deploy BeiDou / GPS dual-mode timing base stations, setting up one Class I reference station for every 100 square kilometers. Each base station outputs a 1PPS pulse signal (rising edge accuracy ±30ns) and an IRIG-B code time message (containing UTC time and leap second information), and then distributes the time signal to secondary nodes via fiber optic PTP.

[0249] Specifically, device-level time synchronization is implemented.

[0250] Each secondary node is connected to at least two primary nodes. The BMC (Best Master Clock) algorithm is used to select the optimal time source. The airborne equipment receives the IRIG-B code through a dedicated fiber optic network. The Flight Management System (FMS) uses an FPGA to implement the clock discipline circuit and hardware timestamps the PTP messages to avoid operating system scheduling delays.

[0251] Preferably, time synchronization is achieved using the IEEE 1588v2 precision protocol, with ±200ns synchronization achieved through Wi-Fi 6 air interface timestamps.

[0252] Specifically, time alignment at the data processing layer.

[0253] High-priority airborne equipment receives time signals via PTP-over-Aviation-Ethernet with a synchronization error of <10μs, and the data logger writes precise timestamps.

[0254] Low-power devices employ a lightweight NTP protocol, synchronizing via 4G / 5G networks with an error of <10ms. Within the cluster, microsecond-level relative synchronization is achieved through TDMA time slot allocation. This method ensures a unified time reference value.

[0255] Through step S503, this embodiment successfully constructed a unified spatiotemporal reference framework, laying the foundation for subsequent flight conflict detection.

[0256] In one embodiment of the low-altitude flight target conflict identification method of this application, it may further include the following:

[0257] Step S601: Divide the target spatial index space into multiple sub-space index layers according to the height layer. The height layers include 120 meters, 300 meters, 600 meters, 1000 meters and 4000 meters. The 4000-meter height layer corresponds to the large-scale spatial index layer, the 300-meter, 600-meter and 1000-meter height layers correspond to the medium-scale spatial index layers, and the 120-meter height layer corresponds to the refined spatial index layer.

[0258] Step S602: Divide the time dimension into multiple calculation cycles, and perform a fast conflict scan on the large-scale spatial index layer within each calculation cycle to filter out potential conflict regions;

[0259] Step S603: Perform a detailed analysis of the potential conflict area to determine whether there is a potential flight conflict risk.

[0260] Optionally, in this embodiment, based on a unified spatiotemporal reference, a fast traversal calculation is performed to discover potential conflicts.

[0261] Specifically, in terms of time, the time dimension is divided into multiple computation cycles, with periods of 15 seconds, 1 minute, 15 minutes, and 30 minutes, respectively. An independent task queue is allocated for parallel computation in each cycle. For each computation cycle time slice, in terms of space, it is traversed in layers according to different heights, such as 120 meters, 300 meters, 600 meters, 1000 meters, and 4000 meters.

[0262] Understandably, short cycles (15 seconds) are used for real-time conflict early warning, prioritizing the calculation of highly dynamic targets (such as drones); medium cycles (1 minute) are used for conflict scanning of conventional aircraft (such as helicopters and general aviation aircraft); and long cycles (more than 15 minutes) are used for airspace situation analysis (such as traffic hotspot prediction).

[0263] Specifically, in the spatial dimension, the grid is divided according to altitude levels, employing a "coarse screening + fine calculation" strategy to reduce computational load. The airspace grid is divided into multiple subspaces according to altitude levels (120 meters, 300 meters, 600 meters, 1000 meters, 4000 meters). Since the spatial grid is constructed from top to bottom and from large to small, the grid size corresponding to altitude levels of 4000 meters and above is large-scale, while the grid size corresponding to altitude levels of 120 meters and below is small-scale, and the rest are medium-scale. First, in the large-scale grid (4000-meter altitude level), rapid collision detection is performed based on the aircraft's position and velocity vectors to filter out potential conflict areas. For the filtered areas, local calculations are triggered in the finer grid (the next scale) until the smallest conflict area is found, reducing unnecessary computation.

