Low-altitude unmanned aerial vehicle flight dynamic monitoring and airspace refinement management method

Abstract

This invention discloses a method for dynamic monitoring of low-altitude unmanned aerial vehicle (UAV) flight and refined airspace management, belonging to the field of low-altitude traffic management technology. The method acquires the motion characteristics of UAVs and non-cooperative organisms within an airspace grid in real time, calculates the dynamic flux and spatial density of biological activity, dynamically expands the safe isolation distance for UAVs based on the flux, and calculates the effective usable volume of the target airspace grid in conjunction with the spatial density, then obtains the dynamic flight capacity of the grid through three-dimensional spatial geometric calculation, and finally uses this capacity as a constraint, combined with the number of UAVs in the grid and the outflow rate, calculates the permitted inflow rate, and triggers flow restriction or detour commands for external UAVs. This invention overcomes the limitations of static safety intervals and single-aircraft avoidance, realizing dynamic and elastic control and proactive pre-scheduling of macroscopic airspace capacity, maximizing airspace utilization while ensuring flight safety.

Description

Technical Field

[0001] This invention belongs to the field of low-altitude traffic management technology, and in particular relates to a method for monitoring the flight dynamics of low-altitude unmanned aerial vehicles and for refined airspace management. Background Technology

[0002] In recent years, with the explosive growth of the low-altitude economy, the application of low-altitude drones in logistics delivery, urban inspection, and other fields has become increasingly widespread, and low-altitude airspace has gradually become a high-density traffic operation space. Low-altitude airspace generally refers to a limited three-dimensional space range with a vertical altitude of 1,000 to 3,000 meters, which is designated according to different regional characteristics, terrain conditions, and actual needs, and is suitable for the routine flight of various aircraft, especially unmanned aerial vehicles.

[0003] However, low-altitude airspace is not only the operational range of drones, but also an important natural spatial layer for flocks of birds, seasonal migration of migratory birds, and the activity of some high-altitude migratory insects. When drones fly densely at low altitudes, they are very likely to encounter flocks of birds or insects at that altitude, which can lead to flight safety accidents such as bird strikes.

[0004] Current airspace management and monitoring systems have the following technical limitations in addressing dynamic environmental risks: First, existing airspace capacity assessments mostly employ static division and fixed safety intervals, failing to consider the intensity of biological activity in the environment as a dynamic variable. This can easily lead to a severe lack of safety redundancy in low-altitude airspace when faced with sudden bird alarms or migratory bird movements. Second, existing collision avoidance strategies largely rely on single-machine sensing for temporary avoidance, lacking capacity early warning and flow scheduling mechanisms based on biological density at the macro-grid level, which can easily trigger local airspace congestion or even chain collisions. Finally, management models that partially consider environmental risks mainly rely on preset empirical thresholds for coarse classification, making it difficult to achieve precise quantitative mapping between environmental risks and the three-dimensional physical properties of low-altitude airspace. This makes it difficult to dynamically adjust available capacity according to actual collision risks, resulting in a significant deficiency in achieving a refined balance between safety assurance and airspace utilization. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method for monitoring the flight dynamics of low-altitude unmanned aerial vehicles (UAVs) and for refined airspace management, thus solving the aforementioned problems.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for monitoring the flight dynamics of low-altitude unmanned aerial vehicles (UAVs) and for refined airspace management, comprising:

[0007] Real-time acquisition of UAV status data and motion characteristic data of non-cooperative low-altitude organisms within the target airspace grid; calculation of biological activity dynamic flux characterizing the intensity of biological crossing threat; and biological spatial density within the grid.

[0008] Based on the physical geometry properties of UAVs and the system control response delay, the safe isolation distance of UAVs is dynamically expanded using the bioactivity dynamics flux, and the effective usable volume of the target airspace grid is calculated using the biospace density and the expanded safe isolation distance.

[0009] By combining the effective available volume with the expanded safe isolation distance, a three-dimensional spatial geometric solution is performed to obtain the dynamic flight capacity of the target airspace grid;

[0010] Using dynamic flight capacity as a constraint, and combining the actual number of UAVs in the target airspace grid at the current moment with the outflow rate, the grid permission inflow rate is calculated. Based on the grid permission inflow rate, refined intervention commands are triggered for external UAVs to achieve closed-loop control of flight scheduling.

[0011] Based on the above technical solutions, the present invention also provides the following optional technical solutions:

[0012] Further technical solutions: The UAV status data specifically includes the average absolute velocity vector of the UAV swarm within the target airspace grid; the motion characteristic data of non-cooperative low-altitude organisms specifically includes the absolute velocity vector of each detected biological target within the target airspace grid, as well as the total number of biological targets detected; the target airspace grid has a fixed three-dimensional geometric volume.

[0013] Further technical solutions: Calculate the biodynamic flux characterizing the intensity of biological crossing threats, and the bio-spatial density of the grid, specifically including:

[0014] Calculate the vector difference between the absolute velocity vector of each biological target and the average absolute velocity vector of the UAV swarm to obtain the characteristics of the relative motion speed distribution;

[0015] Based on the distribution characteristics of relative motion speed and the total number of biological targets detected, the aggregation calculation is performed, and the spatial dimension is normalized according to the total three-dimensional geometric volume to output the biodynamic flux.

[0016] The biological spatial density is obtained by calculating the ratio of the total number of biological targets detected to the total three-dimensional geometric volume.

[0017] Further technical solutions: The physical geometric properties of the UAV specifically include the windward cross-sectional area of ​​the UAV along the flight direction, and the basic physical braking safety distance of the UAV in an environment without biological interference; the system control response delay is specifically the total physical response delay time from the detection of biological targets by the monitoring system to the execution of avoidance actions by the UAV.

[0018] Further technical solutions: dynamically expanding the safe isolation distance of the UAV and calculating the effective usable volume of the target airspace grid, specifically including:

[0019] Construct a multidimensional collision prediction feature quantity that includes biological activity dynamic flux, windward cross-sectional area, and total physical response delay time;

[0020] Establish an amplification mapping rule that is positively correlated with the expected feature quantity of multi-dimensional collision, apply the amplification mapping rule to the basic physical braking safety distance of the UAV, and obtain the expanded safety isolation distance;

[0021] Based on the cubic envelope characteristics of biological spatial density and expanded safe isolation distance, the proportion of the repulsive volume of the spatial domain to biological activity is calculated.

[0022] The total 3D geometric volume is reduced by the amount of space lost due to the proportion of the repulsive volume, and after applying non-negative boundary constraints, the effective usable volume is output.

[0023] Further technical solutions: Perform three-dimensional spatial geometric calculations to obtain the dynamic flight capacity of the target airspace grid, specifically including:

[0024] Calculate the ratio between the effective usable volume and the volume of the security isolation envelope constructed by a single UAV based on the expanded security isolation distance;

[0025] Perform a floor or truncation operation on the multiple relationship, and use the obtained discrete integer value as the dynamic flight capacity.

[0026] A further technical solution: In the process of calculating the grid permission inflow rate, the fine-grained scheduling time step set by the airspace management system is used as the instruction update cycle parameter.

