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CN-121999640-A - Airspace situation awareness system and method for electric vertical take-off and landing aircraft (eVTOL)

CN121999640ACN 121999640 ACN121999640 ACN 121999640ACN-121999640-A

Abstract

The invention discloses an airspace situation awareness system and method of an electric vertical take-off and landing aircraft (eVTOL). The system includes an edge computing node and eVTOL terminal devices. The system is characterized in that edge computing nodes collect multi-source data in real time, an airspace is divided into Beidou grid code airspace units and marked as red, yellow, green and other risk categories, a situation target range is dynamically and adaptively determined according to the real-time flight speed of eVTOL, the edge nodes only screen risk data in the range and transmit the risk data to terminal equipment with low delay after optimization, and the terminal equipment visually presents a traffic light type airspace situation on a cockpit interface in a three-dimensional mode. The invention obviously expands the perception boundary through edge-end coordination, dynamic range adaptation and visual display, the cognitive load of the driver is reduced, and the full-speed-domain flight safety is ensured.

Inventors

  • CHENG CHENGQI
  • WU XUEMIN
  • HU XUELIAN

Assignees

  • 北斗伏羲信息技术有限公司

Dates

Publication Date
20260508
Application Date
20260104

Claims (20)

  1. 1. An airspace situation awareness system of an electromotive vertical take-off and landing aircraft (eVTOL) comprises terminal equipment arranged on eVTOL and at least one edge computing node arranged on the periphery of a route, and is characterized by comprising one or more processors and a memory, wherein the memory is stored with computer executable instructions, when the instructions are executed by the one or more processors, the system achieves the functions of a situation generating module, a situation transmitting module, a driving situation displaying module and a risk displaying module, wherein the situation generating module is configured to be executed by the edge computing node and is used for acquiring multi-source airspace data in real time, dividing a peripheral airspace taking eVTOL as a center into a plurality of geospatial grid airspace units, marking the airspace units as one of at least two preset risk categories based on a preset situation assessment model, the dynamic range adapting module is configured to acquire the current flight speed of eVTOL in real time, dynamically and adaptively determine a situation target range to be displayed according to the flight speed by utilizing a preset speed-range mapping relation, the situation transmitting module is configured to be used for displaying the situation data in the three-dimensional situation, the situation transmitting module is configured to be used for displaying the risk category of the airspace on the terminal equipment, the terminal equipment is configured to be a three-dimensional risk category, and the terminal equipment is configured to be displayed in the airspace category.
  2. 2. The system of claim 1, wherein the pre-set risk categories are color-coded schemes based on traffic light logic, specifically including a red category representing high risk or forbidden traffic, a yellow category representing cautious or potentially conflicting traffic, and a green category representing safe or permitted traffic.
  3. 3. The system of claim 2, wherein the demarcation criteria for labeling an airspace unit as the red, yellow, or green category includes at least one of a level based on a Probabilistic Collision Risk Index (PCRI) with other aircraft, whether the airspace unit overlaps with a known temporary or permanent no-fly area, a quality level of communication or navigation signals within the airspace unit, air traffic flow control instructions issued by an airspace management center, or a risk level of real-time weather data, such as wind shear or micro-downburst, within the airspace unit.
  4. 4. A system according to claim 3, characterized in that the specific logic for performing the partitioning criteria is configured to label one spatial unit as a red class when it meets either one of its calculated Probabilistic Collision Risk Index (PCRI) above a first threshold or its spatial range overlapping the forbidden zone boundary in the active state exceeds 50% or its internal beidou signal availability is below 95%, and to label one spatial unit as a yellow class when it does not meet the red class condition but its Probabilistic Collision Risk Index (PCRI) is between the first and second threshold and its predicted latest intersection time is less than a preset time window, and to label it as a green class for all other spatial units that do not meet the red or yellow class condition, wherein the update frequency of the partitioning logic is configured to be not below 5Hz to ensure real-time of the situation.
  5. 5. The system of claim 1, wherein the speed-range mapping is configured such that the magnitude of the situational target range is positively correlated with the current flight speed of eVTOL.
  6. 6. The system of claim 5, wherein the dynamic range adaptation module is specifically configured to divide the eVTOL flight speed into a plurality of speed segments, and to preset a fixed situation target range radius for each speed segment, e.g., 1 km range radius when the speed is in a low speed segment of 0-30km/h, 2.5 km range radius when the speed is in a medium speed segment of 31-80km/h, and 5 km range radius when the speed is in a high speed segment of 81-120 km/h.
  7. 7. The system of claim 6, wherein the dynamic range adaptation module is further configured to employ a fine adjustment strategy based on continuous functions and hysteresis processing, wherein the radius R of the situation target range is continuously calculated by the formula R = Rmin + k log (V/vref+1), where Rmin is the minimum display radius, k is the speed influence coefficient, V is the current flight speed, and Vref is the reference speed, and wherein to prevent frequent jumps in the display range due to small fluctuations in speed, the module is further configured with a hysteresis decision logic to trigger the updating of the situation target range only if the absolute value of the calculated difference between the new radius Rnew and the current radius Rcurrent exceeds a preset radius change threshold and the state duration exceeds a preset time threshold, thereby improving display stability and user experience.
