A bus station unmanned aerial vehicle taking-off and landing facility integrated planning method

Abstract

The application provides a bus station unmanned aerial vehicle taking-off and landing facility integrated planning method, and belongs to the technical field of unmanned aerial vehicle taking-off and landing facility planning. The application collects various ground area bus station and other information, analyzes unmanned aerial vehicle service demand characteristics through big data, sets key nodes, lays a solid foundation for planning, makes the taking-off and landing facility layout more in line with actual demand, and improves the use efficiency. Secondly, the quantitative index calculation and the analytic hierarchy process are used to scientifically determine the unmanned aerial vehicle taking-off and landing facility quantity priority of each ground area, reasonably allocate limited resources, and preferentially meet the demand of key areas. Thirdly, when screening the initial taking-off and landing points, various constraint factors such as safety protection and airspace management are comprehensively considered in combination with the priority, so that the points meet the requirements in various aspects and the layout is reasonable. Finally, the core taking-off and landing points are determined by screening the transferable nodes, the taking-off and landing points are classified and constructed into an efficient network, and the planning scheme is integrated into a comprehensive and systematic planning scheme.

Description

Technical Field

[0001] This invention relates to the field of drone take-off and landing facility planning technology, and in particular to an integrated planning method for drone take-off and landing facilities at bus stations. Background Technology

[0002] With the rapid expansion of the low-altitude economy, the demand for drone technology in various scenarios such as urban last-mile logistics delivery, emergency rescue response, public transportation station safety inspection, and route status monitoring has exploded. However, the planning and construction of take-off and landing sites has become a key bottleneck restricting its large-scale application.

[0003] The significant differences between drones and other aircraft, coupled with the lack of a unified hierarchical division and coordination mechanism in existing facilities, result in a fragmented system. The lack of effective connections and coordination between different types and sizes of take-off and landing facilities hinders the formation of an efficient network structure. This makes seamless coordination and efficient scheduling of drones during operation difficult, reducing overall operational efficiency. For example, in some cities, the lack of coordinated planning between small and medium-sized vertical take-off and landing sites forces drones to travel longer distances during transport, increasing operating and time costs.

[0004] There is an imbalance in the spatial layout of low-altitude take-off and landing facilities. On the one hand, there is a shortage of facilities in areas with concentrated demand, such as logistics parks, which cannot meet the ever-increasing logistics and distribution needs. With the rapid development of e-commerce, urban logistics and distribution volumes are constantly increasing, and the demand for low-altitude take-off and landing facilities is becoming increasingly urgent. However, the existing facility layout cannot meet this demand, resulting in low logistics and distribution efficiency and affecting the normal operation of the urban economy. On the other hand, the facilities are not rationally located in densely populated areas and ecologically sensitive areas.

[0005] Therefore, it is necessary to provide an integrated planning method for drone take-off and landing facilities at public transportation stations to solve the above-mentioned technical problems. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides an integrated planning method for unmanned aerial vehicle (UAV) take-off and landing facilities at public transportation stations. This method aims to solve the problems of fragmented planning systems and unbalanced spatial layouts in existing UAV urban take-off and landing facility planning methods.

[0007] This invention provides an integrated planning method for unmanned aerial vehicle (UAV) take-off and landing facilities at public transportation stations, comprising the following steps: S1. Divide the ground areas corresponding to the urban low-altitude areas, and collect basic information on public transport stations, environmental parameters, building layout and population density in each ground area; S2. Based on the building layout and population density of each ground area, analyze the service demand characteristics of drones in each ground area, and combine the route network of bus stations to set the collaborative and conflict nodes of drones in each ground area. S3. Obtain the distribution density and service radius indicators of collaborative and conflicting nodes of UAVs in various ground areas, and use the analytic hierarchy process to prioritize the number of UAV take-off and landing facilities in various ground areas. S4. Obtain available bus station locations and, through preset constraint factors and the priority of the number of drone take-off and landing facilities in various ground areas, select initial take-off and landing points; among which, constraint factors include safety protection, airspace management, and work sequence. S5. Based on the collaborative and conflicting nodes of UAVs in various ground areas, the system uses data statistical analysis to select transit nodes, thereby identifying core take-off and landing points from the initial take-off and landing points, constructing a hierarchical and categorized take-off and landing facility network, and generating a planning scheme.