[0264] Preferably, the fast collision detection adopts a spatial index-based fast detection method. In the index space, the R-tree index is used to index the aircraft position, quickly query the nearby targets, and use the direction-velocity vector method to predict the conflict. The relative velocity vector of the two aircraft is calculated. If the intersection angle is less than the threshold and the distance decreases, it is marked as a potential conflict.

[0265] Through step S603, this embodiment successfully obtained potential conflict regions based on secondary fine-grained conflict detection, laying the foundation for subsequent real-time conflict analysis and early warning.

[0266] In one embodiment of the low-altitude flight target conflict identification method of this application, it may further include the following:

[0267] Step S701: Construct a short-term trajectory prediction algorithm based on the aircraft dynamics model, predict the flight trajectory of the flight target according to the short-term trajectory prediction algorithm, introduce meteorological data and airspace constraints to constrain the flight trajectory after the flight trajectory prediction, and determine the corresponding predicted flight trajectory.

[0268] Step S702: Predict the minimum distance between the flight targets based on the predicted flight trajectory, and determine the corresponding minimum horizontal distance and minimum vertical distance.

[0269] Optionally, in this embodiment, multi-source heterogeneous sensors are used to collect aircraft status data in the potential conflict area in real time. This status data includes, but is not limited to, position, velocity, heading, and acceleration. Data preprocessing includes Kalman filtering for noise reduction and spatiotemporal alignment to ensure the reliability of the input data.

[0270] Based on real-time aircraft status data, a dynamic model is used to predict future trajectories and trigger conflict prediction. Preferably, a point mass model is used for fixed-wing aircraft and a maneuver constraint model is used for multi-rotor aircraft. Historical trajectory data is trained using an LSTM neural network to optimize short-term prediction accuracy. Real-time meteorological data (wind field, turbulence) and airspace restrictions (no-fly zones, flight paths) are introduced to constrain the prediction data to obtain the final predicted flight trajectory.

[0271] For example, when a logistics drone is flying in strong crosswind conditions, the system combines its dynamic model (maximum wind resistance of 15 m / s) and real-time wind field data to predict its future trajectory (it may reach point A or point B in 15 seconds).

[0272] Next, based on the aforementioned baseline trajectory, we will comprehensively consider uncertainties such as the aircraft's maneuverability and weather conditions to calculate the position confidence interval and probability of conflict of the baseline trajectory, and assess the uncertainty of the above trajectory prediction.

[0273] For example, at a certain moment, the trajectory prediction model gives the predicted position of the aircraft as point A (X0, Y0, Z0) and its velocity as (Vx0, Vy0, Vz0). Uncertainty quantification, on the other hand, analyzes the possible deviation range of the position due to various uncertainties based on this position and velocity.

[0274] Specifically, uncertainty quantification is achieved through Monte Carlo simulation. Sensor accuracy errors, model errors, environmental disturbances, and the predicted trajectory data output from the aforementioned trajectory prediction modeling are input into the Monte Carlo simulation to obtain the predicted trajectory position confidence interval and the probability conflict risk map. The position confidence interval is used to predict the possible deviation range around the predicted position; the probability conflict risk map is used to display the probability distribution of collisions between aircraft.

[0275] Monte Carlo simulations were used to simulate 1000 flight trajectories to obtain the position confidence interval (deviation range) for the predicted trajectory. This position confidence interval directly reflects the reliability of the trajectory prediction. If the position confidence interval (deviation range) is too large, it indicates high uncertainty in the trajectory prediction, requiring improvements to certain aspects of the trajectory prediction modeling. For example, if the uncertainty of meteorological data is found to cause the position confidence interval to be too large, then it is advisable to consider introducing a more accurate meteorological data source or improving the way meteorological data is processed in the trajectory prediction model.

[0276] In practical applications, trajectory prediction modeling and uncertainty quantification are iterative processes. First, preliminary trajectory prediction results are obtained through trajectory prediction modeling. Then, Monte Carlo simulation is performed. Based on the results of uncertainty quantification, the shortcomings of the trajectory prediction model are identified, and the trajectory prediction model is improved. Then, uncertainty quantification is performed again. This process is repeated until a near-accurate flight prediction trajectory and trajectory deviation range are obtained.

