A vertical take-off and landing site selection method, device, equipment and medium

Abstract

The application relates to the technical field of low-altitude traffic, and discloses a vertical take-off and landing site selection method, device, equipment and medium, acquires joint transport demand elements and traffic guide elements of each grid position in a target space, performs preliminary site selection in the target space in priority of the traffic guide elements, and constructs an initial site selection set of the target space; constructs a multi-target site selection model of the target space according to the joint transport demand elements; and performs multi-target iterative optimization on the initial site selection set according to the multi-target site selection model, so as to obtain an iterative site selection set of the target space. The method has the beneficial effect that the actual site selection process is guided by the traffic guide elements, existing traffic resources are effectively reused, seamless connection of the vertical take-off and landing site and the existing traffic system is realized, a comprehensive three-dimensional traffic system is formed through synergistic effects among various traffic modes, and the actual demand of the low-altitude passenger transport scene is met.

Description

Technical Field

[0001] This application relates to the field of low-altitude transportation technology, and in particular to a method, apparatus, equipment and medium for selecting a vertical take-off and landing site. Background Technology

[0002] Low-altitude transportation refers to the transport of goods or personnel using aircraft such as drones or electric vertical takeoff and landing (eVTOL) aircraft in airspace below 1,000 to 3,000 meters. This fills gaps in the existing transportation system and improves the timeliness and accessibility of the regional integrated transportation system. Low-altitude transportation requires corresponding vertical takeoff and landing fields for aircraft to load and unload goods and board and alight passengers. The site selection process for these fields needs to consider various factors, including but not limited to regional traffic demand and scenario requirements. Related technologies are typically adapted to single non-passenger scenarios such as low-altitude logistics. However, the factors to be considered in non-passenger scenarios and passenger scenarios are completely different, making it impossible to directly apply these technologies to passenger scenarios. Therefore, there is an urgent need to propose a site selection method suitable for low-altitude passenger transportation scenarios. Summary of the Invention

[0003] This application provides a method, apparatus, equipment, and medium for selecting a vertical take-off and landing site. It utilizes traffic guidance elements to guide the actual site selection process, effectively reuses existing traffic resources, and achieves seamless integration between the vertical take-off and landing site and the existing transportation system. Through the synergistic effect of multiple modes of transportation, a comprehensive three-dimensional transportation system is formed, which meets the actual needs of low-altitude passenger transport scenarios.

[0004] To achieve the above objectives, the main technical solutions adopted in this application include: In a first aspect, embodiments of this application provide a method for selecting a vertical take-off and landing site, the method comprising: Obtain the intermodal transport demand elements and traffic guidance elements for each grid location in the target space, perform preliminary site selection based on the traffic guidance elements in the target space, and construct the initial site selection set for the target space; A multi-objective location model for the target space is constructed based on the intermodal transport demand elements; the initial location set is iteratively optimized based on the multi-objective location model to obtain the iterative location set for the target space.

[0005] The vertical take-off and landing field location method proposed in this application obtains the intermodal transport demand elements and traffic guidance elements for each grid location in the target space, and constructs a multi-objective location model and an initial location set for the target space based on the intermodal transport demand elements and traffic guidance elements. Based on the initial location set, multi-objective iterative optimization is performed according to the multi-objective location model to iteratively select locations in the target space, obtaining an iterative location set as the final location result. Compared with related technologies, considering that passengers in low-altitude passenger transport scenarios also need to use other modes of transportation to fill the gaps between low-altitude transportation segments, this application also obtains the traffic guidance elements for each grid location in addition to the intermodal transport demand elements. The traffic guidance elements are used to guide the iterative optimization process of the multi-objective location model, enabling priority consideration of traffic guidance elements during the iterative location process. This results in location results that seamlessly integrate with the existing transportation system, effectively reusing existing transportation resources and forming a comprehensive three-dimensional transportation system through the synergistic effect of multiple transportation modes, thereby meeting the actual needs of low-altitude passenger transport scenarios.

[0006] Optionally, the intermodal transport demand elements can be obtained through the following methods: Obtain the travel time and travel cost data for each of the multiple candidate intermodal transport modes for any grid location in each of the grid locations; For any candidate intermodal transport mode among the multiple candidate intermodal transport modes, the travel time data and travel cost data of any candidate intermodal transport mode are fused according to the preference difference parameter to construct the intermodal transport utility function of any candidate intermodal transport mode; Based on the intermodal utility function, a preference selection profile is generated for any candidate intermodal mode to obtain the preference selection probability of any candidate intermodal mode. Based on the preference selection probability, demand analysis is performed on the traffic flow data of any grid location to obtain the intermodal demand elements for any candidate intermodal mode in any grid location.

[0007] Optionally, the traffic guidance elements can be obtained in the following ways: A traffic accessibility analysis is performed on the target space to obtain the traffic connection nodes in each grid location; The spatial location of the traffic connection node is used as the traffic guidance element.

[0008] Optionally, the preliminary site selection prioritizing the traffic guidance elements in the target space, and the construction of an initial site selection set for the target space, includes: Random location selection is performed in the target space to obtain an initial random location set; Candidate guidance grids are selected from the target space based on the traffic guidance elements; preliminary site selection is performed on the candidate guidance grids to obtain the initial guidance site selection; If the number of candidate guide grids is less than the preset number of addresses, supplementary addresses are selected at grid positions other than the candidate guide grids to obtain initial supplementary addresses, and an initial guide address set is obtained based on the initial guide addresses and the initial supplementary addresses. The initial random location set and the initial guided location set are merged to obtain the initial location set.

[0009] Optionally, the step of performing multi-objective iterative optimization on the initial location set according to the multi-objective location model to obtain the iterative location set of the target space includes: The initial location set is subjected to crossover mutation to obtain a descendant location set. The descendant location set and the initial location set are then merged to obtain the set to be screened. The fitness of the set to be screened is evaluated to obtain an iterative fitness index, and the set to be screened is then subjected to elite retention screening based on the iterative fitness index to obtain an intermediate site selection set. The intermediate location set is used as the initial location set. The crossover and mutation process is repeated on the initial location set to obtain the offspring location set. The offspring location set and the initial location set are merged to obtain the set to be screened. The fitness of the set to be screened is evaluated to obtain the iterative fitness index. The set to be screened is then subjected to elite retention screening based on the iterative fitness index to obtain the intermediate location set. This process is repeated until the iteration limit is reached. The intermediate location set at the iteration limit is used as the iterative location set.

[0010] Optionally, the method further includes: Based on multiple suitability evaluation indicators in a preset suitability evaluation system, multiple candidate locations in the iterative site selection set are evaluated respectively to obtain the indicator scores of each candidate location; wherein, the preset suitability evaluation system is constructed by comparing the importance of the multiple suitability evaluation indicators; Based on the weights of the various suitability evaluation indicators, the scores of the indicators are comprehensively evaluated to obtain the site suitability of each of the multiple candidate locations.

