Infrastructure service range analysis method, apparatus and electronic device

Abstract

The application provides an infrastructure service range analysis method, device and electronic equipment, and relates to the field of space planning. The infrastructure service range analysis method comprises the following steps: determining a starting point data set and an ending point data set for OD analysis of at least one target area including a basic service infrastructure; calculating an OD distance corresponding to the basic service infrastructure based on a road network data set, the starting point data set and the ending point data set; and determining a service range of the basic service infrastructure according to the OD distance corresponding to each basic service infrastructure. The infrastructure service range analysis method provided by the embodiment of the application can efficiently and accurately determine the infrastructure service range in a large range.

Description

Infrastructure service scope analysis methods, devices and electronic equipment Technical Field

[0001] This application relates to the field of spatial planning, and more specifically, to a method, apparatus, and electronic device for analyzing the service range of infrastructure. Background Technology

[0002] As global urbanization accelerates and urban populations and spatial extent continue to expand, the balance between supply and demand for infrastructure services faces severe challenges. Basic infrastructure services include public service facilities (such as hospitals, schools, parks, and libraries) and municipal infrastructure (such as fire stations, sewage treatment plants, public transportation stops, and 5G base stations). The rationality of the spatial distribution of basic infrastructure services directly affects the efficiency of social resource allocation and the quality of life for residents.

[0003] Traditional methods for delineating the service area of ​​infrastructure rely on Dijkstra's algorithm to calculate the shortest distance to obtain the shortest path from the starting point, followed by spatial interpolation. However, this method significantly increases the computation time cost in large-scale road networks with more than 100,000 nodes, such as city-level or national-level road networks, and its accuracy is easily affected by road network density and point location offset. Summary of the Invention

[0004] The purpose of this application is to provide an infrastructure service range analysis method, apparatus, and electronic device. This infrastructure service range analysis method combines grid and road network-based shortest path calculation, and uses distributed computing to calculate the shortest path between each grid point and the target infrastructure service point. While maintaining calculation accuracy, it improves calculation efficiency and can efficiently and accurately determine the infrastructure service range over a large area.

[0005] In a first aspect, embodiments of this application provide an infrastructure service range analysis method, which includes: determining a starting dataset and an ending dataset for OD analysis of the basic service facilities based on a target area including at least one basic service facility; calculating the OD distance corresponding to the basic service facilities based on the road network dataset, the starting dataset, and the ending dataset; and determining the service range of each basic service facility based on the OD distance corresponding to each basic service facility.

[0006] In the above implementation process, the infrastructure service range analysis method provided in this application combines grid and road network-based shortest path calculation. Based on the actual road network, it calculates the shortest path cost from infrastructure location (starting point) to each spatial unit (end point), which can accurately quantify the spatiotemporal accessibility of public services under the real urban road network, thereby providing powerful and intuitive data-driven decision support for urban renewal, facility site selection optimization and scientific allocation of public resources.

[0007] Optionally, in this embodiment of the application, determining the starting point dataset and the ending point dataset for OD analysis of the basic service facilities based on the target area including at least one basic service facility includes: taking all basic service facility points in the target area as starting points, determining the starting points located on the road network as target starting points, and constructing the starting point dataset from the target starting points; dividing the target area into grids, taking all grid center points as ending points, determining the ending points located on the road network as target ending points, and constructing the ending point dataset from the target ending points.

[0008] In the above implementation process, in the infrastructure service range analysis method provided in the embodiments of this application, before performing OD distance calculation, a road network dataset with rigorous topological relationships and complete attribute information is constructed, which provides a highly simulated traffic environment basis for OD cost calculation and significantly improves the accuracy and realism of the accessibility analysis results.

[0009] Optionally, in this embodiment of the application, the method further includes: if the starting point in the starting point dataset is not on the road network, vertically mapping the starting point to the nearest road network and determining the point mapped to the road network as the target starting point; if the ending point in the ending point dataset is not on the road network, vertically mapping the ending point to the nearest road network and determining the point mapped to the road network as the target ending point; and calculating the starting cost distance from the starting point to the target starting point and the cost distance from the ending point to the target ending point.

[0010] In the above implementation process, the infrastructure service range analysis method provided in this application effectively unifies the analysis unit and refines the evaluation granularity by standardizing the study area into a high-precision geographic grid and taking its center point as the demand endpoint; and by combining the Beidou grid coding system and road network topology verification, it ensures that the starting point and the endpoint are accurately associated with the road network in space and logic, providing a reliable data foundation for OD cost distance calculation.

