Airspace situation awareness system and method of electric vertical take-off and landing aircraft (eVTOL)

Abstract

The invention discloses an airspace situation awareness system and method of an electric vertical take-off and landing aircraft (eVTOL). The system comprises an edge computing node and eVTOL terminal equipment. The method is characterized in that an edge computing node collects multi-source data in real time, divides an airspace into Beidou grid code airspace units and marks the Beidou grid code airspace units as risk categories such as red, yellow and green; the system dynamically and adaptively determines a situation target range according to the real-time flight speed of the eVTOL; the edge node only screens the risk data in the range, and transmits the risk data to the terminal equipment in a low-delay manner after optimization; and the terminal equipment presents a'traffic light 'type airspace situation on a cockpit interface in a three-dimensional visualization manner. According to the method, through edge-end cooperation, dynamic range adaptation and visual display, the perception boundary is obviously expanded, the cognitive load of a driver is reduced, and the full-speed-domain flight safety is ensured.

Description

Technical Field

[0001] This invention relates to the field of aircraft safety technology, and more particularly to an airspace situational awareness system and method for electric vertical takeoff and landing (eVTOL) aircraft. More specifically, this invention utilizes geospatial grids for refined airspace management and, combined with the real-time flight status of the eVTOL, provides pilots with an intuitive, dynamic, and low-latency airspace situational awareness and decision support solution, suitable for complex low-altitude application scenarios such as urban air traffic (UAM). Background Technology

[0002] Urban air mobility (UAM), as a crucial component of future urban three-dimensional transportation networks, is experiencing rapid development. Electric vertical takeoff and landing (eVTOL) aircraft, due to their vertical takeoff and landing capabilities, low noise levels, and zero emissions, are considered key vehicles for realizing UAM, demonstrating enormous potential in urban commuting, freight transport, and emergency rescue. The typical flight airspace for eVTOLs is concentrated in the low-altitude urban region between 100 and 500 meters. This airspace environment is exceptionally complex, containing not only numerous other aircraft (such as other eVTOLs, drones, and traditional helicopters) but also various static and dynamic obstacles, such as tall buildings, power lines, areas of weather change, and temporarily designated controlled airspace. This poses an unprecedented challenge to the airspace situational awareness capabilities of aircraft pilots.

[0003] Currently, eVTOL pilots primarily rely on two traditional methods to obtain airspace situational information. The first is direct visual observation. However, the range and reliability of visual perception are severely limited by numerous factors. Under ideal clear weather conditions, pilots have a good ability to identify unobstructed targets within 500 meters, but when the distance exceeds 1 kilometer, target details become difficult to distinguish. In low-visibility conditions such as dense fog, heavy rain, or nighttime, the effective visibility range is drastically reduced to less than 100 meters, rendering visual perception almost ineffective. More importantly, in the modern urban "concrete jungle," tall buildings, bridges, and other structures create numerous "blind spots," making it impossible for pilots to detect potential threats behind obstructions, which can easily lead to close-range conflict incidents.

[0004] The second type is traditional airborne radar and navigation systems. Existing eVTOL systems commonly use short-range detection radars, with a detection range typically between 1 and 3 kilometers, which can compensate for the limitations of visual perception to some extent. However, the way they present information is not intuitive. Radar systems usually display targets on the screen as raw point clouds or simple flight paths. Pilots need to combine this information with the target's speed, heading, and other data, using mental calculations to assess collision risk and determine the level of airspace congestion. In busy airspaces (e.g., multiple aircraft simultaneously over a hub), this information processing method is inefficient, easily leading to information overload and decision-making delays for pilots. Furthermore, this radar data is not correlated with the refined airspace units such as geospatial grids used in modern airspace management, nor does it intuitively overlay and display key management information such as airspace flight permissions (e.g., no-fly zones, restricted-fly zones), making it difficult for pilots to make quick and accurate judgments during high-speed flight.

[0005] Meanwhile, existing air-to-ground data links also have bottlenecks. While airspace management centers can grasp the macroscopic airspace situation, there is often a data transmission latency of 100 to 300 milliseconds or even higher when transmitting this information to aircraft. Furthermore, the transmitted data packets usually contain information from all grids over a large area, resulting in high data redundancy. This not only consumes valuable communication bandwidth but also increases the burden on eVTOL terminals for data parsing, causing the situational information ultimately seen by the pilot to be delayed and unable to meet the stringent real-time requirements of high-speed flight scenarios.

[0006] Furthermore, a commonly overlooked issue is that most existing situational awareness solutions employ fixed display ranges, failing to match the variable flight speeds of eVTOL. eVTOL's flight speed range is extensive, from 0 km / h during hovering to 60-120 km / h during cruise, and even exceeding 150 km / h in emergencies. During high-speed cruise, pilots need to anticipate airspace conditions at greater distances to allow sufficient reaction time; while during low-speed takeoff and landing or hovering, a focused, detailed view of the immediate surroundings is crucial. An excessively large display range can introduce irrelevant information, interfering with attention to critical areas. Current technology cannot dynamically adjust the situational awareness target range according to flight speed, leading to contradictions such as "not seeing far enough" or "information being too cluttered" during critical flight phases.