[0011] Optionally, the preset suitability evaluation system can be obtained through the following methods: The optimal and worst indicators are determined from the multiple suitability evaluation indicators, and the importance of other indicators in the multiple suitability evaluation indicators is compared to obtain the optimal and worst relative importance of the multiple suitability evaluation indicators. The multiple suitability evaluation indicators are weighted according to the optimal and worst relative importance, and the initial indicator weights corresponding to each of the multiple suitability evaluation indicators are obtained. The weight calculation deviation of the optimal and worst relative importance is determined. The consistency result of the optimal and worst relative importance is calculated based on the deviation calculated according to the preset consistency index and the weight. If the consistency results meet the preset consistency conditions, the preset suitability evaluation system is constructed based on the multiple suitability evaluation indicators and their corresponding initial indicator weights.

[0012] Secondly, embodiments of this application provide a vertical take-off and landing site selection device, the device comprising: The initial site selection module is used to obtain the intermodal transport demand elements and traffic guidance elements of each grid location in the target space, perform preliminary site selection with priority given to the traffic guidance elements in the target space, and construct the initial site selection set of the target space. The iterative location selection module is used to construct a multi-objective location selection model for the target space based on the intermodal transport demand elements; and to perform multi-objective iterative optimization on the initial location selection set based on the multi-objective location selection model to obtain an iterative location selection set for the target space.

[0013] Thirdly, embodiments of this application provide a computer device, including: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the method described in any of the above embodiments.

[0014] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer instructions, which are used to cause a computer to perform the method described in any one of the above embodiments. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the specific embodiments of this application or 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 this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0016] Figure 1 A step diagram illustrating the vertical take-off and landing site selection method provided in this application embodiment; Figure 2 This is a schematic diagram of the grid positions and their position codes within the target space in an embodiment of this application; Figure 3This is a flowchart illustrating the steps for obtaining intermodal transport demand elements in an embodiment of this application. Figure 4 This is a flowchart illustrating the steps for obtaining traffic guidance elements in an embodiment of this application. Figure 5 This is a diagram illustrating the steps involved in constructing the initial location set in an embodiment of this application. Figure 6 This is a flowchart illustrating the steps of multi-objective iterative optimization in the embodiments of this application; Figure 7 This is a flowchart illustrating the steps involved in evaluating the suitability of candidate locations in an embodiment of this application. Figure 8 This is a flowchart illustrating the steps involved in constructing a preset suitability evaluation system in an embodiment of this application. Figure 9 A block diagram of a vertical take-off and landing site selection device provided in the embodiments of this application; Figure 10 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] Low-altitude transportation refers to the transport of goods or personnel using aircraft such as drones or electric vertical takeoff and landing (eVTOL) aircraft in airspace below 1,000 to 3,000 meters. This fills gaps in the existing transportation system and improves the timeliness and accessibility of the regional integrated transportation system. Low-altitude transportation requires corresponding vertical takeoff and landing fields for aircraft to load and unload goods and board and alight passengers. The site selection process for these fields needs to consider various factors, including but not limited to regional traffic demand and scenario requirements. Related technologies are typically adapted to single non-passenger scenarios such as low-altitude logistics. However, the factors to be considered in non-passenger scenarios and passenger scenarios are completely different, making it impossible to directly apply these technologies to passenger scenarios. Therefore, there is an urgent need to propose a site selection method suitable for low-altitude passenger transportation scenarios.

[0019] To address the aforementioned issues, this application provides a method, apparatus, equipment, and medium for selecting vertical take-off and landing (VTOL) sites. This method acquires intermodal transport demand elements and traffic guidance elements for each grid location in the target space, performs preliminary site selection prioritizing traffic guidance elements within the target space, and constructs an initial site selection set for the target space. It then constructs a multi-objective site selection model for the target space based on the intermodal transport demand elements. Finally, it performs multi-objective iterative optimization on the initial site selection set based on the multi-objective site selection model to obtain an iterative site selection set for the target space.

[0020] The vertical take-off and landing field location method provided in this application obtains the intermodal transport demand elements and traffic guidance elements of each grid location in the target space, and constructs a multi-objective location model and an initial location set for the target space based on the intermodal transport demand elements and traffic guidance elements; on the basis of the initial location set, multi-objective iterative optimization is performed according to the multi-objective location model to perform iterative location selection in the target space, and obtains an iterative location set as the final location result.

[0021] Compared with related technologies, considering that passengers in low-altitude passenger transport scenarios also need to use other modes of transportation to fill the gaps between low-altitude transportation segments, this application, based on the intermodal transport demand elements, also obtains traffic guidance elements for each grid location. The traffic guidance elements are used to guide the iterative optimization process of the multi-objective site selection model, so that the traffic guidance elements can be given priority in the iterative site selection process, resulting in site selection results that can be seamlessly connected with the existing transportation system. This effectively reuses existing transportation resources and forms a comprehensive three-dimensional transportation system through the synergistic effect between multiple modes of transportation, thereby meeting the actual needs of low-altitude passenger transport scenarios.

[0022] According to an embodiment of this application, a method for selecting a vertical take-off and landing site is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Also, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0023] Reference Figure 1 As shown, this embodiment provides a method for selecting a vertical takeoff and landing site, the method comprising: S100. Obtain the intermodal transport demand elements and traffic guidance elements of each grid location in the target space, perform preliminary site selection with priority given to traffic guidance elements in the target space, and construct the initial site selection set of the target space.

[0024] S200. Construct a multi-objective location model for the target space based on intermodal transport demand elements; perform multi-objective iterative optimization on the initial location set based on the multi-objective location model to obtain an iterative location set for the target space.

[0025] The target space can be a physical space where multiple vertical take-off and landing fields need to be constructed. The grid positions can be multiple positions obtained by dividing the target space into grids, with each grid position having the same size. In this embodiment, the size of the grid position is 100 meters × 100 meters. Each grid position can be equipped with different intermodal transport methods, including but not limited to electric vertical take-off and landing aircraft, private cars, taxis, airport buses, and subways.

[0026] Intermodal transport demand can represent the demand for any mode of transportation at any grid location, and can be obtained based on the traffic volume at that grid location and the probability of passengers choosing any mode of transportation. It should be noted that this embodiment mainly focuses on the location selection of vertical take-off and landing (VTOL) sites for electric vertical take-off and landing (EVTOL) aircraft. Therefore, the intermodal transport demand in this embodiment mainly represents the demand for EVTOL aircraft at any grid location, and can be obtained based on the traffic volume at that grid location and the probability of passengers choosing EVTOL aircraft. Traffic guidance elements can represent the existing transportation systems at any grid location that can connect to the VTOL site, and are given priority consideration during the VTOL site selection process. For example, traffic guidance elements can include, but are not limited to, the specific locations of existing transportation systems such as high-speed rail stations, bus stations, subway stations, and highway points, such as latitude and longitude.

[0027] Specifically, for each grid location in the target space, a traffic demand analysis is performed on each grid location to determine the demand for passengers using electric vertical takeoff and landing (EVTOL) aircraft, thus obtaining the intermodal transport demand elements for each grid location. Simultaneously, a traffic system scan is conducted within the spatial range of each grid location to query the existing traffic systems within each grid location and return the specific locations of these existing traffic systems, thereby obtaining the traffic guidance elements for each grid location.

