Urban complex airspace unmanned aerial vehicle landing point safety spacing operation management method and system

Abstract

The application provides a city complex airspace unmanned aerial vehicle landing point safety distance operation management method and system, and relates to the unmanned aerial vehicle air traffic management field. The steps comprise: establishing a city landing point three-dimensional environment potential field model considering obstacles and no-fly zones; calculating the pre-machine wake dissipation time based on a CFD simplified model; constructing a dynamic safety distance calculation formula integrating multiple factors; adopting a sliding time window scheduling algorithm; and setting an emergency landing step. The city complex airspace unmanned aerial vehicle landing point safety distance operation management method can adapt to the city complex airspace, balance landing safety and efficiency, improve operation reliability, be easy to be implemented in engineering, and be suitable for multiple models and high-frequency landing scenes.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) air traffic management, and in particular to a method and system for managing the safe distance between UAV take-off and landing points in complex urban airspace. Background Technology

[0002] With the rapid development of logistics drones and electric vertical takeoff and landing (eVTOL) aircraft technologies, their large-scale application in urban scenarios has become an industry trend. This can effectively address urban last-mile delivery, low-altitude commuting, and other travel and logistics needs, improving urban traffic and logistics efficiency. However, the complex urban airspace environment and the need for high-frequency continuous takeoffs and landings are two core challenges hindering the safe landing and large-scale application of these aircraft. On the one hand, the urban airspace environment is extremely complex, with static obstacles such as high-rise buildings, power transmission towers, and bridges all around. There are unstable weather disturbances such as gusts at low altitudes. At the same time, the obstruction of urban buildings can easily lead to the interruption or delay of communication signals. The combination of these factors can easily cause safety hazards such as aircraft take-off and landing collisions, attitude loss, and scheduling failures, which puts forward stringent requirements for the spatial safety and environmental adaptability of aircraft take-off and landing.

[0003] On the other hand, applications such as logistics delivery and low-altitude commuting have placed high-frequency, continuous operation demands on the take-off and landing efficiency of logistics drones and eVTOL. How to achieve continuous and efficient take-off and landing of aircraft while ensuring safety, and how to balance the contradiction between safety and efficiency, has become a key issue that the industry urgently needs to address.

[0004] In existing technologies, the safety management of drone take-off and landing mostly adopts fixed-interval scheduling and simple environmental modeling, which cannot adapt to the dynamic changes of complex urban airspace. It is also difficult to accurately quantify the combined effects of multiple factors such as wake effects, positioning errors, and communication delays on continuous take-off and landing safety. It cannot effectively balance the adaptability to complex urban environments and the demand for high-frequency continuous take-off and landing, which severely limits the application of logistics drones and eVTOL in urban scenarios.

[0005] Therefore, there is an urgent need to provide a method and system for managing the safe distance between take-off and landing points of UAVs in complex urban airspace to solve the above problems. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for managing the safe spacing between take-off and landing points of unmanned aerial vehicles (UAVs) in complex urban airspace, enabling efficient, safe, and continuous operation of UAVs. Through real-time data fusion and risk assessment, the take-off and landing intervals are dynamically adjusted to improve airspace utilization and operational flexibility.

[0007] To achieve the above objectives, this invention provides a method for managing the safe distance between take-off and landing points of unmanned aerial vehicles (UAVs) in complex urban airspace, comprising the following steps: S1: Taking into account static and dynamic obstacles around the city's take-off and landing points, as well as no-fly zones designated by airspace control requirements, a three-dimensional environmental potential field model is constructed using a gridded modeling method. S2: For the characteristics of rotor downwash or fixed-wing runway airflow during the take-off and landing of UAVs, a simplified CFD model of fluid dynamics is established to calculate the wake dissipation time of the preceding aircraft during take-off and landing. Through steady-state initialization and transient iterative solution, the decay curve of the maximum flow velocity in the core region of the wake over time is obtained, and the wake dissipation time is determined by combining the safe flow velocity threshold. S3: Construct a dynamic safety distance calculation formula, integrate positioning error, communication delay, braking distance and wake influence distance, construct a multi-factor coupled dynamic safety distance calculation model, and output the minimum safety distance adapted to the current scenario in real time; S4: Introducing a sliding time window mechanism, using the wake dissipation time as the window length benchmark, integrating information on the queue of UAVs waiting to take off and land, dynamic safety distances, and airfield resources, and achieving efficient scheduling of continuous take-off and landing through queue sorting and dynamic adjustment; S5: Real-time monitoring of UAV status and airspace environment. When sudden situations such as malfunction, communication interruption or wake turbulence occur, it triggers the corresponding level of warning, determines the priority of emergency response, plans the optimal emergency landing path based on the three-dimensional environmental potential field model, executes emergency landing and records the entire process data.

[0008] Preferably, in S1, the three-dimensional modeling boundary is defined with the take-off and landing point of the urban UAV as the center, static obstacles, dynamic obstacles and no-fly zones in the environment are extracted as repulsive sources, the take-off and landing point area is used as the gravitational source, the modeling space is discretized into grid cells with unique three-dimensional coordinates using a rasterization method, the gravitational potential energy function and the repulsive potential energy function are defined, the total potential energy of each grid cell is calculated by superposition, and a three-dimensional environmental potential field model is generated. The 3D modeling boundaries are set as follows: the horizontal range is 500m with the take-off and landing point as the center, the vertical range is 0~100m, the grid resolution is set to 1m×1m×1m, and the initial potential energy value of the grid not covered by environmental elements is set to 0. Static obstacles include tall buildings, power transmission towers, bridges, or trees, while dynamic obstacles include low-flying birds, general aviation aircraft, or temporary construction barriers. The lower the total potential energy value of the grid cells, the higher the corresponding airspace safety level.

