A multi-node coordinated airspace control method and device for urban low-altitude traffic

Abstract

The present invention proposes a multi-node collaborative airspace control method and device for urban low-altitude transportation. The method includes: obtaining perception data from a real-time perception system of urban low-altitude aircraft bodies and urban airspace infrastructure; assigning task scheduling and sorting, determining the local congestion coefficient and the path start and end point pairs of the current aircraft's assigned tasks; optimizing the initial spline path by constructing a path cost function to generate a sequence of path control points and a total path cost; dynamically adjusting the path based on distributed collaborative control to generate the final path for the aircraft during actual execution; mapping the final path to the urban airspace, generating a complete scheduling sequence for the aircraft in the urban airspace, and sending it to the aircraft control system for execution. The present invention can ensure collaborative cooperation between aircraft, improve the utilization efficiency of airspace resources, and ensure flight safety in the context of increasingly complex urban low-altitude airspace, thereby laying the foundation for the intelligent development of low-altitude transportation systems.

Description

Technical Field

[0001] The present invention belongs to the field of low-altitude unmanned aerial vehicles, and in particular relates to a multi-node collaborative airspace control method and device for urban low-altitude transportation. Background Art

[0002] With the acceleration of urbanization, urban low-altitude transportation has become a vital transportation sector. In particular, the use and management of low-altitude airspace in applications such as unmanned aerial vehicles (UAVs), aerial taxis (AAVs), and logistics distribution has become a pressing challenge. Existing airspace management systems primarily rely on traditional ground-based control centers. Aircraft path planning and scheduling rely on predetermined flight plans, and communication between aircraft and control centers often suffers from latency. While this approach works effectively for simple flight missions, it presents numerous issues when faced with the complex demands of urban low-altitude transportation.

[0003] First, existing airspace management systems typically rely on centralized decision-making mechanisms, meaning all dispatch and instructions are uniformly controlled and coordinated by a central system. This centralized management approach not only faces bottlenecks in computing power and communication bandwidth, but is also prone to single-point failures. As the number of aircraft increases and airspace density increases, traditional centralized control systems struggle to cope with the dynamic and complex urban airspace environment, suffering from response delays and low decision-making efficiency. Furthermore, low-altitude flight environments are plagued by unpredictable factors, such as climate change and airspace emergencies. This requires aircraft to be highly adaptable, while traditional predetermined path planning methods often lack flexibility and are unable to adjust flight paths in real time, potentially leading to collisions or violations of flight safety in emergency situations.

[0004] Secondly, current path planning methods are mostly based on static models and fail to account for real-time dynamic environments. When performing missions, aircraft need to perceive their surroundings in real time and adjust their flight paths based on environmental changes. However, existing path planning algorithms often neglect real-time coordination between aircraft and fail to fully consider their physical constraints (such as their maximum speed and turning radius). As a result, these algorithms may not meet the dual requirements of flight safety and efficiency during actual flight. With the commercialization and large-scale use of low-altitude airspace, the number of aircraft has increased dramatically. Ensuring that each aircraft's path planning is both safe and efficient in utilizing limited airspace resources has become an urgent issue.

[0005] While existing technologies enable a certain degree of scheduling and path planning through automated systems, they still face numerous limitations. Centralized scheduling systems lack efficient real-time response, static path planning methods cannot handle dynamic flight environments, and effective coordination between aircraft is lacking. Existing technologies also lack sufficient adaptive capabilities, making it difficult to achieve dynamic coordination and conflict avoidance among multiple aircraft in complex low-altitude environments. Summary of the Invention

[0006] The purpose of the present invention is to propose a multi-node collaborative airspace control method and device for urban low-altitude traffic, which solves the bottleneck problem of the existing technology in low-altitude traffic airspace management and provides a more flexible, efficient and safe low-altitude traffic control method.

[0007] In order to achieve the above-mentioned object, a first aspect of the present invention provides a multi-node coordinated airspace control method for urban low-altitude traffic, the method comprising:

[0008] Acquire and preprocess the perception data of the real-time perception system of urban low-altitude aircraft and urban airspace infrastructure to generate a global perception data set; wherein the global perception data set includes: three-dimensional position data, linear velocity and attitude angle, relative altitude, position and track vector of neighboring aircraft, distance and direction of static obstacles ahead, and local airspace density;

[0009] Determining, based on the global perception data set, a local congestion coefficient and a path start-end point pair of the current aircraft assignment task by performing real-time airspace availability analysis and current aircraft assignment task scheduling sorting;

[0010] Constructing an initial spline path based on the path start and end point pairs, constructing a path cost function with minimizing the local congestion coefficient as the optimization goal to optimize the initial spline path, and generating a path control point sequence and a total path cost;

[0011] Taking any aircraft as the target aircraft, the target aircraft and its neighboring aircraft are combined to perform dynamic path adjustment based on distributed cooperative control. The dynamically adjusted path control point sequence is generated as the final path of the aircraft during actual execution, as well as the total cost of the new path.

[0012] Acquiring urban airspace, mapping the final path to the urban airspace, generating a complete scheduling sequence for the aircraft in the urban airspace, packaging the complete scheduling sequence into a structured scheduling plan, and sending it to the aircraft control system for execution;

[0013] If the aircraft resource request fails, the aircraft control system marks the conflicting resource segment as a scheduling failure segment and notifies the corresponding aircraft to enter a task delay or queue waiting for resource release; if the aircraft resource request succeeds, it is marked as a successful resource unit set, indicating that the path has been successfully allocated to the executable airspace in the current scheduling cycle;

[0014] Among them, once the aircraft receives the scheduling confirmation signal, it enters the automatic takeoff preparation state. The aircraft control system will perform flight path tracking with reference to the successful resource unit set. At the same time, the operation of the aircraft is monitored in real time to ensure that its path execution is consistent with resource occupancy.

[0015] In one possible example, the real-time perception system includes GNSS, IMU, barometer, ADS-B receiver and visual sensor;

[0016] The GNSS is used to provide the three-dimensional position data of the aircraft; the IMU is used to provide the linear velocity and attitude angle of the aircraft; the barometer is used to provide the relative altitude of the aircraft; the ADS-B receiver provides the position and track vector of the adjacent aircraft; and the visual sensor is used to provide obstacle detection results within the forward line of sight of the aircraft, that is, the distance and direction of the static obstacles in front.

[0017] The sensing data includes three-dimensional position data, linear velocity and attitude angle, relative altitude, position and track vector of adjacent aircraft, and distance and direction of static obstacles ahead;

[0018] The pre-processing includes:

[0019] Unifying the sensory data into a common timestamp and geographic coordinate framework;

[0020] Obtain aircraft position information, construct a cube region centered on any aircraft based on the aircraft position information, and record the frequency of aircraft appearance per unit time within each grid cell of the cube region as a dynamic density indicator. Apply trilinear interpolation to all dynamic density indicators to generate the local airspace three-dimensional density of any query point.

