Multi-objective siting method for eVTOL takeoff and landing points

Abstract

The application discloses an eVTOL landing site location method, comprising the following steps: determining an initial candidate landing site set based on a three-dimensional city model constructed according to city geographic space data combined with a GIS platform; establishing a mixed integer programming model with the maximum coverage area, the minimum flight energy consumption and the minimum flight time as the target; using a simulated annealing algorithm to perform iterative search based on the initial candidate landing site set to generate an initial layout scheme meeting preset conditions as an initial solution; using an exact solver to take the initial solution as a search starting point to perform accurate optimization on the mixed integer programming model and output an optimal eVTOL landing site layout. The application comprehensively considers the multi-objective trade-off between the complex three-dimensional space constraints of the city and the operation of the eVTOL, can plan a landing site layout scheme that is safe, efficient and optimal in service coverage, and can effectively improve the operation efficiency of the urban air transportation system.

Description

Technical Field

[0001] This invention relates to the field of urban air traffic planning technology, and to a multi-objective site selection method for eVTOL (electric vertical take-off and landing aircraft) take-off and landing points based on GIS 3D modeling and hybrid optimization algorithms. In particular, it relates to an eVTOL take-off and landing point selection method that combines GIS 3D spatial analysis with multi-objective hybrid operations research optimization. Background Technology

[0002] With the acceleration of urbanization and the increasing severity of ground traffic congestion, urban air mobility (UAM) has attracted widespread attention as an emerging transportation mode. eVTOL, with its vertical takeoff and landing, low noise, and environmental friendliness, has become a core vehicle for UAM. However, the efficient operation of UAM is highly dependent on the rational layout of ground infrastructure.

[0003] Existing research on vertical airport (vertical take-off and landing point) location currently employs three methods: goal-oriented optimization, K-means clustering algorithm, and Geographic Information System (GIS). First, vertical airport site selection optimization is considered a subproblem of the Hub Location Problem (HLP), primarily involving determining the location of hub facilities and designing the hub network to optimize objectives such as cost or service. Campbell first proposed the HLP model, which has since been widely applied in vertical airport layout research. Rath and Chow optimized the vertical airport network to meet the demand coverage of air taxis, formulating the problem as an optimization problem maximizing passenger volume. Second, Lim and Hwang used origin-destination (OD) data from the Seoul metropolitan area as input. The K-means algorithm divides these geographically dispersed OD points into K clusters, with the centroid of each cluster considered as the initial location of the vertical airport; however, this method has limitations when dealing with complex location objectives. Rajendran and Zack, and Sinha and Rajendran, respectively proposed a constrained clustering method using the CLARA algorithm. Fadhil studied suitable areas for vertical airports in Munich and Los Angeles, with the selection of these areas limited by factors such as restricted airspace, military zones, and schools. Brunelli et al. created a digital model in Bologna and used building height, type, and obstacle height to assess site suitability.

[0004] While existing optimization methods can achieve mathematically optimal solutions, they often neglect factors such as market coverage and geographical constraints, making them ill-suited to the complexities of urban air transportation systems. Although K-means is computationally efficient, its reliance on random initial solutions means that poor initial point selection can significantly impact the quality of the results. Furthermore, this algorithm primarily relies on distance and data similarity, which are typically used to determine candidate facility locations rather than final site selection. Clustering methods alone cannot adequately account for the multidimensional constraints of urban space, such as topography, airspace limitations, and ground transportation accessibility.

[0005] In contrast, GIS methods can comprehensively analyze spatial and environmental factors to assess the suitability of vertical airport site selection. However, most studies still remain at the level of spatial analysis, lacking systematic consideration of UAM network structure and operational efficiency, and failing to fully take into account multiple objectives such as demand coverage, cost minimization, and travel time optimization. Summary of the Invention

[0006] The purpose of this invention is to provide a multi-objective site selection method for eVTOL take-off and landing points based on GIS 3D modeling and hybrid optimization algorithms, given the complex urban spatial structure and the dual requirements of air traffic for service coverage and operational efficiency. Based on a vertical take-off and landing point (vertiport) site selection framework that considers urban 3D spatial constraints and multi-objective optimization, the method achieves scientific and optimized layout of take-off and landing points by integrating multi-source data and mixed integer programming.