[0277] For example, the initially constructed trajectory prediction model may not have fully considered the impact of turbulence on multi-rotor UAVs. During uncertainty quantification, it was found that due to the uncertainty of turbulence, the position confidence interval was too large, leading to a high risk of probabilistic collisions. Therefore, the trajectory prediction model was improved by adding a correction term for the impact of turbulence, and uncertainty quantification was performed again to verify the improvement effect.

[0278] It is understandable that the flight prediction trajectories and trajectory deviation ranges obtained above are the flight prediction trajectories and trajectory deviation ranges of flight targets within the potential conflict area. Therefore, the minimum distance between flight targets can be calculated based on these prediction trajectories and trajectory deviation ranges. The minimum horizontal distance and minimum vertical distance are calculated based on the coordinates of the predicted trajectory points and the deviation range, and the distance range is compared with the distance threshold to obtain the conflict probability. A probability conflict risk map is generated. Through risk prediction, the areas and probabilities of high conflict risk between flight targets can be seen intuitively, and measures can be taken to provide early warning and manual intervention to reduce the probability of real-time conflict.

[0279] Through step S702, this embodiment successfully calculates the minimum distance between aircraft within the potential conflict area based on the trajectory prediction model and uncertainty quantification, thereby achieving real-time conflict identification between aircraft and improving the flight efficiency and safety of low-altitude aircraft.

[0280] To support real-time conflict identification for large-scale aircraft and improve the flight efficiency and safety of low-altitude aircraft, this application provides an embodiment of a low-altitude flight target conflict identification device for implementing all or part of the aforementioned low-altitude flight target conflict identification method. See [link to embodiment]. Figure 2 The low-altitude flight target conflict identification device specifically includes the following components:

[0281] The spatial reference construction module 10 is used to determine the corresponding basic scale of the spatial grid according to the preset target airspace characteristics and preset aircraft performance characteristics, to perform multi-level grid division of the target airspace according to the preset difference logic, the geographic coordinates of the target airspace range and the basic scale of the spatial grid, to determine the corresponding spatial grid, to construct the topological relationship between each spatial grid, and to determine the corresponding distributed grid reference space.

[0282] The index space construction module 20 is used to receive flight data of low-altitude flying targets, perform altitude reference conversion and time reference synchronization on the flight data, determine the corresponding flight target reference data, associate the flight target reference data with the distributed grid reference space, and determine the corresponding target airspace index space.

[0283] The flight conflict detection module 30 is used to perform altitude layering of the target airspace index space according to a preset altitude value, determine multiple corresponding index layers, perform secondary fine-grained flight conflict detection on each index layer according to a preset time period, determine whether there is a potential flight conflict risk, and if so, acquire real-time status data of flight targets in the potential flight conflict area, predict the flight trajectory of the real-time status data of the flight targets according to a preset aircraft dynamics model, determine the minimum distance between the corresponding flight targets, and determine whether there is an actual flight conflict risk based on the minimum distance.

[0284] As described above, the low-altitude flight target conflict identification device provided in this application embodiment can obtain the basic scale of the spatial grid based on the characteristics of the target airspace and the performance characteristics of the aircraft. It then divides the target airspace into multi-level grids based on difference logic and the basic scale of the spatial grid to construct a distributed grid reference space. The device performs altitude reference conversion and time reference synchronization on the flight data in the reference space and associates it with the reference space to obtain the target airspace index space. It performs altitude-level layering of the target airspace index space and performs secondary fine-grained flight conflict detection within each index space layer according to a preset time period to determine whether there is a potential flight conflict risk. If so, it acquires real-time status data of the flight targets, predicts the minimum distance between flight targets, and determines whether there is an actual flight conflict risk. This enables real-time conflict identification of large-scale aircraft and improves the flight efficiency and safety of low-altitude aircraft.

