A low-altitude flight plan approval optimization method based on four-dimensional space-time capacity constraint

Abstract

This invention provides an optimization method for low-altitude flight plan approval based on four-dimensional spatiotemporal capacity constraints. The method first collects and standardizes four-dimensional profile data of approved and newly applied flight plans, divides the low-altitude airspace into four-dimensional units, and initializes capacity thresholds. Then, it maps approved plans to these four-dimensional units, calculates capacity occupancy, and checks the capacity constraints of newly applied plans. Subsequently, it detects conflict types and classifies conflict areas into risk levels. Next, it sorts conflict-affected plans according to task priority, and uses single or combined strategies of time, altitude, and speed, along with optimization algorithms, to achieve minimum disturbance adjustment. Finally, it outputs approval opinions. This invention achieves dynamic and precise assessment of low-altitude airspace spatiotemporal capacity, avoids flight conflicts at the source, improves approval efficiency and airspace resource utilization, and is suitable for large-scale, multi-task parallel operation scenarios in low-altitude environments. It can be applied to various low-altitude operation management platforms.

Description

Technical Field

[0001] This invention relates to the field of low-altitude airspace operation management and intelligent scheduling technology, and in particular to an optimization method for low-altitude flight plan approval based on four-dimensional spatiotemporal capacity constraints. Background Technology

[0002] In recent years, the low-altitude economy has developed rapidly, with the application scenarios of low-altitude drones expanding continuously, including logistics, urban patrol, emergency rescue, smart security, and urban air traffic. The number of aircraft in low-altitude airspace has continued to grow, and the operational mode of low-altitude airspace is gradually shifting from single-aircraft, low-density operation to large-scale, networked, and multi-tasking parallel operation. Flight plan approval, as a core link in ensuring low-altitude flight safety and maintaining airspace operational order, directly affects the utilization efficiency and operational safety level of low-altitude airspace resources due to its efficiency and accuracy.

[0003] Current low-altitude flight plan approval mechanisms primarily rely on static spatial conflict detection or simple time window judgments for review, with core methods including track spatial overlap detection and no-fly zone constraint verification. However, low-altitude flight activities exhibit significant spatiotemporal coupling characteristics. Flight tracks not only contain three-dimensional spatial location information (longitude, latitude, and altitude) but also time-dimensional information on arrival at and passage through various points, forming a four-dimensional operational profile with temporal attributes. Traditional approval methods, focusing only on point-based spatial or temporal conflicts, have several shortcomings: First, the approval process is overly conservative. To avoid direct conflicts, the system often restricts the route selection or takeoff and landing times of new flight plans, resulting in low efficiency in the utilization of low-altitude airspace resources. Second, potential conflicts are exposed late. Because there is no systematic assessment of the overall airspace capacity occupancy in the future, potential airspace congestion or flight conflicts are often only discovered during actual flights, significantly increasing the risks of low-altitude operations. Third, the system lacks dynamic adjustment capabilities and cannot optimize and adjust newly applied flight plans in real time under capacity constraints, making it difficult to adapt to high-density operation scenarios with multiple parallel low-altitude tasks. Fourth, the centralized conflict detection and approval model is prone to high computational complexity and long response latency in high-density flight application scenarios, limiting scalability and real-time performance. To ensure response speed, approval accuracy is often sacrificed.

[0004] While some studies have proposed the concept of using four-dimensional trajectories for low-altitude airspace management, existing landing systems only conduct compliance or point-based conflict detection during the flight plan approval process. They fail to perform capacity assessments based on the four-dimensional profiles of approved flight paths, broken down by time period and airspace unit, and cannot provide actionable minimum disturbance adjustment suggestions for new applications. Therefore, there is an urgent need to construct a four-dimensional spatiotemporal capacity modeling and constraint mechanism for large-scale low-altitude operations. This mechanism would provide a unified assessment of airspace spatiotemporal resource occupancy during the approval phase and enable intelligent optimization and adjustment of flight plans under capacity constraints, thereby improving the efficiency of low-altitude airspace resource utilization and operational safety. Summary of the Invention

[0005] The purpose of this invention is to provide an optimization method for low-altitude flight plan approval based on four-dimensional spatiotemporal capacity constraints. This method aims to solve the technical problems of existing low-altitude flight plan approvals, such as the lack of overall airspace spatiotemporal capacity assessment, intelligent adjustment capabilities without capacity constraints, and poor real-time performance and scalability in high-density scenarios. The invention achieves capacity-aware assessment and intelligent minimum disturbance adjustment for low-altitude flight plan approvals, while ensuring low-latency response and scalability of the approval system under high-concurrency applications.

