Hybrid road network planning method and device for post-disaster rescue scene

Abstract

The invention discloses a mixed road network planning method and device for post-disaster rescue, and belongs to the technical field of path planning, and the method mainly comprises the steps: extracting a post-disaster feasible city road network; a cross-country area range is limited, a rasterized road network containing cross-country information is generated, and a feasible cross-country area road network is extracted; and the existing structured road network information and the off-road road network are comprehensively considered, and multi-target road network calculation is performed according to complex road network attributes, so that the traveling trajectory planning of the mixed road network is realized. The method solves the problem that an existing vehicle road network planning algorithm is difficult in road network planning of urban road-cross-country mixed road network factors in post-disaster scenes, and ensures that rescue vehicles can quickly and safely arrive at a target location under post-disaster complex geographical conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of path planning, and particularly to a hybrid road network planning method for post-disaster rescue. Background Art

[0002] The rescue work after natural disasters faces huge challenges. In the post-disaster scenario, some existing road networks are damaged and in an impassable or restricted passage state. Conventional road network-based planning methods are prone to problems where the planned path is infeasible. At the same time, they cannot consider the driving conditions required to cross off-road environments and cannot meet the urgent needs of rescue vehicles for rapid mobility. Structured road networks have stable road attributes, high vehicle passing speeds, and high passenger comfort, and have a higher passing priority. In unstructured off-road environments not covered by the road network, the terrain structure is complex and the step change in passability is obvious, but important maneuvering effects can be achieved in scenarios such as road damage, taking a quick shortcut, and avoiding risk areas. Therefore, how to effectively combine off-road road networks with urban road networks to form hybrid road network information and perform path planning based on this has become an urgent problem to be solved in the field of post-disaster rescue.

[0003] In the prior art, although some studies have focused on path planning in off-road environments, most of them focus on single environmental types and lack solutions that consider off-road and urban road networks together. At the same time, the efficiency and accuracy of path planning algorithms also need to be further improved to meet the urgent needs of post-disaster rescue. Summary of the Invention

[0004] The present disclosure provides a hybrid road network planning method for post-disaster rescue scenarios. Through steps such as rasterization processing of off-road scene maps, off-road range limitation and road network mapping, coupled road network information construction, application of path planning algorithms, and output of planned trajectories and visualization, the urban road network and off-road scenarios are coupled and planned to construct a hybrid road network information model, providing a more efficient and reliable path planning scheme for post-disaster rescue to ensure that rescue vehicles can quickly and safely reach the target location under complex post-disaster geographical conditions.

[0005] Specifically, the hybrid road network planning method for post-disaster rescue scenarios provided by the present disclosure includes the following steps:

[0006] Extraction of feasible urban road network: After receiving disaster relief location information, obtain the urban road network within the possible passing areas, update the road surface information attributes in the road network according to the actual post-disaster situation, and extract the feasible urban road network by combining the road surface attributes and the passability of rescue vehicles;

[0007] Feasible off-road road network extraction: After receiving the disaster relief location information, according to the actual situation after the disaster and combined with the existing urban road network data, manually define the boundary of the off-road range, use the grid method to map model the off-road environment, and divide the off-road road surface into multiple grids according to the DEM (Digital Elevation Model) digital elevation model and the roughness information of the surface attributes, and add the corresponding elevation values and ground attributes to each grid. The grid-based modeling can visually display the undulations and type differences of the off-road road surface, and extract the feasible off-road road network accordingly;

[0008] Integrate road network information: By setting a unified coordinate system and grid size, ensure the consistency and comparability of the off-road and urban road networks at the data level; Map the road network paths within the off-road range to virtual road network nodes and edges, and assign corresponding passing costs and attribute information, including road type, passing speed, length, etc.; Integrate the extracted off-road road network with the urban road network data to form a mixed road network information GraphML file;

[0009] Hybrid road network planning: Considering the impact of different ground types on the passing speed, dynamically adjust the path weights to generate the planned disaster relief path; Optimize the path planning method based on the Dijkstra algorithm.

[0010] Preferably, it further includes the following steps:

[0011] Output the planned trajectory and visualization: After the path is verified to be feasible, output the planned rescue path trajectory, including detailed node coordinates, path length, estimated passing time and other information;

[0012] When new disaster information is detected, automatically re-plan the path and generate a new disaster relief trajectory to ensure the continuity and effectiveness of the rescue operation.

[0013] A hybrid road network planning device for post-disaster rescue scenarios applying the above method, including:

[0014] Feasible urban road network extraction module: Used to extract the feasible urban road network according to the road surface conditions within the post-disaster road network and the passability of rescue vehicles;

[0015] Feasible off-road road network extraction module: Used to map model the off-road environment using the grid method based on the manually defined off-road range and extract the feasible off-road road network;

[0016] Road network information integration module: Used to map the road network paths within the off-road range to virtual road network nodes and edges based on a unified coordinate system and grid size, and integrate the extracted off-road road network with the urban road network data to form a mixed road network information GraphML file;

[0017] Hybrid road network planning module: It is used to perform path planning based on the constructed Hybrid road network GraphML file, utilize the optimal path algorithm, consider the impact of different ground types on the passing speed, dynamically adjust the path weights, and generate dynamic planned disaster relief paths.

