A method for on-demand resource allocation for urban air traffic safety

Abstract

The application provides a method for on-demand resource allocation for urban air traffic safety, mainly solves the problem of collision of personal air vehicles (PAVs) caused by the contradiction between resource demand and distribution in the existing UAM, and the implementation scheme is as follows: a communication model between PAVs and base stations (BS) is established; the perception reaction time of PAVs is calculated, a three-dimensional safety distance mathematical model related to the perception reaction time, the current vehicle speed and the braking capacity factor is established; a three-dimensional safety distance driven resource optimization problem related to communication resources and computing resources is modeled; according to the three-dimensional safety distance mathematical model, the objective function of the three-dimensional safety distance driven resource optimization problem is minimized, and the allocation of communication resources and computing resources is completed. The application can improve the safety of UAM, relieve the contradiction between resource supply and demand, improve the communication quality of PAVs, reduce the probability of safety accidents, and can be used for safe travel of urban air traffic.

Description

Technical Field

[0001] This invention belongs to the field of transportation technology, specifically relating to a method for on-demand resource allocation in urban air traffic, which can be used to improve the flight safety of personal aircraft in air traffic scenarios and alleviate the contradiction between resource supply and demand. Background Technology

[0002] In recent years, rapid urbanization has exacerbated the contradiction between limited transportation resources and ever-increasing transportation demand, necessitating the development of available spaces other than land-based land use. Urban air mobility (UAM), a mode of transportation dominated by aircraft in low-altitude airspace, holds great promise for alleviating traffic congestion and has sparked extensive research.

[0003] While urban air mobility is developing rapidly, it also faces numerous safety challenges. In UAM (Urban Aerial Mobility), Personal Air Vehicles (PAVs) possess a high degree of freedom during flight, requiring attention not only to avoid horizontal collisions but also to potential longitudinal safety hazards. Furthermore, PAVs fly at speeds far exceeding those of ground-based vehicles; for example, the PAL-VOne from PAL-V Europe and the Geely Terrafugia Transition have maximum speeds of 180 km / h and 185 km / h, respectively. Therefore, the consequences of a mid-air collision would be unimaginable. To ensure the safety of PAVs during flight, safe distances must be maintained in every direction within UAM.

[0004] In response to flight safety issues in UAM (Airborne Collision Avoidance), researchers have conducted a series of studies. The TCAS (Traffic Collision Avoidance System), an air traffic control system that prevents mid-air collisions without relying on the ground, is widely used in civil aviation transport aircraft.

[0005] In their paper "Conflict Management Considering a Smooth Transition of Aircraft into Adjacent Airspace", Hong et al. designed a two-tiered architecture to reduce the complexity of adjacent airspaces and avoid conflicts, thereby mitigating conflicts caused by air traffic control.

[0006] In their paper "Vision-based Collision Avoidance for Personal Aerial Vehicles using Dynamic Potential Fields", Rehmatullah et al. proposed a PAVs collision avoidance system that uses a dynamic repulsive potential function to avoid obstacles by using repulsive control forces, thus assisting PAVs operators in making safe driving decisions.

[0007] While existing methods can address collision issues in UAM (Universal Airspace Communication), their lack of systematic resource analysis makes it difficult to guarantee communication quality for dense and high-speed PAVs (Programmable Air Vehicles), thus limiting the effectiveness of these collision avoidance methods in UAM. To address this, Knoblock et al. proposed an AI-based resource management method in their paper "Investigation and Evaluation of Advanced Spectrum Management Concepts for Aeronautical Communications," which dynamically optimizes communication resources by predicting communication demands across the entire airspace. However, in UAM, available resources gradually decrease with increasing altitude due to the combined effects of antenna angle and communication distance. To ensure flight safety, PAVs must increase their resource requirements. However, this method lacks analysis of the individual resource needs of PAVs, thus failing to effectively resolve the contradiction between resource demand and distribution in UAM, and cannot guarantee communication quality for high-speed moving PAVs, potentially leading to safety incidents. Summary of the Invention

[0008] The purpose of this invention is to overcome the shortcomings of the above-mentioned technologies and propose an on-demand resource allocation method for urban air traffic safety. With the goal of minimizing the three-dimensional safety distance, this method can improve safety in UAM while alleviating the contradiction between resource supply and demand, improving the communication quality of PAVs, and reducing the probability of safety accidents.

