Method, system and device for generating airport road coverage polygon and storage medium

Abstract

The invention discloses a method, system and device for generating an airport road coverage polygon, and a storage medium. The method comprises the following steps: obtaining geographic coordinates of an airport center point; generating a circle by taking the airport center point as a reference, and generating a plurality of sampling points on the circumference of the circle according to a preset sampling rule to obtain a sampling point set; acquiring road network data, and calculating the shortest path from the center point of the field to each sampling point in the sampling point set based on the road network data to obtain a shortest path set; traversing each shortest path in the shortest path set, calculating target points on the shortest path, the highway distance between the target points and the airport center point being equal to a preset highway distance, and obtaining a target point set; and connecting all the target points in the target point set to generate an airport road coverage polygon. For airports with different topographic conditions, conditions in all directions around the airports can be considered more comprehensively through reasonable sampling distances.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of transportation planning and geographic information systems, and relates to a method, system, device and storage medium for generating airport highway coverage polygon. BACKGROUND

[0002] In the field of airport planning and transportation accessibility assessment, existing technologies mainly rely on circular coverage models and linear distance analysis methods. Circular coverage models take the airport as the center and draw a circle with a fixed radius (such as 100 kilometers) to represent the service range of the airport. However, this method has significant limitations, especially in areas with complex terrain or non-homogeneous road network structure, such as mountainous areas or urban dense areas. There is often a huge difference between straight-line distance and actual highway distance, leading to serious overestimation or underestimation of the actual service range of the airport.

[0003] Linear distance analysis methods usually only assess the straight-line distance between the airport and the town center, ignoring the complexity of actual traffic routes and accessibility, which may lead to inaccurate decision-making basis when planning and evaluating the service potential of the airport to the surrounding areas. For example, in mountainous areas, the straight-line distance between two locations may be short, but the actual highway distance may be greatly extended due to terrain barriers, even beyond the service range of the airport. In urban dense areas, although the physical distance may be short, real factors such as traffic congestion may also increase the time cost, thus reducing the coverage efficiency of the airport.

[0004] In addition, traditional GIS tools often use static buffer analysis methods when dealing with airport coverage, i.e., establishing a fixed radius area centered on the airport. This method cannot dynamically respond to the impact of road network changes (such as new highways) on airport coverage, nor can it adapt to the needs of multi-airport collaborative layout optimization, because in a multi-airport environment, the mutual influence between airports and the overlap of coverage ranges need to be accurately calculated to obtain a reasonable layout.

[0005] In summary, the existing technologies have the following shortcomings in airport planning and site selection, regional transportation accessibility assessment, and multi-airport collaborative layout optimization: 1. Ignoring terrain barriers and road network detour effects: Circular coverage models and linear distance analysis methods do not fully consider the impact of actual terrain and road network conditions on airport service range.

[0006] 2. Static evaluation limitations: Traditional GIS tools rely on static buffer analysis, which cannot dynamically reflect the impact of road network changes on airport coverage.

[0007] 3. Multi-airport collaborative layout optimization problem: existing technologies are difficult to accurately calculate the mutual influence and coverage overlap between multiple airports, limiting their application in multi-airport collaborative layout optimization. SUMMARY

[0008] The purpose of the present application is to overcome the above-mentioned shortcomings of the prior art, and to provide a method, system, device and storage medium for generating an airport road coverage polygon, which can more comprehensively consider the conditions around the airport in all directions by reasonable sampling distance for airports with different terrain conditions.

[0009] To achieve the above-mentioned purpose, the following technical solutions are adopted: A method for generating an airport road coverage polygon, comprising the following processes: Obtaining the geographic coordinates of the airport center point; Generating a circle with the airport center point as the reference, and generating a plurality of sampling points on the circumference of the circle according to a preset sampling rule to obtain a sampling point set; Obtaining road network data and calculating the shortest path from the airport center point to each sampling point in the sampling point set based on the road network data to obtain a shortest path set; Traversing each shortest path in the shortest path set to calculate the target point on the shortest path whose road distance from the airport center point is equal to the preset road distance to obtain a target point set; Connecting all target points in the target point set to generate an airport road coverage polygon.