[0285] To further illustrate this solution, this application also provides a specific application example of using the aforementioned low-altitude flight target conflict identification device to implement the low-altitude flight target conflict identification method, which specifically includes the following:

[0286] Step 1: Establish a distributed spatial grid baseline. First, determine the basic grid scale based on airspace characteristics, and divide the airspace into different levels of grids according to n-difference logic. A top-down approach is adopted, starting with the largest grid and progressively subdividing it to form a grid tree structure. Each grid is assigned a unique code for easy indexing. Simultaneously, topological relationships between grids are established to support correlation analysis between adjacent grids. The performance characteristics of the aircraft are fully considered during grid division to ensure that the grid scale is adapted to flight characteristics.

[0287] Step 2: Achieve unified spatiotemporal reference. A unified spatiotemporal reference framework will be established to address the differences in characteristics among different types of aircraft. Regarding altitude references, altitude references used by different aircraft (such as geometric altitude, barometric altitude, etc.) will be uniformly converted to a standard reference. Regarding time references, a unified time coordinate system will be established to solve the time synchronization problem for different equipment. Through reference unification, the position descriptions and time synchronization of multiple entities, including civil aviation, general aviation, and unmanned aerial vehicles, will be achieved within the same framework.

[0288] Step 3: Perform fast traversal computation. In the time dimension, traversal computation is performed with periods of 15 seconds, 1 minute, 15 minutes, and 30 minutes. For each time slice, in the spatial dimension, traversal is performed in layers with different heights: 120 meters, 300 meters, 600 meters, 1000 meters, and 4000 meters. Parallel computation is employed, first performing a fast collision scan on the large-scale grid to identify potential collision regions, and then performing a refined analysis on these regions. During the traversal process, the hierarchical structure of the grid is fully utilized to achieve rapid computational convergence.

[0289] Step 4: Perform precise conflict identification. For potential conflict areas identified during the rapid traversal, initiate the precise identification process. First, acquire real-time status data of aircraft within the area, including position, speed, and heading. Then, based on the aircraft's dynamics model, predict their flight trajectories for the next 15 seconds. Considering factors such as aircraft maneuverability and weather conditions, assess the uncertainty of the trajectory prediction. Finally, through trajectory simulation, calculate the minimum distance between aircraft to determine if there is an actual risk of conflict.

[0290] From a hardware perspective, in order to support real-time conflict identification of large-scale aircraft and improve the flight efficiency and safety of low-altitude aircraft, this application provides an embodiment of an electronic device for implementing all or part of the aforementioned low-altitude flight target conflict identification method. The electronic device specifically includes the following components:

[0291] The system comprises a processor, memory, a communications interface, and a bus; wherein the processor, memory, and communications interface communicate with each other via the bus; the communications interface is used to realize information transmission between the low-altitude flight target conflict identification method and core business systems, user terminals, and related databases and other related devices; the logic controller can be a desktop computer, tablet computer, or mobile terminal, etc., and this embodiment is not limited to these. In this embodiment, the logic controller can be implemented with reference to the embodiments of the low-altitude flight target conflict identification method in the present embodiment, and the contents of the embodiments of the low-altitude flight target conflict identification method are incorporated herein, and repeated parts will not be described again.

[0292] It is understood that the user terminal may include smartphones, tablet computers, network set-top boxes, portable computers, desktop computers, personal digital assistants (PDAs), in-vehicle devices, smart wearable devices, etc. Among these, the smart wearable devices may include smart glasses, smartwatches, smart bracelets, etc.

[0293] In practical applications, parts of the low-altitude target conflict identification method can be executed on the electronic device side as described above, or all operations can be completed in the client device. The choice can be made based on the processing power of the client device and the limitations of the user's usage scenario. This application does not impose any limitations on this. If all operations are completed in the client device, the client device may further include a processor.

[0294] The aforementioned client device may have a communication module (i.e., a communication unit) that can communicate with a remote server to achieve data transmission. The server may include a server on the task scheduling center side; in other implementation scenarios, it may also include a server on an intermediate platform, such as a server on a third-party server platform that has a communication link with the task scheduling center server. The server may include a single computer device, a server cluster consisting of multiple servers, or a distributed server structure.

[0295] Figure 3 This is a schematic block diagram illustrating the system configuration of the electronic device 9600 according to an embodiment of this application. Figure 3 As shown, the electronic device 9600 may include a central processing unit 9100 and a memory 9140; the memory 9140 is coupled to the central processing unit 9100. It is worth noting that... Figure 3 This is an example; other types of structures can also be used to supplement or replace this structure to achieve telecommunications functions or other functions.

