Multi-node cooperative airspace control method and device for urban low-altitude traffic

Abstract

The invention provides a multi-node collaborative airspace control method and device for urban low-altitude traffic. The method comprises the following steps: acquiring sensing data of a real-time sensing system of an urban low-altitude aircraft body and an urban airspace infrastructure; assigning task scheduling sorting, and determining a local congestion coefficient and a path starting and ending point pair of a current aircraft assignment task; constructing a path cost function to optimize the initial spline path, and generating a path control point sequence and a path total cost; generating a final path of the aircraft during actual execution based on dynamic path adjustment of distributed cooperative control; and mapping the final path to the urban airspace, generating a complete scheduling sequence of the aircraft in the urban airspace, and sending the complete scheduling sequence to an aircraft control system for execution. Under the background that the urban low-altitude airspace is increasingly complex, cooperation among the aircrafts can be guaranteed, the utilization efficiency of airspace resources is improved, meanwhile, flight safety is guaranteed, and therefore a foundation is laid for intelligent development of a low-altitude traffic system.

Description

Technical Field

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

[0002] With the acceleration of the urbanization process, urban low-altitude traffic has gradually become an important traffic field. Especially in application scenarios such as unmanned aerial vehicles (UAVs), air taxis (AAVs), and logistics distribution, the use and management of low-altitude airspace have become urgent problems to be solved. The existing airspace management systems mainly rely on traditional ground control centers. The path planning and scheduling of aircraft depend on pre-determined flight plans, and there is often a time delay in the communication between the aircraft and the control center. Although this method can operate effectively in simple flight tasks, it exposes many problems when facing complex urban low-altitude traffic demands.

[0003] First of all, the existing airspace management systems usually rely on a centralized decision-making mechanism, which means that all scheduling and instructions are uniformly controlled and allocated by the central system. This centralized management method not only faces bottlenecks in computing power and communication bandwidth but also is prone to single-point failure problems. In the case of an increase in the number of aircraft and airspace density, the traditional centralized control system is difficult to cope with the dynamic and complex urban airspace environment, resulting in response delays and low decision-making efficiency. In addition, there are many unpredictable factors in the low-altitude flight environment, such as climate change and airspace emergencies. This requires aircraft to have strong adaptability, while the traditional pre-determined path planning method often lacks flexibility and cannot adjust the flight path in real time, resulting in the possibility of collisions or violations of flight safety for aircraft in emergency situations.

[0004] Secondly, most current path planning methods are based on static models and cannot take into account the real-time dynamic environment. When an aircraft executes a task, it needs to perceive the surrounding environment in real time and adjust its flight path according to the changes in the environment. However, most existing path planning algorithms ignore the real-time collaboration between aircraft and do not fully consider the physical constraints of aircraft (such as the maximum speed and turning radius of aircraft), resulting in the inability to 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 sharply. How to ensure that the path planning of each aircraft is not only safe but also can efficiently use limited airspace resources has become an urgent problem to be solved.

[0005] Although existing technologies perform a certain degree of scheduling and path planning through automated systems, there are still many limitations. Systems based on centralized scheduling cannot achieve efficient real-time response. Static path planning methods cannot handle dynamic flight environments and lack effective cooperation between aircraft. Existing technologies lack sufficient adaptability and are difficult to achieve dynamic cooperation and conflict avoidance among multiple aircraft in low-altitude complex environments. Summary of the Invention

[0006] The object 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 in the management of low-altitude traffic airspace in existing technologies and provides a more flexible, efficient and safe low-altitude traffic control method.

[0007] To achieve the above object, in the first aspect of the present invention, a multi-node collaborative airspace control method for urban low-altitude traffic is provided. The method includes: Obtain the perception data of the real-time perception system of the urban low-altitude aircraft body 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 height, position and track vector of neighboring aircraft, distance and direction of static obstacles ahead, local airspace density; According to the global perception data set, determine the local congestion coefficient and the path start and end point pairs of the current aircraft allocation task through real-time airspace availability analysis and current aircraft allocation task scheduling and sorting; Construct an initial spline path according to the path start and end point pairs, construct a path cost function with minimizing the local congestion coefficient as the optimization target, optimize the initial spline path, and generate a path control point sequence and a total path cost; Take any aircraft as the target aircraft, and perform dynamic path adjustment based on distributed cooperative control by combining the target aircraft with its neighboring aircraft, generate the final path for the aircraft to actually execute as the dynamic adjusted path control point sequence, and a new total path cost; Obtain the urban airspace, map the final path to the urban airspace, generate a complete scheduling sequence of the aircraft on the urban airspace, package the complete scheduling sequence into a structured scheduling plan, and send it to the aircraft control system for execution; Wherein, if the aircraft resource request fails, the aircraft control system marks the conflict resource segment as a scheduling failure segment and notifies the corresponding aircraft to enter a state of task delay or queuing for resource release; if the aircraft resource request is successful, it is marked as a set of successful resource units, indicating that the path has been successfully allocated to the executable airspace in the current scheduling cycle; 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 set of successful resource units. Meanwhile, the operation of the aircraft is monitored in real time to ensure that its path execution is consistent with resource occupancy.

[0008] In a feasible example, the real-time perception system includes a GNSS, an IMU, a barometer, an ADS-B receiver, and a vision sensor; wherein 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; the barometer is used to provide the relative altitude of the aircraft; the ADS-B receiver provides the positions and track vectors of neighboring aircraft; the vision sensor is used to provide the detection results of obstacles within the forward line-of-sight range of the aircraft, that is, the distance and direction of static obstacles ahead; then the perception data includes three-dimensional position data, linear velocity and attitude angle, relative altitude, positions and track vectors of neighboring aircraft, and the distance and direction of static obstacles ahead; wherein the preprocessing includes: Unify the perception data into the same timestamp and geographic coordinate framework; Obtain the aircraft position information. According to the aircraft position information, construct a cubic region centered on any aircraft, and record the frequency of aircraft appearance per unit time in each grid cell of the cubic region as a dynamic density index. Use the trilinear interpolation method for all dynamic density indices to generate the three-dimensional density of the local airspace at any query point; Unify and organize all processed data into a global perception data set.