[0007] This invention is achieved through the following technical solution: A multi-target location selection method for eVTOL takeoff and landing points includes the following steps: Based on a 3D city model constructed using urban geospatial data and a GIS platform, an initial set of candidate take-off and landing points is determined. Establish a mixed-integer programming model with the objectives of maximizing coverage area, minimizing flight energy consumption, and minimizing flight time; The simulated annealing algorithm is used to iteratively search based on the initial candidate take-off and landing point set to generate an initial layout scheme that meets the preset conditions, which serves as the initial solution. Using an exact solver, with the initial solution as the starting point, the mixed integer programming model is precisely optimized to output the optimal eVTOL take-off and landing point layout.

[0008] Preferably, the objective function of the mixed-integer programming model includes: ; in, Describe the objective function. Indicates from the take-off and landing point to the take-off and landing point Energy consumed by flight This is a binary variable indicating whether to select the takeoff and landing point. Fly to the take-off and landing point , Indicates from the take-off and landing point to the take-off and landing point Flight time is defined by λ, a weighting factor used to balance the relationship between energy consumption, flight time, and coverage area. This is a binary variable indicating whether to select a takeoff or landing point. , Indicates the take-off and landing point The coverage area , , These are maximum energy consumption, maximum flight time, and maximum coverage area, respectively.

[0009] Preferably, determining the initial candidate take-off and landing point set includes: Within the pre-defined candidate take-off and landing point area, based on the FAA vertical airport design standards, buildings whose height exceeds a set height threshold are identified. Taking the center of the building as the center and a preset multiple of the minimum circumscribed sphere radius determined based on eVTOL size parameters as the benchmark, a safe zone radius is set. Coordinate points that meet the take-off and landing standards are selected outside the safe zone radius as the initial candidate take-off and landing point set.

[0010] Preferably, the determination of the preset take-off and landing point candidate area includes: Based on a 3D city model, a buffer analysis is performed with preset target facilities as nodes to determine facility buffer zones. Based on population density raster data, population density data analysis is performed on the facility buffer zones to determine densely populated areas with population density that meets a preset population density threshold. Based on a digital elevation model, the terrain slope of the densely populated areas is calculated, and areas with slope less than the slope threshold are extracted as preset take-off and landing point candidate areas.

[0011] Preferably, the mixed-integer programming model is solved based on the following preset constraints: The cumulative flight energy consumption of all eVTOLs must not exceed the battery capacity limit, and the flight time of a single eVTOL mission must not exceed the set maximum endurance time. Multiple eVTOL aircraft take off under a preset minimum time interval constraint and perform missions in a preset order; Establish a path connection only between the two selected take-off and landing points; The Euclidean distance between the two selected take-off and landing points is greater than or equal to the minimum safe distance; MTZ sub-loop elimination constraint is used to restrict the access order of candidate take-off and landing points in the route, prevent illegal closed-loop paths from appearing during the search process, and ensure that the generated solution is a complete access path.

[0012] Preferably, when using the simulated annealing algorithm to perform iterative search based on the initial candidate take-off and landing point set, the following preset strategy is adopted: The cooling factor is dynamically adjusted based on the real-time acceptance rate. When the real-time acceptance rate is below the threshold, a slow cooling strategy is used to maintain search activity. When the real-time acceptance rate is above the threshold, a fast cooling strategy is used to increase selection pressure. Within the preset standard range, a preset standard cooling speed is used. The real-time acceptance rate is the proportion of items accepted within the k-th temperature cycle.

[0013] Preferably, when using the simulated annealing algorithm for iterative search based on the initial candidate take-off and landing point set, the solution acceptance probability is periodically adjusted by an adjustment factor to balance global search capability and convergence speed, including: ； ； In the formula, This represents the probability of accepting the solution. Indicates the adjustment factor. Represents the base probability. This indicates the maximum number of iterations at the current temperature.

[0014] Preferably, the urban geospatial data includes digital elevation models, building outline data, and remote sensing imagery.

[0015] The multi-objective site selection method for eVTOL take-off and landing points of the present invention comprehensively considers the urban spatial environment, combines three-dimensional spatial analysis with geographic information system (GIS) for multi-objective hybrid operation optimization, and selects take-off and landing points for eVTOL. This method can form good regional interconnection and path coverage, take into account the accessibility of central urban areas and peripheral areas, and improve the efficiency of operation and scheduling.