[0028] It is understandable that, in addition to intermodal transport demand factors and traffic guidance factors, low-altitude traffic risk analysis can be performed on each grid location to assess the degree of risk that would result from adding vertical take-off and landing fields to each grid location, thus obtaining intermodal transport risk factors. In some embodiments, intermodal transport risk factors can be obtained by performing low-altitude traffic risk analysis based on factors such as urban area maps, population density, shielding effects, obstacles, and no-fly zones for each grid location. The specific methods for low-altitude traffic risk analysis can employ basic and general techniques in this field, and are not specifically limited here.

[0029] In this embodiment, the intermodal cost elements for each grid location can also be determined based on the vertical take-off and landing (VTOL) construction plan for the target space. These intermodal cost elements can be the construction cost of building a VTOL at any grid location. It is understood that the intermodal cost elements can be determined based on the VTOL level of the constructed VTOL, which can include primary, intermediate, and advanced VTOL. Primary VTOLs have a smaller footprint and lower service capacity, serving only as temporary parking for electric vertical take-off and landing (EVL) aircraft. Intermediate VTOLs have a larger footprint than primary VTOLs and higher service capacity. Advanced VTOLs have a larger footprint than intermediate VTOLs and higher service capacity. Both intermediate and advanced VTOLs have relatively complete related facilities, allowing for multiple stops for EVL aircraft and providing transportation services for passengers.

[0030] Furthermore, an initial set of site selections for the target space is constructed, which includes... An initial set of locations is used for multi-objective iterative optimization. The initial location set can be mapped using chromosome encoding. In this embodiment, the chromosome encoding method includes: each genome consists of 3 genes, where the first 2 genes are positional codes representing the specific coordinates of the initial location; the third gene is a takeoff and landing field level code representing the takeoff and landing field level of the corresponding location. It can be understood that the length of the chromosome used to map and represent the initial location set is... , which includes Each genome. For example, the process of encoding chromosomes in the target space can be referred to... Figure 2 As shown, Genome 1 indicates that an intermediate-level take-off and landing field is expected to be built at the grid position in the 1st row and 4th column of the target space; Genome 2 indicates that an advanced-level take-off and landing field is expected to be built at the grid position in the 2nd row and 3rd column of the target space; and Genome 3 indicates that a primary-level take-off and landing field is expected to be built at the grid position in the 4th row and 2nd column of the target space.

[0031] In some embodiments, the initial location set may be obtained by combining an initial random location set and an initial guided location set. The initial random location set may be obtained by selecting locations in the target space in a completely random manner, while the initial guided location set may be obtained by selecting locations in the target space based on traffic guidance elements, thereby preferentially selecting grid positions close to the traffic guidance elements. When chromosome encoding is used, the process of generating the initial random location set may include: initializing a length of... An empty list is used as a chromosome, containing One genome; random integers are generated for each genome in the chromosome, wherein the values ​​of the first two genes of any genome are within the coordinate range of the target space, and the values ​​of the third gene are within the level range of the vertical take-off and landing field.

[0032] Understandably, fully random location selection ensures the diversity of the initial random location set and the breadth of the solution space, avoiding getting trapped in local optima during multi-objective iterative optimization. In this embodiment, the initial location set is obtained by mixing the initial random location set and the initial guided location set, effectively combining the advantages of both random and guided location selection. This overcomes the problem that a single method cannot balance diversity and convergence, generating an initial location set that meets the needs of passenger transport scenarios and possesses diversity, thereby improving the convergence speed of multi-objective iterative optimization.

[0033] Furthermore, based on the intermodal transport demand elements, intermodal transport risk elements, and intermodal transport cost elements of each grid location, a multi-objective model is constructed for the target space based on the intermodal transport demand elements, intermodal transport risk elements, and intermodal transport cost elements, resulting in a multi-objective location model with multiple objective functions, which can be used for vertical take-off and landing field location selection in the target space.

[0034] In some embodiments, the objective function of the multi-objective site selection model may include maximizing low-altitude demand coverage, minimizing low-altitude operational risk, and minimizing construction cost. Maximizing low-altitude demand coverage can be constructed based on intermodal transport demand factors to maximize the satisfaction of intermodal transport demand in the target space. Minimizing low-altitude operational risk can be constructed based on intermodal transport risk factors to minimize the risks posed by constructing a vertical take-off and landing field to the target space. Minimizing construction cost can be constructed based on intermodal transport cost factors to minimize the construction cost of the vertical take-off and landing field, thereby reducing the economic burden on the target space.

[0035] For example, for a grid position located in the i-th row and j-th column of the target space, the objective function to maximize low-altitude demand coverage can be expressed as: in, For the grid position in row i and column j, the intermodal transport demand element is... The attenuation coefficient between the grid position in row i and column j and the grid position in row a and column b; The location coefficient for the grid position in the i-th row and j-th column indicates whether the grid position is selected for the construction of a vertical take-off and landing field; This represents the total number of rows in the raster space. This represents the total number of columns in the raster position within the target space. Attenuation coefficient. It can be represented as: in, Let be the length of the line connecting the center points of the grid positions in the i-th row and j-th column and the grid positions in the a-th row and b-th column. This is the maximum service range for a vertical take-off and landing (VTOL) field. Beyond this maximum service range, the service capability of the VTOL field gradually decreases with increasing distance. This is the service capacity boundary distance of the vertical takeoff and landing (VTOL) field. Beyond this boundary distance, the VTOL field cannot provide service. Location coefficient. It can be represented as: Each grid position also corresponds to a takeoff and landing field level, which can be represented as: in, The takeoff and landing field level is the grid position in the i-th row and j-th column.

[0036] The objective function for minimizing the risk of low-altitude operation can be expressed as: in, The operational risk for the grid position in row i and column j.

[0037] The objective function for minimizing construction costs can be expressed as: in, The construction cost of a vertical takeoff and landing field at the grid position in the i-th row and j-th column can be expressed as: in, The construction cost of the primary take-off and landing field; The construction cost of an intermediate take-off and landing field; The construction cost of advanced take-off and landing fields.

[0038] The multi-objective location model also includes constraints, such as the total number of vertical take-off and landing (VTOL) fields, coverage constraints, service capacity constraints, fairness constraints, and distance constraints. Among these, the constraint on the total number of VTOL fields can be determined based on the VTOL field construction plan for the target space, and is used to limit the total number of VTOL fields constructed in the target space. For example, the constraint on the total number of VTOL fields can be expressed as: in, This represents the total number of vertical take-off and landing fields that should be constructed in the target space.

[0039] Coverage constraints can be set for common attributes of vertical takeoff and landing (VTOL) fields to ensure that newly constructed VTOL fields can meet intermodal transport requirements in the target space to a certain extent. For example, a coverage constraint can be expressed as: in, This represents the global intermodal transport demand elements within the target space. It is understandable that, based on the aforementioned coverage constraints, newly constructed vertical takeoff and landing (VTOL) fields within the target space must meet 50% of the intermodal transport demand within that target space.

[0040] Service capacity constraints can be the service capacity settings for each grid location of any newly established vertical takeoff and landing (VTOL) field. They are used to limit the service capacity allocation of each newly established VTOL field and avoid planning errors. For example, a service capacity constraint can be expressed as: in, The set of service coverage grids for the vertical take-off and landing field at the grid position in row i and column j; Let be the maximum service capacity of the vertical take-off and landing field (VTOL) located at the grid position in the i-th row and j-th column. It is understood that, based on the above service capacity constraint, for any newly established VTOL in the target space, the sum of the intermodal transport demands of all grid positions covered by that field cannot exceed the service capacity of that newly established VTOL.