[0008] Preferably, the specific steps of S1 include: S101. Divide the urban low-altitude airspace into several ground areas according to administrative divisions or functional zones, and establish a mapping database between ground areas and low-altitude areas. S102. Based on multiple sensors, collect basic information on public transport stations, environmental parameters, building layout and population density in various ground areas.

[0009] Preferably, the specific steps of S2 include: S201. Analyze the service demand of UAVs in different building types based on building layout, including the temporal and spatial distribution of demand. S202. Obtain the bus route network topology of various ground areas, analyze the connection relationship and passenger flow distribution characteristics between bus stations through big data analysis methods, identify nodes that need to provide collaborative services, and establish a spatial distribution map of collaborative nodes. S203. Based on the bus station route network, identify the nodes where conflicts occur during the operation of drones in various ground areas.

[0010] Preferably, the specific steps of S3 include: S301. Count the number of UAV collaborative and conflicting nodes in each ground area, and calculate the node distribution density index based on the area. S302. Based on the performance characteristics and actual operational needs of the UAV, obtain the distribution density index and service radius index of the collaborative and conflicting nodes. S303. Based on the distribution density index and service radius index of collaborative and conflicting nodes, the hierarchical analysis method is used to prioritize the number of UAV take-off and landing facilities in various ground areas. The priority is specifically divided into high priority, medium priority and low priority.

[0011] Preferably, the specific steps of S4 include: S401. Obtain the locations of available bus stations from the basic information collected on bus stations in various ground areas; S402. Pre-set the specific requirements and standards for each constraint factor of safety protection, airspace management, and work sequence, and screen the available bus station locations based on the priority of the number of UAV take-off and landing facilities in each ground area to select the initial take-off and landing points that meet the requirements.

[0012] Preferably, the specific steps of S5 include: S501. Based on the collaborative and conflicting nodes of UAVs in various ground areas, use data statistical analysis methods to analyze the connection relationship between each collaborative and conflicting node, and select collaborative or conflicting nodes with relay functions as relay nodes. S502. Based on the transit nodes, evaluate the network centrality of each candidate core point, further screen the initial take-off and landing points, and determine the core take-off and landing points. S503. Based on the functions of the core take-off and landing points and the initial take-off and landing points, classify them into different levels, construct a hierarchical and classified take-off and landing facility network, and integrate it into a planning scheme.

[0013] Compared with related technologies, the integrated planning method for unmanned aerial vehicle (UAV) take-off and landing facilities at bus stations provided by this invention has the following beneficial effects: This invention comprehensively collects information from various aspects, including public transportation stations, in different ground areas. Through big data analysis of drone service demand characteristics and the identification of key nodes, it lays a solid foundation for planning, making the layout of take-off and landing facilities more aligned with actual needs and improving efficiency. Secondly, it scientifically prioritizes the number of drone take-off and landing facilities in different ground areas using quantitative index calculations and the analytic hierarchy process (AHP), rationally allocating limited resources and prioritizing the needs of key areas. Furthermore, when selecting initial take-off and landing sites, it comprehensively considers various constraints such as safety protection and airspace management, combined with priorities, to ensure that the sites meet requirements in all aspects and are rationally laid out. Finally, by selecting transit nodes, it identifies core take-off and landing sites, classifies and categorizes these sites to construct an efficient network, and integrates them into a comprehensive and systematic planning scheme, achieving efficient, orderly, and safe drone operation in urban low-altitude areas. Attached Figure Description

[0014] Figure 1This is a flowchart illustrating an integrated planning method for unmanned aerial vehicle (UAV) take-off and landing facilities at public transportation stations, as per the present invention. Detailed Implementation