[0009] 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 the GNSS and IMU to generate the state estimation vector of the aircraft.

[0010] In a feasible example, according to the global perception data set, by performing real-time airspace availability analysis and current aircraft assignment task scheduling sorting, determine the local congestion coefficient and the path start and end point pairs of the current aircraft assignment task, specifically including: Take any aircraft as the target aircraft. With the current position of the target aircraft as the center and combined with the movement direction, construct a predicted flight path channel, and calculate the congestion coefficient along the predicted channel at the current position to analyze whether the three-dimensional density of the corresponding local airspace in the flight path channel exceeds the set threshold to determine whether there is potential congestion on this 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 imposed on the path during the task scheduling process; Obtain the task set of the target aircraft, perform scheduling and sorting, and generate the current optimal task; Assign the current optimal task to the target aircraft, and at the same time generate the initial path start point and the initial target point for task execution as the path start and end point pair; if the path start point cannot directly reach the initial target point due to density restrictions, airspace blockades, or dangerous obstacles ahead, a sub-optimal task fallback strategy will be triggered, re-screen alternative tasks from the task set, and re-execute the scheduling scoring mechanism until the task is successfully assigned or the airspace situation is updated; Among them, the obtaining the task set of the target aircraft, performing scheduling and sorting, and generating the current optimal task specifically includes: Each task will be assigned a comprehensive scheduling score, which is calculated by combining the Euclidean distance from the current position of the aircraft to the task start point, the average congestion coefficient on the expected execution path of the task, the remaining time window length of the task, and the priority constant corresponding to the task type.

[0011] In a feasible example, constructing an initial spline path according to the path start and end point pair, constructing a path cost function with minimizing the local congestion coefficient as the optimization goal, and optimizing the initial spline path to generate a sequence of path control points and the total path cost specifically includes: Construct an initial spline path according to the path start and end point pair, where the initial spline path is composed of a sequence of path control points, and the intermediate path control points are initially uniformly generated by linear interpolation; the initial spline path is constructed as a cubic B-spline; Construct a path cost function with minimizing the local congestion coefficient as the optimization goal to generate the total path cost; among them, the path optimization process adopts a path control point position adjustment strategy based on gradient descent, and the optimization variable is the intermediate control points; Generate an optimized sequence of path control points, which is the input for optimizing the path curve.

[0012] In a feasible example, using any aircraft as the target aircraft, and performing dynamic path adjustment based on distributed cooperative control by combining the target aircraft with its neighboring aircraft to generate the sequence of path control points after dynamic adjustment as the final path for the aircraft to actually execute, and the new total path cost, specifically includes: Each aircraft maintains a locally perceived window during operation, which obtains the path status of nearby aircraft through short-range communication, including the sequence of path control points, the current execution progress, and the total path cost of nearby aircraft; among them, each broadcast only sends the hash compression information of the nearest path control points within the current window, and attaches the total cost value; Construct a path conflict estimation function based on the spatial overlap between its own path control points and those of neighboring paths, and generate a path conflict estimation value to reflect the degree of path interference between its own aircraft and neighboring aircraft; Optimize the total path cost according to the path conflict estimation value to generate a dynamically adjusted sequence of path control points and a new total path cost.

[0013] In a feasible example, when the path conflict estimation value exceeds a preset threshold, it is determined that there is a potential conflict in the current path and adjustment is required. Specifically, it includes: Select the path with the most concentrated conflicts from the current path of the aircraft, and apply perturbations to this path to guide the path away from the paths of high-density or high-priority aircraft; wherein, the perturbation direction of the perturbation is jointly determined by the local cost gradient and the normal offset, and aiming at minimizing the perturbation, it is achieved through fast gradient descent.

[0014] In a feasible example, the acquisition of the urban airspace, mapping the final path to the urban airspace, generating a complete scheduling sequence of 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: Take the urban airspace as a standard four-dimensional resource unit to form an urban airspace resource coordinate system; Obtain the three-dimensional coordinates of the path control points of the target aircraft, and according to the set time step, assign a timestamp to each path control point to represent the expected flight time of the aircraft at this 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, and form a complete scheduling sequence; Package the complete scheduling sequence into a structured scheduling plan and send it to the aircraft control system.

[0015] In the second aspect of the present invention, a multi-node collaborative airspace control device for urban low-altitude traffic is provided. The device includes: A real-time perception module for obtaining the perception data of the real-time perception system of the urban low-altitude aircraft body and urban airspace infrastructure, performing preprocessing, and generating a global perception data set; wherein, the global perception data set includes: three-dimensional position data, linear velocity and attitude angle, relative height, positions and track vectors of neighboring aircraft, distances and directions of static obstacles ahead, and local airspace density; An aircraft scheduling module for determining the local congestion coefficient and the start and end point pairs of the paths of the current aircraft assignment tasks by performing real-time airspace availability analysis and current aircraft assignment task scheduling sorting according to the global perception data set; A path planning module, configured to construct an initial spline path according to the start and end points of the path, construct a path cost function with minimizing the local congestion coefficient as the optimization objective to optimize the initial spline path, and generate a path control point sequence and the total path cost; A cooperative control module, configured to use any aircraft as the target aircraft, and perform dynamic path adjustment based on distributed cooperative control by combining the target aircraft with its neighboring aircraft, generate the path control point sequence after dynamic adjustment as the final path for the aircraft to actually execute, and the new total path cost; A resource scheduling module, configured to obtain the urban airspace, map the final path to the urban airspace, generate a complete scheduling sequence of 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; Wherein, 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 state of task delay or queuing waiting for resource release; if the aircraft resource request is successful, it is marked as a set of successful resource units, indicating that this path has been successfully allocated to the executable airspace in the current scheduling period; Wherein, once the aircraft receives a scheduling confirmation signal, it enters the automatic takeoff preparation state. The aircraft control system performs flight path tracking with reference to the set of successful resource units. 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.

