Unmanned aerial vehicle airspace resource allocation method based on low-altitude economy

Abstract

The invention discloses an unmanned aerial vehicle airspace resource allocation method based on low-altitude economy, and the method comprises the steps: constructing an airspace network under a three-dimensional cylindrical coordinate system at a preset height layer, and calculating a minimum safety distance from a mass center to an airspace pipe wall under the margin constraint based on the model parameters of an unmanned aerial vehicle; calculating a width upper limit and a width lower limit of the dynamic isolation belt; solving the maximum traffic flow by using the airspace flow optimization model; performing elastic segmentation on a time window of the empty channel network by using a space-time diversity reservation algorithm; when the carrying capacity of the same air channel in the air channel network reaches the upper limit, the number of flight air channels is adjusted according to the width adjustment range of the dynamic isolation belt, and airspace resource allocation is carried out by using the collaborative optimization model; according to the method, through space resource division, time resource division and dynamic time slice scheduling, the airspace traffic capacity and the resource utilization rate of the unmanned aerial vehicle can be effectively improved, and therefore collaborative optimization of efficient utilization of airspace resources and flight safety is achieved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of unmanned aerial vehicle airspace management, and particularly relates to a method for allocating unmanned aerial vehicle airspace resources based on low-altitude economy. BACKGROUND

[0002] The unmanned aerial vehicle traffic management system (UTM) is a digital management and control platform for realizing safe and efficient collaborative management of low-altitude unmanned aerial vehicle operation through real-time airspace dynamic monitoring, intelligent conflict prediction and automatic scheduling decision. Realizing efficient management of low-altitude traffic is the focus of research by experts in the relevant fields at home and abroad. At present, a plurality of frameworks, platform architectures and supporting implementation projects of unmanned aerial vehicle airspace management systems have been developed globally for responding to current commercial service demands and social expectations and simultaneously solving the problem of low utilization rate of unmanned aerial vehicle airspace resources. However, it should be noted that the development of UTM in various countries is still in the initial stage and still has many limitations. The configuration of airspace resources needs to be accurately scheduled and allocated in combination with the hardware parameters and operation mechanisms of various unmanned aerial vehicles.

[0003] Low-altitude economy generally refers to airspace 1000 meters and below as a running space, taking unmanned aerial vehicles, electric vertical take-off and landing aircraft (eVTOL) and other new aircraft as carriers, and forming a new economic form through technological innovation and industrial integration. From the perspective of industrial economics, low-altitude economy not only expands the boundaries of traditional aviation industry, but also injects new growth momentum into regional economic development by building a new type of infrastructure for "space-air integration". At present, with the gradual opening of airspace management and the increasing perfection of technical standards, low-altitude economy is showing great market potential and social value on a global scale. Low-altitude economy mainly includes low-altitude manufacturing, low-altitude flight, low-altitude support and comprehensive services, involving the first, second and third industries. Unmanned aerial vehicle transportation is a key part of low-altitude economy, which is promoting changes in the logistics, transportation and agricultural industries. At present, the management of unmanned aerial vehicle airspace between cities mainly relies on a three-dimensional layered model, which divides the flight airspace into different speed levels in the vertical direction and allocates airspace resources in combination with different task types. High-altitude airspace is used for high-speed logistics, medium-altitude airspace is used for manned transportation, and low-altitude airspace is used for light equipment inspection. The inherent defects of manned transportation in the medium-altitude airspace gradually appear, and traditional lightweight unmanned aerial vehicle logistics cannot solve the problems of high efficiency and safety of mass unmanned aerial vehicle logistics, which limits the scale expansion of unmanned aerial vehicle transportation networks between cities.

[0004] Therefore, the resource allocation method for low-altitude economy in the prior art cannot take into account the new airspace planning of plane resource optimization and safety redundancy, resulting in low levels of airspace traffic capacity and resource utilization of unmanned aerial vehicles. SUMMARY

[0005] To solve the above problems, the application provides a UAV airspace resource allocation method based on low-altitude economy, comprising the following steps:

[0006] Constructing an airway network under a three-dimensional column coordinate system at a preset altitude layer, taking it as an application scenario of UAV in low-altitude economic airspace resource allocation, and calculating a minimum safety distance of a center of mass of the UAV from a wall of the airway under a preset margin constraint based on model parameters of the UAV;

[0007] Based on a pre-designed intelligent dynamic isolation belt system, calculating dynamic isolation belt width parameter adjustment information between airways in the airway network, and calculating an upper limit and a lower limit of the width of the dynamic isolation belt based on the dynamic isolation belt width parameter adjustment information;

[0008] Using a pre-constructed airspace flow optimization model to solve the maximum traffic flow of the airway network; wherein the airspace flow optimization model is constructed based on a 0-1 knapsack optimization algorithm;

[0009] Using a space-time diversity reservation algorithm to elastically segment a time window of the airway network to obtain an initial scheme of airway time slot allocation;

[0010] Based on the initial scheme of airway time slot allocation, fusing urban subway dispatching principles and UAV dynamics constraints to construct an adaptive allocation system of airspace time slot resources, and based on a subway moving block system theory, adjusting a same airway capacity when the same airway capacity in the airway network reaches an upper limit to obtain an adjusted number of flight airways;

[0011] According to the adjusted number of flight airways, using a pre-constructed collaborative optimization model to allocate airspace resources; wherein the collaborative optimization model is an intelligent airspace management system for inter-city manned UAV transportation, constructed in combination with a manned safety enhanced airway model and a space-time-physiological rhythm coupling algorithm.

