Road network automatic synthesis method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a road network automatic synthesis method and device, electronic equipment and a storage medium, and belongs to the technical field of surveying and mapping geographic information. The method comprises the following steps: marking a feature road; deleting the road mesh suspension lines which are smaller than the minimum length threshold value of the road and do not belong to the characteristic road to obtain first road network structured information; deleting the first candidate road which has the lowest road grade and does not belong to the feature road to obtain second road network structured information; deleting the second candidate road which has the lowest road grade and does not belong to the feature road to obtain third road network structured information; and performing mesh merging based on the third road network structured information to obtain target mesh structured information and target road network structured information. According to the embodiment of the invention, the structured road network is comprehensively extracted, the road network shape structural features and the road network density difference are reasonably maintained after integration, the road network integration efficiency and the road network integration rationality are improved, and the manual integration workload of the road network is reduced.

Description

Technical Field

[0001] The present application relates to the field of surveying and mapping geographic information technology, and particularly to a method and device for automatic generalization of road networks, an electronic device, and a storage medium. Background Art

[0002] In the related art, traditional road network generalization models focus more on considering geometric graphic comprehensive constraints when comprehensively selecting roads, and less on considering the influence of road attribute values and other geographical elements on road selection generalization, and insufficient consideration of the characteristics of road networks. The automation of road automatic generalization is insufficient, which affects the rationality and efficiency of road comprehensive selection.

[0003] In summary, the technical problems existing in the related art need to be improved. Summary of the Invention

[0004] The main purpose of the embodiments of the present application is to propose a method and device for automatic road generalization, an electronic device, and a storage medium, aiming to extract a structured road network, better maintain the characteristics of the road network and the difference in road network cell density, and improve the efficiency and rationality of road generalization.

[0005] To achieve the above object, on the one hand, an embodiment of the present application proposes a method for automatic road generalization, the method comprising the following steps: Marking characteristic roads based on initial road network structured information, the initial road network structured information including road network cell boundary lines and road network cell suspension lines; Deleting the road network cell suspension lines in the initial road network structured information that are less than the road minimum length threshold and do not belong to the characteristic roads, to obtain the first road network structured information after deletion; Marking first candidate roads based on the initial cell structured information, the first road network structured information, and a road network cell line density threshold, and deleting the first candidate roads with the lowest road grade and not belonging to the characteristic roads in the first road network structured information, to obtain the second road network structured information after deletion, the initial road network structured information including road grade information and road length information; Marking second candidate roads based on the initial cell structured information and the second road network structured information, and deleting the second candidate roads with the lowest road grade and not belonging to the characteristic roads in the second road network structured information, to obtain the third road network structured information after deletion; Performing cell merging based on the third road network structured information to obtain target cell structured information and target road network structured information.

[0006] To achieve the above object, on the other hand, an embodiment of the present application proposes a device for automatic generalization of road networks, the device comprising: A marking module, configured to mark characteristic roads based on initial road network structured information, where the initial road network structured information includes road network mesh boundary lines and road network mesh suspension lines; A first integration module, configured to delete the road network mesh suspension lines in the initial road network structured information that are less than the minimum road length threshold and do not belong to the characteristic roads, so as to obtain the first deleted road network structured information; A second integration module, configured to mark first candidate roads based on the initial mesh structured information, the first road network structured information, and a road network eye line density threshold, and delete the first candidate roads in the first road network structured information that have the lowest road grade and do not belong to the characteristic roads, so as to obtain the second deleted road network structured information, where the initial road network structured information includes road grade information and road length information; A third integration module, configured to mark second candidate roads based on the initial mesh structured information and the second road network structured information, and delete the second candidate roads in the second road network structured information that have the lowest road grade and do not belong to the characteristic roads, so as to obtain the third deleted road network structured information; A merging module, configured to perform mesh merging based on the third road network structured information to obtain target mesh structured information and target road network structured information.

[0007] To achieve the above object, on the other hand, an embodiment of the present application proposes an electronic device, where the electronic device includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the method described above is implemented.

[0008] To achieve the above object, on the other hand, an embodiment of the present application proposes a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described above is implemented.

[0009] The embodiments of the present application at least include the following beneficial effects: The present application provides a method and device for automatic generalization of road networks, an electronic device, and a storage medium. This solution marks characteristic roads based on the initial road network structured information, which is beneficial for maintaining the characteristics of the road network and understanding the functional structure of the road network; deletes the road network mesh hanging lines in the initial road network structured information that are less than the minimum road length threshold and do not belong to the characteristic roads, and obtains the first road network structured information after deletion, retaining the main road structure and reducing complexity; marks the first candidate roads based on the initial mesh structured information, the first road network structured information, and the road network line density threshold, and deletes the first candidate roads with the lowest road grade and not belonging to the characteristic roads in the first road network structured information, obtaining the second road network structured information after deletion, which is beneficial for streamlining the road network structure, maintaining the difference in road network mesh density, and improving the rationality of road generalization; marks the second candidate roads based on the initial mesh structured information and the second road network structured information, and deletes the second candidate roads with the lowest road grade and not belonging to the characteristic roads in the second road network structured information, obtaining the third road network structured information after deletion, streamlining the road network structure; performs mesh merging based on the third road network structured information to obtain the target mesh structured information and the target road network structured information, which is beneficial for reducing redundant roads and meshes, making the road network more streamlined and effective. The shape structure characteristics of the road network after generalization, the difference in road network line density are reasonably maintained, and the data quality such as the correctness of the topological relationship between roads, the integrity of attribute values, and geometric accuracy is good, improving the efficiency of road network generalization and optimizing the rationality of road network generalization, greatly reducing the manual workload of road network generalization. Description of the Drawings

[0010] Figure 1 is the flowchart of the method for automatic generalization of road networks provided by the embodiments of the present application; Figure 2 is Figure 1 the flowchart of step S103 in Figure 3 is Figure 1 the flowchart of step S105 in Figure 4 is the flowchart of the data preprocessing steps of the method for automatic generalization of road networks provided by the embodiments of the present application; Figure 5 is a specific implementation flowchart when the method for automatic generalization of road networks provided by the embodiments of the present application is applied to a road automatic generalization selection system; Figure 6 is the Delaunay triangle classification and median line extraction connection diagram provided by the embodiments of the present application; Figure 7 is the schematic diagram of the road center line extraction process provided by the embodiments of the present application; Figure 8 is the road center line effect diagram provided by the embodiment of the present application; Figure 9 is the schematic diagram of the road integration network construction provided by the embodiment of the present application; Figure 10 is the schematic diagram of the road broken chain processing provided by the embodiment of the present application; Figure 11 is the flow chart of the detection and automatic link processing of the pseudo node provided by the embodiment of the present application; Figure 12 is the schematic diagram of the road break point connection provided by the embodiment of the present application; Figure 13 is the schematic diagram of the structured information representation of the road mesh boundary line and the suspension line provided by the embodiment of the present application; Figure 14 is the schematic diagram of the road mesh structured information representation provided by the embodiment of the present application; Figure 15 is the road mesh structure diagram provided by the embodiment of the present application; Figure 16 is the schematic diagram of the road network data of various scales provided by the embodiment of the present application; Figure 17 is the schematic diagram of the position relationship thumbnail of the road after simplification and the source double-line road before generalization provided by the embodiment of the present application; Figure 18 is the enlarged schematic diagram of the position relationship between the road after simplification and the source double-line road before generalization provided by the embodiment of the present application; Figure 19 is the attribute table of the road network after generalization provided by the embodiment of the present application; Figure 20 is the schematic diagram of the comparison of road attribute data before and after the generalization of the road network provided by the embodiment of the present application; Figure 21 is the flow chart of the implementation of the road network selection model based on the constraint face weight graph theory provided by the embodiment of the present application; Figure 22 is the schematic diagram of the structure of the road network automatic generalization device provided by the embodiment of the present application; Figure 23 is the schematic diagram of the hardware structure of the electronic device provided by the embodiment of the present application. Specific implementation manners

[0011] In order to make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application. When the following description involves the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with the embodiments of the present application. They are only examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the appended claims.

