A method and device for evaluating the capacity of a vertical take-off and landing field for a drone

Abstract

The application discloses a kind of unmanned plane vertical take-off and landing field capacity evaluation method and device, the method according to unmanned plane vertical take-off and landing field topological structure, abstract unmanned plane operation flow, determine the queuing system in unmanned plane operation flow;According to the operation mode of vertical take-off and landing field, respectively establish the queuing theory model of each queuing system in unmanned plane operation flow;According to the parameter set in the process of unmanned plane vertical take-off and landing and vertical take-off and landing field field operation parameter, determine the average service speed of each queuing system, obtain the relationship curve of the average queuing time under the stable state of each queuing system and the average arrival speed of unmanned plane;According to the maximum unmanned plane queuing time accepted by each queuing system and the relationship curve, determine the operation capacity of each queuing system;According to network flow theory, the node with minimum operation capacity of each queuing system in unmanned plane operation flow is regarded as blocked flow, and the overall operation capacity of vertical take-off and landing field is obtained.The application lays the foundation for unmanned plane flow intelligent regulation and control.

Description

Technical Field

[0001] This invention belongs to the field of unmanned aerial vehicle (UAV) technology, and in particular relates to a method and apparatus for assessing the capacity of a UAV vertical take-off and landing field. Background Technology

[0002] Compared to the relatively mature land, water, and civil aviation transportation, urban air transportation is still in the exploratory stage, with significant room for future development. With the further opening of my country's low-altitude airspace, the "last mile" logistics delivery method in cities is undergoing transformation, driving the development of a series of issues such as the layout, design, capacity assessment, and traffic control of urban logistics drone vertical take-off and landing sites.

[0003] Currently, existing methods for assessing the capacity of vertical take-off and landing sites abroad mainly employ time-dependent linear programming and other methods. Although these methods can yield relatively accurate capacity assessment results, their complexity and time consumption result in poor practicality and fail to meet the timeliness and convenience requirements of UAV operations in urban air traffic. Research on the capacity assessment of UAV vertical take-off and landing sites in my country is still in its early stages, and a complete and effective method for assessing the capacity of vertical take-off and landing sites is lacking. Summary of the Invention

[0004] The purpose of this invention is to provide a method and apparatus for assessing the capacity of a vertical take-off and landing (VTOL) field for unmanned aerial vehicles (UAVs). Based on the operational mode of the VTOL field, queuing theory models are established for each queuing system in the UAV operation process to determine the operational capacity of each queuing system. According to network flow theory, the node with the smallest operational capacity in each queuing system in the UAV operation process is taken as the congested flow, thus obtaining the overall operational capacity of the VTOL field. This invention addresses the problems of immature methods and incomplete theories in the initial exploratory stage of urban air traffic development in my country.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] This invention provides a method for assessing the capacity of a UAV vertical takeoff and landing field, comprising:

[0007] Based on the topology of the UAV vertical take-off and landing field, the UAV operation process is abstracted, and the queuing system in the UAV operation process is determined;

[0008] Based on the operating mode of the vertical take-off and landing field, queuing theory models of each queuing system in the UAV operation process are established respectively. The queuing theory model is expressed as a functional relationship between the average queuing time of the queuing system in a steady state and the average arrival flow of UAVs and the average service speed of the queuing system. The steady state refers to the state in which the average arrival flow of UAVs is less than the average service speed of the queuing system.

[0009] Based on the parameters set during the vertical take-off and landing of UAVs and the on-site operation parameters of the vertical take-off and landing field, the average service speed of each queuing system is determined. Combined with the queuing theory model of each queuing system, the relationship curve between the average queuing time and the average arrival flow of UAVs under the steady state of each queuing system is obtained.

[0010] The operating capacity of each queuing system is determined based on the maximum queuing time of each queuing system and the aforementioned relationship curve.

[0011] Based on network flow theory, the node with the smallest operating capacity in each queuing system in the UAV operation process is taken as the congested flow, and the overall operating capacity of the vertical take-off and landing field is obtained.

[0012] Furthermore, based on the topology of the UAV vertical take-off and landing field, the abstracted UAV operation process includes: the UAV descends from the nearby airspace to the terminal area of ​​the vertical take-off and landing field, lands on the landing platform through vertical take-off and landing operations, and taxis to the parking apron; after performing operations on the parking apron, it requests take-off, moves to the take-off platform through the taxiway, and takes off vertically to leave the vertical take-off and landing field;

[0013] The queuing system for determining the drone operation process includes: an entry queuing system at the landing platform, a ground queuing system at the apron, and a departure queuing system at the takeoff platform.

[0014] Furthermore, based on the operational mode of the vertical take-off and landing field, queuing theory models are established for each queuing system in the UAV operation process, including:

[0015] If the vertical takeoff and landing field adopts an isolated operation mode, then,

[0016] The entry queuing system adopts a single service counter queuing model, which is represented as M / M / 1 / ∞ / ∞;

[0017] For ground queuing systems, a single-queue, multi-service-station queuing model is adopted, denoted as M / M / c / ∞ / ∞;

[0018] The departure queuing system adopts the M / M / 1 / N / ∞ queuing model;

[0019] If the vertical takeoff and landing field adopts a mixed operation mode, then,

[0020] The entry queuing system and the exit queuing system are in the same queuing system, using the M / M / 1 / ∞ / ∞ queuing model;

[0021] The M / M / c / ∞ / ∞ queuing model is used for ground queuing systems.