[0296] In one embodiment, the low-altitude target conflict identification method function can be integrated into the central processing unit 9100. The central processing unit 9100 can be configured to perform the following control:

[0297] Step S101: Determine the corresponding basic scale of the spatial grid based on the preset target airspace characteristics and preset aircraft performance characteristics; divide the target airspace into multi-level grids based on the preset difference logic, the geographic coordinates of the target airspace range and the basic scale of the spatial grid; determine the corresponding spatial grids; construct the topological relationship between each spatial grid; and determine the corresponding distributed grid reference space.

[0298] Step S102: Receive flight data of low-altitude flying targets, perform altitude reference conversion and time reference synchronization on the flight data, determine the corresponding flight target reference data, associate the flight target reference data with the distributed grid reference space, and determine the corresponding target airspace index space.

[0299] Step S103: The target airspace index space is height-stratified according to a preset altitude value to determine multiple corresponding index layers. Secondary fine-grained flight conflict detection is performed on each index layer according to a preset time period to determine whether there is a potential flight conflict risk. If so, real-time status data of flight targets in the potential flight conflict area is obtained. Flight trajectory prediction is performed on the real-time status data of the flight targets according to a preset aircraft dynamics model to determine the minimum distance between the corresponding flight targets. The existence of an actual flight conflict risk is determined based on the minimum distance.

[0300] As described above, the electronic device provided in this application embodiment obtains the basic scale of the spatial grid based on the characteristics of the target airspace and the performance characteristics of the aircraft. It then divides the target airspace into multi-level grids based on difference logic and the basic scale of the spatial grid, constructing a distributed grid reference space. The flight data in the reference space undergoes altitude reference conversion and time reference synchronization, and is associated with the reference space to obtain the target airspace index space. The target airspace index space is then hierarchically layered, and secondary fine-grained flight conflict detection is performed within each index space layer according to a preset time period to determine whether there is a potential flight conflict risk. If so, real-time status data of the flight targets is obtained, the minimum distance between flight targets is predicted, and the existence of an actual flight conflict risk is determined. This enables real-time conflict identification of large-scale aircraft, improving the flight efficiency and safety of low-altitude aircraft.

[0301] In another embodiment, the low-altitude flight target conflict identification method can be configured separately from the central processing unit 9100. For example, the low-altitude flight target conflict identification method can be configured as a chip connected to the central processing unit 9100, and the function of the low-altitude flight target conflict identification method can be realized through the control of the central processing unit.

[0302] like Figure 3 As shown, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily need to include these components. Figure 3 All components shown; in addition, the electronic device 9600 may also include Figure 3 For components not shown, please refer to existing technologies.

[0303] like Figure 3 As shown, the central processing unit 9100, sometimes also referred to as a controller or operating control, may include a microprocessor or other processor device and / or logic device, which receives input and controls the operation of various components of the electronic device 9600.

[0304] The memory 9140 may be, for example, one or more of a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices. It may store the aforementioned failure-related information, and also store a program for executing that information. The central processing unit 9100 may execute the program stored in the memory 9140 to perform information storage or processing, etc.

[0305] Input unit 9120 provides input to central processing unit 9100. Input unit 9120 may be, for example, a keypad or touch input device. Power supply 9170 provides power to electronic device 9600. Display 9160 displays images and text. Display may be, for example, an LCD display, but is not limited thereto.

[0306] The memory 9140 can be a solid-state memory, such as a read-only memory (ROM), random access memory (RAM), a SIM card, etc. It can also be a memory that retains information even when power is off, can be selectively erased, and contains more data; examples of this type of memory are sometimes referred to as EPROMs. The memory 9140 can also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application / function storage unit 9142 for storing application programs and function programs or processes for executing the operation of the electronic device 9600 via the central processing unit 9100.

[0307] The memory 9140 may also include a data storage unit 9143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 may include various drivers for the electronic device's communication functions and / or for performing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0308] The communication module 9110 is a transmitter / receiver that sends and receives signals via the antenna 9111. The communication module 9110 is coupled to the central processing unit 9100 to provide input signals and receive output signals, which is the same as in a conventional mobile communication terminal.

