An air space traffic control method, system and computer readable storage medium

Abstract

The application discloses a kind of airspace traffic control method, system and computer readable storage medium.The method comprises: establishing the digital traffic rule model of quantifying low-altitude traffic rules into computable parameters including priority weight and behavior determination threshold;Real-time acquisition of the state information of aircraft in airspace;Based on the digital traffic rule model, the traffic right state of the three-dimensional airspace grid involved in the current and expected path of the aircraft is dynamically adapted, and the traffic right state is used to represent the traffic right level of different aircrafts to the grid;And when it is judged that there is flight conflict, based on the adapted traffic right state, generate and issue avoidance instruction containing specific maneuver parameters for the aircraft to be avoided for the aircraft to be avoided.This application deeply integrates abstract traffic rules with three-dimensional grid situation, dynamically adapts the grid permission state driven by rules, realizes the transformation from passive alarm to active prediction conflict avoidance, can remotely and in advance eliminate flight conflict, and automatically generate accurate avoidance instruction, thereby improving the operation safety and control efficiency of low-altitude airspace.

Description

Technical Field

[0001] This invention relates to the field of low-altitude airspace management technology, and more specifically, to an airspace traffic control method, system, and computer-readable storage medium, particularly applicable to the coordinated flight control of multiple types of aircraft (such as manned aircraft, drones, etc.) in urban air traffic (UAM) scenarios. Background Technology

[0002] With the rapid development of urban air traffic (UAM) and the low-altitude economy, the number and flight frequency of various aircraft, such as drones and electric vertical takeoff and landing (eVTOL) aircraft, are growing exponentially. To ensure the safety and order of low-altitude flights, airspace management systems based on three-dimensional grids have emerged. These systems typically divide low-altitude airspace into standardized three-dimensional grid units, and by monitoring the position and status of aircraft within the grid, they achieve traffic situational awareness and basic flight management.

[0003] However, existing low-altitude grid-based management systems still face numerous challenges in practical applications. A common problem is the disconnect between general traffic rules and grid-based situational awareness systems. Traditional air traffic rules, such as flight priority and avoidance principles, are mostly macro-level guiding principles, lacking a digital and quantitative model that can be directly understood and executed by computers. This results in the grid system's status (e.g., occupied, idle) failing to accurately reflect actual passage permissions under rule constraints. For example, within the same grid airspace, the passage priorities of emergency rescue helicopters and commercial cargo drones should be vastly different, but existing systems often fail to differentiate them based on grid status. Aircraft avoidance decisions still heavily rely on manual judgment or independent onboard sensing systems, leading to inefficient and inconsistent decision-making.

[0004] The methods for avoiding flight conflicts are relatively passive. Most systems only trigger warnings or avoidance procedures after detecting that two or more aircraft are approaching each other in space and time or meet certain collision risk thresholds. This "post-event" response mechanism fails to make full use of traffic rules for proactive and forward-looking risk prediction. For example, in head-on flight scenarios, there is a lack of unified, rule-based avoidance direction agreements; in merging scenarios, due to ambiguous priority determination, aircraft may take high-risk actions such as emergency hovering or violent maneuvers to avoid collisions, which not only affects flight efficiency but also threatens the attitude stability of the aircraft.

[0005] For specific airspaces with high density and complexity, such as takeoff and landing sites and transportation hubs, management methods are relatively crude. Takeoff and landing aircraft paths frequently intersect, and incidents of drones straying into manned aircraft takeoff and landing protection zones occur from time to time. This indicates that the existing grid management model fails to provide functional, fine-grained division and rule-based linkage for such critical areas, resulting in low takeoff and landing efficiency and high safety risks.

[0006] For dynamically changing airspace restrictions such as controlled areas, no-fly zones, and temporary severe weather zones, the existing grid system fails to achieve real-time and efficient linkage. Updates to airspace restriction information are often delayed, and aircraft may inadvertently enter risky airspace due to a lack of timely access to the latest area status, posing a serious safety hazard.

[0007] Therefore, how to digitally model abstract low-altitude traffic rules and deeply integrate them with three-dimensional grid situational awareness to achieve a rule-driven, proactive, and predictive dynamic traffic control, thereby effectively solving technical problems such as ambiguous flight priorities, passive conflict avoidance, and disordered management in specific areas, is a technical challenge that urgently needs to be addressed in this field. Summary of the Invention

[0008] This invention provides an airspace traffic control method, system, and computer-readable storage medium, which solves the technical problems of low-altitude traffic rules being disconnected from grid situation, passive flight conflict avoidance, and chaotic management of specific airspaces in the prior art.