[0007] Therefore, how to overcome the limitations of existing visual and radar perception and provide eVTOL drivers with an airspace situational awareness solution that offers intuitive information presentation, sufficient reaction time, and real-time, low-redundancy data transmission is a technical challenge that urgently needs to be addressed in the current UAM field. Summary of the Invention

[0008] The main objective of this invention is to overcome the aforementioned deficiencies of the prior art and provide an airspace situational awareness system and method for electric vertical takeoff and landing (eVTOL) aircraft, which aims to significantly improve the pilot's situational awareness, decision-making efficiency, and flight safety in complex low-altitude environments.

[0009] To achieve the above objectives, the present invention provides an airspace situational awareness system for an electric vertical takeoff and landing (eVTOL) aircraft, comprising terminal equipment deployed on the eVTOL and at least one edge computing node deployed around the flight path. The system includes one or more processors and a memory storing computer-executable instructions. When the instructions are executed by the one or more processors, the system performs the functions of the following modules:

[0010] • Situation generation module, configured to be executed by the edge computing node, is used to collect multi-source airspace data in real time, divide the surrounding airspace centered on the eVTOL into multiple geospatial grid airspace units, and label the airspace units as one of at least two preset risk categories based on a preset situation assessment model.

[0011] • The dynamic range adaptation module is configured to acquire the current flight speed of the eVTOL in real time, and dynamically and adaptively determine a situational target range that needs to be displayed based on the flight speed and using a preset speed-range mapping relationship.

[0012] • A data transmission module configured to filter risk category data of spatial units located within the target situation range from the edge computing node and transmit it to the terminal device on the eVTOL via a communication link; and

[0013] • Situation display module, configured to be executed by the terminal device, for presenting in three dimensions a visual representation of airspace units within the target area that are marked with risk categories on the cockpit display interface of the eVTOL.

[0014] Furthermore, to achieve the above objectives, the present invention also provides an airspace situational awareness method for electric vertical takeoff and landing (eVTOL) aircraft, comprising the following steps:

[0015] • At least one edge computing node deployed around the flight path collects multi-source airspace data in real time and divides the surrounding airspace centered on the eVTOL into multiple BeiDou grid code airspace units. Based on a preset situation assessment model, the airspace units are labeled as one of at least two preset risk categories.

[0016] • The current flight speed of the eVTOL is acquired in real time, and based on the flight speed, a range of situational targets that need to be displayed is dynamically and adaptively determined using a preset speed-range mapping relationship;

[0017] • The edge computing node filters out risk category data for spatial units within the target situation range and transmits it to the terminal device on the eVTOL via a communication link; and

[0018] • On the cockpit display interface of the eVTOL, airspace units within the range of the situational target, labeled with risk categories, are presented in a three-dimensional visual representation.

[0019] Compared with the prior art, the technical solution provided by the present invention has at least the following beneficial effects:

[0020] 1. Significantly expands the perception boundary and eliminates blind spots: By fusing multi-source data and generating situational awareness remotely, this invention extends the driver's effective perception range from hundreds of meters to several kilometers, enabling it to "penetrate" obstacles such as buildings and gain real-time insight into the airspace behind obstructed areas. This increases risk warning time several times compared to traditional solutions, providing drivers with ample decision-making margin for safe avoidance.

[0021] 2. Significantly reduces cognitive load and improves decision-making efficiency: This invention abstracts complex airspace data into intuitive "red, yellow, and green" risk categories or continuous risk potential fields, greatly simplifying the presentation of information. Pilots no longer need to expend effort interpreting raw data; instead, they can intuitively understand the airspace situation within seconds, much like observing traffic lights. This allows them to allocate more attention to flight control itself, reducing the misjudgment rate caused by information overload.

[0022] 3. Ensuring sufficient reaction time and achieving safety across the entire speed range: This invention uniquely and dynamically correlates the range of situational targets with the flight speed of the eVTOL. During high-speed flight, the display range is automatically expanded to ensure "seeing far enough," effectively guaranteeing reaction time; during low-speed flight, the focus is on close targets to avoid information redundancy. This adaptive mechanism ensures that the pilot receives optimal situational information support throughout the entire flight envelope of the eVTOL.