[0309] Based on different communication technologies, multiple communication modules 9110 can be configured in the same electronic device, such as cellular network modules, Bluetooth modules, and / or wireless LAN modules. The communication module 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and receive audio input from the microphone 9132, thereby realizing typical telecommunications functions. The audio processor 9130 may include any suitable buffer, decoder, amplifier, etc. Furthermore, the audio processor 9130 is also coupled to a central processing unit 9100, enabling on-device recording via the microphone 9132 and on-device playback of stored sound via the speaker 9131.

[0310] Embodiments of this application also provide a computer-readable storage medium capable of implementing all steps of the low-altitude target conflict identification method with a server or client as the execution subject in the above embodiments. The computer-readable storage medium stores a computer program that, when executed by a processor, implements all steps of the low-altitude target conflict identification method with a server or client as the execution subject in the above embodiments. For example, when the processor executes the computer program, it implements the following steps:

[0311] Step S101: Determine the corresponding basic scale of the spatial grid based on the preset target airspace characteristics and preset aircraft performance characteristics; divide the target airspace into multi-level grids based on the preset difference logic, the geographic coordinates of the target airspace range and the basic scale of the spatial grid; determine the corresponding spatial grids; construct the topological relationship between each spatial grid; and determine the corresponding distributed grid reference space.

[0312] Step S102: Receive flight data of low-altitude flying targets, perform altitude reference conversion and time reference synchronization on the flight data, determine the corresponding flight target reference data, associate the flight target reference data with the distributed grid reference space, and determine the corresponding target airspace index space.

[0313] Step S103: The target airspace index space is height-stratified according to a preset altitude value to determine multiple corresponding index layers. Secondary fine-grained flight conflict detection is performed on each index layer according to a preset time period to determine whether there is a potential flight conflict risk. If so, real-time status data of flight targets in the potential flight conflict area is obtained. Flight trajectory prediction is performed on the real-time status data of the flight targets according to a preset aircraft dynamics model to determine the minimum distance between the corresponding flight targets. The existence of an actual flight conflict risk is determined based on the minimum distance.

[0314] As described above, the computer-readable storage medium provided in this application embodiment obtains the basic scale of the spatial grid based on the characteristics of the target airspace and the performance characteristics of the aircraft. It then divides the target airspace into multi-level grids based on difference logic and the basic scale of the spatial grid, constructing a distributed grid reference space. Flight data in the reference space undergoes altitude reference conversion and time reference synchronization, and is associated with the reference space to obtain the target airspace index space. The target airspace index space is then hierarchically layered, and secondary fine-grained flight conflict detection is performed within each index space layer according to a preset time period to determine if there is a potential flight conflict risk. If so, real-time status data of the flight targets is obtained, the minimum distance between flight targets is predicted, and the existence of an actual flight conflict risk is determined. This enables real-time conflict identification of large-scale aircraft, improving the flight efficiency and safety of low-altitude aircraft.

[0315] Embodiments of this application also provide a computer program product capable of implementing all steps of the low-altitude flight target conflict identification method with the execution subject being a server or client in the above embodiments. When executed by a processor, this computer program / instruction implements the steps of the low-altitude flight target conflict identification method. For example, the computer program / instruction implements the following steps:

[0316] Step S101: Determine the corresponding basic scale of the spatial grid based on the preset target airspace characteristics and preset aircraft performance characteristics; divide the target airspace into multi-level grids based on the preset difference logic, the geographic coordinates of the target airspace range and the basic scale of the spatial grid; determine the corresponding spatial grids; construct the topological relationship between each spatial grid; and determine the corresponding distributed grid reference space.

[0317] Step S102: Receive flight data of low-altitude flying targets, perform altitude reference conversion and time reference synchronization on the flight data, determine the corresponding flight target reference data, associate the flight target reference data with the distributed grid reference space, and determine the corresponding target airspace index space.