[0011] Optionally, in this embodiment of the application, calculating the OD distance corresponding to the basic service facilities based on the road network dataset, the starting point dataset, and the ending point dataset includes: dividing the ending point dataset into multiple ending point subsets; allocating independent processes to calculate the minimum OD distance between each target starting point in the starting point dataset and the target ending point in each ending point subset, and generating a target dataset including the target starting point, the target ending point, and the minimum OD distance; determining the minimum value among the multiple minimum OD distances of multiple target ending points corresponding to the same target starting point in the target dataset as the minimum OD distance value of the target starting point, and updating the target dataset.

[0012] In the above implementation process, the infrastructure service range analysis method provided in this application solves the practical problem that spatial locations and road network data are difficult to match accurately by vertically mapping the origin and destination points that deviate from the road network to the nearest road node and calculating the access cost, thus ensuring the accessibility and computability of all origin and destination points on the road network in OD analysis.

[0013] Optionally, in this embodiment of the application, calculating the minimum OD distance between each target starting point in the starting point dataset and the target endpoints in each endpoint subset includes: determining the detectable road nodes in the endpoint subset; calculating the OD distance between the target starting point and the detectable road nodes; and determining the minimum value among the OD distances as the minimum OD distance between the target starting point and multiple target endpoints in the target endpoint subset.

[0014] In the above implementation process, the infrastructure service range analysis method provided in this application decomposes the calculation of large-scale OD cost matrix into multiple independent sub-tasks that can be processed in parallel, realizing high-speed search of paths between massive origin and destination points, and showing significant effects in large-scale road networks.

[0015] Optionally, in this embodiment of the application, the method further includes: when the starting point corresponding to the target starting point is not on the road network, adding the starting point cost distance to the OD distance between the target starting point and the detectable road node as the updated OD distance; and / or, when the ending point corresponding to the target ending point is not on the road network, adding the ending point cost distance to the OD distance between the target starting point and the detectable road node as the updated OD distance.

[0016] In the above implementation process, the infrastructure service range analysis method provided in this application uses the A* heuristic search algorithm and interruption threshold control to eliminate invalid calculation branches while ensuring the optimality of path search, and concentrates computing resources within a reasonable service space range; by processing the endpoint subset in batches and computing in parallel, the large-scale OD matrix calculation is successfully decomposed into multiple efficient subtasks that can be solved independently, thereby improving the efficiency of massive path calculation at the city scale.

[0017] Optionally, in this embodiment of the application, determining the service range of a basic service facility based on the OD distance corresponding to each basic service facility includes: merging the minimum OD distance in the target dataset into the geographic grid vector layer, and determining the optimal reachability cost from the basic service facility to the grid cell; and performing hierarchical rendering of the map based on the optimal reachability cost to obtain the service range of the basic service facility.

[0018] In the above implementation process, by superimposing the vertical mapping cost from the origin and destination to the road network with the actual driving cost calculated based on the road network, the common problem of facilities and demand points deviating from the road network in real-world analysis is effectively solved. This significantly improves the accuracy and real-world interpretability of the OD distance calculation results, and provides a reliable data foundation for the refined assessment of facility service coverage.

[0019] Secondly, embodiments of this application provide an infrastructure service range analysis device, which includes: a dataset construction module, an OD distance calculation module, and a visualization module; the dataset construction module is used to determine a starting dataset and an ending dataset for OD analysis of the basic service facilities based on a target area including at least one basic service facility; the OD distance calculation module is used to calculate the OD distance corresponding to the basic service facilities based on the road network dataset, the starting dataset, and the ending dataset; and the visualization module is used to determine the service range of each basic service facility based on its corresponding OD distance.

[0020] Thirdly, embodiments of this application provide an electronic device, which includes a memory and a processor. The memory stores program instructions, and when the processor executes the program instructions, it performs the steps in any of the methods described in the first aspect above.

[0021] Fourthly, embodiments of this application also provide a computer-readable storage medium storing computer program instructions, which, when read and executed by a processor, perform the steps in any implementation of the first aspect described above.