[0016] This invention comprehensively considers the geographical constraints of urban three-dimensional space and the multi-objective requirements of eVTOL operation. By combining GIS spatial analysis and hybrid optimization algorithms, it achieves scientific planning of take-off and landing point selection. It can plan a safe, efficient, and optimal take-off and landing point layout scheme, which not only ensures flight safety and physical feasibility, but also effectively improves service coverage and reduces operating costs, thereby effectively improving the operational efficiency of urban air traffic systems. Attached Figure Description

[0017] Figure 1This is a flowchart of the eVTOL multi-target location method for take-off and landing points according to an embodiment of the present invention. Detailed Implementation

[0018] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only for explaining the present invention and are not intended to limit the present invention.

[0019] The method in this application embodiment constructs a three-dimensional city model by integrating digital elevation models, building outlines, remote sensing images, and multi-source data on population distribution. It then filters candidate points through spatial overlay analysis, establishes a mixed integer programming model with the goal of maximizing coverage area and minimizing flight energy consumption and flight time, and solves the model using a hybrid optimization algorithm to obtain the optimal take-off and landing point layout, thus realizing the scientific planning of take-off and landing point site selection.

[0020] like Figure 1 As shown in the figure, the eVTOL take-off and landing point selection method based on GIS 3D modeling and hybrid optimization algorithm in this embodiment of the invention includes the following steps: S1. Based on the three-dimensional city model constructed using urban geospatial data and a GIS platform, determine the initial set of candidate take-off and landing points; S2. Establish a mixed-integer programming model with the objectives of maximizing coverage area, minimizing flight energy consumption, and minimizing flight time; S3. Using the simulated annealing algorithm, an iterative search is performed based on the initial candidate take-off and landing point set to generate an initial layout scheme that meets the preset conditions, which serves as the initial solution; S4. Using an exact solver, with the initial solution as the starting point, perform exact optimization on the mixed integer programming model and output the optimal eVTOL take-off and landing point layout.

[0021] This application, based on a GIS platform, constructs a spatial 3D model to analyze spatial factors such as terrain, demand point distribution, and population density, and selects potentially feasible candidate take-off and landing point areas as an initial candidate take-off and landing point set. Then, based on this, a mixed integer programming (MIP) model incorporating selection decisions is established with the optimization objectives of maximizing coverage area, minimizing total flight energy consumption, and minimizing total flight time. By constructing a multi-objective MIP model, the take-off and landing point areas are optimized. By maximizing coverage area and minimizing flight energy consumption and flight time, the location layout of eVTOL take-off and landing points is optimized, making the layout not only efficient but also in line with real-world needs.

[0022] In one embodiment, the three-dimensional city model of this application is based on a GIS platform, using a digital elevation model (DEM) as a terrain reference, overlaying a remote sensing image base map with building outline data containing floor information, and generating the model through stretching. More preferably, by overlaying the remote sensing image base map, the building height is estimated using the floor field in the building outline data and then stretched to generate a three-dimensional building model, ensuring that the building base height matches the digital elevation model (DEM) terrain data.

[0023] After generating a 3D city model, analysis is performed based on the model to determine buffer zones for target facilities. Population density analysis is then overlaid to identify candidate areas for pre-defined take-off and landing points. Target facilities include urban transportation hubs, medical centers, and commercial centers.

[0024] For example, urban transportation hubs, medical centers, and commercial centers are extracted as key nodes, and buffer zones are established for each (e.g., a 1km buffer zone for commercial centers and a 500m buffer zone for medical centers); combined with population density raster data, nodes with population densities exceeding a set threshold (e.g., 1000 people / km²) are identified. 2 The process involves several steps: First, the intersection of the DEM data with slope less than 2% is extracted as a preliminary candidate area. Then, the slope information of the DEM data is calculated, and flat areas within the preliminary candidate areas are extracted as potential take-off and landing sites, i.e., flat take-off and landing zones. Simultaneously, based on the FAA (Federal Aviation Administration) vertical airport design standards, high-rise buildings exceeding 20 meters in height are identified, and a safety buffer zone (e.g., with a radius of 60 meters) is established centered on these buildings. Points falling within the safety buffer zone are eliminated, ultimately determining a set of safe and usable candidate points. Finally, based on the urban 3D spatial model, buffer zone analysis and overlay analysis are used to screen out candidate take-off and landing points that meet traffic, medical, commercial needs, and terrain safety requirements, thus determining the initial candidate take-off and landing points.