[0006] According to one objective of the present invention, the present invention provides an optimization method for low-altitude flight plan approval based on four-dimensional spatiotemporal capacity constraints, comprising the following steps: Step 1: Data acquisition and preprocessing. Collect four-dimensional profile data of approved flight plans and newly applied flight plans. Divide the low-altitude airspace into four-dimensional units and initialize the capacity threshold. Standardize the track data. The four-dimensional unit is formed by combining three-dimensional spatial units and time slices. Step 2: Airspace capacity assessment, mapping approved flight plans to four-dimensional cells and calculating capacity occupancy, performing capacity constraint checks on newly applied flight plans, and marking overcapacity cells; Step 3: Conflict identification and risk analysis. Detect conflict types between new application plans and approved plans on a four-dimensional grid, and classify the risk level of conflict areas based on flight error-related factors. Step 4: Intelligent optimization and adjustment of flight plans. Conflicting plans are sorted according to flight mission priority, and single or combined adjustment strategies of time, altitude, and speed are adopted. The optimal adjustment scheme with the least disturbance is selected through optimization algorithms. Step 5: Approval decision output. Based on the capacity assessment, conflict analysis and optimization adjustment results, generate and output the approval opinion for the new application plan.

[0007] Furthermore, in step 1, the four-dimensional profile data includes longitude, latitude, altitude, time, and flight speed information; the three-dimensional spatial unit is a regular grid dividing the low-altitude airspace according to longitude, latitude, and altitude; the time slice is a time unit divided according to fixed time intervals; the trajectory of each flight plan is represented as a time parameterized function: ; in: Let i be the spatial plane coordinates of flight plan i at time t. Let i be the flight altitude at time t. Let t be a time variable. i start t is the start time of flight plan i. i end The end time of flight plan i; the set of approved flight plans is: , where n is the number of approved flight plans.

[0008] Furthermore, in step 1, the division accuracy of the three-dimensional spatial unit, the time interval of the time slice, and the capacity threshold of the four-dimensional unit are all determined based on airspace management rules, airspace level, or historical flight density statistics.

[0009] Furthermore, step 2 specifically includes: Step 2.1 Four-dimensional profile mapping: Map the time parameterization function of each approved flight plan to a four-dimensional cell G(i,j,k,l) ​​in the low-altitude airspace, and accumulate capacity occupancy information, where i,j,k are the spatial indices of the three-dimensional spatial cell, and l is the time index of the time slice; Step 2.2 Dynamic Capacity Calculation: Define an upper capacity limit C for each four-dimensional cell. max (i,j,k,l), calculate the capacity usage C. used (i,j,k,l) ​​and capacity utilization rate U(i,j,k,l); Step 2.3 Capacity Constraint Check: Map the new flight plan application to a four-dimensional cell, calculate the capacity occupancy increment, and mark the four-dimensional cells that exceed the capacity.

[0010] Furthermore, in step 2.2, the formula for calculating the capacity usage is: ; in, Let G(i,j,k,l) ​​be the trajectory occupancy identifier function. If the m-th approved flight plan occupies a four-dimensional cell G(i,j,k,l), then... (i,j,k,l)=1, otherwise (i,j,k,l)=0; Capacity utilization rate is defined as: ; when hour, This indicates that the four-dimensional unit has reached or exceeded the capacity threshold; In step 2.3, if any four-dimensional unit satisfies C used (i,j,k,l)+1>C max If (i,j,k,l), then the four-dimensional cell is determined to be an overcapacity cell.

[0011] Furthermore, in step 3, the conflict types include spatial overlap conflicts, temporal overlap conflicts, combined conflicts, and direct running conflicts; the direct running conflicts are obtained through continuous trajectory spacing verification, and the verification formula is: ; Where: x a ,y a ,z a Let x be the spatial coordinates of plan a at time t. b ,y b ,z b Let the spatial coordinates of plan b at time t be denoted as ; when At that time, D safe The preset safe flight distance is considered a direct operational conflict.

[0012] Furthermore, in step 3, the flight error-related factors include historical flight errors, trajectory prediction uncertainties, aircraft positioning errors, and air-to-ground communication delays; the risk levels are divided into three levels: low, medium, and high.