[0022] Fifthly, embodiments of this application also provide a computer program product, the computer program product including a computer program / instruction, which, when executed by a processor, implements the steps in any of the implementations of the first aspect described above. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 is a flowchart of the infrastructure service scope analysis method provided in an embodiment of this application;

[0025] Figure 2 is a schematic diagram of the road network structure provided in an embodiment of this application;

[0026] Figure 3 is a schematic diagram of the grid and the relationship between the start and end points provided in an embodiment of this application;

[0027] Figure 4 is a flowchart of OD distance calculation provided in an embodiment of this application;

[0028] Figure 5 is a flowchart illustrating an example of minimum OD distance provided in an embodiment of this application;

[0029] Figure 6 is a schematic diagram of OD distance calculation provided in an embodiment of this application;

[0030] Figure 7 is a schematic diagram of the results provided in the embodiments of this application;

[0031] Figure 8 is a schematic diagram of the infrastructure service range analysis device provided in an embodiment of this application;

[0032] Figure 9 is a schematic diagram of the structure of the electronic device provided in the embodiment of this application. Detailed Implementation

[0033] The technical solutions of the embodiments of this application will now be described with reference to the accompanying drawings. For example, the flowcharts and block diagrams in the drawings illustrate the architecture, functions, and operations of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagram may represent a module, program segment, or part of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or can be implemented using a combination of dedicated hardware and computer instructions. In addition, the functional modules in the various embodiments of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0034] In the fields of urban planning and public management, the comprehensive utilization of multi-source urban monitoring data to conduct dynamic analysis of the spatiotemporal accessibility of infrastructure service coverage at the urban scale can accurately quantify the spatial service efficiency and equity of various public service facilities such as education, healthcare, transportation, and green spaces. The assessment results can provide reliable data support and decision-making basis for optimizing urban spatial structure and scientifically allocating and refining the scheduling of public service resources, thus helping to build a livable, resilient, and intelligent urban living environment.

[0035] Traditional methods for delineating the service area of ​​infrastructure rely on Dijkstra's algorithm to calculate the shortest distance to obtain the shortest path from the starting point, followed by spatial interpolation. However, this method is computationally inefficient in large-scale road networks with more than 100,000 nodes, such as city-level or national-level road networks; it also has a significant impact on the calculation results for points spatially distant from the road; furthermore, the spatial density of the road network has a significant impact on the spatial interpolation results.

[0036] In addition, there are other methods for defining infrastructure service areas, such as using APIs, using ArcGIS network analysis to calculate service areas, and using professional software FME to perform calculations.

[0037] The inventors found that one method, using map APIs (such as Gaode and Baidu) to crawl the travel time from the grid centroid to the facility point and then generating a high-precision isochronous circle through interpolation, relies on API quotas and requires advanced GIS skills. Using ArcGIS's built-in network analysis tools to calculate service areas within a specified threshold based on the actual road network results in poor interpolation performance in sparse areas. Leveraging the powerful integration capabilities of FME software, either by calling map APIs or processing local road networks, is complex to configure and requires a high level of expertise.

[0038] Based on this, this application proposes an infrastructure service range analysis method, apparatus, and electronic device. This infrastructure service range analysis method combines grid-based and road network-based shortest path calculations, using distributed computing to calculate the shortest path between each grid point and the target infrastructure service facility point. While maintaining computational accuracy, it improves computational efficiency, enabling efficient and accurate determination of the infrastructure service range over a large area. It also quantitatively evaluates the spatial service effectiveness of various public service facilities, providing effective data support for urban planning optimization and public resource allocation decisions.

[0039] Please refer to Figure 1, which is a flowchart of the infrastructure service scope analysis method provided in the embodiment of this application; this application provides a flowchart of an infrastructure service scope analysis method, which can be executed by the electronic device shown in Figure 9.

[0040] The infrastructure service coverage analysis method includes the following steps:

[0041] Step S100: Based on the target area including at least one basic service facility, determine the starting dataset and the ending dataset for OD analysis of the basic service facility.

[0042] In step S100 above, the starting point dataset and the ending point dataset in the target area to be studied are determined. The target area usually includes multiple basic service facility points. To perform OD analysis on the basic service facilities, the starting point and the ending point need to be clearly defined to form the starting point dataset and the ending point dataset.

[0043] The Origin dataset typically represents the geographic location vector data of basic service facility points, or nodes on the road network with the lowest vertical mapping cost from the basic service facility point. The Destination dataset represents service demand points, which can be generated by dividing the target area into grids, with the center point of each grid serving as a demand point.

[0044] In the embodiments of this application, the basic service facilities studied can be public service facilities, such as hospitals, schools, parks, and libraries; municipal infrastructure, such as fire stations, sewage treatment plants, public transportation stations, and 5G base stations.

[0045] It should be noted that OD analysis, or Origin-Destination Analysis, is a core analytical method in the field of traffic engineering. It is mainly used to study the origin, destination, mode, and spatiotemporal distribution patterns of traffic trips.