[0018] Compared with the prior art, the beneficial effects of the present disclosure are as follows: ①Improve rescue efficiency: By integrating off-road scenarios and urban road networks, more comprehensive hybrid road network information is formed, providing more path options for post-disaster rescue and shortening the rescue time; ②Enhance path safety: Considering the complexity of the off-road environment and road damage conditions comprehensively, through rasterization processing and recording of elevation and ground attribute information, the safety and feasibility of the planned path are ensured; ③Achieve rapid response: Utilize the Dijkstra path planning algorithm to achieve rapid calculation and output of paths, providing strong support for post-disaster emergency response. Brief Description of the Drawings

[0019] By describing the exemplary embodiments of the present disclosure in more detail with reference to the accompanying drawings, the above and other objects, features, and advantages of the present disclosure will become more apparent. Among them, in the exemplary embodiment mode of the present disclosure, the same reference numerals generally represent the same components.

[0020] Figure 1 It is an exemplary flowchart of a hybrid road network planning method for post-disaster rescue scenarios according to the present disclosure;

[0021] Figure 2 It is a schematic diagram of a hybrid road network planning for post-disaster rescue scenarios. Detailed Description of the Embodiments

[0022] The preferred embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the preferred embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to make the present disclosure more thorough and complete, and to fully convey the scope of the present disclosure to those skilled in the art.

[0023] The present disclosure provides a hybrid road network planning method for post-disaster rescue scenarios to solve the problem of difficult road network planning in post-disaster scenarios for existing vehicle road network planning algorithms that involve factors of urban road-off-road hybrid road networks. Based on an adaptive path planning algorithm, it comprehensively considers the feasible regions of structured road networks and unstructured off-road environments to achieve the effect of rapid road network travel strategy planning that synthesizes off-road information in discontinuous road networks. The method includes four parts: extraction of feasible urban road networks, extraction of feasible off-road road networks, fusion of road network information, and solution calculation of hybrid road network planning; among them: the part of extracting feasible urban road networks dynamically updates the existing road network attribute information according to real-time feedback of the disaster-stricken area; the part of extracting feasible off-road road networks generates and corrects in real time a rasterized road network containing off-road information for the off-road area selected by the decision maker; the part of fusing road network information fuses urban and off-road road networks through scale and feature alignment to ensure their consistency at the data level for supporting hybrid road network planning; the part of road network planning solution calculation comprehensively considers the fused hybrid road network information and performs multi-objective road network solution calculation according to the complex road network attributes to achieve road network travel trajectory planning.

[0024] In an exemplary embodiment, the hybrid road network planning process for post-disaster rescue according to the present disclosure is as shown in the appendix Figure 1 and mainly includes the following steps:

[0025] 1. Extraction of feasible urban road networks:

[0026] In post-disaster rescue scenarios, it is first necessary to obtain and extract the urban road network information of the disaster area. Urban road networks usually include various infrastructure such as roads, blocks, bridges, etc. The extraction of feasible urban road networks mainly involves the following steps:

[0027] 1.1 Receiving disaster relief location information: During post-disaster rescue, the coordinates of the specific disaster relief location are first received through the disaster relief command center or the emergency management system. These coordinates are used to determine the core area where the path needs to be planned.

[0028] 1.2 Obtaining urban road network data: Obtain the urban road network information of the disaster area and its surrounding areas from existing urban planning data or real-time map databases. Save this information in the standardized GraphML file format, including the type, width, length, and speed limit attributes of the roads.

[0029] 1.3 Road surface information update: According to the actual situation after the disaster, update the road surface attributes of each road in the urban road network. For example, if road damage, closure, etc. occur due to earthquakes or floods, then adjust the corresponding attributes in the corresponding nodes and road edge information in the road network data GraphML file so that the damaged urban road network roads can be identified and avoided during subsequent path planning.

[0030] 1.4 Analysis of the Passability of Disaster Relief Vehicles: Combining the passability requirements corresponding to the types of dispatched disaster relief vehicles, considering factors such as the width, load-bearing capacity, and slope of the roads, filter all the road network edge attributes in the GraphML file to evaluate the passability of each road in the urban road network, so as to ensure that the extracted feasible road network can meet the driving requirements of disaster relief vehicles.

[0031] 1.5 Extracting the Feasible Urban Road Network: Based on the above analysis and evaluation, extract the roads that disaster relief vehicles can pass through in the current post-disaster situation to form the information of the feasible urban road network. This information will be used for subsequent coupling and integration with the off-road road network.