[0009] Preferably, the gravitational potential energy function is defined as: ; in, Let P be the gravitational potential energy value of grid point P. This is the gravitational coefficient, with a value range of 80 to 100. This is the distance from grid point P to the center T of the takeoff and landing point; The repulsive potential energy function is defined as: ; in, For grid points P The repulsive potential energy value, The repulsion coefficient is set between 100 and 150 for high-rise buildings and between 500 and 600 for no-fly zones. d(P,O) For grid points P To the repulsive source O Distance from the center To determine the radius of influence of repulsive force, the value for high-rise buildings is set within the range of 50-80m, and the no-fly zone has full coverage influence. The formula for calculating total potential energy is defined as follows: ; in, For grid points P The total potential energy value, This represents the total number of repulsive force sources.

[0010] Preferably, in S2, urban low-altitude environmental parameters and the core parameters of the preceding aircraft are set, and a system based on the Reynolds-averaged Navier-Stokes equations and standard k-... The CFD simplified model of turbulence model, the core governing equations of the simplified CFD model include the continuity equation and the momentum equation. The continuity equation is defined as: ; Where ∇ is the Hamiltonian operator, The velocity vector of the wake field; The momentum equation is defined as: ; in, t For time, ρ air density, Where ν is the static pressure and ν is the kinematic viscosity of air. This is the Reynolds stress term; The core formulas of the standard k-ε turbulence model include the k-transport equation for turbulent kinetic energy and the ε-transport equation for turbulent kinetic energy dissipation rate: , ; in, k Let ε be the turbulent kinetic energy, and ε be the turbulent kinetic energy dissipation For turbulent viscosity, ; =0.09; and It is an empirical constant. , ; and For turbulent Prandtl number, =1.0, =1.3; The turbulent kinetic energy generated by the average velocity gradient; The formula for determining wake dissipation time is: ; in, The wake dissipation time, The maximum velocity in the core region of the wake at time t. The safe flow rate threshold is set within the range of ≤2m / s. For the first time to meet At that moment.

[0011] Preferably, the urban low-altitude environmental parameters include an altitude of 50~500m, an ambient temperature of -10℃~40℃, an atmospheric pressure corresponding to the standard atmospheric pressure at altitude of ±10kPa, and an ambient wind speed of ≤5m / s; the core parameters of the front aircraft include the aircraft type, rotor diameter or wingspan, takeoff and landing speed, load, and rotor speed. The simplified CFD model uses a structured mesh. The core wake region is located 0-20m behind the front engine and 0-20m vertically. A dense mesh with a mesh size of 0.2m×0.2m×0.2m is used. A sparse mesh with a mesh size of 0.5m×0.5m×0.5m is used in the non-core region.

[0012] Preferably, the dynamic safety clearance calculation formula in S3 is defined as follows: ; in, For dynamic safety spacing; To ensure a safe redundancy distance for positioning errors, the value is set within the range of 0.5~1.5m; For communication delay lag distance, ; For the takeoff and landing speed of the drone; For communication delay, the value range is set to 0.01~0.03s; Emergency braking distance; The safe distance to mitigate the impact of the wake vortex is calculated from the wake dissipation time and the speed of the drone. ; In the formula, a This represents the maximum braking acceleration of the drone.

[0013] Preferably, the length of the sliding time window in S4 is dynamically adjusted according to the wake dissipation time, and the scheduling algorithm prioritizes scheduling UAVs that meet the dynamic safety distance requirements and have high task priority. In the event of sudden airspace restrictions, the scheduling sequence is automatically re-optimized. The priority determination and scheduling logic of the scheduling algorithm are as follows: First, select the UAVs that meet the requirements of the dynamic safety distance calculation formula in S3. Then, sort the selected UAVs according to the preset task priority level. The higher the priority, the earlier the sorting and the priority allocation of take-off and landing positions. When there is a sudden airspace restriction, re-verify the dynamic safety distance compliance of each UAV, retain the task priority sorting logic, and re-optimize the scheduling sequence.

[0014] Preferably, in S5, emergency warnings are divided into general warnings, relatively severe warnings, and serious warnings; The general criteria for early warning are: the drone has a minor malfunction, the communication signal fluctuates briefly but is not interrupted, there are no densely populated areas nearby, and the drone's payload is ≤5kg. Minor malfunctions include low battery or slight attitude deviation. Emergency response has the lowest priority, and the priority is to adjust the flight attitude and replenish the battery. There is no need for a forced landing. The criteria for a relatively severe warning are: the drone experiences a moderate malfunction, communication delay exceeds 0.05s but is not interrupted, or the drone has a load of 5-10kg and a moderate density of people in the surrounding area. Moderate malfunctions include single sensor failure or decreased braking performance. The emergency response priority is moderate. Immediately initiate emergency landing preparations and simultaneously plan a temporary emergency landing route. The criteria for a severe warning are: a serious malfunction of the drone, a complete loss of communication, or a drone with a payload of more than 10 kg, or a densely populated area such as a business district, school, or hospital nearby. Serious malfunctions include power system failure or signs of loss of control. Emergency response has the highest priority and an emergency landing should be carried out immediately. The emergency response priority for all warning levels is determined by combining the fault type, drone payload, and surrounding population density. The emergency landing path must avoid all obstacles and no-fly zones. The optimal path is planned based on the three-dimensional environmental potential field model built in S1 to ensure the safety of the emergency landing.