[0010] Preferably, before generating the sampling point set, the geographic coordinates of the airport center point are converted into projection coordinates; the step of generating the sampling point set includes generating a geometric circle with the projection coordinates of the converted airport center point as the center and a preset radius value, and generating a plurality of sampling points on the circumference of the geometric circle to obtain a projection coordinate sampling point set; the projection coordinates of each sampling point in the projection coordinate sampling point set are converted into geographic coordinates to form the sampling point set.

[0011] Preferably, the step of calculating the shortest path from the airport center point to each sampling point in the sampling point set includes taking the airport center point as the starting point and the sampling point as the ending point, calling a path planning interface of an online map service to obtain a shortest path vertex set between the starting point and the ending point, and the shortest path vertex set constitutes the shortest path.

[0012] Preferably, the step of calculating the shortest path from the airport center point to each sampling point in the sampling point set comprises: loading local road network data into a topological analysis component; in the topological analysis component, finding road segments adjacent to the airport center point and the sampling point, and respectively calculating a nearest point A closest to the airport center point and a nearest point B closest to the sampling point on the road network; establishing a graph data structure based on the road network data, and using a preset shortest path algorithm to calculate a shortest path vertex set between the nearest point A and the nearest point B; supplementing the nearest point A to the start position of the shortest path vertex set and supplementing the nearest point B to the end position of the shortest path vertex set to form the shortest path.

[0013] Preferably, the step of calculating the target point on the shortest path with a road distance equal to the preset road distance from the airport center point comprises: traversing each vertex of the shortest path to calculate the segment distance between adjacent two vertices; starting from the start point of the shortest path, accumulating each segment distance to obtain an accumulated distance; when the accumulated distance is greater than the preset road distance, determining that the target point falls within the road segment corresponding to the current segment distance, and locating the target point in the road segment by linear interpolation.

[0014] Preferably, after the coverage polygon is generated, the method further comprises: checking whether the coverage polygon has self-intersecting edges; if there are self-intersecting edges, recording the intersection point of the two self-intersecting edges, deleting all vertices between the start vertices of the two self-intersecting edges, and inserting the intersection point between the start vertices of the two self-intersecting edges to eliminate the self-intersection.

[0015] Preferably, the preset road distance is 100 kilometers; and the step of generating a plurality of sampling points on the circumference of the circle according to the preset sampling rule comprises: sampling at an interval of 10 kilometers on the circumference of the circle with a radius of 100 kilometers to generate the sampling points.

[0016] A system for generating an airport road coverage polygon, comprising: a geographic coordinate acquisition module configured to acquire a geographic coordinate of an airport center point; a sampling point generation module configured to generate a circle with the airport center point as a reference, generate a plurality of sampling points on the circumference of the circle according to a preset sampling rule, and obtain a sampling point set; a shortest path calculation module configured to acquire road network data, and calculate a shortest path from the airport center point to each sampling point in the sampling point set based on the road network data to obtain a shortest path set; a target point calculation module configured to traverse each shortest path in the shortest path set, calculate a target point on the shortest path with a road distance equal to a preset road distance from the airport center point, and obtain a target point set; A polygon generation module is configured to connect all the target points in the target point set to generate an airport highway coverage polygon.

[0017] A computer device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the method for generating an airport highway coverage polygon when executing the computer program.

[0018] A computer readable storage medium stores a computer program, and the computer program implements the steps of the method for generating an airport highway coverage polygon when executed by a processor.

[0019] Compared with the prior art, the present application has the following beneficial effects: Compared with the circular model, the present application greatly reduces the error in the analysis of the coverage range of a mountainous airport, can truly reflect the 100-kilometer highway reachable range of the airport, and can also be converted into the 1.5-hour highway reachable range of the airport according to the average speed. The present application supports the generation of multiple coverage layers by modifying the radius value (such as 50 / 150 kilometers) to assist in evaluating the synergistic effect of an airport cluster. For airports in different terrain conditions, the present application can more comprehensively consider the conditions in all directions around the airport by reasonably selecting the sampling distance.

[0020] According to the road network data, a path planning algorithm is used to plan a path with the center point of the airport as the starting point and the sampling point as the ending point to obtain a path point set. This process combines the actual road network information, so that the calculated path is more consistent with the actual highway traffic conditions. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 It is a schematic diagram of the Gaussian Kriging coordinates (X, Y) of the present application; Figure 2 It is a schematic diagram of the sampling point position of the present application; Figure 3 It is a schematic diagram of finding a 100-kilometer distance point on the shortest path line of the present application; Figure 4 It is a schematic diagram of the polygon self-intersection processing principle of the present application; Figure 5 It is a schematic diagram of the 100-kilometer coverage range of a plain area airport of the present application; Figure 6 It is a schematic diagram of the 100-kilometer coverage range of a mountainous airport of the present application. DETAILED DESCRIPTION

[0022] In the following, the technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of embodiments of the present application, rather than all embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work should fall within the protection scope of the present application.