[0012] It can be understood that the terms "first", "second", etc. used in the present application may be used herein to describe various concepts, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if", "when" as used herein may be interpreted as "when...", "while...", or "in response to determining".

[0013] The terms "at least one", "a plurality of", "each", "any one", etc. used in the present application, at least one includes one, two or more than two, a plurality of includes two or more than two, each refers to each of the corresponding plurality, and any one refers to any one of the plurality.

[0014] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application and are not intended to limit the present application.

[0015] Before elaborating on the embodiments of the present application in detail, some nouns and terms involved in the embodiments of the present application will be described first. The nouns and terms involved in the embodiments of the present application are applicable to the following explanations.

[0016] 1) Delaunay triangle (Delaunay Triangulation) is a triangulation method. Given a set of points on a plane, Delaunay triangle triangulation can connect these points into triangles such that there are no other points inside the circumcircle of each triangle.

[0017] 2) Automatic generalization of road networks refers to the automation of simplifying, generalizing and optimizing road networks in multi-scale maps through algorithms and programs, and realizing the automatic derivation of various small-scale road data from large-scale road data to meet the cartographic requirements of maps at different scales.

[0018] In the related art, the traditional road network synthesis model focuses more on considering the comprehensive geometric constraints for the comprehensive selection of roads, and less on the influence of the attribute values of roads and other geographical elements on the comprehensive road selection, and insufficient consideration is given to the characteristics of the road network. The automation of road automatic synthesis is insufficient, which affects the rationality and efficiency of road comprehensive selection.

[0019] In summary, the technical problems existing in the related art need to be improved.

[0020] In view of this, an automatic road network synthesis method, device, equipment and medium are provided in the embodiments of the present application. This solution marks characteristic roads based on the initial road network structured information, which is beneficial to maintaining the characteristics of the road network and understanding the functional structure of the road network; deletes the road network eye suspension lines in the initial road network structured information that are less than the minimum road length threshold and do not belong to the characteristic roads to obtain the first road network structured information after deletion, retaining the main road structure and reducing complexity; marks the first candidate roads based on the initial mesh structured information, the first road network structured information and the road network line density threshold, and deletes the first candidate roads with the lowest road grade and not belonging to the characteristic roads in the first road network structured information to obtain the second road network structured information after deletion, which is beneficial to streamlining the road network structure, maintaining the difference in road network eye density, and improving the rationality of road synthesis; marks the second candidate roads based on the initial mesh structured information and the second road network structured information, and deletes the second candidate roads with the lowest road grade and not belonging to the characteristic roads in the second road network structured information to obtain the third road network structured information after deletion, streamlining the road network structure; performs mesh merging based on the third road network structured information to obtain the target mesh structured information and the target road network structured information, which is beneficial to reducing redundant roads and meshes, making the road network more streamlined and effective. The morphological structure characteristics of the road network after synthesis, the difference in road network line density are reasonably maintained, and the data quality such as the correctness of the topological relationship between roads, the integrity of attribute values and geometric accuracy is good, improving the efficiency of road network synthesis and optimizing the rationality of road network synthesis, and greatly reducing the manual workload of road network synthesis.

[0021] The road network automatic generalization method provided by the embodiments of this application relates to the field of surveying and mapping geographic information. The road network automatic generalization method provided by the embodiments of this application can be applied to a terminal, or to a server, or can also be software running on a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, a vehicle-mounted terminal, etc., but is not limited thereto; the server side can be configured as an independent physical server, or can be configured as a server cluster or a distributed system composed of multiple physical servers, or can also be configured as a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network; the software can be an application that implements the road network automatic generalization method, etc., but is not limited to the above forms.

[0022] This application can be used in many general-purpose or special-purpose computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and so on. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0023] Figure 1 is an optional flowchart of the road network automatic generalization method provided by the embodiments of this application, Figure 1 The method in may include but is not limited to steps S101 to S105.

[0024] Step S101, mark characteristic roads based on the initial road network structured information.

[0025] Specifically, the initial road network structured information includes road network mesh boundary lines and road network mesh suspension lines.

[0026] In some embodiments, extract the initial road network structured information from the source road data.

[0027] Specifically, obtain source road data; extract road centerlines based on the source road data through the Delaunay triangulation method; fuse the road centerlines of multiple road layers to obtain fused road data; perform chain break processing, overlapping line deletion processing, and road breakpoint processing on the fused road data to obtain original road information; convert road line features into road mesh surfaces based on the original road information to determine road meshes and road mesh boundary lines; determine internal hanging lines and external hanging lines of the road mesh based on the original road information and the road mesh; construct a topological relationship model based on the original road information, the road mesh, the road mesh boundary lines, the internal hanging lines of the road mesh, and the external hanging lines of the road mesh. Among them, the topological relationship model includes initial road network structured information and initial mesh structured information, and the road mesh refers to the road mesh surface.

[0028] Optionally, use a graph theory model to select and mark characteristic roads based on the initial road network structured information through the semantic constraint method of attribute values.

[0029] In some embodiments, use a spatial analysis method to analyze the initial road network structured information to select and mark characteristic roads.

[0030] It can be understood that in response to the first instruction, display the initial road network structured information on the map page, and in response to the road selection instruction, display the characteristic roads selected by the user on the map page. Among them, the first instruction is triggered when the map page is opened or refreshed, and the first instruction is used to display the map page, and the road selection instruction is triggered when the user selects a characteristic road.

[0031] In this embodiment, marking characteristic roads based on the initial road network structured information is beneficial to maintaining the characteristics of the road network, understanding the functional structure of the road network, and providing data support for subsequent road selection.

[0032] Step S102, delete the road mesh hanging lines in the initial road network structured information that are less than the road minimum length threshold and do not belong to the characteristic roads to obtain the first road network structured information after deletion.

[0033] Specifically, the road mesh hanging lines include the internal hanging lines of the road mesh and the external hanging lines of the road mesh.

[0034] It can be understood that for the hanging line roads on the periphery of the road mesh, considering that the external hanging lines of the road mesh are the main bridges connecting the comprehensive regional road network and another regional road network, all the external hanging lines of the road mesh are completely retained during the selection process.

[0035] In some embodiments, the hanging lines outside the road mesh in the initial road network structured information are retained; the hanging lines inside the road mesh that are less than the road minimum length threshold and do not belong to the feature roads in the initial road network structured information are deleted to obtain the first road network structured information after deletion.

[0036] Specifically, traverse the road meshes in sequence, find the hanging line with the lowest level in the mesh, and determine whether the length of the corresponding hanging line is less than the comprehensive threshold (i.e., the road minimum length threshold). If so, retain the road element and re-find the hanging line with the lowest level in the mesh. Otherwise, delete the road mark.

[0037] Among them, the road minimum length threshold can be determined by the user or automatically determined by the system.