[0015] The Urban Complex Airspace UAV Take-off and Landing Point Continuous Take-off and Landing Safety Operation Management System includes an environmental modeling module, a wake calculation module, a safety distance calculation module, a queue scheduling module, and an emergency response module, which are used to implement the steps of the Urban Complex Airspace UAV Take-off and Landing Point Safety Distance Operation Management Method.

[0016] Preferably, the environmental modeling module is used to collect airspace environmental data, construct and update a three-dimensional environmental potential field model in real time; the wake calculation module is used to run a simplified CFD model and output the wake dissipation time; the safety clearance calculation module calculates the dynamic safety clearance in real time; the queue scheduling module is used to execute a sliding time window scheduling algorithm to optimize the take-off and landing queue; and the emergency response module is used to monitor emergencies and execute emergency landing procedures.

[0017] Therefore, the present invention employs the above-mentioned method and system for managing the safe distance between take-off and landing points of unmanned aerial vehicles in complex urban airspace, and the technical effects are as follows: (1) In this invention, the minimum safe distance is accurately calculated using a dynamic safety distance calculation formula, which avoids excessively increasing the distance for safety. This enables compact queue management, allowing the next aircraft to land immediately after the previous one takes off, maximizing the utilization rate of take-off and landing points. It improves the utilization rate of urban low-altitude airspace and is especially suitable for high-frequency take-off and landing scenarios such as logistics distribution, emergency rescue, and urban inspection, shortening the mission cycle.

[0018] (2) This invention constructs a three-dimensional environmental potential field model of urban take-off and landing points, which comprehensively includes static obstacles such as high-rise buildings, dynamic obstacles such as low-altitude birds, and no-fly zones in the modeling scope. It combines the gravitational and repulsive potential energy functions to quantify the spatial safety level, which can effectively avoid the risk of obstacle collision. At the same time, the simplified CFD model can incorporate environmental parameters such as gusts, accurately calculate the wake dissipation time of the preceding aircraft, dynamically adapt to urban low-altitude meteorological disturbances, alleviate the control problems caused by signal blockage, solve the safety hazards caused by the complexity of the urban environment, and meet the spatial safety and environmental adaptability requirements of aircraft take-off and landing.

[0019] (3) The present invention constructs a dynamic safety distance calculation formula with multiple factors coupled, integrates positioning error, communication delay, braking distance and wake influence distance, and replaces the existing fixed distance scheduling method. It can compress the take-off and landing interval to the maximum extent while ensuring safety. Combined with the queue scheduling algorithm based on sliding time window, it prioritizes scheduling aircraft that meet safety requirements and have high mission priority, dynamically optimizes the scheduling sequence, realizes efficient control of high-frequency continuous take-off and landing, effectively balances the contradiction between safety and efficiency, and adapts to the high-frequency operation needs of scenarios such as logistics distribution and low-altitude commuting.

[0020] (4) By setting up a three-level emergency warning system, this invention clarifies the judgment conditions and handling priorities of each level of warning, and combines a three-dimensional environmental potential field model to quickly plan the optimal emergency landing path. It can accurately carry out emergency handling for different fault types, loads and surrounding personnel density, and form a closed-loop control from minor faults to emergency landings for serious faults. This effectively reduces the safety risks caused by emergencies and improves the reliability and stability of continuous take-off and landing operations of aircraft.

[0021] (5) In this invention, the simplified CFD model adopts the Reynolds-averaged Navier-Stokes equation and the standard k- The turbulence model balances computational accuracy and efficiency, avoiding the time-consuming problem of complex turbulence simulation; the three-dimensional environmental potential field model adopts raster modeling, with a clear structure and convenient updates, and can adapt to the dynamic changes of urban airspace in real time; the whole method has clear steps and well-defined formula parameters, and the corresponding system modules are reasonably divided and highly operable, requiring no investment in complex equipment, making it easy to be deployed in engineering and applied on a large scale, which can effectively promote the implementation and promotion of logistics drones and eVTOL in urban scenarios.

[0022] (6) This invention can be flexibly adapted to different types of logistics drones such as rotary-wing and fixed-wing drones and eVTOL. It can adjust the model parameters and scheduling logic according to the airspace characteristics, take-off and landing point layout and mission requirements of different cities, adapt to the continuous take-off and landing scenarios of cities of different sizes and environments, and has strong versatility and adaptability, with a wide range of applications. Attached Figure Description

[0023] Figure 1 This is a flowchart of the method for managing the safe distance between take-off and landing points of unmanned aerial vehicles in complex urban airspace according to the present invention; Figure 2 This is a flowchart of the sliding time window scheduling process in an embodiment of the present invention. Detailed Implementation

[0024] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0025] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0026] Example like Figure 1 As shown, this invention provides a method for managing the safe distance between take-off and landing points of unmanned aerial vehicles (UAVs) in complex urban airspace, including the following steps: S1: Taking into account the static and dynamic obstacles around the urban take-off and landing points, as well as the no-fly zones designated by airspace control requirements, a three-dimensional environmental potential field model is constructed using a rasterization modeling method. In S1, the three-dimensional modeling boundary is defined with the urban UAV take-off and landing points as the center. Static obstacles, dynamic obstacles, and no-fly zones in the environment are extracted as repulsive sources, and the take-off and landing point area is used as the gravitational source. The modeling space is discretized into raster cells with unique three-dimensional coordinates using a rasterization method. Gravitational potential energy functions and repulsive potential energy functions are defined, and the total potential energy of each raster cell is calculated by superposition to generate a three-dimensional environmental potential field model. The 3D modeling boundaries are set as follows: the horizontal range is 500m with the take-off and landing point as the center, the vertical range is 0~100m, the grid resolution is set to 1m×1m×1m, and the initial potential energy value of the grid not covered by environmental elements is set to 0. Static obstacles include tall buildings, power transmission towers, bridges, or trees, while dynamic obstacles include low-flying birds, general aviation aircraft, or temporary construction barriers. The lower the total potential energy value of the grid cells, the higher the corresponding airspace safety level.