[0025] In this application, based on the initial screening area, the specific coordinate points that meet the take-off and landing standards are extracted by further combining terrain slope constraints and high-rise building safety obstacle avoidance constraints, forming a set of safety candidate points.

[0026] In this application, the mixed integer programming model aims to maximize coverage area, minimize flight energy consumption, and minimize flight time. It is a multi-objective location optimization model, and its objective function expression is as follows: ; In the formula, Indicates from the take-off and landing point to the take-off and landing point The energy (electrical energy) consumed during flight. This is a binary variable indicating whether to select the takeoff and landing point. Fly to the take-off and landing point , Indicates from the take-off and landing point to the take-off and landing point Flight time As a weighting factor, it is used to balance the relationship between energy consumption, flight time, and coverage area. This is a binary variable indicating whether to select a takeoff or landing point. , Indicates the take-off and landing point The coverage area The total energy consumption limit for all eVTOLs. This is the maximum total flight time consumed. The total coverage area size for all demand points (required take-off and landing points).

[0027] Among them, weighting factors To balance the coverage area of ​​takeoff and landing points, flight energy consumption, and time costs, sensitivity analysis methods can be used to adjust within a set range. The value of is determined by observing the changing trends of the various components of the objective function, such as by plotting the weighting factors. Based on the response curve to the target, the optimal weighting factor is determined. .

[0028] Optionally, in this application, the mixed-integer programming model needs to satisfy the following constraints, such as energy consumption and time constraints, flight interval and logic constraints, physical distance constraints, sub-loop elimination constraints, etc., which are specifically explained as follows: Energy consumption and time constraints: The cumulative flight energy consumption of all eVTOLs shall not exceed the battery capacity limit, and the flight time of a single mission shall not exceed the set maximum endurance. Examples include: This means that the flight energy consumption of all eVTOLs must not exceed the maximum energy consumption. .

[0029] Flight spacing and logical constraints: When multiple eVTOLs are performing missions, a minimum takeoff interval must be met. And only when the take-off and landing points and When all are selected (i.e.) =1 and =1, This is a binary variable indicating whether to select a takeoff or landing point. Only after this condition is met can a path connection be established. Examples include: ( =1); This indicates that each eVTOL aircraft must adhere to a certain time interval during takeoff and execute tasks sequentially. The minimum interval time, , Indicates the first The first take-off and landing point , The takeoff time of the aircraft.

[0030] Physical distance constraint: The Euclidean distance between any two selected take-off and landing points must be greater than the minimum safe clearance. ,include: ;in, Indicates the take-off and landing point to the take-off and landing point The distance between two take-off and landing points is a certain distance, meaning the distance between them cannot be less than a certain value. This indicates the minimum safe distance.

[0031] MTZ Subloop Constraint Elimination: Introducing the MTZ Constraint Formula This prevents disconnected sub-loops from forming in path planning and ensures path continuity. The MTZ constraint formula represents the MTZ sub-loop elimination constraint, used to prevent the formation of disconnected sub-loops. The total number of hypothetical candidate points. For each take-off and landing point Introduce a continuous variable to represent the takeoff and landing points. The order of visits along the route, For each take-off and landing point Introduce a continuous variable to represent the takeoff and landing points. The access sequence number in the flight route.

[0032] Furthermore, in this application, the mixed-integer programming model also needs to satisfy the following constraints: ; The above formula means that only when the take-off and landing points and take-off and landing points Only when all points are selected can the takeoff and landing points be considered. to the take-off and landing point The flight path. This constraint ensures the logical consistency of the flight path, meaning that flight connections cannot be established between unselected take-off and landing points.

[0033] Furthermore, the mixed-integer programming model of this application also needs to satisfy the following constraints: ; in, Indicates from the take-off and landing point The first day of departure The flight sequence of eVTOLs, Show from the take-off and landing point The first day of departure The flight sequence of the eVTOL aircraft, as shown in the above formula, represents the flight order of the first... frame and the first The eVTOL aircraft must fly in strict accordance with the flight sequence.