[0038] Exemplarily, for the hanging lines inside the road mesh, according to the shortest road length expression condition of the corresponding cartographic specification of the comprehensive target scale (for example, according to the regulations of the basic scale map compilation specification, the minimum expression length of the road map on the 1:25000 topographic map is 1 cm, that is, the actual road length is 250 m), the road lines that are less than the specification length threshold and are not feature roads are deleted.

[0039] In this embodiment, deleting the hanging lines of the road mesh that are less than the road minimum length threshold and do not belong to the feature roads in the initial road network structured information to obtain the first road network structured information after deletion is beneficial to retaining the main road structure, reducing complexity, and improving processing efficiency.

[0040] Step S103, mark the first candidate roads based on the initial mesh structured information, the first road network structured information, and the road network line density threshold, and delete the first candidate roads with the lowest road grade and not belonging to the feature roads in the first road network structured information to obtain the second road network structured information after deletion.

[0041] Specifically, the initial road network structured information includes road grade information and road length information, and the first candidate roads are the roads in the meshes where the road network line density is greater than the road network line density threshold.

[0042] In some embodiments, determine whether the number of roads in the first road network structured information is greater than the square root model comprehensive threshold (i.e., the target number of roads). If so, calculate the road network line density, determine the meshes where the road network line density exceeds the road network line density threshold, and use the roads included in the meshes as the first candidate roads.

[0043] Among them, the target number of roads can be determined by the user or automatically determined by the system; the road network line density threshold can also be determined by the user or automatically determined by the system, and is not limited thereto.

[0044] It can be understood that if the number of roads is less than or equal to the square root model comprehensive threshold, the road selection ends.

[0045] Optionally, delete the first candidate road with the lowest road grade, the shortest length, and not belonging to the characteristic road in the first road network structured information to obtain the second road network structured information after deletion.

[0046] In some embodiments, calculate the current road network line density of each mesh based on the initial mesh structured information and the first road network structured information; determine the meshes with the current road network line density greater than the road network line density threshold as candidate meshes, mark the hanging lines in the candidate meshes as the first candidate roads, and use the first candidate road with the lowest road grade as the first preselected road.

[0047] Further, if the first preselected road only includes a single road and the first preselected road does not belong to the characteristic road, delete the first preselected road from the first road network structured information to obtain the second road network structured information after deletion.

[0048] Optionally, if the first preselected road includes multiple roads, determine the road with the shortest length in the first preselected road as the first selected road. If the first selected road does not belong to the characteristic road, delete the first selected road from the first road network structured information to obtain the second road network structured information after deletion.

[0049] Exemplarily, perform a judgment on the road network line density. According to the mapping specification, for the road network meshes with a line density greater than the specification line density threshold, find the hanging road with the smallest internal grade and the shortest length inside. If the found hanging road is not a characteristic road, mark it for deletion.

[0050] In this embodiment, marking the first candidate roads based on the initial mesh structured information, the first road network structured information, and the road network line density threshold, and deleting the first candidate roads with the lowest road grade and not belonging to the characteristic road in the first road network structured information to obtain the second road network structured information after deletion is beneficial to streamlining the road network structure, maintaining the difference in road network mesh density, and improving the rationality of road synthesis.

[0051] Step S104, mark the second candidate roads based on the initial mesh structured information and the second road network structured information, and delete the second candidate roads with the lowest road grade and not belonging to the characteristic road in the second road network structured information to obtain the third road network structured information after deletion.

[0052] Specifically, the initial mesh structured information includes the mesh area, and the second candidate roads are the mesh boundary lines of the road network meshes with the smallest mesh area.

[0053] In some embodiments, it is determined whether the number of roads in the second road network structure information is greater than the square root model synthesis threshold (i.e., the target number of roads). If so, the road mesh with the smallest mesh area is determined based on the initial mesh structure information; based on the second road network structure information, the boundary lines of the road mesh with the smallest mesh area are marked (or determined) as the second candidate roads.

[0054] Further, the second candidate road with the lowest road grade is used as the second preselected road.

[0055] Optionally, if the second preselected road includes only a single road and the second preselected road does not belong to the feature road, the second preselected road is deleted from the second road network structure information to obtain the third road network structure information after deletion; if the second preselected road includes multiple roads, the road with the shortest length in the second preselected road is determined as the second selected road. If the second selected road does not belong to the feature road, the second selected road is deleted from the second road network structure information to obtain the third road network structure information after deletion.

[0056] In this embodiment, the second candidate roads are marked based on the initial mesh structure information and the second road network structure information, and the second candidate roads with the lowest road grade and not belonging to the feature road in the second road network structure information are deleted to obtain the third road network structure information after deletion, which is beneficial to streamlining the road network structure, reducing the computational complexity of subsequent processing, and improving the efficiency and accuracy of road synthesis.

[0057] Step S105: Based on the third road network structure information, perform mesh merging to obtain the target mesh structure information and the target road network structure information.

[0058] In some embodiments, through mesh merging, the mesh surfaces on both sides of the deleted road are merged into one to simplify the network structure. Among them, the merged road network structure reflects the final road connectivity and functional distribution, and the target road network is more compact.

[0059] Optionally, the meshes on both sides of the deleted road are merged; the topological structure information of the meshes is updated; the remaining number of roads is calculated. If the target number of roads is reached, the target mesh structure information and the target road network structure information are obtained.

[0060] It can be understood that if the target number of roads is not reached, return to step S104.

[0061] In some embodiments, mesh merging is performed based on the third road network structure information to obtain the current mesh structure information and the current road network structure information after mesh merging; the number of target roads is obtained, and the current number of roads in the current road network structure information is calculated; if the current number of roads is greater than the number of target roads, the current road network structure information is used as the second road network structure information, and the current mesh structure information is used as the initial mesh structure information, and the step of marking the second candidate road based on the initial mesh structure information and the second road network structure information is returned until the current number of roads is less than or equal to the number of target roads; if the current number of roads is less than or equal to the number of target roads, the current mesh structure information is used as the target mesh structure information, and the current road network structure information is used as the target road network structure information.

[0062] Among them, find another adjacent road network line to be merged, merge the two network lines to obtain a new feature network line for storage, improve the attribute information such as the boundary line between the new surface and the old surface, and find the starting point of the boundary line to be deleted. Determine whether the starting point is a graph breakpoint. If so, connect the graph breakpoint line at the starting point, integrate the attribute information, update the boundary information of the merged road network line, and then find the end point of the boundary line to be deleted. If not, directly find the end point of the boundary line to be deleted.

[0063] Further, determine whether the end point is a graph breakpoint. If so, connect the graph breakpoint line at the end point, integrate the attribute information, update the boundary information of the merged road network line, and then determine whether the remaining number of roads is greater than the square root model comprehensive threshold. If not, directly determine whether the remaining number of roads is greater than the square root model comprehensive threshold. If the remaining number of roads is less than or equal to the square root model comprehensive threshold, the road synthesis selection ends; otherwise, return to the step of finding the road network line with the smallest area.