[0012] Optionally, the calculation formula of the minimum safety distance of the center of mass of the UAV from the wall of the airway under the preset margin constraint is as follows:

[0013] wherein, represents the minimum safety distance; is a wing span of the UAV; is a turbulence offset; is a control margin; is a sensor error margin;

[0014] In the formula, the turbulence offset​ The expression is as follows: ;

[0015] The control margin The expression is as follows: ;

[0016] The sensor error margin The expression is as follows: ;

[0017] Wherein, is the UAV body density; is the UAV cruising speed; is the viscosity coefficient; is the attitude control system response time; represents the positioning accuracy; is the UAV reference cruising speed.

[0018] Optionally, the calculation formula of the dynamic isolation bandwidth parameter adjustment information is as follows: ;

[0019] Wherein, represents the dynamic isolation bandwidth parameter adjustment information at time t; represents the UAV wing span; represents the UAV cruising speed; represents the high air density; represents the cruising lift coefficient; represents the track control accuracy; represents the turbulence disturbance amplification factor; represents the maximum braking distance;

[0020] In the formula, the expression of the maximum braking distance is as follows: ;

[0021] Wherein, represents the out-of-control acceleration limit;

[0022] The calculation formula of the lower limit of the width of the dynamic isolation zone is as follows: ;

[0023] Wherein, represents the lower limit of the width of the dynamic isolation zone; represents the energy dissipated when the UAV collides; represents the effective airspace cross-sectional area;

[0024] The upper limit of the width of the dynamic isolation zone is calculated as follows: ;

[0025] Wherein, represents the upper limit of the width of the dynamic isolation zone.

[0026] Optionally, the airspace flow optimization model comprises the following construction process:

[0027] A target function is constructed with the maximum aircraft airspace flow of the unmanned aerial vehicle as the target;

[0028] According to the target function, corresponding constraint conditions are formulated; the constraint conditions include information synchronization constraints and space capacity constraints;

[0029] Based on the target function and the constraint conditions, a 0-1 knapsack algorithm is used to construct an airspace flow optimization model;

[0030] Wherein, the expression of the target function is as follows: ;

[0031] Wherein, represents the target function; represents the priority weight of the unmanned aerial vehicle i; represents the scheduling information of the unmanned aerial vehicle i; represents a constraint function vector; n represents the number of unmanned aerial vehicles;

[0032] The expression of the information synchronization constraint is as follows: ;

[0033] Wherein, is a local state vector; is a consistent state vector; is a convergence rate coefficient; is a maximum synchronization error; is a system-specific unit index; is a system unit set;

[0034] The expression of the space capacity constraint is as follows: ; ;

[0035] Wherein, represents the space capacity at time t; represents the minimum safety interval at time t; represents the length of the unmanned aerial vehicle body; represents the effective cross-sectional area of the airway at time t; represents the average area of the unmanned aerial vehicle; Indicates the airway radius; This represents the dynamic isolation strip width at time t.

[0036] Optionally, the expression for the spatiotemporal diversity reservation algorithm is as follows: ;

[0037] in, This represents the initial scheme for allocating air channel time slots at time t; It is a smoothing factor; For minimum time slot constraints, Total time required for drone takeoff; This represents the maximum total number of drones.

[0038] In the formula, the minimum time slot constraint The expression is as follows: ; ;

[0039] in, For cruise parameters; The speed of the drone; For braking deceleration; This refers to the emergency obstacle avoidance distance. To control command delays.

[0040] Optionally, the formula for calculating the capacity of the same airway in the airway network is as follows: ;

[0041] in, Indicates the capacity of the same airway within the airway network; This indicates the maximum number of drones operating in parallel on the same airway. This represents the total number of air lanes currently in use.

[0042] In the formula, the maximum number of parallel UAVs on the same airway The expression is as follows: ;

[0043] in, Indicates the curve coefficient of the air track; The distance between the two cities; Indicates the drone's flight speed; Minimum time slot constraint;

[0044] The expression for adjusting the capacity of the same airway to obtain the adjusted number of airways is as follows: ;

[0045] wherein, represents the adjusted flight airway quantity; is the total quantity of airways after dynamic adjustment.

[0046] Optionally, the collaborative optimization model comprises a construction process as follows:

[0047] Through the first decision architecture, the air space throughput is combined, and the isolation band deviation is taken as the core consideration factor to establish an upper macroscopic optimization model. The maximum throughput objective function is as follows: ;

[0048] wherein, is the airway moment capacity; is the average cruising speed represents the dynamic isolation band width at time t; is the optimal width of the dynamic isolation band; is the safety weight coefficient; is the discount factor; T represents the total time;

[0049] Through the second decision architecture, the actual flight status and the actual task priority weight of the unmanned aerial vehicle are considered to establish a microscopic control model, the deflection risk of the unmanned aerial vehicle is evaluated, and the deflection cost objective function is constructed as follows: ;

[0050] wherein, represents the deflection cost objective function; is the ideal heading angle of the unmanned aerial vehicle i; is the actual heading angle of the unmanned aerial vehicle i, is the priority weight of the unmanned aerial vehicle i; M represents the total number of unmanned aerial vehicles; is the maximum synchronization error; is the system unit set; L represents the safety boundary penalty function; represents the real-time state vector of the unmanned aerial vehicle i; represents the safety state reference vector of the unmanned aerial vehicle;

[0051] According to the maximum throughput objective function and the deflection cost objective function, a collaborative optimization model is constructed in combination with a manned safety enhanced airway model and a space-time-physiological rhythm coupling algorithm.

[0052] Optionally, the expression of the manned safety enhanced airway model is as follows: ;

[0053] wherein, R human represents the manned safety enhanced airway model; b represents the wing span of the unmanned aerial vehicle. For turbulence deviation; For control margin; For sensor error margin; For manned mass; For kinetic impact; For cabin structure strength;

[0054] The expression of the space-time-physiological rhythm coupling algorithm is as follows: ;

[0055] Wherein, Fatigue coefficient; Actual arrival time of passenger j; Theoretical arrival time of passenger j; Comfort weight coefficient; Flight time; Circadian rhythm function.