[0016] In a third aspect of the present invention, an electronic device is provided. The device includes a processor, a memory, and a communication interface. The processor, the memory, and the communication interface are interconnected and complete communication with each other. The memory stores executable program code. The communication interface is used for wireless communication. The processor is used to retrieve the executable program code stored on the memory and execute some or all of the steps described in any method of the first aspect.

[0017] In a fourth aspect of the present invention, a computer-readable storage medium is provided. Electronic data is stored in the computer-readable storage medium. 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 the present application.

[0018] In a fifth aspect of the present invention, a computer program product is provided. The computer program product includes 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 described in the first aspect of the present application. The computer program product can be a software installation package.

[0019] The beneficial technical effects of the present invention are at least as follows: The present invention adopts a distributed collaborative control mechanism, enabling each aircraft to not only make autonomous decisions but also cooperate with other aircraft to dynamically adjust routes and airspace usage. Through this distributed system, the aircraft can respond in real time to changes in the surrounding environment and no longer rely on a single central control, thus greatly enhancing the flexibility and emergency response ability of the system.

[0020] In addition, to cope with the dynamic environment in the low-altitude airspace, the present invention proposes a dynamic route planning method based on real-time data. The aircraft can not only plan routes according to the current flight state and target tasks but also perceive the surrounding environment in real time (such as weather, airspace density, relative position of aircraft, etc.) and dynamically adjust the route during flight. This method effectively avoids the limitations of traditional route planning methods and can provide safer and more efficient route options for aircraft in complex environments.

[0021] More importantly, the present invention introduces an optimization mechanism for physical constraints to ensure that when the aircraft conducts route planning and collaborative scheduling, it can follow the actual flight physical characteristics and safety standards. These physical constraints include the maximum speed of the aircraft, the minimum safety interval, the turning radius, etc., which are often overlooked in traditional route planning. By incorporating these physical constraints into the decision-making process of the aircraft, the present invention can effectively avoid flight accidents caused by the aircraft ignoring its own performance limitations and ensure the feasibility and safety of route planning.

[0022] In summary, through the combination of distributed collaborative control, dynamic route planning, and physical constraint optimization, the present invention solves the bottleneck problems in the management of low-altitude traffic airspace in the prior art and provides a more flexible, efficient, and safe low-altitude traffic control method. This innovative solution can ensure the 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 a foundation for the intelligent development of the low-altitude traffic system. Brief Description of the Drawings

[0023] The present invention is further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation to the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to the following drawings without creative efforts.

[0024] Figure 1 It is a flowchart of a multi-node collaborative airspace control method for urban low-altitude traffic according to an embodiment of the present invention.

[0025] Figure 2 It 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.

[0026] Figure 3 This is a structural block diagram of an electronic device provided by an embodiment of the present invention. Detailed implementation manners

[0027] The embodiments of the present invention will be described in detail below. Examples of the embodiments are shown in the drawings, where the same or similar reference numerals denote the same or similar elements or elements with the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and are only used to explain the present invention and should not be construed as a limitation to the present invention.

[0028] In the present invention, the full name of GNS is Global Navigation Satellite System, which generally refers to all satellite navigation systems, including global, regional, and enhanced ones, such as GPS in the United States, Glonass in Russia, Galileo in Europe, the Beidou satellite navigation system in China, and related enhanced systems, such as WAAS (Wide Area Augmentation System) in the United States, EGNOS (European Geostationary Navigation Overlay Service) in Europe, and MSAS (Multi-functional Transport Satellite Augmentation System) in Japan, etc., and also covers other satellite navigation systems under construction and to be constructed in the future.

[0029] IMU is an inertial measurement unit, which is the core device for autonomous navigation by measuring the acceleration and angular velocity of the carrier's movement, and is composed of sensors such as gyroscopes and accelerometers. By real-time calculating the acceleration and angular velocity data, it can continuously output the three-dimensional attitude, displacement, and velocity information of the carrier.

[0030] ADS-B, the full name is Automatic Dependent Surveillance - Broadcast System, can automatically (once per second) obtain parameters from on-board equipment, and without manual operation or interrogation, it can broadcast information such as the position, altitude, speed, heading, and identification number of the aircraft to other aircraft or ground stations, enabling air traffic controllers to monitor the aircraft status in real time.

[0031] As Figure 1 shown, an embodiment of the present invention provides a multi-node collaborative airspace control method for urban low-altitude traffic. The method includes: S1. Obtain the perception data of the real-time perception system of the urban low-altitude aircraft body 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, the position and track vector of adjacent aircraft, the distance and direction of static obstacles ahead, and local airspace density.

[0032] Due to the dense buildings, diverse types of aircraft, and complex flight paths in urban low-altitude airspace, it faces the problem of the interweaving of high-frequency operation of multiple aircraft and dynamic changes in airspace. Therefore, to achieve safe and efficient airspace collaborative control, the goal of this step is to provide a directly callable structured data model for subsequent steps such as path planning, mission scheduling, and conflict prediction by real-time sensing of the aircraft's own state and the local airspace environment.

[0033] Specifically, this step sets the object based on common aircraft with urban low-altitude flight capabilities, and relies on its on-board sensors (including GNSS, IMU, barometer, vision sensors, ADS-B receiving modules, etc.) and the perception infrastructure deployed in the city (such as ADS-B ground receiving stations) for data collection. After all data is collected, it needs to complete synchronization, denoising, spatial alignment, and structured processing, and finally output a perception data structure covering the current state of the aircraft and the local airspace distribution.

[0034] Among them, the data input of this step comes from the real-time perception systems of urban low-altitude aircraft bodies and urban airspace infrastructure, specifically including: GNSS: Provide three-dimensional position data ; IMU: Provide linear velocity and attitude angles ; Barometer: Provide the relative altitude of the aircraft ; ADS-B receivers (on-board and ground): Provide the positions and track vectors of surrounding aircraft; Vision sensors: Provide the detection results of obstacles within the forward line of sight (the distance between the target bounding box and the depth) .

[0035] For example, when a delivery drone takes off from a building complex, its GNSS initializes the current position, the IMU starts inertial navigation tracking, and the barometer calibrates the starting altitude; at the same time, the ADS-B module scans the positions and velocity vectors of other flying aircraft within a radius of 3 kilometers, and the vision module detects whether there are static obstacles (such as residual goods on the temporary apron) in front of the take-off platform. The above information will be passed as path starting point constraints into the path planning module.