[0009] To achieve the above objectives, the first aspect of the present invention provides an airspace traffic control method, comprising:

[0010] A digital traffic rule model is established, which quantifies low-altitude traffic rules into computable parameters that include priority weights and behavior determination thresholds;

[0011] Real-time acquisition of the status information of at least one aircraft in the airspace, the status information including the aircraft's position, heading and identity information;

[0012] Based on the digital traffic rule model, the access permission status of the three-dimensional airspace grid involved in the current and expected paths of the aircraft is dynamically adapted. The access permission status is used to characterize the access permission level of different aircraft to the three-dimensional airspace grid.

[0013] When a flight conflict is determined, based on the adapted access permission status, an avoidance command containing specific maneuver parameters is generated and issued to the aircraft to be avoided.

[0014] Preferably, the establishment of the digital traffic rule model includes: constructing multi-dimensional priority rules based on the aircraft type, mission type, and operating status, and assigning unique priority weight values ​​to combinations under different dimensions.

[0015] Preferably, the construction of the multi-dimensional priority rules specifically includes: establishing a priority weight system with at least eight levels, wherein the priority weight values ​​are distributed within a preset range; the task types at least cover emergency rescue, public transportation and commercial freight, and assigning the highest priority weight value to emergency rescue tasks.

[0016] Preferably, the priority weight system is configured as follows: the priority weight Wems for emergency rescue tasks is set in the range of [0.9, 1.0]; the priority weight Wuam for manned transportation tasks is set in the range of [0.7, 0.89]; and the priority weight Wcargo for commercial freight tasks is set in the range of [0.5, 0.69]. Furthermore, when the aircraft status switches to an emergency state, its final priority weight Wfinal is updated using the formula Wfinal = max(Wbase, Wemergency), where Wbase is the base priority weight and Wemergency is a preset emergency state weight value, ensuring that the aircraft in the emergency state obtains the highest passage permission.

[0017] Preferably, the access permission status includes: green light status, indicating priority passage; yellow light status, indicating waiting or yielding as instructed; and red light status, indicating prohibition of entry.

[0018] Preferably, the dynamic adaptation of the access permission status of the three-dimensional airspace grid includes: when it is determined that the priority of the first aircraft is higher than that of the second aircraft, and the predicted trajectories of the two conflict, setting the relevant grids on the expected path of the first aircraft to a green light state, and forcibly setting the relevant grids on the conflicting path of the second aircraft to a red light state or a yellow light state.

[0019] Preferably, the triggering and execution logic of the dynamic adaptation is configured as follows: when the priority weight W1 of the first aircraft is greater than the priority weight W2 of the second aircraft, and the closest distance between their predicted trajectories within a future time window Tpredict (e.g., 30 to 60 seconds) is less than the safe distance threshold Dsafe (preferably 50 to 200 meters, dynamically adjusted according to the aircraft speed), the system sets the path grid of the first aircraft to a green light state for the next N (e.g., 10 to 20, assuming a time step of 1 second) time steps. At the same time, it sets the M consecutive (e.g., 5 to 10) grids on the path of the second aircraft that are less than the warning threshold Daalert (e.g., 300 to 500 meters) in the spatiotemporal distance from the conflict point to a red light state, and sets the grids behind the conflict point to a yellow light state. The condition for the yellow light state to be lifted is that the first aircraft has passed the conflict point and the distance between it and the second aircraft is greater than the safe clearance distance Dclear (e.g., 100 meters).

[0020] Preferably, the generation and issuance of avoidance instructions containing specific maneuver parameters includes: generating a right turn heading adjustment instruction for one or two aircraft in a head-on conflict scenario; and generating deceleration or altitude adjustment instructions for low-priority aircraft in a merging scenario.

[0021] Preferably, generating a right-turn heading adjustment command for one or two of the aircraft specifically includes: calculating the relative velocity vector Vrel and relative position vector Prel of the conflicting parties; calculating a target right-turn heading angle θturn based on the rules for head-on avoidance in the digital traffic rule model, wherein the heading angle θturn ensures that the lateral distance between the new track after the turn and the other party's track is continuously greater than the minimum safe distance; and generating an avoidance command containing the target right-turn heading angle θturn.

[0022] Preferably, the method further includes: for specific airspaces such as take-off and landing fields or hubs, pre-dividing the internal three-dimensional space into functional grids, the functional grids including take-off channel grids, landing channel grids and waiting grids; and binding exclusive passage rules to different types of functional grids.

[0023] Preferably, the exclusive passage rules include: setting the passage priority of the landing channel grid to be higher than that of the takeoff channel grid; requiring all aircraft entering the specific airspace to first enter the waiting grid, and then enter the takeoff or landing channel grid in sequence according to the dispatch instructions.

[0024] A second aspect of the present invention provides an airspace traffic control system, including a processor, a memory, and a communication interface. The memory stores computer-executable instructions, and when the processor executes the computer-executable instructions, it implements the method described in the first aspect of the present invention.