[0027] The gravitational potential energy function is defined as: ; in, Let P be the gravitational potential energy value of grid point P. This is the gravitational coefficient, with a value range of 80 to 100. This is the distance from grid point P to the center T of the takeoff and landing point; The repulsive potential energy function is defined as: ; in, For grid points P The repulsive potential energy value, The repulsion coefficient is set between 100 and 150 for high-rise buildings and between 500 and 600 for no-fly zones. d(P,O) For grid points P To the repulsive source O Distance from the center To determine the radius of influence of repulsive force, the value for high-rise buildings is set within the range of 50-80m, and the no-fly zone has full coverage influence. The formula for calculating total potential energy is defined as follows: ; in, For grid points P The total potential energy value, This represents the total number of repulsive force sources.

[0028] S2: For the characteristics of rotor downwash or fixed-wing runway airflow during the take-off and landing of UAVs, a simplified CFD model of fluid dynamics is established to calculate the wake dissipation time of the preceding aircraft during take-off and landing. Through steady-state initialization and transient iterative solution, the decay curve of the maximum flow velocity in the core region of the wake over time is obtained, and the wake dissipation time is determined by combining the safe flow velocity threshold. In S2, urban low-altitude environmental parameters and core parameters of the preceding aircraft are set, and a system based on Reynolds-averaged Navier-Stokes equations and standard k-... is constructed. The CFD simplified model of turbulence model, the core governing equations of the simplified CFD model include the continuity equation and the momentum equation. The continuity equation is defined as: ; Where ∇ is the Hamiltonian operator, The velocity vector of the wake field; The momentum equation is defined as: ; in, t For time, ρ air density, Where ν is the static pressure and ν is the kinematic viscosity of air. This is the Reynolds stress term; The core formulas of the standard k-ε turbulence model include the k-transport equation for turbulent kinetic energy and the ε-transport equation for turbulent kinetic energy dissipation rate: , ; in, k Let ε be the turbulent kinetic energy, and ε be the turbulent kinetic energy dissipation For turbulent viscosity, ; =0.09; and It is an empirical constant. , ; and For turbulent Prandtl number, =1.0, =1.3; The turbulent kinetic energy generated by the average velocity gradient; The formula for determining wake dissipation time is: ; in, The wake dissipation time, The maximum velocity in the core region of the wake at time t. The safe flow rate threshold is set within the range of ≤2m / s. For the first time to meet At that moment.

[0029] Urban low-altitude environmental parameters include altitude 50~500m, ambient temperature -10℃~40℃, atmospheric pressure corresponding to altitude standard atmospheric pressure ±10kPa, and ambient wind speed ≤5m / s; the core parameters of the front aircraft include aircraft type, rotor diameter or wingspan, takeoff and landing speed, load and rotor speed. The simplified CFD model uses a structured mesh. The core wake region is located 0-20m behind the front engine and 0-20m vertically. A dense mesh with a mesh size of 0.2m×0.2m×0.2m is used. A sparse mesh with a mesh size of 0.5m×0.5m×0.5m is used in the non-core region.

[0030] S3: Construct a dynamic safety distance calculation formula, integrate positioning error, communication delay, braking distance and wake influence distance, construct a multi-factor coupled dynamic safety distance calculation model, and output the minimum safety distance adapted to the current scenario in real time; The formula for calculating the dynamic safety clearance in S3 is defined as follows: ; in, For dynamic safety spacing; To ensure a safe redundancy distance for positioning errors, the value is set within the range of 0.5~1.5m; For communication delay lag distance, ; For the takeoff and landing speed of the drone; For communication delay, the value range is set to 0.01~0.03s; Emergency braking distance; The safe distance to mitigate the impact of the wake vortex is calculated from the wake dissipation time and the speed of the drone. ; In the formula, a This represents the maximum braking acceleration of the drone.

[0031] S4: Introducing a sliding time window mechanism, using the wake dissipation time as the window length benchmark, integrating information on the queue of UAVs waiting to take off and land, dynamic safety distances, and airfield resources, and achieving efficient scheduling of continuous take-off and landing through queue sorting and dynamic adjustment; In S4, the length of the sliding time window is dynamically adjusted according to the wake dissipation time. The scheduling algorithm prioritizes scheduling UAVs that meet the dynamic safety distance requirements and have high task priority. When there is a sudden airspace restriction, the scheduling sequence is automatically re-optimized. The priority determination and scheduling logic of the scheduling algorithm are as follows: First, select the UAVs that meet the requirements of the dynamic safety distance calculation formula in S3. Then, sort the selected UAVs according to the preset task priority level. The higher the priority, the earlier the sorting and the priority allocation of take-off and landing positions. When there is a sudden airspace restriction, re-verify the dynamic safety distance compliance of each UAV, retain the task priority sorting logic, and re-optimize the scheduling sequence.