[0036] First, all data needs to be unified within the same timestamp and geographical coordinate framework. The GNSS and ADS-B position data are transformed and aligned using the WGS84 coordinate system. The outputs of the IMU and barometer need to be transformed into the speed and attitude in the global coordinate system based on the unified aircraft coordinate system. The system uses an Extended Kalman Filter (EKF) to perform joint state estimation on the data from GNSS and IMU, improving the trajectory stability in cases such as short-term occlusion and sensor fluctuations. The state estimation model is as follows: where, is the state estimation vector of the aircraft at time , including the position , speed , and attitude ; is the state transition matrix (dynamically adjusted according to the attitude), is the control input matrix, is the combination of IMU acceleration and angular velocity, and is the system process noise. This filter performs an update once per second and can stabilize the trajectory output based on the IMU inertial input in case of short-term GNSS signal loss.

[0037] Secondly, to generate the 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 region centered on the local aircraft (for example, with a radius of 1.5 km, divided into equally spaced grids). The frequency of aircraft appearance within each grid cell per unit time is recorded as the dynamic density index . Due to the relatively coarse data granularity, the present invention uses the trilinear interpolation method to improve spatial continuity. The formula is as follows: where, represents the local airspace density at any query point, are the observed density values of the eight adjacent grid cells where the path control point is located, are the weights from the path control point to each grid point, satisfying . The density value will be used as an avoidance or passage determination factor in subsequent task scheduling and path optimization.

[0038] Meanwhile, the vision sensor is used to monitor the distance and direction vector of the obstacle in front of the aircraft. Combining with the current speed of the aircraft, the potential collision time is calculated. If , then mark this path as a dangerous path and exclude it as a hard constraint during the path initial point generation stage. ​​​​​​​​

[0039] Finally, all processed data are organized into a global perception dataset: 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 be used as dynamic feedback input for path adjustment and airspace scheduling. The output of this step is a structured global perception dataset .

[0040] S2. According to the global perception data set, by performing real-time airspace availability analysis and current aircraft assignment task scheduling and sorting, determine the local congestion coefficient and the path start and end point pair of the current aircraft assignment task, In the process of urban low-altitude traffic system operation, 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 is very likely to cause 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 of multiple aircraft and optimize airspace use to ensure the safety and efficiency of overall airspace operations.

[0041] 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 neighboring aircraft ; and the distance to the static obstacle ahead With 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, mission type (such as freight, patrol), etc.

[0042] First, the system will perform a real-time airspace availability analysis on the local airspace it perceives. 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 , determine whether there is potential congestion on this path. If there is a high-density section in the predicted area, it is marked as a non-passable area, and a soft penalty constraint is imposed on the path during the task scheduling process. The prediction mechanism is as follows: Among them, represents the congestion coefficient along the predicted channel at the current position, is an indicator function, which is 1 if the density exceeds the threshold, and 0 otherwise. This indicator will be used as a task scheduling weight factor to participate in the design of the subsequent scheduling objective function, and is used to balance the shortest path of the task and the airspace load.

[0043] Subsequently, the task scheduling module will perform scheduling and sorting on the task set received by the aircraft. Each task will be assigned a comprehensive scheduling score , which combines the current position of the aircraft with the distance to the task starting point , the predicted airspace congestion risk , the urgency of the task time window , and the priority coefficient of the task type for joint calculation. The calculation formula of the scheduling score is as follows: Among them, is the Euclidean distance from the current position of the aircraft to the task starting point, is the average congestion coefficient on the predicted execution path of the task, is the remaining time window length of the task, is the priority constant corresponding to the task type (such as 1.5 for medical rescue tasks and 1.0 for ordinary freight), is a small constant to prevent division by zero. This scoring mechanism allows the system to comprehensively consider the importance of the task, the complexity of the path, and time constraints, and realize the adaptive scheduling of multi-aircraft tasks in a dynamic airspace.

[0044] Furthermore, after the scheduling and sorting are completed, the system will select the current optimal task and assign it to the aircraft, and at the same time generate the initial path starting point and the initial target point of the task execution as the input boundary conditions for the next path planning. If the path starting point cannot be directly reached due to density restrictions, airspace blockade, or the presence of dangerous obstacles ahead , the system will trigger the sub-optimal task fallback strategy and re-screen alternative tasks from ​, and re - execute the scheduling scoring mechanism until the task is successfully allocated or the airspace situation is updated.

[0045] It can be understood that the output of this step is two items: one is the task identifier and the pair of start and end points of the path for the currently assigned aircraft ; the other is the local airspace congestion coefficient centered on this aircraft ; this value will participate in the path cost calculation as an avoidance control input in the subsequent path planning step.

[0046] To sum up, the innovation points of this step are as follows: First, an airspace local congestion degree index is constructed based on the predicted flight path, realizing the real - time evaluation 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 joint modeling ability for actual multi - source dynamic constraints; third, a congestion path penalty mechanism and a fallback scheduling strategy are designed to ensure that the aircraft still has the task switching ability when airspace resources are tight, improving the overall robustness of the system.

[0047] S3. Construct an initial spline path according to the pair of start and end points of the path, and construct a path cost function with minimizing the local congestion coefficient as the optimization goal to optimize the initial spline path, generating a sequence of path control points and the total path cost.

[0048] Specifically, on the premise that the aircraft has been assigned a task and obtained the pair of start and end points of the path as well as the airspace congestion coefficient at the current position , the purpose of this step is to generate an initial path that meets the physical flyability of the aircraft for the aircraft. The path should avoid congested areas as much as possible, have a continuously executable geometric shape, and provide an optimization starting point for subsequent dynamic adjustment and cooperative obstacle avoidance of the aircraft.

[0049] This step first constructs an initial spline path based on the pair of start and end points of the task , where represents the normalized path parameter. The path is defined by control points , where , , and the intermediate control points are initially uniformly generated by linear interpolation. Considering the requirements of path continuity and curvature in the urban airspace, is constructed as a cubic B - spline to ensure the continuous differentiability and local controllability of the curve.