[0064] Steps S101 to S105 illustrated in the embodiments of the present application, by marking characteristic roads based on the initial road network structured information, are beneficial to maintaining the characteristics of the road network; deleting the road network mesh hanging lines in the initial road network structured information that are less than the minimum road length threshold and do not belong to the characteristic roads to obtain the first road network structured information after deletion, retaining the main road structure and reducing complexity; marking the first candidate roads based on the initial mesh structured information, the first road network structured information, and the road network eye line density threshold, and deleting the first candidate roads with the lowest road grade and not belonging to the characteristic roads in the first road network structured information to obtain the second road network structured information after deletion, maintaining the difference in road network eye density and improving the rationality of road synthesis; marking the second candidate roads based on the initial mesh structured information and the second road network structured information, and deleting the second candidate roads with the lowest road grade and not belonging to the characteristic roads in the second road network structured information to obtain the third road network structured information after deletion; performing mesh merging based on the third road network structured information to obtain the target mesh structured information and the target road network structured information, which is beneficial to reducing redundant roads and meshes, making the road network more concise and effective, reasonably maintaining the morphological structure characteristics of the road network, the difference in road network line density, having good data quality such as the correctness of the topological relationship between roads, the integrity of attribute values, and geometric accuracy, improving the efficiency of road network synthesis and optimizing the rationality of road network synthesis, and greatly reducing the manual synthesis workload of the road network.

[0065] Please refer to Figure 2 , in some embodiments, step S103 may include but is not limited to steps S201 to S204: Step S201, calculating the current road network eye line density of each mesh based on the initial mesh structured information and the first road network structured information.

[0066] It can be understood that the initial mesh structured information includes at least one mesh.

[0067] In step S201 of some embodiments, it is determined whether the number of the first roads in the first road network structured information is greater than the square root model synthesis threshold. If so, the current road network eye line density of each mesh is calculated; if not, the selection ends.

[0068] Among them, the square root model synthesis threshold can be defined by the user, or the system default value can be used, or it can be automatically determined by the system, and is not limited thereto.

[0069] Step S202, determining the meshes with the current road network eye line density greater than the road network eye line density threshold as candidate meshes, marking the roads with the lowest road grade in the candidate meshes as the first candidate roads, and taking the first candidate roads with the lowest road grade as the first preselected roads.

[0070] In step S202 of some embodiments, the meshes with the current road network eyeliner density greater than the road network eyeliner density threshold are determined as candidate meshes.

[0071] It can be understood that the candidate meshes can be a single mesh or multiple meshes, and the present application does not limit this.

[0072] Further, the roads in the candidate meshes are marked (or determined) as the first candidate roads, and the first candidate road with the lowest road grade is used as the first preselected road.

[0073] It can be understood that the first preselected road can be a single road or multiple roads, and the present application does not limit this.

[0074] Step S203, if the first preselected road only includes a single road and the first preselected road does not belong to the characteristic road, then delete the first preselected road from the first road network structured information to obtain the second road network structured information after deletion.

[0075] In step S203 of some embodiments, the single first preselected road that does not belong to the characteristic road is deleted, and the second road network structured information is obtained after deletion.

[0076] Step S204, if the first preselected road includes multiple roads, then determine the road with the shortest length in the first preselected road as the first selected road. If the first selected road does not belong to the characteristic road, then delete the first selected road from the first road network structured information to obtain the second road network structured information after deletion.

[0077] In step S204 of some embodiments, if the first preselected road includes multiple roads, then select the first selected road with the shortest length from the first preselected roads.

[0078] Further, delete the first selected road that does not belong to the characteristic road, and the second road network structured information is obtained after deletion.

[0079] Please refer to Figure 3 , in some embodiments, step S105 may include but is not limited to steps S301 to S304: Step S301, perform mesh merging based on the third road network structured information to obtain the current mesh structured information and the current road network structured information after mesh merging.

[0080] In step S301 of some embodiments, merge the meshes on both sides of the deleted road and update the topological structured information of the meshes.

[0081] Step S302, obtain the target road quantity and calculate the current road quantity of the current road network structured information.

[0082] It is understandable that the number of target roads can be set by the user or automatically determined by the system or use the system default value.

[0083] In step S302 of some embodiments, calculate the current number of roads in the current road network structure information after mesh merging.

[0084] Step S303, if the current number of roads is greater than the number of target roads, then use the current road network structure information as the second road network structure information, use the current mesh structure information as the initial mesh structure information, and return to the step of marking the second candidate roads based on the initial mesh structure information and the second road network structure information until the current number of roads is less than or equal to the number of target roads.

[0085] It is understandable that if the current number of roads is greater than the number of target roads, then the current road network structure information does not meet the system requirements and road synthesis needs to be continued.

[0086] Furthermore, use the current road network structure information as the second road network structure information, use the current mesh structure information as the initial mesh structure information, and return to the step of marking the second candidate roads based on the initial mesh structure information and the second road network structure information until the current number of roads is less than or equal to the number of target roads.

[0087] Step S304, if the current number of roads is less than or equal to the number of target roads, then use the current mesh structure information as the target mesh structure information and use the current road network structure information as the target road network structure information.

[0088] In step S304 of some embodiments, if the current number of roads is less than or equal to the number of target roads, then the road selection is completed, use the current mesh structure information as the target mesh structure information, and use the current road network structure information as the target road network structure information.

[0089] Please refer to Figure 4 , in some embodiments, the road network automatic synthesis method provided by the embodiments of the present application further includes a data preprocessing step, and the data preprocessing step may include but is not limited to steps S401 to S403: Step S401, obtain source road data.

[0090] In step S401 of some embodiments, obtain source road data from a multi-source database or a multi-source data interface.

[0091] Step S402, extract the road center line based on the source road data by the Delaunay triangulation method.

[0092] In step S402 of some embodiments, a Delaunay triangulation network is constructed to generate a road centerline.

[0093] Optionally, the Delaunay triangulation network is generated by the point-by-point insertion method, and the Delaunay triangulation is performed on the double-line road area.

[0094] Furthermore, the Delaunay triangles are classified and recognized, and the triangles generated by the triangulation are divided into three categories during the network construction process.

[0095] In some embodiments, the centerline is extracted according to the Delauany triangulation network, the road at the intersection is straightened, and the road centerline is smoothed.

[0096] Step S403: The road centerlines of multiple road layers are fused to obtain fused road data.

[0097] In step S403 of some embodiments, road fusion network construction is realized. An empty feature class is generated with all the road layer feature classes to be networked comprehensively as templates (mathematical basis and corresponding fields); different layer feature classes are traversed one by one, and the corresponding features are added to the newly established empty feature class, and the corresponding attribute information is matched and updated; a new FROMLAYER field is created, and the name value of the source layer of the road feature is written into it to obtain fused road data.

[0098] Step S404: The fused road data is subjected to chain break processing, overlapping line deletion processing, and road break point processing to obtain the original road information.

[0099] In step S404 of some embodiments, in order to meet the requirements of the road network selection model based on the constraint surface weight graph theory, the boundary lines of the merged multi-source road network meshes are subjected to chain break processing, and the corresponding two road features at the intersection points are broken.

[0100] Furthermore, overlapping line deletion processing is realized. A line feature buffer surface is generated based on the topological tolerance, and the overlapping features (i.e., overlapping road lines) completely contained therein are identified through spatial query, and the overlapping features are deleted.

[0101] In some embodiments, the corresponding roads with pseudo nodes or the roads with non-existent pseudo nodes broken at the break points are connected to form a new road network line feature class, and the attribute values of the connected road network lines are integrated at the same time.

[0102] Step S405: Based on the original road information, the road line features are converted into road network mesh surfaces, and the road network meshes and the boundary lines of the road network meshes are determined.

[0103] Specifically, the road network mesh is the road network mesh surface.