[0009] The technical approach to achieving the objective of this invention is as follows: by establishing a resource-related three-dimensional secure distance model, a stable communication connection between PAVs and base stations is realized; by establishing a three-dimensional secure distance-driven on-demand resource allocation strategy, the stringent security requirements in UAM are met.

[0010] Based on the above ideas, the implementation steps of the present invention include the following:

[0011] (1) Establish a communication model between Personal Air Vehicles (PAVs) and Base Stations (BS):

[0012] (1a) Calculate the NLoS probability when PAVs communicate with BS respectively. Path loss of NLoS links Channel gain between PAVs and BS

[0013] (1b) Substituting the above parameters into the maximum data transmission rate formula, we obtain the communication model between Personal Air Vehicles (PAVs) and Base Stations (BS):

[0014]

[0015] Where, r n,k (t) represents the maximum link transmission rate between PAVs and BS, where B n,k (t) represents the bandwidth of PAVs, P n,k (t) represents the power of PAVs, σ 2 Indicates noise power, a n,k (t) indicates whether there is a blockage between PAVs and BS. When a blockage exists, a n,k (t) = 1, otherwise a n,k (t) = 0;

[0016] (2) Calculate the collision warning delay during the transmission of the collision warning signal. Processing latency and return latency Establish the perceptual reaction time τ of PAVs n,k (t):

[0017]

[0018] (3) In scenarios of PAVs climbing and descending traffic, establish a relationship with the perception reaction time τ. n (t), Current vehicle speed v n (t) and braking capacity a n,k (t) Factor-related three-dimensional safety distance mathematical model n→m (t):

[0019]

[0020] Among them, a m,k+1 Let a represent the acceleration of the vehicle k+1 ahead on the m-th floor. m,k-1 Let v represent the acceleration of the following car k-1 on the m-th layer. m v n The average velocities τ of the m-th and n-th layers are respectively. n,k (t) represents the perceptual reaction time of PAV in the nth layer, l m,k (t) represents the same-level safety distance of the m-th layer;

[0021] (4) Based on the security-based resource requirements of PAVs and the resource distribution in the scenario, establish communication resource B n,k (t) and computing resources The related three-dimensional safety distance-driven resource optimization problem P;

[0022]

[0023] stl n→m (t)≥l m (t)

[0024]

[0025]

[0026] in, Let k represent the objective function of the three-dimensional safety distance driven resource optimization problem. n This represents the number of PAVs in the nth layer, l n→m (t) represents the mathematical model of the three-dimensional safety distance, l m (t) represents the safety distance between the same floor in the m-th floor. and Let represent the available communication resources and computing resources in the nth layer of the Urban Air Traffic Management (UAM), respectively.

[0027] (5) Based on the three-dimensional safety distance mathematical model l n→m (t), the objective function of the resource optimization problem P driven by minimizing the three-dimensional safety distance. The objective function of the three-dimensional safety distance driven resource optimization problem P is obtained after minimization. and the corresponding allocated communication resources and computing resources Complete the communication resource B n,k (t) and computing resources The allocation.

[0028] Compared with the prior art, the present invention has the following advantages:

[0029] This invention improves the safety of urban air traffic management (UAM) systems and effectively reduces the probability of accidents by constructing a three-dimensional mathematical model of safe distance related to factors such as perception reaction time, current vehicle speed, and braking capability, compared to existing air collision avoidance methods.

[0030] Meanwhile, because this invention comprehensively considers the safety resource requirements of personal aerial vehicles (PAVs) and the resource distribution in the scenario, it drives resource optimization by modeling the three-dimensional safety distance related to communication and computing resources, thereby realizing on-demand allocation of resources in UAM. Compared with existing flight safety research, this invention can alleviate the resource supply and demand contradiction caused by antenna angle and communication distance while ensuring flight safety, and improve the communication quality of PAVs. Attached Figure Description

[0031] Figure 1 This is a flowchart illustrating the implementation process of the present invention;

[0032] Figure 2 This is a schematic diagram of a scenario for the present invention;

[0033] Figure 3This is a schematic diagram of the movement of PAVs climbing traffic in this invention;

[0034] Figure 4 This is a simulation diagram of the coordinates and driving state of PAVs in this invention;

[0035] Figure 5 A comparative simulation diagram of the communication resource requirements and allocation of the present invention;

[0036] Figure 6 This is a simulation diagram comparing the computational resource requirements and allocation of the present invention. Detailed Implementation

[0037] The embodiments and effects of the present invention will be further described in detail below with reference to the accompanying drawings.