[0013] Furthermore, in step 4, after sorting by flight mission priority, the flight plans with higher priority remain unchanged, while only the flight plans with lower priority are optimized and adjusted; the adjustment strategy is specifically as follows: Time fine-tuning: Calculate the time adjustment amount Adjusted flight time ; Height optimization: Calculate height adjustment amount Adjusted flight altitude ; Speed ​​Adjustment: Calculate the speed adjustment amount Adjusted cruise speed ; Where, N k To reduce the number of flight plans within the four-dimensional unit, Cap k λ is the capacity threshold. t t is the time adjustment factor. i For the original transit time, Hl ayer For the height-level hierarchical interval, s∈{ 1,1} represents the upward or downward direction, z i The original cruising altitude, λ v v is the speed adjustment coefficient. i This is the original cruising speed.

[0014] Furthermore, in step 4, a multi-objective optimization algorithm or heuristic algorithm is used to achieve minimum perturbation optimization, and the objective function is set as the perturbation cost function: ; Among them, w t For time-adjusted weighting coefficients, w h For highly adjusted weighting coefficients, w v w is the weighting coefficient for speed adjustment. t +w h +w v =1; The constraints of the optimization algorithm include: the capacity usage of each four-dimensional unit does not exceed its capacity limit, the adjusted trajectory meets the requirements of safe distance between routes and no-fly zones, and the adjusted parameters meet the performance constraints of the aircraft itself.

[0015] Furthermore, in step 2, a heatmap of spatial capacity occupancy or a time-spatial occupancy matrix is ​​generated based on the capacity occupancy situation; in step 4, if there are multiple optimal adjustment schemes with the same disturbance cost, they are sorted a second time according to task execution time and energy consumption cost; in step 5, the approval opinions include two types: direct approval and approval with the attached optimal optimization adjustment scheme.

[0016] The technical solution of this invention constructs a low-altitude flight plan approval system through a four-dimensional spatiotemporal dimension. First, it completes the standardized processing of multi-source flight data and the four-dimensional unitization of airspace. Then, it conducts refined capacity assessment and multi-type conflict detection. Combined with task priority, it achieves intelligent, minimal-disturbance adjustment of flight plans, and finally outputs accurate approval opinions. This method overcomes the limitations of traditional static point-based conflict detection, can predict airspace congestion risks, avoid flight conflicts at the source, and simultaneously considers the real-time nature of approval and the efficiency of airspace resource utilization. It achieves dynamic, safe, and efficient management and control of low-altitude airspace, and is suitable for large-scale, multi-task parallel operation scenarios in low-altitude environments. Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is a flowchart of an embodiment of the present invention; Detailed Implementation The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0020] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. Furthermore, the terms "installed," "connected," and "linked" should be interpreted broadly; for example, they may refer to a fixed connection, a detachable connection, or an integral connection; they may refer to a mechanical connection or an electrical connection; they may refer to a direct connection or an indirect connection through an intermediate medium; and they may refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0021] Example 1 like Figure 1 As shown, a method for optimizing the approval of low-altitude flight plans based on four-dimensional spatiotemporal capacity constraints is proposed. The core of this method lies in using the four-dimensional profile information (longitude, latitude, altitude, and time) of approved flight plans to conduct a refined assessment of airspace capacity. During the approval stage, new flight plans are dynamically and intelligently optimized under capacity constraints to achieve safe and efficient utilization of high-density low-altitude airspace. Specifically, the method includes the following steps: Step 1: Data acquisition and preprocessing. Collect four-dimensional profile data of approved flight plans and newly applied flight plans, divide the low-altitude airspace into four-dimensional units and initialize the capacity threshold, and standardize the flight track data. Step 2: Airspace capacity assessment, mapping approved flight plans to four-dimensional cells and calculating capacity occupancy, performing capacity constraint checks on newly applied flight plans, and marking overcapacity cells; Step 3: Conflict identification and risk analysis. Detect conflict types between new application plans and approved plans on a four-dimensional grid, and classify the risk level of conflict areas based on flight error factors. Step 4: Intelligent optimization and adjustment of flight plans. Conflicting plans are sorted according to task priority, and single or combined adjustment strategies of time, altitude, and speed are adopted. The optimal adjustment scheme with the least disturbance is selected through optimization algorithms. Step 5: Approval decision output. Based on the capacity assessment, conflict analysis and optimization adjustment results, generate and output the approval opinion for the new application plan.