[0021] All processed data are organized into a global perception dataset.

[0022] Among them, when an abnormality occurs in the real-time perception system, an extended Kalman filter is used to perform joint state estimation on the data from GNSS and IMU to generate a state estimation vector of the aircraft.

[0023] In a feasible example, determining the local congestion coefficient and the path start and end point pair of the current aircraft assignment task by performing real-time airspace availability analysis and current aircraft assignment task scheduling and sorting based on the global perception dataset specifically includes:

[0024] Taking any aircraft as the target aircraft, the current position of the target aircraft is taken as the center, combined with the movement direction, to construct a predicted flight path channel. The congestion coefficient of the current position along the predicted channel is calculated. This is used to analyze whether the corresponding local airspace three-dimensional density within the flight path channel exceeds the set threshold and determine whether there is potential congestion on the path. Among them, if there is a high-density section in the predicted area, it is marked as an impassable area, and a soft penalty constraint is applied to the path during the task scheduling process.

[0025] Obtaining the task set of the target aircraft, performing scheduling and sorting, and generating the current optimal task;

[0026] The currently optimal task is assigned to the target aircraft, and the initial path starting point and initial target point for the task execution are generated for it as the path start-end point pair; if the path starting point cannot directly reach the initial target point due to density restrictions, airspace blockades, or dangerous obstacles ahead, the suboptimal task fallback strategy will be triggered, and alternative tasks will be re-screened from the task set, and the scheduling scoring mechanism will be re-executed until the task is successfully assigned or the airspace situation is updated;

[0027] The step of obtaining the task set of the target aircraft, performing scheduling and sorting, and generating the currently optimal task specifically includes:

[0028] Each mission is assigned a comprehensive scheduling score, which is calculated based on the Euclidean distance from the aircraft's current location to the mission's starting point, the average congestion coefficient on the mission's expected execution path, the length of the mission's remaining time window, and the priority constant corresponding to the mission type.

[0029] In a feasible example, constructing an initial spline path based on the path start and end point pairs, optimizing the initial spline path by constructing a path cost function with minimizing the local congestion coefficient as the optimization goal, and generating a path control point sequence and a total path cost, specifically includes:

[0030] Constructing an initial spline path according to the path start and end point pairs, wherein the initial spline path is composed of a sequence of path control points, and intermediate path control points are initially uniformly generated by linear interpolation; the initial spline path is constructed as a cubic B-spline;

[0031] The path cost function is constructed with the optimization goal of minimizing the local congestion coefficient to generate the total path cost. The path optimization process adopts the path control point position adjustment strategy based on gradient descent, and the optimization variable is the intermediate control points;

[0032] Generate an optimized path control point sequence as input for optimizing the path curve.

[0033] In a feasible example, any aircraft is used as the target aircraft, and the target aircraft is combined with its neighboring aircraft to perform dynamic path adjustment based on distributed cooperative control. The dynamically adjusted path control point sequence is generated as the final path when the aircraft is actually executed, and the total cost of the new path specifically includes:

[0034] Each aircraft maintains a local perception window during operation. This window obtains the path status of nearby aircraft through short-range communication, including the path control point sequence, current execution progress and total path cost of nearby aircraft. Each broadcast only sends the most recent information in the current window. Hash compression information of each path control point, with a total cost value;

[0035] Based on the spatial overlap between the own path control point and the neighboring path control points, a path conflict estimation function is constructed to generate a path conflict estimation value, which is used to reflect the degree of path interference between the own aircraft and the neighboring aircraft;

[0036] The total path cost is optimized according to the path conflict estimation value to generate a dynamically adjusted path control point sequence and a new path total cost.

[0037] In a feasible example, when the path conflict estimation value exceeds a preset threshold, it is determined that the current path has a potential conflict and needs to be adjusted, specifically including:

[0038] A path with the most conflicts is selected from the current path of the aircraft, and a perturbation is applied to the path to guide the path away from high-density or high-priority aircraft paths; wherein the perturbation direction is determined by the local cost gradient and the normal offset, with the goal of minimizing the perturbation, which is achieved through fast gradient descent.

[0039] In a feasible example, acquiring urban airspace, mapping the final path to the urban airspace, generating a complete scheduling sequence for the aircraft in the urban airspace, packaging the complete scheduling sequence into a structured scheduling plan, and sending it to the aircraft control system for execution, specifically includes:

[0040] The urban airspace is converted into a standard four-dimensional resource unit to form an urban airspace resource coordinate system;

[0041] Obtain the three-dimensional coordinates of the target aircraft's path control points. Based on a set time step, assign a timestamp to each path control point, indicating the aircraft's estimated flight time at that path control point. Combine the three-dimensional coordinates with the timestamp to generate the four-dimensional resource unit corresponding to the path control point in the urban airspace resource grid, forming a complete scheduling sequence.

[0042] The complete scheduling sequence is packaged into a structured scheduling plan and sent to the aircraft control system.

[0043] In a second aspect of the present invention, a multi-node coordinated airspace control device for urban low-altitude traffic is provided, the device comprising:

[0044] A real-time perception module is used to obtain perception data from the real-time perception system of urban low-altitude aircraft and urban airspace infrastructure, perform preprocessing, and generate a global perception data set; wherein the global perception data set includes: three-dimensional position data, linear velocity and attitude angle, relative altitude, position and track vector of neighboring aircraft, distance and direction of static obstacles ahead, and local airspace density;

[0045] An aircraft scheduling module is configured to determine a local congestion coefficient and a path start-end point pair of a current aircraft assignment task by performing real-time airspace availability analysis and scheduling sorting of the current aircraft assignment task based on the global perception data set;

[0046] A path planning module is used to construct an initial spline path based on the path start and end point pairs, optimize the initial spline path by constructing a path cost function with minimizing the local congestion coefficient as the optimization goal, and generate a path control point sequence and a total path cost;

[0047] The collaborative control module is used to use any aircraft as the target aircraft and perform dynamic path adjustment based on distributed collaborative control with the target aircraft and its neighboring aircraft, generating a sequence of dynamically adjusted path control points as the final path for the aircraft during actual execution, as well as the total cost of the new path;

[0048] a resource scheduling module, configured to acquire the urban airspace, map the final path to the urban airspace, generate a complete scheduling sequence for the aircraft in the urban airspace, package the complete scheduling sequence into a structured scheduling plan, and send it to the aircraft control system for execution;

[0049] If the aircraft resource request fails, the aircraft control system marks the conflicting resource segment as a scheduling failure segment and notifies the corresponding aircraft to enter a task delay or queue waiting for resource release; if the aircraft resource request succeeds, it is marked as a successful resource unit set, indicating that the path has been successfully allocated to the executable airspace in the current scheduling cycle;

[0050] Among them, once the aircraft receives the scheduling confirmation signal, it enters the automatic takeoff preparation state. The aircraft control system will perform flight path tracking with reference to the successful resource unit set. At the same time, the operation of the aircraft is monitored in real time to ensure that its path execution is consistent with resource occupancy.