[0027] Further technical solutions: Calculate the grid permit import rate, specifically including:

[0028] Assess the airspace capacity margin between dynamic flight capacity and the actual number of UAVs:

[0029] Distribute the remaining airspace capacity over a fine-grained scheduling time step to obtain the basic permit injection rate;

[0030] The base permit injection rate is linearly superimposed with the outflow rate within the target spatial grid at the current moment to output the grid permit inflow rate.

[0031] Further technical solutions: Specific control methods for triggering refined intervention commands for external drones based on the grid permission import rate include:

[0032] Determine the numerical boundaries of the grid permitted inflow rate;

[0033] If the grid's permitted inflow rate is less than or equal to zero, the target airspace grid is determined to be in a state of capacity saturation or high risk of biological collision, and an entry denial command is issued to external drones that intend to fly into the target airspace grid.

[0034] If the grid's permitted inflow rate is greater than zero, it is determined that the target airspace grid has the capacity to accept it, and an access command is issued to the external UAV intending to fly into the target airspace grid.

[0035] This invention provides a method for monitoring the flight dynamics of low-altitude unmanned aerial vehicles (UAVs) and for refined airspace management, which has the following advantages compared with existing technologies:

[0036] 1. To address the problem of insufficient safety redundancy in emergency situations caused by the fixed safety interval in existing technologies, this invention extracts the intensity and spatial density of biological crossing threats, dynamically expands the safety isolation distance of the UAV, and calculates the effective usable volume. This technology breaks through the rigid limitations of static designation, enabling the system to adaptively provide a larger physical safety buffer space for the UAV when encountering sudden bird frights or high-density biological groups, thereby reducing the probability of collisions with non-cooperative targets;

[0037] 2. To address the problem that existing technologies rely excessively on temporary avoidance by individual drones and lack macro-level early warning and control, this invention, based on the dynamic flight capacity calculation using three-dimensional spatial geometry, further derives the grid permission inflow rate. This technology achieves a shift from "passive avoidance after the fact" to "proactive flow control before the fact." The system can detect the grid's capacity saturation trend in advance and proactively trigger queuing, speed reduction, or detour diversion commands for external drones, effectively avoiding localized traffic paralysis caused by multiple drones simultaneously entering densely populated biological areas.

[0038] 3. Addressing the problem that existing technologies rely on empirical thresholds for coarse-grained classification, making it difficult to balance safety and efficiency, this invention precisely quantifies biological activity characteristics into geometric volume loss in three-dimensional space and directly translates them into specific control commands for access or rejection. This technology allows airspace management to move away from a "one-size-fits-all" no-fly zone approach, instead flexibly releasing or shrinking airspace capacity based on actual biological risks. While strictly adhering to safety standards, it maximizes the operational potential and refined scheduling capabilities of low-altitude airspace. Attached Figure Description

[0039] Figure 1 This is a schematic diagram of the process of the present invention.

[0040] Figure 2 This is a diagram showing the transfer of core parameters and the flow of model data.

[0041] Figure 3 This is a logic control diagram for triggering refined intervention commands. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0043] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0044] Please see Figure 1 The present invention provides a method for monitoring the flight dynamics of low-altitude unmanned aerial vehicles (UAVs) and for refined airspace management, comprising:

[0045] Real-time acquisition of UAV status data and motion characteristic data of non-cooperative low-altitude organisms within the target airspace grid; calculation of biological activity dynamic flux characterizing the intensity of biological crossing threat; and biological spatial density within the grid.

[0046] Based on the physical geometry properties of UAVs and the system control response delay, the safe isolation distance of UAVs is dynamically expanded using the bioactivity dynamics flux, and the effective usable volume of the target airspace grid is calculated using the biospace density and the expanded safe isolation distance.

[0047] By combining the effective available volume with the expanded safe isolation distance, a three-dimensional spatial geometric solution is performed to obtain the dynamic flight capacity of the target airspace grid;

[0048] Using dynamic flight capacity as a constraint, and combining the actual number of UAVs in the target airspace grid at the current moment with the outflow rate, the grid permission inflow rate is calculated. Based on the grid permission inflow rate, refined intervention commands are triggered for external UAVs to achieve closed-loop control of flight scheduling.

[0049] The UAV status data specifically includes the average absolute velocity vector of the UAV swarm within the target airspace grid; the motion characteristic data of non-cooperative low-altitude organisms specifically includes the absolute velocity vector of each detected biological target within the target airspace grid, as well as the total number of biological targets detected; the target airspace grid has a fixed three-dimensional geometric volume.

[0050] The physical geometry properties of the drone specifically include the windward cross-sectional area of ​​the drone along the flight direction, and the basic physical braking safety distance of the drone in an environment without biological interference; the system control response delay is specifically the total physical response delay time from the detection of biological targets by the monitoring system to the drone executing avoidance actions.

[0051] In the process of calculating the grid permission inflow rate, the fine-grained scheduling time step set by the airspace management system is used as the instruction update cycle parameter.

[0052] The following example will provide a more detailed explanation of the above technical solution:

[0053] Suppose that at location A in the low-altitude airspace of a city, the airspace management system divides this airspace into multiple target airspace grids. Within a specific target airspace grid... In this system, dynamic monitoring and management of drone flights are required. At a certain moment... The system uses deployed radar and visual sensors to acquire, in real time, the average absolute velocity vector of the drone swarm within the grid, as well as the absolute velocity vector and total number of detected bird flocks (non-cooperative low-altitude creatures). For example, the system detects 50 drones within the grid with an average velocity of 10. At the same time, 20 birds were detected, with an average speed of 15. Furthermore, the flock of birds was flying towards the drone swarm.

[0054] Based on this real-time data, the system first calculates the biodynamic flux and biospace density of the grid. For example, by weighted averaging the relative velocities of bird flocks and drone swarms, and combining this with the number of birds and the grid volume, the biodynamic flux is calculated to be 0.05. The biological spatial density is 0.001. These figures quantify the intensity and density of the potential threat posed by bird flocks to drone flights at the current moment.

[0055] Subsequently, the system uses these biological activity parameters to dynamically adjust the safe isolation distance of the UAV. Assuming a basic physical braking safety distance of 10 meters in the absence of biological interference, the system dynamically expands this safety isolation distance based on the calculated biological activity dynamic flux, taking into account the UAV's windward cross-sectional area and system control response delay. For example, under the current biological threat intensity, the safety isolation distance is dynamically expanded to 15 meters. Next, the system uses this expanded safety isolation distance and biological spatial density to calculate the effective usable volume of the target airspace grid. Specifically, the system subtracts the volume occupied by the safe zones of 20 birds each with a radius of 15 meters from the total grid volume to obtain the actual effective usable volume for safe flight of the UAV.

[0056] After obtaining the effective usable volume, the system combines the expanded safety isolation distance with three-dimensional spatial geometric calculations to obtain the dynamic flight capacity of the target airspace grid. For example, the system calculates that the grid can safely accommodate a maximum of 40 UAVs at the current moment. This capacity is a dynamically changing value that reflects the actual carrying capacity of the airspace under biological threats in real time.

[0057] Using this dynamic flight capacity as a constraint, the system combines the current number of drones in the grid (e.g., 35 drones are currently flying in the grid) and the outflow rate (e.g., 5 drones are expected to fly out in the next scheduling time step) to calculate the grid's permitted inflow rate. Assuming the system's fine-grained scheduling time step is set to 10 seconds, the system calculates that the maximum permitted inflow rate for external drones into the grid at the current moment is 0.5 drones / second.