[0022] Specifically, in step 1, the four-dimensional profile data includes longitude, latitude, altitude, time, and flight speed information. The four-dimensional unit is formed by combining three-dimensional spatial units and time slices. The three-dimensional spatial unit is a regular grid that divides the low-altitude airspace according to longitude, latitude, and altitude, and the time slice is a time unit divided according to fixed time intervals.

[0023] In step 1, the trajectory of each flight plan is represented as a time-parameterized function: ; in: Let i be the spatial plane coordinates of flight plan i at time t. Let i be the flight altitude at time t. Let t be a time variable. i start t is the start time of flight plan i. i end The end time of flight plan i; the set of approved flight plans is: , where n is the number of approved flight plans.

[0024] In step 1, the division accuracy of the three-dimensional spatial unit, the time interval of the time slice, and the capacity threshold of the four-dimensional unit are all determined based on airspace management rules, airspace level, or historical flight density statistics.

[0025] Specifically, in step 2, the airspace capacity assessment includes: Step 2.1 Four-dimensional profile mapping: Map the time parameterization function of each approved flight plan to a four-dimensional cell G(i,j,k,l) ​​in the low-altitude airspace, and accumulate capacity occupancy information, where i,j,k are the spatial indices of the three-dimensional spatial cell, and l is the time index of the time slice; Step 2.2 Dynamic Capacity Calculation: Define an upper capacity limit C for each four-dimensional cell. max(i,j,k,l), calculate the capacity usage C. used (i,j,k,l) ​​and capacity utilization rate U(i,j,k,l); Step 2.3 Capacity Constraint Check: Map the new flight plan application to a four-dimensional cell, calculate the capacity occupancy increment, and mark the four-dimensional cells that exceed the capacity.

[0026] In step 2.2, the formula for calculating the capacity usage is: ; in, Let G(i,j,k,l) ​​be the trajectory occupancy identifier function. If the m-th approved flight plan occupies a four-dimensional cell G(i,j,k,l), then... (i,j,k,l)=1, otherwise (i,j,k,l)=0; Capacity utilization rate is defined as: ; when hour, This indicates that the four-dimensional unit has reached or exceeded the capacity threshold.

[0027] In step 2.3, there exists any four-dimensional element that satisfies: +1> If so, the four-dimensional unit is determined to be an overcapacity unit.

[0028] Specifically, in step 3, the conflict types include spatial overlap conflict, temporal overlap conflict, and combined conflict, as well as direct running conflict after checking the spacing between continuous trajectories.

[0029] The formula for checking the spacing between continuous trajectories is: ; Where: x a ,y a ,z a Let x be the spatial coordinates of plan a at time t. b ,y b ,z b Let the spatial coordinates of plan b at time t be denoted as ; when At that time, D safe The preset safe flight distance is considered a direct operational conflict.

[0030] In step 3, the reference factors for risk level classification include historical flight errors, trajectory prediction uncertainty, aircraft positioning errors, and air-to-ground communication delays. The risk levels are divided into three levels: low, medium, and high.

[0031] Specifically, in step 4, after sorting the conflict-related flight plans according to the priority of the flight missions, the flight plans with higher priority remain unchanged, and only the flight plans with lower priority are optimized and adjusted.

[0032] Step 4 involves adjustments including time fine-tuning, height optimization, and speed adjustment, specifically: 4.1.1 Time fine-tuning: Calculate the time adjustment amount Adjusted flight time ; 4.1.2 Height Optimization: Calculate the height adjustment amount Adjusted flight altitude ; 4.1.3 Speed ​​Adjustment: Calculate the speed adjustment amount Adjusted cruise speed ; Where, N k To reduce the number of flight plans within the four-dimensional unit, Cap k λ is the capacity threshold. t t is the time adjustment factor. i For the original transit time, Hl ayer For the height-level hierarchical interval, s∈{ 1,1} represents the upward or downward direction, z i The original cruising altitude, λ v v is the speed adjustment coefficient. i This is the original cruising speed.

[0033] In step 4, a multi-objective optimization algorithm or heuristic algorithm is used to achieve minimum perturbation optimization, and the objective function is set as the perturbation cost function: ; Among them, w t For time-adjusted weighting coefficients, w h For highly adjusted weighting coefficients, w v w is the weighting coefficient for speed adjustment. t +w h +w v =1.