[0050] In order to make the path actively avoid local high - congestion areas while satisfying the constraints of the pair of start and end points of the path, the present invention introduces a path cost function based on congestion weight as the optimization goal: Among them, is the th path control point, is the length of this path, represents the local airspace congestion coefficient at the control point (output by step 2), is the congestion penalty weight coefficient. This objective function aims to reduce the degree of the path crossing the high congestion area on the premise of ensuring a short path, so as to achieve the balance between task scheduling and airspace resource utilization.

[0051] 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 , and the optimization steps are as follows: Initialization: Generate intermediate control points according to linear interpolation; Calculate the current path cost ; For each control point calculate the gradient of the cost function , where the derivative of is obtained by numerical approximation; Update the control point: , where is the learning rate; Repeat steps 2 - 4 until convergence or the maximum number of iterations is reached.

[0052] The finally optimized path control point sequence is the input of the path curve , which is used to generate a path with continuity, low congestion rate and physical feasibility. Since the path curve is controlled by splines, the aircraft control system can obtain the curvature change of the path through derivative calculation to ensure that it does not exceed its turning ability and inertial constraints during execution.

[0053] It can be understood that the output of this step includes two items: one is the path control point sequence , which is used as the input for the initial path tracking control of the aircraft; the other is the total path cost , which is used by the coordination module for path optimization and load evaluation.

[0054] To sum up, the innovation points of this step include: (1) The congestion coefficient Directly embed the path optimization cost function and perform penalty modeling in combination with the path length; (2) propose an adaptive optimization strategy for path control points based on gradient descent, without the need to newly introduce aircraft states or environmental perception inputs, and the logic is completely closed-loop to the task scheduling output; (3) provide high-quality executable inputs for the subsequent cooperative control module through structured output of path control points and cost evaluation. This solution not only optimizes the resource distribution adaptability of the path but also ensures that the path complies with the aircraft operation limitations and has good practical deployment capabilities.

[0055] S4. Take any aircraft as the target aircraft, and perform dynamic path adjustment based on distributed cooperative control by combining the target aircraft with its neighboring aircraft, generating a sequence of path control points after dynamic adjustment as the final path for the aircraft to actually execute, as well as the total cost of the new path.

[0056] Among them, in the low-altitude airspace of cities, multiple aircraft may simultaneously perform different tasks in dense areas. Factors such as path conflicts, temporary airspace closures, and changes in task priorities will cause the pre-planned initial path (output in step 3) to face problems of failure or inefficiency during actual execution. Therefore, the goal of this step is to build a cooperative control mechanism that fully relies on local communication, does not require central scheduling, and has dynamic adaptation capabilities during the task execution process, enabling the aircraft to adaptively adjust the path to avoid conflicts, give way to high-priority tasks, and reduce its own cost, based on the initial path and the total cost (also output in step 3).

[0057] Specifically, first, each aircraft maintains a locally perceived window during operation. This window obtains the path status of nearby aircraft through short-range communication (such as LTE-V2X, Wi-Fi Mesh, or millimeter wave), including its sequence of path control points , the current execution progress (i.e., has executed up to the control point ) and the total path cost . To ensure scalability and high-frequency communication efficiency, only the hash-compressed information of the nearest control points within the current window is sent each time a broadcast is made, along with the total cost value.

[0058] Next, the system constructs a path conflict estimation function based on the spatial overlap degree between its own path control points and those of neighboring aircraft, reflecting the path interference degree between its own aircraft and neighboring . The design of the conflict function needs to have spatial continuity and path execution consistency, and is defined as follows: Among them, is the A control point is the control point of the neighboring aircraft at the same step size is the scaling factor for controlling the distance sensitivity is the anti-zero constant. This function not only considers the path space overlap, but also comprehensively considers the total cost (priority) of neighboring tasks, making high-cost tasks have a higher weight in the conflict assessment, so as to obtain a higher priority in conflict resolution.

[0059] When exceeds the threshold the system determines that there is a potential conflict in the current path and needs to be adjusted. At this time, a local perturbation optimization strategy is used to fine-tune the path. The specific operation is as follows: Select the path with the most concentrated conflicts from the current path and apply a perturbation to guide the path away from the paths of high-density or high-priority aircraft. The perturbation direction is jointly determined by the local cost gradient and the normal offset, and the optimization objective function is: where the first term is the perturbation amplitude regularization term, which controls the path perturbation 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 and recalculate the new conflict degree of this path point after adjustment is the balance coefficient. This local optimization problem is achieved by fast gradient descent, and the obtained is used to update the path.

[0060] The updated set of path control points is denoted as and the total cost is recalculated as and its calculation still uses the path total cost function defined in step 3 (including the path length and the local airspace cost). If where is the update threshold), then accept the current round of perturbation, otherwise roll back to keep the original path.

[0061] Furthermore, to maintain the system convergence and communication efficiency, this step synchronizes the path control points and the total cost every fixed time and adopts an asynchronous update mechanism: each aircraft independently maintains its own path without waiting for the feedback of neighbors to complete. The collaborative adjustment and path update are executed in parallel based on distributed communication to ensure low latency and high fault tolerance.

[0062] It can be understood that the output of this step includes two items: the first is the sequence of path control points after dynamic adjustment, that 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 task completion efficiency and airspace load balance.

[0063] To sum up, the innovation points of this step are reflected in multiple aspects: (1) A conflict estimation function combining path space overlap and task priority is proposed , which is used to dynamically evaluate the risk of path coordination conflicts; (2) A feasible local perturbation optimization mechanism is designed to perform distributed path fine-tuning through a regularized-conflict joint objective function to ensure the stability of path adjustment; (3) An asynchronous cooperation and hash compression communication strategy is proposed, making the cooperation mechanism highly real-time and network scalable. This method highly meets the collaborative control requirements of multiple low-altitude aircraft operating simultaneously in the city. Without relying on central control, it realizes the adaptive coordination of paths among multiple aircraft.