[0051] In the third aspect of the present invention, an electronic device is provided, which includes a processor, a memory, and a communication interface. The processor, memory, and communication interface are interconnected and perform communication with each other. The memory stores executable program code, the communication interface is used for wireless communication, and the processor is used to call the executable program code stored in the memory to execute some or all of the steps described in any method of the first aspect.

[0052] In a fourth aspect of the present invention, a computer-readable storage medium is provided, in which electronic data is stored. When the electronic data is executed by a processor, it is used to execute the electronic data to implement some or all of the steps described in the first aspect of this application.

[0053] In a fifth aspect of the present invention, a computer program product is provided, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, wherein the computer program is operable to cause a computer to perform some or all of the steps described in the first aspect of the present application. The computer program product may be a software installation package.

[0054] The beneficial technical effects of the present invention are at least as follows:

[0055] This invention utilizes a distributed collaborative control mechanism, enabling each aircraft to not only make autonomous decisions but also coordinate with other aircraft to dynamically adjust their routes and airspace usage. This distributed system enables aircraft to respond to changes in their surroundings in real time, eliminating reliance on a single central control system. This significantly enhances the system's flexibility and emergency response capabilities.

[0056] Furthermore, to cope with the dynamic environment of low-altitude airspace, this paper proposes a dynamic path planning method based on real-time data. This method not only allows the aircraft to plan its path based on its current flight status and target mission, but also allows it to perceive the surrounding environment (such as weather, airspace density, and relative position) in real time, dynamically adjusting its path during flight. This method effectively avoids the limitations of traditional path planning methods and provides safer and more efficient path selection for aircraft in complex environments.

[0057] More importantly, this invention incorporates a physical constraint optimization mechanism to ensure that aircraft adhere to actual flight physics and safety standards during path planning and coordinated scheduling. These physical constraints include the aircraft's maximum speed, minimum safe separation, and turning radius, factors often overlooked in traditional path planning. By incorporating these physical constraints into the aircraft's decision-making process, this invention effectively avoids flight accidents caused by ignoring its own performance limitations, ensuring the feasibility and safety of path planning.

[0058] In summary, this invention, through the combination of distributed collaborative control, dynamic path planning, and physical constraint optimization, addresses the bottlenecks of existing technologies in low-altitude traffic airspace management, providing a more flexible, efficient, and safe low-altitude traffic control method. This innovative solution can ensure collaborative cooperation between aircraft, improve the utilization efficiency of airspace resources, and ensure flight safety in the context of the increasingly complex urban low-altitude airspace, thus laying the foundation for the intelligent development of low-altitude traffic systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The present invention is further described with reference to the accompanying drawings. However, the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative effort.

[0060] Figure 1 This is a flow chart of a multi-node collaborative airspace control method for urban low-altitude traffic according to an embodiment of the present invention.

[0061] Figure 2 This is a framework diagram of a multi-node collaborative airspace control device for urban low-altitude traffic according to an embodiment of the present invention.

[0062] Figure 3 This is a structural block diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0063] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention and are not to be construed as limiting the present invention.

[0064] In the present invention, GNS stands for Global Navigation Satellite System, which refers to all satellite navigation systems, including global, regional and enhanced systems, such as the United States' GPS, Russia's Glonass, Europe's Galileo, China's BeiDou satellite navigation system, and related augmentation systems, such as the United States' WAAS (Wide Area Augmentation System), Europe's EGNOS (European Geostationary Navigation Overlay System) and Japan's MSAS (Multifunctional Transport Satellite Augmentation System), etc., and also covers other satellite navigation systems under construction and to be built in the future.

[0065] An IMU is an inertial measurement unit (IMU). It's a core device for autonomous navigation, measuring the acceleration and angular velocity of a vehicle's motion. It consists of sensors such as a gyroscope and accelerometer. By calculating acceleration and angular velocity data in real time, it continuously outputs the vehicle's three-dimensional attitude, displacement, and velocity.

[0066] ADS-B, the full name of which is Automatic Dependent Surveillance Broadcast System, can automatically (once per second) obtain parameters from onboard equipment and broadcast the aircraft's position, altitude, speed, heading, identification number and other information to other aircraft or ground stations without manual operation or inquiry, allowing controllers to monitor the aircraft's status in real time.

[0067] like Figure 1 As shown, an embodiment of the present invention provides a multi-node coordinated airspace control method for urban low-altitude traffic, the method comprising:

[0068] S1. Acquire the perception data of the real-time perception system of urban low-altitude aircraft and urban airspace infrastructure, perform preprocessing, and generate a global perception data set; wherein, the global perception data set includes: three-dimensional position data, linear velocity and attitude angle, relative altitude, position and track vector of neighboring aircraft, distance and direction of static obstacles ahead, and local airspace density.

[0069] Urban low-altitude airspace, with its dense buildings, diverse aircraft types, and complex paths, faces the challenges of high-frequency multi-aircraft operations and dynamic airspace changes. Therefore, to achieve safe and efficient coordinated airspace control, this step aims to provide a structured data model that can be directly used for subsequent steps such as path planning, task scheduling, and conflict prediction by sensing the aircraft's status and the local airspace environment in real time.

[0070] Specifically, this step uses common aircraft capable of low-altitude urban flight as the primary target. Data is collected from these aircraft's onboard sensors (including GNSS, IMUs, barometers, visual sensors, and ADS-B receivers) and urban perception infrastructure (such as ADS-B ground receiving stations). After collection, all data undergoes synchronization, denoising, spatial alignment, and structuring, ultimately outputting a perception data structure that captures the aircraft's current state and local airspace distribution.

[0071] The data input for this step comes from the real-time perception system of urban low-altitude aircraft and urban airspace infrastructure, specifically including:

[0072] GNSS: Provides three-dimensional position data ;

[0073] IMU: Provides linear velocity and posture angle ;

[0074] Barometer: Provides relative altitude of the aircraft ;

[0075] ADS-B receivers (airborne and ground-based): provide the location of surrounding aircraft With the track vector ;

[0076] Visual sensor: Provides obstacle detection results within the forward viewing range (distance between the target bounding box and depth) ).