[0023] 4. Real-time situational awareness synchronization is achieved, ensuring information validity: Through an "edge-end" collaborative architecture and a series of data optimization strategies, this invention reduces the end-to-end latency of air-to-ground situational awareness data from hundreds of milliseconds to less than 80 milliseconds, and reduces data redundancy by more than 99%. This ensures that the pilot sees the actual situation of the current airspace on the display interface, providing a solid and reliable data foundation for safe and efficient flight decisions. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a schematic diagram of the architecture of an eVTOL airspace situational awareness system according to an embodiment of the present invention.

[0026] Figure 2 This is a flowchart of an eVTOL spatial situational awareness method according to an embodiment of the present invention.

[0027] Figure 3 This is a schematic diagram illustrating the working logic of a dynamic range adaptation module according to an embodiment of the present invention.

[0028] Figure 4 This is a schematic diagram of a situation display interface on a cockpit head-up display (HUD) according to an embodiment of the present invention. Wherein: 400: Head-up display (HUD); 401: Red grid; 402: Yellow grid; 403: Green grid; 404: Recommended flight direction.

[0029] Figure 5 This is a schematic diagram illustrating a continuously changing situation visualization effect based on a risk potential field according to another embodiment of the present invention. Wherein: 501: Energy cloud; 502: Continuous color temperature change. Detailed Implementation

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

[0031] Example 1: System Architecture and Functional Modules

[0032] See Figure 1This embodiment discloses an eVTOL airspace situational awareness system. The system adopts an edge-end collaborative distributed architecture, mainly including terminal equipment (101) deployed on the eVTOL aircraft, and at least one edge computing node (102) deployed on ground infrastructure (such as communication base stations) around the predetermined flight path. This architecture forwards a large number of computing tasks to the edge side, which is closer to the data source and the user, thereby effectively reducing data transmission latency and the burden on the core network.

[0033] The system relies on one or more processors and corresponding memory in terms of hardware. The memory stores computer-executable instructions, which, when executed by the processor, enable the system to coordinate the operation of the following major functional modules.

[0034] The situation generation module (110) typically runs on an edge computing node (102). Its core responsibility is to aggregate information and generate driver-friendly situation data.

[0035] First, this module collects multi-source spatial data in real time. These data sources include, but are not limited to:

[0036] • Airspace management center data: such as geofence data of permanent no-fly zones (e.g., airport clear zones) and temporary no-fly zones (e.g., emergency control zones) received via dedicated links, as well as airspace traffic control instructions.

[0037] • Surrounding aircraft data: Real-time location (usually based on BeiDou high-precision positioning), speed, heading and other dynamic information of other surrounding eVTOLs, drones, helicopters, etc. are collected through 5G networks or ADS-B and other means.

[0038] • eVTOL self-sensing data: Receives information on nearby obstacles uploaded by the terminal device (101) and detected by its own millimeter-wave radar, photoelectric sensors and other devices.

[0039] • Environmental and signal data: Integrate data from meteorological stations (collecting wind speed, visibility, etc.) and signal monitoring equipment (collecting communication / navigation signal strength) deployed within the airspace grid.

[0040] After acquiring multi-source heterogeneous data, the module uses data fusion algorithms such as Kalman filtering to calibrate and optimize target position, velocity and other information from different sources, generating unified and high-precision dynamic information of airspace targets.

[0041] Next, the module divides the surrounding airspace centered on the target eVTOL into a series of refined three-dimensional airspace units (111) based on BeiDou grid code technology. The accuracy of the grid can be dynamically adapted to the scene. For example, a high-precision grid of 20m×20m×10m is used in the densely built-up urban core area, while a grid of 50m×50m×20m is used in the open suburbs.

[0042] The most crucial step is that the module, based on a pre-defined situation assessment model, labels each airspace unit (111) with a risk category. In a preferred embodiment, this risk category is based on a color coding scheme according to traffic light logic, specifically including:

[0043] • Red category: Represents high risk or prohibition of passage.

[0044] • Yellow category: Indicates that careful observation is required or that there is a potential conflict.

[0045] • Green category: Indicates that it is currently safe or that passage is permitted.

[0046] The criteria for classifying a spatial unit into a specific category are multi-dimensional and include at least one or more of the following:

[0047] • Collision Risk: Based on the Probabilistic Collision Risk Index (PCRI) rating with other aircraft. The PCRI is a comprehensive mathematical model that considers not only the target's relative position, speed, and heading, but also quantifies the eVTOL's own maneuverability limitations (such as maximum rate of climb and turning radius) and the uncertainties in sensor data.

[0048] • Airspace compliance: Determine whether the airspace unit overlaps with the geographical boundaries of known temporary or permanent no-fly zones.

[0049] • Signal quality: Assess whether the quality level of communication or navigation signals (such as BeiDou signals) within the airspace unit meets the requirements for safe flight.

[0050] • Traffic control instructions: In response to traffic control instructions issued by the airspace management center for specific airspace units.

[0051] • Meteorological risk: Based on real-time meteorological data, assess whether there are hazardous weather phenomena in the airspace unit, such as the risk level of wind shear or microburst.