[0064] S5. Obtain the urban airspace, map the final path to the urban airspace, generate a complete scheduling sequence of 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.

[0065] Among them, after the aircraft completes path planning, dynamic collision avoidance, and cooperative control, its final flight path and the total cost are already clear. These outputs represent the best flight plan of the aircraft in the current low-altitude urban airspace, including the spatial path and the comprehensive scheduling cost. To achieve actual operation, the system needs to uniformly dock this path with the urban airspace resource map and complete the final decision on executable scheduling to ensure that the aircraft has legal and conflict-controllable airspace occupancy rights during the takeoff to landing process.

[0066] Specifically, first, the scheduling platform divides the urban airspace into standard four-dimensional resource units , where is the three-dimensional spatial direction, is the time dimension. These resource units are discretely divided according to the preset spatial and time step sizes. The spatial direction uses as the grid granularity (such as 50 meters per grid), and the time direction uses as the time step (such as 1 second per step) to form a unified urban airspace resource coordinate system. All aircraft path mapping and scheduling processes must be based on this basic coordinate framework to ensure the consistency of scheduling alignment and conflict detection.

[0067] For the path control point , it represents the position of the th target control point of the aircraft in the path, which is output by step 4 and is the flight point finally determined after dynamic cooperative adjustment. The system assigns a timestamp to each control point according to the set time step ​ , which represents the predicted flight time of the aircraft at this path control point, and the calculation method is as follows: Subsequently, map the three-dimensional space coordinates and time tags to discrete grid coordinates, and calculate the four-dimensional resource unit corresponding to this control point in the urban airspace resource grid: The entire path After this operation, a complete scheduling sequence of the aircraft on the airspace resources is formed: The meanings of the variables are as follows: : The th control point in the path, generated by the path optimization module, representing the th expected position of the aircraft during flight; : The expected time step of control point , generated by accumulating at fixed time intervals; : The three-dimensional continuous space coordinates of the control point; : The space step size of the urban airspace grid division; : The standard time step size used for urban airspace scheduling; : The floor operation, used to classify continuous coordinates into discrete grids; : The resource unit corresponding to the aircraft at control point , that is, the space-time grid occupied from the perspective of the scheduling system; : The aircraft The set of resource units that need to be locked during the whole process from takeoff to landing.

[0068] Furthermore, the scheduling system then performs unified sorting and conflict handling on the set of all aircraft. The sorting basis is the total cost , which represents the comprehensive performance of this path in dimensions such as airspace resource utilization efficiency, safety, and conflict prevention ability. The lower the total cost, the more friendly the path is to the overall system scheduling. Therefore, its obtains the priority locking right. The system traverses the resource requests of each aircraft in turn. When multiple aircraft compete for a certain resource unit , the system will retain the resource requests of those with lower total costs and eliminate the conflict segments of other requests.

[0069] Understandably, for paths where resource requests fail, the system no longer triggers path replanning. Instead, it directly marks the conflicting resource segments as scheduling failure segments and notifies the corresponding aircraft to enter a state of task delay or queuing for resource release, waiting to attempt scheduling again in a subsequent cycle. For aircraft that successfully obtain all resources, their will be marked as a successful resource unit set , indicating that this path has been successfully allocated to the executable airspace in the current scheduling cycle, and the system will lock this path in the overall scheduling table.

[0070] The system packages this scheduling result into a structured scheduling plan and sends it to the aircraft control system. The scheduling content includes: : The complete airspace resource occupation path of this aircraft during the execution cycle; the resource position corresponding to each time step ; the takeoff and landing time windows for task execution; the takeoff permission flag: indicating whether this aircraft is allowed to take off in the current cycle (a boolean flag). ;

[0071] Furthermore, once the aircraft receives the scheduling confirmation signal, it can enter the automatic takeoff preparation state. Its flight control system will execute flight path tracking with as a reference. At the same time, the system scheduling module will start real-time monitoring of the operation of this aircraft to ensure that its path execution is consistent with resource occupation.

[0072] For example, the task path of a delivery drone contains 10 control points, corresponding to a flight time of 10 seconds. After mapping by standard time steps and spatial grids, the system generates a resource path . If this path conflicts with an aircraft with a higher priority from the 4th to the 7th second, the system will remove this section of the path, mark it as a scheduling failure segment, and update the task takeoff time to the 8th second, and replan the remaining resource usage time period. After receiving this scheduling plan, the aircraft enters a countdown preparation for the 8th second, and automatically takes off and executes the task when the scheduling time arrives.

[0073] In summary, this step completes the standardized mapping from aircraft path control points to urban airspace resources, conflict handling, and final scheduling release, ensuring that each aircraft has a clear, unique, and controllable airspace channel in a high-density low-altitude urban scenario.

[0074] As Figure 2 shown, in another embodiment of the present invention, a multi-node collaborative airspace control device for urban low-altitude transportation is provided. The device includes: The real-time perception module 101 is used to obtain the perception data of the real-time perception system of the urban low-altitude aircraft body 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 height, position and flight track vector of adjacent aircraft, distance and direction of static obstacles ahead, and local airspace density; The aircraft scheduling module 102 is used to determine the local congestion coefficient and the path start and end point pairs of the current aircraft allocation task by performing real-time airspace availability analysis and current aircraft allocation task scheduling and sorting according to the global perception data set; The path planning module 103 is used to construct an initial spline path according to 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 sequence of path control points and the total path cost; The cooperative control module 104 is used to take any aircraft as the target aircraft, and perform dynamic path adjustment based on distributed cooperative control by combining the target aircraft with its neighbor aircraft, generate a sequence of path control points after dynamic adjustment as the final path for the aircraft to actually execute, and the new total path cost; The resource scheduling module 105 is used to obtain the urban airspace, map the final path to the urban airspace, generate a complete scheduling sequence of the aircraft on the urban airspace, package the complete scheduling sequence into a structured scheduling plan, and send it to the aircraft control system for execution; Wherein, if the aircraft resource request fails, the aircraft control system marks the conflict resource segment as a scheduling failure segment and notifies the corresponding aircraft to enter a state of task delay or queuing waiting for resource release; if the aircraft resource request is successful, it is marked as a set of successful resource units, indicating that the path has been successfully allocated to the executable airspace in the current scheduling cycle; Wherein, 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 set of successful resource units, and at the same time, monitor the operation of the aircraft in real time to ensure that its path execution is consistent with resource occupancy.