[0025] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect of the present invention.

[0026] The present invention, through the above technical solution, has at least the following beneficial effects:

[0027] 1. This invention transforms abstract traffic rules into a computable digital model, thereby driving the dynamic adaptation of access permission status in a three-dimensional airspace grid. The presence of a high-priority aircraft will proactively render its path as a "green light," and forcibly render the grid containing a low-priority aircraft on a conflict path as a "red light" or "yellow light." This mechanism directly "projects" traffic rules onto the airspace grid, realizing a shift from passive warning to proactive, predictive conflict avoidance. It can resolve flight conflicts remotely and in advance, significantly reducing the flight conflict rate from 12% to below 1%, thus improving the overall safety of airspace operations.

[0028] 2. Based on the rule-adapted grid state, the system can automatically and quickly generate precise avoidance commands containing specific maneuver parameters, reducing the aircraft's avoidance decision time from the traditional 5 seconds to 0.5 seconds, and the emergency avoidance response time to no more than 100 milliseconds. Simultaneously, refined grid management for specific areas such as takeoff and landing fields enables automated scheduling, increasing takeoff and landing field throughput by 50% and effectively improving airspace resource utilization.

[0029] 3. The constructed digital traffic rule model has good scalability and can adapt to rule iterations through parameter updates. The system can link control zones, no-fly zones, and meteorological data in real time, dynamically reflecting various restriction rules as a grid state, ensuring that aircraft always operate in compliant and safe airspace, and can increase the airspace compliance flight rate from 85% to 99.5%, meeting the needs of refined management of large-scale urban air traffic in the future. Attached Figure Description

[0030] 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.

[0031] Figure 1 This is a hardware structure block diagram of an airspace traffic control system according to an embodiment of the present invention.

[0032] Figure 2 This is a flowchart of an airspace traffic control method according to an embodiment of the present invention.

[0033] Figure 3 This is a schematic diagram illustrating the dynamic adaptation of access permission status of a three-dimensional spatial grid under rule-driven conditions according to an embodiment of the present invention.

[0034] Figure 4 This is a detailed flowchart illustrating the generation of avoidance instructions for head-on collision scenarios according to an embodiment of the present invention.

[0035] Figure 5 This is a schematic diagram illustrating the functional grid division of a specific airspace for a takeoff and landing field according to an embodiment of the present invention. Explanation of reference numerals in the attached figures: 100: Airspace traffic control system; 101: Processor; 102: Memory; 103: Communication interface; 301: First aircraft; 302: Second aircraft; 303: Predicted trajectory of the first aircraft; 304: Predicted trajectory of the second aircraft; 305: Conflict point; 306: Green light status grid; 307: Red light status grid; 308: Yellow light status grid; 500: Takeoff and landing field; 501: Landing lane grid; 502: Takeoff lane grid; 503: Holding grid. Detailed Implementation

[0036] 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.

[0037] Example 1

[0038] This embodiment provides an airspace traffic control system and method based on traffic rule adaptation. The system aims to deeply integrate traditional low-altitude traffic rules with modern three-dimensional gridded airspace management technology. By constructing a closed-loop technical system of "rule modeling - grid adaptation - conflict resolution - command generation," it achieves precise, proactive, and efficient control over low-altitude flight activities.

[0039] See Figure 1 This diagram illustrates the hardware structure of the airspace traffic control system described in this embodiment. The system can be deployed at a ground control center, edge computing node, or cloud server. System 100 may include a processor 101, a memory 102, and a communication interface 103.

[0040] The processor 101 may be a computing unit such as a central processing unit (CPU), a graphics processing unit (GPU), an application-specific integrated circuit (ASIC), or a field-programmable gate array (FPGA). Its core function is to execute computer-executable instructions stored in the memory 102 to implement the various functional modules and method steps described in this invention.

[0041] The memory 102 may be random access memory (RAM), read-only memory (ROM), solid-state drive (SSD), or other forms of non-volatile storage media. The memory 102 stores the operating system, application programs, and the airspace traffic control program, which is the core of this invention. When the program is loaded and executed by the processor 101, the system 100 is able to perform functions such as traffic rule modeling, grid state adaptation, and conflict resolution.

[0042] The communication interface 103 may include a wireless communication module (such as 5G / 4G, Wi-Fi, satellite communication) and a wired communication interface (such as an Ethernet interface), responsible for data exchange with aircraft in the airspace, ground sensor networks (such as radar, ADS-B receivers), meteorological data service centers, and higher-level airspace management centers. Through the communication interface 103, the system 100 can obtain the status information of the aircraft in real time and distribute control commands downwards.