[0038] Reference Figure 2 The scenario used in this invention includes a base station (BS) and personal aerial vehicles (PAVs). The BS provides communication and computing resources to the PAVs, which fly in low-altitude airspace according to a "layered" airspace pattern. The airspace is divided into N layers, with K layers in the nth layer. n A car, and

[0039] This example demonstrates on-demand resource allocation based on the city's air traffic scenario to effectively reduce the probability of safety accidents and improve the communication quality of PAVs.

[0040] Reference Figure 1 The implementation steps of this example include the following:

[0041] Step 1: Establish a communication model between Personal Air Vehicles (PAVs) and Base Stations (BS).

[0042] 1.1) Based on the line-of-sight (LoS) probability formula for air-to-ground communication, and the sum of the LoS probability and the non-line-of-sight (NLoS) probability, calculate the NLoS probability when PAVs communicate with BS.

[0043]

[0044] in, This represents the Loss of Sorrow (LoS) probability when PAVs communicate with BS, μ1 and μ2 represent two constants with different values ​​related to the environment, and h n,k (t) represents the vertical height between PAVk and BS in the nth layer, d n,k (t) represents the Euclidean distance between the two;

[0045] 1.2) Calculate the path loss of the NLoS link according to the path loss formula.

[0046]

[0047] Among them, f n,k (t) represents the channel frequency;

[0048] 1.3) Calculate the channel gain between PAVs and BS according to the channel gain formula.

[0049]

[0050] Where g0 represents the Euclidean distance d between BS and PAV. n,k (t) represents the channel gain at 1m;

[0051] 1.4) Substituting the above parameters into the maximum data transmission rate formula, we obtain the communication model between PAVs and BS:

[0052]

[0053] Where, r n,k (t) represents the maximum link transmission rate between PAVs and BS, where B n,k (t) represents the bandwidth of PAVs, P n,k (t) represents the power of PAVs, σ 2 Indicates noise power, a n,k (t) indicates whether there is a blockage between PAVs and BS. When a blockage exists, a n,k (t) = 1, otherwise a n,k (t) = 0.

[0054] Step 2: Calculate the collision warning delay during the collision warning signal transmission process. Processing latency and return latency Establish the perceptual reaction time τ of PAVs n,k (t).

[0055] 2.1) Calculate the collision warning delay according to the transmission delay formula.

[0056]

[0057] Among them, c n,k+1 (t) represents the data size of the warning message, r n,k+1 (t) represents the transmission rate between PAV and BS;

[0058] 2.2) Calculate the processing delay according to the processing delay formula.

[0059]

[0060] Where, η n,k (t) represents the computational complexity. This indicates the computing power allocated to PAV;

[0061] 2.3) Calculate the return delay according to the transmission delay formula.

[0062]

[0063] Among them, c n,k (t) represents the size of the calculated result;

[0064] 2.4) Substituting the above parameters into the sensory reaction time formula, we obtain the sensory reaction time τ of PAVs. n,k (t):

[0065]

[0066] Step 3: In scenarios where PAVs are climbing and descending traffic, establish a relationship with the perception reaction time τ. n,k (t), Current vehicle speed v n,k (t) and braking capacity a n,k (t) Factor-related three-dimensional safety distance mathematical model n→m (t).

[0067] 3.1) Based on the kinematic method of collision avoidance, calculate the safe distance l of PAVs flying at the same level in low-altitude airspace. n,k (t):

[0068]

[0069] Among them, v n τ represents the average driving speed at the nth level. n,k (t) represents the perceptual response time of PAV in the nth layer. This represents the maximum acceleration of car k at present. This represents the maximum acceleration of the vehicle in front (k+1).