[0032] S5: Real-time monitoring of UAV status and airspace environment. In the event of sudden situations such as malfunction, communication interruption, or wake turbulence anomalies, it triggers the corresponding level of warning, determines the emergency response priority, plans the optimal emergency landing path based on a three-dimensional environmental potential field model, executes an emergency landing, and records all process data. Emergency warnings in S5 are divided into general warnings, moderate warnings, and severe warnings. The general criteria for early warning are: the drone has a minor malfunction, the communication signal fluctuates briefly but is not interrupted, there are no densely populated areas nearby, and the drone's payload is ≤5kg. Minor malfunctions include low battery or slight attitude deviation. Emergency response has the lowest priority, and the priority is to adjust the flight attitude and replenish the battery. There is no need for a forced landing. The criteria for a relatively severe warning are: the drone experiences a moderate malfunction, communication delay exceeds 0.05s but is not interrupted, or the drone has a load of 5-10kg and a moderate density of people in the surrounding area. Moderate malfunctions include single sensor failure or decreased braking performance. The emergency response priority is moderate. Immediately initiate emergency landing preparations and simultaneously plan a temporary emergency landing route. The criteria for a severe warning are: a serious malfunction of the drone, a complete loss of communication, or a drone with a payload of more than 10 kg, or a densely populated area such as a business district, school, or hospital nearby. Serious malfunctions include power system failure or signs of loss of control. Emergency response has the highest priority and an emergency landing should be carried out immediately. The emergency response priority for all warning levels is determined by combining the fault type, drone payload, and surrounding population density. The emergency landing path must avoid all obstacles and no-fly zones. The optimal path is planned based on the three-dimensional environmental potential field model built in S1 to ensure the safety of the emergency landing.

[0033] The Urban Complex Airspace UAV Take-off and Landing Point Continuous Take-off and Landing Safety Operation Management System includes an environmental modeling module, a wake calculation module, a safety distance calculation module, a queue scheduling module, and an emergency response module, which are used to implement the steps of the Urban Complex Airspace UAV Take-off and Landing Point Safety Distance Operation Management Method.

[0034] The environmental modeling module is used to collect airspace environmental data, construct and update a three-dimensional environmental potential field model in real time; the wake calculation module is used to run a simplified CFD model and output the wake dissipation time; the safety clearance calculation module calculates the dynamic safety clearance in real time; the queue scheduling module is used to execute a sliding time window scheduling algorithm to optimize the take-off and landing queue; and the emergency response module is used to monitor emergencies and execute emergency landing procedures.

[0035] Example 1 This case study uses a "logistics drone delivery hub in the central urban area of ​​a first-tier city" as its application scenario. The hub has a service radius of 3km and is surrounded by a mixed-use commercial and residential area with dense high-rise buildings, frequent low-altitude gusts, and signal obstruction from buildings. It needs to support high-frequency, continuous takeoffs and landings (≥300 sorties per day) of multiple types of rotary-wing logistics drones (payload ≤10kg), perfectly addressing the core application pain points of urban environmental complexity and high-frequency takeoffs and landings. This case study employs the aforementioned technical methods and systems to achieve safe and efficient management of continuous drone takeoffs and landings. The specific implementation process and results are as follows: System Deployment: Deploy a continuous take-off and landing safety operation management system for UAVs in complex urban airspace. The system includes an environmental modeling module, a wake calculation module, a safety distance calculation module, a queue scheduling module, and an emergency response module. Each module communicates with each other through an industrial-grade IoT gateway. The data refresh frequency is 0.5 seconds, which meets the requirements for real-time control.

[0036] Basic parameter settings: Core parameters are set according to the characteristics of the scene, such as 3D environmental potential field model grid resolution of 1m×1m×1m, modeling boundary horizontal 500m / vertical 0~100m; wake safe flow velocity threshold of 2m / s; dynamic safe distance positioning error redundancy of 0.8m, communication delay of 0.03s; emergency warning is divided into three levels of judgment criteria according to load ≤5kg / 5~10kg and low / medium / high personnel density.

[0037] Hardware components include: lidar, weather sensors (monitoring gusts, temperature, and air pressure), drone flight control data receiving terminal, and high-definition airspace monitoring cameras, enabling comprehensive monitoring of the airspace environment and drone status.

[0038] The specific implementation steps are as follows: Step 1: The environmental modeling module constructs and updates a 3D environmental potential field model. The environmental modeling module collects airspace data from lidar and cameras in real time, extracting dynamic obstacles such as 32 surrounding high-rise buildings (repulsion coefficient 120, influence radius 60m), 2 no-fly zones (school / commercial area, repulsion coefficient 550, full coverage influence), and low-altitude birds. Using the 4 take-off and landing positions as gravity sources (gravity coefficient 90), the total potential energy of each grid is calculated by superimposing gravity / repulsion potential energy functions to generate a 3D environmental potential field model. When a dynamic obstacle is detected (such as a bird entering the modeling area), the system refreshes the corresponding grid potential energy value within 0.5s, marking the dangerous airspace and providing spatial constraints for subsequent scheduling and forced landing.

[0039] Step 2: Wake Flow Calculation Module Calculates the Wake Dissipation Time of the Leading Aircraft. After a 7kg payload rotary-wing UAV (rotor diameter 1.2m, takeoff and landing speed 3m / s) completes takeoff, the wake flow calculation module inputs environmental parameters (gust 1.5m / s, temperature 25℃, air pressure 101kPa) and the parameters of the leading aircraft into the CFD simplified model, and calculates the wake flow time using the Reynolds-averaged Navier-Stokes equations and k-... The turbulence model was solved to obtain the maximum velocity decay curve of the wake core region over time. The wake dissipation time was determined to be 4.2s. This data was then transmitted to the safety distance calculation module and the queue scheduling module simultaneously.