[0041] Fairness constraints can be designed based on the principle of "one vertical take-off and landing yard per district," requiring the construction of at least one new vertical take-off and landing yard in each administrative region. This ensures that the newly constructed yards can fairly serve all administrative regions, guaranteeing traffic fairness among them. For example, a fairness constraint can be expressed as: in, For the target space belonging to the administrative region A set of regional grid cells; This refers to the set of administrative regions in the target space.

[0042] Distance constraints can be established based on the mutual influence between adjacent vertical takeoff and landing (VTOL) fields. For VTOL fields that are too close together, airflow interference may occur, and their flight paths are prone to intersecting, increasing safety risks for low-altitude traffic. Therefore, when the distance between adjacent VTOL fields is less than the minimum constraint distance, they influence each other, and simultaneous construction is not permitted. For example, the distance constraint can be expressed as: in, The location coefficient for the grid position in row a and column b indicates whether the grid position is selected for the construction of a vertical take-off and landing field; This is the minimum constraint distance.

[0043] Furthermore, using a multi-objective location selection model, the initial location selection set is subjected to multi-objective iterative optimization. In each iteration round, the initial location selection set is subjected to population optimization. The population-optimized set is used as the initial location selection set for the optimization process of the next iteration round, thereby realizing the multi-round evolution of the initial location selection set and finally obtaining the iterative location selection set of the target space as the final location selection result.

[0044] The vertical take-off and landing field location method provided in this embodiment obtains the intermodal transport demand elements and traffic guidance elements of each grid location in the target space, and constructs a multi-objective location model and an initial location set for the target space based on the intermodal transport demand elements and traffic guidance elements; on the basis of the initial location set, multi-objective iterative optimization is performed according to the multi-objective location model to perform iterative location selection in the target space, and obtains an iterative location set as the final location result.

[0045] Compared with related technologies, considering that passengers in low-altitude passenger transport scenarios also need to use other modes of transportation to fill the gaps between low-altitude transportation segments, this application, based on the intermodal transport demand elements, also obtains traffic guidance elements for each grid location. The traffic guidance elements are used to guide the iterative optimization process of the multi-objective site selection model, so that the traffic guidance elements can be given priority in the iterative site selection process, resulting in site selection results that can be seamlessly connected with the existing transportation system. This effectively reuses existing transportation resources and forms a comprehensive three-dimensional transportation system through the synergistic effect between multiple modes of transportation, thereby meeting the actual needs of low-altitude passenger transport scenarios.

[0046] Reference Figure 3 As shown, in one embodiment of this application, the intermodal transport demand elements are obtained in the following manner: S112. Obtain the travel time and travel cost data for each of the multiple candidate intermodal transport modes for any grid location.

[0047] S114. For any candidate intermodal transport mode among multiple candidate intermodal transport modes, the travel time data and travel cost data of any candidate intermodal transport mode are fused according to the preference difference parameter to construct the intermodal transport utility function of any candidate intermodal transport mode.

[0048] S116. Based on the intermodal utility function, characterize the preference choice of any candidate intermodal mode to obtain the preference choice probability of any candidate intermodal mode.

[0049] S118. Based on the preference selection probability, perform demand analysis on the traffic flow data of any grid location to obtain the intermodal demand elements for any candidate intermodal mode in any grid location.

[0050] Among them, travel time data and travel cost data can be obtained in advance through historical data statistics. Travel time data can be used to represent the total time passengers spend on transportation when using the corresponding candidate intermodal transport mode, and travel cost data can be used to represent the total cost passengers spend on transportation when using the corresponding candidate intermodal transport mode.

[0051] Specifically, for any given grid location with multiple feasible candidate intermodal transport modes, the travel time and cost data for each candidate mode are obtained. For any given candidate intermodal transport mode, the travel time and cost data are fused based on a preference difference parameter to construct an intermodal utility function for that mode. It should be noted that the preference difference parameter can be pre-defined, corresponding to different types of passengers, representing their preferences when selecting a mode of transport. For example, some passengers are more time-sensitive and tend to choose modes of transport with shorter total travel times. For these time-sensitive passengers, the preference difference parameter corresponding to the travel time data can be a large negative value, indicating that the longer the total travel time for any given candidate intermodal transport mode, the lower its travel utility and the less likely it is to be chosen by time-sensitive passengers.

[0052] For example, the combined transport utility function can be expressed as: in, Candidate intermodal transport methods The combined transport utility function; To select candidate intermodal transport methods Travel time data at the time; To select candidate intermodal transport methods Travel cost data at the time; and The preference difference parameter can follow a preset preference distribution, such as a negative log-normal distribution; The random error term can follow a Type I extreme value distribution.

[0053] Furthermore, after obtaining the intermodal utility function for any candidate intermodal mode, the probability of a passenger choosing that particular candidate intermodal mode from all candidate intermodal modes is calculated based on the intermodal utility function. Then, these probabilities are summed according to the probability distributions of different passenger types to obtain the preference selection probability for all passengers choosing that particular candidate intermodal mode. For example, the preference selection probability can be expressed as: in, Passengers can choose candidate intermodal transport modes based on their preferences. The probability of preference selection; Candidate intermodal transport methods The utility correction factor represents the passenger's preference for candidate intermodal transport methods. Fixed preferences; The probability density function of the preset preference distribution to which the preference difference parameter is obeyed; The variance of the preset preference distribution; Let be the total number of candidate intermodal transport modes. The integration process of the above equation can be implemented using the Monte Carlo simulation method.

[0054] Furthermore, traffic flow data for any given grid location is obtained to represent the total traffic flow demand at that given grid location. Methods for obtaining traffic flow data may include: acquiring historical traffic data related to that given grid location, including data with destinations at that given grid location and data with origins at that given grid location; and statistically analyzing the historical traffic data within a unit of time to obtain the traffic flow data for that given grid location.

[0055] After obtaining traffic flow data, demand analysis is performed on the traffic flow data at any given grid location based on preference selection probabilities. The probability of allocating the traffic flow data to any candidate intermodal transport mode is calculated, thus obtaining the intermodal transport demand element for that candidate intermodal transport mode at that given grid location. For example, the intermodal transport demand element can be represented as: in, This represents the traffic flow data for the grid location in the i-th row and j-th column. It is understood that this application primarily focuses on the location of vertical takeoff and landing (VTOL) sites for electric vertical takeoff and landing (EVTOL) aircraft, and the intermodal transport demand element mentioned can be considered as the traffic flow for EVTOL aircraft trips at any grid location.

[0056] Reference Figure 4 As shown, in one embodiment of this application, traffic guidance elements are obtained in the following manner: S122. Perform traffic accessibility analysis on the target space to obtain the traffic connection nodes in each grid location.

[0057] S124. Spatial positioning of transportation connection nodes, using the spatial location of transportation connection nodes as a traffic guidance element.

[0058] Specifically, a traffic system scan is performed in the target space to identify the existing traffic systems contained within it. The existing traffic systems are then categorized according to their spatial extent at each grid location, determining the traffic connection capacity they can provide at each grid location. This yields traffic connection nodes at each grid location, representing the available modes of transportation for passengers traveling between sections using electric vertical takeoff and landing (EVTOL) aircraft. For example, traffic connection nodes may include, but are not limited to, high-speed rail stations, bus stations, subway stations, and highway points.