[0058] Finally, the system triggers refined intervention commands for external drones based on the grid's permitted inflow rate. For example, if an external drone intends to fly into the grid, the system determines that the grid's permitted inflow rate (0.5 drones / second) is greater than zero, indicating that the grid has the capacity to accept drones. Therefore, the system issues an admission command to the external drone and controls its actual physical inflow rate to be less than or equal to 0.5 drones / second. If the calculated grid's permitted inflow rate is less than or equal to zero, for example, due to a sharp increase in bird numbers leading to capacity saturation, the system issues a denial command to the external drone and guides it to hover and wait or fly around to other available grids. Through this series of steps, dynamic, refined, and closed-loop management of drone flights in low-altitude airspace is achieved, effectively addressing the risk of biological collisions.

[0059] Based on the above examples, the technical solution of this embodiment makes a significant contribution to the prior art. Existing airspace management systems mostly employ static partitioning and fixed safety intervals, which can easily lead to a severe lack of safety redundancy in low-altitude airspace when faced with sudden bird frights or migratory bird movements. For example, in the above example, if static management is used, even if the number and speed of bird flocks change, the safety isolation distance and airspace capacity will not be dynamically adjusted, potentially allowing drones to enter despite biological threats, thereby increasing the risk of collisions. This embodiment achieves accurate quantification and dynamic perception of biological threats by acquiring real-time biological motion characteristic data and calculating biological activity dynamic flux and biological spatial density. This allows for the dynamic expansion of the drone's safety isolation distance and the calculation of the real-time changing effective available volume and dynamic flight capacity. This dynamic adjustment mechanism significantly improves the safety redundancy of airspace and avoids the safety hazards caused by static management.

[0060] Furthermore, most existing collision avoidance strategies rely on individual drones' perception for temporary avoidance, lacking a capacity warning and traffic scheduling mechanism based on bio-density at the macro-grid level. This can easily lead to local airspace congestion or even cascading collisions. In the example above, this embodiment uses dynamic flight capacity as a constraint, combined with the actual number of drones and their outflow rate, to calculate the grid-permitted inflow rate, and triggers refined intervention commands to external drones based on this rate. This grid-level capacity warning and traffic scheduling mechanism can provide closed-loop control of airspace traffic at a macro level, avoiding local congestion and cascading reactions that may result from individual drone avoidance. For example, when the grid capacity is saturated, the system will proactively refuse entry to external drones, rather than waiting for individual drones to detect danger before taking avoidance measures, thereby reducing risks at the source.

[0061] Finally, management models that partially consider environmental risks mainly rely on preset empirical thresholds for coarse classification, making it difficult to achieve precise quantitative mapping between environmental risks and the three-dimensional physical properties of low-altitude airspace. They also struggle to dynamically adjust available capacity based on actual collision risks, resulting in significant deficiencies in achieving a refined balance between safety assurance and airspace utilization. This embodiment introduces precise quantitative indicators such as biodynamic flux and biospace density, and combines them with the physical geometric properties of the UAV and system control response delay to construct a mathematical model for dynamically expanding the safety isolation distance and calculating the effective available volume, achieving a precise mapping between environmental risks and airspace physical properties. This enables the airspace management system to dynamically and precisely adjust available capacity based on actual collision risks, thereby maximizing the utilization of low-altitude airspace while ensuring flight safety. For example, when the biological threat is low, the safety isolation distance expansion is small, and the airspace capacity is large, improving airspace utilization efficiency; while when the biological threat is high, the safety isolation distance expansion is large, and the airspace capacity decreases accordingly, prioritizing flight safety.

[0062] Preferably, the calculation of the biodynamic flux characterizing the intensity of the bio-crossing threat, and the bio-spatial density of the grid, specifically includes:

[0063] Calculate the vector difference between the absolute velocity vector of each biological target and the average absolute velocity vector of the UAV swarm to obtain the characteristics of the relative motion speed distribution;

[0064] Based on the distribution characteristics of relative motion speed and the total number of biological targets detected, the aggregation calculation is performed, and the spatial dimension is normalized according to the total three-dimensional geometric volume to output the biodynamic flux.

[0065] The ratio of the total number of biological targets detected to the total three-dimensional geometric volume is used to obtain the biological spatial density.

[0066] Specifically, the mathematical formulas for calculating the biodynamic flux characterizing the intensity of biological crossing threats, and the biospace density of the grid are as follows:

[0067]

[0068]

[0069] in, Indicates the first Each spatial grid at time The biodynamic flux, in units of ; Represents the total three-dimensional geometric volume, in units of ; Indicates at time The total number of biological targets detected; Indicates the traversal index number of the biological target; This represents the absolute velocity vector of each biological target, in units of... ; Indicates at time The average absolute velocity vector, in units of ; Indicates the first The relative motion rate of each biological target with respect to the drone, in units of ; Indicates the first Each spatial grid at time Biological spatial density, in units of .

[0070] Biological activity dynamics flux This is an indicator that measures the overall activity level and potential collision risk of biological targets relative to a swarm of drones within a specific airspace grid. The concept lies in quantifying the threat intensity posed by individual biological entities traversing a swarm of drones. This flux can be calculated based on real-time monitored individual biological target velocities, the average velocity of the drone swarm, and the grid volume. For example, various detection methods such as radar, lidar, visual sensors, or acoustic sensors can be used to acquire real-time trajectory and velocity information of biological targets within the airspace, combined with the drones' own flight data, to calculate the biological activity dynamics flux. (Biological spatial density) This density characterizes the degree of aggregation of biological targets within a specific airspace grid, i.e., the number of biological individuals per unit volume. This density reflects the congestion level of the airspace and the likelihood of multiple encounters between UAVs and biological targets. The biological spatial density can be calculated by statistically analyzing the total number of biological targets detected within a specific airspace grid over a specific time period and dividing by the fixed three-dimensional geometric volume of that grid. For example, once the monitoring system identifies and counts all biological individuals within the grid, this definition can be directly applied for calculation. In the above formula, Indicates the first The total three-dimensional geometric volume of a spatial grid is a predefined and fixed physical quantity used to define the physical boundary of the spatial grid. Indicates the first Each spatial grid at time The total number of biological targets monitored, a figure obtained through real-time detection and counting. Indicates the first The absolute velocity vector of each biological target is measured in real time by the biological target tracking system. Indicates the first A swarm of drones within a single airspace grid at any given time The average absolute velocity vector is obtained through the UAV flight management system or onboard sensors. Indicates the first The relative speed of motion of a biological target with respect to a drone is a parameter for assessing collision risk.

[0071] This application's approach standardizes and quantifies the calculation process of biological activity dynamic flux and biological spatial density by introducing precise mathematical formulas. Specifically, biological activity dynamic flux... The calculation comprehensively considers the grid volume, the number of biological targets, and the relative motion rate of each biological target relative to the UAV swarm, thus dynamically and accurately reflecting the intensity of the potential crossing threat posed by biological targets to UAV flight. Simultaneously, the biological spatial density... The calculation directly quantifies the number of biological targets per unit volume, intuitively reflecting the degree of airspace congestion. These precisely calculated parameters serve as inputs for subsequent steps (e.g., dynamically expanding the safe isolation distance of UAVs, calculating the effective usable volume of the target airspace grid), ensuring that the entire low-altitude UAV flight dynamic monitoring and airspace refinement management system can make decisions based on reliable, physically meaningful data. In this way, this application avoids the empirical judgments or fuzzy estimations that may exist in traditional methods, significantly improving the accuracy and real-time performance of biological threat assessment, and providing a solid data foundation for achieving refined airspace management.