[0046] Step S200: Based on the road network dataset, the starting point dataset, and the ending point dataset, calculate the OD distance corresponding to the basic service facilities.

[0047] In step S200 above, based on the road network dataset, the starting point dataset, and the ending point dataset, the movement cost in a real-world environment is simulated. By calculating the minimum cost path from each starting point (i.e., basic service facility point) to each ending point (i.e., grid center point) in the starting point dataset, the abstract service capacity is transformed into a concrete and quantifiable spatiotemporal cost.

[0048] The road network dataset includes road geometry information (length, width, coordinates), grade, speed limit, direction, topology, and other attributes.

[0049] Step S300: Determine the service range of each basic service facility based on the OD distance corresponding to each basic service facility.

[0050] In step S300 above, an OD cost matrix is ​​formed based on the calculated OD distance. This matrix accurately records the minimum cost value from each facility point to each grid cell. The OD cost matrix can be spatialized and classified according to a predetermined service threshold (such as a 15-minute living circle).

[0051] For example, all grid cells reachable within 15 minutes are included in the facility's effective service area. GIS technology is then used to connect these cells and render them into continuous service areas (such as isochronous maps), or to generate an accessibility heatmap of the entire region, clearly showing the areas of strength, weakness, and blind spots in service coverage. This visualizes the calculation results, providing a direct representation of the spatial differentiation of infrastructure services.

[0052] As shown in Figure 1, the infrastructure service range analysis method provided in this application combines grid and road network-based shortest path calculation. Based on the actual road network, it calculates the shortest path cost from infrastructure points (starting points) to various spatial units (end points), which can accurately quantify the spatiotemporal accessibility of public services under the real urban road network, thereby providing powerful and intuitive data-driven decision support for urban renewal, facility site selection optimization and scientific allocation of public resources.

[0053] In an optional embodiment, please refer to Figure 2, which is a schematic diagram of the road network structure provided in the embodiment of this application; before step S200, a road network dataset is constructed.

[0054] First, the road network data is standardized by extracting basic attributes of road segments, including direction (one-way, two-way), length (meters), and travel time (minutes). Two-way roads are then split into two one-way links (forward and reverse) to ensure that the road network direction aligns with actual traffic rules.

[0055] Furthermore, establish the association between nodes and road segments, as shown in Figure 2 at the points where roads intersect and where they do not. The green dots in Figure 2 represent road intersections. Each road node (intersection) is associated with all the road segments it connects to, clarifying the reachability between nodes. For non-connected road segments such as elevated roads, intersection nodes should be avoided. Correct any topological errors in the road network (e.g., disconnected roads, unconnected endpoints of two roads), ensuring the continuity and integrity of the road network.

[0056] Construct a road network structure named Road_ND. In road network construction, a network dataset is built based on urban road vector data, and reasonable topology parameters are set, such as allowing arbitrary vertex turns (Any_Vertex), to simulate real traffic environments. The impedance parameters of the network dataset can be set to travel time or distance, adjusted according to the analysis requirements to improve the realism of path matching.

[0057] As shown in Figure 2, in the infrastructure service range analysis method provided in this application embodiment, a road network dataset with rigorous topological relationships and complete attribute information is constructed before calculating OD distance. This provides a highly realistic traffic environment basis for OD cost calculation, significantly improving the accuracy and realism of the accessibility analysis results.

[0058] Please refer to Figure 3, which is a schematic diagram of the grid and start-end point relationship provided in an embodiment of this application. In an optional embodiment of this application, the step S100 above, which determines the start-end dataset and end-end dataset for OD analysis of the basic service facilities based on the target area including at least one basic service facility, can be implemented in the following ways:

[0059] All basic service facilities points within the target area are taken as starting points, and the starting points located on the road network are determined as target starting points. The starting point dataset is composed of target starting points.

[0060] The target area is divided into grids, and all grid center points are used as endpoints. The endpoints located on the road network are determined as target endpoints, and the endpoint dataset is composed of target endpoints.

[0061] For example, using the BeiDou grid coding system, the target area, i.e., the study area, is divided into a regular grid of 50m × 50m. Spatial feature conversion tools (such as the Feature To Point tool in ArcGIS) are used to convert the grid's areal features into point features (grid center points), which serve as the destination dataset. Infrastructure location data within the study area are directly used as the origin dataset. The length of the urban road network data is calculated (unit: meters), and the road network topology relationships are constructed.

[0062] In Figure 3, the purple rings represent infrastructure points, i.e., the origins. The blue dots represent grid points, i.e., the destinations, and the yellow area represents the study area.