[0070] 3.2) Calculate the safe distance l in the PAVs climbing traffic scenario according to the kinematic formula. n→n+1 (t) and the safe distance l in a descending traffic scenario n→n-1 (t):

[0071] 3.2.1) Reference Figure 3 The safe distance l of PAVs in a climbing traffic scenario is calculated based on kinematic formulas. n→n+1 (t):

[0072]

[0073] Among them, a n+1,k+1 Let a represent the acceleration of the car k+1 ahead on the (n+1)th floor. n+1,k-1 Let v represent the acceleration of the following car k-1 on the (n+1)th layer. n+1 Let l represent the average velocity of the (n+1)th layer. n+1,k (t) represents the safety distance between the same layer in the (n+1)th layer;

[0074] 3.2.2) Calculate the safe distance l of PAVs in a descending traffic scenario using kinematic formulas. n→n-1 (t):

[0075]

[0076] Among them, a n-1,k+1 Let a represent the acceleration of the car k+1 ahead on the (n-1)th floor. n-1,k-1 Let v represent the acceleration of the following car k-1 on the (n-1)th layer. n-1 Let l represent the average velocity of the (n-1)th layer. n-1,k (t) represents the safety distance between the same layer in the (n-1)th layer;

[0077] 3.3) Based on the safe distance l during ascent n→n+1 (t) and the safe distance during descent l n→n-1 (t), thus obtaining the mathematical model of the three-dimensional safety distance in UAM. n→m (t):

[0078]

[0079] Among them, a m,k+1 Let a represent the acceleration of the vehicle k+1 ahead on the m-th floor. m,k-1 Let v represent the acceleration of the following car k-1 on the m-th layer. m Let l represent the average velocity of the m-th layer. m,k (t) represents the same-layer safety distance for the m-th layer.

[0080] Step 4: Based on the security-based resource requirements and resource distribution in the scenario, model and communicate resource B. n,k (t) and computing resources The related three-dimensional safety distance-driven resource optimization problem P.

[0081] 4.1) Minimizing the total stereo safe distance of PAVs in the UAM scene is set as the objective function of the resource optimization problem P, expressed as follows:

[0082]

[0083] Among them, B n,k(t) represents the communication resources allocated to each PAV. Computational resources allocated to each PAV;

[0084] 4.2) Set the safety constraints and resource constraints as the constraints of the resource optimization problem P:

[0085] 4.2.1) Let the safety constraint be the three-dimensional safety distance l of PAVs. n→m (t) is not less than the same-layer safety distance l. m (t):

[0086] l n→m (t)≥l m (t);

[0087] 4.2.2) Assume the resource constraint is that the sum of resources allocated to each layer of PAVs is within the range of available resources in each layer of UAM:

[0088]

[0089]

[0090] Where, k n This represents the number of PAVs in the nth layer. and Let represent the communication resources and computing resources available in the nth layer of UAM, respectively.

[0091] 4.3) Based on the above objective function and constraints, establish a connection with communication resource B. n,k (t) and computing resources Related three-dimensional safety distance driven resource optimization problem P:

[0092]

[0093] Step 5, based on the three-dimensional safety distance mathematical model l n→m (t), the objective function of the resource optimization problem P driven by minimizing the three-dimensional safety distance. Complete the communication resource B n,k (t) and computing resources The allocation.

[0094] 5.1) Determine the state space S t Let l be the mathematical model of three-dimensional safety distance. n→m (t) Relevant Personal Air Vehicle (PAV) status and resource distribution in Urban Air Traffic (UAM):

[0095]

[0096] Among them, (xn,k (t),y n,k (t),z n (t) represents the coordinates of PAV, v n,k (t) represents the speed of PAV, a n,k (t) represents the acceleration of PAV, c n,k (t) represents the size of the warning message issued by PAV, r n,k (t) represents the data transmission rate between PAV and BS. These parameters are all related to the three-dimensional safe distance mathematical model. n→m (t) The state of the associated PAV; These represent the distribution of communication resources and computing resources in the UAM space, respectively.

[0097] 5.2) Transfer the motion space A t Let B be the communication resource allocated to each PAV. n,k (t) and computing resources

[0098] 5.3) The reward function R t Set to negative

[0099] 5.4) Based on the above settings, the Depth Deterministic Policy Gradient (DDPG) algorithm is used, according to the action space A. t Action a in t Using reward function R t Calculate the reward value r t And through the state space S t The interaction continuously learns and iteratively solves the three-dimensional safety distance-driven resource optimization problem P until the maximum reward value r is obtained. t,max The objective function of the three-dimensional safety distance driven resource optimization problem P after minimization is obtained. and the corresponding allocated communication resources and computing resources Complete the on-demand allocation of urban air transportation resources.