[0043] Based on the above system architecture, the method flow provided in this embodiment is as follows: Figure 2 As shown.

[0044] Step S201: Establish a digital traffic rules model.

[0045] This step is fundamental to the invention, and its core lies in transforming abstract, qualitative traffic rules into quantifiable parameters and logic that can be processed by a computer. This digital traffic rule model is built by processor 101 and maintained in memory 102, serving as a rule engine to provide a basis for subsequent decision-making. The model specifically includes the modeling of the following sub-modules:

[0046] 1. Priority Rule Modeling: The system constructs a multi-dimensional priority weight system containing at least eight levels. The priority determination sequence follows this logic: Emergency Status > Mission Type > Manned / Unmanned > Aircraft Type > Size. Specifically, distressed aircraft are assigned the highest priority. Under normal conditions, priority is determined by mission type, such as emergency rescue / medical transport missions having higher priority than other missions. Then, the priority is compared sequentially by whether the aircraft is manned (manned aircraft > unmanned aircraft), aircraft type (e.g., helicopters > other fixed-wing or multi-rotor aircraft), and size (large > small).

[0047] To achieve quantitative calculation, the system assigns a clear priority weight W value range to different task types. For example, the priority weight Wems for emergency rescue tasks is set in the range of [0.9, 1.0]; the priority weight Wuam for passenger transport tasks (such as UAM passenger transport) is set in the range of [0.7, 0.89]; and the priority weight Wcargo for commercial freight tasks is set in the range of [0.5, 0.69].

[0048] When any aircraft switches to emergency status due to malfunction or other reasons, its final priority weight Wfinal will be updated using the following formula:

[0049] Wfinal = max(Wbase, Wemergency)

[0050] Here, Wbase is the basic priority weight calculated for the aircraft based on its mission, aircraft type, etc.; Wemergency is a preset extremely high emergency state weight value (e.g., 0.95). This formula ensures that any aircraft in an emergency state receives the highest access privileges. The aircraft's identity information (including but not limited to aircraft type, mission type, and operational status) can be bound and reported through onboard devices such as the BeiDou grid 3D positioning eSIM card.

[0051] 2. Digitization of Avoidance Rules: Transforming general aerial avoidance principles into specific behavioral judgment thresholds and command parameters. For example:

[0052] The "avoid head-on flight" rule is quantified as follows: when the relative heading angle of two aircraft is between 170° and 190°, it is determined to be a head-on flight scenario, and the system will trigger the generation of an avoidance command to turn to the right by 30° to 45°.

[0053] * The “overtaking” rule is quantified as follows: the overtaking aircraft must maintain a horizontal safety distance of not less than 50 meters and a vertical safety distance of not less than 20 meters from the overtaken aircraft.

[0054] 3. Modeling specific aircraft and airspace rules:

[0055] * Helicopter-specific rules: A three-dimensional spherical space with a radius of 200 meters, centered on the helicopter's real-time position, is designated as the core protection zone. The grid within this zone is exclusive to other low-priority drones. Furthermore, it is stipulated that when a helicopter needs to avoid a fixed-wing aircraft, its heading adjustment threshold should be no less than 15°.

[0056] * Altitude Stratification Rules: Low-altitude airspace is divided into different operational layers based on altitude. For example, micro-drones are restricted to flight altitudes below 120 meters; conventional drones to below 300 meters; and manned aircraft such as helicopters operate at altitudes below 1000 meters. To increase precision, each altitude layer can be further subdivided into 20-meter intervals.

[0057] * Area Restriction Rules: Information such as controlled areas, no-fly zones, and severe weather zones is converted into grid attribute parameters. For example, when the wind speed is greater than or equal to 15 m / s or the visibility is less than or equal to 500 meters, the affected grid will be marked as impassable.

[0058] Step S202: Obtain aircraft status information in real time.

[0059] System 100 receives status information in real time and continuously from all managed aircraft within the airspace via its communication interface 103. This information is typically reported in the form of data packets and includes at least: the aircraft's unique identifier, real-time three-dimensional position (e.g., latitude, longitude, and altitude), velocity vector, heading angle, and current mission status (e.g., cruise, takeoff and landing, emergency response). This data forms the basis for subsequent situational analysis and conflict assessment.

[0060] Step S203: Dynamically adapt the access permission status of the three-dimensional spatial grid based on the rule model.

[0061] This step is the core innovation of this invention. It "projects" the rule model established in step S201 onto a three-dimensional airspace grid, so that the grid's state is no longer a simple "occupied / idle" state, but rather reflects the "access rights" of traffic rules. The processor 101 analyzes the state of each aircraft and its expected flight path in real time, and dynamically assigns one of the following three access rights states to the three-dimensional grid involved in the path based on the digital traffic rule model (at the computer system level, this state can be mapped to a specific state bit, enumeration value, or Boolean combination, such as 00-red light, 01-yellow light, 10-green light):

[0062] * Green light status: Indicates priority passage. Typically assigned to grids on the current and future paths of high-priority aircraft.