[0022] Furthermore, if the vertical take-off and landing field adopts an isolated operation mode, then,

[0023] The queuing theory model for the entry queuing system is as follows:

[0024]

[0025] in, Let μ1 be the average queuing time for drones in the entry queuing system, μ1 be the average service speed of the entry queuing system, and λ1 be the average arrival flow of drones in the entry queuing system.

[0026] The queuing theory model for ground queuing systems is as follows:

[0027]

[0028]

[0029] in, This represents the average queuing time for drones in the ground queuing system. Let λ2 represent the average length of drones in the ground queuing system, λ2 and μ2 represent the average arrival flow and average service speed of drones in the ground queuing system, respectively, c2 represent the number of helipads in the vertical take-off and landing field, and ρ2 represent the average occupancy rate of the helipads. This indicates the probability that the tarmac is completely empty.

[0030] ρ2 is represented as:

[0031] Represented as:

[0032] n represents the number of drones on the helipad;

[0033] The queuing theory model for the departure queuing system is as follows:

[0034]

[0035]

[0036] in, The average queuing time for drones in the departure queuing system. The average queue length of drones in the departure queuing system. λ3 and μ3 represent the average takeoff flow of UAVs and the average service speed of the departure queuing system, respectively. This represents the probability that the takeoff platform is idle.

[0037]

[0038] Furthermore, determining the average service speed of each queuing system based on the parameters set during the UAV's vertical takeoff and landing process and the on-site operational parameters of the vertical takeoff and landing site includes:

[0039] Based on the altitude H of the final approach point in the drone's vertical takeoff and landing procedure. a Drone descent rate V a The distance L from the landing platform to the taxiway entrance t and the ground gliding speed v of the drone t The average landing time of the drone is calculated as follows:

[0040]

[0041] Among them, T a This indicates the average landing time of the drone;

[0042] The average takeoff time of the drone is calculated as follows:

[0043]

[0044] Among them, T d H represents the average takeoff time of the drone. d V is the altitude of the drone's departure point. d This refers to the takeoff climb speed;

[0045] Based on the distance L from the vertical takeoff and landing field taxiway to the apron p The ground gliding speed of the drone, v t Time t for loading and unloading goods and equipment inspection p The average turnaround time for drones is calculated as follows:

[0046]

[0047] Among them, T p This indicates the average turnaround time for drones;

[0048] The average service speed of each queuing system is calculated as follows:

[0049]

[0050] Furthermore, determining the operating capacity of each queuing system based on the maximum queuing time for drones accepted by each queuing system and the relationship curve includes:

[0051] Determine the maximum acceptable drone queuing time for each queuing system, find the point corresponding to the maximum acceptable drone queuing time on the relationship curve of the queuing system, and take the average arrival flow of drones corresponding to the point as the operating capacity of the queuing system.

[0052] Furthermore, based on network flow theory, the node with the smallest operating capacity in each queuing system during the UAV operation process is taken as the congested flow, thus obtaining the overall operating capacity of the vertical take-off and landing field, including:

[0053] The minimum operating capacity of each queuing system is taken as the overall operating capacity of the entire vertical take-off and landing field.

[0054] The present invention also provides a device for assessing the capacity of a UAV vertical take-off and landing field, comprising:

[0055] The initial module is used to abstract the UAV operation process and determine the queuing system in the UAV operation process based on the topology of the UAV vertical take-off and landing field.

[0056] The modeling module is used to establish queuing theory models for each queuing system in the UAV operation process according to the operation mode of the vertical take-off and landing field. The queuing theory model is expressed as a function of the average queuing time of the queuing system in a steady state, the average arrival flow of UAVs, and the average service speed of the queuing system. The steady state refers to the state in which the average arrival flow of UAVs is less than the average service speed of the queuing system.

[0057] The correlation module is used to determine the average service speed of each queuing system based on the parameters set during the vertical take-off and landing of the UAV and the on-site operation parameters of the vertical take-off and landing site. Combined with the queuing theory model of each queuing system, the relationship curve between the average queuing time and the average arrival flow of UAVs under the steady state of each queuing system is obtained.

[0058] The determination module is used to determine the operating capacity of each queuing system based on the maximum queuing time of each queuing system and the relationship curve.

[0059] The output module is used to determine the overall operating capacity of the vertical take-off and landing field by taking the node with the smallest operating capacity in each queuing system in the UAV operation process as the congested flow, based on network flow theory.

[0060] Furthermore, the modeling module is specifically used for,

[0061] For vertical takeoff and landing fields employing an isolated operation mode, the queuing theory models for each queuing system are established as follows:

[0062] The entry queuing system adopts a single service counter queuing model, which is represented as M / M / 1 / ∞ / ∞;

[0063] For ground queuing systems, a single-queue, multi-service-station queuing model is adopted, denoted as M / M / c / ∞ / ∞;

[0064] The departure queuing system adopts the M / M / 1 / N / ∞ queuing model;

[0065] For a vertical takeoff and landing field employing a hybrid operation mode, the queuing theory models for each queuing system are established as follows:

[0066] The entry queuing system and the exit queuing system are in the same queuing system, using the M / M / 1 / ∞ / ∞ queuing model;

[0067] The M / M / c / ∞ / ∞ queuing model is used for ground queuing systems.