[0023] It should be noted that the terms "first", "second", and the like in the specification of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to include only those steps or units clearly listed, but can include other steps or units that are not clearly listed or inherent to the process, method, product or device.

[0024] The method for generating an airport highway coverage polygon described in the embodiments includes the following processes: Step 1: Obtain the geographic coordinates of the center point of the airport; generate a circle with the center point of the airport as the reference, and generate a plurality of sampling points on the circumference of the circle according to a preset sampling rule to obtain a sampling point set.

[0025] Step 2: Obtain road network data, and calculate the shortest path from the center point of the airport to each sampling point in the sampling point set based on the road network data to obtain a shortest path set.

[0026] Step 3: Traverse each shortest path in the shortest path set, and calculate the target point on the shortest path whose highway distance from the center point of the airport is equal to a preset highway distance to obtain a target point set.

[0027] Step 4: Connect all target points in the target point set to generate an airport highway coverage polygon.

[0028] The generation of the geometric circle with a specific radius in step 1 includes converting the latitude and longitude coordinates of the airport center point to the Gauss-Kruger projection coordinate system, so as to accurately sample under the projection coordinate system. Technically, the use of the Gauss-Kruger projection coordinate system facilitates the calculation of sampling points and simplifies the calculation of spherical distance. In principle, this embodiment utilizes the mathematical principle of projection transformation to convert latitude and longitude coordinates into plane coordinates, facilitating subsequent geometric calculation and sampling point positioning. In terms of effects, through the conversion of the Gauss-Kruger projection coordinate system, this embodiment can more accurately calculate the position of the sampling point, thereby improving the accuracy of the coverage calculation. In other embodiments, other projection coordinate systems can also be used to solve the problem of sampling point calculation in different geographical areas.

[0029] The adjustable geometric circle radius in step 1 allows the generation of multiple levels of coverage circle layers, suitable for different levels of airport coverage range evaluation requirements. Technically, the technical solution of this embodiment can generate coverage circle layers of different levels by adjusting the radius of the geometric circle, meeting the evaluation requirements of different coverage ranges in airport planning. In principle, the technology of adjusting the radius value is based on geometric principles and can generate circles of different sizes, thereby obtaining a set of evaluation points of different coverage ranges. In terms of effects, the technology in this embodiment can generate multiple levels of coverage circle layers, providing more comprehensive and detailed coverage range evaluation for airport planning. In other embodiments, other shapes of coverage models, such as ellipses, can also be used to solve the problem of coverage range evaluation under specific terrain conditions.

[0030] The equidistant sampling length in step 1 is automatically adjusted based on the terrain and road network density to adapt to sparse road network areas and improve calculation efficiency. Technically, the technical solution of this embodiment can adapt to sampling requirements under different terrains and road network densities by automatically adjusting the equidistant sampling length, thereby improving the calculation efficiency. In principle, the automatic adjustment technology of sampling length is based on road network analysis and terrain evaluation principles and can dynamically adjust the density of sampling points according to the sparseness of the road network and the complexity of the terrain. In terms of effects, the technology in this embodiment can adjust the number of sampling points according to the actual situation, reducing unnecessary calculations and improving the efficiency of airport coverage range evaluation. In other embodiments, other sampling strategies, such as random sampling, can also be used to solve the problem of sampling efficiency under specific terrain conditions.

[0031] The shortest path calculation in Step 2 employs an online map service API that can access and utilize real-time updated road network data from multiple online map service providers for path planning. Technically, this embodiment can obtain the latest road network data by calling the online map service API, ensuring the real-time and accuracy of path planning. In principle, the API utilizes the characteristics of network services to outsource complex path planning calculations to professional map service providers, reducing the burden of local calculations. In terms of effects, the technology in this embodiment can quickly and accurately calculate the shortest path from the airport center point to each evaluation point, improving the efficiency and accuracy of coverage range calculation. In other embodiments, the path planning problem in offline environments can also be solved by loading local road network data combined with GIS components.