[0104] In some embodiments, converting road line elements into road mesh surfaces specifically includes the following steps: 1) Use the DataManagementTools.FeatureToPolygon interface tool in ArcEngine to convert the road line element class to obtain the road mesh surface element class; 2) Add boundary BOUNADRY, area AREA, and road mesh density DENSITY fields to the newly generated road mesh surface element class: 3) Traverse the generated road mesh surfaces, calculate the area values of the corresponding road meshes, and store them in the AREA area field attribute values; 4) Use the ArcEngine geometric element attributes to extract the boundary lines of the road meshes, perform buffer analysis on the boundary lines of the road meshes, and generate buffer surface elements; 5) Perform overlay analysis on the boundary lines of the buffer surface elements and the road mesh line element class, find the road mesh boundary lines corresponding to each mesh surface element, and store the IDs of the road boundary lines of the corresponding road meshes in the BOUNADRY field attribute values to facilitate the tracking and updating of the road mesh boundary lines during the mesh merging operation in the graph theory model; 6) Calculate the road density of the road meshes and store the corresponding calculated values in the DENSITY field attribute values.

[0105] Step S406: Determine the internal hanging lines and external hanging lines of the road meshes based on the original road information and the road meshes.

[0106] In step S406 of some embodiments, determine the hanging roads inside and outside the road meshes. The hanging lines of the roads are divided into internal hanging lines of the road meshes and external hanging lines of the road meshes.

[0107] Step S407: Construct a topological relationship model based on the original road information, road meshes, road mesh boundary lines, internal hanging lines of the road meshes, and external hanging lines of the road meshes.

[0108] Specifically, the topological relationship model includes initial road network structured information and initial mesh structured information.

[0109] Furthermore, use a newly created feature class to store the processing results (i.e., road meshes, road mesh boundary lines, internal hanging lines of the road meshes, and external hanging lines of the road meshes), add a new FROM field to track the elements in the comprehensive processing process, record the source information of the elements, establish the ID relationship between the elements of the newly created feature class and the original elements (i.e., the original road information), and achieve dynamic integration through attribute mapping rules. Among them, the numerical attributes take the average value, and the non-numerical attributes are merged and stored.

[0110] In step S407 of some embodiments, a topological relationship model is constructed. The topological model includes the boundary lines of road meshes and the topological relationship formed by suspended roads.

[0111] Taking the road automatic generalization selection system as an example, Figure 5 is a specific implementation flowchart when the road network automatic generalization method provided by the embodiments of the present application is applied to the road automatic generalization selection system. Figure 5 The method in may include but is not limited to the following steps: Step 1, road centerline extraction.

[0112] Construct a Delaunay triangulation network to generate the road centerline. Specifically, in this paper, Delaunay triangulation is used to implement the extraction of the road centerline. The specific process is as follows: (1) Perform Delaunay triangulation on the double-line road area. Specifically, the point-by-point insertion method mentioned above is adopted to generate the Delaunay triangulation network; (2) Classify and identify the Delaunay triangles. During the process of constructing the network, the triangles generated by the triangulation are divided into three categories. Exemplarily, the Delaunay triangle classification and centerline extraction connection diagram is as Figure 6 shown.

[0113] Among them, the triangles with only one adjacent triangle on one side are class I triangles, the triangles with two adjacent triangles on two sides are class II triangles, and the triangles with three adjacent triangles on three sides are class III triangles. Class I triangles mainly appear at the entrances and exits of roads, class III triangles appear at road intersections, and the others are classified as class II triangles.

[0114] Further, to realize the construction of the road centerline network, when extracting the road centerline, the point connection and line construction are carried out in the following way: for class I triangles, connect the midpoint of the only adjacent side to the opposite triangle vertex; for class II triangles, connect the midpoints of the two adjacent sides; for class III triangles, connect the midpoints of the three sides to the centroid. Among them, the adjacent side refers to the common side of two adjacent triangles. Start searching from class III triangles and terminate at class I triangles or class III triangles to obtain one edge of the network. Until all class III triangles are processed, then start searching from class I triangles and terminate at class I triangles, so as to extract and form the road centerline network.

[0115] Optionally, perform straightening processing on the road lines at intersections: When extracting the road centerline for the triangles at T-shaped intersections during the process of constructing the network, straightening processing is required. The main method is to first find the road intersection point, then cut off the straight line at a certain length at the road intersection point, compare the angles of the remaining lines after cutting, and when the angles of the two lines meet a certain range, connect the two lines and merge them into one line. When it is a crossroads, find the intersection point as the node of the network, and finally form the road network.

[0116] In some embodiments, road smoothing is performed: The road centerline extracted through the above steps still needs to be smoothed, and the Bezier interpolation smoothing interface method in ArcEngine can be used for curve fitting processing.

[0117] Exemplarily, the schematic diagram of the road centerline extraction process is as Figure 7 shown. Among them, the schematic diagram of the source road data is as Figure 7 (a) shown in Figure 7 the schematic diagram of constructing Delaunay triangles is as Figure 7 (b) shown in Figure 7 the schematic diagram of extracting the centerline according to the Delauany triangulation network is as Figure 7 (c) shown in

[0118] Furthermore, the effect diagram of the road centerline is as Figure 8 shown, where the dark line is the original double-line road and the light line is the extracted road centerline.

[0119] Step 2, road fusion and network construction.

[0120] In some embodiments, heterogeneous data is integrated through a unified mathematical basis, and the original layer source information is retained. To meet the requirements of the constraint surface weight graph theory road network selection model, multi-source road feature classes need to be processed for fusion and network construction.

[0121] Specifically, the process of automatically realizing road fusion and network construction includes: (1) Generate an empty feature class with all the road layer feature classes to be subjected to network generalization as templates (mathematical basis and corresponding fields); (2) Traverse different layer feature classes one by one, add the corresponding features to the newly created empty feature class, and match and update the corresponding attribute information; (3) Create a new FROMLAYER field and write the road feature source layer name value into it.

[0122] It can be understood that the main purpose of doing this is to track the source of the fused features during the later comprehensive processing, ensure that each feature in the fused road feature class has a corresponding layer source flag, and be able to accurately and reasonably integrate the attribute values of the features when performing geometric processing on the road data in the later stage; at the same time, the corresponding geometric original state of the road features after generalization can be restored according to the FROMLAYER field, and the road features can be classified and extracted through the attribute value of this field, and then reasonable symbolization operations can be performed on the road feature class after the comprehensive operation.

[0123] Exemplarily, the schematic diagram of road integration network construction is as Figure 9 shown.

[0124] Step 3: Road break chain processing.

[0125] In some embodiments, in order to meet the requirements of the road network selection model based on the constrained surface weight graph theory, the boundary lines of the merged multi-source road network meshes are processed for break chains, and the corresponding two road elements at the intersection points are interrupted.

[0126] Exemplarily, the schematic diagram of road break chain processing is as Figure 10 shown.

[0127] Step 4: Road overlapping line deletion.

[0128] In some embodiments, a line element buffer surface is generated based on topological tolerance, and the overlapping elements completely contained therein are identified through spatial queries, and the overlapping elements are deleted.

[0129] Step 5: Road break point processing.

[0130] In some embodiments, the corresponding roads with pseudo nodes or the roads with non-existing pseudo nodes interrupted are connected at the break points to form a new road network line element class, and at the same time, the attribute values of the connected road networks are integrated.