[0077] For example, before a delivery drone takes off from a building complex, its GNSS initializes its current position, the IMU starts inertial tracking, and the barometer calibrates its starting altitude. Simultaneously, the ADS-B module scans the position and velocity vectors of other flying aircraft within a 3-kilometer radius, and the vision module detects whether there are static obstructions in front of the takeoff platform (such as residual cargo on a temporary apron). This information is then passed into the path planning module as a constraint on the path starting point.

[0078] First, all data must be unified into the same timestamp and geographic coordinate framework. GNSS and ADS-B position data are converted and aligned using the WGS84 coordinate system. The IMU and barometer outputs must be converted to velocity and attitude in a global coordinate system based on the unified aircraft coordinate system. The system uses an extended Kalman filter (EKF) to perform joint state estimation on data from GNSS and IMU, improving trajectory stability in situations such as short-term occlusion and sensor fluctuations. The state estimation model is as follows:

[0079]

[0080] in, For the aircraft at time The state estimation vector, including the position ,speed ,attitude ; is the state transfer matrix (dynamically adjusted as the posture changes), is the control input matrix, is the combination of IMU acceleration and angular velocity, is the system process noise. This filter is updated once per second and can stabilize the trajectory output based on the IMU inertial input in the event of a short GNSS signal loss.

[0081] Secondly, to generate real-time density information of the local airspace, the system will use the position information of surrounding aircraft received by ADS-B to construct a cubic area centered on the aircraft (for example, with a radius of 1.5 kilometers and divided into equally spaced grids). The frequency of aircraft appearances per unit time in each grid cell is recorded as a dynamic density indicator. Since the data granularity is relatively coarse, the present invention uses trilinear interpolation to improve spatial continuity. The formula is as follows:

[0082]

[0083] in, represents the local spatial density of any query point, is the observation density value of the eight adjacent grid cells where the path control point is located, is the weight of the path control point to each grid point, satisfying The density value will be used as a factor in determining whether to avoid or pass in subsequent task scheduling and path optimization.

[0084] At the same time, the visual sensor is used to monitor the distance to obstacles in front of the aircraft With direction vector , combined with the current speed of the aircraft Calculating potential collision time ,like The path is marked as a dangerous path and excluded as a hard constraint in the path initial point generation stage.

[0085] Finally, all processed data are organized into a global perception dataset:

[0086]

[0087] in, It's time The global perception dataset contains the aircraft's own status, the relative information of neighboring aircraft, the local airspace density, and the position and direction of static obstacles within the line of sight. This data structure will be passed to the task scheduling algorithm and path initial solution generation module in the subsequent steps, and will serve as dynamic feedback input for path adjustment and airspace scheduling. The output of this step is a structured global perception dataset. .

[0088] S2. Determine the local congestion coefficient and the path start and end point pairs of the current aircraft assignment task by performing real-time airspace availability analysis and current aircraft assignment task scheduling based on the global perception data set.

[0089] During the operation of the urban low-altitude traffic system, the mission objectives are diverse, the airspace resources are limited, and the aircraft status is constantly changing. Especially during the morning and evening rush hours, the aircraft paths frequently cross, which easily leads to airspace congestion and resource conflicts. This step aims to build a global perception dataset based on the global perception dataset generated in step 1. , dynamically analyze the status of local airspace resources, and combine the aircraft's mission information, flight priority, current location and other factors to carry out collaborative task scheduling and airspace utilization optimization of multiple aircraft to ensure the safety and efficiency of overall airspace operations.

[0090] Specifically, the input data is , which includes the position of the aircraft itself ,speed , attitude angle , pressure altitude ; Local airspace three-dimensional density ; The position and track vector of the adjacent aircraft ; and the distance to the static obstacle ahead and direction In task scheduling, this step will also receive the task set issued by the upper task system , each task Contains constraint information such as the mission start point, end point, time window, and mission type (such as freight or patrol).

[0091] First, the system will perform real-time airspace availability analysis on the perceived local airspace. As the center, combined with its movement direction , the system builds a predicted flight path channel , and analyze the mesh density within the channel Whether it exceeds the set threshold , to determine whether the path has potential congestion. If there is a high-density section in the predicted area, it is marked as an impassable area, and a soft penalty constraint is applied to the path during task scheduling. The prediction mechanism is as follows:

[0092]

[0093] in, Indicates the congestion coefficient of the current location along the predicted channel, It is an indicator function, which is 1 if the density exceeds the threshold, otherwise it is 0. This indicator will be used as a task scheduling weight factor in the subsequent scheduling objective function design to balance the shortest path of the task and the airspace load.

[0094] Then, the task scheduling module will set the task received by the aircraft Perform scheduling and sorting. Each task will be assigned an overall scheduling score , the score is combined with the current position of the aircraft Distance from the mission starting point , predict airspace congestion risks 、Task time window urgency , Task type priority coefficient Calculated together. The calculation formula for the scheduling score is as follows:

[0095]

[0096] in, is the Euclidean distance from the current position of the aircraft to the starting point of the mission, is the average congestion coefficient on the task’s expected execution path, is the remaining time window length of the task, is the priority constant corresponding to the mission type (e.g., 1.5 for medical rescue missions and 1.0 for general cargo missions). To prevent the small constant from dividing by zero. This scoring mechanism allows the system to comprehensively consider the mission importance, path complexity and time constraints, and realize the adaptive scheduling of multi-aircraft missions in dynamic airspace.

[0097] Furthermore, after the scheduling is completed, the system will select the current optimal task Assigned to the aircraft and generate the initial path starting point for its mission execution With the initial target point , as the input boundary conditions for the next step of path planning. If there are density restrictions, airspace blockades, or dangerous obstacles ahead The starting point of the path cannot be directly reached , the system will trigger the suboptimal task fallback strategy, Re-screen alternative tasks , and re-execute the scheduling scoring mechanism until the task is successfully assigned or the airspace situation is updated.

[0098] As you can understand, the output of this step is two items: one is the current aircraft assignment task The task identifier and the path start and end point pair ; The second is the congestion coefficient of the local airspace centered on the aircraft , which will be used as the avoidance control input in the subsequent path planning steps to participate in the path cost calculation.

[0099] To summarize, the innovations of this step are: First, a local airspace congestion index is constructed based on the predicted flight channel, realizing real-time assessment of the availability of urban dynamic airspace resources; second, the task scheduling scoring model simultaneously integrates path risk, task urgency and aircraft position, reflecting the ability to jointly model actual multi-source dynamic constraints; third, a congested path penalty mechanism and fallback scheduling strategy are designed to ensure that aircraft still have the ability to switch tasks when airspace resources are tight, thereby improving the overall robustness of the system.

[0100] S3. Construct an initial spline path based on the path start and end point pairs, construct a path cost function with minimizing the local congestion coefficient as the optimization goal, optimize the initial spline path, and generate a path control point sequence and a total path cost.