[0072] The following is a specific example to illustrate this. As a particular implementation method, assume a specific spatial grid. In, its total three-dimensional geometric volume Given, for example, 1000 cubic meters. At a certain moment... The monitoring system detected that there were within the grid. A biological target, for example, detecting 5 birds. For each detected biological target... The system obtains its current absolute velocity vector. Simultaneously, the system also obtained the average absolute velocity vector of the drone swarm within the grid. To calculate the dynamic flux of biological activity. The system will first calculate each biological target Magnitude of relative motion speed to the drone swarm Then, sum the relative motion rates of all biological targets and divide the sum by the grid volume. For example, if there are three birds within the grid, calculate their relative velocities relative to the swarm of drones, then sum these three relative velocities and divide by the grid volume. Simultaneously, to calculate the biological spatial density... The system directly reports the total number of biological targets monitored. Divide by mesh volume For example, if the grid volume is 1000 cubic meters and 5 birds are detected, the biological spatial density is 5 / 1000. Through these calculations, the system can obtain the threat intensity of biological activity within the grid to the UAV flight and the density of biological distribution at the current moment.

[0073] Through the above technical solution, this application provides a precise and quantitative method for calculating the dynamic flux of biological activity and the spatial density of organisms. This calculation method, based on physical quantities and mathematical models, overcomes the limitations of empirical or qualitative assessments that may exist in traditional methods, making the assessment of low-altitude biological threats more objective and accurate. Since the dynamic flux of biological activity and the spatial density of organisms are key input parameters for subsequent dynamic expansion of safety isolation distance, calculation of effective usable volume, and dynamic flight capacity, their precise calculation directly improves the sensitivity and accuracy of the entire airspace management system in perceiving biological risks. This not only ensures the safety of UAVs flying in complex low-altitude environments and effectively avoids collisions with non-cooperative biological targets, but also enables airspace capacity assessment and scheduling decisions based on more reliable data, thereby achieving efficient utilization and refined management of low-altitude airspace resources and avoiding waste of airspace resources or safety risks caused by overly conservative or overly aggressive assessments.

[0074] Preferably, the safe isolation distance of the dynamically expanded UAV, and the effective usable volume of the target airspace grid, specifically include:

[0075] Construct a multidimensional collision prediction feature quantity that includes biological activity dynamic flux, windward cross-sectional area, and total physical response delay time;

[0076] Establish an amplification mapping rule that is positively correlated with the expected feature quantity of multi-dimensional collision, apply the amplification mapping rule to the basic physical braking safety distance of the UAV, and obtain the expanded safety isolation distance;

[0077] Based on the cubic envelope characteristics of biological spatial density and expanded safe isolation distance, the proportion of the repulsive volume of the spatial domain to biological activity is calculated.

[0078] Subtract the space lost due to the proportion of the repulsive volume from the total 3D geometric volume, and output the effective usable volume after applying non-negative boundary constraints;

[0079] Specifically, the mathematical model for calculating the safe isolation distance of dynamically expanding UAVs and the effective usable volume of the target airspace grid is as follows:

[0080]

[0081]

[0082] in, Indicates the first Each spatial grid at time The expanded safety separation distance, in units of ; This indicates the basic physical braking safety distance for drones, in units of... ; Represents the dynamic flux of biological activity, in units of ; This represents the cross-sectional area facing the wind, in units of... ; Represents the total physical response delay time, in units of ; Indicates the first Each spatial grid at time Effective usable volume, in units of ; Represents the total three-dimensional geometric volume, in units of ; Pi is a constant. Represents biological spatial density, in units of .

[0083] Among them, the safe isolation distance of dynamically expanding drones It refers to the first Each spatial grid at time Considering the threat of biological activity, the minimum safe distance that a drone needs to maintain is not fixed but dynamically adjusted according to real-time biological threats. Its purpose is to ensure that the drone has sufficient reaction time to perform avoidance maneuvers in environments with biological interference, thereby significantly reducing the risk of collision with biological targets. This distance can be calculated using a mathematical model, such as the exponential function model shown in this application. This model comprehensively considers the basic physical braking safe distance of a drone in an environment without biological interference. Biological activity dynamics flux The windward cross-sectional area of ​​the UAV along its flight direction And the total physical response time from when the surveillance system detects a biological target to when the drone performs an avoidance maneuver. Multiple factors, including the movement trajectory of biological targets and the maneuverability of drones, can be considered. Furthermore, the required minimum safe distance can be dynamically predicted and adjusted based on real-time sensor data and predictive algorithms.

[0084] Calculate the effective usable volume of the target spatial grid. It refers to the first Each spatial grid at time After deducting the space occupied by biological targets and their dynamically expanded safe isolation distance, the actual three-dimensional spatial volume that the UAV can safely use is determined. Its purpose is to accurately quantify the actual carrying capacity of the airspace grid, providing fundamental data for subsequent flight capacity calculations and scheduling decisions, and preventing UAVs from entering areas with dense biological activity or high risk. This volume can be calculated using a mathematical model, such as the formula shown in this application, which calculates the volume of the three-dimensional space that the UAV can safely use. The total 3D geometric volume of a spatial grid Biological spatial density and the safe isolation distance after dynamic expansion Factors such as these are taken into account, and the calculation results are ensured to be non-negative. Alternatively, three-dimensional spatial geometric modeling and voxelization techniques can be used to model each biological target and its dynamically expanded safety isolation area as three-dimensional obstacles. Then, the voxel set occupied by these obstacles can be subtracted from the total mesh volume to obtain the effective usable volume.

[0085] The proposed solution first acquires real-time UAV status data and motion characteristic data of non-cooperative low-altitude organisms within the target airspace grid, and then calculates the biodynamic flux characterizing the intensity of the biological crossing threat. and the biological spatial density of the grid Based on this, and considering the physical and geometric properties of the drone (such as the windward cross-sectional area of ​​the drone along its flight direction)... Basic physical braking safety distance for drones in environments free from biological interference (Available through the product specification manual) and system control response latency (i.e., the total physical response time from when the monitoring system detects a biological target to when the drone performs an avoidance maneuver). ), utilizing the dynamic flux of biological activity The safe isolation distance of the drone is dynamically expanded using an exponential function model. This exponential model ensures that the required safety distance increases non-linearly with the increase of biological threats, thus providing a more robust safety buffer. Subsequently, the dynamically expanded safety isolation distance is utilized. and biological spatial density Calculate the effective usable volume of the target spatial grid. This computational model accurately quantifies the actual usable airspace under biological threats by subtracting the volume occupied by biological targets and their safe isolation zones from the total grid volume. This mechanism of dynamically adjusting safety distances and calculating effective usable volume enables airspace management systems to adaptively adjust the safety margin of UAVs based on real-time biological threat conditions and accurately assess the actual carrying capacity of airspace, thereby maximizing airspace utilization efficiency while ensuring flight safety.