[0034] Furthermore, the mixed-integer programming model of this application also needs to satisfy the following constraints: ; in, It contains the set of coordinates of all the selected candidate points.

[0035] In one embodiment, this application preferably employs a simulated annealing algorithm (… SA This invention employs a hybrid optimization strategy that combines a discrete location selection subproblem with an exact solver (such as the Gurobi solver) to solve the multi-objective location optimization model and output the optimal take-off and landing point layout scheme. The invention decomposes the location selection problem into a discrete location selection subproblem and a continuous path optimization subproblem.

[0036] In this application, as a preferred embodiment, when using the simulated annealing algorithm to iteratively search based on the initial candidate take-off and landing point set, a composite neighborhood structure including addition, deletion, and exchange operations is adopted to locally perturb the current take-off and landing point layout scheme to generate a new solution; simultaneously, during the iterative search process, adaptive adjustments are made based on the following preset strategy: The cooling factor of the algorithm is dynamically adjusted based on the dynamically changing real-time acceptance rate. When the real-time acceptance rate is below a set threshold, a slow cooling strategy is used to maintain search activity; when the real-time acceptance rate is above the set threshold, a fast cooling strategy is used to increase selection pressure. A preset standard cooling rate is used within a preset standard range. The real-time acceptance rate is the... The proportion accepted within the temperature cycle.

[0037] For example, in this application, based on dynamically changing acceptance rates Dynamically adjust the cooling factor ,include: ; In this application, the acceptance rate Defined as in the first The proportion accepted within the wheel temperature cycle. Cooling factor. As shown in the piecewise function above, this piecewise function adjusts the cooling rate based on the real-time acceptance rate: when the acceptance rate falls below a minimum threshold... Use slow cooling to maintain search activity; when the acceptance rate is above the maximum threshold. At that time, rapid cooling is used to enhance the selected pressure; between the minimum threshold With the maximum threshold The interval refers to the standard cooling rate used within the normal range, as shown in the piecewise function above.

[0038] In a preferred embodiment, the simulated annealing algorithm periodically adjusts the solution acceptance probability during the iterative search process using a temperature-adaptive adjustment factor to balance global search capability and convergence speed, ensuring a high-quality initial angle output, as shown in the following equation. The final solution acceptance probability is obtained by multiplying the base probability by the dynamic adjustment factor. Wherein, the adjustment factor... By periodically adjusting the solution acceptance probability during the search process, it is possible to effectively avoid getting trapped in local optima and improve performance. That is, the adjustment factor changes periodically with the number of iterations to dynamically correct the acceptance probability during the search process.

[0039] ； ； Using simulated annealing algorithm ( SA After obtaining the initial solution, the initial solution output by the simulated annealing algorithm is used as a warm start input to the exact solver (such as Gurobi). The exact solver is used to solve the constructed mixed integer programming (MIP) model to determine the final take-off and landing point coordinates, and finally output the optimal eVTOL take-off and landing point layout.

[0040] In summary, this application uses the initial set of candidate take-off and landing points as the solution input, and performs a mixed solution of the above multi-objective optimization model. First, the simulated annealing algorithm is used to obtain the initial layout scheme, and then the initial layout scheme is used as the initial solution input to the exact solver to solve the mixed integer programming model that includes take-off and landing point location decisions and path planning variables. Finally, the optimal take-off and landing point layout is output.

[0041] This invention constructs a three-dimensional urban model by integrating multi-source data such as digital elevation models, building outlines, remote sensing images, and population distribution. Buffer analysis, overlay analysis, and terrain slope constraints are used to screen preliminary candidate points that meet safety and operational conditions. Based on these preliminary candidate points, a mixed integer programming model is constructed with the objectives of maximizing demand coverage, minimizing flight energy consumption, and minimizing flight time. A hybrid optimization strategy combining simulated annealing and the Gurobi solver is used to solve the model, achieving scientific planning for takeoff and landing point selection.

[0042] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention. Therefore, the embodiments should be regarded as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, it is intended that all variations falling within the meaning and scope of the equivalents of the claims be included within the invention.