[0101] Specifically, the aircraft has been assigned a task And get the path start and end point pair and the airspace congestion coefficient of the current location This step aims to generate an initial path for the aircraft that is physically flyable, based on the premise that the path should avoid congestion as much as possible, have a continuously executable geometry, and provide an optimized starting point for subsequent dynamic adjustments and coordinated obstacle avoidance with the aircraft.

[0102] This step firstly compares the starting and ending points of the task path Construct an initial spline path ,in Represents the normalized path parameters. The path is composed of control points Definition, where , , the intermediate control points are initially generated uniformly by linear interpolation. Considering the path continuity and curvature requirements in urban airspace, It is constructed as a cubic B-spline to ensure the continuous differentiability and local controllability of the curve.

[0103] In order to make the path satisfy the constraints of the path start and end points while actively avoiding local high congestion areas, the present invention introduces a path cost function based on congestion weights As optimization goal:

[0104]

[0105] in, For the Path control points, is the path length, Represents control points The local airspace congestion coefficient at (output from step 2), is the congestion penalty weight coefficient. This objective function aims to reduce the degree of paths crossing high congestion areas while ensuring that the paths are short, so as to achieve a balance between task scheduling and airspace resource utilization.

[0106] Furthermore, the path optimization process adopts a control point position adjustment strategy based on gradient descent, and the optimization variable is the intermediate control points , the optimization steps are as follows:

[0107] Initialization: According to Linear interpolation generates intermediate control points;

[0108] Calculate the current path cost ;

[0109] For each control point Calculate the gradient of the cost function ,in right The derivative of is obtained by numerical approximation;

[0110] Update control points: ,in is the learning rate;

[0111] Repeat steps 2–4 until convergence or the maximum number of iterations is reached.

[0112] The final optimized path control point sequence The path curve The input is used to generate a continuous, low-congestion, and physically feasible path. Because the path curve is controlled by a spline, the aircraft control system can calculate the curvature of the path through derivative calculations to ensure that the vehicle's steering capability and inertia constraints are not exceeded during execution.

[0113] It can be understood that the output of this step includes two items: one is the path control point sequence , as the initial path tracking control input of the aircraft; the second is the total path cost , used for collaborative modules to perform path optimization and load evaluation.

[0114] In summary, the innovations of this step include: (1) the congestion coefficient Directly embed the path optimization cost function and combine it with the path length for penalty modeling; (2) propose a gradient descent-based adaptive optimization strategy for path control points, eliminating the need for new aircraft state or environmental perception inputs, and the logic is completely closed-loop within the task scheduling output; (3) By structured output of path control points and cost evaluations, high-quality executable input is provided for subsequent collaborative control modules. This solution not only optimizes the resource distribution adaptability of the path, but also ensures that the path complies with aircraft operational constraints, demonstrating excellent practical deployment capabilities.

[0115] S4. Take any aircraft as the target aircraft, and perform dynamic path adjustment based on distributed collaborative control with the target aircraft and its neighboring aircraft to generate a dynamically adjusted path control point sequence as the final path when the aircraft is actually executed, as well as the total cost of the new path.

[0116] In urban low-altitude airspace, multiple aircraft may perform different tasks in a dense area at the same time. Factors such as path conflicts, temporary airspace closures, and changes in mission priorities may cause the pre-planned initial path to (Output of step 3) faces the problem of failure or inefficiency in actual execution. Therefore, the goal of this step is to calculate the initial path and total cost during the task execution. (Same as the output of step 3), build a collaborative control mechanism that relies entirely on local communication, does not require central scheduling, and has dynamic adaptability, so that the aircraft can adaptively adjust its path to avoid conflicts, let high-priority tasks and reduce its own costs.

[0117] Specifically, first, each aircraft maintains a local perception window during operation, which obtains the path status of nearby aircraft, including their path control point sequence, through short-range communication (such as LTE-V2X, Wi-Fi Mesh or millimeter wave). , Current execution progress (i.e., execution to the control point ) and the total path cost To ensure scalability and high-frequency communication efficiency, each broadcast only sends the most recent The hashed information of each control point is compressed, and the total cost value is attached.

[0118] Next, the system constructs a path conflict estimation function based on the spatial overlap between its own path control points and the neighboring path control points. , reflecting the aircraft itself With neighbors The design of the conflict function needs to have spatial continuity and path execution consistency, which is defined as follows:

[0119]

[0120] in, For the current aircraft control points, is the control point of the neighboring aircraft at the same step length, To control the scaling factor for distance sensitivity, To prevent division by zero constants. This function not only considers the path space overlap, but also comprehensively considers the total cost (priority) of neighboring tasks, so that high-cost tasks have a higher weight in conflict evaluation and thus receive a higher priority in conflict resolution.

[0121] when Exceeding the threshold When the system determines that there is a potential conflict in the current path, it needs to be adjusted. At this time, the local perturbation optimization strategy is used to fine-tune the path. The specific operation is: Choose the path with the most conflicts , applying a disturbance on this path To guide the path away from high-density or high-priority aircraft paths. The perturbation direction is determined by the local cost gradient and the normal offset. The optimization objective function is:

[0122]

[0123] Among them, the first term is the disturbance amplitude regularization term, which controls the path disturbance not to be too large to avoid causing a sharp deviation of the path and affecting the flyability; the second term is the conflict cost term, Recalculate the new conflict degree of the path point after adjustment; is the balance coefficient. This local optimization problem is implemented by fast gradient descent, and the obtained Used to update the path.

[0124] The updated path control point set is recorded as , the total cost is recalculated as , which is calculated using the path total cost function defined in step 3 (including path length and local spatial cost). If (in is the update threshold), then the perturbation of this round is accepted, otherwise it rolls back and keeps the original path.

[0125] Furthermore, in order to maintain system convergence and communication efficiency, this step is repeated every fixed time. Path control points and total cost are synchronized once, using an asynchronous update mechanism: each aircraft independently maintains its own path without waiting for feedback from neighbors. Coordinated adjustments and path updates are performed in parallel based on distributed communication, ensuring low latency and high fault tolerance.

[0126] It can be understood that the output of this step includes two items: the first is the dynamically adjusted path control point sequence , which is the final path when the aircraft actually executes; the second is the total cost of the new path , which is called by the scheduling system when calculating the statistical task completion efficiency and airspace load balance.

[0127] In summary, the innovations of this step are reflected in many aspects: (1) A conflict estimation function combining the path space overlap and task priority is proposed. , used to dynamically assess the risk of path coordination conflicts; (2) an implementable local perturbation optimization mechanism was designed, which uses a regularization-conflict joint objective function to perform distributed path fine-tuning to ensure path adjustment stability; (3) an asynchronous coordination and hash compression communication strategy was proposed, which makes the coordination mechanism highly real-time and network scalable. This method is highly consistent with the requirements of coordinated control of multiple aircraft operating simultaneously at low altitudes in cities, and achieves adaptive coordination of paths among multiple aircraft without relying on central control.