[0032] In another implementation of Step 2, the shortest path calculation employs a combination of locally preloaded GIS component road network data and Dijkstra algorithm. Technically, this embodiment's technical solution ensures path planning even without network connection by preloading local road network data and using Dijkstra algorithm for shortest path calculation. In principle, Dijkstra algorithm is a classic shortest path algorithm that can effectively find the shortest path between two points in a graph data structure. In terms of effects, the technology in this embodiment can provide a stable and reliable path planning solution independently of network services, suitable for various airport planning scenarios. In other embodiments, other path planning algorithms such as A algorithm can be used to solve path planning problems under specific conditions.

[0033] The conversion of the target point's latitude and longitude coordinates in Step 3 includes using the spherical distance formula to determine the accurate position of the target point on the shortest path under the influence of the Earth's curvature. Technically, this embodiment's technical solution ensures the accuracy of target point positioning by considering the Earth's curvature through the application of the spherical distance formula. In principle, the spherical distance formula is based on the spherical model of the Earth to calculate the great circle distance between two points, suitable for distance calculation in latitude and longitude coordinates. In terms of effects, the technology in this embodiment can accurately locate target points at specific kilometers from the airport center point, providing key data points for polygon generation. In other embodiments, other distance calculation methods such as the ellipsoid model can be used to solve target point positioning problems under high precision requirements.

[0034] The target point determination in step 3 includes using a linear interpolation method to accurately locate the target point position on the shortest path line that is a specific number of kilometers away from the airport center point. The specific number of kilometers can be adjusted as needed. In terms of technology, the technical solution of the embodiment can accurately locate the target point that is a specific number of kilometers away from the airport center point on the shortest path line through the linear interpolation method, thereby improving the positioning accuracy. In terms of principle, the linear interpolation technology can accurately calculate the position of the target point on the linear path based on the mathematical interpolation principle. In terms of effect, the technology in the embodiment can generate accurate 100-kilometer road distance points, which provide key data points for polygon generation. In other embodiments, other interpolation algorithms such as spline interpolation can also be used to solve the target point positioning problem on a nonlinear path.

[0035] The processing of the target point set in all directions in step 4 includes using a special algorithm to eliminate duplicate points and solving the self-intersection problem of the polygon through a coordinate transformation method to ensure clear boundaries. In terms of technology, the technical solution of the embodiment generates clear and error-free polygon boundaries by eliminating duplicate points and handling the self-intersection problem, thereby improving the visualization effect of the coverage analysis. In terms of principle, the technology of eliminating duplicate points and handling the self-intersection problem is based on the principles of polygon geometry, ensuring the correctness and aesthetics of the polygon. In terms of effect, the technology in the embodiment can generate accurate and self-intersection-free polygons, which provide intuitive and clear graphical representations for airport coverage analysis. In other embodiments, other polygon processing algorithms such as the Douglas-Peucker algorithm can also be used to solve the polygon optimization problem under a high-complexity road network.

[0036] The method also includes accurately determining the polygon boundary of the road coverage range through an optimization strategy, wherein the optimization strategy uses coordinate transformation and linear interpolation. In terms of technology, the technical solution of the embodiment improves the accuracy and computational efficiency of the polygon boundary through the optimization strategy using coordinate transformation and linear interpolation technology. In terms of principle, the coordinate transformation and linear interpolation technology is based on mathematical and geometric principles and can effectively process the point set to generate an optimized polygon boundary. In terms of effect, the technology in the embodiment can generate a road coverage range polygon with clear boundaries and no self-intersection, which provides accurate geographic information for airport planning. In other embodiments, other optimization algorithms such as genetic algorithms can also be used to solve the polygon boundary optimization problem under a large-scale data set.

[0037] The method further comprises step 5, rendering the highway coverage polygon on the GIS platform, and the polygon is optimized to keep the boundary clear and without self-intersection. In terms of technology, the technical solution of the embodiment visualizes the calculated highway coverage polygon through the rendering function of the GIS platform, which facilitates the airport planning personnel to intuitively understand the coverage. In principle, the rendering technology of the GIS platform can convert geographic data into map images based on the principles of computer graphics. In terms of effect, the technology in the embodiment can generate an intuitive and clear airport coverage map, providing strong decision support for airport site selection and planning. In other embodiments, other GIS platforms or custom rendering algorithms can also be used to solve map rendering problems in specific environments.