[0075] Figure 3 It is a structural block diagram of an electronic device provided by an embodiment of the present application. As Figure 3 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 complete communication with each other. The memory 902 may store one or more computer programs, and one or more computer programs may be configured to be executed by one or more processors 901 to implement the methods described in the above embodiments.

[0076] The processor 901 may include one or more processing cores. The processor 901 connects various parts within the entire electronic device 900 through various interfaces and circuits. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 902, and by invoking data stored in the memory 902, it performs various functions of the electronic device 900 and processes data. Optionally, the processor 901 may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor 901 may integrate a combination of one or several of a central processing unit (CPU), a graphics processing unit (GPU), and a modem, etc. It can be understood that the above-mentioned modem may not be integrated into the processor 901 and may be implemented separately through a communication chip.

[0077] The memory 902 may include random access memory (RAM) and may also include read-only memory (ROM). The memory 902 is used to store instructions, programs, code, code sets, or instruction sets. The memory 902 may include a program storage area and a data storage area. Among them, 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 during the use of the electronic device 900.

[0078] It can be understood that the electronic device 900 may include more or fewer structural elements than those in the above structural block diagram. For example, it includes a power module, physical buttons, a WiFi (Wireless Fidelity) module, a speaker, a Bluetooth module, sensors, etc., which are not limited herein.

[0079] The above-mentioned electronic device 900 may be a detection device or a part of a detection device.

[0080] An embodiment of the present application provides a computer-readable storage medium. Program data is stored in the computer-readable storage medium. When the program data is executed by a processor, it is used to execute some or all of the steps of any one of the multi-node collaborative airspace control methods for urban low-altitude traffic described in the above method embodiments.

[0081] An embodiment of the present application also provides a computer program product. The computer program product includes 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 one of the multi-node collaborative airspace control methods for urban low-altitude traffic described in the above method embodiments. The computer program product can be a software installation package.

[0082] It should be noted that the above-described work process is only illustrative and does not limit the protection scope of the present invention. In actual applications, those skilled in the art can select some or all of them according to actual needs to achieve the purpose of the solution of this embodiment, and no limitation is made here.

[0083] In addition, for the technical details not described in detail in this embodiment, reference can be made to the parameter operation method provided in any embodiment of the present invention, which will not be elaborated here.

[0084] It should be noted that in this article, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or system. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, method, article or system including that element.

[0085] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0086] Through the description of the above embodiments, those skilled in the art can clearly understand that the above embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence, or the part that makes a contribution to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as a read-only memory / random access memory, magnetic disk, optical disc), and includes several instructions to cause a terminal device (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in various embodiments of the present invention.

[0087] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be similarly included in the patent protection scope of the present invention.

Claims

1. A multi-node collaborative airspace control method for urban low-altitude transportation, characterized in that The method includes: Obtaining the perception data of the real-time perception system of the urban low-altitude aircraft body and urban airspace infrastructure, performing preprocessing, and generating a global perception data set; wherein, the global perception data set includes: three-dimensional position data, linear velocity and attitude angle, relative height, position and flight path vector of adjacent aircraft, distance and direction of static obstacles ahead, and local airspace density; According to the global perception data set, by performing real-time airspace availability analysis and current aircraft allocation task scheduling and sorting, determining the local congestion coefficient and the path start and end point pairs of the current aircraft allocation task; Constructing an initial spline path according to the path start and end point pairs, constructing a path cost function with minimizing the local congestion coefficient as the optimization goal, and optimizing the initial spline path to generate a path control point sequence and a total path cost; Taking any aircraft as the target aircraft, performing dynamic path adjustment based on distributed cooperative control by combining the target aircraft with its neighboring aircraft, generating the path control point sequence after dynamic adjustment as the final path for the aircraft to actually execute, and a new total path cost; Obtaining the urban airspace, mapping the final path to the urban airspace, generating a complete scheduling sequence of the aircraft on the urban airspace, packaging the complete scheduling sequence into a structured scheduling plan, and sending it to the aircraft control system for execution; Wherein, if the aircraft resource request fails, the aircraft control system marks the conflict resource segment as a scheduling failure segment and notifies the corresponding aircraft to enter the state of task delay or queuing for resource release; if the aircraft resource request is successful, it is marked as a set of successful resource units, indicating that the current path has been successfully allocated to the executable airspace in the current scheduling cycle; Wherein, once the aircraft receives the scheduling confirmation signal, it enters the automatic takeoff preparation state, and the aircraft control system performs flight path tracking with reference to the set of successful resource units. 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 collaborative airspace control method for urban low-altitude transportation according to claim 1, characterized in that, The real-time perception system includes GNSS, IMU, barometer, ADS-B receiver, and visual sensor; Wherein 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 height of the aircraft; The ADS-B receiver provides the position and flight path vector of adjacent aircraft; the visual sensor is used to provide the detection result of obstacles within the forward line-of-sight range of the aircraft, that is, the distance and direction of static obstacles ahead; Then, the perception data includes three-dimensional position data, linear velocity and attitude angle, relative height, position and flight path vector of adjacent aircraft, distance and direction of static obstacles ahead; Wherein the preprocessing includes: Unifying the perception data into the same time stamp and geographical coordinate framework; Obtain the position information of the aircraft, construct a cubic region centered on any aircraft according to the aircraft position information, and record the frequency of aircraft appearance per unit time in each grid cell of the cubic region as a dynamic density index. Use the trilinear interpolation method for all dynamic density indices to generate the three-dimensional density of the local airspace at any query point; Unify and organize all processed data into a global perception data set; Among them, when the real-time perception system fails, use the extended Kalman filter to perform joint state estimation on the data from GNSS and IMU to generate the state estimation vector of the aircraft.