[0015] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0016] Example In the specific implementation process, such as Figure 1 As shown, an integrated planning method for unmanned aerial vehicle (UAV) take-off and landing facilities at public transportation stations includes the following steps: S1. Divide the ground areas corresponding to the urban low-altitude areas, and collect basic information on public transport stations, environmental parameters, building layout and population density in each ground area; S2. Based on the building layout and population density of each ground area, analyze the service demand characteristics of drones in each ground area, and combine the route network of bus stations to set the collaborative and conflict nodes of drones in each ground area. S3. Obtain the distribution density and service radius indicators of collaborative and conflicting nodes of UAVs in various ground areas, and use the analytic hierarchy process to prioritize the number of UAV take-off and landing facilities in various ground areas. S4. Obtain available bus station locations and, through preset constraint factors and the priority of the number of drone take-off and landing facilities in various ground areas, select initial take-off and landing points; among which, constraint factors include safety protection, airspace management, and work sequence. S5. Based on the collaborative and conflicting nodes of UAVs in various ground areas, the system uses data statistical analysis to select transit nodes, thereby identifying core take-off and landing points from the initial take-off and landing points, constructing a hierarchical and categorized take-off and landing facility network, and generating a planning scheme.

[0017] In the specific implementation process, the specific steps of S1 include: S101. Divide the urban low-altitude airspace into several ground areas according to administrative divisions or functional zones, and establish a mapping database between ground areas and low-altitude areas.

[0018] Specifically, the division is based on the city's administrative divisions, such as districts and counties, or by functional zones, including commercial areas, residential areas, industrial areas, and cultural and educational areas. Geographic Information System (GIS) technology is used to precisely divide the ground areas corresponding to the city's low-altitude airspace. GIS digitizes the city's geospatial information, dividing the city into independent ground areas by drawing boundary lines on a map. For example, a city might be divided into several areas, such as Area A (commercial area), Area B (residential area), and Area C (industrial area). After the ground area division is completed, each ground area is assigned a unique identifier. Simultaneously, based on the correspondence between low-altitude airspace and ground areas, the low-altitude airspace is also divided accordingly, establishing a one-to-one mapping relationship with the ground areas.

[0019] In this embodiment, the city has five administrative districts: Dongcheng District, Xicheng District, Nancheng District, Beicheng District, and Zhongcheng District. The research team divided the ground area corresponding to the city's low-altitude airspace into these five regions according to administrative divisions. Then, using GIS technology, the boundaries of each administrative district were accurately drawn on an electronic map. Next, an identification code was assigned to each administrative district, such as 01 for Dongcheng District and 02 for Xicheng District. Simultaneously, based on the actual situation of the city's low-altitude airspace, the low-altitude airspace range corresponding to each administrative district was determined. For example, the low-altitude airspace range corresponding to Dongcheng District is 50-150 meters in altitude, with the horizontal range extending outwards from the Dongcheng District boundary by a certain distance. Finally, these mapping relationship data are stored in a database named "Urban Low-Altitude and Ground Area Mapping Database" for convenient subsequent querying and use.

[0020] S102. Based on multiple sensors, collect basic information on public transport stations, environmental parameters, building layout and population density in various ground areas.

[0021] Specifically, sensors are installed within bus depots to collect basic information such as depot area, number of bus stops, and location of charging facilities. Environmental sensors are installed at different locations on the ground to collect environmental parameters such as air quality, temperature, humidity, and wind speed. Building layout information is obtained using devices such as LiDAR and cameras; these devices are used to scan and photograph buildings within the ground area to obtain 3D models and location information of buildings. Population counting sensors are installed in densely populated areas such as communities and commercial districts, or population density data is obtained through cooperation with relevant departments. Finally, the received data is organized and classified, and different types of data from different ground areas are stored in corresponding databases.

[0022] In the specific implementation process, the specific steps of S2 include: S201. Based on building layout analysis, analyze the service demand of UAVs in different building types and areas, including the temporal and spatial distribution of demand.