[0038] The technical solution of the embodiment generates a circle with a specific radius by taking the airport center point as the starting point, and performs equidistant sampling on the circumference to obtain a series of evaluation points, which are used for subsequent path calculation, ensuring the comprehensiveness and accuracy of the coverage. In principle, the shortest path is calculated using GIS components or online map service APIs, which takes into account actual road network data, terrain, and road conditions, providing more realistic path planning. In terms of effect, the technology in the embodiment locates target points of a specific distance and processes the set of points to generate a polygon of the airport highway coverage, eliminating the bias of the circular model and improving the accuracy of airport coverage analysis. In other embodiments, the sampling length and radius value can be adjusted to solve the calculation efficiency and coverage accuracy problem under different terrain conditions.

[0039] The above method is further illustrated with actual cases as follows: Step 1: Generate a circle with a specific radius by taking the airport center point as the center, and perform equidistant sampling to obtain a set of sampling points (latitude and longitude coordinates).

[0040] (1) As shown in Figure 1 , use Proj4 or other projection calculation components to transform the airport center point coordinates (latitude and longitude description) to Gauss-Krige projection coordinates (north as the positive direction of the vertical axis, east as the positive direction of the horizontal axis). For convenience of mathematical calculation, in this solution, the positive east direction is stored as the X axis and the positive north direction as the Y axis when using Gauss-Krige coordinates.

[0041] Note that the Gauss-Krige projection coordinates are in meters, which can be easily used for geometric calculations.

[0042] (2) Take the airport center point coordinates (Gauss-Krige coordinates) as the center to generate a geometric circle with a radius of 100 kilometers (other radius values can be configured as needed, such as calculating a highway coverage distance of 150 kilometers with a radius of 150 kilometers).

[0043] (3) Calculate the circumference of the geometric circle (the circumference of a circle with a radius of 100 kilometers is about 628 kilometers). Take 10 kilometers as the initial sampling length (in areas with a sparse road network, the sampling length can be appropriately increased to reduce the amount of calculation). Divide it into 63 parts (62.8 rounded off). Calculate the actual sampling angle in reverse (using polar coordinates to easily calculate the position of the sampling point) as 5.714 (360÷63=5.714).

[0044] (4) Iterate through the azimuth angle corresponding to each sampling angle, and calculate the position of the circumferential sampling point with the airport center point coordinates (Gaussian coordinates) as the reference point and 100 kilometers as the length. All sampling points are converted to latitude and longitude coordinates through Proj4 and added to the set.

[0045] Step 2: Find the shortest path from the center point (latitude and longitude coordinates) of the computer field to a certain sampling point (latitude and longitude coordinates).

[0046] Option 1: Use the API of an online map service (most online maps provide API access interfaces for route planning) to obtain the shortest path; (1) Using the airport center point as the starting point and the sampling point as the ending point, call the API interface of the online map service. For example, the Web API interface format of a certain online map service is: https: / / api.map.baidu.com / direction / v2 / driving?origin=origin latitude,origin longitude&destination=destination latitude,destination longitude&ak=your AK

[0047] (2) After executing this API using any programming language, the system will return a JSON string, one of which describes the latitude and longitude coordinates of each point on the shortest path. Parse the string of this attribute value, extract the coordinates of each point and add them to the shortest path vertex set (latitude and longitude coordinates).

[0048] Option 2: Load local road network data and obtain the shortest path by programmatically calling commonly used GIS components; (1) Use GIS components (such as SharpMap) to read road network data (usually SHP files) and load it into topology analysis components (such as NetTopologySuite).

[0049] (2) Using the spatial index in the topology analysis component, quickly find the road segment set near the start point (airport center point) and end point (sampling point), traverse all nearby road segments, calculate the nearest distance to each road segment, and get the nearest point corresponding to the minimum distance as the nearest point on the road network. According to this method, the nearest points A and B of the start point (airport center point) and end point (sampling point) to the road network are obtained.

[0050] (3) Analyze each road segment in the road network, build a graph data structure (nodes and edges are the basic data sources), and write Dijkstra algorithm to realize the shortest path vertex set (latitude and longitude coordinates) of A and B in the road network.