[0128] S5. Acquire the urban airspace, map the final path to the urban airspace, generate a complete scheduling sequence for the aircraft in the urban airspace, package the complete scheduling sequence into a structured scheduling plan, and send it to the aircraft control system for execution.

[0129] Among them, after the aircraft completes path planning, dynamic collision avoidance and coordinated control, its final flight path and total cost These outputs represent the optimal flight plan for the aircraft in the current urban low-altitude airspace, including the spatial path and comprehensive scheduling cost. To achieve actual operation, the system must integrate this path with the urban airspace resource map and make the final executable scheduling decision, ensuring that the aircraft has legal and conflict-controllable airspace occupation rights from takeoff to landing.

[0130] Specifically, first, the dispatching platform divides the urban airspace into standard four-dimensional resource units ,in is the three-dimensional direction of space, These resource units are discretely divided according to the preset space and time steps, and the spatial direction is is the grid size (e.g. 50 meters per grid), and the time direction is A unified urban airspace resource coordinate system is formed based on the time step (e.g., 1 second per step). All aircraft path mapping and scheduling processes must use this basic coordinate framework to ensure scheduling alignment and conflict detection consistency.

[0131] For path control points , indicating that the aircraft is in the path The position of the target control point is output by step 4 and is the final flight point after dynamic collaborative adjustment. The system sets the time step , assigning a timestamp to each control point , which represents the estimated flight time of the aircraft at the control point on the path, is calculated as follows:

[0132]

[0133] Then, the three-dimensional space coordinates and time labels are mapped into discrete grid coordinates, and the four-dimensional resource unit corresponding to the control point in the urban airspace resource grid is calculated:

[0134]

[0135] The entire path After this operation, a complete scheduling sequence of aircraft on airspace resources is formed:

[0136]

[0137] The meanings of the variables are as follows:

[0138] : The first control point, generated by the path optimization module, represents the first control point of the aircraft in flight. expected positions;

[0139] : Control point The expected time steps are generated by accumulating them at fixed time intervals;

[0140] : three-dimensional continuous space coordinates of the control points;

[0141] : The spatial step size of urban airspace grid division;

[0142] : standard time step used in urban airspace scheduling;

[0143] : Round down operation, used to convert continuous coordinates into discrete grids;

[0144] :The aircraft is at the control point The resource unit corresponding to the location, i.e., the space-time grid occupied from the perspective of the scheduling system;

[0145] : Aircraft The set of resource units that need to be locked during the entire process from takeoff to landing.

[0146] Furthermore, the dispatch system then The collection performs unified sorting and conflict resolution. The sorting is based on the total cost , represents the comprehensive performance of the path in terms of airspace resource utilization efficiency, safety, conflict prevention capability, etc. The lower the total cost, the more friendly the path is to the overall system scheduling, so its Obtain priority locking rights. The system traverses each aircraft resource request in turn. When a resource unit When multiple aircraft compete, the system will retain the resource request of the one with the lowest total cost and eliminate the conflicting segments of other requests.

[0147] It is understandable that for a path where resource request fails, the system will no longer trigger path re-planning, but will directly mark the conflicting resource segment as a scheduling failure segment and notify the corresponding aircraft to enter a task delay or queue waiting for resource release, waiting for the next cycle to try scheduling again. A collection of resource units that will be marked as successful , indicating that the path has been successfully allocated to the executable airspace in the current scheduling cycle, and the system will lock this path in the scheduling master table.

[0148] The system packages the scheduling results into a structured scheduling plan and sends it to the aircraft control system. The scheduling content includes: : The complete airspace resource occupation path of the aircraft during the execution cycle; each time step Corresponding resource location ;Takeoff and landing time window for mission execution;Takeoff permission flag: indicates whether the aircraft is allowed to take off in the current cycle (Boolean flag).

[0149] Furthermore, once the aircraft receives the dispatch confirmation signal, it will enter the automatic takeoff preparation state, and its flight control system will For reference execution flight path tracking, at the same time, the system scheduling module will start real-time monitoring of the aircraft's operation to ensure that its path execution is consistent with resource occupancy.

[0150] For example, a delivery drone mission path Contains 10 control points, corresponding to 10 seconds of flight time. The system generates resource paths after mapping the standard time step and space grid. If the route conflicts with a higher-priority aircraft between the 4th and 7th seconds, the system removes this route, marks it as a scheduling failure, updates the mission's takeoff time to the 8th second, and reschedules the remaining resource usage time. Upon receiving this schedule, the aircraft begins its 8th-second countdown and automatically takes off and executes its mission when the scheduled time arrives.

[0151] In summary, this step completes the standardized mapping, conflict resolution, and final dispatch release from aircraft path control points to urban airspace resources, ensuring that each aircraft has a clear, unique, and controllable airspace channel in high-density, low-altitude urban scenarios.

[0152] like Figure 2 As shown, in another embodiment of the present invention, a multi-node collaborative airspace control device for urban low-altitude traffic is provided, the device comprising:

[0153] The real-time perception module 101 is used to obtain perception data from the real-time perception system of urban low-altitude aircraft and urban airspace infrastructure, perform preprocessing, and generate a global perception data set; wherein the global perception data set includes: three-dimensional position data, linear velocity and attitude angle, relative altitude, position and track vector of neighboring aircraft, distance and direction of static obstacles ahead, and local airspace density;

[0154] An aircraft scheduling module 102 is configured to determine a local congestion coefficient and a path start-end point pair of a current aircraft assignment task by performing real-time airspace availability analysis and scheduling sorting of the current aircraft assignment task based on the global perception dataset;

[0155] A path planning module 103 is configured to construct an initial spline path based on the path start-end point pair, optimize the initial spline path by constructing a path cost function with minimization of the local congestion coefficient as the optimization goal, and generate a path control point sequence and a total path cost;

[0156] The cooperative control module 104 is configured to use any aircraft as a target aircraft, perform dynamic path adjustment based on distributed cooperative control with the target aircraft in combination with its neighboring aircraft, and generate a dynamically adjusted path control point sequence as the final path for the aircraft to actually execute, as well as the total cost of the new path;

[0157] The resource scheduling module 105 is configured to obtain the urban airspace, map the final path to the urban airspace, generate a complete scheduling sequence for the aircraft in the urban airspace, package the complete scheduling sequence into a structured scheduling plan, and send it to the aircraft control system for execution;

[0158] If the aircraft resource request fails, the aircraft control system marks the conflicting resource segment as a scheduling failure segment and notifies the corresponding aircraft to enter a task delay or queue waiting for resource release; if the aircraft resource request succeeds, it is marked as a successful resource unit set, indicating that the path has been successfully allocated to the executable airspace in the current scheduling cycle;

[0159] Among them, once the aircraft receives the scheduling confirmation signal, it enters the automatic takeoff preparation state. The aircraft control system will perform flight path tracking with reference to the successful resource unit set. At the same time, the operation of the aircraft is monitored in real time to ensure that its path execution is consistent with resource occupancy.