  8. 8. The system of claim 1, wherein the data transmission module is further configured with a data optimization unit for performing optimization on the risk category data prior to transmitting the data.
  9. 9. The system of claim 8, wherein the optimization process includes at least one of a course relevance screening to screen only spatial unit data associated with the eVTOL current planned course and its alternate reduced course, a data lossless compression to compress the data using a fast lossless compression algorithm, or a priority scheduling to assign different transmission priorities to the data packets according to the risk categories to ensure that data of a high risk category is sent preferentially.
  10. 10. The system of claim 9 wherein the priority scheduling unit is finely configured to mark packets of the red class of airspace unit as highest priority requiring less than 80 milliseconds of end-to-end delay, to mark packets of the yellow class of airspace unit as next highest priority requiring less than 150 milliseconds of delay, to mark packets of the green class of airspace unit as normal priority allowing delay to be within 300 milliseconds, and wherein the data transmission module, based on the priority mark, preferentially discards or delays sending low priority packets when the network is congested and reserves proprietary bandwidth or enables Forward Error Correction (FEC) mechanisms for high priority packets to ensure that most critical risk warning information can be delivered with highest quality and lowest delay under any network conditions.
  11. 11. The system of claim 1, wherein the cockpit display interface is a heads-up display (HUD) or a head-mounted display (HMD), and the situation display module superimposes the airspace unit in a semi-transparent three-dimensional color block over the driver's real external field of view.
  12. 12. The system of claim 1, wherein the communication link is a dual-link redundancy communication architecture based on a combination of a primary communication link of a 5G private network and a backup communication link based on beidou short messages.
  13. 13. The system of claim 3, wherein the situation display module is further configured to generate a continuous risk potential field based on the Probabilistic Collision Risk Index (PCRI) and render in a visual form of an energy cloud, wherein the higher the risk area, the closer the color of the energy cloud is to warmth and the higher the density, while downsampling the area away from eVTOL using a dynamic level of detail (LOD) technique to optimize system resource occupancy.
  14. 14. A airspace situation awareness method for an electromotive vertical take-off and landing aircraft (eVTOL) is characterized by comprising the steps of collecting multisource airspace data in real time through at least one edge computing node deployed around an air route, dividing the surrounding airspace centered on eVTOL into a plurality of geospatial grid airspace units, marking the airspace units as one of at least two preset risk categories based on a preset situation assessment model, acquiring the current flight speed of eVTOL in real time, dynamically and adaptively determining a situation target range to be displayed according to the flight speed by utilizing a preset speed-range mapping relation, screening out risk category data of the airspace units located in the situation target range through the edge computing node, sending the risk category data to terminal equipment on eVTOL through a communication link, and displaying the risk category marked airspace units in the situation target range in a three-dimensional visual mode on a cockpit display interface of eVTOL.
  15. 15. The method according to claim 14, wherein the preset risk categories are color-coded schemes based on traffic light logic, in particular comprising red, yellow and green categories, representing high risk, potential risk and safety, respectively.
  16. 16. The method of claim 15, wherein labeling a bin as the red, yellow, or green class of partitioning criteria comprises at least one of a level based on a probabilistic risk of collision index, a quality level of communication or navigation signals overlapping a no-fly zone, an air traffic flow control command, or real-time weather data.
  17. 17. The method of claim 16, wherein the specific logic steps of performing the partitioning criteria are labeling a spatial bin as a red class when it is detected that its calculated Probabilistic Collision Risk Index (PCRI) is above a first threshold or that its spatial extent overlaps more than 50% of the boundary of the active no-fly zone, labeling a bin as a yellow class when it is detected that the bin does not meet the red class condition but its PCRI is between the first and second thresholds and the predicted latest intersection time is less than a preset time window, labeling a bin as a green class for all other bins that do not meet the red or yellow class condition, and performing the partitioning logic at a frequency cycle of no less than 5 Hz.
  18. 18. The method of claim 14, wherein the speed-range mapping relationship is configured such that the magnitude of the situational target range is positively correlated with the current flight speed of eVTOL.
  19. 19. The method according to claim 18, wherein the step of dynamically determining the situation target range comprises comparing the eVTOL flight speed with a plurality of preset speed segments, selecting a preset situation target range radius corresponding to the current speed segment according to the comparison result, selecting a range radius of 1km when the speed is in a low speed segment of 0-30km/h, and selecting a range radius of 5 km when the speed is in a high speed segment of 81-120 km/h.
  20. 20. The method of claim 19, wherein the step of dynamically determining the attitude goal range further includes continuously calculating a goal radius R by the formula R = Rmin + k log (V/Vref + 1), where Rmin is a minimum display radius, k is a speed influencing factor, and V is a current flight speed, and performing a hysteresis determination step of performing updating of the attitude goal range to avoid frequent jumps in the display range only if the absolute value of the difference between the calculated new radius Rnew and the current radius Rcurrent exceeds a preset radius change threshold and the state duration exceeds a preset time threshold.