[0051] Dijkstra algorithm is a classical algorithm for finding single-source shortest paths in graphs proposed by Dutch computer scientist Edsger Dijkstra in 1956. The algorithm is mainly used to solve the shortest path problem from a vertex (source point) to all other vertices in a weighted directed graph or undirected graph, and its core feature is to select the nearest unvisited vertex to the source point for expansion each time. At present, the pseudo code of this algorithm can be found in most algorithm class books, and this function can also be implemented using various open source libraries (such as Dijkstra.NET).

[0052] (4) Add A to the beginning of the shortest path vertex set and B to the end of the shortest path vertex set to form the final shortest path vertex set (latitude and longitude coordinates).

[0053] Step 3: Find the target point with a highway distance of 100 kilometers in a certain path.

[0054] (1) Traverse the shortest path vertex set (latitude and longitude coordinates) to generate a temporary point set PTS, and add each vertex coordinate in the shortest path vertex set to the set PTS by transforming it into Gauss-Kriging coordinates.

[0055] (2) Traverse each element in PTS, calculate the distance between the adjacent two vertices, and save it to the distance set DS.

[0056] (3) Traverse each element (distance) in DS from the beginning, and accumulate the current element and all previous elements. If the accumulation result is greater than 100 kilometers, it means that the target point with a highway distance of 100 kilometers falls within the current segment.

[0057] In the current segment (corresponding to the adjacent two vertices Pa and Pb in the shortest path vertex set), locate the point Px with a highway distance of 100 kilometers by linear interpolation ( Figure 3 to be supplemented), and convert Px to latitude and longitude coordinates to get the target point.

[0058] Step 4: For each sampling point of the 100km circle, repeat the execution of Step 2 and Step 3, and generate an optimized polygon (which may have repeated points or self-intersection problems) according to the obtained target point set.

[0059] (1) Eliminate the repetition. Traverse the target point set in reverse order, and if a point has the same coordinates as the previous point, delete it from the set.

[0060] (2) Bubble method traverses to check whether the edge formed by each two adjacent vertices and the edge of other adjacent vertices has self-intersection, and if not, the target polygon can be directly generated, otherwise, record the index of the two intersecting edges and the intersection point coordinate value.

[0061] (3) For the case of self-intersection, the self-intersection of the polygon can be eliminated according to the polygon self-intersection processing method described in the description. Figure 4

[0062] Assuming that the starting vertex indexes of the two self-intersecting edges are Pa and Pb, delete all vertices in the vertex set between Pa and Pb, insert the intersection point of the two self-intersecting edges after Pa and before Pb, and the obtained polygon is the polygon with self-intersection eliminated.

[0063] (4) Repeat the execution of sub-step (2) to check whether the polygon has self-intersection, and if still has self-intersection, continue to process according to the method of sub-step (3).

[0064] Step 5: Connect the vertex set remaining after Step 4 in turn to form the 100km coverage polygon, and render the airport coverage polygon display result on the GIS platform.

[0065] Figure 5 is a typical plain area airport 100km coverage range generated by using the present scheme. The highway network in the plain area is dense, and the coverage range is close to the circle.

[0066] Figure 6 is a typical mountainous area airport 100km coverage range generated by using the present scheme. The highway network in the mountainous area is sparse due to the influence of the terrain, and the shape of the coverage range presents irregular characteristics.

[0067] The following is an apparatus embodiment of the present application, which can be used to execute the method embodiments of the present application. For details not disclosed in the apparatus embodiment, please refer to the method embodiments of the present application.

[0068] ​In another embodiment of the present application, a system for generating an airport road coverage polygon is provided, which can be used to implement the method for generating an airport road coverage polygon. Specifically, the system for generating an airport road coverage polygon comprises a geographic coordinate acquisition module, a sampling point generation module, a shortest path calculation module, a target point calculation module, and a polygon generation module.

[0069] The geographic coordinate acquisition module is configured to acquire the geographic coordinates of the airport center point.

[0070] The sampling point generation module is configured to generate a circle with the airport center point as the center, and generate a plurality of sampling points on the circumference of the circle according to a preset sampling rule, thereby obtaining a sampling point set.

[0071] The shortest path calculation module is configured to acquire road network data, and calculate the shortest path from the airport center point to each sampling point in the sampling point set based on the road network data, thereby obtaining a shortest path set.

[0072] The target point calculation module is configured to traverse each shortest path in the shortest path set, and calculate a target point on the shortest path that has a road distance from the airport center point equal to a preset road distance, thereby obtaining a target point set.