[0160] Figure 3 This is a structural block diagram of an electronic device provided in an embodiment of the present application. Figure 3 As shown, the electronic device 900 may include one or more of the following components: a processor 901, a memory 902 and a communication interface 903. The processor 901, the memory 902 and the communication interface 903 are interconnected and perform communication with each other. The memory 902 may store one or more computer programs, and the one or more computer programs may be configured to implement the methods described in the above embodiments when executed by one or more processors 901.

[0161] The processor 901 may include one or more processing cores. The processor 901 uses various interfaces and lines to connect the various parts of the entire electronic device 900, and performs various functions and processes data of the electronic device 900 by running or executing instructions, programs, code sets or instruction sets stored in the memory 902, and calling data stored in the memory 902. Optionally, the processor 901 can be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), and programmable logic array (PLA). The processor 901 can integrate one or more combinations of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. It is understandable that the above-mentioned modem may not be integrated into the processor 901, but may be implemented separately through a communication chip.

[0162] The memory 902 may include a random access memory (RAM) or a read-only memory (ROM). The memory 902 may be used to store instructions, programs, codes, code sets, or instruction sets. The memory 902 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc. The data storage area may also store data created by the electronic device 900 during use.

[0163] It is understandable that the electronic device 900 may include more or fewer structural elements than those in the above structural block diagram, for example, a power module, physical buttons, a WiFi (Wireless Fidelity) module, a speaker, a Bluetooth module, a sensor, etc., which are not limited here.

[0164] The electronic device 900 may be a detection device or a part of a detection device.

[0165] An embodiment of the present application provides a computer-readable storage medium, wherein program data is stored in the computer-readable storage medium. When the program data is executed by a processor, the program data is used to execute part or all of the steps of any one of the multi-node collaborative airspace control methods for urban low-altitude traffic recorded in the above method embodiments.

[0166] The present application also provides a computer program product, comprising a non-transitory computer-readable storage medium storing a computer program. The computer program is operable to cause a computer to execute some or all of the steps of any of the multi-node coordinated airspace control methods for urban low-altitude traffic described in the aforementioned method embodiments. The computer program product may be a software installation package.

[0167] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of the present invention. In practical applications, technicians in this field can select part or all of it according to actual needs to achieve the purpose of the embodiment scheme, and no limitation is made here.

[0168] In addition, for technical details not fully described in this embodiment, please refer to the parameter operation method provided in any embodiment of the present invention, and will not be repeated here.

[0169] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or system comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or system. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or system comprising the element.

[0170] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0171] Through the above description of the embodiments, those skilled in the art will clearly understand that the methods of the above embodiments can be implemented using software plus the necessary general-purpose hardware platform. Of course, hardware can also be used, but in many cases the former is a more preferred implementation method. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory / random access memory, a magnetic disk, or an optical disk) and includes a number of instructions for enabling a terminal device (such as a mobile phone, computer, server, air conditioner, or network device) to execute the methods described in the various embodiments of the present invention.

[0172] The above are only preferred embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A multi-node coordinated airspace control method for urban low-altitude traffic, characterized in that: The method comprises: Acquire and preprocess the perception data of the real-time perception system of urban low-altitude aircraft and urban airspace infrastructure to generate a global perception data set; wherein the global perception data set includes: three-dimensional position data, linear velocity and attitude angle, relative altitude, position and track vector of neighboring aircraft, distance and direction of static obstacles ahead, and local airspace density; Determining, based on the global perception data set, a local congestion coefficient and a path start-end point pair of the current aircraft assignment task by performing real-time airspace availability analysis and current aircraft assignment task scheduling sorting; Constructing an initial spline path based on the path start and end point pairs, constructing a path cost function with minimizing the local congestion coefficient as the optimization goal to optimize the initial spline path, and generating a path control point sequence and a total path cost; Taking any aircraft as the target aircraft, the target aircraft and its neighboring aircraft are combined to perform dynamic path adjustment based on distributed cooperative control. The dynamically adjusted path control point sequence is generated as the final path of the aircraft during actual execution, as well as the total cost of the new path. Acquiring urban airspace, mapping the final path to the urban airspace, generating a complete scheduling sequence for the aircraft in the urban airspace, packaging the complete scheduling sequence into a structured scheduling plan, and sending it to the aircraft control system for execution; If the aircraft resource request fails, the aircraft control system marks the conflicting resource segment as a scheduling failure segment and notifies the corresponding aircraft to enter a task delay or queue waiting for resource release; if the aircraft resource request succeeds, it is marked as a successful resource unit set, indicating that the current path has been successfully allocated to the executable airspace in the current scheduling cycle; Among them, once the aircraft receives the scheduling confirmation signal, it enters the automatic takeoff preparation state. The aircraft control system will perform flight path tracking with reference to the successful resource unit set. At the same time, the operation of the aircraft is monitored in real time to ensure that its path execution is consistent with resource occupancy.

2. A multi-node coordinated airspace control method for urban low-altitude traffic according to claim 1, characterized in that: The real-time perception system includes GNSS, IMU, barometer, ADS-B receiver and visual sensor; The GNSS is used to provide three-dimensional position data of the aircraft; the IMU is used to provide the linear velocity and attitude angle of the aircraft; and the barometer is used to provide the relative altitude of the aircraft. The ADS-B receiver provides the position and track vector of the adjacent aircraft; the visual sensor is used to provide obstacle detection results within the forward line of sight of the aircraft, that is, the distance and direction of the static obstacles in front; Then, the sensing data includes three-dimensional position data, linear velocity and attitude angle, relative altitude, position and track vector of neighboring aircraft, and distance and direction of static obstacles ahead; The pre-processing includes: Unifying the sensory data into a common timestamp and geographic coordinate framework; Obtain aircraft position information, construct a cube region centered on any aircraft based on the aircraft position information, and record the frequency of aircraft appearance per unit time within each grid cell of the cube region as a dynamic density indicator. Apply trilinear interpolation to all dynamic density indicators to generate the local airspace three-dimensional density of any query point. All processed data are organized into a global perception dataset; Among them, when an abnormality occurs in the real-time perception system, an extended Kalman filter is used to perform joint state estimation on the data from GNSS and IMU to generate a state estimation vector of the aircraft.