[0318] Step S103: The target airspace index space is height-stratified according to a preset altitude value to determine multiple corresponding index layers. Secondary fine-grained flight conflict detection is performed on each index layer according to a preset time period to determine whether there is a potential flight conflict risk. If so, real-time status data of flight targets in the potential flight conflict area is obtained. Flight trajectory prediction is performed on the real-time status data of the flight targets according to a preset aircraft dynamics model to determine the minimum distance between the corresponding flight targets. The existence of an actual flight conflict risk is determined based on the minimum distance.

[0319] As described above, the computer program product provided in this application embodiment obtains the basic scale of the spatial grid based on the characteristics of the target airspace and the performance characteristics of the aircraft. It then divides the target airspace into multi-level grids based on difference logic and the basic scale of the spatial grid, constructing a distributed grid reference space. The flight data in the reference space undergoes altitude reference conversion and time reference synchronization, and is associated with the reference space to obtain the target airspace index space. The target airspace index space is then hierarchically layered, and secondary fine-grained flight conflict detection is performed within each index space layer according to a preset time period to determine whether there is a potential flight conflict risk. If so, real-time status data of the flight targets is obtained, the minimum distance between flight targets is predicted, and the existence of an actual flight conflict risk is determined. This enables real-time conflict identification of large-scale aircraft, improving the flight efficiency and safety of low-altitude aircraft.

[0320] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0321] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0322] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0323] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0324] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A method for identifying collisions of low-altitude flying targets, characterized in that, The method includes: A grid scale rule is constructed based on airspace type. The corresponding basic grid scale is determined according to the grid scale rule and preset target airspace characteristics. The airspace type includes urban airspace, suburban airspace, and mountainous airspace. A corresponding grid scale threshold is determined based on preset aircraft performance characteristics. The basic grid scale is constrained by the grid scale threshold to determine the corresponding spatial grid basic scale. The aircraft performance characteristics are the aircraft's flight attribute characteristics. The preset grid scale threshold is based on the aircraft's maximum speed and maneuver radius attributes. The target airspace is divided into multi-level grids based on preset difference logic, the geographic coordinates of the target airspace range, and the spatial grid basic scale to determine the corresponding spatial grids. The topological relationships between the spatial grids are constructed to determine the corresponding distributed grid reference space. Receive flight data of low-altitude flying targets, perform altitude reference conversion and time reference synchronization on the flight data, determine the corresponding flight target reference data, associate the flight target reference data with the distributed grid reference space, and determine the corresponding target airspace index space; The target airspace index space is height-stratified according to a preset altitude value to determine multiple corresponding index layers. Secondary fine-grained flight conflict detection is performed on each index layer according to a preset time period to determine whether there is a potential flight conflict risk. If so, real-time status data of flight targets in the potential flight conflict area is obtained. Flight trajectory prediction is performed on the real-time status data of flight targets according to a preset aircraft dynamics model to determine the minimum distance between corresponding flight targets. The existence of actual flight conflict risk is determined based on the minimum distance.

2. The low-altitude target conflict identification method according to claim 1, characterized in that, The step of dividing the target airspace into multi-level grids based on preset difference logic, the geographic coordinates of the target airspace range, and the basic scale of the spatial grid, and determining the corresponding spatial grid, includes: The differential logic basis is set according to the minimum safe interval between flight targets, wherein the differential logic basis is the scale reduction ratio between control grid levels; The latitude and longitude coordinates of the boundary vertex of the target airspace range are used as the initial range of the top-level grid, and the basic scale of the spatial grid is used as the minimum range. The initial range is divided into hierarchical progressive grids according to the difference logic cardinality to determine the corresponding spatial grids that conform to the minimum range.

3. The low-altitude target conflict identification method according to claim 2, characterized in that, The process of constructing the topological relationships between the various spatial grids and determining the corresponding distributed grid reference space includes: A unique code generation operation is performed on the spatial grid based on the space filling curve to determine the unique code corresponding to each spatial grid. A graph database is constructed to record the unique codes and spatial relationships of each of the aforementioned spatial grids, thereby determining the corresponding distributed grid reference space.