[0043] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A multi-target location method for eVTOL takeoff and landing points, characterized in that, Including the following steps: Based on a 3D city model constructed using urban geospatial data and a GIS platform, an initial set of candidate take-off and landing points is determined. Establish a mixed-integer programming model with the objectives of maximizing coverage area, minimizing flight energy consumption, and minimizing flight time; The simulated annealing algorithm is used to iteratively search based on the initial candidate take-off and landing point set to generate an initial layout scheme that meets the preset conditions, which serves as the initial solution. Using an exact solver, with the initial solution as the starting point, the mixed integer programming model is precisely optimized to output the optimal eVTOL take-off and landing point layout.

2. The eVTOL multi-target location method for takeoff and landing points according to claim 1, characterized in that, The objective function of the mixed-integer programming model includes: ; in, Describe the objective function. Indicates from the take-off and landing point to the take-off and landing point Energy consumed by flight This is a binary variable indicating whether to select the takeoff and landing point. Fly to the take-off and landing point , Indicates from the take-off and landing point to the take-off and landing point Flight time is defined by λ, a weighting factor used to balance the relationship between energy consumption, flight time, and coverage area. This is a binary variable indicating whether to select a takeoff or landing point. , Indicates the take-off and landing point The coverage area , , These are maximum energy consumption, maximum flight time, and maximum coverage area, respectively.

3. The eVTOL take-off and landing point multi-target location method according to claim 1, characterized in that, The determination of the initial candidate take-off and landing point set includes: Within the pre-defined candidate take-off and landing point area, based on the FAA vertical airport design standards, buildings whose height exceeds a set height threshold are identified. Taking the center of the building as the center and a preset multiple of the minimum circumscribed sphere radius determined based on eVTOL size parameters as the benchmark, a safe zone radius is set. Coordinate points that meet the take-off and landing standards are selected outside the safe zone radius as the initial candidate take-off and landing point set.

4. The eVTOL multi-target location method for takeoff and landing points according to claim 3, characterized in that, The determination of the preset take-off and landing point candidate area includes: Based on a 3D city model, a buffer analysis is performed with preset target facilities as nodes to determine facility buffer zones. Based on population density raster data, population density data analysis is performed on the facility buffer zones to determine densely populated areas with population density that meets a preset population density threshold. Based on a digital elevation model, the terrain slope of the densely populated areas is calculated, and areas with slope less than the slope threshold are extracted as preset take-off and landing point candidate areas.

5. The eVTOL multi-target location method for takeoff and landing points according to claim 1, characterized in that, The solution to the mixed-integer programming model is based on the following preset constraints: The cumulative flight energy consumption of all eVTOLs must not exceed the battery capacity limit, and the flight time of a single eVTOL mission must not exceed the set maximum endurance time. Multiple eVTOL aircraft take off under a preset minimum time interval constraint and perform missions in a preset order; Establish a path connection only between the two selected take-off and landing points; The Euclidean distance between the two selected take-off and landing points is greater than or equal to the minimum safe distance; MTZ sub-loop elimination constraint is used to restrict the access order of candidate take-off and landing points in the route, prevent illegal closed-loop paths from appearing during the search process, and ensure that the generated solution is a complete access path.

6. The eVTOL multi-target location method for takeoff and landing points according to claim 1, characterized in that, When using the simulated annealing algorithm to perform iterative search based on the initial candidate takeoff and landing point set, the following preset strategy is employed: The cooling factor is dynamically adjusted based on the real-time acceptance rate. When the real-time acceptance rate is below the threshold, a slow cooling strategy is used to maintain search activity. When the real-time acceptance rate is above the threshold, a fast cooling strategy is used to increase selection pressure. Within the preset standard range, a preset standard cooling speed is used. The real-time acceptance rate is the proportion of items accepted within the k-th temperature cycle.

7. The eVTOL multi-target location method for takeoff and landing points according to claim 1, characterized in that, When using the simulated annealing algorithm for iterative search based on an initial set of candidate takeoff and landing points, the solution acceptance probability is periodically adjusted by an adjustment factor to balance global search capability and convergence speed, including: ； ； In the formula, This represents the probability of accepting the solution. Indicates the adjustment factor. Represents the base probability. This indicates the maximum number of iterations at the current temperature.

8. The eVTOL multi-target location method for takeoff and landing points according to claim 1, characterized in that, The exact solver includes at least the Gurobi exact solver.

9. The eVTOL multi-target location method for take-off and landing points according to claim 1, characterized in that, The urban geospatial data includes digital elevation models, building outline data, and remote sensing imagery.

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