[0052] To ensure real-time situational awareness, the update frequency of this classification logic is configured to be no less than 5Hz. A specific example of the classification logic is as follows: a spatial unit is labeled as red if it meets any of the following conditions: its calculated PCRI is higher than a first threshold (e.g., 0.8); its spatial extent overlaps with the boundary of an activated no-fly zone by more than 50%; or the availability of BeiDou signals within it is less than 95%. A spatial unit is labeled as yellow if it does not meet the red category conditions, but its PCRI is between the first threshold (0.8) and the second threshold (e.g., 0.4), and its predicted Time of Closest Intersection (TCA) based on the current motion situation is less than a preset time window (e.g., 60 seconds). All other spatial units that do not meet the red or yellow category conditions are labeled as green.

[0053] A dynamic range adaptation module (120) can run on an edge computing node (102) or a terminal device (101), but is typically implemented on the edge node side to reduce the interaction of transmission decisions. It obtains the current flight speed V from the eVTOL in real time and dynamically and adaptively determines a situational target range (112) to be displayed based on a preset speed-range mapping relationship. The core idea of ​​this mapping relationship is that the size of the situational target range is positively correlated with the current flight speed of the eVTOL.

[0054] See Figure 3 This mapping relationship can be implemented in multiple ways.

[0055] A simpler approach is to divide the eVTOL's flight speed into multiple discrete speed segments, and pre-define a fixed target range radius R for each segment. For example:

[0056] • Low speed range (301) (0-30km / h, such as take-off, landing, and hovering): The range radius is set to 1 kilometer, at which time the driver pays more attention to close-range details.

[0057] • Medium speed range (302) (31-80km / h, such as city cruising): The range radius is set to 2.5 kilometers, taking into account the medium distance situation.

[0058] • High-speed section (303) (81-120km / h, such as rapid commuting): Set the range radius to 5 kilometers to ensure sufficient long-range warning.

[0059] Another, more refined approach is to employ an adjustment strategy based on continuous functions and hysteresis. The radius R of the situation target range can be continuously calculated using the following formula:

[0060] R = Rmin + k × log(V / Vref + 1)

[0061] Where Rmin is the minimum display radius (e.g., 1 km), k is an adjustable speed influence coefficient, V is the current flight speed, and Vref is a reference speed (e.g., 10 km / h). This formula makes the radius R increase smoothly and non-linearly with the increase of speed V.

[0062] Furthermore, to prevent frequent jumps in the display range caused by minor speed fluctuations (such as those caused by gusts of wind), which could interfere with the driver's visual focus, this module is equipped with a delayed judgment logic. That is, the system continuously calculates the new target radius R_new, but only triggers an update of the situational target range when the absolute value of the difference between R_new and the currently used radius R_current exceeds a preset radius change threshold (e.g., 500 meters), and this difference persists for more than a preset time threshold (e.g., 2 seconds). This mechanism significantly improves display stability and user experience.

[0063] The data transmission module (130), also located on the edge computing node (102), is tasked with efficiently and reliably transmitting the generated situation information to eVTOL. Before sending the data, it first selects the portion of the situation target range (112) from all the generated grid situation data based on the situation target range (112) determined by the dynamic range adaptation module (120).

[0064] To further improve transmission efficiency, this module integrates a data optimization unit (131) to perform at least one optimization process:

[0065] • Route correlation filtering: It is not only based on distance filtering, but also combines the current planned routes of eVTOL and their alternate routes to filter out only the airspace unit data that are spatially related to these routes, and remove data that are obviously unrelated in direction and location.

[0066] • Lossless data compression: Using fast lossless compression algorithms such as LZ4 or Zstandard, which have low computational overhead and fast compression / decompression speed, the filtered data packets are compressed, which can reduce the data volume by 30%-50%.

[0067] • Priority Scheduling: This is the core mechanism for ensuring the real-time nature of critical information. This unit assigns different transmission priorities to data packets based on the risk category of the airspace unit. For example, a refined configuration might be: marking data packets from red-category airspace units as highest priority (Priority 0), requiring an end-to-end latency of less than 80 milliseconds; marking yellow-category data packets as second-highest priority (Priority 1), requiring a latency of less than 150 milliseconds; and marking green-category data packets as normal priority (Priority 2), allowing a latency of less than 300 milliseconds. When transmitting over the communication link, network devices provide differential service based on this priority marking, prioritizing the transmission of high-priority data packets during network congestion. They can even reserve dedicated bandwidth or enable forward error correction (FEC) mechanisms for the highest-priority data packets, ensuring that the most critical risk warning information is delivered with the highest quality and lowest latency under any network conditions.