[0086] Windward cross-sectional area Acquisition and calibration methods

[0087] CAD model projection method (suitable for customized models): The 3D design digital model of the UAV is directly imported into the modeling software. The normal vector of the projection plane is set to be parallel to the UAV's designed cruise flight direction, and the software automatically calculates the internal area of ​​the projection outline.

[0088] The physical grid projection counting method utilizes the principles of optical projection and standard graph paper for area calculation. The specific steps are as follows:

[0089] Step 1 (Attitude Fixation): In a dark room or at night, fix the UAV under test with a bracket and adjust its pitch angle to a standard horizontal cruise flight attitude.

[0090] Step 2 (Light Source and Screen Setup): Place a standard-sized grid (e.g., with a side length of [missing information]) directly behind the drone. Use a backdrop or cardboard with a square grid pattern. Place a strong point light source at a considerable distance directly in front of the drone (ensuring the light is approximately parallel).

[0091] Step 3 (Contour Drawing): The light projects the outline of the drone's windward side onto the grid paper behind it. The tester uses a pen to trace the closed projection outline of the drone on the grid paper along the edge of the shadow.

[0092] Step 4 (Mesh Counting for Area): Remove the grid paper and manually count the number of grids contained within the outline. Count a complete grid as 1, and count grids that do not fill a full grid at the outline edge as 0.5. Multiply the total number of grids by the area of ​​a single standard grid (e.g., ...). This gives the windward cross-sectional area of ​​the drone. .

[0093] Basic physical braking safety distance Experimental calibration method:

[0094] This parameter characterizes the shortest physical distance required for the UAV to come to a complete stop (or decelerate to a safe cruise threshold) within an undisturbed, ideal airspace from the triggering of a braking command. Calibration must be performed through closed-loop kinematic testing at a test flight field.

[0095] Step 1 (Extreme Cruise): In a test airspace with no wind or light wind, control the drone at the maximum cruise speed designed for this model. Flight in a straight, level line.

[0096] Step 2 (Emergency Braking Trigger): The ground station sends an emergency hovering or full-power reverse thrust braking command to the UAV, and at the same time, the high-precision RTK-GPS system records the initial coordinates at the moment of triggering.

[0097] Step 3 (Trajectory Calculation): After the UAV's velocity relative to the ground decreases to zero and its attitude stabilizes, record the termination coordinates. Calculate the three-dimensional straight-line distance between the two points, which is the pure mechanical braking distance. .

[0098] Step 4 (System Delay Compensation): Combining the total delay time of UAV flight control command communication and mechanical execution. The final calibration formula is: (in (This is the engineering safety redundancy factor, typically taken as 1.1 to 1.2)

[0099] The following is a concrete example to illustrate this. Suppose at a certain moment... , No. Biological activity dynamics flux of a spatial grid for Biological spatial density for The basic physical braking safety distance for drones for The windward cross-sectional area of ​​the drone along its flight direction for Total physical response delay of the system for The total three-dimensional geometric volume of this spatial grid. for First, calculate the safe isolation distance after dynamic expansion according to the formula. Substituting the values, we can obtain Next, the dynamically expanded safety isolation distance is utilized. and biological spatial density Calculate the effective usable volume Substituting the values, we get... .in ,but .therefore, This indicates that at this level of biological threat intensity, the spatial grid has no usable volume. If the biological threat is lower, for example... for ,but ,at this time ,but This indicates that even with low biological activity flux, a high biological spatial density can result in zero effective usable volume. If the biological spatial density is even lower, for example... for ,at this time The above calculations clearly demonstrate how the safe isolation distance and effective usable volume dynamically adjust based on real-time biological threats.

[0100] Through the above technical solution, this application can dynamically adjust the safe isolation distance of UAVs based on real-time bio-activity dynamics flux and bio-spatial density, thereby avoiding the problems of over-conservatism or insufficient safety that may result from a fixed safe distance. Simultaneously, by accurately calculating the effective usable volume of the airspace grid, this application provides a more accurate and reliable data foundation for subsequent airspace capacity assessment and UAV scheduling, enabling the airspace management system to more effectively balance flight safety and airspace utilization efficiency, and significantly improving the adaptability and robustness of low-altitude UAV flight dynamic monitoring and refined airspace management methods.

[0101] Preferably, three-dimensional spatial geometric calculations are performed to obtain the dynamic flight capacity of the target airspace grid, specifically including:

[0102] Calculate the ratio between the effective usable volume and the volume of the security isolation envelope constructed by a single UAV based on the expanded security isolation distance;

[0103] Perform floor or truncation operations on the multiple relationship, and use the obtained discrete integer values ​​as dynamic flight capacity;

[0104] Specifically, the formula for calculating the dynamic flight capacity of the target airspace grid by performing three-dimensional spatial geometric calculations is as follows:

[0105]

[0106] in, Indicates the first Each spatial grid at time Dynamic flight capacity; This represents the floor function; Indicates the effective usable volume, in units of ; This represents the expanded safe separation distance, in units of... .

[0107] This application's solution solves the problem of insufficient data for precise airspace management by performing three-dimensional spatial geometric calculations on the target airspace grid to obtain its dynamic flight capacity. Specifically, it addresses the issue of not being able to directly perform precise airspace management based solely on the available effective volume and safe isolation distance. Safety isolation distance after dynamic expansion Based on this, this scheme introduces a precise calculation model. This model will effectively utilize the available volume. Consider the total space available for drone use, and the dynamically expanded safety isolation distance. This is considered as the minimum safe space required for each drone in the current biological threat environment. (The effective usable volume is then considered.) Divide by the product of the security isolation cube required for a single drone This allows us to determine the theoretically achievable number of drones within the given spatial grid. Since the number of drones must be an integer, a floor function is used. To ensure the practical operability of the calculation results, thus obtaining the first... Each spatial grid at time Dynamic flight capacity that can be safely accommodated This calculation method, which comprehensively considers factors such as complex biological threats, UAV physical attributes, and system response latency, directly transforms the effective usable volume and safe isolation distance into a quantifiable and specific indicator for airspace scheduling. It enables airspace management systems to dynamically and accurately assess the carrying capacity of each grid based on real-time environmental changes, providing a solid data foundation for subsequent UAV inflow rate calculations and the triggering of refined intervention commands, thus greatly improving the precision and security of airspace management.

[0108] It should be noted that this invention employs a spherical model (i.e., introduces a spherical model) when calculating the biological repulsion volume. (coefficients), while a cube model (i.e., coefficients) was used when calculating the dynamic flight capacity of the UAV. This difference in geometric design is not an oversight in mathematical derivation, but rather a precise adaptation to the actual business scenarios of low-altitude traffic engineering: First, for non-cooperative low-altitude organisms, due to the highly unpredictable and isotropic flight trajectories of birds or insect swarms (i.e., equal probability of maneuvering in any direction in three-dimensional space), their repulsive envelope of traversing threats is modeled as a continuous perfect sphere, which most realistically and rigorously conforms to the biological collision hazard boundaries in the physical world. Second, for controlled-flight drones, modern low-altitude airspace management systems (UTMs) typically employ a spatial voxelization grid scheduling logic to reduce computational overhead and achieve efficient flight path allocation. That is, the system no longer divides complex spherical flight corridors for drones, but instead discretizes the safe isolation space into individual grids with sides of length [missing information]. The standard cubic isolator voxel. Therefore, the effective usable volume... Divide directly by the volume of a single cubic element. This aligns with the engineering practice standards for allocating flight sorties based on discrete block grids in digital airspace. This hybrid geometric modeling approach, which deducts biological threats spherically and discretizes UAV capacity using blocks, ensures absolute safety redundancy in risk assessment while also considering the computational economy of the underlying data structure of the airspace scheduling system.