[0023] Specifically, buildings in various ground areas are classified in detail. Common building types include residential, commercial, industrial, and public facilities (such as schools, hospitals, and government offices). For different building types, the services that drones may provide are analyzed. For example, questionnaires or historical data collection are used to clarify the demand for drone services in different building types. In residential areas, drones are mostly used for express delivery and emergency supplies transportation; in commercial areas, they are mostly used for goods delivery and advertising; in industrial areas, they are mostly used for parts transportation and equipment inspection; and in public facility areas, they are mostly used for medical supplies transportation and document delivery. Statistics on different types of buildings are also compiled. The study examines the changing demand for drone services across different building types and time periods. For example, in residential areas, delivery demand occurs mostly during the day and night, with peak times after get off work. In commercial areas, delivery demand is concentrated during business hours. In industrial areas, equipment inspections are conducted at specific times on weekdays. Finally, the study determines the geographical distribution of drone service demand across different building types. For instance, in large residential communities, different buildings may have varying delivery demands, with buildings near the entrance potentially having lower demand and those inside the community having higher demand. Similarly, in commercial areas, different shopping malls and stores may have spatially different demands for delivery services.

[0024] In this embodiment, a specific area of ​​a city is used as an example. This area includes residential, commercial, and industrial zones. In the residential zone, an online survey revealed a high demand for drone delivery, especially between 7-9 PM, as most residents are at home during this time. Spatially, buildings in the center of the residential area have a stronger demand for drone delivery due to their greater distance from delivery stations. In the commercial zone, the demand for drone delivery from shopping malls and stores is concentrated during business hours from 10 AM to 4 PM, with stores closer to the center of the commercial zone experiencing higher demand due to higher foot traffic and more orders. In the industrial zone, factories' demand for drone component transportation is concentrated between 9-11 AM and 2-4 PM on weekdays, with different workshops varying their component needs spatially based on their production schedules.

[0025] S202. Obtain the bus route network topology of various ground areas, analyze the connection relationship and passenger flow distribution characteristics between bus stations through big data analysis methods, identify nodes that need to provide collaborative services, and establish a spatial distribution map of collaborative nodes.

[0026] Specifically, bus route data for various ground areas is obtained from urban traffic management departments or bus companies, including information such as the starting point, destination, and stops along the route. This data is then visualized using GIS technology to construct a bus route network topology, showcasing the connections between different bus stations. Passenger flow data is collected through bus card systems, onboard cameras, and other equipment, including the number of passengers boarding and alighting at different times for each route and stop. Data mining or statistical analysis methods from big data analytics are then used to analyze the passenger flow data, understanding the temporal distribution patterns (such as morning and evening peak hours and off-peak hours) and spatial distribution characteristics (such as differences in passenger flow density in different areas). Based on the connections between bus stations and the passenger flow distribution characteristics, nodes requiring coordinated services are identified. For example, some large bus hubs, due to the convergence of multiple bus lines and high passenger flow, may need drones to coordinate with buses to achieve rapid transportation of passenger luggage and allocation of emergency supplies; some bus stops located near commercial or residential areas, due to the dense population in the surrounding area, have a high demand for services such as drone delivery and can also serve as collaborative service nodes; the identified collaborative nodes are marked on a GIS map to establish a spatial distribution map of collaborative nodes.

[0027] S203. Based on the bus station route network, identify the nodes where conflicts occur during the operation of drones in various ground areas.