3. The multi-node coordinated airspace control method for urban low-altitude traffic according to claim 1 is characterized in that: The determining of a local congestion coefficient and a path start-end point pair of a current aircraft assignment task by performing real-time airspace availability analysis and current aircraft assignment task scheduling and sorting based on the global perception data set specifically includes: Taking any aircraft as the target aircraft, the current position of the target aircraft is taken as the center, combined with the movement direction, to construct a predicted flight path channel. The congestion coefficient of the current position along the predicted channel is calculated. This is used to analyze whether the corresponding local airspace three-dimensional density within the flight path channel exceeds the set threshold and determine whether there is potential congestion on the path. Among them, if there is a high-density section in the predicted area, it is marked as an impassable area, and a soft penalty constraint is applied to the path during the task scheduling process. Obtaining the task set of the target aircraft, performing scheduling and sorting, and generating the current optimal task; The currently optimal task is assigned to the target aircraft, and the initial path starting point and initial target point for the task execution are generated for it as the path start-end point pair; if the path starting point cannot directly reach the initial target point due to density restrictions, airspace blockades, or dangerous obstacles ahead, the suboptimal task fallback strategy will be triggered, and alternative tasks will be re-screened from the task set, and the scheduling scoring mechanism will be re-executed until the task is successfully assigned or the airspace situation is updated; The step of obtaining the task set of the target aircraft, performing scheduling and sorting, and generating the currently optimal task specifically includes: Each task is assigned a comprehensive scheduling score, which is calculated based on the Euclidean distance from the current position of the aircraft to the starting point of the task, the average congestion coefficient on the task's expected execution path, the length of the task's remaining time window, and the priority constant corresponding to the task type.

4. The multi-node coordinated airspace control method for urban low-altitude traffic according to claim 1 is characterized in that: The initial spline path is constructed based on the path start and end point pairs, and a path cost function is constructed to optimize the initial spline path with minimizing the local congestion coefficient as the optimization goal to generate a path control point sequence and a total path cost, specifically including: Constructing an initial spline path according to the path start and end point pairs, wherein the initial spline path is composed of a sequence of path control points, and intermediate path control points are initially uniformly generated by linear interpolation; the initial spline path is constructed as a cubic B-spline; The path cost function is constructed with the optimization goal of minimizing the local congestion coefficient to generate the total path cost. The path optimization process adopts the path control point position adjustment strategy based on gradient descent, and the optimization variable is the intermediate Path control points; Generate an optimized path control point sequence as input for optimizing the path curve.

5. The multi-node coordinated airspace control method for urban low-altitude traffic according to claim 1 is characterized in that: The method uses any aircraft as the target aircraft, combines the target aircraft with its neighboring aircraft to perform dynamic path adjustment based on distributed cooperative control, and generates a dynamically adjusted path control point sequence as the final path of the aircraft during actual execution, as well as the total cost of the new path, specifically including: Each aircraft maintains a local perception window during operation. This window obtains the path status of nearby aircraft through short-range communication, including the path control point sequence, current execution progress and total path cost of nearby aircraft. Each broadcast only sends the most recent information in the current window. Hash compression information of each path control point, with a total cost value; Based on the spatial overlap between the own path control point and the neighboring path control points, a path conflict estimation function is constructed to generate a path conflict estimation value, which is used to reflect the degree of path interference between the own aircraft and the neighboring aircraft; The total path cost is optimized according to the path conflict estimation value to generate a dynamically adjusted path control point sequence and a new path total cost.

6. The multi-node coordinated airspace control method for urban low-altitude traffic according to claim 1 is characterized in that: The process of acquiring the urban airspace, mapping the final path to the urban airspace, generating a complete scheduling sequence for the aircraft in the urban airspace, packaging the complete scheduling sequence into a structured scheduling plan, and sending the plan to the aircraft control system for execution specifically includes: The urban airspace is converted into a standard four-dimensional resource unit to form an urban airspace resource coordinate system; Obtain the three-dimensional coordinates of the target aircraft's path control points. Based on a set time step, assign a timestamp to each path control point, indicating the aircraft's estimated flight time at that path control point. Combine the three-dimensional coordinates with the timestamp to generate the four-dimensional resource unit corresponding to the path control point in the urban airspace resource grid, forming a complete scheduling sequence. The complete scheduling sequence is packaged into a structured scheduling plan and sent to the aircraft control system.

7. A multi-node coordinated airspace control device for urban low-altitude traffic, characterized in that: The device comprises: A real-time perception module is used to obtain perception data from the real-time perception system of urban low-altitude aircraft and urban airspace infrastructure, perform preprocessing, and generate a global perception data set; wherein the global perception data set includes: three-dimensional position data, linear velocity and attitude angle, relative altitude, position and track vector of neighboring aircraft, distance and direction of static obstacles ahead, and local airspace density; An aircraft scheduling module is configured to determine a local congestion coefficient and a path start-end point pair of a current aircraft assignment task by performing real-time airspace availability analysis and scheduling sorting of the current aircraft assignment task based on the global perception data set; A path planning module is used to construct an initial spline path based on the path start and end point pairs, optimize the initial spline path by constructing a path cost function with minimizing the local congestion coefficient as the optimization goal, and generate a path control point sequence and a total path cost; The collaborative control module is used to use any aircraft as the target aircraft and perform dynamic path adjustment based on distributed collaborative control with the target aircraft and its neighboring aircraft, generating a sequence of dynamically adjusted path control points as the final path for the aircraft during actual execution, as well as the total cost of the new path; a resource scheduling module, configured to acquire the urban airspace, map the final path to the urban airspace, generate a complete scheduling sequence for the aircraft in the urban airspace, package the complete scheduling sequence into a structured scheduling plan, and send it to the aircraft control system for execution; If the aircraft resource request fails, the aircraft control system marks the conflicting resource segment as a scheduling failure segment and notifies the corresponding aircraft to enter a task delay or queue waiting for resource release; if the aircraft resource request succeeds, it is marked as a successful resource unit set, indicating that the path has been successfully allocated to the executable airspace in the current scheduling cycle; Among them, once the aircraft receives the scheduling confirmation signal, it enters the automatic takeoff preparation state. The aircraft control system will perform flight path tracking with reference to the successful resource unit set. At the same time, the operation of the aircraft is monitored in real time to ensure that its path execution is consistent with resource occupancy.

8. An electronic device, characterized in that: The device comprises: A processor, a memory, and a communication interface, wherein the processor, the memory, and the communication interface are interconnected and perform communication work among each other; The memory stores executable program code, and the communication interface is used for wireless communication; The processor is used to call the executable program code stored in the memory and execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that A computer program for electronic data exchange is stored, wherein the computer program enables a computer to execute the method according to any one of claims 1 to 6.

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