[0100] The technical effects of the present invention will be further explained below with reference to simulation experiments:

[0101] 1. Simulation conditions

[0102] Simulation software: Python is used;

[0103] Simulation Scenario: The simulation environment is a cubic urban area with sides of 200m. A Base Station (BS) with an antenna height of 25m is located at the center of the simulation environment. 50 Parking Vehicles (PAVs) are randomly distributed on the ground and in low-altitude airspace at heights of 100m and 200m. The speed limits for each level are 50 km / h, 120 km / h, and 180 km / h, respectively. The coordinates and driving status of the PAVs in the simulation scenario are shown below. The location of the scatter plot represents the coordinates of the PAVs, and the driving status is as follows: Figure 4 As shown.

[0104] 2. Simulation content and result analysis:

[0105] Simulation 1: Using the above simulation scenario and conditions, the present invention is simulated in... Figure 4 Simulations were performed on demand allocation of communication resources in the scenario, and the results are as follows: Figure 5 As shown in the diagram. The color depth represents the amount of communication resources; a lighter color indicates more communication resources. The unit of color depth is Hz. The color of a square represents the communication resource demand of PAVs, the color of a triangle represents the allocated communication resources, and a circle indicates that the communication resource demand has not been met.

[0106] from Figure 5 As can be seen, the squares and triangles are similar in color, indicating that the present invention allocates communication resources in the UAM scenario on demand, effectively alleviating the contradiction between communication resource demand and communication resource distribution. Figure 5 The five circles in the diagram indicate that the present invention can meet the communication resource requirements of 90% of PAVs in the current UAM scenario.

[0107] Simulation 2: Using the above simulation scenarios and conditions, the present invention is simulated in... Figure 4 The simulation was performed by allocating computing resources on demand in the scenario, and the results are as follows: Figure 6 As shown in the diagram. The color depth represents the amount of computing resources; a lighter color indicates more computing resources. The unit of color depth is Hz. The color of a square represents the computing resource requirements of a PAV, the color of a triangle represents the allocated computing resources, and a circle indicates that the computing resource requirements are not being met.

[0108] from Figure 6 As can be seen, the squares and triangles are similar in color, indicating that the present invention allocates computing resources in the UAM scene on demand. Figure 6 The eight circles in the diagram indicate that the present invention can meet the computational resource requirements of 84% of PAVs in the current UAM scenario.

[0109] The above description is merely a specific example of the present invention and does not constitute any limitation on the present invention. Obviously, those skilled in the art, after understanding the content and principles of the present invention, may make various modifications and changes in form and details without departing from the principles and structure of the present invention. However, these modifications and changes based on the ideas of the present invention are still within the scope of protection of the claims of the present invention.

Claims

1. A method for on-demand resource allocation for urban air traffic safety, characterized in that, Includes the following steps: (1) Establish a communication model between Personal Air Vehicles (PAVs) and Base Stations (BS): (1a) Calculate the NLoS probability when PAVs communicate with BS respectively. Path loss of NLoS links Channel gain between PAVs and BS ; (1b) Substituting the above parameters into the maximum data transmission rate formula, we obtain the communication model between Personal Air Vehicles (PAVs) and Base Stations (BS): ； in, This represents the maximum link transmission rate between PAVs and BS. This indicates the bandwidth of PAVs. Indicates the power of PAVs. Indicates noise power. This indicates whether there is a blockage between PAVs and BS. When a blockage exists... ,on the contrary ; (2) Calculate the collision warning delay during the transmission of the collision warning signal. Processing delay and return latency Establish the perceptual reaction time of PAVs : ； (3) In scenarios where PAVs are climbing and descending traffic, establish a perception response time. Current vehicle speed and braking ability Mathematical model of three-dimensional safety distance related to factors : ； in, Indicates the first The front of the layer acceleration, Indicates the first Following vehicle on the floor acceleration, , The first Layer and first The average velocity of the layer, Indicates the first The sensing response time of PAV in the layer. For the first Safety distance between floors; (4) Based on the security-based resource requirements of PAVs and the resource distribution in the scenario, establish communication resources and computing resources Related issues related to three-dimensional safety distance driven resource optimization ; ； ； ； ； in, The objective function represents the resource optimization problem driven by three-dimensional safety distance. Indicates the first The number of PAVs in the layer Mathematical models representing three-dimensional safety distances Indicates the first Safety distance between floors on the same floor and These represent the UAM (Urban Air Traffic Management) levels respectively. The available communication and computing resources in the layer, and ; (5) Based on the mathematical model of three-dimensional safety distance Minimize the three-dimensional safety distance to drive resource optimization problem objective function The minimized three-dimensional safety distance-driven resource optimization problem is obtained. objective function and the corresponding allocated communication resources and computing resources Complete the communication resources and computing resources The allocation.