[0068] The communication link (132) used in this system is a dual-link redundant communication architecture to achieve extremely high reliability. The main communication link is based on a 5G private network (or network slicing), which can provide large bandwidth and air interface latency as low as less than 50ms. The backup communication link uses BeiDou short message communication. When the main link is interrupted due to signal blockage or electromagnetic interference, the system will automatically switch to the backup link. Although its bandwidth is very low, it is sufficient to transmit the most critical text-formatted warning information (such as "Red light grid 3 kilometers ahead, emergency avoidance"), ensuring that situational awareness is never interrupted.

[0069] The situation display module (140), which runs on the eVTOL terminal device (101), is responsible for presenting the received situation data to the driver in the most intuitive way.

[0070] See Figure 4 In one implementation, the cockpit display interface is preferably a head-up display (HUD) (400) or a helmet-mounted display (HMD). This module precisely registers and overlays the received airspace units, in the form of semi-transparent three-dimensional color blocks, onto the pilot's actual external field of vision. Red grids (401), yellow grids (402), and green grids (403) are rendered with different colors and approximately 60% transparency, clearly conveying risk information without excessively obstructing the pilot's observation of the real environment. Simultaneously, the system also uses prominent arrows on the HUD to recommend flight directions (404), guiding the eVTOL through safe green grid areas.

[0071] See Figure 5In another, more advanced implementation, to overcome the information jumps and "cliff effect" that discrete color blocks may cause, the situation display module (140) generates a continuous risk potential field based on the received continuous Probabilistic Collision Risk Index (PCRI) values ​​and renders it as a visualization of an energy cloud (501). In this view, the higher the risk area, the closer the color of the energy cloud is to a warm tone (a continuous color temperature change from green to yellow to red) (502), the higher the saturation, and the higher the visual density. This gradual visualization method is more in line with human intuitive perception and allows the driver to understand the distribution and evolution trend of risks more precisely.

[0072] Meanwhile, to optimize the rendering computational resources of the terminal device (101), this module also employs Dynamic Level of Detail (LOD) technology. It combines the driver's eye-tracking data to perform high-resolution, high-refresh-rate detailed rendering of the potential field in the central area of ​​the driver's gaze point, while downsampling or using a simplified model for approximate rendering of areas at the edge of the field of vision or at a distance. This significantly reduces system resource consumption while ensuring the clarity of key information, ensuring that the refresh rate of the situation display remains stable above 30Hz even in extremely complex spatial scenarios.

[0073] Example 2: Method Flow

[0074] See Figure 2 This embodiment discloses an eVTOL airspace situational awareness method, which can be executed by the aforementioned system, and specifically includes the following steps:

[0075] Step S201: Collect multi-source data and generate grid situation.

[0076] This step is performed by an edge computing node. It periodically (e.g., every 200 milliseconds) collects the latest airspace data from multiple sources, including the airspace management center, surrounding aircraft, the target eVTOL itself, and environmental sensors. This data is then processed using a data fusion algorithm, and the airspace surrounding the eVTOL is divided into three dimensions based on the BeiDou grid code. Finally, the aforementioned situation assessment model (whether based on a discrete red-yellow-green classification or a continuous PCRI value model) is applied to calculate and label the current risk category or risk potential value for each grid cell.

[0077] Step S202: Obtain eVTOL flight speed in real time.

[0078] The system acquires the current precise flight speed output by the eVTOL flight control system in real time at a high frequency (e.g., 10Hz) via the CAN bus or other airborne data bus.

[0079] Step S203: Dynamically determine the situation display range.

[0080] Based on the real-time velocity obtained in step S202, the system executes dynamic range adaptation logic. As described in Embodiment 1, this can be achieved by matching the preset radius corresponding to the velocity segment through a lookup table, or by calculating using the continuous function R = Rmin + k * log(V / Vref + 1). Simultaneously, hysteresis judgment logic is applied to determine whether the current situational target range needs to be updated, thereby avoiding frequent jumps in the displayed range.

[0081] Step S204: Filter, optimize and package situational data.

[0082] Based on the situational target range determined in step S203, the edge computing node filters out the data to be transmitted from all the generated grid situations. Then, it performs optimization operations on this data, including secondary filtering based on flight path correlation, lossless compression, and allocation of transmission priorities according to risk level, ultimately generating a concise and efficient data packet.

[0083] Step S205: Transmit data through redundant links.

[0084] Edge computing nodes send optimized data packets to the target eVTOL via a dual-link system: primary (5G private network) and backup (BeiDou short message service). During transmission, the network provides differential services based on the priority marking of data packets to ensure low-latency delivery of high-risk warnings.

[0085] Step S206: 3D visualization is presented on the cockpit interface.

[0086] The terminal device on eVTOL receives and decompresses the data packets. Based on the data content, the situation display module renders a three-dimensional, semi-transparent situation view on the HUD or HMD. This can be discrete red, yellow, and green three-dimensional color blocks, or a continuously grading energy cloud. The display module also automatically zooms in and adjusts the level of detail based on driver interaction (such as eye movements or voice commands) or specific scenarios (such as approaching a turning point on the flight path), providing the driver with optimal decision support.