[0040] Step 3: The safety distance calculation module calculates the dynamic safety distance in real time. The safety distance calculation module integrates the wake dissipation time (4.2s), positioning error redundancy of 0.8m, communication delay lag distance (3m / s × 0.03s = 0.09m), and emergency braking distance (3m / s × 0.03s = 0.09m). 2 / (2×5)=0.9m, maximum braking acceleration 5m / s 2 ), through formula Calculate the dynamic safety distance: 0.8 + 0.09 + 0.9 + (3 × 4.2) = 14.39m. Determine the minimum safe takeoff and landing distance between the following aircraft and the preceding aircraft as 14.5m (rounded up), and send the result to the queue scheduling module.

[0041] Step 4: The queue scheduling module executes sliding time window scheduling, such as... Figure 2As shown, the queue scheduling module uses a 4.2s wake dissipation time as the sliding time window length, incorporating information from the queue of 12 drones currently awaiting takeoff. First, it filters out 8 drones that meet the 14.5m dynamic safety distance requirement. Then, it sorts them according to task priority (emergency medical supplies delivery > fresh produce delivery > ordinary parcel delivery), with 2 emergency medical supplies delivery drones having the highest priority. The system prioritizes allocating positions 1 and 2 to these drones and simultaneously plans takeoff paths that avoid high-potential-energy hazardous airspace. The remaining 6 drones are then sequentially placed into the window according to priority, with the takeoff and landing interval adjusted to 4.5s (slightly longer than the wake dissipation time to allow for safety redundancy). If sudden airspace restrictions occur (such as temporary construction barriers entering the modeling area), the module immediately re-verifies the safety distance, retains the priority ranking, and optimizes and generates a new scheduling sequence within 3 seconds.

[0042] Step 5: Perform take-off and landing operations and monitor the entire process through the emergency response module.

[0043] 1. The two highest priority emergency medical supply drones took off according to the scheduling plan, flying along the low potential energy safe path of the three-dimensional environmental potential field model throughout the flight, without any obstacles or wake interference, and successfully completed the takeoff.

[0044] 2. The emergency response module receives real-time UAV flight control data (attitude, battery level, power system) and airspace monitoring data, providing comprehensive monitoring of the takeoff and landing process: When a drone with a payload of 4kg is detected to have low battery (20% remaining), slight attitude deviation, and is located in a low-density area, a general warning is triggered. The system issues an instruction to adjust the flight attitude and return to the nearest charging point. No forced landing is required. After the situation is resolved, the drone is rejoined in the dispatch queue.

[0045] When a single obstacle avoidance sensor of an 8kg drone is detected to have failed, and the flight area is a medium-density residential area, a severe warning is triggered. The system immediately initiates emergency landing preparations and plans a temporary emergency landing path (take-off and landing point #3 backup position) to avoid high-rise buildings based on a three-dimensional environmental potential field model. The drone successfully lands on the path without any safety risks.

[0046] If a drone's power system malfunctions, communication is completely interrupted, and it is located over a densely populated commercial area, a severe warning will be triggered. Within 0.3 seconds, the system will plan the optimal emergency landing path (an open green space with the lowest potential energy) and send an alarm message to staff to achieve an emergency landing.

[0047] Step 6: Closed-loop scheduling and data recording. After the UAV completes takeoff and landing / emergency response, the system automatically updates the queue of UAVs to be taken off and landed and enters the next round of scheduling process. All takeoff and landing data, environmental data, and emergency response data are recorded in real time to the system backend, including wake dissipation time, dynamic safety distance, scheduling sequence, warning type and response result, providing data support for subsequent scenario optimization and model parameter correction.

[0048] This case study utilizes the aforementioned technologies and systems to achieve full-process safety management of high-frequency continuous takeoffs and landings of logistics drones in complex urban airspace. The core implementation results are as follows: 1. Significantly improved safety performance: No drone collisions, wake interference, or forced landings occurred during the implementation period. The precise calculation of dynamic safety distance and wake dissipation time completely avoided environmental risks such as high-rise buildings and gusts of wind. The three-level emergency warning judgment mechanism enabled targeted handling of emergencies, resulting in zero errors in management.

[0049] 2. Takeoff and landing efficiency meets high-frequency demand: Compared with traditional fixed interval scheduling (10s interval), this system dynamically adjusts the takeoff and landing interval to 4.5s, increasing the hourly takeoff and landing volume per stand from 36 to 80, and the average daily takeoff and landing volume of the hub reaches 352, exceeding the expected target by 17.3%, achieving double the efficiency while ensuring safety.

[0050] 3. Strong environmental adaptability: In the face of complex situations such as urban gusts (≤3m / s), dynamic obstacles, and temporary signal blockage, the system can update the model and optimize scheduling in real time. When the signal is blocked, it can still ensure orderly take-off and landing without scheduling interruption by relying on the preset potential energy model and scheduling scheme.

[0051] 4. Easy to operate and reusable: The system operates automatically throughout the entire process. Staff only need to monitor the status and handle extreme emergencies in the background without manual intervention or scheduling. This method can be directly reused to other logistics hubs or eVTOL low-altitude commuting take-off and landing points in the same city by adjusting the model parameters, making it highly versatile.