[0063] Optionally, the correlation between the starting point and the road network can be verified using the "Near" tool in ArcGIS to ensure that the starting point can be calculated.

[0064] As shown in Figure 3, the infrastructure service range analysis method provided in this application effectively unifies the analysis units and refines the evaluation granularity by standardizing the study area into a high-precision geographic grid and using its center point as the demand endpoint. Furthermore, by combining the BeiDou grid coding system with road network topology verification, it ensures the precise spatial and logical association between the starting point and the endpoint and the road network, providing a reliable data foundation for OD cost distance calculation.

[0065] In an optional implementation, if the starting point in the starting point dataset is not on the road network, the starting point is vertically mapped to the nearest road network, and the point mapped to the road network is determined as the target starting point. Furthermore, the starting cost distance from the starting point to the target starting point is calculated.

[0066] In an optional implementation, if the endpoint in the endpoint dataset is not on the road network, the endpoint is vertically mapped to the nearest road network, and the point mapped to the road network is determined as the target endpoint. Furthermore, the endpoint cost distance from the endpoint to the target endpoint is calculated.

[0067] In this embodiment of the application, if the starting point or the ending point is already on the road network, then the starting point or the ending point is directly regarded as the target starting point or the target ending point during the calculation process.

[0068] In the above implementation process, the starting cost distance or ending cost distance represents the vertical distance / time from the starting point and the ending point to the nearest road.

[0069] Therefore, the infrastructure service range analysis method provided in this application solves the practical problem of the difficulty in accurately matching spatial locations with road network data by vertically mapping the origin and destination points that deviate from the road network to the nearest road node and calculating the access cost, thus ensuring the accessibility and computability of all origin and destination points on the road network in OD analysis.

[0070] Please refer to Figure 4, which is a flowchart of OD distance calculation provided in an embodiment of this application. In an optional embodiment of this application, the above step S200, based on the road network dataset, the starting point dataset, and the ending point dataset, calculates the OD distance corresponding to the basic service facilities, which can be achieved through the following steps:

[0071] Step S210: Divide the endpoint dataset into multiple endpoint subsets.

[0072] In step S210 above, the complete endpoint dataset is divided into multiple endpoint subsets of appropriate size, thereby splitting a large-scale, computationally intensive OD analysis task into multiple independent, parallel-processable computational subtasks.

[0073] Step S220: Allocate independent processes to calculate the minimum OD distance between each target starting point in the starting point dataset and each target endpoint in the endpoint subset, and generate a target dataset including the target starting point, target endpoint, and minimum OD distance.

[0074] In step S220 above, a process pool is configured according to the number of CPU cores of the computing device, and the endpoint dataset is divided into several endpoint subsets, also known as several sub-batches, using the rule of "number of threads × batch size", to avoid memory bottlenecks caused by excessive computing load.

[0075] Each subset of endpoints is assigned an independent computation process, which uses network analysis algorithms to calculate in parallel the minimum OD cost (such as shortest time or shortest path) between all starting points and all endpoints within that subset. Each process runs independently without interfering with others, and the final output is a target dataset containing the target starting point, target endpoint, and corresponding minimum cost.

[0076] Step S230: Determine the minimum OD distance among multiple minimum OD distances of multiple target endpoints corresponding to the same target starting point in the target dataset as the minimum OD distance value of the target starting point, and update the target dataset.

[0077] In step S230 above, since each starting point is calculated with points in multiple subsets of endpoints, the same starting point may correspond to multiple endpoints, resulting in multiple temporary minimum OD distance values. This embodiment of the application performs a global reduction on the output results of all parallel processes. For each starting point, the minimum value is selected from all its temporary OD distance results and determined as the final minimum OD distance value for that facility point. The final target dataset is then updated to ensure that one starting point corresponds to one endpoint and one minimum OD value for subsequent analysis.

[0078] As shown in Figure 4, the infrastructure service range analysis method provided in this application decomposes the calculation of a large-scale OD cost matrix into multiple independent sub-tasks that can be processed in parallel, realizing high-speed search of paths between massive origin and destination points, and showing significant effects in large-scale road networks.

[0079] Please refer to Figure 5, which is a flowchart illustrating an example of minimum OD distance provided in an embodiment of this application. In an optional implementation, as mentioned above, a network analysis algorithm is used to calculate the minimum OD cost between all starting points and all ending points within the subset in parallel. Here, the A* algorithm is used as an example to illustrate the calculation of the minimum OD cost between the target starting point and the target ending point.