[0087] Example 3: Application Scenarios

[0088] Taking the eVTOL commuter route between the city center CBD and the international airport in a first-tier city as an example, the route is 25 kilometers long and flies at an altitude of 300-400 meters. It passes through commercial areas with high-rise buildings, suburbs with low population density, and the edge of the airport's airspace with strict airspace management.

[0089] 1. System Deployment: An edge computing node is deployed every 5 kilometers along the flight path, integrating radar and BeiDou receivers. On the commuter eVTOL fleet, HUDs supporting this system, 5G private network communication modules, and BeiDou short message terminals are installed. The airspace is pre-divided into BeiDou grids of varying precision, and the airport airspace is defined as a permanent red grid area.

[0090] 2. Flight process:

[0091] • Takeoff Phase: The eVTOL takes off from the CBD's vertical takeoff and landing airport at a speed below 30 km / h. The system automatically sets the situational target range to 1 km, and the HUD clearly displays a green safety passage grid within a 200-meter radius of the takeoff and landing field, as well as the top areas of nearby buildings marked with yellow grids.

[0092] • City Cruise: eVTOL accelerates to 80km / h, entering the city cruise phase. The system smoothly expands the display range to 3km. At this time, the grid above a commercial area 2.5km ahead, displayed on the HUD, turns yellow. Tap the central control screen or look at the grid, and the auxiliary information area will display: "There is an uncoordinated drone within the grid (speed 50km / h, distance 2km), it is recommended to reduce speed to 60km / h." The driver follows the advice, slightly reduces speed, remains vigilant, and safely passes through the area.

[0093] • High-speed cruising and avoidance: After leaving the city, eVTOL accelerates to 120km / h. The display range automatically expands to 5 kilometers. The system pre-renders a red grid area representing the airport's airspace in the far distance of the HUD's field of vision. At the same time, a system-recommended detour route planned along the green grid is highlighted. The pilot has more than 150 seconds to calmly adjust the direction according to the recommended route, avoiding the dangerous situation that may occur in traditional solutions where the pilot only discovers the no-fly zone and has to take emergency evasive action when approaching it.

[0094] • Communication Interruption Scenario: While traversing an industrial area with a complex electromagnetic environment, the 5G signal experienced a brief interruption. The system immediately switched to the BeiDou short message backup link and sent a critical instruction to the cockpit within one second: "Temporary no-fly zone (firefighting) 4 kilometers ahead, please turn left to enter backup route A." Although the detailed situation map could not be updated, the most critical early warning information ensured the continued safety of the flight.

[0095] Through the application of this system, the flight safety rate, punctuality rate and airspace utilization efficiency of this route have been significantly improved, while the pilots' flight workload has been greatly reduced.

[0096] Example 4: Alternatives and Technological Variations

[0097] This invention is not limited to the specific technical details described in the above embodiments. Various variations are possible without departing from the core concept of this invention.

[0098] • Variants of the risk assessment model: In addition to rule-based and PCRI-based risk assessments, deep learning-based risk prediction models can be introduced. By training on a large amount of historical radar data, flight data, and airspace conflict event data, this model can more accurately predict the probability of a conflict occurring in a certain airspace unit within a short period of time (e.g., 30-60 seconds), thereby achieving more forward-looking situational awareness.

[0099] • Variations in human-computer interaction: In addition to visual displays, the system can also integrate three-dimensional spatial audio alerts. For example, when a potential threat is detected on the left, the driver will hear a specific frequency alert through an earpiece worn in the left ear, with the distance and intensity of the sound correlated with the distance and severity of the threat. This multi-sensory information presentation method can further reduce the burden on a single visual channel.

[0100] • Variations in communication technology: In addition to 5G and BeiDou short message service, the system can also integrate low-orbit satellite internet (such as Starlink) as a third communication backup, which is particularly suitable for eVTOLs performing missions in remote areas or across sea routes, ensuring reliable situational data updates from any location.

[0101] In summary, this invention effectively solves a series of technical challenges in existing eVTOL airspace situational awareness through innovative system architecture, dynamic adaptive algorithms, and intuitive visualization solutions, providing strong technical support for achieving safe and efficient urban air traffic.