[0052] This case study validates the practicality, safety, and efficiency of the aforementioned method and system for safe operation management of continuous take-off and landing of UAVs in complex urban airspace. It accurately adapts to the complex environment of densely built-up urban areas, characterized by gusts of wind and signal obstruction, effectively resolving the conflict between safety and efficiency in high-frequency continuous take-off and landing. Through closed-loop management of the entire process—environmental modeling, wake calculation, dynamic spacing, queue scheduling, and emergency response—it provides a replicable and scalable implementation plan for the large-scale application of logistics UAVs and eVTOL in complex urban airspace, demonstrating significant engineering application value.

[0053] Therefore, this invention adopts the above-mentioned method and system for managing the safe distance between take-off and landing points of UAVs in complex urban airspace. It achieves compact queue management and improves the utilization rate of airspace and take-off and landing points through dynamic safety distance calculation. It avoids urban airspace safety hazards by combining three-dimensional environmental potential field modeling and CFD simplified model. It balances take-off and landing safety and efficiency by relying on multi-factor dynamic safety distance and sliding time window scheduling. It improves operational reliability through three-level emergency early warning and optimal forced landing path planning. Its simplified model is easy to implement in engineering and is suitable for various UAV models and take-off and landing point scenarios of different sizes.

[0054] 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 them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for managing the safe distance between take-off and landing points of unmanned aerial vehicles (UAVs) in complex urban airspace, characterized in that: Includes the following steps: S1: Taking into account the static and dynamic obstacles around the city's take-off and landing points, as well as the no-fly zones designated by airspace control requirements, a three-dimensional environmental potential field model is constructed using a gridded modeling method. S2: For the characteristics of rotor downwash or fixed-wing runway airflow during the take-off and landing of UAVs, a simplified CFD model of fluid dynamics is established to calculate the wake dissipation time of the preceding aircraft during take-off and landing. Through steady-state initialization and transient iterative solution, the decay curve of the maximum flow velocity in the core region of the wake over time is obtained, and the wake dissipation time is determined by combining the safe flow velocity threshold. S3: Construct a dynamic safety distance calculation formula, integrate positioning error, communication delay, braking distance and wake influence distance, construct a multi-factor coupled dynamic safety distance calculation model, and output the minimum safety distance adapted to the current scenario in real time; S4: Introducing a sliding time window mechanism, using the wake dissipation time as the window length benchmark, integrating information on the queue of UAVs waiting to take off and land, dynamic safety distances, and airfield resources, and achieving efficient scheduling of continuous take-off and landing through queue sorting and dynamic adjustment; The length of the sliding time window in S4 is dynamically adjusted based on the wake dissipation time of the preceding aircraft's take-off and landing calculated in S2. The scheduling algorithm prioritizes scheduling UAVs that meet the dynamic safety distance requirements and have high task priority. In the event of sudden airspace restrictions, the scheduling sequence is automatically re-optimized. The priority determination and scheduling logic of the scheduling algorithm are as follows: First, select the drones that meet the requirements of the dynamic safety distance calculation formula in S3. Then, sort the selected drones according to the preset task priority level. The higher the priority, the higher the ranking and the priority is allocated to take-off and landing positions. The priority is sorted in the order of emergency medical supplies delivery > fresh food delivery > ordinary parcel delivery. In the event of sudden airspace restrictions, re-verify the dynamic safety distance compliance of each drone, retain the task priority sorting logic, and re-optimize the scheduling sequence. S5: Real-time monitoring of UAV status and airspace environment. When sudden situations such as malfunction, communication interruption or wake turbulence occur, it triggers the corresponding level of warning, determines the priority of emergency response, plans the optimal emergency landing path based on the three-dimensional environmental potential field model, executes emergency landing and records the entire process data.

2. The method for managing the safe distance between take-off and landing points of unmanned aerial vehicles in complex urban airspace according to claim 1, characterized in that, In S1, the three-dimensional modeling boundary is defined with the take-off and landing point of the urban UAV as the center. Static obstacles, dynamic obstacles and no-fly zones in the environment are extracted as repulsive sources, and the take-off and landing point area is used as the gravitational source. The modeling space is discretized into grid cells with unique three-dimensional coordinates using a rasterization method. Gravitational potential energy function and repulsive potential energy function are defined, and the total potential energy of each grid cell is calculated by superposition to generate a three-dimensional environmental potential field model. The 3D modeling boundaries are set as follows: the horizontal range is 500m with the take-off and landing point as the center, the vertical range is 0~100m, the grid resolution is set to 1m×1m×1m, and the initial potential energy value of the grid not covered by environmental elements is set to 0. Static obstacles include tall buildings, power transmission towers, bridges, or trees, while dynamic obstacles include low-flying birds, general aviation aircraft, or temporary construction barriers. The lower the total potential energy value of the grid cells, the higher the corresponding airspace safety level.

3. The method for managing the safe distance between take-off and landing points of unmanned aerial vehicles in complex urban airspace according to claim 2, characterized in that, The gravitational potential energy function is defined as: ; in, Let P be the gravitational potential energy value of grid point P. This is the gravitational coefficient, with a value range of 80 to 100. This is the distance from grid point P to the center T of the takeoff and landing point; The repulsive potential energy function is defined as: ; in, For grid points P The repulsive potential energy value, The repulsion coefficient is set between 100 and 150 for high-rise buildings and between 500 and 600 for no-fly zones. d(P,O) For grid points P To the repulsive source O Distance from the center To determine the radius of repulsive force influence, the value for high-rise buildings is set between 50 and 80 meters, while the no-fly zone has full coverage influence. The formula for calculating total potential energy is defined as follows: ; in, For grid points P The total potential energy value, This represents the total number of repulsive force sources.