[0056] Based on the same inventive concept, the present application also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the steps of the method.

[0057] Based on the same inventive concept, the present application also provides an electronic device, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to realize the steps of the method.

[0058] Advantages:

[0059] 1. The present application constructs an airspace flow optimization model based on 0-1 knapsack optimization algorithm at the transportation density peak period, which is beneficial to improve the airspace capacity, resource utilization rate and reduce the calculation delay;

[0060] 2. The present application constructs a conflict probability model based on the dynamic characteristics of unmanned aerial vehicles, adopts Monte Carlo simulation verification, and suppresses the front and rear collision probability to Times per sortie, which is two orders of magnitude lower than the traditional RRT* (Rapidly-exploring Random Tree Star, heuristic algorithm) algorithm based on geometric separation;

[0061] 3. The application integrates subway mobile block system theory and proposes a distributed space-time conflict detection algorithm. Under the condition of information synchronization delay ≤ 50 ms, the algorithm supports concurrent scheduling decisions of 1500 times per second, which is 20 times faster than centralized route planning and A* algorithm. Through the GPU-accelerated integer linear programming (ILP) solver, the time slot allocation optimization time is shortened from seconds to milliseconds compared with the traditional genetic algorithm.

[0062] 4. The application adopts three-dimensional column coordinate system modeling and elastic airway number adjustment mechanism to support mixed operation of multiple types of aircraft in the 300-1000 meter height layer of urban clusters. The system greatly improves the throughput compared with the fixed airway strategy such as EU U-space standard in the mixed flight scene of eVTOL manned and unmanned aircraft and logistics unmanned aircraft. BRIEF DESCRIPTION OF DRAWINGS

[0063] Figure 1 The overall implementation process schematic diagram of the inter-city unmanned aerial vehicle logistics airway resource allocation method provided by the application is provided.

[0064] Figure 2 The three-dimensional model schematic diagram of the unmanned aerial vehicle flight airway in the inter-city unmanned aerial vehicle logistics airway resource allocation method provided by the application is provided.

[0065] Figure 3 The three-dimensional model schematic diagram of the dynamic isolation zone in the inter-city unmanned aerial vehicle logistics airway resource allocation method provided by the application is provided.

[0066] Figure 4 The airspace flow optimization model schematic diagram in the inter-city unmanned aerial vehicle logistics airway resource allocation method provided by the application is provided.

[0067] Figure 5 The time slot allocation model schematic diagram in the inter-city unmanned aerial vehicle logistics airway resource allocation method provided by the application is provided.

[0068] Figure 6 The intelligent airspace management system structure schematic diagram for inter-city manned unmanned aerial vehicle transportation in the inter-city unmanned aerial vehicle logistics airway resource allocation method provided by the application is provided.

[0069] Figure 7 The simulated dynamic scheduling process schematic diagram in the inter-city unmanned aerial vehicle logistics airway resource allocation method provided by the application is provided.

[0070] Figure 8 The performance quantitative comparison schematic diagram of the inter-city unmanned aerial vehicle logistics airway resource allocation method provided by the application and the traditional fixed airspace management model (fixed model) is provided. DETAILED DESCRIPTION

[0071] The application will be further described in detail below in conjunction with the accompanying drawings of the specification.

[0072] As Figure 1 shown, the application proposes a low-altitude economy-based unmanned aerial vehicle airspace resource allocation method, which realizes efficient utilization of airspace resources and collaborative optimization of flight safety through spatial resource division, time resource division, and dynamic time slice scheduling. Compared with traditional unmanned aerial vehicle cargo technology, this method has higher positioning solution accuracy and higher regional allocation scheduling. The specific steps include the following:

[0073] Step 1: Construct an airway network in a three-dimensional column coordinate system at a preset altitude layer (e.g., 300-1000 meters), use it as an application scenario for super-high-speed airspace and high-speed airspace design, and calculate the minimum safety distance of the unmanned aerial vehicle mass center from the airway pipe wall under the constraints of turbulence disturbance, control error, sensor noise, and other margins based on unmanned aerial vehicle model parameters.

[0074] Based on the actual application scenario of inter-city logistics distribution, an airway network framework in a three-dimensional column coordinate system is constructed at a medium-low altitude layer of 300-1000 meters. This framework will serve as the basic platform for super-high-speed airspace and high-speed airspace design. Meanwhile, this accurate three-dimensional column coordinate system model provides a mathematical basis for subsequent airspace planning.

[0075] The unmanned aerial vehicle flight airway three-dimensional model is as Figure 2 shown. The unmanned aerial vehicle flight airway is a three-dimensional channel system specially designed for inter-city low-altitude transportation. It uses a parallel pipe structure to build a large-scale, standardized transportation network framework. This three-dimensional channel forms isolated transportation corridors in the horizontal plane, and the flight boundaries are determined through legal procedures to provide exclusive navigation space for unmanned aerial vehicles. Unmanned aerial vehicles in the channel form a cluster formation according to the pre-set speed level, and the heading angle limiting mechanism ensures the orderliness of group flight, forming a directional flow mode similar to a subway system.

[0076] After establishing the coordinate system, various influencing factors need to be considered. According to the model parameters of different unmanned aerial vehicles, the turbulence disturbance, control error, and sensor noise encountered during flight are calculated in detail. By incorporating these parameters into the calculation model, the influence of various margin constraints on flight safety can be accurately evaluated.