Description

Airspace situation awareness system and method for electric vertical take-off and landing aircraft (eVTOL) Technical Field The invention relates to the technical field of aircraft safety, in particular to a airspace situation sensing system and method of an electric vertical take-off and landing aircraft (eVTOL). More specifically, the invention utilizes the geospatial grid to carry out airspace refined management, combines the real-time flight state of eVTOL, provides an intuitive, dynamic and low-delay airspace situation awareness and decision-making auxiliary scheme for a driver, and is suitable for complex low-altitude application scenes such as urban air traffic (UAM). Background Urban air traffic (UAM) is rapidly evolving as an important component of future urban three-dimensional traffic networks. The electric vertical take-off and landing aircraft (eVTOL) is regarded as a key carrier for realizing UAM due to the vertical take-off and landing capability, lower noise level and zero emission characteristics, and has great potential in the fields of urban commute, cargo transportation, emergency rescue and the like. eVTOL is concentrated in urban low altitudes of 100 meters to 500 meters, which is an extremely complex airspace environment, and not only is a large number of other aircrafts (such as other eVTOL, unmanned planes and traditional helicopters) exist, but also various static and dynamic obstacles such as high-rise buildings, power transmission cables, meteorological change areas and temporarily planned controlled airspace are spread, which constitutes an unprecedented challenge for the airspace situation awareness capability of aircrafts and drivers. Currently, eVTOL drivers rely mainly on two traditional approaches to obtain airspace situation information. The first is direct visual observation by the naked eye. However, the range and reliability of visual perception is severely limited by a number of factors. Under ideal sunny weather, a driver has better recognition capability on a non-shielding target within 500 meters, but when the distance exceeds 1 km, the detail of the target is difficult to distinguish. Under the condition of low visibility such as dense fog, heavy rain or night, the effective sight distance is sharply shortened to within hundred meters, and the visual perception is almost ineffective. More importantly, in the modern urban steel forest, buildings such as high-rise buildings, bridges and the like form a large number of visual blind areas, and a driver cannot perceive potential threats behind a shielding object, so that a close-range collision event is extremely easy to cause. The second is an onboard conventional radar and navigation system. The short-distance detection radar commonly carried in the prior eVTOL has the detection distance of usually 1 to 3 km, and can make up the defect of visual perception to a certain extent. However, the manner in which it presents information is not intuitive. Radar systems typically display targets on a screen in the form of an original point cloud or a simple path line, and drivers need to evaluate collision risk and determine busyness of airspace through mental arithmetic by combining information such as speed, heading and the like of the targets. In areas of heavy traffic (e.g., multiple aircraft are present above a hub) this information processing approach is inefficient and highly prone to overload of driver information and decision delays. In addition, the radar data are not associated with refined airspace units such as geospatial grids adopted by modern airspace management, and key management information such as flight permission (such as forbidden flight and limited flight) of an airspace cannot be intuitively displayed in a superimposed manner, so that a driver is difficult to quickly and accurately judge in high-speed flight. At the same time, the existing space data link also has a bottleneck. The airspace management center is able to grasp macroscopic airspace situations, but when transmitting such information to an aircraft, there is often a data transmission delay of 100 to 300 milliseconds or more. In addition, the transmitted data packet generally contains all grid information in a large range, and the data redundancy is high, so that precious communication bandwidth is occupied, the burden of carrying out data analysis by eVTOL terminals is increased, situation information finally seen by a driver is lagged, and the harsh requirement on real-time performance in a high-speed flight scene cannot be met. Furthermore, a problem that is generally ignored is that existing situation display schemes mostly employ a fixed display range and fail to match the eVTOL variable flight speeds. eVTOL has a wide range of flight speed coverage, from 0km/h at hover to 60-120km/h at cruise, even over 150km/h in emergency situations. When the vehicle is cruising at a high speed, a driver needs to perceive the condition of a more distan