[0063] * Yellow light status: Indicates waiting or yielding as instructed. This is typically assigned to the grid of a low-priority aircraft that has a potential conflict with a high-priority aircraft, or to a target altitude layer grid that is already occupied.

[0064] * Red light status: Indicates entry is prohibited. This is typically assigned to conflict grids already locked by high-priority aircraft, no-fly zones, grids covered by severe weather, or grids within helicopter core protection zones.

[0065] like Figure 3 As shown, this dynamic adaptation process is rule-driven. Assume that the priority weight W1 of the first aircraft 301 (e.g., an emergency rescue helicopter) is significantly higher than the priority weight W2 of the second aircraft 302 (e.g., a logistics drone). The system predicts through trajectory forecasting that the predicted trajectories 303 and 304 of the two aircraft will conflict at a future conflict point 305. The specific triggering logic is as follows: when W1 > W2, and the closest distance between the two aircraft within the future prediction time window Tpredict is less than a preset safe distance threshold Dsafe, the adaptation mechanism is activated. For example, when a high-priority aircraft enters a grid, the grid and surrounding grids within a three-times-safe-distance range occupied by low-priority aircraft will be marked with a red light.

[0066] Upon activation, the system sets the path grid of the first aircraft 301 for the next N time steps to a green light state (306), ensuring its priority passage. Simultaneously, it forcibly sets the M consecutive grids on the path of the second aircraft 302 that are less than the warning threshold Daalert at the conflict point 305 to a red light state (307), preventing it from proceeding. Grids behind the conflict point may be set to a yellow light state (308), instructing them to wait. The condition for the yellow light state to be lifted is typically: the first aircraft 301 has safely passed the conflict point, and the distance between it and the second aircraft 302 has returned to a safe clearance distance Dclear.

[0067] In this way, traffic rules are visualized and concretized as "traffic lights" in the airspace grid, providing clear and intuitive passage guidance for aircraft and achieving proactive conflict avoidance from the source.

[0068] Step S204: Generate and issue an avoidance command containing specific maneuver parameters.

[0069] When the system determines that there is a flight conflict through grid state adaptation (for example, an aircraft is about to enter a red light grid), the processor 101 will generate and issue specific, executable avoidance instructions for the aircraft that needs to be avoided (usually the lower priority one) based on the adapted access permission state.

[0070] See Figure 4 Taking a head-on collision scenario as an example, the instruction generation process is as follows:

[0071] Step S401: The system detects an oncoming collision scenario (e.g., relative heading between 170° and 190°).

[0072] Step S402: Calculate the relative velocity vector Vrel and relative position vector Prel of the two conflicting parties.

[0073] Step S403: Invoke the rules regarding head-on avoidance in the digital traffic rule model, namely the "yield to the right" principle.

[0074] Step S404: Based on this rule, processor 101 calculates an optimal target right turn heading angle θturn. The goal of this heading angle calculation is to ensure that the lateral separation between the new track after the turn and the other track can consistently be greater than the minimum safe separation. For example, the calculated θturn is 35° to the right.

[0075] Step S405: Generate an avoidance command that includes the target’s right turn heading angle and other parameters (such as maintaining current speed and altitude).

[0076] Step S406: Send the instruction to the aircraft that needs to be avoided via communication interface 103.

[0077] The instructions also differ for other conflict scenarios:

[0078] * Cross-merging scenario: Generate commands such as "decelerate to 30km / h" or "descend / ascend 20 meters" for low-priority aircraft to avoid being separated from high-priority aircraft in time or space.

[0079] * Overtaking Scenario: Generates the instruction "Rise 10 meters to the right to enter the overtaking channel, and resume the original trajectory after the overtaking is completed" for the aircraft performing the overtaking maneuver.

[0080] * Emergency Situation: When a distressed aircraft triggers the highest priority, the system will issue mandatory instructions to all other aircraft in its path, such as "immediately move to the left to wait at a safety grid 500 meters away".

[0081] Through this series of steps, the present invention achieves refined and intelligent control of low-altitude traffic. The beneficial effects are significant: rule-driven proactive avoidance reduces the flight conflict rate from 12% to below 1%; automated decision-making shortens the aircraft avoidance decision time from the traditional 5 seconds to 0.5 seconds; and the response time in emergency situations is no more than 100ms, providing a solid guarantee for safe flight.

[0082] Example 2

[0083] This embodiment focuses on a refined grid management scheme for specific airspaces such as take-off and landing fields or transportation hubs. This scheme is a further specific application of the method described in Embodiment 1.