[0034] The constraints of the optimization algorithm include: ① The capacity usage of each four-dimensional unit must not exceed its capacity limit; ② The flight path after the flight plan adjustment meets the requirements of safe distance between routes and no-fly zones; ③ The parameters after the flight plan adjustment meet the performance constraints of the aircraft itself.

[0035] Specifically, in step 5, the approval opinion includes two types: if the new application plan can meet the capacity constraints and conflict avoidance requirements without adjustment, it will be approved directly; if adjustment is required, the optimal adjustment plan will be output together with the approval opinion.

[0036] In step 2, a heatmap of airspace capacity occupancy or a time-space occupancy matrix is ​​generated based on the capacity occupancy status to visually display the spatiotemporal capacity distribution of airspace.

[0037] In step 4, if there are multiple optimal adjustment schemes with the same disturbance cost, they are sorted a second time according to task execution time and energy consumption cost.

[0038] The method of this invention is applicable to low-altitude operation management platforms, unmanned aerial vehicle (UAV) monitoring systems, low-altitude airspace scheduling systems, and low-altitude integrated operation support platforms.

[0039] Example 2 This embodiment uses the approval of drone flight plans in the low-altitude airspace of a city as an application scenario. This airspace is a low-altitude open area surrounding the city's core area, with a flight altitude of 0-120 meters. It primarily carries two types of flight missions: drone logistics and urban inspection. There are currently 80 approved flight plans and 15 new flight plans awaiting approval. Figure 1 As shown, the specific implementation steps of the low-altitude flight plan approval optimization method based on four-dimensional spatiotemporal capacity constraints of the present invention are as follows: Step 1: Data Acquisition and Preprocessing 1.1 Acquisition of Approved Plans: Collect four-dimensional profile data of 80 approved flight plans, including the latitude and longitude of each waypoint, flight altitude (accuracy 0.1 meters), estimated time of passage (accuracy 1 second), and cruise speed (2~8 m / s). 1.2 Acquisition of new application plans: Collect the track points, flight altitude, estimated take-off and landing time and air speed of 15 flight plans pending approval, including 10 logistics plans and 5 inspection plans; 1.3 Airspace Division and Capacity Initialization: The low-altitude airspace was divided into a three-dimensional spatial unit grid with longitude of 0.001°, latitude of 0.001°, and altitude of 20 meters, resulting in a total of 100×100×6=60,000 three-dimensional spatial units. The time dimension was divided into time slices of 5 minutes each, with 288 time slices set for the next 24 hours, forming a four-dimensional unit G(i,j,k,l). Based on the airspace management rules and historical flight density, the upper limit C of the capacity of a typical four-dimensional unit was set. max =4, the upper limit of the four-dimensional unit capacity C for high-risk areas near buildings and transportation hubs. max =2; 1.4 Data standardization: Uniformly convert the spatial coordinates of all track data to the WGS84 coordinate system, the timestamps to Beijing time, the speed unit to m / s, and the altitude unit to meters; represent the flight trajectory of each flight plan as a time-parametric function: ; where: is the spatial plane coordinate; is the flight altitude; is the time variable; The set of approved flight plans is: .

[0040] Step 2: Airspace capacity assessment 2.1 Four-dimensional profile mapping: Map the time-parametric functions of the tracks of 80 approved flight plans to four-dimensional cells one by one, record the occupied flight plan numbers in each four-dimensional cell, and complete the accumulation of capacity occupancy information; 2.2 Dynamic capacity calculation: Calculate the capacity usage C used (i,j,k,l) and the capacity utilization rate U(i,j,k,l) of each four-dimensional cell, generate a capacity occupancy heat map of this airspace, where the capacity utilization rate U≥0.8 in 32 four-dimensional cells, and the capacity utilization rate in 8 four-dimensional cells ≥1, which are over-capacity cells, mainly concentrated in the airspace above the core logistics distribution channels and urban main roads; 2.3 Capacity constraint check: Map 15 newly applied flight plans to four-dimensional cells, calculate the capacity occupancy increment, and find that 7 of the newly applied plans will cause C used +1>C max in 12 four-dimensional cells, and mark these 12 four-dimensional cells as over-capacity conflict cells.