[0131] Specifically, for the road segments with pseudo nodes, geometric connection and attribute value consistency processing are performed; for the roads where the break points are not pseudo nodes, corresponding geometric connections are made, and the attribute value of the new road element after connection takes the average value of the numerical attribute values of the two old roads before merging or the same value of the non-numerical attribute values. If the attribute values of the corresponding fields of the two old roads are different and non-numerical, then both attribute values are written into the newly merged element, and they are distinguished by this ID number and separated by ",". During the process of connecting the road break points, the newly established elements are geometrically processed and tracked using the newly created field attribute values, and the ID numbers of the roads before merging are recorded. After the integration is completed, re-breaking and attribute value processing are performed.

[0132] Exemplarily, the flowchart of pseudo node detection and automatic link processing is as Figure 11 shown, and the schematic diagram of road break point connection is as Figure 12 shown.

[0133] Step 6: Mesh boundary information extraction.

[0134] In some embodiments, the method of constructing road network meshes is to convert road line elements into road network mesh surfaces. The specific steps are as follows: 1) Use the DataManagementTools.FeatureToPolygon interface tool in ArcEngine to convert the road line feature class to obtain the road mesh surface feature class; 2) Add boundary BOUNADRY, area AREA, and road mesh density DENSITY fields to the newly generated road mesh surface feature class: 3) Traverse the generated road mesh surfaces, calculate the area values of the corresponding road meshes, and store them in the AREA field attribute values; 4) Use ArcEngine geometric feature attributes to extract the boundary lines of the road meshes, perform buffer analysis on the boundary lines of the road meshes, and generate buffer surface features; 5) Perform overlay analysis on the boundary lines of the buffer surface features and the road mesh line feature class to find the road mesh boundary lines corresponding to each mesh surface feature, and store the IDs of the road boundary lines of the corresponding road meshes in the BOUNADRY field attribute values to facilitate the tracking and updating of the road mesh boundary lines during the mesh merging operation in the graph theory model in the later stage; 6) Calculate the road density of the road meshes and store the corresponding calculated values in the DENSITY field attribute values.

[0135] Step 7, extraction of hanging road information.

[0136] It should be noted that the structured object of the road mesh topological relationship includes not only the road meshes and their boundary lines, but also the hanging roads inside and outside the road meshes. The hanging lines of the roads are divided into the hanging lines inside the road meshes and the hanging lines outside the road meshes. According to the distribution characteristics of the actual roads, the peripheral hanging roads mainly refer to the outermost boundary roads in the comprehensive area, which are mainly bridges connecting the road networks in the comprehensive area and other area ranges; the selection of the hanging lines inside the meshes is mainly determined by the road length, grade, and the line density of the road mesh lines.

[0137] In some embodiments, the extraction steps of the outermost hanging lines of the meshes are as follows: first merge all the road meshes; then find all the road lines outside the boundaries of the merged road meshes through spatial analysis, and determine these roads as the peripheral hanging roads of the meshes. The main process of extracting the hanging roads inside the meshes: traverse the road meshes, use the spatial query interface of AE to find the roads falling inside the meshes, and the found roads are the hanging lines inside the meshes.

[0138] Step 8, integration of road attribute values.

[0139] In some embodiments, a new feature class is used to store the processing results. A new FROM field is created to track the features in the comprehensive processing process, record the source information of the features, establish the ID relationship between the features of the new feature class and the original features, and achieve dynamic integration through attribute mapping rules. Among them, the numerical attributes take the average value, and the non-numerical attributes are stored in combination.

[0140] Step 9, road centerline extraction.

[0141] In some embodiments, the road centerline is re-extracted after the integration of road attribute values.

[0142] Step 10, construction of the structured topological model of the road mesh.

[0143] In some embodiments, the topological model constructs the topological relationship formed by the boundary lines of the road mesh and the hanging roads.

[0144] Exemplarily, the topological model is represented in the form of an attribute table. Figure 13 It is a schematic diagram of the structured information representation of the road mesh boundary line and the hanging line. Figure 14 It is a schematic diagram of the structured information representation of the road mesh. Among them, Shape represents the geometric shape of the road mesh; Area represents the area of the road region; Boundary represents the boundary line of the road mesh region; Dangle represents the unconnected or isolated line segments in the road mesh; Density represents the density of the road mesh, reflecting the distribution of roads in a unit area.

[0145] Furthermore, the road mesh structure diagram is as Figure 15 shown. Among them, the gray surface represents the road mesh, the light-colored lines represent the boundary roads of the road mesh and the hanging roads outside the road mesh, and the dark-colored lines represent the hanging roads inside the road mesh.

[0146] Step 11, selection of characteristic roads for marking.

[0147] a. Selection is made according to the semantic constraint method of attribute values.

[0148] Using the graph theory model to select roads focuses more on considering geometric graphic relationships. However, roads are different from geographical line elements such as contour lines. They are line elements with rich attribute data. The traditional graph theory model for road selection can be optimized by using characteristic road constraints. Therefore, a method for selecting characteristic roads according to the semantic constraints of attribute values is generated. The specific way of function implementation is to input relevant road names or keywords for road selection, or use SQL conditional query statements to query and then select roads with specific semantics.

[0149] b. Selection is made according to the spatial position relationship constraint method.

[0150] Select roads according to the constraints of spatial position relationship. This selection method mainly uses spatial analysis methods, loads relevant road appurtenance element layers for spatial judgments such as proximity and inclusion, marks relevant roads as characteristic roads, and conducts selection. For example, load ferry point data, conduct proximity analysis query, and mark the roads connected to the ferry as characteristic roads to achieve the purpose of selection.

[0151] c. Select visually by manual interaction click.

[0152] Select visually by manual interaction click. This selection method mainly aims at the situation where using the graph theory model for selection may cause some roads to be discontinuous. If this situation occurs, the comprehensive result can be judged. If it is found that there are such discontinuous road situations, relevant roads can be re-selected as characteristic road segments by manual interaction click.

[0153] Step 12, Select hanging roads.

[0154] In some embodiments, control and select all hanging roads according to the minimum road length threshold, find the roads with lengths less than the threshold, and at the same time judge whether they are characteristic roads. If so, retain them; otherwise, directly discard them. Optionally, for the hanging lines inside the road mesh, according to the shortest road length expression conditions of the corresponding mapping specifications of the comprehensive target scale (for example, according to the regulations of the national basic scale map compilation specifications, the minimum expression length of the road map on the 1:25,000 topographic map is 1 cm, that is, the actual road length is 250 m), delete the road lines that are less than the specified length threshold and are not characteristic roads.

[0155] Furthermore, conduct road mesh line density judgment. According to the mapping specifications, for the road meshes with line densities greater than the specified line density threshold, find the hanging roads with the lowest level and the shortest length inside them. If the found hanging roads are not characteristic roads, mark them for deletion. For the hanging line roads outside the road mesh, considering that these road elements are the bridges mainly connecting the road network of the comprehensive area and the road network of another area, all these roads are retained during the selection process.

[0156] Step 13, Select the boundary lines of the road mesh.

[0157] In some embodiments, according to the road mesh line density threshold and the comprehensive target quantity of the roads, discard the non-characteristic roads with the lowest level and the shortest length. Extract all road meshes within the comprehensive area, re-sort them from small to large according to the area, and find the road mesh with the smallest area.

[0158] Further, extract all the roads that make up the smallest-area road network meshes, find one or several roads with the lowest road grade. If there is only one road with the lowest grade, determine whether it is a characteristic road. If it is not a characteristic road, discard this road; otherwise, continue to search for roads with low grade and short length. If the number is greater than 1, find the road with the shortest length, and at the same time determine whether it is a characteristic road. If it is, retain it; otherwise, discard it.