[0077] The geometric parameter design of the tubular airway needs to consider the dynamic characteristics of the unmanned aerial vehicle and the structural engineering requirements. The determination of the cross-sectional size needs to consider the influence of the wing span, turbulence deviation, and control margin space superposition. When modeling with a three-dimensional column coordinate system, the airway center axis is aligned with the ideal flight trajectory of the unmanned aerial vehicle. Then, the minimum safety distance of the aircraft center of gravity to the pipe wall can be solved by the corresponding boundary constraint equation, which is expressed as follows: ;

[0078] wherein, is the wingspan of the UAV, is the turbulence offset, is the control margin, is the sensor error margin, wherein the turbulence offset follows the Reynolds number correction model, which is expressed as follows: ;

[0079] wherein the Reynolds number characterizes the flow state, is the cruising speed of the UAV, and the typical Reynolds number is: ;

[0080] and the control margin is derived from the dynamic response time constraint: ;

[0081] wherein is is the response time of the attitude control system, which can be obtained by testing the UAV model. The sensor error margin is associated with the positioning accuracy , which is expressed as follows: ;

[0082] Based on the above calculation results, a key safety parameter needs to be determined, i.e., the minimum safety distance that must be maintained between the UAV's center of mass and the airway wall under various interference conditions. The determination of this distance not only meets the basic flight safety requirements, but also reserves sufficient buffer space for unexpected situations, thereby ensuring the reliability and safety of the entire logistics distribution system. After reserving a 15% engineering margin, the final airway radius can be obtained: ;

[0083] Step 2: Design an intelligent dynamic isolation strip system and calculate the adjustment range of the width parameter of the dynamic isolation strip between the UAV airways. Based on the dynamic characteristics of the UAV and the shear stress threshold of the isolation strip magnetorheological material, the upper limit of the width is derived, and the lower limit of the width is derived from the cross-layer conflict probability model.

[0084] After completing the accurate design of the airway radius, only a reasonable airway structure is not enough to cope with the complex and variable urban low-altitude operating environment. The isolation strip between the airways serves as a key barrier to prevent cross-airway interference and collision, and the accuracy of its width parameter directly affects the safety and throughput efficiency of the entire airspace system.

[0085] Dynamic isolation zone 3D model as follows Figure 3 As shown, the core of dynamic buffer zone design lies in establishing a dynamic balance mechanism between airspace resource utilization efficiency and operational risk prevention. In intercity transportation, to further control transportation risks, dynamic buffer zones must also be set up between each airway to cope with emergencies in the airspace environment. When airway transport density is low, the buffer zone width can be as large as possible to minimize airway risk. When transport density is high, the buffer zone width is reduced to improve transport efficiency, but at the same time, the probability of risk must be minimized. Buffer zone width parameters. The expression for the dynamic adjustment design equation is as follows: ;

[0086] in It is the wingspan of the drone. It is the drone's cruising speed. That's the air density at an altitude of about 1000 meters. It is the cruise lift coefficient. It refers to the accuracy of trajectory control. It is the turbulence disturbance amplification factor. That is the maximum braking distance.

[0087] Based on the dynamic characteristics of UAVs, the upper limit of the guardrail width can be preliminarily determined by analyzing their maximum braking distance. The braking performance of the UAV directly affects its obstacle avoidance capability in emergency situations, thus determining the minimum buffer space required for the guardrail. The expression for the maximum braking distance of the UAV is as follows: ;

[0088] Where V is the drone's cruising speed. It is the runaway acceleration limit.

[0089] To further optimize the design of the isolation zone, a more in-depth analysis of the properties of the magnetorheological material used in the isolation zone is necessary. By calculating the shear stress threshold of the magnetorheological material under different magnetic fields, the maximum tolerance of the isolation zone to drone collisions or compression can be derived. Based on this, the upper limit of the isolation zone width can be adjusted to ensure its effective function even in extreme conditions.

[0090] The shear stress of the magnetorheological material in the isolation zone must meet the energy dissipation requirements of the drone collision, and the width parameter of the isolation zone... The upper limit is constrained by the shear stress threshold of the magnetorheological material, as expressed below: ;

[0091] in This refers to the energy that needs to be dissipated during a drone collision. is the effective airspace cross-sectional area, is the separation strip width parameter.

[0092] To balance the efficiency of the airway network, the minimum width lower limit of the separation strip needs to be derived based on the cross-layer conflict probability model. This model calculates the minimum width that the separation strip can allow under the premise of ensuring safety by analyzing the flight trajectory intersection probability of unmanned aerial vehicles at different altitudes, thereby achieving the best balance between safety and airspace utilization. The separation strip width parameter The lower limit is determined by the cross-layer conflict probability model, expressed by an exponential probability density function as follows: ;

[0093] where is the flight path control accuracy, is the separation strip width parameter.

[0094] Solving The lower limit ensures that the conflict probability is suppressed to 10 -6 times per thousand sorties or less.

[0095] When adjusting the width of the separation strip, attention should be paid to the adjustment rate of the parameters to avoid serious consequences caused by the non-uniformity of information among various units in the airspace system. The elastic separation strip width adjustment rate is constrained by the Lyapunov stability criterion, and the inequality expression is as follows: ;

[0096] Through this model, the maximum number of airways N can be calculated, which is the maximum unmanned aerial vehicle flight airspace flow that the model can achieve without considering the spacing between unmanned aerial vehicles. The expression is as follows: ;

[0097] where is the maximum unmanned aerial vehicle flight airspace flow, is the unmanned aerial vehicle cruising speed, is the effective airspace cross-sectional area, is the efficiency coefficient related to the redundancy of the scheduling system, is the minimum safety interval

[0098] Based on the elastic separation strip width parameter of the unmanned aerial vehicle dynamics characteristics, the specific range of the dynamic interval can be determined, with the lower limit constrained by the conflict probability and the upper limit determined by the material stress threshold, which is not lower than the elastic separation strip width adjustment time. After obtaining the maximum number of airways through this model, the airspace flow can be effectively improved under the premise of ensuring the safety threshold.