[0028] Specifically, clear rules are established for drone operations at low altitudes, including flight altitude, speed, and flight path. For example, regulations stipulate that drones must not fly higher than 120 meters and at a speed exceeding 50 km / h within a specific area, and their flight paths must avoid obstacles such as buildings and high-voltage lines. An overlay analysis of the bus station route network and the drone's operating space is performed, considering environmental factors around the bus stations, such as building height and road layout, to analyze potential conflict points between drones and bus stations, bus routes, and other obstacles when flying according to the regulations. Based on the analysis results, nodes where drones may conflict during operation are identified in various ground areas. For example, near some bus stations, due to the height of buildings, drones may need to change their flight paths to avoid them, thus conflicting with the bus station's access routes; conflicts may also occur at intersections where bus routes intersect with the drone's planned flight path. The identified conflict nodes are then recorded in detail, including the location of the conflict and the possible type of conflict (e.g., conflict with buildings, conflict with bus routes, etc.).

[0029] In this embodiment, there is a bus terminal near an industrial area in a city. The area contains some tall chimneys and factory buildings, which drones must avoid during flight according to drone operation rules. Analysis revealed that when drones fly along their planned routes, they create a narrow passage between the bus terminal entrance / exit and the chimneys. This can easily lead to collisions when multiple drones are flying simultaneously or buses are entering / exiting the terminal. Therefore, the entrance / exit of this bus terminal is identified as a conflict node. Additionally, at a nearby intersection of a bus route and the drone's planned flight path, the drone may collide with the bus while flying at low altitude due to the bus's height; this intersection is also identified as a conflict node.

[0030] In the specific implementation process, the specific steps of S3 include: S301. Count the number of UAV collaborative and conflicting nodes in each ground area, and calculate the node distribution density index based on the area.

[0031] Specifically, professional personnel will conduct a detailed statistical analysis of the identified collaborative and conflicting nodes within each ground area. This will be achieved through on-site inspections, reviewing relevant planning records, and utilizing Geographic Information Systems (GIS) to ensure no node is overlooked. Accurate area data for each ground area will be obtained from urban planning departments or relevant geographic information databases. Based on the statistically obtained number of nodes and area, the distribution density index of collaborative and conflicting nodes within each ground area will be calculated using the formula: Distribution Density = Number of Nodes / Area.

[0032] In this embodiment, a city's urban area is included, comprising multiple bus stations, older residential areas, and some small commercial streets. Detailed statistics identified 30 collaborative nodes (e.g., collaboration points between bus stations and express delivery points) and 20 conflict nodes (e.g., locations that may conflict with high-voltage lines, tall buildings, etc.). The total area is 8 square kilometers. Based on the formula, the node density in this area is (30+20)÷8=6.25 nodes / square kilometer.

[0033] S302. Based on the performance characteristics of the UAV and actual operational needs, obtain the distribution density index and service radius index of collaborative and conflicting nodes.

[0034] Specifically, the performance parameters of the drones to be deployed are obtained, and the actual operational needs of the drones are assessed through questionnaires, such as the urgency of delivery tasks, coverage requirements, and service frequency. The relationship between distribution density and service radius is analyzed; higher distribution density indicates that drones need to take off and land frequently within a smaller area, while the service radius limits the drones' operational range.

[0035] In this embodiment, if a logistics company considers to launch drone delivery services in an industrial area of ​​a city, its drones have a flight time of 40 minutes and a flight speed of 60 km / h. A questionnaire survey revealed that businesses in the industrial area expect to receive their packages within 30 minutes. Considering the task execution time and safety margin, the service radius of the drones is determined to be 12 km. Simultaneously, analysis of the industrial area reveals a high density of collaborative nodes (such as the collaboration points between enterprise warehouses and package receiving points) and conflict nodes (such as chimneys and high-voltage equipment within factories). To meet the needs of efficient delivery and avoid conflicts, the service radius is further adjusted to 10 km to ensure that the drones can operate safely and efficiently within a suitable range.

[0036] S303. Based on the distribution density index and service radius index of collaborative and conflicting nodes, the hierarchical analysis method is used to prioritize the number of UAV take-off and landing facilities in various ground areas. The priority is specifically divided into high priority, medium priority and low priority.