4. The low-altitude target conflict identification method according to claim 1, characterized in that, The process of performing altitude reference conversion and time reference synchronization on the flight data to determine the corresponding flight target reference data includes: An altitude reference transformation matrix is ​​constructed to perform standard data transformation on the heterogeneous altitude data in the flight data, and the corresponding unified altitude reference value is determined. A three-level time synchronization network is deployed, and the flight data is sorted based on the time synchronization network to determine the corresponding unified time reference value. The three-level time synchronization network includes a first-level node, a second-level node, and a third-level node. The first-level node is used to connect to the Beidou atomic clock time source, the second-level node is used to synchronize the time difference through the fiber optic PTP protocol, and the third-level node is used to synchronize the time difference using NTP-over-4G. By combining the unified altitude reference value and the unified time reference value, the corresponding flight target reference data is determined.

5. The low-altitude target conflict identification method according to claim 1, characterized in that, The step of performing height-level layering of the target airspace index space according to a preset altitude value to determine multiple corresponding index layers, and performing secondary fine-grained flight conflict detection on each index layer according to a preset time period to determine whether there is a potential flight conflict risk, includes: The target spatial index space is divided into multiple sub-space index layers according to the height layer. The height layers include 120 meters, 300 meters, 600 meters, 1000 meters and 4000 meters. The 4000-meter height layer corresponds to the large-scale spatial index layer, the 300-meter, 600-meter and 1000-meter height layers correspond to the medium-scale spatial index layers, and the 120-meter height layer corresponds to the refined spatial index layer. The time dimension is divided into multiple calculation cycles, and a fast conflict scan is performed on the large-scale spatial index layer within each calculation cycle to filter out potential conflict regions. A detailed analysis of the potential conflict areas is conducted to determine whether there is a risk of potential flight conflict.

6. The low-altitude flight target conflict identification method according to claim 1, characterized in that, The step of predicting the flight trajectory of the flight target based on the real-time state data of the flight target according to the preset aircraft dynamics model, and determining the minimum distance between the corresponding flight targets, includes: A short-term trajectory prediction algorithm is constructed based on an aircraft dynamics model. The flight trajectory of the flight target is predicted according to the short-term trajectory prediction algorithm. Meteorological data and airspace constraints are introduced to constrain the flight trajectory after the flight trajectory prediction, and the corresponding predicted flight trajectory is determined. The minimum distance between the flight targets is predicted based on the predicted flight trajectory, and the corresponding minimum horizontal distance and minimum vertical distance are determined.

7. A low-altitude target conflict identification device, characterized in that, The device includes: The spatial reference construction module is used to construct grid scale rules based on airspace type, determine the corresponding basic grid scale according to the grid scale rules and preset target airspace characteristics, wherein the airspace type includes urban airspace, suburban airspace, and mountainous airspace; determine the corresponding grid scale threshold according to preset aircraft performance characteristics, constrain the basic grid scale according to the grid scale threshold, and determine the corresponding spatial grid basic scale, wherein the aircraft performance characteristics are the flight attribute characteristics of the aircraft, and the preset grid scale threshold is based on the aircraft's maximum speed and maneuver radius attribute characteristics; divide the target airspace into multi-level grids according to preset difference logic, the geographic coordinates of the target airspace range, and the spatial grid basic scale, determine the corresponding spatial grid, construct the topological relationship between the spatial grids, and determine the corresponding distributed grid reference space; The index space construction module is used to receive flight data of low-altitude flying targets, perform altitude reference conversion and time reference synchronization on the flight data, determine the corresponding flight target reference data, associate the flight target reference data with the distributed grid reference space, and determine the corresponding target airspace index space. The flight conflict detection module is used to perform altitude layering of the target airspace index space according to a preset altitude value, determine multiple corresponding index layers, perform secondary fine-grained flight conflict detection on each index layer according to a preset time period, determine whether there is a potential flight conflict risk, if so, acquire real-time status data of flight targets in the potential flight conflict area, predict the flight trajectory of the real-time status data of the flight targets according to a preset aircraft dynamics model, determine the minimum distance between the corresponding flight targets, and determine whether there is an actual flight conflict risk based on the minimum distance.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the low-altitude flight target conflict identification method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the low-altitude flight target conflict identification method according to any one of claims 1 to 6.

Images