[0080] In step S220 above, allocating independent processes to calculate the minimum OD distance between each target starting point in the starting point dataset and each target endpoint in the endpoint subset can be achieved through the following steps:

[0081] Step S221: Determine the detectable road nodes in the endpoint subset.

[0082] In step S221 above, based on a preset mid-segment threshold, such as 60 minutes or 10 kilometers, points whose theoretical costs far exceed the reasonable service range are filtered out from the study area, and finally a set of detectable road nodes that are within the threshold range and can be calculated is obtained.

[0083] Step S222: Calculate the OD distance between the target starting point and the detectable road node.

[0084] In step S222 above, starting from the target origin, and using the set of detectable road nodes obtained in the previous step as the potential target, the A* algorithm is started for parallel computation. The A* algorithm maintains an open list (list to be tested) and a closed list (list already tested), iteratively evaluates the cost f(n) = g(n) + h(n) of each detectable road node (where g(n) is the actual time cost from the origin to the current node, and h(n) is the heuristically estimated cost to the target node, such as the straight-line distance), and selects the globally optimal node to expand the search until the shortest path to all detectable road nodes is found or the interruption threshold is exceeded.

[0085] Step S223: Determine the minimum value in the OD distance as the minimum OD distance between multiple target endpoints in the subset of target starting point and target ending point.

[0086] In step S223 above, after calculating the path for all detectable road nodes within a subset of endpoints, all successfully solved paths are traversed and compared, and the path with the smallest cumulative cost (i.e., OD distance) is selected. This minimum value represents the optimal possible cost for the target origin to reach any endpoint within the geographical range of this subset of endpoints.

[0087] As shown in Figure 5, the infrastructure service range analysis method provided in this application uses the A* heuristic search algorithm and interruption threshold control to eliminate invalid calculation branches while ensuring the optimality of path search, and concentrates computing resources within a reasonable service space. By processing the endpoint subset in batches and computing in parallel, the large-scale OD matrix calculation is successfully decomposed into multiple efficient subtasks that can be solved independently, thereby improving the efficiency of massive path calculation at the city scale.

[0088] Please refer to Figure 6, which is a schematic diagram of OD distance calculation provided in the embodiment of this application; in the optional implementation of the embodiment of this application, when the starting point corresponding to the target starting point is not on the road network, the starting point cost distance is superimposed on the OD distance between the target starting point and the detectable road node as the updated OD distance; and / or, when the ending point corresponding to the target ending point is not on the road network, the ending point cost distance is superimposed on the OD distance between the target starting point and the detectable road node as the updated OD distance.

[0089] Taking Figure 6 as an example, in Figure 6, neither the starting point nor the ending point is on the road network.

[0090] To calculate the path cost of OD1 and the path cost of OD2, we first need to vertically map points D1, D2, and O onto the nearest road network, as shown in Figure 6: mapping point D1' for D1, mapping point D2' for D2, and mapping point O' for O. Furthermore, the path cost of OD1 = L1 + L2 + L3 + L4, and the path cost of OD2 = L1 + L5 + L6.

[0091] As shown in Figure 6, by superimposing the vertical mapping costs (L1, L4, L6) from the origin and destination to the road network with the actual driving costs (L2+L3, L5) calculated based on the road network, the common problem of facilities and demand points deviating from the road network in real-world analysis is effectively solved. This significantly improves the accuracy and real-world interpretability of the OD distance calculation results, and provides a reliable data foundation for the refined assessment of facility service coverage.

[0092] Please refer to Figure 7, which is a schematic diagram of the results provided in the embodiment of this application; in an optional embodiment of this application, the determination of the service range of the basic service facility based on the OD distance corresponding to each basic service facility in step S300 above can be achieved through the following steps:

[0093] Step S310: Merge the minimum OD distance in the target dataset into the geographic grid vector layer and determine the optimal reachability cost from the basic service facilities to the grid cells.

[0094] Step S320: Based on the optimal reach cost, perform hierarchical rendering of the map to obtain the service range of basic service facilities.

[0095] In steps S310 to S320 above, the minimum cost OD calculation results selected from each batch are merged into the geographic grid vector layer to complete the extraction of the minimum cost path for each grid unit. This grid result represents the optimal reachability cost from the infrastructure point to the grid unit. By hierarchically visualizing the optimal cost results, the final infrastructure service range result is generated.