[0109] As a specific implementation method, suppose at a certain moment The first step has been calculated through the aforementioned steps. Effective usable volume of each target spatial grid The safe isolation distance is 10,000 cubic meters and dynamically expanded. The grid is 20 meters. At this point, to determine the maximum number of drones that the grid can safely accommodate, the system will perform a three-dimensional spatial geometric calculation. Specifically, the effective usable volume will be... Divide by the cube of the safe isolation distance, i.e. cubic meters. Then, divide 10,000 cubic meters by 8,000 cubic meters to get 1.25. Finally, round the result down to the nearest integer. Therefore, the spatial grid at the current moment... Dynamic flight capacity It was determined to be a single sortie. This means that although it could potentially accommodate more in terms of volume, the grid can only safely hold one drone at this moment, considering the minimum safe isolation space required for each drone under the current biological threat.

[0110] The above technical solution directly transforms the two key parameters—dynamically expanded safety separation distance and effective usable volume—into a specific and quantifiable dynamic flight capacity indicator through precise three-dimensional spatial geometric calculations. This solves the problem that airspace managers find it difficult to intuitively and accurately assess airspace carrying capacity in complex dynamic environments. This dynamic flight capacity... This data can reflect the actual carrying capacity limit of airspace under biological threats in real time, providing a direct and reliable basis for subsequent calculation of grid permission inflow rates. Therefore, based on this precise capacity data, the airspace management system can make more timely and accurate scheduling decisions, effectively avoiding airspace overload and potential biological collision risks, thereby significantly improving the safety of low-altitude UAV flights, airspace utilization efficiency, and overall management level.

[0111] Preferably, calculating the grid permission inflow rate specifically includes:

[0112] Assess the airspace capacity margin between dynamic flight capacity and the actual number of UAVs;

[0113] Distribute the remaining airspace capacity over a fine-grained scheduling time step to obtain the basic permit injection rate;

[0114] The base permit injection rate is linearly superimposed with the outflow rate within the target spatial grid at the current moment to output the grid permit inflow rate;

[0115] Specifically, the specific model formula for calculating the grid permitted inflow rate is as follows:

[0116]

[0117] in, Indicates the first Each spatial grid at time Mesh permit import rate, in units of ; Indicates at time Monitored outflow rate, in units of ; Indicates dynamic flight capacity; Indicates at time The actual number of drones as monitored and statistically analyzed; Indicates the fine-grained scheduling time step, in units of .

[0118] Specifically, the grid license import rate This refers to the maximum allowable rate by which an airspace management system permits external drones to safely enter a specific airspace grid. Its purpose is to provide a quantified, real-time access standard to avoid airspace overload or potential biological collision risks. This rate can be dynamically adjusted based on real-time airspace conditions, for example, through calculation and updates by the airspace management system based on preset strategies or algorithms, or through manual intervention. Actual outflow rate. This refers to the rate at which the number of drones normally departing from a target airspace grid changes over time at a specific moment. This rate can be obtained by real-time monitoring of drone crossings at the airspace grid boundaries, for example, through radar, visual sensors, or the drone's own location reporting system. The acquisition method can include counting the number of drones leaving the grid and averaging it over a time window, or estimating it based on drone flight plans using a predictive model. Dynamic flight capacity This represents the maximum number of drones that a target airspace grid can safely accommodate at a specific time. This capacity is calculated based on the effective available volume of the airspace and the dynamically expanded safe isolation distance, reflecting the actual carrying capacity of the airspace considering biological threats. Its purpose is to provide an upper limit reference for calculating the grid's permitted inflow rate, ensuring that the airspace does not exceed its safe carrying limit due to excessive inflow. Actual number of drones. This refers to the total number of drones actually present within a target airspace grid at a specific moment. This number can be obtained in real time by an airspace monitoring system that counts all registered or detected drones within the grid. For example, the system can continuously track the position of each drone and determine whether it is within the current grid range, thus accumulating the count. Fine-grained scheduling time step. This is the periodic parameter used by the airspace management system to update fine-grained scheduling instructions. For example, it can be set to anywhere from a few seconds to tens of seconds. Fine-grained scheduling time step. Specific value selection scheme:

[0119] Get the lower limit of system hardware physical refresh rate : Obtain the slowest scan cycle of the current airspace surveillance equipment (such as radar, photoelectric sensors) and the minimum communication interval of the airspace management system data link, and take the larger of the two as the lower limit of the system's physical refresh. This lower limit ensures that the system will not issue overclocking commands beyond the underlying hardware's execution capabilities (such as setting...) under extremely congested conditions. ).step

[0120] Calculate the upper limit of safe silence based on space travel limits and biological invasion dynamics. To prevent UAVs from "blindly flying beyond their designated boundaries" during the silent interval between two scheduling command cycles or from failing to respond adequately to sudden non-cooperative biological invasions, the system employs dual constraint calculations, combining the physical boundary of the grid with the biological early warning response time. Specifically, it obtains the minimum physical space side length of the target airspace grid. Maximum speed limit for drones within the grid The system is set up for drones. Within a very short timeframe of traversing the grid, at least one must be issued to it. Second (usually in engineering) Intervention commands. Simultaneously, obtain the longest tolerance warning response time constant of the surveillance system to highly dynamic, non-cooperative biological invasions. (For example, taking experience points) to Taking the smaller of the two values, the formula for the maximum safe silence time limit is derived as follows: .

[0121] Dynamic interpolation calculation and extreme value boundary protection based on real-time spatial saturation rate. (Time acquisition) Actual number of UAVs within the target airspace grid And the dynamic flight capacity obtained through three-dimensional spatial geometric calculations in the preliminary steps. To prevent division-by-zero system failures caused by capacity reduction in extreme environments, and to prevent scheduling frequencies from exceeding physical limits due to spatial overcapacity, the system constructs a piecewise interpolation model with extreme value truncation protection. Its specific logic control and calculation formulas are as follows: If or If the airspace is determined to be in a high-risk state of capacity failure or severe overcapacity, the system triggers the fastest response mechanism and directly outputs the lower limit of the system hardware physical refresh: ;like and If the airspace is determined to be within the normal adjustable range, then negative correlation linear interpolation calculation is performed using the airspace saturation rate: ,in, This represents the fine-grained scheduling time step of the final output, in units of... The aforementioned extreme value boundary protection model ensures that, regardless of how severe the sudden environmental conditions, the system scheduling time step is always strictly constrained within a certain range. Within an absolutely safe and physically feasible closed interval.