[0099] Step 3: Constructing airspace flow optimization model, under the condition of ensuring the information uniformity of each unit of airspace system and the information synchronization limit threshold (less than 50 ms), the 0-1 knapsack optimization algorithm is used to solve the maximum traffic flow.

[0100] After the scientific setting of the width adjustment interval of the dynamic isolation belt, the safety buffer mechanism between the air corridors has been established. However, the safety isolation itself is not the ultimate goal. If only the range of the isolation belt is determined without further calculating the limit flow that each air corridor can carry under this safety constraint, it is difficult to achieve the coordinated maximization of safety and efficiency from the system level. It is necessary to construct an airspace flow optimization model within the physical boundary delimited by the dynamic isolation belt, accurately solve the maximum number of unmanned aerial vehicle flights that can safely pass through each air corridor and the entire network in unit time, and provide quantitative basis for global scheduling decision-making, so as to convert the safety redundancy brought by the isolation belt into controllable operation capacity.

[0101] In the model construction stage, it is necessary to ensure the high uniformity of information between each unit of airspace system, and strictly control the information synchronization delay to be no more than 50 ms. Real-time and accurate data synchronization is the basis for ensuring the efficient and coordinated operation of air corridor network. Any delay or information inconsistency may lead to scheduling errors or safety hazards.

[0102] As shown in Figure 4 The airspace flow optimization model constructed by the application is shown in the figure. In the unmanned aerial vehicle airspace management system, the core goal of airspace flow optimization is to maximize the number of unmanned aerial vehicles that can pass through a unit space in unit time under the premise of ensuring the safety of unmanned aerial vehicles. Based on the information synchronization and air corridor capacity constraint conditions, a mathematical model is established;

[0103] The information synchronization constraint is that the data state of all airspace units including unmanned aerial vehicles, air corridors, ground control stations, etc. needs to be consistent, that is, the information synchronization delay should not be greater than 50 ms, so as to avoid the decision risk caused by possible decision lag; the air corridor capacity constraint is that the number of unmanned aerial vehicles that a single air corridor can accommodate at the same time is limited due to the physical size constraint of unmanned aerial vehicles and air corridors, dynamic isolation belt and safety interval limitation, and the upper limit of air corridor capacity;

[0104] Under the premise of meeting the above information synchronization requirements, the 0-1 knapsack algorithm is further used to optimize the airspace flow distribution. The dynamic weight mechanism is introduced to enable the algorithm to adjust the priority according to the real-time traffic conditions; combined with heuristic rules, the calculation efficiency under large-scale airspace network is improved. Through this optimization algorithm, the maximum traffic flow under the given air corridor resource constraint can be accurately calculated, so as to realize the efficient utilization of airspace resources.

[0105] Based on the constraint condition and its optimization factor, the sum of priority weight of all scheduled UAVs is maximized, the high value and high emergency task is guaranteed to pass, and as many UAVs as possible are arranged to run. The objective function is as follows: ;

[0106] wherein, represents whether the UAV is scheduled, is the priority weight, is the constraint function vector, is the upper limit of system capacity.

[0107] The key constraint factor is modeled as follows:

[0108] The distributed consistency protocol is used to ensure state synchronization in information synchronization constraint aspect, and the specific formula is as follows: ;

[0109] wherein is the local state vector, is the consistent state vector, is the convergence rate coefficient, is the maximum synchronization error, is the system specific unit index, is the system unit set.

[0110] The distributed consistency protocol expression ensures that the state of all nodes will converge to a globally consistent state at an exponential rate, and the maximum error will not be exceeded.

[0111] In the space aspect, the three-dimensional airway capacity model is introduced to determine the maximum number of UAVs that can be accommodated in an airway at the same time, and the specific formula is as follows: ; ;

[0112] wherein, is the airway radius, is the isolation bandwidth, is the minimum safety interval, is the effective cross-sectional area of the airway, is the average area of the UAV. In addition, the priority weight of UAV passing can be set, the emergency task weight is 1.5, and the regular task weight is 1.

[0113] Step 4: Using the space-time diversity reservation algorithm, the time dimension and space dimension are decoupled, and on this basis, a time slot allocation model is constructed. According to the real-time reservation density, the airway time window is flexibly segmented.

[0114] Through the space-time diversity reservation algorithm, the collaborative decoupling processing is realized in the time dimension and the space dimension. The double-dimension decoupling mechanism can effectively separate the space-time constraints of airway resources, so that the system can independently optimize the time allocation and the space layout, thereby significantly improving the scheduling flexibility.

[0115] As Figure 5 shown is a constructed time slot allocation model constructed by the application. The space-time diversity reservation algorithm is developed from the time and space angles. In the space diversity layer, the system divides the flight space of unmanned aerial vehicle transportation between cities into flight airways and dynamic isolation belts, wherein the odd airways are forward transportation channels, and the even numbered airways are reverse return channels. The layout is isolated according to the basic strategy of heading separation, so that the unmanned aerial vehicles in the same flight airway only have a one-way following relationship, and the collision risk is fundamentally eliminated.

[0116] At the same time, in order to further eliminate the collision risk, the algorithm performs gridding processing in all same-direction flight airways in the vertical direction of the three-dimensional space, that is, different direction airway barriers are arranged in the middle of the same-direction flight airways. The top airway is used as an emergency response special airway and is not used in non-emergency situations. Each airway gap is further strengthened by the dynamic isolation belt.