[0102] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An airspace situational awareness system for an electric vertical takeoff and landing (eVTOL) aircraft, comprising terminal equipment deployed on the eVTOL and at least one edge computing node deployed around the flight path, characterized in that, The system includes: one or more processors; and a memory storing computer-executable instructions. When the instructions are executed by the one or more processors, the system performs the functions of the following modules: a situation generation module, configured to be executed by the edge computing node, for real-time acquisition of multi-source airspace data, dividing the surrounding airspace centered on the eVTOL into multiple geospatial grid airspace units, and labeling the airspace units as one of at least two preset risk categories based on a preset situation assessment model; a dynamic range adaptation module, configured to acquire the current flight speed of the eVTOL in real time, and dynamically and adaptively determine a situation target range to be displayed based on the flight speed and using a preset speed-range mapping relationship; a data transmission module, configured to filter out the risk category data of the airspace units located within the situation target range from the edge computing node, and send it to the terminal device on the eVTOL via a communication link; and a situation display module, configured to be executed by the terminal device, for three-dimensionally visually presenting the airspace units within the situation target range that are labeled with risk categories on the cockpit display interface of the eVTOL.

2. The system according to claim 1, characterized in that, The preset risk categories are based on a color coding scheme according to traffic light logic, specifically including: a red category representing high risk or prohibition of passage; a yellow category representing caution or potential conflict; and a green category representing safety or permission to pass.

3. The system according to claim 2, characterized in that, The situation generation module is used to classify an airspace unit into the red, yellow, or green category based on at least one of the following criteria: the Probabilistic Collision Risk Index (PCRI) level based on other aircraft; whether the airspace unit overlaps with a known temporary or permanent no-fly zone; the quality level of communication or navigation signals within the airspace unit; air traffic control instructions issued by the airspace management center; or real-time meteorological data within the airspace unit, such as the risk level of wind shear or microburst.

4. The system according to claim 3, characterized in that, The specific logic for executing the classification criteria in the situation generation module is configured as follows: when a spatial unit meets any of the following conditions, it is marked as a red category: its calculated probabilistic collision risk index (PCRI) is higher than a first threshold, or the overlap rate between its spatial range and the boundary of the active no-fly zone exceeds 50%, or the availability of BeiDou signals within it is lower than 95%; when a spatial unit does not meet the red category conditions, but its probabilistic collision risk index (PCRI) is between the first and second thresholds, and the predicted nearest intersection time is less than a preset time window, it is marked as a yellow category; for all other spatial units that do not meet the red or yellow category conditions, they are marked as green categories. The update frequency of the classification logic is configured to be no less than 5Hz to ensure the real-time performance of the situation.

5. The system according to claim 1, characterized in that, The speed-range mapping is configured such that the size of the situational target range is positively correlated with the current flight speed of the eVTOL.

6. The system according to claim 5, characterized in that, The dynamic range adaptation module is specifically configured to divide the flight speed of the eVTOL into multiple speed segments and preset a fixed situational target range radius for each speed segment; for example, when the speed is in the low-speed segment of 0-30km / h, the range radius is 1 kilometer; when the speed is in the medium-speed segment of 31-80km / h, the range radius is 2.5 kilometers; and when the speed is in the high-speed segment of 81-120km / h, the range radius is 5 kilometers.

7. The system according to claim 6, characterized in that, The dynamic range adaptation module is further configured to employ a refined adjustment strategy based on continuous functions and hysteresis processing. The radius R of the situation target range is continuously calculated using 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. Furthermore, to prevent frequent changes in the display range due to minor speed fluctuations, the module is also configured with a hysteresis judgment logic. That is, the update of the situation target range is triggered only when 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 duration of this state exceeds a preset time threshold. This improves display stability and user experience.

8. The system according to claim 1, characterized in that, Before sending data, the data transmission module is also equipped with a data optimization unit, which is used to perform optimization processing on the risk category data.

9. The system according to claim 8, characterized in that, The optimization process includes at least one of the following: route correlation filtering, used to filter out only airspace unit data related to the currently planned eVTOL route and its alternate landing route; lossless data compression, used to compress the data using a fast lossless compression algorithm; or priority scheduling, used to assign different transmission priorities to data packets according to the risk category, so as to ensure that data of the high-risk category is sent first.

10. The system according to claim 9, characterized in that, The priority scheduling unit is finely configured to: mark data packets of red category airspace units as the highest priority, requiring an end-to-end latency of less than 80 milliseconds; mark data packets of yellow category airspace units as the second highest priority, requiring a latency of less than 150 milliseconds; and mark data packets of green category airspace units as ordinary priority, allowing a latency of less than 300 milliseconds. The data transmission module, based on the priority marking, prioritizes discarding or delaying the transmission of low-priority data packets when the network is congested, and reserves dedicated bandwidth or enables forward error correction (FEC) mechanism for high-priority data packets to ensure that the most critical risk warning information can be delivered with the highest quality and lowest latency under any network conditions.

11. The system according to claim 1, characterized in that, The cockpit display interface is a head-up display (HUD) or a helmet display (HMD), and the situation display module overlays the airspace unit as a semi-transparent three-dimensional color block onto the driver's actual external field of vision.