[0068] Furthermore, the association module is specifically used for,

[0069] Based on the altitude H of the final approach point in the drone's vertical takeoff and landing procedure. a Drone descent rate V a The distance L from the landing platform to the taxiway entrance t and the ground gliding speed v of the drone t The average landing time of the drone is calculated as follows:

[0070]

[0071] Among them, T a This indicates the average landing time of the drone;

[0072] The average takeoff time of the drone is calculated as follows:

[0073]

[0074] Among them, T d H represents the average takeoff time of the drone. d V is the altitude of the drone's departure point. d This refers to the takeoff climb speed;

[0075] Based on the distance L from the vertical takeoff and landing field taxiway to the apron p The ground gliding speed of the drone, v t Time t for loading and unloading goods and equipment inspection p The average turnaround time for drones is calculated as follows:

[0076]

[0077] Among them, T p This indicates the average turnaround time for drones;

[0078] The average service speed of each queuing system is calculated as follows:

[0079]

[0080] By substituting the average service speed of each queuing system into the queuing theory model of each queuing system, the relationship curve between the average queuing time and the average arrival flow of UAVs under the steady state of each queuing system is obtained.

[0081] The beneficial effects of this invention are as follows:

[0082] This invention provides a method for assessing the capacity of a vertical take-off and landing (VTOL) field for unmanned aerial vehicles (UAVs). Based on the operational mode of the VTOL field, queuing theory models are established for each queuing system in the UAV operation process to determine the operational capacity of each queuing system. According to network flow theory, the node with the smallest operational capacity in each queuing system in the UAV operation process is identified as the congested flow, thus obtaining the overall operational capacity of the VTOL field. This invention addresses the problems of immature methods and incomplete theories in the initial exploratory stage of urban air traffic development in my country. It contributes to the construction of a complete urban air traffic system, improves the efficiency of urban UAV operations, ensures UAV safety, and lays the foundation for intelligent traffic control of UAVs. Attached Figure Description

[0083] Figure 1 A flowchart of the method for assessing the capacity of a UAV vertical take-off and landing field provided in an embodiment of the present invention;

[0084] Figure 2 This is a schematic diagram of the operation process of a UAV vertical take-off and landing field in an embodiment of the present invention;

[0085] Figure 3 This is a state transition diagram of the queuing system in an embodiment of the present invention;

[0086] Figure 4 This is a curve showing the relationship between the average queuing time of the queuing system in this embodiment of the invention and the arrival flow of drones;

[0087] Figure 5 This is a schematic diagram of the drone queuing system connection in an embodiment of the present invention;

[0088] Figure 6 This is a schematic diagram of the UAV operation network flow model provided in an embodiment of the present invention. Detailed Implementation

[0089] The embodiments of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without innovative effort are within the scope of protection of the present invention.

[0090] Example 1

[0091] This embodiment provides a method for assessing the capacity of a UAV vertical takeoff and landing field. (See also...) Figure 1 ,include:

[0092] Step S1: Establish the topology of the vertical take-off and landing field, abstract the UAV operation process, analyze the number and operation mode of ground facilities in various places, and extract the factors affecting the capacity of the take-off and landing field;

[0093] Step S2: Based on the operating mode of the vertical take-off and landing field, establish queuing theory models for each queuing system in the UAV operation process; where the queuing theory model represents the functional relationship between the average queuing time of the queuing system under steady state and the average arrival speed of the UAV and the average service speed of the queuing system;

[0094] Step S3: Based on the parameters set during the vertical take-off and landing of the UAV and the on-site operation parameters of the vertical take-off and landing field, determine the average service speed of each queuing system. Combine the queuing theory model of each queuing system to obtain the relationship curve between the average queuing time of each queuing system and the average arrival speed of the UAV. And based on the acceptable queuing time of each queuing system and the relationship curve between the average queuing time of each queuing system and the average arrival speed of the UAV, obtain the operating capacity of each queuing system.

[0095] Step S4: Based on network flow theory, the node with the smallest capacity in the UAV operation process is taken as the congested flow to obtain the overall operating capacity of the vertical take-off and landing field.

[0096] It should be noted that a drone vertical take-off and landing field is an area used for the landing, take-off, and taxiing of drones with vertical take-off and landing capabilities.

[0097] In this embodiment, a vertical takeoff and landing field topology is established, and the UAV operation process is abstracted, as follows:

[0098] A vertical takeoff and landing (VTOL) area includes: takeoff and landing platforms, taxiways, aprons, hangars, etc. For an abstract overview of the UAV's operational process within a VTOL area, please refer to [link / reference needed]. Figure 2 ,

[0099] The drone descends from nearby airspace to the terminal area of ​​the vertical take-off and landing field, lands on the platform via vertical take-off and landing operations, and, depending on its own performance (whether it has taxiing capabilities), chooses to taxi autonomously or be transported to the apron by a taxiing vehicle. In the apron turnover area, personnel or automated equipment perform operations such as loading and unloading of cargo and equipment testing. If special operations such as charging or repair are required, the drone is transported to the hangar for completion. After the drone completes its turnover on the apron, it will request take-off, move to the take-off platform via the taxiway, and take off vertically from the take-off and landing field to complete the next mission.

[0100] In this embodiment, based on the above-described UAV operation process, the number and operation modes of ground facilities in various locations are analyzed, and the influencing factors of take-off and landing site capacity are extracted, as follows:

[0101] The operational mode of a vertical takeoff and landing (VTOL) field affects its capacity. The operational modes of the takeoff and landing platforms can be referenced from the multi-runway operational modes of civil aviation airports, and are divided into segregated operation mode and mixed operation mode. Segregated operation mode refers to dividing the takeoff and landing platform into two parts, one for landing and the other for takeoff; mixed operation mode refers to all takeoff and landing platforms being able to handle both takeoff and landing.