[0117] In terms of time diversity, a dynamic time slot adjustment algorithm is constructed to realize the effect of elastic segmentation of the airway time window by the system according to the real-time reservation density. On this basis, a resource reservation mechanism is introduced, and the airway resources need to be reserved in advance. In order to facilitate the system to plan the resources transported on a certain airway in a certain time slot in advance, the reservation flights are used to determine the before and after time slots of each class, and the expression is as follows: ;

[0118] Among them is a smoothing factor, is the minimum time slot constraint;

[0119] In addition, when establishing the time diversity model, the physical constraints of the unmanned aerial vehicle cluster taking off from the same platform also need to be considered. Due to safety factors, the unmanned aerial vehicles on the same airway need to meet a certain time interval when taking off, and the minimum safety interval is determined by the kinematic equation and the probability constraint, and the formula is as follows: ;

[0120] Among them is the cruise parameter, is the cruise speed, is the braking deceleration, is the emergency obstacle avoidance distance, is the control command delay;

[0121] Among them, the safety braking distance expression is as follows: ;

[0122] Step 5: Build a space-time slot resource adaptive allocation system that combines the principles of urban subway dispatching and the dynamics of unmanned aerial vehicles. Based on the Moving Block System theory, divide the airway flight area into dynamic virtual blocks. When the capacity of the same airway reaches the upper limit, adjust the number of flight airways using dynamic isolation belts.

[0123] To achieve flexible expansion and security verification of multi-dimensional resources, the model introduces urban subway dispatching and unmanned aerial vehicle dynamics constraints, and builds an adaptive allocation system for space-time slot resources. Based on the Moving Block System theory, divide the airway flight area into dynamic virtual blocks. The block length is determined by the following expression: ;

[0124] Combined with the airway bending coefficient , the parallel capacity of the same flight airway can be obtained , and the expression is as follows: ; ;

[0125] At the implementation level, the flight density and traffic state of each virtual block are monitored in real time through the deployed distributed sensor network. When the capacity of an airway approaches the upper limit, the system will intelligently trigger the dynamic isolation belt adjustment mechanism, using advanced magnetorheological material technology to adjust the physical layout and number configuration of the flight airway in real time, allowing the airway resources to be further utilized. The expression is as follows: ;

[0126] where is the total number of airways after dynamic adjustment.

[0127] This process fully integrates the principles of moving block in subway dispatching, optimizing the allocation of airspace resources by dynamically adjusting block boundaries, ensuring safe flight spacing and maximizing airway usage efficiency.

[0128] The basic collision probability model under the limit capacity condition can be used to evaluate whether the airway resource division is sufficiently safe. Assuming that unmanned aerial vehicles are randomly and independently distributed in the airway, their collision events follow a Poisson process, the hourly arrival rate of the system when setting the corresponding parallel airways can be obtained, and the expression is as follows: ;

[0129] The Poisson distribution probability calculation expression is as follows: ;

[0130] When the calculated collision probability in the horizontal direction under the minimum time slot condition is almost equal to zero, it indicates that the risk of collision between the front and rear unmanned aerial vehicles on the same airway is negligible.

[0131] The system successfully migrates the mature scheduling theory of ground rail transit to the field of three-dimensional airspace management, realizes the elastic expansion of airspace resources through dynamic division of virtual blocks, and constructs a truly adaptive allocation algorithm suitable for low-altitude scenarios.

[0132] Step 6: Construct an intelligent airspace management system for inter-city manned unmanned aerial vehicle transportation, combine the constructed manned safety enhanced airway model, develop the space-time-physiological rhythm coupling algorithm, and establish a series of measures such as mixed traffic flow hierarchical control framework to establish a multi-dimensional collaborative optimization model to achieve breakthroughs in safety and operating efficiency.

[0133] Based on the special safety requirements of manned flight, a safety enhanced airway model is constructed. This model not only needs to consider the geometric constraints of traditional airspace planning, but also needs to optimize the airway structure according to the operating characteristics of manned unmanned aerial vehicles to ensure the stability and reliability of the flight path. Combined with real-time data monitoring and artificial intelligence prediction technology, this system can also dynamically adapt to unexpected factors such as sudden weather and airspace congestion, ensuring the efficiency and safety of the entire transportation network.

[0134] As Figure 6 shown is an intelligent airspace management system for inter-city manned unmanned aerial vehicle transportation constructed by the application. In view of the flexibility of humans compared to static objects, this algorithm adds human safety specificity constraints based on the original safe airway design, focusing on solving the risk problems of passenger mass, kinetic impact, and cabin structure strength specific to the manned scenario. The specific formula is as follows: ;

[0135] Manned mass, Kinetic impact, Cabin structure strength.

[0136] Considering that humans are different from goods in terms of flexibility, in order to solve the conflict between the punctuality rate of inter-city unmanned aerial vehicle transportation flights and the physiological comfort of passengers, and the contradiction between space utilization rate and flight safety, the above-mentioned space-time separation reservation algorithm is optimized, rhythm cost is included as an optimization factor, and a space-time-physiological rhythm coupling algorithm is further developed. This algorithm needs to consider factors such as passenger physiological comfort, circadian rhythm changes, and flight duration to dynamically adjust the flight strategy. The formula is as follows: ;

[0137] Circadian rhythm function: ;

[0138] wherein is a fatigue coefficient, is a passenger actual arrival time, is a customer ideal arrival time, is a comfort weight coefficient, is a flight time.

[0139] In order to coordinate efficient and safe operation of manned UAVs and logistics UAVs in a shared airspace, the present application builds a double-deck decision architecture through a Markov decision process to realize coordinated optimization of macro flow allocation and micro trajectory control. The first decision architecture combines airspace throughput and deviation of the isolation strip as core consideration factors, establishes an upper macro optimization model, and the objective function is as follows: ;

[0140] wherein is a capacity of the airway at the time, is an average cruising speed, is an actual width of the dynamic isolation strip, is an optimal width of the dynamic isolation strip, is a safety weight coefficient, is a discount factor. The dynamic adjustment can be made to meet the actual deployment needs.