[0159] Step 14, pseudo-node detection and elimination.

[0160] In some embodiments, traverse each road. If the attributes of the current road and the adjacent road are the same, it is considered that the node between these two roads is a pseudo-node, and then connect these two roads.

[0161] Step 15, road break point recovery.

[0162] In some embodiments, merge the meshes on both sides of the discarded road; update the topological structure information of the meshes. Further, calculate the number of remaining roads. If the target value is reached, exit; otherwise, go to the step of "extract all road network meshes in the comprehensive area and re-sort them from small to large in terms of area, and find the road network mesh with the smallest area".

[0163] Exemplarily, schematic diagrams of road network data at various scales are as Figure 16 shown. Among them, the schematic diagram of the original road network data at 1:1000 is as Figure 16 shown in (a) of Figure 16 ; the schematic diagram of the extracted road center line data at 1:1000 is as Figure 16 shown in (b) of Figure 16 ; the schematic diagram of the comprehensive road network data at 1:10000 is as Figure 16 shown in (c) of Figure 16 ; the schematic diagram of the comprehensive road network data at 1:25000 is as

[0164] shown in (d) of

[0165] ; the schematic diagram of the comprehensive road network data at 1:50000 is as

[0166]

[0167] Among them, the schematic diagram of the positional relationship between the road after simplification and the original double-line road before generalization is as follows Figure 17 shown, and the enlarged schematic diagram of the positional relationship between the road after simplification and the original double-line road before generalization is as follows Figure 18 shown. Among them, Figure 18 it includes double-line roads and single-line roads obtained after centerline extraction, road selection, and simplification.

[0168] Exemplarily, the attribute table of the road network with a scale of 1:10000 obtained after road selection and road simplification of the local urban road network with a scale of 1:1000 is as follows Figure 19 shown, and the schematic diagram of the comparison of road attribute data before and after the generalization of the local urban road network with a scale of 1:1000 to a road network with a scale of 1:10000 obtained after road selection and road simplification is as follows Figure 20 shown. Among them, the annotation of control 1 is the road name of the corresponding single-line element, and the annotation of control 2 is the road name of the corresponding simplified single-line road.

[0169] Specifically, the flow chart of the implementation of the constraint-based face weight graph theory road network selection model is as follows Figure 21 shown. Among them, first, the road line elements and road network eye surface elements are obtained, then the generalization parameters are set, the selection of characteristic roads is marked, the road network eyes are traversed sequentially, the lowest-level hanging line in the eye is found, and it is judged whether the length of the corresponding hanging line is less than the generalization threshold. If so, the road element is retained, and the lowest-level hanging line in the eye is searched again. If not, the road mark is deleted.

[0170] Furthermore, it is judged whether the number of remaining roads is greater than the square root model generalization threshold. If so, the road network eyes are traversed sequentially, the line density of the road network is calculated, and it is judged whether the remaining line density is greater than the line density generalization threshold. If so, the lowest-level and shortest-length hanging line is found, and it is judged whether the hanging line is a characteristic road. If so, the road element is retained, and the lowest-level hanging line in the eye is searched again. If not, the road mark is deleted.

[0171] After that, it is judged whether the remaining number is greater than the square root model generalization threshold. If so, the road network eye with the smallest area is found, then the boundary line of the road network eye surface with the smallest area is found, and it is judged whether the number of the lowest-level edges is greater than 1. If not, the road mark is deleted. If so, the shortest edge is found among the lowest-level edges, and it is judged whether the shortest edge is a characteristic road. If so, the road element is retained, and the lowest-level mesh boundary line is searched again. If not, the road mark is deleted.

[0172] Further, search for another merged adjacent road network line, merge the two network lines to obtain a new feature network line for storage, improve the attribute information such as the boundary line between the new surface and the old surface, and search for the starting point of the boundary line to be deleted. Determine whether the starting point is a graph judgment point. If so, connect the graph judgment line at the starting point to integrate the attribute information, update the boundary information of the merged road network line, and then search for the ending point of the boundary line to be deleted. If not, directly search for the ending point of the boundary line to be deleted.

[0173] After that, determine whether the ending point is a graph judgment point. If so, connect the graph judgment line at the ending point, integrate the attribute information, update the boundary information of the merged road network line, and then determine whether the remaining number of roads is greater than the square root model comprehensive threshold. If not, directly determine whether the remaining number of roads is greater than the square root model comprehensive threshold. If the remaining number of roads is less than or equal to the square root model comprehensive threshold, the road comprehensive selection ends. Otherwise, return to the step of searching for the road network line with the smallest area.

[0174] The road network selection comprehensive model in the embodiment of the present application is a reasonable and effective comprehensive model. Practice shows that: this algorithm better takes into account the overall characteristics of the road network, and the network characteristics are well maintained; the selection using characteristic road constraints during the selection process maintains good connectivity for high-level roads, which improves the disadvantage that the traditional graph theory model for road selection focuses more on geometric figure comprehensive constraint conditions and less on the influence of road attribute values and other geographical elements on road selection comprehensive; at the same time, the road network mesh density constraint is considered during the selection process, so the difference in road network mesh density is also well maintained after selection; for the selection part of road synthesis, using the program of this system can reduce the workload by 80% to 90% compared with manually making a comprehensive map, and at the same time avoid human errors in manual operations.

[0175] Guided by the road network comprehensive principle, the embodiment of the present application analyzes the network structure characteristics of the road network, selects reasonable road automatic synthesis operators and comprehensive models, realizes the road network comprehensive system through system design, conducts road comprehensive example verification, and analyzes and evaluates the road comprehensive quality. The network structure characteristics of the road network after synthesis and the difference in road network line density are reasonably maintained. The data quality such as the correctness of the topological relationship between roads, the integrity of attribute values, and geometric accuracy is good. The road network comprehensive system has achieved good results in terms of adaptability, comprehensive efficiency, and automation degree. The road network comprehensive system has certain application value and can provide reference for the further research of road network synthesis in the future.

[0176] Please refer to Figure 22 , the embodiment of the present application also provides a road network automatic synthesis device, which can implement the above road network automatic synthesis method. The device includes: A marking module 2201 is configured to mark characteristic roads based on initial road network structured information, where the initial road network structured information includes road network mesh boundary lines and road network mesh suspension lines; A first integration module 2202 is configured to delete road network mesh suspension lines in the initial road network structured information that are less than the minimum road length threshold and do not belong to the characteristic roads, so as to obtain the first deleted road network structured information; A second integration module 2203 is configured to mark first candidate roads based on the initial mesh structured information, the first road network structured information, and the road network line density threshold, and delete the first candidate roads with the lowest road grade and not belonging to the characteristic roads in the first road network structured information, so as to obtain the second deleted road network structured information, where the initial road network structured information includes road grade information and road length information; A third integration module 2204 is configured to mark second candidate roads based on the initial mesh structured information and the second road network structured information, and delete the second candidate roads with the lowest road grade and not belonging to the characteristic roads in the second road network structured information, so as to obtain the third deleted road network structured information; A merging module 2205 is configured to perform mesh merging based on the third road network structured information, so as to obtain the target mesh structured information and the target road network structured information.

[0177] An embodiment of the present application further provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the above-mentioned road network automatic integration method is implemented. The electronic device can be any intelligent terminal including a tablet computer, an in-vehicle computer, etc.