2. The method according to claim 1, characterized in that: In step (1a), the NLoS probability is calculated when PAVs communicate with BS. It is calculated based on the line-of-sight (LoS) probability formula for air-to-ground communication and the sum of the LoS probability and the non-line-of-sight (NLoS) probability, as shown below: ； in, This represents the line-of-sight (LoS) probability when PAVs communicate with BS. , This represents two constants with different values ​​related to the environment. Represents the PAV in the nth layer Vertical height between BS, This represents the Euclidean distance between the two.

3. The method according to claim 1, characterized in that: In step (1a), the path loss of the NLoS link is calculated. It is calculated based on the path loss formula, as shown below: ； in, This represents the Euclidean distance between the two. Indicates the channel frequency.

4. The method according to claim 1, characterized in that: In step (1a), the channel gain between PAVs and BS is calculated. It is calculated based on the channel gain formula, as shown below: ； in, This represents the Euclidean distance between the two. This represents the Euclidean distance between BS and PAV. Channel gain at 1m.

5. The method according to claim 1, characterized in that: In step (2), the collision warning delay is calculated. Processing delay and return latency The formula is as follows: ； ； ； in, The size of the warning message. Indicates the vehicle in front Transmission rate between BS and B Indicates computational complexity. This indicates the computing power allocated to PAV. This indicates the size of the processed result. Indicates the current vehicle The transmission rate between the BS and the server.

6. The method according to claim 1, characterized in that: In step (3), establish and perceive reaction time. Current vehicle speed and braking ability Mathematical model of three-dimensional safety distance related to factors The implementation is as follows: (3a) Calculate the safe distance of PAVs at the same level when flying in low-altitude airspace based on the kinematic method of collision avoidance. : ； in, Indicates that it is located at the th The average driving speed of the floor, Indicates the first The sensing response time of PAV in the layer. Indicates the current vehicle Maximum acceleration, Indicates the vehicle in front The maximum acceleration; (3b) Calculate the safe distance in the PAVs climbing traffic scenario according to the kinematic formula. and safe distance in descending traffic scenarios : ； ； in, Indicates the first The front of the layer acceleration, Indicates the first Following vehicle on the floor acceleration, , They represent the first Layer and first The average velocity of the layer, Indicates the first The sensing response time of PAV in the layer. For the first Safety distance between floors; Indicates the first The front of the layer acceleration, Indicates the first Following vehicle on the floor acceleration, Indicates the first The average velocity of the layer, For the first Safety distance between floors; (3c) Based on the safe distance during ascent and safe distance during descent The mathematical model of the three-dimensional safety distance in UAM is obtained. : ； in, Indicates the first The front of the layer acceleration, Indicates the first Following vehicle on the floor acceleration, , The first Layer and first The average velocity of the layer, Indicates the first The sensing response time of PAV in the layer. For the first Safety distance between floors.

7. The method according to claim 1, characterized in that: In step (5), the mathematical model of three-dimensional safety distance is used. Minimize the three-dimensional safety distance to drive resource optimization problem objective function The implementation is as follows: (5a) State space Set as a mathematical model of three-dimensional safety distance The status of relevant Personal Air Vehicles (PAVs) and the distribution of resources in Urban Air Traffic Management (UAM); (5b) Move space Configure to allocate communication resources to each PAV and computing resources ; (5c) The reward function Set to negative ; (5d) Based on the above settings, the Depth Deterministic Policy Gradient (DDPG) algorithm is used, according to the execution action space. Actions in Using reward function Calculate reward value and through the state space Through continuous interaction and learning, it iteratively solves the resource optimization problem driven by three-dimensional safety distance. until the maximum reward value is achieved. The minimized three-dimensional safety distance-driven resource optimization problem is obtained. objective function and the corresponding allocated communication resources and computing resources .

8. The method according to claim 7, characterized in that: State space in step (5a) , means as follows: ； in, Represents the coordinates of PAV. This indicates the speed at which the PAV travels. This represents the acceleration of PAV. This indicates the size of the warning message issued by PAV. These parameters represent the data transmission rate between PAV and BS, and are all related to the three-dimensional safe distance mathematical model. The status of the relevant PAV; , These represent the distribution of communication resources and computing resources in the UAM space, respectively.

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