[0122] The solution in this application utilizes the dynamic flight capacity of the airspace grid. The actual number of drones in the grid Actual outflow rate of drones and fine-grained scheduling time steps Combined, this constructs a real-time, dynamic mesh permission import rate. The computational model. The core of this model lies in first calculating the remaining capacity of the current airspace grid, i.e., the dynamic flight capacity. Compared with the actual number of drones The difference represents the additional number of drones the airspace can accommodate at the current moment. Then, this remaining capacity is divided by the fine-tuning scheduling time step. This is then converted into a rate that can be incorporated in the next scheduling cycle. Based on this, the actual outflow rate of drones normally flying out of the grid is further considered. This means that whenever a drone leaves the grid, a corresponding amount of airspace capacity is released, allowing an equal number of external drones to safely merge in without increasing the total number of drones within the grid or exceeding its dynamic flight capacity. In this way, the system can assess the airspace's capacity in real time and accurately, quantifying it into actionable merge rate commands. This calculation method not only considers the static geometric characteristics of the airspace, but more importantly, it incorporates the influence of biodynamic flux and biospace density on safe isolation distances and effective available volume, thus enabling dynamic flight capacity... This accurately reflects the airspace carrying capacity under biological threats. Therefore, the grid permit inflow rate calculated based on this... It can ensure that airspace resources are utilized to the maximum extent while maintaining airspace security, and achieve refined traffic management.

[0123] The following is a concrete example to illustrate this. Suppose at a certain moment... , No. Dynamic flight capacity of each airspace grid The actual number of drones in the grid is 10 sorties. There were 8 sorties. Simultaneously, the actual outflow rate of drones flying normally out of the grid was monitored. It is 0.1 (That is, one drone flies out on average every 10 seconds), with a set fine-grained scheduling time step. 10 Based on the above model formula, the mesh permission inflow rate can be calculated. This means that within the next 10-second scheduling period, the airspace grid can safely accommodate a total of 3 external drones. (Schedules), while taking into account that one drone flies out, so that the total number of drones in the grid will not exceed the dynamic flight capacity.

[0124] Through the aforementioned technical solution, the airspace management system can obtain a real-time, dynamic drone inflow rate limit that takes into account biological threats. This allows the system to precisely control the entry of external drones based on the actual airspace capacity, existing traffic conditions, and drone outflow, effectively avoiding airspace saturation and significantly reducing the risk of collisions between drones and non-cooperative low-altitude organisms. Furthermore, by combining dynamic flight capacity with actual stock and outflow rates, this solution can maximize the utilization of airspace resources, optimize drone traffic, and achieve efficient and safe refined management of low-altitude airspace.

[0125] Preferably, the specific control methods for triggering refined intervention commands for external UAVs based on the grid permission import rate include:

[0126] Determine the numerical boundaries of the grid permitted inflow rate;

[0127] If the grid's permitted inflow rate is less than or equal to zero, the target airspace grid is determined to be in a state of capacity saturation or high risk of biological collision. An entry denial command is issued to the external UAV that intends to fly into the target airspace grid, and the external UAV is triggered to perform evasion actions such as hovering and waiting in the current grid or flying around an adjacent available grid with remaining capacity.

[0128] If the grid permission merge rate is greater than zero, it is determined that the target airspace grid has the capacity to accept it. An access command is issued to the external UAV that intends to fly into the target airspace grid, and the actual physical flight rate of the external UAV is controlled to be less than or equal to the grid permission merge rate.

[0129] Among them, the grid permission import rate is determined. The numerical boundary is the starting point of the entire intervention mechanism. This step, by numerically judging the calculated grid allowable inflow rate, can clarify whether the current airspace grid is in a saturated, high-risk state, or has acceptance capacity. Its role is to transform continuous rate values ​​into discrete decision states, providing a basis for subsequent command issuance. Specifically, by setting one or more thresholds, continuous allowable inflow rate values ​​can be divided into different intervals, each interval corresponding to a different airspace state or management strategy. For example, a zero threshold can be set to divide the rate into positive and non-positive value zones, corresponding to acceptance capacity and no acceptance capacity / high-risk state, respectively. In addition, fuzzy logic or machine learning models can be combined, based on... By analyzing the numerical values ​​and trends of these values, we can dynamically assess the risk level and acceptance capacity of airspace, thereby enabling more refined boundary judgments rather than simply comparing thresholds.

[0130] If the permitted inflow rate of a grid is less than or equal to zero, the target airspace grid is determined to be in a state of capacity saturation or high risk of biological collision. An entry denial command is issued to any external UAV attempting to enter the target airspace grid, triggering the external UAV to either hover in place within its current grid or fly around an adjacent available grid with remaining capacity. When the permitted inflow rate of an airspace grid is zero or negative, it indicates that the grid cannot safely accommodate more UAVs or that there is a high risk of biological collision. This step aims to proactively avoid potential hazards and ensure airspace safety through mandatory denial and avoidance commands. The entry denial command can be sent to the flight control system of the external UAV via a wireless communication link. The command includes the target grid's entry denial identifier and a suggested avoidance strategy code. Upon receiving the command, the external UAV's autonomous navigation system will either hover in place or replan its flight path accordingly. The triggering of avoidance actions can be coordinated by the airspace management system, which recommends detour routes for the denied UAV based on the real-time status of surrounding grids, or requires the UAV to hover in place until the target grid becomes capable of accepting it.

[0131] If the grid's permitted merging rate is greater than zero, the target airspace grid is deemed capable of receiving drones. An access command is then issued to any external drone intending to enter the target airspace grid, controlling the external drone's actual physical merging rate to be less than or equal to the grid's permitted merging rate. When the permitted merging rate of the airspace grid is positive, it indicates that the grid still has safe reception capacity. This step aims to allow external drones to enter while ensuring safety, and to precisely control their entry rate to maintain stable and efficient airspace operation. The access command can be sent to the external drone via data link, containing confirmation of permission and a specific maximum permitted merging rate. Upon receiving this command, the external drone's flight control system automatically adjusts its flight speed to ensure it does not exceed this limit. Rate control is achieved by continuously monitoring the external drone's actual merging rate and comparing it with the permitted merging rate. If the actual merging rate exceeds the permitted merging rate, the airspace management system can resend the adjustment command, or the drone can autonomously adjust based on the received permitted rate.

[0132] The solution in this application addresses the grid permission import rate. Numerical boundary checks simplify complex dynamic airspace states into clear decision-making criteria. When the check shows that the grid's permitted inflow rate is less than or equal to zero, the system immediately identifies that the current target airspace grid is in a state of capacity saturation or high risk of biological collision. At this time, to avoid potential conflicts and dangers, the system issues an entry denial command to all external UAVs intending to enter the grid. This command not only prevents new UAVs from entering but also triggers external UAVs to perform evasive maneuvers such as hovering in place or flying around adjacent available grids, thereby proactively eliminating the risks caused by excessive inflow. Conversely, when the grid's permitted inflow rate is greater than zero, the system determines that the target airspace grid has the capacity to accept UAVs. At this time, it issues an entry permission command to external UAVs and strictly controls their actual physical inflow rate to not exceed the calculated permitted inflow rate. This refined intervention mechanism, based on dynamic permission inflow rate, transforms a series of complex parameters—such as the calculated biodynamic flux, biospace density, dynamically expanded safe isolation distance, effective available volume, and dynamic flight capacity—into executable, closed-loop scheduling commands. This ensures the safety and efficiency of UAV flight while considering biohazards and airspace capacity limitations. In this way, the airspace management system can respond to changes in the airspace environment in real time and with precision, effectively avoiding airspace congestion and biohazard collision risks, and achieving refined and adaptive management of low-altitude UAV flights.