[0041] Step 3: Conflict identification and risk analysis 3.1 Conflict detection: Conduct conflict detection on 7 conflict-related newly applied plans. Among them, 4 plans have combined conflicts (spatial + temporal overlap and over-capacity), 2 plans have spatial overlap conflicts, and 1 plan has a direct operation conflict with an approved plan (the spatial distance D(t)=5 meters < Dsafe=10 meters at the same moment, and Dsafe is the preset safe flight spacing for UAVs in this airspace); 3.2 Risk level assessment: Combine the historical flight errors (average 3 meters), positioning errors (average 2 meters), and communication delays (average 0.5 seconds) in this airspace to classify the risk levels of 12 conflict four-dimensional cells and 1 direct operation conflict area: 3 four-dimensional cells and 1 direct operation conflict area are high-risk, 5 four-dimensional cells are medium-risk, and 4 four-dimensional cells are low-risk.

[0042] Step 4: Intelligent optimization and adjustment of flight plans 4.1 Adjustment Strategy Design: First, all conflict-related flight plans were prioritized by mission type: Emergency Logistics Plans > General Logistics Plans > Urban Patrol Plans. Higher-priority flight plans remained unchanged. For the seven low-priority new conflict-related application plans, different adjustment strategies were adopted based on risk levels: High-risk areas: For the two ordinary logistics plans, a combination of time fine-tuning and speed adjustment strategy is adopted; for the one inspection plan, a combination of high optimization and time fine-tuning strategy is adopted. Medium-risk areas: A single strategy of fine-tuning the timing of two logistics plans; Low-risk areas: A single strategy of speed adjustment is adopted for one logistics plan and one inspection plan; taking one ordinary logistics plan F5 in a high-risk area as an example, its congestion four-dimensional unit k1 occupies N k1 =5, Cap k1 =2, preset λ t =60 seconds, λ v =0.2m / s, calculate the time adjustment Δt5 = 60 × (5 2) = 180 seconds, speed adjustment Δv5 = 0.2 × (5 2) = 0.6 m / s; Original transit time t5 = 10:00, adjusted t5′ = 10:03; Original cruising speed v5 = 5 m / s, adjusted v5′ = 5.6 m / s; Taking a logistics plan F9 in a medium-risk area as an example, its congestion four-dimensional unit k2 occupies N k2 =4, Cap k2 =4, λ t =30 seconds, calculate the time adjustment Δt9 = 30 × (4 4) = 0 seconds, fine-tune to the adjacent time slice l+1, and pass through a time delay of 5 minutes.

[0043] 4.2 Optimization Algorithm Solution: Particle swarm optimization is adopted as the multi-objective optimization algorithm, and the perturbation cost objective function is set as follows: ; Based on the mission characteristics of this airspace, a weighting coefficient w is set. t =0.5, w h =0.3, w v =0.2; Set constraints: ① All four-dimensional units C used ≤C max② The distance between the adjusted flight path and the no-fly zone is ≥5 meters; ③ The speed adjustment range of the UAV is 2~10m / s, and the altitude adjustment range is 0~120 meters; ④ The spatial distance between any two trajectories at the same time is ≥10 meters; The disturbance cost of all feasible adjustment schemes is solved by particle swarm optimization algorithm, and one optimal adjustment scheme with the minimum disturbance cost is selected for each of the 7 conflict-related plans, where the maximum disturbance cost J is... max =120, minimum disturbance cost J min =20, all adjustment schemes meet the constraints.

[0044] Step 5: Approval Decision Output The system generated approval opinions for 15 new application plans: 8 plans did not require adjustment and were directly approved; 7 conflict-related plans were approved along with the optimal optimization adjustment plan. The adjusted four-dimensional profile data, disturbance costs, and conflict elimination results were output to the drone monitoring platform, which automatically completed the approval and sent the adjustment results to the drone pilot's terminal.

[0045] The application results of this embodiment show that, after adopting the method of the present invention, the collision detection accuracy of the city's low-altitude airspace reaches 100%, the collision elimination rate of the overcapacity area reaches 100%, and the average response time for flight plan approval is controlled within 2 seconds. Compared with the traditional approval method, the approval efficiency is improved by more than 80%, and the airspace resource utilization efficiency is improved by more than 50%, effectively realizing the safe and efficient operation of high-density low-altitude airspace.