[0141] The second decision layer is in-depth to the actual environment of the UAV, and establishes a micro control model in view of the actual flight status of the UAV, the actual task priority weight, safety and other factors, and the objective function is constructed as follows: ;

[0142] is an ideal heading angle of the UAV, is an actual heading angle of the UAV, is a priority weight;

[0143] Combined with real-time data monitoring and artificial intelligence prediction technology, the system can also dynamically adapt to unexpected weather, airspace congestion and other uncertain factors to ensure the efficiency and safety of the entire transportation network. The implementation of this series of measures will lay a solid foundation for the large-scale operation of future inter-city manned UAV transportation. Based on the Poisson potential field equation, the present application grids the three-dimensional airspace, adopts a multi-grid method to accelerate convergence, and iteratively solves to build an emergency passage generation algorithm, designs a temporary emergency passage, and the specific algorithm is as follows: ;

[0144] For the open channel permeability, the calculation formula is: ;

[0145] For the emergency demand density, is the potential field function, the gradient of which points to the optimal rescue path, is the attenuation coefficient.

[0146] By integrating the above key technologies, a multi-dimensional collaborative optimization model is constructed, which needs to balance between safety, operation efficiency, energy consumption management and passenger comfort and other multiple goals, so as to promote the breakthrough application of manned unmanned aerial vehicle transportation in intelligent airspace management.

[0147] The above merely describes specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which shall be covered within the protection scope of the present application.

[0148] Simulation parameters: based on the wing span of a typical VTOL (for example, b=12m) and comprehensive calculation of turbulence offset , control margin and sensor error margin , the minimum safety distance d is obtained by formula , and the final increase of 15% engineering margin is rounded to determine . This diameter can provide a safe yaw and attitude control margin for the unmanned aerial vehicle.

[0149] According to the formula , K is the turbulence disturbance amplification factor, taking 1.5; S brake is the maximum braking distance; is the flight path control accuracy, taking 0.3m; b is the wing span 12m), and the lower limit is calculated to be 28 meters; at the same time, the upper limit should be calculated to be 42 meters by the shear stress threshold of the magneto-rheological material.

[0150] The maximum number of airways is 7 working airways (including 1 emergency airway) in the 300-1000 meter airspace. When the isolation belt is adjusted to the minimum width (28m), it can be expanded to 9; the hourly arrival rate threshold is set to 90 times / hour; the average take-off cycle (T launch ) of the unmanned aerial vehicle in the same airway is set to 240 seconds.

[0151] Simulation result comparison:

[0152] ​As Figure 7 shown, it shows the dynamic scheduling process based on 24-hour simulation of the present algorithm. It can be clearly seen from the figure that the demand of system input has obvious peak and valley. The method proposed in the present application can intelligently respond to this change: in the low demand period, the system maintains more empty lane number 7 and larger isolation bandwidth 42 meters, and preferentially guarantees the flight safety redundancy; when it is monitored that the demand exceeds the preset threshold, i.e. 90, the system automatically triggers the adjustment mechanism, and the number of empty lanes is increased to 9 at the 9th hour, so as to expand the theoretical capacity of the system from 90 flights / hour to 120 flights / hour. It is worth noting that the instantaneous backlog generated at the 8th hour is successfully transferred to the 11th hour with redundant capacity for processing, and finally all demands are digested without backlog, which proves the excellent dynamic adaptability and scheduling ability of the present application.

[0153] As Figure 8 shown, it is the performance quantitative comparison of the present application (dynamic model) and the traditional fixed airspace management model (fixed model). The data show that the total processing capacity of the dynamic model is increased from 1924 to 2231, the demand satisfaction rate is increased from 86.24% to 100%, and the capacity utilization rate is increased from 66.81% to 77.47%; all three key indicators are significantly better than the fixed model. What is particularly important is that the dynamic model realizes zero backlog, while the fixed model has a final backlog of 307. The comparison results fully prove that the performance gain realized by the dynamic isolation belt mechanism of the present application is not obtained by sacrificing safety, but significantly improves the utilization efficiency of airspace resources and the overall throughput of the system under the premise of intelligently guaranteeing safety, and realizes the cooperative optimization of safety and efficiency.

Claims

1. A method for allocating UAV airspace resources based on low-altitude economy, characterized in that, The method includes the following steps: Step 1: Construct an airway network in a three-dimensional cylindrical coordinate system at a preset altitude layer, and use it as an application scenario for UAV resource allocation in low-altitude economic airspace. Calculate the minimum safe distance between the UAV's center of mass and the airway wall under preset margin constraints based on the UAV's model parameters. Step 2: Based on the pre-designed intelligent dynamic isolation zone system, calculate the dynamic isolation zone width parameter adjustment information between airways in the airway network, and calculate the upper limit and lower limit of the dynamic isolation zone width based on the dynamic isolation zone width parameter adjustment information; Step 3: Solve for the maximum throughput of the airway network using a pre-built airspace traffic optimization model; wherein the airspace traffic optimization model is constructed based on the 0-1 knapsack optimization algorithm; Step 4: Using the spatiotemporal diversity reservation algorithm, the time windows of the air channel network are flexibly divided to obtain the initial scheme for air channel time slot allocation; Step 5: Based on the initial airspace time slot allocation scheme, integrate the urban subway scheduling principle and UAV dynamic constraints to construct an adaptive allocation system for airspace time slot resources. Based on the subway moving block system theory, when the capacity of the same airspace in the airspace network reaches the upper limit, adjust the capacity of the same airspace to obtain the adjusted number of flight airspaces. Step 6: Based on the adjusted number of flight lanes, airspace resources are allocated using a pre-built collaborative optimization model; wherein, the collaborative optimization model is an intelligent airspace management system for intercity manned UAV transportation, which is constructed by combining a manned safety-enhanced flight lane model and a spatiotemporal-physiological rhythm coupling algorithm.