[0084] Takeoff and landing fields are among the areas with the most intensive flight activity and the highest risk of conflict. To address this, this invention proposes pre-dividing the three-dimensional space inside and around the field into functional grids and assigning specific access rules to different functional grids.

[0085] See Figure 5 A typical takeoff and landing field 500 is divided into multiple functional grid regions:

[0086] * Landing lane grid 501: Dedicated to aircraft in the final approach phase. This area grid has extremely high passage priority.

[0087] * Takeoff lane grid 502: Dedicated to aircraft preparing for takeoff and initial climb.

[0088] * Waiting Grid 503: A safe airspace located near the takeoff and landing field for aircraft preparing for takeoff or waiting for landing clearance to hover or circle.

[0089] * Unmanned Aerial Vehicle (UAV) Restricted Area (not shown in the diagram): A three-dimensional space is designated around the take-off and landing site (e.g., within a radius of 500 meters). The grid of this area is permanently or dynamically set to a red light state to prohibit unauthorized UAVs from entering, thereby ensuring the safety of manned aircraft take-off and landing.

[0090] The following set of dedicated access rules are tied to these functional grids:

[0091] 1. Landing Priority Rule: The landing lane grid 501 is set to have a higher priority than the takeoff lane grid 502. When an aircraft enters the approach phase (e.g., within 1 kilometer of the takeoff and landing field), the system will automatically lock the landing lane grid 501 involved in its expected landing path to a green light status.

[0092] 2. Ordered Scheduling Rules: All aircraft wishing to use the takeoff and landing field, whether taking off or landing, must first enter waiting grid 503 and switch to a waiting state (yellow light). The system then sorts and schedules aircraft based on the occupancy of the landing lanes. For example, when an aircraft completes landing and leaves the landing lane, the system sends an entry command to the first aircraft waiting to land in waiting grid 503, while simultaneously setting the corresponding landing lane grid 501 back to green. For takeoff requests, the system will set the takeoff lane grid 502 to green during periods of landing lane vacancy and instruct the waiting aircraft to take off.

[0093] Through this refined functional grid division and rule binding, the operational order within the takeoff and landing field has been standardized. The right-of-way for approaching aircraft is guaranteed, and landing delays have been significantly reduced from an average of 120 seconds to 20 seconds; takeoff and landing paths are effectively separated, and the incidence of conflict incidents within the area has decreased significantly. Both the throughput and operational safety of the entire takeoff and landing field have been improved, with throughput increasing by 50%.

[0094] Example 3

[0095] This embodiment provides a variant or alternative to the aircraft identity information binding and reporting mechanism in Embodiment 1.

[0096] In Example 1, the use of an onboard BeiDou grid 3D positioning eSIM card to bind and report aircraft identity information (such as aircraft type and mission type) was mentioned. This is an efficient and reliable method because it deeply integrates location services with identity authentication. However, in some scenarios, there may be aircraft that do not possess such dedicated hardware.

[0097] In one variant embodiment, the binding and reporting of identity information can be achieved through other technical means. For example, the system can be compatible with and integrate information from different sources:

[0098] 1. Flight Plan Database: Before a flight, the aircraft operator needs to submit a flight plan to the airspace management system, which includes detailed information such as aircraft type, mission nature (e.g., aerial photography, logistics, emergency), and payload. When the system receives the real-time location report of the aircraft, it can associate it with the flight plan database through its unique identifier (e.g., serial number or registration number) to obtain its identity and priority attributes.

[0099] 2. ADS-B (Automatic Dependent Surveillance-Broadcast) Signals: For aircraft equipped with ADS-B Out devices, the broadcast signals contain not only dynamic information such as position and speed, but also static information such as aircraft type and call sign. Airspace traffic control systems can acquire this information through a network of deployed ground-based ADS-B receivers and use it as a basis for determining basic priorities.

[0100] 3. Dedicated Communication Link Authentication: When an aircraft establishes a connection with the control system via an encrypted dedicated communication link, it needs to undergo authentication. During the authentication process, the aircraft can report its detailed configuration and mission information.

[0101] By integrating the aforementioned multiple information sources, the system can establish digital identity profiles for a wider range of aircraft types (including some traditional aircraft or those without deeply integrated dedicated hardware) and assign them appropriate priority weights, thereby seamlessly incorporating them into the dynamic control system of this invention. This flexibility enhances the system's universality and compatibility.

[0102] In summary, this invention constructs a digital traffic rule model and uses it to drive the dynamic adaptation of three-dimensional airspace grid access permission states, ultimately generating precise avoidance instructions. This effectively transforms abstract traffic rules into executable machine behavior. This not only improves the safety and efficiency of low-altitude flight but also provides a complete and reliable technical solution for large-scale, high-density urban air traffic management in the future. Ultimately, the airspace compliance rate can be increased from 85% to 99.5%, and airspace utilization can be improved by 40%.