[0059] Furthermore, within any given grid location, the traffic connection nodes within that grid location are spatially located to obtain their spatial positions, which are then returned as traffic guidance elements. For example, the spatial positions may include the latitude and longitude of the aforementioned traffic connection nodes.

[0060] Reference Figure 5 As shown, as one embodiment of this application, preliminary site selection prioritizing traffic guidance elements is performed in the target space to construct an initial site selection set for the target space, including: S132. Randomly select locations in the target space to obtain an initial set of random locations.

[0061] S134. Select candidate guidance grids from the target space based on traffic guidance elements; perform preliminary site selection in the candidate guidance grids to obtain the initial guidance site selection.

[0062] S136. If the number of candidate guide grids is lower than the preset number of addresses, supplementary addresses are made in grid positions other than the candidate guide grids to obtain initial supplementary addresses, and an initial guide address set is obtained based on the initial guide address and the initial supplementary addresses.

[0063] S138. Merge the initial random location set and the initial guided location set to obtain the initial location set.

[0064] Specifically, the initial random location set can be obtained by selecting locations in the target space in a completely random manner. By selecting locations completely randomly, the diversity of the initial random location set and the breadth of the solution space can be ensured, and the problem of getting stuck in local optima during multi-objective iterative optimization can be avoided.

[0065] Furthermore, the traffic guidance elements include the traffic connection nodes and their spatial locations within each grid position. Based on the traffic guidance elements, all grid positions containing traffic connection nodes are selected from all grid positions as filter guidance grids. It is understood that selecting guidance grids containing traffic connection nodes can provide transportation connection services for passengers traveling using electric vertical takeoff and landing (EVTOL) aircraft, thereby connecting the EVTOL field with the existing transportation system and achieving synergistic effects among various modes of transportation.

[0066] Furthermore, initial guide addressing is obtained by performing preliminary addressing in the candidate guide grid in a completely random manner. When chromosome encoding is used, the process of obtaining the initial guide addressing may include: initializing the length to... An empty list is used as a chromosome, containing One genome; randomly select any grid position from the screening guide grid, fill the first two genes of the first genome with the coordinates of this grid position, and generate a random integer for the third gene of the first genome to obtain the mapping code representation of the first initial position in the initial guide location; repeat the above steps to fill all genomes in the chromosome to obtain the initial guide location. The numerical range of the third gene is within the level range of the vertical takeoff and landing field.

[0067] In some embodiments, random perturbations may be introduced during the initial guidance site selection process. For example, random perturbations may be introduced during the process of randomly selecting any grid position from the screening guidance grid and during the process of generating a random integer for the third gene of any genome, in order to preserve randomness and improve the diversity of the initial guidance site selection while ensuring priority traffic guidance elements.

[0068] Furthermore, if the number of candidate guide grids is less than the preset number of locations, making it impossible to meet the expected construction needs by selecting grid positions solely from the candidate guide grids, then grid positions other than the candidate guide grids are randomly selected from all grid positions. These selected grid positions are used as supplementary locations, and initial supplementary locations are obtained based on their coordinates. If only initial guide locations are obtained, they can be used as the initial guide location set. If both initial guide locations and initial supplementary locations are obtained, they can be merged, and the merged result can be used as the initial guide location set.

[0069] Furthermore, after obtaining the initial random location set and the initial guided location set, the two are merged to obtain the initial location set.

[0070] Reference Figure 6 As shown, in one embodiment of this application, the initial location set is subjected to multi-objective iterative optimization based on a multi-objective location selection model to obtain an iterative location set for the target space, including: S210. Perform crossover mutation on the initial site selection set to obtain the offspring site selection set, and merge the offspring site selection set and the initial site selection set to obtain the set to be screened.

[0071] S220. Evaluate the fitness of the set to be screened to obtain the iterative fitness index, and perform elite retention screening on the set to be screened based on the iterative fitness index to obtain the intermediate location set.

[0072] S230. Using the intermediate location set as the initial location set, repeat the above process of crossover and mutation on the initial location set to obtain the offspring location set. Merge the offspring location set and the initial location set to obtain the set to be screened. Evaluate the fitness of the set to be screened to obtain the iterative fitness index. Based on the iterative fitness index, perform elite retention screening on the set to be screened to obtain the intermediate location set. Repeat this process until the iteration limit is reached. Use the intermediate location set when the iteration limit is reached as the iterative location set.

[0073] Specifically, in any iteration of the multi-objective iterative optimization, the initial location set is first subjected to crossover and mutation to generate a child location set that differs from the initial location set. The initial location set and the child location set are then merged to obtain the set to be selected. In some embodiments, the roulette wheel mutation method can be used to mutate the initial location set during the crossover and mutation process to generate the child location set. The population size can be set to 500, the crossover probability can be set to 0.8, the mutation probability can be set to 0.02, and the mutation method can be random factor mutation.

[0074] Furthermore, a fitness function is designed based on the objective function of the multi-objective location selection model to evaluate the degree to which each location in the selection set satisfies the location selection objective. The fitness function is used to evaluate the fitness of the selection set, obtaining the fitness index of each location in the selection set, which serves as the iterative fitness index. For example, the fitness function can be expressed as: in, The first fitness function can be obtained from the objective function that maximizes low-altitude demand coverage. The second fitness function can be obtained from the objective function that minimizes the risk of low-altitude operation. The third fitness function can be derived from the objective function of minimizing construction costs.

[0075] Furthermore, based on the constraints of the multi-objective location selection model, location locations that do not meet any of the constraints are screened out from the set to be screened, and elite retention screening is performed on the set to be screened according to the iterative fitness index to retain the better location locations that meet the preset number of location selections, thus obtaining the intermediate location set.

[0076] Furthermore, before reaching the iteration limit, the intermediate location set is used as the initial location set, and the aforementioned iteration process is repeated to gradually update the intermediate location set and the iteration fitness index until the iteration limit is reached. When the iteration limit is reached, the intermediate location set at this point is output as the final result, serving as the iteration location set. For example, the iteration limit can be set to 300 iterations.

[0077] Reference Figure 7 As shown in one embodiment of this application, the method further includes: S310. Based on multiple suitability evaluation indicators in the preset suitability evaluation system, evaluate multiple candidate locations in the iterative site selection set respectively to obtain the indicator scores of each candidate location; wherein, the preset suitability evaluation system is constructed by comparing the importance of multiple suitability evaluation indicators.

[0078] S320. Based on the weights of the various suitability evaluation indicators, the indicator scores are comprehensively evaluated to obtain the site suitability of each of the multiple candidate locations.

[0079] The preset suitability evaluation system can be a system containing multiple suitability evaluation indicators, each with a different weight representing its importance. For example, the preset suitability evaluation system can be constructed using methods such as the Best-Worst Method (BWM), the Analytical Hierarchy Process (AHP), and the Analytical Network Process (ANP). In this embodiment, the preset suitability evaluation system can be constructed using the Best-Worst Method by comparing the importance of multiple suitability evaluation indicators.