[0096] Taking Figure 7 as an example, the points in Figure 7 represent the locations of middle schools, the green lines represent the road network, and the color from blue to red indicates an increasing distance from the facility points. In this study, the shortest travel distance to the nearest middle school for each grid is stored as an attribute in the grid data. Users can customize the distance classification criteria for the service area according to their actual needs and quickly extract the analysis results within a specified service radius. Simultaneously, the data also includes the identification information of the corresponding nearest middle school, supporting filtering by individual facilities or combinations, facilitating service coverage analysis of specific middle schools or groups of middle schools. This result can also be spatially overlaid with socioeconomic data such as population distribution and residential units to accurately assess the convenience of residents in different areas accessing educational services, thereby providing a quantitative decision-making basis for optimizing school layout and equitable allocation of educational resources.

[0097] Therefore, the infrastructure service range analysis method provided in this application realizes the transformation from abstract numerical values ​​to spatial visualization services by spatially associating the calculated minimum OD cost data with a geographic grid; and performs hierarchical rendering based on cost thresholds, ultimately generating a visualization map that intuitively reflects the strength and spatial differentiation characteristics of infrastructure service coverage, providing direct decision support for accurately identifying service blind spots and optimizing facility layout.

[0098] Please refer to Figure 8, which is a schematic diagram of the modules of the infrastructure service range analysis device provided in the embodiment of this application; in an optional embodiment of this application, the infrastructure service range analysis device 100 includes: a dataset construction module 110, an OD distance calculation module 120, and a visualization module 130.

[0099] The dataset construction module 110 is used to determine the starting dataset and the ending dataset for OD analysis of the basic service facilities based on the target area including at least one basic service facility.

[0100] The OD distance calculation module 120 is used to calculate the OD distance corresponding to basic service facilities based on the road network dataset, the starting point dataset, and the ending point dataset.

[0101] The visualization module 130 is used to determine the service range of each basic service facility based on the OD distance corresponding to each basic service facility.

[0102] In an optional embodiment, during the process of determining the start-point dataset and end-point dataset for OD analysis of the basic service facilities based on the target area including at least one basic service facility, the dataset construction module 110 is specifically used to: take all basic service facility points within the target area as start points, determine the start points located on the road network as target start points, and construct the start-point dataset from the target start points; divide the target area into grids, take all grid center points as end points, determine the end points located on the road network as target end points, and construct the end-point dataset from the target end points.

[0103] In an optional embodiment, the infrastructure service range analysis device 100 is further configured to: vertically map the starting point to the nearest road network when the starting point in the starting point dataset is not on the road network, and determine the point mapped to the road network as the target starting point; vertically map the ending point to the nearest road network when the ending point in the ending point dataset is not on the road network, and determine the point mapped to the road network as the target ending point; and calculate the starting cost distance from the starting point to the target starting point and the cost distance from the ending point to the target ending point.

[0104] In an optional embodiment, during the process of calculating the OD distance corresponding to the basic service facilities based on the road network dataset, the starting point dataset, and the ending point dataset, the OD distance calculation module 120 is specifically used to: divide the ending point dataset into multiple ending point subsets; allocate independent processes to calculate the minimum OD distance between each target starting point in the starting point dataset and the target ending point in each ending point subset, and generate a target dataset including the target starting point, the target ending point, and the minimum OD distance; determine the minimum value among the multiple minimum OD distances of multiple target ending points corresponding to the same target starting point in the target dataset as the minimum OD distance value of the target starting point, and update the target dataset.

[0105] In an optional embodiment, during the process of calculating the minimum OD distance between each target starting point in the starting point dataset and the target endpoints in each endpoint subset, the OD distance calculation module 120 is specifically used to: determine the detectable road nodes in the endpoint subset; calculate the OD distance between the target starting point and the detectable road nodes; and determine the minimum value of the OD distance as the minimum OD distance between the target starting point and multiple target endpoints in the target endpoint subset.

[0106] In an optional embodiment, the OD distance calculation module 120 is further configured to: when the starting point corresponding to the target starting point is not on the road network, add the starting point cost distance to the OD distance between the target starting point and the detectable road node to obtain an updated OD distance; and / or, when the ending point corresponding to the target ending point is not on the road network, add the ending point cost distance to the OD distance between the target starting point and the detectable road node to obtain an updated OD distance.

[0107] In an optional embodiment, during the process of determining the service range of a basic service facility based on the OD distance corresponding to each basic service facility, the OD distance calculation module 120 is used to: merge the minimum OD distance in the target dataset into the geographic grid vector layer and determine the optimal reachability cost from the basic service facility to the grid cell; and perform hierarchical rendering of the map based on the optimal reachability cost to obtain the service range of the basic service facility.