[0103] 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. A method for airspace traffic control, characterized in that, include: A digital traffic rule model is established, which quantifies low-altitude traffic rules into computable parameters that include priority weights and behavior determination thresholds; Real-time acquisition of the status information of at least one aircraft in the airspace, the status information including the aircraft's position, heading and identity information; Based on the digital traffic rule model, the access permission status of the three-dimensional airspace grid involved in the current and expected paths of the aircraft is dynamically adapted. The access permission status is used to characterize the access permission level of different aircraft to the three-dimensional airspace grid. When a flight conflict is determined, an avoidance command is generated and issued to the aircraft to be avoided based on the adapted access permission status.

2. The method according to claim 1, characterized in that, The establishment of the digital traffic rules model includes: Based on the aircraft type, mission type, and operational status, multi-dimensional priority rules are constructed, and unique priority weight values ​​are assigned to combinations under different dimensions.

3. The method according to claim 2, characterized in that, The construction of multi-dimensional priority rules specifically includes: Establish a priority weighting system with at least eight levels, wherein the priority weight values ​​are distributed within a preset range; the task types at least cover emergency rescue, public transportation and commercial freight, and assign the highest priority weight value to emergency rescue tasks.

4. The method according to claim 3, characterized in that, The priority weighting system is configured as follows: the priority weight Wems for emergency rescue tasks is set in the range of [0.9, 1.0]; the priority weight Wuam for manned transportation tasks is set in the range of [0.7, 0.89]; and the priority weight Wcargo for commercial freight tasks is set in the range of [0.5, 0.69]. Furthermore, when the aircraft status switches to an emergency state, its final priority weight Wfinal is updated using the formula Wfinal = max(Wbase, Wemergency), where Wbase is the base priority weight and Wemergency is the preset emergency state weight value, ensuring that aircraft in an emergency state obtain the highest access rights.

5. The method according to claim 1, characterized in that, The access permission status includes: green light status, indicating priority passage; yellow light status, indicating waiting or yielding as instructed; and red light status, indicating prohibition of entry.

6. The method according to claim 5, characterized in that, The dynamic adaptation of access permission status for the three-dimensional spatial grid includes: When the priority of the first aircraft is determined to be higher than that of the second aircraft, and the predicted trajectories of the two conflict, the relevant grids on the expected path of the first aircraft are set to green light status, and the relevant grids on the conflicting path of the second aircraft are forcibly set to red light status or yellow light status.

7. The method according to claim 6, characterized in that, The dynamic adaptation triggering and execution logic is configured as follows: when the priority weight W1 of the first aircraft is greater than the priority weight W2 of the second aircraft, and the closest distance between their predicted trajectories within the future time window Tpredict is less than the safe distance threshold Dsafe, the system sets the path grid of the first aircraft to a green light state for the next N time steps. At the same time, it sets the M consecutive grids on the path of the second aircraft that are less than the warning threshold Daalert to a red light state, and sets the grids behind the conflict point to a yellow light state. The condition for the yellow light state to be lifted is that the first aircraft has passed the conflict point and the distance between it and the second aircraft is greater than the safe clearance distance Dclear.

8. The method according to claim 1, characterized in that, The generation and issuance of avoidance commands containing specific maneuver parameters includes: In the event of a head-on collision, generate a heading adjustment command to turn right for one or two of the aircraft. For cross-merging scenarios, generate deceleration or altitude adjustment commands for low-priority aircraft.

9. The method according to claim 8, characterized in that, The process of generating a right turn heading adjustment command for one or two of the aircraft specifically includes: Calculate the relative velocity vector Vrel and relative position vector Prel of the two conflicting parties; based on the rules for head-on avoidance in the digital traffic rule model, calculate a target right turn heading angle θturn, which ensures that the lateral distance between the new track after the turn and the other party's track is always greater than the minimum safe distance; generate an avoidance instruction containing the target right turn heading angle θturn.

10. The method according to claim 1, characterized in that, The method further includes: For specific airspaces such as takeoff and landing fields or hubs, the three-dimensional space inside them is pre-divided into functional grids, including takeoff channel grids, landing channel grids, and holding grids; and exclusive passage rules are bound to different types of functional grids.

11. The method according to claim 10, characterized in that, The specific access rules include: Set the passage priority of the landing lane grid to be higher than that of the takeoff lane grid; All aircraft entering the specified airspace must first enter the waiting grid and then enter the takeoff or landing channel grid in sequence according to the dispatch instructions.