[0178] Please refer to Figure 23 , Figure 23 which schematically shows the hardware structure of an electronic device in another embodiment. The electronic device includes: A processor 2301, which can be implemented in a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is configured to execute relevant programs to implement the technical solutions provided by the embodiments of the present application; The memory 2302 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM), etc. The memory 2302 can store an operating system and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 2302 and are called by the processor 2301 to execute the automatic road network synthesis method of the embodiments of this application; The input / output interface 2303 is used to implement information input and output; The communication interface 2304 is used to implement communication interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or through wireless means (such as mobile network, WIFI, Bluetooth, etc.); The bus 2305 transmits information between various components of the device (such as the processor 2301, the memory 2302, the input / output interface 2303, and the communication interface 2304); Among them, the processor 2301, the memory 2302, the input / output interface 2303, and the communication interface 2304 are communicatively connected to each other inside the device through the bus 2305.

[0179] The embodiments of this application also provide a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the automatic road network synthesis method.

[0180] It can be understood that the content in the above method embodiments is applicable to the device embodiments, the equipment embodiments, and the storage medium embodiments. The functions specifically implemented by this storage medium embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those of the above method embodiments.

[0181] As a non-transitory computer-readable storage medium, the memory can be used to store non-transitory software programs and non-transitory computer-executable programs. In addition, the memory can include high-speed random access memory, and can also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory optionally includes a memory remotely disposed relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above networks include, but are not limited to, the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0182] The preferred embodiments of the embodiments of the present application have been described above with reference to the accompanying drawings, which does not limit the scope of the rights of the embodiments of the present application. Any modifications, equivalent replacements, and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of the present application shall be within the scope of the rights of the embodiments of the present application.

Claims

1. An automatic generalization method for road networks, characterized in that The method includes the following steps: Mark characteristic roads based on initial road network structured information, where the initial road network structured information includes road network mesh boundary lines and road network mesh suspension lines; Delete the road network mesh suspension lines in the initial road network structured information that are less than the minimum road length threshold and do not belong to the characteristic roads, to obtain the first road network structured information after deletion; Mark first candidate roads based on the initial mesh structured information, the first road network structured information, and the road network eye line density threshold, and delete the first candidate roads in the first road network structured information that have the lowest road grade and do not belong to the characteristic roads, to obtain the second road network structured information after deletion. The initial road network structured information includes road grade information and road length information; Mark second candidate roads based on the initial mesh structured information and the second road network structured information, and delete the second candidate roads in the second road network structured information that have the lowest road grade and do not belong to the characteristic roads, to obtain the third road network structured information after deletion; Perform mesh merging based on the third road network structured information to obtain target mesh structured information and target road network structured information.

2. The method according to claim 1, characterized in that, The marking of characteristic roads based on the initial road network structured information includes one of the following: Use a graph theory model to select and mark characteristic roads based on the initial road network structured information through the semantic constraint method of attribute values; Alternatively, use a spatial analysis method to analyze the initial road network structured information to select and mark characteristic roads; Alternatively, in response to a first instruction, display the initial road network structured information on a map page, and in response to a road selection instruction, display the characteristic roads selected by the user on the map page.

3. The method according to claim 1, wherein The road network mesh suspension lines include internal road network mesh suspension lines and external road network mesh suspension lines. The deletion of the road network mesh suspension lines in the initial road network structured information that are less than the minimum road length threshold and do not belong to the characteristic roads to obtain the first road network structured information after deletion includes: Retain the external road network mesh suspension lines in the initial road network structured information; Delete the internal road network mesh suspension lines in the initial road network structured information that are less than the minimum road length threshold and do not belong to the characteristic roads, to obtain the first road network structured information after deletion.

4. The method according to claim 1, wherein The marking of first candidate roads based on the initial mesh structured information, the first road network structured information, and the road network eye line density threshold, and the deletion of the first candidate roads in the first road network structured information that have the lowest road grade and do not belong to the characteristic roads to obtain the second road network structured information after deletion. The initial road network structured information includes road grade information and road length information includes: Calculate the current road network eye line density of each mesh based on the initial mesh structured information and the first road network structured information; Determine the meshes with the current road network eyeliner density greater than the road network eyeliner density threshold as candidate meshes, mark the roads in the candidate meshes as the first candidate roads, and use the first candidate road with the lowest road grade as the first preselected road; If the first preselected road only includes a single road and the first preselected road does not belong to the feature road, delete the first preselected road from the first road network structured information to obtain the second road network structured information after deletion; If the first preselected road includes multiple roads, determine the road with the shortest length in the first preselected road as the first selected road. If the first selected road does not belong to the feature road, delete the first selected road from the first road network structured information to obtain the second road network structured information after deletion.

5. The method according to claim 1, characterized in that The initial mesh structured information includes the mesh area. Marking the second candidate roads based on the initial mesh structured information and the second road network structured information includes: Determine the road network mesh with the smallest mesh area based on the initial mesh structured information; Based on the second road network structured information, mark the roads that make up the road network mesh with the smallest mesh area as the second candidate roads.

6. The method according to claim 1, wherein The mesh merging based on the third road network structured information to obtain the target mesh structured information and the target road network structured information includes: Perform mesh merging based on the third road network structured information to obtain the current mesh structured information and the current road network structured information after mesh merging; Obtain the target road quantity and calculate the current road quantity of the current road network structured information; If the current road quantity is greater than the target road quantity, use the current road network structured information as the second road network structured information, use the current mesh structured information as the initial mesh structured information, and return to the step of marking the second candidate roads based on the initial mesh structured information and the second road network structured information until the current road quantity is less than or equal to the target road quantity; If the current road quantity is less than or equal to the target road quantity, use the current mesh structured information as the target mesh structured information and the current road network structured information as the target road network structured information.

7. The method according to claim 1, wherein The method further includes: Obtain the source road data; Extract the road centerlines based on the source road data through the Delaunay triangulation method; Fuse the road centerlines of multiple road layers to obtain the fused road data; Perform chain breaking, overlapping line deletion, and road breakpoint processing on the fused road data to obtain the original road information; Convert the road line elements into road network mesh surfaces based on the original road information, and determine the road network meshes and the road network mesh boundary lines; Determine the internal hanging lines and external hanging lines of the road network meshes based on the original road information and the road network meshes; Construct a topological relationship model based on the original road information, the road mesh, the road mesh boundary line, the internal suspension line of the road mesh, and the external suspension line of the road mesh. The topological relationship model includes the initial road network structured information and the initial mesh structured information.

8. An automatic generalization device for road networks, characterized in that, The device includes: A marking module for marking characteristic roads based on the initial road network structured information, where the initial road network structured information includes the road mesh boundary line and the road mesh suspension line; A first integration module for deleting the road mesh suspension lines in the initial road network structured information that are less than the road minimum length threshold and do not belong to the characteristic roads, to obtain the first road network structured information after deletion; A second integration module for marking first candidate roads based on the initial mesh structured information, the first road network structured information, and the road mesh line density threshold, and deleting the first candidate roads in the first road network structured information that have the lowest road grade and do not belong to the characteristic roads, to obtain the second road network structured information after deletion. The initial road network structured information includes road grade information and road length information; A third integration module for marking second candidate roads based on the initial mesh structured information and the second road network structured information, and deleting the second candidate roads in the second road network structured information that have the lowest road grade and do not belong to the characteristic roads, to obtain the third road network structured information after deletion; A merging module for performing mesh merging based on the third road network structured information to obtain the target mesh structured information and the target road network structured information.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the method according to any one of claims 1 to 7.

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