[0102] When the number of drone takeoffs and landings is unbalanced, the mixed operation mode can make fuller use of the takeoff and landing platform resources; conversely, if the drone traffic is large and balanced, the isolated operation mode will be more efficient.

[0103] The number of take-off and landing platforms and helipads plays a decisive role in the capacity of a vertical take-off and landing field. The more take-off and landing platforms there are, the more drones can take off and land per unit time; similarly, the more helipads there are, the more drone turnover services can be provided.

[0104] The number of drones that a hangar can hold also affects the capacity of the vertical take-off and landing field. The more drones a hangar can hold, the more drones can be charged and repaired in the hangar. Conversely, if the hangar can hold fewer drones, there may be situations where drones that need to be charged occupy the landing pad.

[0105] The average takeoff / landing time of a UAV is determined by parameters such as the altitude of the final approach point, the descent rate, the distance from the takeoff and landing platform to the taxiway entrance, and the UAV's ground taxiing speed. Analysis of the UAV operation model reveals that UAV arrival and departure behaviors are discrete events with uncertainty. Furthermore, for each UAV, its landing and takeoff times are random and independent, following a negative exponential distribution; that is, both the arrival and departure flows of the UAVs are Poisson flows.

[0106] In this embodiment, based on the operating mode of the vertical take-off and landing field, queuing theory models for each queuing system are established for different ground facilities, and the indices under steady-state conditions of each queuing system are solved. The specific implementation process is as follows:

[0107] It should be noted that the queuing system at a vertical takeoff and landing (VTOL) field includes an arrival queuing system, a ground queuing system, and a departure queuing system. See details below. Figure 5 .

[0108] A. If the vertical takeoff and landing field adopts an isolated operation mode, then:

[0109] For landing platform ( Figure 5 The entry queuing system with serial number ① in the middle),

[0110] The arrival flow of drones can be considered infinite, meaning there are enough drones in the airspace ready to land. Let λ1 be the number of drones arriving per unit time, or arrival flow rate, and let λ1 be the average landing time of a drone. That is, the average service speed of the landing platform is μ1.

[0111] If λ1 > μ1, meaning the average arrival flow is greater than the average service speed, the queuing system will never reach a steady state. The number of drones arriving per unit time will always be more than the number of drones that complete landing and leave the landing platform, and the queue will become longer and longer. Conversely, if λ1 < μ1, the entry queuing system can reach a steady state.

[0112] In both of the above situations, when λ1 > μ1, it will cause a lot of delays, which does not meet the requirements for UAV operation. Therefore, the main analysis focuses on the capacity of the landing platform under a certain waiting (delay) level when λ1 < μ1.

[0113] A single-landing-platform UAV arrival queuing system operates on a first-come, first-served basis. When a UAV arrives at the landing pad terminal area, if the landing pad is already occupied, it joins the queue and waits, assuming unlimited waiting airspace. This system conforms to the single-server queuing model in queuing theory, which can be represented as M / M / 1 / ∞ / ∞ (abbreviated as M / M / 1). The transition relationships between states are as follows: Figure 3 .

[0114] When analyzing the queuing status of drones in the terminal area, we need to find the probability that the state of the queuing system at any time t under steady state is n (meaning there are n drones in the system). Since the probability in steady state is independent of time, the probability of system state n is:

[0115] For a stable system, the input rate should equal the output rate in each state, such as... Figure 3 The transition rate for the number of drones changing from 0 to 1 is λ1P0, and conversely, the transition rate for the number of drones changing from 1 to 0 is μ1P1. Therefore, for state 0, the equilibrium equation is:

[0116]

[0117] Similarly, for system state n > 0, there is an equilibrium equation:

[0118]

[0119] From equations (1) and (2), we can obtain:

[0120]

[0121] Based on the normalization of probabilities in each state, we have Right now therefore:

[0122]

[0123]

[0124] In equation (5), let This is the ratio of average arrival traffic of drones to average service speed of landing platforms. When n=0, This indicates that the system is in an idle state, meaning the landing platform is not occupied by a drone; conversely, This indicates that at least one drone is in the queuing system and the landing platform is busy; therefore, ρ1 also represents the average utilization rate of the landing platform.

[0125] Based on equation (5), the average number of drones in the entry queuing system can be further derived as follows:

[0126]

[0127] Therefore, the average arrival time of the drone can be calculated.

[0128]

[0129] in, This represents the average arrival time of drones in the entry queuing system. λ1 represents the average number of drones in the entry queuing system (average queue length), and λ1 represents the average arrival flow of drones in the entry queuing system.

[0130] The arrival time of a drone can be divided into two parts: queuing time and landing time. Therefore, the average queuing (delay) time for drones can be expressed as:

[0131]

[0132] In the formula, The average queuing (delay) time for drones in the entry queuing system. This represents the average landing time of drones in the entry queuing system.