[0108] Please refer to Figure 9, which is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. An electronic device 200 provided in this application includes: a processor 201 and a memory 202. The memory 202 stores machine-readable instructions executable by the processor 201. When the machine-readable instructions are executed by the processor 201, they perform the steps in any implementation of the above-described infrastructure service scope analysis method.

[0109] Based on the same inventive concept, embodiments of this application also provide a computer program product, the computer program product including a computer program / instruction, which is executed by a processor using steps in any implementation of the above-described infrastructure service scope analysis method.

[0110] Based on the same inventive concept, embodiments of this application also provide a computer-readable storage medium storing computer program instructions, which, when read and executed by a processor, perform steps in any implementation of the above-described infrastructure service scope analysis method.

[0111] The computer-readable storage medium can be any medium capable of storing program code, such as Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM).

[0112] In this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, without necessarily requiring or implying any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0113] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for analyzing the service scope of infrastructure, characterized in that, The infrastructure service range analysis method includes: determining a start-point dataset and an end-point dataset for OD analysis of a target area including at least one basic service facility; calculating the OD distance corresponding to the basic service facility based on a road network dataset, the start-point dataset, and the end-point dataset; determining the service range of the basic service facility based on the OD distance corresponding to each basic service facility; the step of calculating the OD distance corresponding to the basic service facility based on the road network dataset, the start-point dataset, and the end-point dataset includes: dividing the end-point dataset into multiple end-point subsets; allocating independent processes to calculate the minimum OD distance between each target start point in the start-point dataset and each target end point in each of the end-point subsets, and generating a target dataset including the target start point, the target end point, and the minimum OD distance; and finding the minimum value among multiple minimum OD distances of multiple target end points corresponding to the same target start point in the target dataset. The method further includes: determining the minimum OD distance value of the target starting point and updating the target dataset; calculating the minimum OD distance between each target starting point in the starting point dataset and each target endpoint in the endpoint subset, including: determining the detectable road nodes in the endpoint subset; calculating the OD distance between the target starting point and the detectable road nodes; determining the minimum value among the OD distances as the minimum OD distance between the target starting point and multiple target endpoints in the target endpoint subset; the method further includes: when the starting point corresponding to the target starting point is not on the road network, adding the starting point cost distance to the OD distance between the target starting point and the detectable road nodes as the updated OD distance; and / or, when the endpoint corresponding to the target endpoint is not on the road network, adding the endpoint cost distance to the OD distance between the target starting point and the detectable road nodes as the updated OD distance.

2. The method according to claim 1, characterized in that, The step of determining the starting point dataset and the ending point dataset for OD analysis of the basic service facilities based on the target area including at least one basic service facility includes: taking all basic service facility points in the target area as starting points, determining the starting points located on the road network as target starting points, and constructing the starting point dataset from the target starting points; dividing the target area into grids, taking all grid center points as ending points, determining the ending points located on the road network as target ending points, and constructing the ending point dataset from the target ending points.

3. The method according to claim 2, characterized in that, The method further includes: if the starting point in the starting point dataset is not on the road network, vertically mapping the starting point to the nearest road network and determining the point mapped to the road network as the target starting point; if the ending point in the ending point dataset is not on the road network, vertically mapping the ending point to the nearest road network and determining the point mapped to the road network as the target ending point; and calculating the starting cost distance from the starting point to the target starting point and the cost distance from the ending point to the target ending point.

4. The method according to claim 1, characterized in that, The step of determining the service range of each basic service facility based on its OD distance includes: merging the minimum OD distance in the target dataset into a geographic grid vector layer, and determining the optimal reachability cost from the basic service facility to the grid cell; and performing hierarchical rendering of the map based on the optimal reachability cost to obtain the service range of the basic service facility.

5. An infrastructure service range analysis device, characterized in that, The infrastructure service range analysis device for performing any one of claims 1-4 includes: a dataset construction module, an OD distance calculation module, and a visualization module; the dataset construction module is used to determine a starting dataset and an ending dataset for OD analysis of the basic service facilities based on a target area including at least one basic service facility; the OD distance calculation module is used to calculate the OD distance corresponding to the basic service facility based on the road network dataset, the starting dataset, and the ending dataset; the visualization module is used to determine the service range of the basic service facility based on the OD distance corresponding to each basic service facility.

6. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores program instructions, and when the processor executes the program instructions, it performs the steps of the method according to any one of claims 1-4.

7. A computer program product, characterized in that, The computer program product includes a computer program / instruction that, when executed by a processor, implements the steps of the method according to any one of claims 1-4.

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