[0046] Compared with the prior art, the present invention has the following significant advantages: This invention upgrades the approval of low-altitude flight plans from static capacity control to dynamic fine-tuning based on 4D profiles. By constructing a four-dimensional spatiotemporal capacity constraint model, it achieves a systematic and refined assessment of the spatiotemporal resource occupancy of low-altitude airspace. It can not only detect direct point conflicts, but also predict the risk of airspace congestion in future periods, thereby avoiding flight conflicts from the source and improving the safety of low-altitude operations. This invention achieves intelligent optimization of flight plans with minimal disturbance under capacity constraints. For overcapacity or conflict areas, it makes differentiated adjustments based on task priority. Through single or combined strategies such as time fine-tuning, altitude optimization, and speed adjustment, it minimizes changes to the original flight plan while eliminating conflicts, avoiding the low approval efficiency caused by large-scale manual adjustments and improving approval efficiency. This invention supports the synergistic optimization of multi-dimensional adjustment methods, integrating adjustment strategies of time, altitude, and speed. It can flexibly select adjustment methods according to airspace congestion and flight mission requirements, adapting to different types and priorities of low-altitude flight missions, and improving the versatility and adaptability of the method. This invention constructs a scalable conflict detection and optimization architecture through the design of airspace grid partitioning, four-dimensional profile mapping and incremental conflict detection. It avoids the computational explosion problem caused by centralized comparison analysis, and ensures that the system can maintain low latency response and high approval accuracy in high-concurrency flight plan application scenarios, thereby improving the real-time performance and scalability of the approval system. The airspace capacity occupancy heatmap and time-airspace occupancy matrix generated by this invention can intuitively display the spatiotemporal capacity distribution of airspace, providing a visual basis for decision-making for low-altitude operation management personnel. At the same time, the optimization and adjustment schemes all provide quantitative adjustment parameters, which are highly executable and can be directly applied to the low-altitude operation management platform.

[0047] 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 the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the approval of low-altitude flight plans based on four-dimensional spatiotemporal capacity constraints, characterized in that, Includes the following steps: Step 1: Data acquisition and preprocessing. Collect four-dimensional profile data of approved flight plans and newly applied flight plans. Divide the low-altitude airspace into four-dimensional units and initialize the capacity threshold. Standardize the track data. The four-dimensional unit is formed by combining three-dimensional spatial units and time slices. Step 2: Airspace capacity assessment, mapping approved flight plans to four-dimensional cells and calculating capacity occupancy, performing capacity constraint checks on newly applied flight plans, and marking overcapacity cells; Step 3: Conflict identification and risk analysis. Detect conflict types between new application plans and approved plans on a four-dimensional grid, and classify the risk level of conflict areas based on flight error-related factors. Step 4: Intelligent optimization and adjustment of flight plans. Conflicting plans are sorted according to flight mission priority, and single or combined adjustment strategies of time, altitude, and speed are adopted. The optimal adjustment scheme with the least disturbance is selected through optimization algorithms. Step 5: Approval decision output. Based on the capacity assessment, conflict analysis and optimization adjustment results, generate and output the approval opinion for the new application plan.

2. The low-altitude flight plan approval optimization method based on four-dimensional spatiotemporal capacity constraints according to claim 1, characterized in that, In step 1, the four-dimensional profile data includes longitude, latitude, altitude, time, and flight speed information; the three-dimensional spatial unit is a regular grid dividing the low-altitude airspace according to longitude, latitude, and altitude; the time slice is a time unit divided according to fixed time intervals; the trajectory of each flight plan is represented as a time parameterized function: ； in: Let i be the spatial plane coordinates of flight plan i at time t. Let i be the flight altitude at time t. Let t be a time variable. i start t is the start time of flight plan i. i end The end time of flight plan i; the set of approved flight plans is: , where n is the number of approved flight plans.

3. The low-altitude flight plan approval optimization method based on four-dimensional spatiotemporal capacity constraints according to claim 1, characterized in that, In step 1, the division accuracy of the three-dimensional spatial unit, the time interval of the time slice, and the capacity threshold of the four-dimensional unit are all determined based on airspace management rules, airspace level, or historical flight density statistics.