[0133] For the ground parking apron of the vertical takeoff and landing field ( Figure 5 (The ground queuing system with serial number ② in the middle)

[0134] The flow of drones on the taxiway remains Poisson. Landing drones move from the taxiway to various helipads, find an available helipad, and taxi in to complete tasks such as loading and unloading cargo. If all helipads are occupied, they must queue and wait, following a first-come, first-served rule. Therefore, the vertical takeoff and landing (VTOL) helipad system conforms to the characteristics of a single-queue, multi-service station queuing system in queuing theory, which can be represented by M / M / c / ∞ / ∞. The state probabilities can be derived from the equilibrium state:

[0135]

[0136]

[0137] In the formula, λ2 and μ2 represent the average arrival flow of UAVs in the ground queuing system and the average service speed of the apron, respectively, and c2 is the number of aprons in the vertical take-off and landing field. This indicates the probability that the apron is completely empty, meaning that all aprons are available for use. c represents the probability that there are n drones in the helipad system. When n < c2, it means that the helipad system is not saturated and there are still vacant helipads waiting to be used. When n ≥ c2, the helipad system is saturated and drones need to queue to enter, resulting in delays.

[0138] Furthermore, the number of drones in the ground queuing system (average queue length) and the average queuing (delay) time are as follows:

[0139]

[0140]

[0141] In the formula, The average queue length of drones in a ground-based queuing system. This indicates the average occupancy rate of the helipad, or the average number of drones accepted per helipad. This represents the average number of drones parked on the ground at a vertical take-off and landing (VTOL) site. This indicates the average queuing (delay) time for drones in the ground queuing system.

[0142] For the takeoff platform ( Figure 5 The departure queuing system (numbered ③ in the middle)

[0143] After each drone completes its turnaround on the helipad, it randomly requests takeoff. The request times follow an independent, uniformly negative exponential distribution, thus the departing drones also follow a Poisson flow. If the takeoff platform is available, the requesting drone leaves the helipad and taxis into the platform via the taxiway to perform its takeoff mission; conversely, if the takeoff platform is occupied, the drone waits in place, causing a delay, and takes off in sequence according to the first-come, first-served principle. The number of helipads is the upper limit of the drone takeoff team leader.

[0144] Therefore, the takeoff platform is an M / M / 1 / N / ∞ queuing system, where N = c², and the probabilities of each state at steady state are:

[0145]

[0146] In the formula, λ3 and μ3 represent the average takeoff flow rate of UAVs and the average service speed of the takeoff platform in the departure queuing system. Let n represent the probability of n drone flights in the departure queuing system. This represents the probability that the takeoff platform is idle. The superscript n denotes ρ³ raised to the power of n; due to the limited queuing space, at this time... This does not represent the average utilization rate of the takeoff platform.

[0147] Furthermore, the number of drones (average queue length) and average queuing (delay) time in the departure queuing system are as follows:

[0148]

[0149]

[0150] In the formula, The number of drones in the departure queuing system (average queue length). This represents the average queuing (delay) time in the departure queuing system.

[0151] B. If the vertical take-off and landing field adopts a mixed operation mode, then:

[0152] When take-off and landing platforms are used in a mixed manner, and drones requesting landing and take-off are in the same queuing system, the take-off and landing platforms can be regarded as an M / M / 1 / ∞ / ∞ queuing system if other queuing characteristics remain unchanged; the operating characteristics of the apron remain unchanged, and it is still an M / M / N / ∞ / ∞ queuing system.

[0153] In this embodiment, the average service speed of each queuing system is determined based on the parameters set during the UAV's vertical take-off and landing process and the on-site operational parameters of the vertical take-off and landing site, as detailed below:

[0154] Based on the altitude H of the final approach point in the drone's vertical takeoff and landing procedure. a Drone descent rate V a The distance L from the landing platform to the taxiway entrance t and the ground gliding speed v of the drone t Calculate the average landing time of the drone, which is the average service time of the landing platform:

[0155]

[0156] Among them, T a This indicates the average landing time of the drone;

[0157] Similarly, the average takeoff time of the drone can be calculated, which is the average service time of the takeoff platform:

[0158]

[0159] In the formula, H d V is the altitude of the drone's departure point. d This refers to the takeoff and climb speed.

[0160] Based on the distance L from the vertical takeoff and landing field taxiway to the apron p ground gliding speed of the drone v t Time t for loading and unloading goods and equipment inspection p Calculate the average turnaround time for drones, which is the average service time on the helipad:

[0161]

[0162] Furthermore, using the aforementioned average service time, the average service speed of each queuing system in step S2 is calculated:

[0163]

[0164] The number of service counters in the apron queuing system is determined based on the number of aprons:

[0165] c2 = N p (20)

[0166] In the formula, c2 represents the number of service counters in the multi-service counter queuing system on the apron, and N... p This refers to the number of parking aprons included within the vertical takeoff and landing field.

[0167] In this embodiment, by combining the queuing theory models of each queuing system, the relationship curves between the average queuing time of each queuing system and the average arrival speed of the UAV are obtained, as follows:

[0168] Based on the parameters calculated above, substituting them into the queuing theory models of each queuing system in step S2, we obtain the relationship curves between the average queuing (delay) time of each queuing system and the arrival flow of drones, as shown below. Figure 4 As shown in the figure, W q Let λ represent the average queuing (delay) time of a certain queuing system, and let λ represent the arrival flow of drones in that queuing system.

[0169] In this embodiment, the operating capacity of each queuing system is obtained based on the relationship curve between the acceptable queuing time of each queuing system and the average queuing time of each queuing system and the average arrival speed of drones, as follows:

[0170] Based on the performance limitations and operational requirements of the drone, determine the acceptable queuing (delay) time T. max The corresponding arrival flow λ of the drones is obtained from the relationship curve. max This is the operating capacity of the queuing system.