3. A multi-node collaborative airspace control method for urban low-altitude transportation according to claim 1, characterized in that, According to the global perception data set, by performing real-time airspace availability analysis and current aircraft assigned task scheduling and sorting, determine the local congestion coefficient and the path start and end point pairs of the current aircraft assigned tasks, specifically including: Take any aircraft as the target aircraft, construct a predicted flight path channel centered on the current position of the target aircraft, combined with the movement direction, and calculate the congestion coefficient along the predicted channel at the current position to analyze whether the three-dimensional density of the corresponding local airspace in the flight path channel exceeds the set threshold to determine whether there is potential congestion on this path; among them, if there is a high-density section in the predicted area, mark it as an impassable area and impose a soft penalty constraint on the path during the task scheduling process; Obtain the task set of the target aircraft, perform scheduling and sorting to generate the current optimal task; Assign the current optimal task to the target aircraft, and at the same time generate the initial path start point and the initial target point for task execution as the path start and end point pair; if the path start point cannot directly reach the initial target point due to density restrictions, airspace blockade or the existence of dangerous obstacles ahead, the sub-optimal task fallback strategy will be triggered, re-screen alternative tasks from the task set, and re-execute the scheduling scoring mechanism until the task is successfully assigned or the airspace situation is updated; Among them, the obtaining the task set of the target aircraft, performing scheduling and sorting to generate the current optimal task specifically includes: Each task will be assigned a comprehensive scheduling score, which is calculated by combining the Euclidean distance from the current position of the aircraft to the task start point, the average congestion coefficient on the predicted execution path of the task, the remaining time window length of the task, and the priority constant corresponding to the task type.

4. A multi-node collaborative airspace control method for urban low-altitude transportation according to claim 1, characterized in that, Construct an initial spline path according to the path start and end point pair, and construct a path cost function with minimizing the local congestion coefficient as the optimization goal to optimize the initial spline path to generate a sequence of path control points and the total path cost, specifically including: Construct an initial spline path according to the path start and end point pair, where the initial spline path is composed of a sequence of path control points, and the intermediate path control points are initially uniformly generated by linear interpolation; the initial spline path is constructed as a cubic B-spline; Construct a path cost function with minimizing the local congestion coefficient as the optimization objective to generate the total path cost. Among them, in the path optimization process, a path control point position adjustment strategy based on gradient descent is adopted, and the optimization variable is the intermediate path control points; Generate an optimized sequence of path control points, which is the input for optimizing the path curve.

5. A multi-node collaborative airspace control method for urban low-altitude transportation according to claim 1, characterized in that, Taking any aircraft as the target aircraft, the target aircraft combines with its neighboring aircraft to perform dynamic path adjustment based on distributed cooperative control, generating a sequence of path control points after dynamic adjustment as the final path for the actual execution of the aircraft, as well as the total cost of the new path, specifically including: Each aircraft maintains a locally perceived window during operation. This window obtains the path status of nearby aircraft through short-range communication, including the sequence of path control points of nearby aircraft, the current execution progress, and the total path cost. Among them, each broadcast only sends the hash-compressed information of the nearest path control points within the current window, along with the total cost value. Construct a path conflict estimation function based on the spatial overlap degree between its own path control points and the neighboring path control points, generating a path conflict estimation value, which is used to reflect the path interference degree between its own aircraft and the neighboring aircraft; Optimize the total path cost according to the path conflict estimation value, generating a sequence of path control points after dynamic adjustment and the total cost of the new path.

6. A multi-node collaborative airspace control method for urban low-altitude transportation according to claim 1, characterized in that The obtaining of the urban airspace, mapping the final path to the urban airspace, generating a complete scheduling sequence of the aircraft on the urban airspace, packaging the complete scheduling sequence into a structured scheduling plan, and sending it to the aircraft control system for execution, specifically including: Taking the urban airspace as a standard four-dimensional resource unit to form an urban airspace resource coordinate system; Obtain the three-dimensional coordinates of the path control points of the target aircraft, and according to the set time step, assign a timestamp to each path control point, indicating the expected flight time of the aircraft at this path control point, combining 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, and constituting a complete scheduling sequence; Package the complete scheduling sequence into a structured scheduling plan and send it to the aircraft control system.

7. A multi-node collaborative airspace control device for urban low-altitude transportation, characterized in that, The device includes: A real-time perception module, which is used to obtain the perception data of the real-time perception system of the urban low-altitude aircraft body and the 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 height, position and flight track vector of neighboring aircraft, distance and direction of static obstacles ahead, local airspace density; An aircraft scheduling module, which is used to determine the local congestion coefficient and the path start and end point pairs of the current aircraft assigned tasks according to the global perception data set by performing real-time airspace availability analysis and scheduling sorting of the current aircraft assigned tasks; A path planning module, which is used to construct an initial spline path according to the path start and end point pairs, construct a path cost function with minimizing the local congestion coefficient as the optimization goal to optimize the initial spline path, generating a sequence of path control points and the total path cost; A cooperative control module, which takes any aircraft as the target aircraft, and the target aircraft combines with its neighboring aircraft to perform dynamic path adjustment based on distributed cooperative control, generating a sequence of path control points after dynamic adjustment as the final path for the actual execution of the aircraft, as well as the total cost of the new path; A resource scheduling module, which is used to obtain the urban airspace, map the final path to the urban airspace, generate a complete scheduling sequence of the aircraft on the urban airspace, package the complete scheduling sequence into a structured scheduling plan, and send it to the aircraft control system for execution; Among them, if the aircraft resource request fails, the aircraft control system marks the conflict resource segment as a scheduling failure segment and notifies the corresponding aircraft to enter a state of task delay or queuing waiting for resource release; if the aircraft resource request is successful, it is marked as a set of successful resource units, 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 performs flight path tracking with reference to the set of successful resource units, and at the same time, monitors the operation of the aircraft in real time to ensure that its path execution is consistent with resource occupancy.

8. An electronic device, characterized in that, The device includes: a processor, a memory, and a communication interface, which are interconnected and complete communication with each other; the memory stores executable program code, and the communication interface is used for wireless communication; the processor is used to retrieve the executable program code stored on the memory and execute the method according to any one of claims 1-6.

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

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