[0073] The polygon generation module is configured to connect all the target points in the target point set, thereby generating an airport road coverage polygon.

[0074] In another embodiment of the present application, a terminal device is provided, which comprises a processor and a memory, the memory being configured to store a computer program, the computer program comprising program instructions, and the processor being configured to execute the program instructions stored in the computer storage medium. The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, and are suitable for implementing one or more instructions, and are particularly suitable for loading and executing one or more instructions to implement a corresponding method flow or a corresponding function. The processor in the embodiments of the present application can be used to generate the airport road coverage polygon method, which comprises the following steps: obtaining the geographic coordinates of the airport center point; taking the airport center point as a reference, generating a circle, and generating a plurality of sampling points on the circumference of the circle according to a preset sampling rule to obtain a sampling point set; obtaining road network data, and calculating the shortest path from the airport center point to each sampling point in the sampling point set based on the road network data to obtain a shortest path set; traversing each shortest path in the shortest path set, calculating the target point on the shortest path whose road distance from the airport center point is equal to a preset road distance to obtain a target point set; and connecting all target points in the target point set to generate an airport road coverage polygon.

[0075] In another embodiment of the present application, a computer readable storage medium (Memory) is also provided, which is a memory device in the terminal device, and is configured to store programs and data. It can be understood that the computer readable storage medium herein can include an internal storage medium of the terminal device, and of course can also include an expansion storage medium supported by the terminal device. The computer readable storage medium provides a storage space, which stores an operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and the instructions can be one or more computer programs (including program codes). It should be noted that the computer readable storage medium herein can be a high-speed RAM memory, or a non-volatile memory such as at least one disk memory.

[0076] The one or more instructions stored in the computer readable storage medium can be loaded and executed by the processor to implement the corresponding steps of the method for generating the airport highway coverage polygon in the above embodiments; the one or more instructions stored in the computer readable storage medium are loaded and executed by the processor to perform the following steps: obtaining geographical coordinates of an airport center point; taking the airport center point as a reference, generating a circle, and generating a plurality of sampling points on the circumference of the circle according to a preset sampling rule to obtain a sampling point set; obtaining road network data, and calculating a shortest path from the airport center point to each sampling point in the sampling point set based on the road network data to obtain a shortest path set; traversing each shortest path in the shortest path set, and calculating a target point on the shortest path with a highway distance equal to a preset highway distance from the airport center point to obtain a target point set; and connecting all target points in the target point set to generate an airport highway coverage polygon.

[0077] Those skilled in the art will understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) containing computer usable program code.

[0078] The present application is described with reference to flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts 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, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks. Figure 1 The functions specified in one or more flows and / or blocks.

[0079] These computer program instructions can also be stored in a computer readable storage medium that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer readable storage medium produce a manufactured product including instruction devices that implement the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks. Figure 1 The functions specified in one or more flows and / or blocks.

[0080] These computer program instructions can also be loaded into a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 The flowchart blocks or blocks in the flowcharts Figure 1 The flowchart blocks or blocks in the flowcharts

[0081] The above-mentioned sequence numbers of the embodiments of the present application are only for description, and do not represent advantages or disadvantages of the embodiments.

[0082] In the above-described embodiments of the present application, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0083] In the several embodiments provided by the present application, it should be understood that the disclosed technology can be implemented in other ways. Of course, the device embodiment described above is only illustrative, and for example, the division of units can be a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, unit or module, and can be electrical or other forms.

[0084] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on multiple units. According to actual needs, part or all of the units can be selected to achieve the purpose of the embodiment scheme.

[0085] The above only describes the preferred embodiments of the present application. It should be pointed out that for those skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, and these improvements and refinements should be considered as the protection scope of the present application.

[0086] It is to be understood that the above description is intended to be illustrative, and not restrictive. Many embodiments and applications other than the examples provided would be apparent to those of skill in the art upon reading the above description. The scope of the application should be determined, not with reference to the above description, but should instead be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. The disclosures of all articles and references, including patent applications and publications, are incorporated by reference for all purposes. The omission in the foregoing description of any aspect of the subject matter disclosed herein is not a disclaimer of such subject matter, nor should it be regarded that the applicant has disclaimed any such subject matter, nor should any such omission be regarded as affecting the scope of the disclosed innovations.