[0171] In this embodiment, based on network flow theory, the node with the smallest capacity in the UAV operation process is taken as the congested flow to obtain the overall operating capacity of the vertical take-off and landing field. The specific implementation process is as follows:

[0172] Combining various queuing theory models, the operation of a drone can be abstracted as three queuing systems connected sequentially, such as... Figure 5 As shown, the operating capacity of each queuing system has been calculated in step S3.

[0173] Based on the above process, the key nodes of drone operation can be abstracted, including the drone's final approach point, the drone's taxiing into the waiting point, the drone's taxiing out of the waiting point, and the drone's takeoff and departure point. The paths connecting the key nodes are the three queuing systems.

[0174] like Figure 6 A network flow model N = (V, S, T, A, C) is established, where S is the source set of the network, T is the sink set, and V and A are the vertex set and arc set, respectively. The source and sink vertices correspond to the network's entry and exit points in the actual network, i.e., the final approach point and takeoff / departure point for the UAV. The vertices in the network other than the source and sink vertices are called transit points, i.e., the UAV's taxi-in waiting point and taxi-out waiting point. C is the network's capacity function, a non-negative function defined on the arc set A, which corresponds to the transport capacity on the corresponding route, i.e., the operating capacity of each queuing system mentioned above.

[0175] In network flow, drones enter from the source, pass through transit points, and reach the sink, forming an actual flow that satisfies antisymmetry, capacity constraints, and flow conservation.

[0176] According to maximum flow theory, the flow rate of a maximum flow is equal to the capacity of the minimum cut (cut set), meaning that the flow bottleneck in the network determines the capacity of the entire network flow model. Therefore, the minimum operating capacity of each queuing system in step S3 is considered as the operating capacity of the entire vertical take-off and landing field:

[0177]

[0178] Example 2

[0179] This embodiment also provides a device for assessing the capacity of a UAV vertical takeoff and landing field, including:

[0180] The initial module is used to abstract the UAV operation process and determine the queuing system in the UAV operation process based on the topology of the UAV vertical take-off and landing field.

[0181] The modeling module is used to establish queuing theory models for each queuing system in the UAV operation process according to the operation mode of the vertical take-off and landing field. The queuing theory model is expressed as a function of the average queuing time of the queuing system in a steady state, the average arrival flow of UAVs, and the average service speed of the queuing system. The steady state refers to the state in which the average arrival flow of UAVs is less than the average service speed of the queuing system.

[0182] The correlation module is used to determine the average service speed of each queuing system based on the parameters set during the vertical take-off and landing of the UAV and the on-site operation parameters of the vertical take-off and landing site. Combined with the queuing theory model of each queuing system, the relationship curve between the average queuing time and the average arrival flow of UAVs under the steady state of each queuing system is obtained.

[0183] The determination module is used to determine the operating capacity of each queuing system based on the maximum queuing time of each queuing system and the relationship curve.

[0184] The output module is used to determine the overall operating capacity of the vertical take-off and landing field by taking the node with the smallest operating capacity in each queuing system in the UAV operation process as the congested flow, based on network flow theory.

[0185] In this embodiment, the initial module is specifically used for,

[0186] Based on the topology of the UAV vertical take-off and landing field, the abstract UAV operation process is as follows: The UAV descends from the nearby airspace to the terminal area of ​​the vertical take-off and landing field, lands on the landing platform through vertical take-off and landing operations, and taxis to the parking apron; after operating on the parking apron, it requests take-off, moves to the take-off platform through the taxiway, and takes off vertically to leave the vertical take-off and landing field.

[0187] The queuing systems in the drone operation process include: the arrival queuing system at the landing platform, the ground queuing system at the apron, and the departure queuing system at the takeoff platform.

[0188] In this embodiment, the modeling module is specifically used for,

[0189] For vertical takeoff and landing fields employing an isolated operation mode, the queuing theory models for each queuing system are established as follows:

[0190] The entry queuing system adopts a single service counter queuing model, which is represented as M / M / 1 / ∞ / ∞;

[0191] For ground queuing systems, a single-queue, multi-service-station queuing model is adopted, denoted as M / M / c / ∞ / ∞;

[0192] The departure queuing system adopts the M / M / 1 / N / ∞ queuing model;

[0193] For a vertical takeoff and landing field employing a hybrid operation mode, the queuing theory models for each queuing system are established as follows:

[0194] The entry queuing system and the exit queuing system are in the same queuing system, using the M / M / 1 / ∞ / ∞ queuing model;

[0195] The M / M / c / ∞ / ∞ queuing model is used for ground queuing systems.

[0196] In this embodiment, for a vertical takeoff and landing field employing an isolated operation mode,

[0197] The queuing theory model for the entry queuing system is as follows:

[0198]

[0199] in, Let μ1 be the average queuing time for drones in the entry queuing system, μ1 be the average service speed of the entry queuing system, and λ1 be the average arrival flow of drones in the entry queuing system.

[0200] The queuing theory model for ground queuing systems is as follows:

[0201]

[0202]

[0203] in, This represents the average queuing time for drones in the ground queuing system. Let λ2 represent the average length of drones in the ground queuing system, λ2 and μ2 represent the average arrival flow and average service speed of drones in the ground queuing system, respectively, c2 represent the number of helipads in the vertical take-off and landing field, and ρ2 represent the average occupancy rate of the helipads. This indicates the probability that the tarmac is completely empty.