2. The method for allocating UAV airspace resources based on low-altitude economy according to claim 1, characterized in that: The formula for calculating the minimum safe distance between the center of mass of the UAV and the air duct wall under the preset margin constraint is as follows: ； in, Indicates the minimum safe distance; For the wingspan of the drone; This is the turbulence offset; To control the margin; This is the sensor error margin; In the formula, the turbulence offset The expression is as follows: ； The control margin The expression is as follows: ； The sensor error margin The expression is as follows: ； in, Density of the drone itself; The speed of the drone; The viscosity coefficient; For the attitude control system response time; Indicates positioning accuracy; This is the reference flight speed for the drone.

3. The method for allocating UAV airspace resources based on low-altitude economy according to claim 1, characterized in that: The formula for calculating the dynamic isolation strip width parameter adjustment information is as follows: ； in, This indicates the dynamic adjustment information of the isolation strip width parameter at time t; This indicates the wingspan of the drone; Indicates the drone's flight speed; Indicates air density at altitude; Indicates the cruise lift coefficient; Indicates the accuracy of trajectory control; Indicates the amplification factor of turbulence disturbance; Indicates the maximum braking distance; The expression for the maximum braking distance is as follows: ； in, Indicates the limit of runaway acceleration; The formula for calculating the lower limit of the width of the dynamic isolation zone is as follows: ； in, Indicates the lower limit of the width of the dynamic safety barrier; This represents the energy dissipated during a drone collision; Indicates the effective airspace cross-sectional area; The formula for calculating the upper limit of the width of the dynamic isolation zone is as follows: ； in, This indicates the upper limit of the width of the dynamic isolation zone.

4. The method for allocating UAV airspace resources based on low-altitude economy according to claim 1, characterized in that: The airspace traffic optimization model includes the following construction process: A target function is constructed with the goal of maximizing the airspace traffic of the aforementioned UAV. Based on the objective function, corresponding constraints are formulated; the constraints include: information synchronization constraints and space capacity constraints. Based on the objective function and the constraints, an airspace traffic optimization model is constructed using the 0-1 knapsack algorithm. The objective function is expressed as follows: ； in, Represent the objective function; This represents the priority weight of drone i; Represents the scheduling information of UAV i; represents the constraint function vector; n represents the number of UAVs; The expression for the information synchronization constraint is as follows: ； in, This is the local state vector; A consistent state vector; The convergence rate coefficient; This represents the maximum synchronization error. Index for system-specific units; A set of system units; The expression for the space capacity constraint is as follows: ； ； in, Represents the spatial capacity at time t; This represents the minimum safe interval at time t; Indicates the length of the drone's fuselage; Represents the effective cross-sectional area of ​​the channel at time t; This indicates the average area of ​​the drone; Indicates the airway radius; This represents the dynamic isolation strip width at time t.

5. The method for allocating UAV airspace resources based on low-altitude economy according to claim 1, characterized in that: The expression for the spatiotemporal diversity reservation algorithm is as follows: ； in, This represents the initial scheme for allocating air channel time slots at time t; It is a smoothing factor; For minimum time slot constraints, Total time required for drone takeoff; This represents the maximum total number of drones. In the formula, the minimum time slot constraint The expression is as follows: ； ； in, For cruise parameters; The speed of the drone; For braking deceleration; This refers to the emergency obstacle avoidance distance. To control command delays.

6. The method for allocating UAV airspace resources based on low-altitude economy according to claim 1, characterized in that: The formula for calculating the capacity of the same airway in the airway network is as follows: ； in, Indicates the capacity of the same airway within the airway network; This indicates the maximum number of drones operating in parallel on the same airway. This represents the total number of air lanes currently in use. In the formula, the maximum number of parallel UAVs on the same airway The expression is as follows: ； in, Indicates the curve coefficient of the air track; The distance between the two cities; Indicates the drone's flight speed; Minimum time slot constraint; The expression for adjusting the capacity of the same airway to obtain the adjusted number of airways is as follows: ； in, This indicates the adjusted number of airways; This represents the total number of air lanes after dynamic adjustments.

7. The method for allocating UAV airspace resources based on low-altitude economy according to claim 1, characterized in that: The collaborative optimization model includes the following construction process: By combining the first decision-making framework with airspace throughput and segregation zone deviation as the core consideration factors, a higher-level macro-optimization model is established, with the objective function for maximum throughput as follows: ； in, For the constant passage capacity of the airway; Average cruising speed This represents the dynamic width of the isolation strip at time t; The optimal width for the dynamic isolation zone; This is the safety weighting coefficient; The discount factor is T; T represents the total number of time points. Using a second decision-making architecture, a micro-control model is established based on the actual flight conditions and mission priority weights of the UAV to assess the UAV deflection risk. The deflection cost objective function is constructed as follows: ； in, This represents the objective function for deflection cost; The ideal heading angle for the UAV; The actual heading angle of UAV i Let i represent the priority weight of drone; M represents the total number of drones. This represents the maximum synchronization error. For the system unit set; L represents the safety boundary penalty function; Represents the real-time state vector of drone i; Represents the safety status baseline vector of the UAV; Based on the maximum throughput objective function and the deflection cost objective function, a collaborative optimization model is constructed by combining the manned safety-enhanced airway model and the spatiotemporal-physiological rhythm coupling algorithm.

8. A method for allocating UAV airspace resources based on low-altitude economy according to claim 1 or 7, characterized in that: The expression for the manned safety-enhanced air traffic control model is as follows: ； Among them, R human b represents a manned safety-enhanced airborne model; b represents the wingspan of the UAV. This is the turbulence offset; To control the margin; This is the sensor error margin; For manned mass; For kinetic energy impact; For cabin structural strength; The expression for the spatiotemporal-physiological rhythm coupling algorithm is as follows: ； in, The fatigue coefficient; The actual arrival time of passenger j; For passenger j, the ideal arrival time; This is a comfort weighting coefficient; For flight time; This is a diurnal rhythm function.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 8.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 8.

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