[0080] For example, suitability evaluation indicators may include primary indicators and secondary indicators, with secondary indicators subordinate to primary indicators, as shown in Table 1. Primary indicators may include traffic flow, infrastructure improvements, airspace conditions, meteorological conditions, electromagnetic environment conditions, and noise conditions, etc. Secondary indicators corresponding to traffic flow may include vehicle flow and passenger flow, etc. Vehicle flow can be evaluated based on the average daily vehicle flow of the corresponding candidate location; passenger flow can be evaluated based on the average daily pedestrian flow of the corresponding candidate location.

[0081] The secondary indicators corresponding to the infrastructure upgrade can include remaining land area, communication, navigation, and surveillance. Remaining land area can be evaluated based on the remaining available land area at the corresponding candidate locations after the construction of the vertical take-off and landing field. Communication can be evaluated based on whether there is a public network or dedicated low-altitude communication coverage at the corresponding candidate locations. Navigation can be evaluated based on whether there is obstruction of BeiDou / GNSS satellite signals at the corresponding candidate locations and whether there is coverage with high-precision enhancement services. Surveillance can be evaluated based on whether the corresponding candidate locations are covered by 5G-A integrated sensing equipment, as well as at least one of radar, radio detection equipment, ADS-B, RID equipment, and photoelectric tracking equipment.

[0082] Secondary indicators corresponding to airspace conditions may include flight altitude restrictions, natural obstacle density, and high-rise building density. Flight altitude restrictions can be evaluated based on the highest flight altitude limit in the airspace where the corresponding candidate location is located. Natural obstacle density can be evaluated based on the number of mountains or trees with a height exceeding 120m within a 160m radius of the corresponding candidate location. High-rise building density can be evaluated based on the number of high-rise buildings with a height exceeding 120m within a 160m radius of the corresponding candidate location, or obstacles such as chimneys and high-mast lights.

[0083] The secondary indicators corresponding to meteorological conditions can include the historical frequency of unfavorable meteorological events, which can be evaluated based on the historical annual frequency of meteorological events such as strong winds, low clouds, dense fog, or thunderstorms within a 160m radius of the corresponding candidate location.

[0084] Secondary indicators corresponding to electromagnetic environment conditions may include the average distance to interference sources, which can be evaluated based on the average distance between the candidate location and FM broadcasts, high-voltage lines, power plants, radio stations, and other industrial interference sources.

[0085] Secondary indicators corresponding to noise conditions may include noise levels, which can be evaluated based on the noise decibels at the corresponding candidate locations.

[0086] Table 1. Suitability Evaluation Indicators of the Pre-set Suitability Evaluation System Specifically, for any candidate location in the iterative site selection set, based on multiple suitability evaluation indicators in a pre-defined suitability evaluation system, the candidate location is evaluated to obtain an indicator score for each of the multiple suitability evaluation indicators. It is understood that the indicator score can be obtained from objective quantitative data of the candidate location, or it can be given by expert subjective evaluation.

[0087] Furthermore, in the pre-defined suitability evaluation system, each suitability evaluation indicator corresponds to an evaluation indicator weight. The scores of each of the multiple suitability evaluation indicators are weighted and summed according to their respective weights to obtain the site selection suitability of any candidate location. For example, the site selection suitability of the t-th candidate location in the iterative site selection set can be expressed as: in, Let q be the weight of the second-level indicator under the p-th primary indicator; The score for the second-level indicator under the p-th primary indicator corresponding to the t-th candidate position; This represents the total number of primary indicators, which can be set to 6 in this embodiment. This represents the total number of secondary indicators.

[0088] Understandably, site suitability indicates whether a candidate location is suitable for constructing a vertical take-off and landing (VTOL) field, and the value range of site suitability is... A higher site suitability score indicates that the corresponding candidate location is more suitable for building a vertical take-off and landing (VTOL) field, and can better meet the low-altitude passenger transport needs of the candidate location. Furthermore, site suitability can also be used to provide micro-level guidance for the construction of VTOL fields. By using the scores of candidate locations on different suitability evaluation indicators as guiding data for VTOL field construction, the practical guidance and engineering feasibility of VTOL field site selection results are significantly improved. Moreover, it can be directly linked to the actual construction process, greatly reducing the difficulty of project implementation.

[0089] Reference Figure 8 As shown, in one embodiment of this application, a preset suitability evaluation system is obtained through the following method: S330. Determine the optimal and worst indicators from multiple suitability evaluation indicators, and compare the importance of other indicators among the multiple suitability evaluation indicators to obtain the optimal and worst relative importance of the multiple suitability evaluation indicators.

[0090] S340. Based on the relative importance of the best and worst, assign weights to multiple suitability evaluation indicators to obtain the initial indicator weights corresponding to each of the multiple suitability evaluation indicators, and determine the weight calculation deviation of the relative importance of the best and worst.

[0091] S350. Calculate the deviation based on the preset consistency index and weight, and calculate the consistency result of the best and worst relative importance.

[0092] S360. If the consistency results meet the preset consistency conditions, construct a preset suitability evaluation system based on multiple suitability evaluation indicators and their corresponding initial indicator weights.

[0093] Specifically, among a set of pre-defined suitability evaluation indicators, the optimal and worst indicators are determined through methods such as expert subjective evaluation. The optimal indicator represents the one with the highest importance among the multiple suitability evaluation indicators, and the worst indicator represents the one with the lowest importance. After determining the optimal and worst indicators, for any indicator other than the optimal and worst indicators, its importance is compared with both the optimal and worst indicators to obtain the relative importance of that indicator among the multiple suitability evaluation indicators.

[0094] For example, the optimal and worst relative importance can be represented as an importance scale, including an optimal scale and a worst scale. The optimal scale can be the importance scale of any indicator relative to the optimal indicator, and the worst scale can be the importance scale of any indicator relative to the worst indicator. Between any indicator and the optimal indicator, if the any indicator and the optimal indicator have the same importance, then the optimal scale of the any indicator can be 1; if the optimal indicator is slightly more important than the any indicator, then the optimal scale of the any indicator can be 3; if the optimal indicator is significantly more important than the any indicator, then the optimal scale of the any indicator can be 5; if the optimal indicator is strongly more important than the any indicator, then the optimal scale of the any indicator can be 7; if the optimal indicator is extremely more important than the any indicator, then the optimal scale of the any indicator can be 9. The optimal scale can also include 2, 4, 6, and 8 as the median between adjacent optimal scales.

[0095] It is understandable that the importance of the above-mentioned comparison process, including terms such as equal importance, slightly more important, significantly more important, strongly more important, and extremely more important, can all be subjectively determined by experts in the expert evaluation, with significant being more important than slightly important, strongly important being more important than significant, and extremely important being more important than strongly important.

[0096] Similarly, between any indicator and the worst-case indicator, if the indicator and the worst-case indicator are of equal importance, the worst-case scale for the indicator can be 1; if the indicator is slightly more important than the worst-case indicator, the worst-case scale for the indicator can be 3; if the indicator is significantly more important than the worst-case indicator, the worst-case scale for the indicator can be 5; if the indicator is strongly more important than the worst-case indicator, the worst-case scale for the indicator can be 7; and if the indicator is extremely more important than the worst-case indicator, the worst-case scale for the indicator can be 9. The worst-case scale can also include 2, 4, 6, and 8 as the median between adjacent worst-case scales.