[0204] ρ2 is represented as:

[0205] Represented as:

[0206] n represents the number of drones on the helipad;

[0207] The queuing theory model for the departure queuing system is as follows:

[0208]

[0209]

[0210] in, The average queuing time for drones in the departure queuing system. The average queue length of drones in the departure queuing system. λ3 and μ3 represent the average takeoff flow of UAVs and the average service speed of the departure queuing system, respectively. This represents the probability that the takeoff platform is idle.

[0211]

[0212] In this embodiment, the association module is specifically used for,

[0213] Based on the altitude H of the final approach point in the drone's vertical takeoff and landing procedure. a Drone descent rate V a The distance L from the landing platform to the taxiway entrance t and the ground gliding speed v of the drone t The average landing time of the drone is calculated as follows:

[0214]

[0215] Among them, T a This indicates the average landing time of the drone;

[0216] The average takeoff time of the drone is calculated as follows:

[0217]

[0218] Among them, T d H represents the average takeoff time of the drone. d V is the altitude of the drone's departure point. d This refers to the takeoff climb speed;

[0219] Based on the distance L from the vertical takeoff and landing field taxiway to the apron p ground gliding speed of the drone v t Time t for loading and unloading goods and inspecting equipment p The average turnaround time for drones is calculated as follows:

[0220]

[0221] Among them, T p This indicates the average turnaround time for drones;

[0222] The average service speed of each queuing system is calculated as follows:

[0223]

[0224] In this embodiment, the determining module is specifically used for,

[0225] Determine the maximum acceptable drone queuing time for each queuing system, find the point corresponding to the maximum acceptable drone queuing time on the relationship curve of the queuing system, and take the average arrival flow of drones corresponding to the point as the operating capacity of the queuing system.

[0226] In this embodiment, the output module is specifically used for,

[0227] The minimum operating capacity of each queuing system is taken as the overall operating capacity of the entire vertical take-off and landing field.

[0228] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0229] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0230] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0231] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0232] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for assessing the capacity of a UAV vertical takeoff and landing field, characterized in that, include: Based on the topology of the UAV vertical take-off and landing field, the UAV operation process is abstracted, and the queuing system in the UAV operation process is determined; The queuing system for determining the drone operation process includes: an arrival queuing system at the landing platform, a ground queuing system at the apron, and a departure queuing system at the takeoff platform; Based on the operating mode of the vertical take-off and landing field, queuing theory models of each queuing system in the UAV operation process are established respectively. The queuing theory model is expressed as a functional relationship between the average queuing time of the queuing system in a steady state and the average arrival flow of UAVs and the average service speed of the queuing system. The steady state refers to the state in which the average arrival flow of UAVs is less than the average service speed of the queuing system. The queuing theory model includes: If the vertical takeoff and landing field adopts an isolated operation mode, then, The queuing theory model for the entry queuing system is as follows: ； in, The average queuing time for drones in the entry queuing system. This represents the average service speed of the entry queuing system. The average arrival flow of drones in the entry queuing system; The queuing theory model for ground queuing systems is as follows: ； ； in, This represents the average queuing time for drones in the ground queuing system. This represents the average queue length of drones in a ground-based queuing system. and These represent the average arrival flow of drones in the ground queuing system and the average service speed of the ground queuing system, respectively. This refers to the number of parking aprons in a vertical takeoff and landing field. This indicates the average occupancy rate of the helipad. This indicates the probability that the tarmac is completely empty. Represented as: ; Represented as: ; It indicates that the tarmac has A drone; The queuing theory model for the departure queuing system is as follows: ； ； in, The average queuing time for drones in the departure queuing system. The average queue length of drones in the departure queuing system. , and These represent the average takeoff traffic of drones and the average service speed of the departure queuing system, respectively. This represents the probability that the takeoff platform is idle. ； Based on the parameters set during the vertical take-off and landing of UAVs and the on-site operation parameters of the vertical take-off and landing field, the average service speed of each queuing system is determined. Combined with the queuing theory model of each queuing system, the relationship curve between the average queuing time and the average arrival flow of UAVs under the steady state of each queuing system is obtained. The operating capacity of each queuing system is determined based on the maximum queuing time of each queuing system and the aforementioned relationship curve. Based on network flow theory, the node with the smallest operating capacity in each queuing system in the UAV operation process is taken as the congested flow, and the overall operating capacity of the vertical take-off and landing field is obtained.

2. The method for assessing the capacity of a UAV vertical takeoff and landing field according to claim 1, characterized in that, Based on the topology of the UAV vertical take-off and landing field, the abstracted UAV operation process includes: the UAV descends from the nearby airspace to the terminal area of ​​the vertical take-off and landing field, lands on the landing platform through vertical take-off and landing operations, and taxis to the parking apron; after performing operations on the parking apron, it requests take-off, moves to the take-off platform through the taxiway, and takes off vertically to leave the vertical take-off and landing field.