[0037] Specifically, the system constructs the following layers: The target layer clarifies the priority of the number of drone take-off and landing facilities in each ground area; the criterion layer determines the criteria influencing priority allocation, primarily the distribution density and service radius indicators of coordinating and conflicting nodes. The distribution density indicator reflects the concentration of nodes within a region; high density indicates more frequent drone activity and potentially greater demand for take-off and landing facilities. The solution layer treats each ground area as a specific solution, comparing the merits of these solutions at the target layer based on the analysis of the criterion layer, thereby determining their priority. For the two indicators at the criterion layer (distribution density indicator and service radius indicator), pairwise comparisons are performed on each surface region in the scheme layer to construct a first judgment matrix. For example, comparing the relative importance of surface region A and surface region B on the distribution density indicator, if the distribution density of surface region A is significantly higher than that of surface region B, then the importance of A relative to B can be assigned a larger value, such as 3 or 5. It should be noted that the specific value is determined according to the difference in importance, using a 1-9 scale, where 1 represents equal importance, 3 represents slightly important, 5 represents significantly important, 7 represents strongly important, and 9 represents extremely important. 2, 4, 6, and 8 represent the median values ​​of the above adjacent judgments. Similarly, for the service radius indicator, pairwise comparisons are also performed on each surface region to construct a second judgment matrix. For each judgment matrix, its eigenvector is calculated using the eigenvalue method, and the eigenvector is normalized to obtain the importance weight of each surface region under the distribution density index and service radius index. The comprehensive weight of each surface region is obtained by summing. The surface regions are sorted according to their comprehensive weights. According to the proportion, the region with the highest comprehensive weight is classified as high priority, the region with the medium comprehensive weight is classified as medium priority, and the region with the lowest comprehensive weight is classified as low priority.

[0038] In its implementation, S4 includes the following specific steps: S401. Obtain the locations of available bus stations from the basic information collected on bus stations in various ground areas.

[0039] S402. Pre-set the specific requirements and standards for each constraint factor of safety protection, airspace management, and work sequence, and screen the available bus station locations based on the priority of the number of UAV take-off and landing facilities in each ground area to select the initial take-off and landing points that meet the requirements.

[0040] Specifically, detailed safety protection standards and requirements should be established, including standards for fire-fighting facilities at drone take-off and landing sites, such as the number and type of fire extinguishers and the coverage of fire hydrants; safety isolation zones should be set up to prevent unauthorized personnel from entering the drone take-off, landing, and flight areas, and the height and strength of the isolation zones should meet safety regulations; safe operating procedures for drone take-off and landing should be stipulated, such as speed limits and altitude requirements during take-off and landing. Communication with local aviation authorities should clarify airspace usage rules and restrictions. Based on the daily operating hours of bus depots and the needs of drone use, reasonable work sequences should be set. For example, to avoid conflicts with peak bus operating hours, drone take-off and landing times at bus depots should avoid morning and evening peak hours and be conducted during off-peak hours. Available bus depot locations should be screened, with high-priority areas prioritizing those that meet all constraint factors and have superior conditions as initial take-off and landing points. For medium-priority areas: While meeting the basic constraint factor requirements, and considering construction costs, select suitable bus depot locations as initial take-off and landing points. For example, choose a bus depot location with relatively low construction costs that can cover the drone service needs within a certain surrounding area. For low-priority areas: Given limited resources, prioritize the needs of high-priority and medium-priority areas. If there are still remaining resources, and the bus depot location in that area meets the basic constraint factor requirements, it can be used as a supplementary initial take-off and landing point.

[0041] In this embodiment, it is assumed that a city is divided into three ground areas, A, B, and C. Priority is assigned via step S303: Area A is high priority, Area B is medium priority, and Area C is low priority. In Area A, there is a bus station location with complete safety facilities, including sufficient fire-fighting equipment and safety barriers. Regarding airspace management, it has coordinated with aviation authorities and obtained legal flight permits, with flight altitude and range meeting requirements. In terms of operating hours, it avoids peak bus hours, meeting the needs of UAV operations. Therefore, this location is prioritized as the initial take-off and landing point. In Area B, there is a bus station location that basically meets safety and airspace management requirements, and it is selected as the initial take-off and landing point. In Area C, there is a bus station location that only meets basic safety requirements. Due to limited resources, if there are remaining resources after ensuring the needs of Areas A and B, it can be used as a supplementary initial take-off and landing point.