12. The system according to claim 1, characterized in that, The communication link is a dual-link redundant communication architecture that combines the main communication link based on the 5G private network with the backup communication link based on BeiDou short messages.

13. The system according to claim 3, characterized in that, The situation display module is further configured to generate a continuous risk potential field based on the probabilistic collision risk index (PCRI) and render it in the form of an energy cloud visualization. In this case, the higher the risk area, the warmer the color of the energy cloud and the higher the density. At the same time, dynamic level of detail (LOD) technology is used to downsample and render areas far from eVTOL to optimize system resource usage.

14. A method for airspace situational awareness for electric vertical takeoff and landing (eVTOL) aircraft, characterized in that, Includes the following steps: At least one edge computing node deployed around the flight path collects multi-source airspace data in real time and divides the surrounding airspace centered on the eVTOL into multiple geospatial grid airspace units. Based on a preset situation assessment model, the airspace units are labeled as one of at least two preset risk categories. The current flight speed of the eVTOL is obtained in real time, and based on the flight speed, a situation target range that needs to be displayed is dynamically and adaptively determined using a preset speed-range mapping relationship. The edge computing node filters out the risk category data of airspace units within the scope of the situational target and sends it to the terminal device on the eVTOL via a communication link; and on the cockpit display interface of the eVTOL, the airspace units within the scope of the situational target and marked with risk categories are presented in three-dimensional visualization.

15. The method according to claim 14, characterized in that, The preset risk categories are based on a color coding scheme for traffic lights, specifically including red, yellow, and green categories, which represent high risk, potential risk, and safe, respectively.

16. The method according to claim 15, characterized in that, The criteria for classifying an airspace unit as a red, yellow, or green category include at least one of the following: a level based on a probabilistic collision risk index, whether it overlaps with a no-fly zone, the quality level of communication or navigation signals, air traffic control instructions, or real-time weather data.

17. The method according to claim 16, characterized in that, The specific logical steps for executing the classification criteria are as follows: when a spatial unit is detected to have a Probabilistic Collision Risk Index (PCRI) higher than the first threshold, or when the overlap rate between its spatial range and the boundary of the active no-fly zone exceeds 50%, the unit is marked as a red category; when a spatial unit is detected to not meet the red category conditions, but its PCRI is between the first and second thresholds, and the predicted nearest intersection time is less than a preset time window, the unit is marked as a yellow category; for all other spatial units that do not meet the red or yellow category conditions, they are marked as green categories, and this classification logic is executed cyclically at a frequency of not less than 5 Hz.

18. The method according to claim 14, characterized in that, The speed-range mapping is configured such that the size of the situational target range is positively correlated with the current flight speed of the eVTOL.

19. The method according to claim 18, characterized in that, The step of dynamically determining the situational target range specifically includes: comparing the flight speed of the eVTOL with multiple preset speed segments; and selecting the preset situational target range radius corresponding to the current speed segment based on the comparison results; for example, when the speed is in the low-speed range of 0-30km / h, a range radius of 1 km is selected; when the speed is in the high-speed range of 81-120km / h, a range radius of 5 km is selected.

20. The method according to claim 19, characterized in that, The step of dynamically determining the range of the situation target further includes: continuously calculating the target radius R using the formula R = Rmin + k * log(V / Vref + 1), where Rmin is the minimum display radius, k is the speed influence coefficient, and V is the current flight speed; and performing a lag judgment step, that is, only when 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 duration of this state exceeds a preset time threshold, will the update of the situation target range be performed to avoid frequent jumps in the display range.

21. The method according to claim 14, characterized in that, A data optimization step is also included before sending the risk category data.

22. The method according to claim 21, characterized in that, The data optimization steps include at least one of the following: performing route correlation screening to filter out irrelevant data; compressing the data using a lossless compression algorithm; or assigning different transmission priorities to data packets according to risk categories and scheduling them.

23. The method according to claim 22, characterized in that, The specific steps for allocating priorities and scheduling are as follows: data packets of red category airspace units are marked as the highest priority, requiring their end-to-end latency to be less than 80 milliseconds; data packets of yellow category airspace units are marked as the second highest priority, requiring their latency to be less than 150 milliseconds; data packets of green category airspace units are marked as ordinary priority; when performing the sending step, differential service is performed according to the priority markings, and the transmission quality and timeliness of high-priority data packets are guaranteed first when the network is congested.

24. The method according to claim 14, characterized in that, The transmission steps of the communication link are redundantly executed through a main communication link based on a 5G private network and a backup communication link based on BeiDou short messages.

25. The method according to claim 16, characterized in that, The method further includes: generating a continuous risk potential field based on the probabilistic collision risk index (PCRI), and visually rendering it on the cockpit display interface in the form of an energy cloud with varying colors and densities; while using dynamic level of detail (LOD) technology to simplify the rendering of the potential field in distant areas where visual perception is less important.

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