Claims

1. A method for generating a polygon representing the coverage area of ​​an airport highway, characterized in that, The method comprises the following steps: obtaining the geographic coordinates of the airport center point; generating a circle with the airport center point as the center and generating a plurality of sampling points on the circumference of the circle according to a preset sampling rule to obtain a sampling point set; obtaining road network data and calculating the shortest path from the airport center point to each sampling point in the sampling point set based on the road network data to obtain a shortest path set; traversing each shortest path in the shortest path set to calculate a target point on the shortest path with a road distance equal to a preset road distance from the airport center point to obtain a target point set; and connecting all target points in the target point set to generate an airport road coverage polygon.

2. The method of generating a highway coverage polygon according to claim 1, wherein, Before generating the sampling point set, the method further comprises: transforming the geographic coordinates of the airport center point into projected coordinates; and the step of generating the sampling point set comprises: generating a geometric circle with the projected coordinates of the transformed airport center point as the center and a preset radius value, and generating a plurality of sampling points on the circumference of the geometric circle to obtain a projected coordinate sampling point set; and transforming the projected coordinates of each sampling point in the projected coordinate sampling point set into geographic coordinates to form the sampling point set.

3. The method of generating a highway coverage polygon according to claim 2, wherein, The step of calculating the shortest path from the airport center point to each sampling point in the sampling point set comprises: calling a path planning interface of an online map service with the airport center point as the starting point and the sampling point as the ending point to obtain a shortest path vertex set between the starting point and the ending point, and the shortest path vertex set constitutes the shortest path.

4. The method of generating a highway coverage polygon according to claim 2, wherein, The step of calculating the shortest path from the airport center point to each sampling point in the sampling point set comprises: loading local road network data into a topological analysis component; finding road segments adjacent to the airport center point and the sampling point in the topological analysis component and calculating the nearest point A closest to the airport center point and the nearest point B closest to the sampling point on the road network, respectively; establishing a graph data structure based on the road network data and calculating the shortest path vertex set between the nearest point A and the nearest point B using a preset shortest path algorithm; and supplementing the nearest point A to the starting position of the shortest path vertex set and supplementing the nearest point B to the ending position of the shortest path vertex set to form the shortest path.

5. The method of generating a highway coverage polygon according to claim 1, wherein, The step of calculating the target point on the shortest path with a road distance equal to a preset road distance from the airport center point comprises: traversing each vertex of the shortest path to calculate the segment distance between adjacent two vertices; starting from the starting point of the shortest path, accumulating the segment distances to obtain an accumulated distance; and when the accumulated distance is greater than the preset road distance, determining that the target point falls within the road segment corresponding to the current segment distance and locating the target point in the road segment by linear interpolation.

6. The method of generating a highway coverage polygon according to claim 1, wherein, After generating the coverage polygon, the method further comprises: checking whether the coverage polygon has self-intersecting edges; if there are self-intersecting edges, recording the intersection point of the two self-intersecting edges and deleting all vertices between the starting vertices of the two self-intersecting edges, and inserting the intersection point between the starting vertices of the two edges to eliminate the self-intersection.

7. The method of generating a highway coverage polygon according to claim 1, wherein, The preset road distance is 100 kilometers; the step of generating a plurality of sampling points on the circumference of the circle according to a preset sampling rule comprises: sampling at equal distances on the circumference of the circle with a radius of 100 kilometers with a sampling length of 10 kilometers to generate the sampling points.

8. A system for generating an airport road coverage polygon, the system comprising: The method comprises the steps of: an airport center point geographic coordinate acquisition module, configured to acquire geographic coordinates of an airport center point; a sampling point generation module, configured to generate a circle with the airport center point as a reference, generate a plurality of sampling points on the circumference of the circle according to a preset sampling rule, and obtain a sampling point set; a shortest path calculation module, configured to acquire road network data, and calculate shortest paths from the airport center point to each sampling point in the sampling point set based on the road network data, and obtain a shortest path set; a target point calculation module, configured to traverse each shortest path in the shortest path set, calculate target points on the shortest path with a road distance equal to a preset road distance from the airport center point, and obtain a target point set; a polygon generation module, configured to connect all target points in the target point set to generate an airport road coverage range polygon.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the steps of the method for generating an airport road coverage range polygon according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program is executed by the processor to implement the steps of the method for generating an airport road coverage range polygon according to any one of claims 1 to 7.

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