[0097] It is understandable that for any given indicator, the optimal and worst scales corresponding to the superior and inferior indicators whose relative importance is symmetrical to that indicator can be reciprocals of each other. For example, for any indicator A, if the superior indicator B is significantly more important than indicator A, and indicator A is significantly more important than the inferior indicator C, then the optimal and worst scales corresponding to the superior indicator B and the inferior indicator C are reciprocals of each other.

[0098] Furthermore, a model is constructed for all suitability evaluation indicators based on the optimal and worst relative importance, resulting in a max-min nonlinear model used to allocate the initial indicator weights. For example, the max-min nonlinear model can be expressed as: in, Calculate the bias for the weights; The initial indicator weights are the optimal indicator weights. The initial weights for the worst-performing indicator; Let be the initial indicator weight for the p-th indicator; This is the importance scale of the optimal indicator relative to the p-th indicator; Let p be the importance scale of the p-th indicator relative to the worst-case indicator. Weight calculation bias. This can be used to represent the error between the initial indicator weights and the optimal indicator weights, and can be expressed as: Substituting the optimal and worst relative importance of each suitability evaluation indicator into the maxima-mina nonlinear model, and solving the maxima-mina nonlinear model, we obtain the initial indicator weights corresponding to each suitability evaluation indicator, and then solve for the weight calculation deviation under the initial indicator weights. It can be understood that the initial indicator weights of the secondary indicators can be obtained by averaging the initial indicator weights of their respective primary indicators, which can be expressed as: in, Let be the initial indicator weight for the p-th primary indicator; This represents the total number of secondary indicators under the p-th primary indicator.

[0099] Furthermore, the importance of the optimal and worst indicators is compared to determine the importance scale of the optimal indicator relative to the worst indicator, which serves as the maximum importance span of the pre-set suitability evaluation system. Based on the importance scale of the optimal indicator relative to the worst indicator, the corresponding pre-set consistency indicator is determined. The consistency result of the pre-set suitability evaluation system is obtained by calculating the ratio based on the obtained pre-set consistency indicator and the weight calculation deviation.

[0100] For example, the consistency result can be represented as: in, A pre-defined consistency index is used. This index can be obtained by looking up a pre-designed value table. It can be related to the importance comparison results between the best and worst indices, as shown in Table 2. This is a scale for the importance of the best indicator relative to the worst indicator.

[0101] Table 2. Preset Consistency Index Values Furthermore, the consistency results are compared with a consistency threshold. If the consistency result does not exceed the threshold, the consistency result is deemed to meet the preset consistency conditions, indicating that the evaluation results given by the expert subjective assessment have good mathematical logic consistency and can be used to construct the preset suitability evaluation system. If the consistency result exceeds the threshold, the consistency result is deemed to not meet the preset consistency conditions, indicating that the evaluation results given by the expert subjective assessment have significant logical contradictions and need to be reassessed. When the consistency result meets the preset consistency conditions, the system is constructed based on multiple suitability evaluation indicators and their corresponding initial indicator weights to obtain the preset suitability evaluation system.

[0102] Accordingly, please refer to Figure 9 This application provides a vertical take-off and landing site selection device, which includes: The initial site selection module 910 is used to obtain the intermodal transport demand elements and traffic guidance elements of each grid location in the target space, perform preliminary site selection with priority given to traffic guidance elements in the target space, and construct the initial site selection set of the target space.

[0103] The iterative location module 920 is used to construct a multi-objective location model for the target space based on intermodal transport demand elements; and to perform multi-objective iterative optimization on the initial location set based on the multi-objective location model to obtain the iterative location set for the target space.

[0104] In some alternative implementations, the initial addressing module 910 includes: The data acquisition unit is used to acquire the travel time and travel cost data of various candidate intermodal transport modes for any grid location.

[0105] The utility construction unit is used to fuse the travel time and travel cost data of any candidate intermodal transport mode among multiple candidate intermodal transport modes based on the preference difference parameter, and construct the intermodal utility function of any candidate intermodal transport mode.

[0106] The probability calculation unit is used to characterize the preference selection of any candidate intermodal transport mode based on the intermodal transport utility function, and to obtain the preference selection probability of any candidate intermodal transport mode.

[0107] The demand analysis unit is used to perform demand analysis on traffic flow data at any grid location based on preference selection probability, and obtain the intermodal demand elements for any candidate intermodal mode at any grid location.

[0108] In some optional implementations, the initial addressing module 910 further includes: The traffic accessibility analysis unit is used to perform traffic accessibility analysis on the target space and obtain the traffic connection nodes in each grid location.

[0109] The spatial positioning unit is used to spatially locate traffic connection nodes, using the spatial location of the traffic connection nodes as traffic guidance elements.

[0110] In some optional implementations, the initial addressing module 910 further includes: The random addressing unit is used to perform random addressing in the target space to obtain an initial random addressing set.

[0111] The guidance location unit is used to filter candidate guidance grids from the target space based on traffic guidance elements; preliminary location selection is performed in the candidate guidance grids to obtain the initial guidance location.

[0112] The supplementary addressing unit is used to perform supplementary addressing at grid positions outside the candidate guide grids when the number of candidate guide grids is lower than the preset addressing number, to obtain the initial supplementary addressing, and to obtain the initial guide addressing set based on the initial guide addressing and the initial supplementary addressing.

[0113] The location merging unit is used to merge the initial random location set and the initial guided location set to obtain the initial location set.

[0114] In some alternative implementations, the iterative addressing module 920 includes: The location set mutation unit is used to perform crossover mutation on the initial location set to obtain the offspring location set. The offspring location set and the initial location set are then merged to obtain the set to be screened.

[0115] The elite retention screening unit is used to evaluate the fitness of the set to be screened, obtain the iterative fitness index, and perform elite retention screening on the set to be screened based on the iterative fitness index to obtain the intermediate site selection set.

[0116] The set of iterative units is used to take the intermediate site selection set as the initial site selection set, repeat the above process of crossover and mutation on the initial site selection set to obtain the offspring site selection set, merge the offspring site selection set and the initial site selection set to obtain the set to be screened, evaluate the fitness of the set to be screened to obtain the iterative fitness index, and perform elite retention screening on the set to be screened according to the iterative fitness index to obtain the intermediate site selection set, until the iteration limit is reached; the intermediate site selection set when the iteration limit is reached is used as the iterative site selection set.

[0117] In some alternative embodiments, the device further includes a suitable evaluation module, comprising: The location evaluation unit is used to evaluate multiple candidate locations in the iterative site selection set based on multiple suitability evaluation indicators in the preset suitability evaluation system, and obtain the index scores of each candidate location; wherein, the preset suitability evaluation system is constructed by comparing the importance of multiple suitability evaluation indicators.

[0118] The comprehensive evaluation unit is used to comprehensively evaluate the index scores based on the weights of each of the multiple suitability evaluation indicators, and to obtain the site suitability of each of the multiple candidate locations.

[0119] In some alternative implementations, the evaluation module may further include: The indicator importance comparison unit is used to determine the best and worst indicators from multiple suitability evaluation indicators, and to compare the importance of other indicators among the multiple suitability evaluation indicators to obtain the best and worst relative importance of the multiple suitability evaluation indicators.