3. The method for assessing the capacity of a UAV vertical takeoff and landing field according to claim 2, characterized in that, Based on the operational mode of the vertical take-off and landing field, queuing theory models for each queuing system in the UAV operation process are established, including: If the vertical takeoff and landing field adopts an isolated operation mode, then, The entry queuing system adopts a single service counter queuing model, which is represented as M / M / 1 / ∞ / ∞; For ground queuing systems, a single-queue, multi-service-station queuing model is adopted, denoted as M / M / c / ∞ / ∞; The departure queuing system adopts the M / M / 1 / N / ∞ queuing model; If the vertical takeoff and landing field adopts a mixed operation mode, then, The entry queuing system and the exit queuing system are in the same queuing system, using the M / M / 1 / ∞ / ∞ queuing model; The M / M / c / ∞ / ∞ queuing model is used for ground queuing systems.

4. The method for assessing the capacity of a UAV vertical takeoff and landing field according to claim 3, characterized in that, The determination of the average service speed of each queuing system based on the parameters set during the vertical take-off and landing of the UAV and the on-site operational parameters of the vertical take-off and landing site includes: Based on the height of the final approach point in the drone's vertical takeoff and landing procedure Drone landing descent rate Distance from landing platform to taxiway entrance and drone ground gliding speed The average landing time of the drone is calculated as follows: ， in, This indicates the average landing time of the drone; The average takeoff time of the drone is calculated as follows: ； in, This indicates the average takeoff time of the drone. The altitude of the drone's departure point. This refers to the takeoff climb speed; Based on the distance from the taxiway to the apron of the vertical takeoff and landing field ground gliding speed of drones Time for loading and unloading goods and equipment inspection The average turnaround time for drones is calculated as follows: ； in, This indicates the average turnaround time for drones; The average service speed of each queuing system is calculated as follows: 。 5. The method for assessing the capacity of a UAV vertical takeoff and landing field according to claim 3, characterized in that, The step of determining the operating capacity of each queuing system based on the maximum queuing time of each queuing system and the relationship curve includes: Determine the maximum acceptable drone queuing time for each queuing system, find the point corresponding to the maximum acceptable drone queuing time on the relationship curve of the queuing system, and take the average arrival flow of drones corresponding to the point as the operating capacity of the queuing system.

6. The method for assessing the capacity of a UAV vertical takeoff and landing field according to claim 5, characterized in that, According to network flow theory, the node with the smallest operating capacity in each queuing system during the UAV operation process is taken as the congested flow, thus obtaining the overall operating capacity of the vertical take-off and landing field, including: The minimum operating capacity of each queuing system is taken as the overall operating capacity of the entire vertical take-off and landing field.

7. A device for assessing the capacity of a UAV vertical takeoff and landing site, characterized in that, The apparatus for implementing the UAV vertical takeoff and landing field capacity assessment method of claim 1 includes: The initial module is used to abstract the UAV operation process and determine the queuing system in the UAV operation process based on the topology of the UAV vertical take-off and landing field. The modeling module is used to establish queuing theory models for each queuing system in the UAV operation process according to the operation mode of the vertical take-off and landing field. The queuing theory model is expressed as a function of the average queuing time of the queuing system in a steady state, the average arrival flow of UAVs, and the average service speed of the queuing system. The steady state refers to the state in which the average arrival flow of UAVs is less than the average service speed of the queuing system. The correlation module is used to determine the average service speed of each queuing system based on the parameters set during the vertical take-off and landing of the UAV and the on-site operation parameters of the vertical take-off and landing site. Combined with the queuing theory model of each queuing system, the relationship curve between the average queuing time and the average arrival flow of UAVs under the steady state of each queuing system is obtained. The determination module is used to determine the operating capacity of each queuing system based on the maximum queuing time of each queuing system and the relationship curve. The output module is used to determine the overall operating capacity of the vertical take-off and landing field by taking the node with the smallest operating capacity in each queuing system in the UAV operation process as the congested flow, based on network flow theory.

8. The UAV vertical take-off and landing field capacity assessment device according to claim 7, characterized in that, The modeling module is specifically used for, For vertical takeoff and landing fields employing an isolated operation mode, the queuing theory models for each queuing system are established as follows: The entry queuing system adopts a single service counter queuing model, which is represented as M / M / 1 / ∞ / ∞; For ground queuing systems, a single-queue, multi-service-station queuing model is adopted, denoted as M / M / c / ∞ / ∞; The departure queuing system adopts the M / M / 1 / N / ∞ queuing model; For a vertical takeoff and landing field employing a hybrid operation mode, the queuing theory models for each queuing system are established as follows: The entry queuing system and the exit queuing system are in the same queuing system, using the M / M / 1 / ∞ / ∞ queuing model; The M / M / c / ∞ / ∞ queuing model is used for ground queuing systems.

9. The UAV vertical take-off and landing field capacity assessment device according to claim 8, characterized in that, The association module is specifically used for, Based on the height of the final approach point in the drone's vertical takeoff and landing procedure Drone landing descent rate Distance from landing platform to taxiway entrance and drone ground gliding speed The average landing time of the drone is calculated as follows: ， in, This indicates the average landing time of the drone; The average takeoff time of the drone is calculated as follows: ； in, This indicates the average takeoff time of the drone. The altitude of the drone's departure point. This refers to the takeoff climb speed; Based on the distance from the taxiway to the apron of the vertical takeoff and landing field ground gliding speed of drones Time for loading and unloading goods and equipment inspection The average turnaround time for drones is calculated as follows: ； in, This indicates the average turnaround time for drones; The average service speed of each queuing system is calculated as follows: ； By substituting the average service speed of each queuing system into the queuing theory model of each queuing system, the relationship curve between the average queuing time and the average arrival flow of UAVs under the steady state of each queuing system is obtained.

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