12. An airspace traffic control system, characterized in that, It includes a processor, a memory, and a communication interface. The memory stores computer-executable instructions. When the processor executes the computer-executable instructions, it implements: A digital traffic rule model is established, which quantifies low-altitude traffic rules into computable parameters that include priority weights and behavior determination thresholds; The status information of at least one aircraft in the airspace can be obtained in real time through the communication interface. The status information includes the aircraft's position, heading and identity information. Based on the digital traffic rule model, the access permission status of the three-dimensional airspace grid involved in the current and expected paths of the aircraft is dynamically adapted. The access permission status is used to characterize the access permission level of different aircraft to the three-dimensional airspace grid. When a flight conflict is determined, an avoidance command is generated for the aircraft to be avoided based on the adapted access permission status, and the avoidance command is sent through the communication interface.

13. The system according to claim 12, characterized in that, The processor is configured to build a digital traffic rule model in the following manner: Based on the aircraft type, mission type, and operational status, multi-dimensional priority rules are constructed, and unique priority weight values ​​are assigned to combinations under different dimensions.

14. The system according to claim 13, characterized in that, The processor is further configured to: Establish a priority weighting system with at least eight levels, wherein the priority weight values ​​are distributed within a preset range; the task types at least cover emergency rescue, public transportation and commercial freight, and assign the highest priority weight value to emergency rescue tasks.

15. The system according to claim 14, characterized in that, The priority weight system implemented by the processor execution instructions is configured as follows: the priority weight Wems for emergency rescue tasks is set in the range of [0.9, 1.0]; the priority weight Wuam for manned transportation tasks is set in the range of [0.7, 0.89]; and the priority weight Wcargo for commercial freight tasks is set in the range of [0.5, 0.69]. Furthermore, when the aircraft status switches to an emergency state, its final priority weight Wfinal is updated using the formula Wfinal = max(Wbase, Wemergency), where Wbase is the base priority weight and Wemergency is a preset emergency state weight value, ensuring that the aircraft in the emergency state obtains the highest access permission.

16. The system according to claim 12, characterized in that, The access permission status includes: green light status, indicating priority passage; yellow light status, indicating waiting or yielding as instructed; and red light status, indicating prohibition of entry.

17. The system according to claim 16, characterized in that, The processor is configured to dynamically adapt to the access permission status of the three-dimensional spatial grid in the following manner: When the priority of the first aircraft is determined to be higher than that of the second aircraft, and the predicted trajectories of the two conflict, the relevant grids on the expected path of the first aircraft are set to green light status, and the relevant grids on the conflicting path of the second aircraft are forcibly set to red light status or yellow light status.

18. The system according to claim 17, characterized in that, The triggering and execution logic of the dynamic adaptation executed by the processor is configured as follows: when the priority weight W1 of the first aircraft is greater than the priority weight W2 of the second aircraft, and the closest distance between their predicted trajectories in the future time window Tpredict is less than the safe distance threshold Dsafe, the processor sets the path grid of the first aircraft to a green light state for the next N time steps, and sets the M consecutive grids on the path of the second aircraft that are less than the warning threshold Daalert to a red light state, and sets the grids behind the conflict point to a yellow light state. The condition for the yellow light state to be lifted is that the first aircraft has passed the conflict point and the distance between it and the second aircraft is greater than the safe clearance distance Dclear.

19. The system according to claim 12, characterized in that, The processor is configured to generate and issue avoidance commands containing specific maneuver parameters in the following manner: In the event of a head-on collision, generate a heading adjustment command to turn right for one or two of the aircraft. For cross-merging scenarios, generate deceleration or altitude adjustment commands for low-priority aircraft.

20. The system according to claim 19, characterized in that, The processor is further configured to: Calculate the relative velocity vector Vrel and relative position vector Prel of the two conflicting parties; based on the rules for head-on avoidance in the digital traffic rule model, calculate a target right turn heading angle θturn, which ensures that the lateral distance between the new track after the turn and the other party's track is always greater than the minimum safe distance; generate an avoidance instruction containing the target right turn heading angle θturn.

21. The system according to claim 12, characterized in that, The processor is also configured to: For specific airspaces such as takeoff and landing fields or hubs, the three-dimensional space inside them is pre-divided into functional grids, including takeoff channel grids, landing channel grids, and holding grids; and exclusive passage rules are bound to different types of functional grids.

22. The system according to claim 21, characterized in that, The dedicated access rules executed by the processor include: Set the passage priority of the landing lane grid to be higher than that of the takeoff lane grid; All aircraft entering the specified airspace must first enter the waiting grid and then enter the takeoff or landing channel grid in sequence according to the dispatch instructions.

23. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 11.

24. A computer program product, characterized in that, It includes computer program instructions stored on a non-transitory computer-readable storage medium, which, when executed by a processor, implement the method as described in any one of claims 1 to 11.

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