[0120] The weight deviation calculation unit is used to assign weights to multiple suitability evaluation indicators according to the relative importance of the best and worst, obtain the initial indicator weights corresponding to each of the multiple suitability evaluation indicators, and determine the weight calculation deviation of the relative importance of the best and worst.

[0121] The consistency calculation unit is used to calculate the deviation based on the preset consistency index and weight, and to calculate the consistency result of the best and worst relative importance.

[0122] The condition judgment output unit is used to construct a preset suitability evaluation system based on multiple suitability evaluation indicators and their corresponding initial indicator weights, provided that the consistency results meet the preset consistency conditions.

[0123] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0124] In this embodiment, the vertical take-off and landing site selection device is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0125] Please see Figure 10 , Figure 10 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application, such as... Figure 10 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 10 Take a processor 10 as an example.

[0126] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0127] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0128] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0129] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0130] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0131] This application also provides a computer-readable storage medium. The methods described in this application can be implemented in hardware or firmware, or implemented as recordable on a storage medium, or implemented as computer code downloaded over a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and subsequently stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the methods shown in the above embodiments are implemented.

[0132] This application provides a computer program product including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the method of any embodiment of this application.

[0133] Although embodiments of this application have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of this application, and all such modifications and variations fall within the scope defined by the appended claims.

[0134] It is understood that before using the technical solutions disclosed in the various embodiments of this disclosure, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this disclosure in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0135] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the software or hardware, such as the electronic device, application, server, or storage medium performing the operations of this disclosed technical solution, based on the prompt message.

[0136] As an optional but non-limiting implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0137] It is understood that the above notification and user authorization process are merely illustrative and do not constitute a limitation on the implementation of this disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of this disclosure.

[0138] It is understood that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and related provisions.

[0139] It is understood that in the specific implementation of this application, data such as user information, location information, and navigation data are involved. When the above embodiments of this application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0140] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0141] For ease of description, the above devices are described separately by function as various units. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware.

[0142] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0143] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0144] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0145] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0146] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0147] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0148] The above description is merely an embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of this application should be included within the scope of the claims of this application.

[0149] Although embodiments of this application have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of this application, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for selecting a vertical takeoff and landing site, characterized in that, The method includes: Obtain the intermodal transport demand elements and traffic guidance elements for each grid location in the target space, perform preliminary site selection based on the traffic guidance elements in the target space, and construct the initial site selection set for the target space; A multi-objective location model for the target space is constructed based on the intermodal transport demand elements; the initial location set is iteratively optimized based on the multi-objective location model to obtain the iterative location set for the target space.

2. The method according to claim 1, characterized in that, The intermodal transport demand elements are obtained through the following methods: Obtain the travel time and travel cost data for each of the multiple candidate intermodal transport modes for any grid location in each of the grid locations; For any candidate intermodal transport mode among the multiple candidate intermodal transport modes, the travel time data and travel cost data of any candidate intermodal transport mode are fused according to the preference difference parameter to construct the intermodal transport utility function of any candidate intermodal transport mode; Based on the intermodal utility function, a preference selection profile is generated for any candidate intermodal mode to obtain the preference selection probability of any candidate intermodal mode. Based on the preference selection probability, demand analysis is performed on the traffic flow data of any grid location to obtain the intermodal demand elements for any candidate intermodal mode in any grid location.

3. The method according to claim 1, characterized in that, The traffic guidance elements are obtained in the following ways: A traffic accessibility analysis is performed on the target space to obtain the traffic connection nodes in each grid location; The spatial location of the traffic connection node is used as the traffic guidance element.

4. The method according to claim 1, characterized in that, The preliminary site selection of traffic guidance elements in the target space, prioritizing their selection, and constructing an initial site selection set for the target space, includes: Random location selection is performed in the target space to obtain an initial random location set; Candidate guidance grids are selected from the target space based on the traffic guidance elements; preliminary site selection is performed on the candidate guidance grids to obtain the initial guidance site selection; If the number of candidate guide grids is less than the preset number of addresses, supplementary addresses are selected at grid positions other than the candidate guide grids to obtain initial supplementary addresses, and an initial guide address set is obtained based on the initial guide addresses and the initial supplementary addresses. The initial random location set and the initial guided location set are merged to obtain the initial location set.

5. The method according to claim 1, characterized in that, The step of performing multi-objective iterative optimization on the initial location set according to the multi-objective location selection model to obtain the iterative location set of the target space includes: The initial location set is subjected to crossover mutation to obtain a descendant location set. The descendant location set and the initial location set are then merged to obtain the set to be screened. The fitness of the set to be screened is evaluated to obtain an iterative fitness index, and the set to be screened is then subjected to elite retention screening based on the iterative fitness index to obtain an intermediate site selection set. The intermediate location set is used as the initial location set. The crossover and mutation process is repeated on the initial location set to obtain the offspring location set. The offspring location set and the initial location set are merged to obtain the set to be screened. The fitness of the set to be screened is evaluated to obtain the iterative fitness index. The set to be screened is then subjected to elite retention screening based on the iterative fitness index to obtain the intermediate location set. This process is repeated until the iteration limit is reached. The intermediate location set at the iteration limit is used as the iterative location set.

6. The method according to claim 1, characterized in that, The method further includes: Based on multiple suitability evaluation indicators in a preset suitability evaluation system, multiple candidate locations in the iterative site selection set are evaluated respectively to obtain the indicator scores of each candidate location; wherein, the preset suitability evaluation system is constructed by comparing the importance of the multiple suitability evaluation indicators; Based on the weights of the various suitability evaluation indicators, the scores of the indicators are comprehensively evaluated to obtain the site suitability of each of the multiple candidate locations.

7. The method according to claim 6, characterized in that, The preset suitability evaluation system is obtained through the following method: The optimal and worst indicators are determined from the multiple suitability evaluation indicators, and the importance of other indicators in the multiple suitability evaluation indicators is compared to obtain the optimal and worst relative importance of the multiple suitability evaluation indicators. The multiple suitability evaluation indicators are weighted according to the optimal and worst relative importance, and the initial indicator weights corresponding to each of the multiple suitability evaluation indicators are obtained. The weight calculation deviation of the optimal and worst relative importance is determined. The consistency result of the optimal and worst relative importance is calculated based on the deviation calculated according to the preset consistency index and the weight. If the consistency results meet the preset consistency conditions, the preset suitability evaluation system is constructed based on the multiple suitability evaluation indicators and their corresponding initial indicator weights.

8. A vertical take-off and landing site selection device, characterized in that, The device includes: The initial site selection module is used to obtain the intermodal transport demand elements and traffic guidance elements of each grid location in the target space, perform preliminary site selection with priority given to the traffic guidance elements in the target space, and construct the initial site selection set of the target space. The iterative location selection module is used to construct a multi-objective location selection model for the target space based on the intermodal transport demand elements; and to perform multi-objective iterative optimization on the initial location selection set based on the multi-objective location selection model to obtain an iterative location selection set for the target space.

9. A computer device, characterized in that, include: A memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, the processor executing the computer instructions to perform the method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the method of any one of claims 1 to 7.

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