[0042] In its implementation, S5 includes the following specific steps: S501. Based on the collaborative and conflicting nodes of UAVs in various ground areas, use data statistical analysis methods to analyze the connection relationship between each collaborative and conflicting node, and select collaborative or conflicting nodes with relay functions as relay nodes.

[0043] Specifically, statistical data analysis methods, such as association analysis or path analysis in graph theory, are used to analyze the connection relationships between collaborative and conflicting nodes, calculating indicators such as connection frequency and connection strength. Based on the connection analysis results, it is determined which nodes have relay functions. It should be noted that the criteria for determining nodes with relay functions are: frequent connections with multiple other nodes, location at the intersection of multiple connection paths, and ability to easily switch between different areas or tasks for the UAV. For example, if a collaborative node connects to multiple conflicting nodes in different directions, and the UAV service demands in the areas corresponding to these conflicting nodes differ significantly, then this collaborative node has a relay function. Based on the criteria for determining nodes with relay functions, collaborative or conflicting nodes with relay functions are selected as suitable relay nodes.

[0044] In this embodiment, a commercial center area of ​​a city has multiple bus stations, dividing it into several ground areas. Through the preceding steps, collaborative nodes A, B, and C, and conflicting nodes D, E, and F were identified. Data statistical analysis revealed that collaborative node B not only frequently connects with conflicting nodes D and E, but is also located on the path connecting A and C. Furthermore, the area corresponding to conflicting node D is primarily a business office area, where drones are mainly used for document delivery; the area corresponding to conflicting node E is a shopping mall, where drones are mainly used for goods transportation; and node B's location allows for convenient switching between these two different tasks. Therefore, node B is determined to have a transit function and is selected as a transit node.

[0045] S502. Based on the transit nodes, assess the network centrality of each candidate core point, further screen the initial take-off and landing points, and determine the core take-off and landing points.

[0046] Specifically, network analysis methods are used, employing indicators such as degree centrality, proximity centrality, or betweenness centrality to evaluate the network centrality of each candidate core location. It should be noted that degree centrality reflects the number of direct connections a node has to other nodes, proximity centrality reflects the average distance a node travels to other nodes, and betweenness centrality reflects a node's ability to control the flow of information within the network. By calculating this indicator and applying a preset threshold range, the initial take-off and landing locations are further filtered to determine the core take-off and landing locations.

[0047] In this embodiment, three candidate core points X, Y, and Z were selected from the initial take-off and landing points. After network centrality evaluation, it was found that point Y had high degree centrality, proximity centrality, and betweenness centrality indices, indicating that it is closely connected with other nodes in the network, can quickly reach other nodes, and plays an important control role in the flow of network information. Therefore, point Y was determined as the core take-off and landing point.

[0048] S503. Based on the functions of the core take-off and landing points and the initial take-off and landing points, classify them into different levels, construct a hierarchical and classified take-off and landing facility network, and integrate it into a planning scheme.

[0049] Specifically, landing sites are categorized based on their service targets and task types. For example, core landing sites can be further subdivided into logistics and distribution, emergency rescue, and monitoring and patrol sites; initial landing sites can be categorized into commercial areas, residential areas, and industrial areas based on the characteristics of the surrounding area. Based on these categorizations, a hierarchical and classified network of landing facilities is constructed. The connections and collaboration methods between landing sites at each level and category are clearly defined to form a landing facility network, which is then integrated into a planning scheme.

[0050] 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.

[0051] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0052] 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 process, method, article, or apparatus. Unless otherwise specified, 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 that element.