4. The low-altitude flight plan approval optimization method based on four-dimensional spatiotemporal capacity constraints according to claim 1, characterized in that, Step 2 specifically includes: Step 2.1 Four-dimensional profile mapping: Map the time parameterization function of each approved flight plan to a four-dimensional cell G(i,j,k,l) ​​in the low-altitude airspace, and accumulate capacity occupancy information, where i,j,k are the spatial indices of the three-dimensional spatial cell, and l is the time index of the time slice; Step 2.2 Dynamic Capacity Calculation: Define an upper capacity limit C for each four-dimensional cell max (i,j,k,l), compute the capacity usage C used (i,j,k,l) and the capacity utilization U(i,j,k,l); Step 2.3 Capacity Constraint Check: Map the new flight plan application to a four-dimensional cell, calculate the capacity occupancy increment, and mark the four-dimensional cells that exceed the capacity.

5. The low-altitude flight plan approval optimization method based on four-dimensional spatiotemporal capacity constraints according to claim 4, characterized in that, In step 2.2, the formula for calculating the capacity usage is: ； in, Let G(i,j,k,l) ​​be the trajectory occupancy identifier function. If the m-th approved flight plan occupies a four-dimensional cell G(i,j,k,l), then... (i,j,k,l)=1, otherwise (i,j,k,l)=0; Capacity utilization rate is defined as: ; when hour, This indicates that the four-dimensional unit has reached or exceeded the capacity threshold; In step 2.3, if any four-dimensional cell satisfies C used (i,j,k,l)+1>C max (i,j,k,l), then the four-dimensional cell is determined as an over-capacity cell.

6. The low-altitude flight plan approval optimization method based on four-dimensional spatiotemporal capacity constraints according to claim 1, characterized in that, In step 3, the conflict types include spatial overlap conflict, temporal overlap conflict, combined conflict, and direct execution conflict; The direct running conflict is obtained by checking the continuous trajectory spacing, and the checking formula is: ； wherein: x a ,y a ,z a are the spatial coordinates of plan a at time t, x b ,y b ,z b are the spatial coordinates of plan b at time t; when At that time, D safe The preset safe flight distance is considered a direct operational conflict.

7. The low-altitude flight plan approval optimization method based on four-dimensional spatiotemporal capacity constraints according to claim 1, characterized in that, In step 3, the flight error-related factors include historical flight errors, trajectory prediction uncertainty, aircraft positioning errors, and air-to-ground communication delays; the risk levels are divided into three levels: low, medium, and high.

8. The low-altitude flight plan approval optimization method based on four-dimensional spatiotemporal capacity constraints according to claim 1, characterized in that, In step 4, after sorting by flight mission priority, the flight plans with higher priority remain unchanged, while only the flight plans with lower priority are optimized and adjusted; the adjustment strategy is as follows: Time fine-tuning: Calculate the time adjustment amount Adjusted flight time ; Height optimization: Calculate height adjustment amount Adjusted flight altitude ; Speed ​​Adjustment: Calculate the speed adjustment amount Adjusted cruise speed ; Where, N k To reduce the number of flight plans within the four-dimensional unit, Cap k λ is the capacity threshold. t t is the time adjustment factor. i For the original transit time, Hl ayer For the height-level hierarchical interval, s∈{ 1,1} represents the upward or downward direction, z i The original cruising altitude, λ v v is the speed adjustment coefficient. i This is the original cruising speed.

9. The low-altitude flight plan approval optimization method based on four-dimensional spatiotemporal capacity constraints according to claim 8, characterized in that, In step 4, a multi-objective optimization algorithm or heuristic algorithm is used to achieve minimum perturbation optimization, and the objective function is set as the perturbation cost function: ； Among them, w t For time-adjusted weighting coefficients, w h For highly adjusted weighting coefficients, w v w is the weighting coefficient for speed adjustment. t +w h +w v =1; The constraints of the optimization algorithm include: the capacity usage of each four-dimensional unit does not exceed its capacity limit, the adjusted trajectory meets the requirements of safe distance between routes and no-fly zones, and the adjusted parameters meet the performance constraints of the aircraft itself.

10. The low-altitude flight plan approval optimization method based on four-dimensional spatiotemporal capacity constraints according to any one of claims 1-9, characterized in that, In step 2, a heatmap of spatial capacity occupancy or a time-spatial occupancy matrix is ​​generated based on the capacity occupancy status; in step 4, if there are multiple optimal adjustment schemes with the same disturbance cost, they are sorted a second time according to task execution time and energy consumption cost; in step 5, the approval opinions include two types: direct approval and approval with the attached optimal optimization adjustment scheme.

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