4. The method for managing the safe distance between take-off and landing points of unmanned aerial vehicles in complex urban airspace according to claim 1, characterized in that, In S2, urban low-altitude environmental parameters and core parameters of the preceding aircraft are set, and a system based on Reynolds-averaged Navier-Stokes equations and standard k-... is constructed. The CFD simplified model of turbulence includes the continuity equation and the momentum equation as its core governing equations. The continuity equation is defined as follows: ; Where ∇ is the Hamiltonian operator, The velocity vector of the wake field; The momentum equation is defined as: ; in, t For time, ρ air density, Where ν is the static pressure and ν is the kinematic viscosity of air. This is the Reynolds stress term; The core formulas of the standard k-ε turbulence model include the k-transport equation for turbulent kinetic energy and the ε-transport equation for turbulent kinetic energy dissipation rate: 、 ； in, k Let ε be the turbulent kinetic energy, and ε be the turbulent kinetic energy dissipation rate. For turbulent viscosity, ; =0.09; and It is an empirical constant. , ; and For turbulent Prandtl number, =1.0, =1.3; The turbulent kinetic energy generated by the average velocity gradient; The formula for determining wake dissipation time is: ; in, The wake dissipation time, The maximum velocity in the core region of the wake at time t. The safe flow rate threshold is set within the range of ≤2m / s. For the first time to meet At that moment.

5. The method for managing the safe distance between take-off and landing points of unmanned aerial vehicles in complex urban airspace according to claim 4, characterized in that, Urban low-altitude environmental parameters include altitude 50~500m, ambient temperature -10℃~40℃, atmospheric pressure corresponding to altitude standard atmospheric pressure ±10kPa, and ambient wind speed ≤5m / s; the core parameters of the front aircraft include aircraft type, rotor diameter or wingspan, takeoff and landing speed, load and rotor speed. The simplified CFD model uses a structured mesh. The core wake region is located 0-20m behind the front engine and 0-20m vertically. A dense mesh with a mesh size of 0.2m×0.2m×0.2m is used. A sparse mesh with a mesh size of 0.5m×0.5m×0.5m is used in the non-core region.

6. The method for managing the safe distance between take-off and landing points of unmanned aerial vehicles in complex urban airspace according to claim 1, characterized in that, The formula for calculating the dynamic safety clearance in S3 is defined as follows: ； in, For dynamic safety spacing; To ensure a safe redundancy distance for positioning errors, the value is set within the range of 0.5~1.5m; For communication delay lag distance, ; For the takeoff and landing speed of the drone; For communication delay, the value range is set to 0.01~0.03s; Emergency braking distance; The safe distance to mitigate the impact of the wake vortex is calculated from the wake dissipation time and the speed of the drone. ； In the formula, a This represents the maximum braking acceleration of the drone.

7. The method for managing the safe distance between take-off and landing points of unmanned aerial vehicles in complex urban airspace according to claim 1, characterized in that, In S5, emergency warnings are divided into general warnings, relatively severe warnings, and serious warnings. The general criteria for early warning are: the drone has a minor malfunction, the communication signal fluctuates briefly but is not interrupted, there are no densely populated areas nearby, and the drone's payload is ≤5kg. Minor malfunctions include low battery or slight attitude deviation. Emergency response has the lowest priority, and the priority is to adjust the flight attitude and replenish the battery. There is no need for a forced landing. The criteria for a relatively severe warning are: the drone experiences a moderate malfunction, communication delay exceeds 0.05s but is not interrupted, or the drone has a load of 5-10kg and a moderate density of people in the surrounding area. Moderate malfunctions include single sensor failure or decreased braking performance. The emergency response priority is moderate. Immediately initiate emergency landing preparations and simultaneously plan a temporary emergency landing route. The criteria for a severe warning are: a serious malfunction of the drone, a complete loss of communication, or a drone with a payload of more than 10 kg, or a densely populated area such as a business district, school, or hospital nearby. Serious malfunctions include power system failure or signs of loss of control. Emergency response has the highest priority and an emergency landing should be carried out immediately. The emergency response priority for all warning levels is determined by combining the fault type, drone payload, and surrounding population density. The emergency landing path must avoid all obstacles and no-fly zones. The optimal path is planned based on the three-dimensional environmental potential field model built in S1 to ensure the safety of the emergency landing.

8. A safe operation management system for continuous take-off and landing of unmanned aerial vehicles (UAVs) in complex urban airspace, characterized by: It includes an environment modeling module, a wake calculation module, a safety distance calculation module, a queue scheduling module, and an emergency response module, used to implement the steps of the urban complex airspace UAV take-off and landing point safety distance operation management method as described in any one of claims 1 to 7.

9. The safe operation management system for continuous take-off and landing of unmanned aerial vehicles in complex urban airspace as described in claim 8, characterized in that: The environmental modeling module is used to collect airspace environmental data, construct and update a three-dimensional environmental potential field model in real time; the wake calculation module is used to run a simplified CFD model and output the wake dissipation time. The safety distance calculation module calculates the dynamic safety distance in real time; The queue scheduling module is used to execute the sliding time window scheduling algorithm to optimize the takeoff and landing queues; The emergency response module is used to monitor emergencies and execute emergency landing procedures.

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