[0133] As one specific implementation, the airspace management system can continuously receive and process real-time data from various airspace grids, including grid permission inflow rates. For example, when the system calculates a specific spatial grid... At any moment of for When a grid is detected as saturated or high-risk, the system immediately identifies it. At this point, the system sends a denial-of-entry command via its communication module to all external drones flying towards that grid. This command may include a specific denial code and suggested evasion strategies. For example, for approaching drone A, the command might instruct it to hover in place within its current grid and wait until the target grid is reached. It becomes a positive value; for drone B, if there is another adjacent drone nearby, and If the airspace grid has a positive value, the system may suggest adjusting the flight path and flying around the adjacent grid. Conversely, if the system calculates a different airspace grid... At any moment of for The system will then issue an access command to any external drone intending to fly into the grid, explicitly informing it of the maximum permitted flight speed. Upon receiving this command, the drone's flight control system will automatically adjust its flight speed to ensure that its actual speed upon entering the grid does not exceed [a certain threshold]. This allows for the orderly use of airspace while ensuring safety.

[0134] Through the aforementioned technical solution, this application directly transforms the dynamically calculated grid entry permission rate into executable, refined intervention commands, solving the technical problem of how to translate complex airspace status assessments into actual scheduling actions. Specifically, by clearly defining the numerical boundaries of the entry permission rate, it can promptly identify saturated or high-risk biological collision states in the airspace grid and immediately issue rejection commands and trigger evasive actions, thereby effectively preventing the aggregation of UAVs in dangerous or congested airspace and significantly reducing the risk of biological collisions and airspace conflicts. Simultaneously, when the airspace has the capacity to accept UAVs, by issuing access commands and precisely controlling the entry rate of external UAVs, it ensures the rational use of airspace resources and avoids potential chaos caused by disorderly entry. This closed-loop intervention mechanism based on real-time dynamic assessment makes low-altitude UAV airspace management more intelligent, refined, and safe, greatly improving the overall safety and efficiency of airspace operations.

[0135] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for monitoring the flight dynamics of low-altitude unmanned aerial vehicles (UAVs) and for refined airspace management. Includes, characterized in that, includes: Real-time acquisition of UAV status data and motion characteristic data of non-cooperative low-altitude organisms within the target airspace grid; calculation of biological activity dynamic flux characterizing the intensity of biological crossing threat; and biological spatial density within the grid. Based on the physical geometry properties of UAVs and the system control response delay, the safe isolation distance of UAVs is dynamically expanded using the bioactivity dynamics flux, and the effective usable volume of the target airspace grid is calculated using the biospace density and the expanded safe isolation distance. By combining the effective available volume with the expanded safe isolation distance, a three-dimensional spatial geometric solution is performed to obtain the dynamic flight capacity of the target airspace grid; Using dynamic flight capacity as a constraint, and combining the actual number of UAVs in the target airspace grid at the current moment with the outflow rate, the grid permission inflow rate is calculated. Based on the grid permission inflow rate, refined intervention commands are triggered for external UAVs to achieve closed-loop control of flight scheduling.

2. The method for monitoring the flight dynamics of low-altitude unmanned aerial vehicles and refining airspace management according to claim 1, characterized in that, The UAV status data specifically includes the average absolute velocity vector of the UAV swarm within the target airspace grid; the motion characteristic data of non-cooperative low-altitude organisms specifically includes the absolute velocity vector of each detected biological target within the target airspace grid, as well as the total number of biological targets detected; the target airspace grid has a fixed three-dimensional geometric volume.

3. The method for monitoring the flight dynamics of low-altitude unmanned aerial vehicles and refining airspace management according to claim 2, characterized in that, The calculation of biodynamic flux, characterizing the intensity of biological crossing threats, and the biospatial density of the grid, specifically includes: Calculate the vector difference between the absolute velocity vector of each biological target and the average absolute velocity vector of the UAV swarm to obtain the characteristics of the relative motion speed distribution; Based on the distribution characteristics of relative motion speed and the total number of biological targets detected, the aggregation calculation is performed, and the spatial dimension is normalized according to the total three-dimensional geometric volume to output the biodynamic flux. The biological spatial density is obtained by calculating the ratio of the total number of biological targets detected to the total three-dimensional geometric volume.

4. The method for monitoring the flight dynamics of low-altitude unmanned aerial vehicles and refining airspace management according to claim 3, characterized in that, The physical geometric properties of the drone specifically include the windward cross-sectional area of ​​the drone along the flight direction, and the basic physical braking safety distance of the drone in an environment without biological interference; the system control response delay is specifically the total physical response delay time from the detection of biological targets by the monitoring system to the drone executing avoidance actions.

5. The method for monitoring the flight dynamics of low-altitude unmanned aerial vehicles and refining airspace management according to claim 4, characterized in that, The safe isolation distance of the dynamically expanded UAV is calculated, and the effective usable volume of the target airspace grid is calculated, specifically including: Construct a multidimensional collision prediction feature quantity that includes biological activity dynamic flux, windward cross-sectional area, and total physical response delay time; Establish an amplification mapping rule that is positively correlated with the expected feature quantity of multi-dimensional collision, apply the amplification mapping rule to the basic physical braking safety distance of the UAV, and obtain the expanded safety isolation distance; Based on the cubic envelope characteristics of biological spatial density and expanded safe isolation distance, the proportion of the repulsive volume of the spatial domain to biological activity is calculated. The total 3D geometric volume is reduced by the amount of space lost due to the proportion of the repulsive volume, and after applying non-negative boundary constraints, the effective usable volume is output.

6. The method for monitoring the flight dynamics of low-altitude unmanned aerial vehicles and refining airspace management according to claim 5, characterized in that, Perform three-dimensional spatial geometric calculations to obtain the dynamic flight capacity of the target airspace mesh, specifically including: Calculate the ratio between the effective usable volume and the volume of the security isolation envelope constructed by a single UAV based on the expanded security isolation distance; Perform a floor or truncation operation on the multiple relationship, and use the obtained discrete integer value as the dynamic flight capacity.

7. The method for monitoring the flight dynamics of low-altitude unmanned aerial vehicles and refining airspace management according to any one of claims 1-6, characterized in that, In the process of calculating the grid permission inflow rate, the fine-grained scheduling time step set by the airspace management system is used as the instruction update cycle parameter.

8. The method for monitoring the flight dynamics of low-altitude unmanned aerial vehicles and refining airspace management according to claim 7, characterized in that, The calculation of the grid permit import rate includes: Assess the airspace capacity margin between dynamic flight capacity and the actual number of UAVs: Distribute the remaining airspace capacity over a fine-grained scheduling time step to obtain the basic permit injection rate; The base permit injection rate is linearly superimposed with the outflow rate within the target spatial grid at the current moment to output the grid permit inflow rate.

9. The method for monitoring the flight dynamics of low-altitude unmanned aerial vehicles and refining airspace management according to claim 8, characterized in that, The specific control methods for triggering refined intervention commands for external drones based on the grid permission import rate include: Determine the numerical boundaries of the grid permitted inflow rate; If the grid's permitted inflow rate is less than or equal to zero, the target airspace grid is determined to be in a state of capacity saturation or high risk of biological collision, and an entry denial command is issued to external drones that intend to fly into the target airspace grid. If the grid's permitted inflow rate is greater than zero, it is determined that the target airspace grid has the capacity to accept it, and an access command is issued to the external UAV intending to fly into the target airspace grid.

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