Claims

1. A method for integrated planning of unmanned aerial vehicle (UAV) take-off and landing facilities at public transportation stations, characterized in that, Includes the following steps: S1. Divide the ground areas corresponding to the urban low-altitude areas, and collect basic information on public transport stations, environmental parameters, building layout and population density in each ground area; S2. Based on the building layout and population density of each ground area, analyze the service demand characteristics of drones in each ground area, and combine the route network of bus stations to set the collaborative and conflict nodes of drones in each ground area. S3. Obtain the distribution density and service radius indicators of collaborative and conflicting nodes of UAVs in various ground areas, and use the analytic hierarchy process to prioritize the number of UAV take-off and landing facilities in various ground areas. S4. Obtain available bus station locations and, through preset constraint factors and the priority of the number of drone take-off and landing facilities in various ground areas, select initial take-off and landing points; among which, constraint factors include safety protection, airspace management, and work sequence. S5. Based on the collaborative and conflicting nodes of UAVs in various ground areas, the system uses data statistical analysis to select transit nodes, thereby identifying core take-off and landing points from the initial take-off and landing points, constructing a hierarchical and categorized take-off and landing facility network, and generating a planning scheme.

2. The integrated planning method for unmanned aerial vehicle (UAV) take-off and landing facilities at public transportation stations according to claim 1, characterized in that, The specific steps of S1 include: S101. Divide the urban low-altitude airspace into several ground areas according to administrative divisions or functional zones, and establish a database of mapping relationships between ground areas and low-altitude areas. S102. Based on multiple sensors, collect basic information on public transport stations, environmental parameters, building layout and population density in various ground areas.

3. The integrated planning method for unmanned aerial vehicle (UAV) take-off and landing facilities at public transportation stations according to claim 1, characterized in that, The specific steps of S2 include: S201. Analyze the service demand of UAVs in different building types based on building layout, including the temporal and spatial distribution of demand. S202. Obtain the bus route network topology of various ground areas, analyze the connection relationship and passenger flow distribution characteristics between bus stations through big data analysis methods, identify nodes that need to provide collaborative services, and establish a spatial distribution map of collaborative nodes. S203. Based on the bus station route network, identify the nodes where conflicts occur during the operation of drones in various ground areas.

4. The integrated planning method for unmanned aerial vehicle (UAV) take-off and landing facilities at public transportation stations according to claim 1, characterized in that, The specific steps of S3 include: S301. Count the number of UAV collaborative and conflict nodes in each ground area, and calculate the node distribution density index based on the area. S302. Based on the performance characteristics and actual operational needs of the UAV, obtain the distribution density index and service radius index of the collaborative and conflicting nodes. S303. Based on the distribution density index and service radius index of collaborative and conflicting nodes, the hierarchical analysis method is used to prioritize the number of UAV take-off and landing facilities in various ground areas. The priority is specifically divided into high priority, medium priority and low priority.

5. The integrated planning method for unmanned aerial vehicle (UAV) take-off and landing facilities at public transportation stations according to claim 1, characterized in that, The specific steps of S4 include: S401. Obtain the locations of available bus stations from the basic information collected on bus stations in various ground areas; S402. Pre-set the specific requirements and standards for various constraint factors such as safety protection, airspace management, and work sequence, and screen the available bus station locations based on the priority of the number of UAV take-off and landing facilities in each ground area to select the initial take-off and landing points that meet the requirements.

6. The integrated planning method for unmanned aerial vehicle (UAV) take-off and landing facilities at public transportation stations according to claim 1, characterized in that, The specific steps of S5 include: S501. Based on the collaborative and conflicting nodes of UAVs in various ground areas, use data statistical analysis methods to analyze the connection relationship between each collaborative and conflicting node, and select collaborative or conflicting nodes with relay functions as relay nodes. S502. Based on the transit nodes, evaluate the network centrality of each candidate core point, further screen the initial take-off and landing points, and determine the core take-off and landing points. S503. Based on the functions of the core take-off and landing points and the initial take-off and landing points, classify them into different levels, construct a hierarchical and classified take-off and landing facility network, and integrate it into a planning scheme.

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