[0032] 2. Extraction of the Feasible Off-road Road Network

[0033] During the disaster relief process, the off-road scenario usually refers to the areas outside the coverage of the urban road network, such as mountainous areas, forests, rivers and other environments with complex terrains. Since the road conditions in these areas are complex and changeable, it is impossible to directly rely on the existing urban road network data. Therefore, the present invention extracts the feasible off-road road network through the following steps:

[0034] 2.1 Receiving the Disaster Relief Location Information: Similar to the extraction of the urban road network, first receive and confirm the geographical coordinates of the disaster relief location. These coordinates are used to define the boundaries of the off-road area.

[0035] 2.2 Manually Defining the Boundaries of the Off-road Area: According to the actual terrain conditions of the disaster area and the requirements of the rescue mission, the rescue experts manually define the scope of the off-road scenario that may need to be considered.

[0036] 2.3 Rasterization Modeling: Use the rasterization method to perform map modeling on the selected off-road scenario area. Specifically, divide the off-road area into multiple uniform raster cells, and each cell represents a surface area of a fixed size. The rasterization modeling simplifies the complex off-road environment into several manageable small cells, which is convenient for subsequent path planning and analysis.

[0037] 2.4 Adding Elevation Values and Ground Attributes: In each raster cell, record the elevation value and ground attribute information of the cell. Such information is obtained from the Digital Elevation Model (DEM) and includes the height, slope of the terrain, and the roughness of the ground surface, etc.

[0038] 2.5 Extracting the Feasible Off-road Road Network: According to the elevation and ground attribute information in the rasterization model, combined with the off-road capabilities of the rescue vehicles, exclude the areas that are not suitable for vehicle passage due to overly steep terrain or overly soft ground, and only retain the suitable driving paths. The information of these paths will be mapped to virtual road network nodes and edges, and corresponding passing costs will be assigned to generate the extracted feasible off-road paths.

[0039] 3. Integrating Road Network Information:

[0040] Realize the coupling of the urban road network and the off-road road network, and construct a hybrid road network. The road network integration is realized through the following steps:

[0041] 3.1 Unify the coordinate system and grid size: Before integrating the urban road network and the off-road road network, it is first necessary to ensure the consistency of the two at the data level. Specifically, by setting a unified geographic coordinate system and grid size, ensure that the urban road network and the off-road road network can be superimposed and compared on the same plane.

[0042] 3.2 Map the off-road road network: Map the extracted off-road road network paths into virtual road network nodes and edges. During the mapping process, the off-road paths in each grid cell are represented as one or more nodes, which are connected by edges. The attributes of the edges include road type, passing speed, path length, etc., and different passing costs are assigned according to different ground types.

[0043] 3.3 Integrate the urban road network and the off-road road network: Integrate the above-generated off-road road network data with the extracted urban road network data to form complete hybrid road network information. The integration process includes connecting the virtual nodes in the off-road road network with adjacent urban road network nodes, and ensuring that the attribute information of each path, such as road type, passing speed, etc., is retained during the connection process.

[0044] 3.4 Generate a hybrid road network file: Save the integrated hybrid road network data as a new GraphML file. This file contains all the nodes and edges of the urban road network and the off-road road network, and retains the detailed attribute information of each path, which is used as the basic data input for subsequent path planning.

[0045] 4. Hybrid road network planning:

[0046] After obtaining and integrating the urban road network and off-road road network information, enter the path planning stage. In this embodiment, a hybrid road network planning method based on the optimal path algorithm is used, which considers the influence of different ground types on the passing speed, dynamically adjusts the path weights, and generates the optimal disaster relief path.

[0047] 4.1 Selection of path planning algorithm: To ensure the efficiency and accuracy of path planning, an optimal path planning method based on the Dijkstra algorithm is adopted. This algorithm gradually expands the starting node until the target node is found, and selects the path with the minimum cost during this process.

[0048] 4.2 Influence of ground type: In a mixed road network, since urban roads and off-road roads have different traffic conditions, the path weight needs to be adjusted dynamically during the path planning process. For example, urban roads are usually flatter and vehicles travel faster, while off-road roads may reduce the vehicle's travel speed due to large terrain undulations and high road roughness. Therefore, during the path planning process, the system will dynamically adjust the speed according to the ground type of each path, thereby affecting the total weight of the path. In this process, the relevant weights are determined based on the node and edge attributes in the GraphML file.

[0049] 4.3 Generate disaster relief path: Based on the consideration of all path weights, the optimal path algorithm is used to generate the optimal disaster relief path. The path should shorten the travel time as much as possible and ensure driving safety while meeting the vehicle traffic requirements. The generated path includes a detailed node sequence from the starting point to the end point, and contains the path attribute information between each node.

[0050] 4.4 Path verification: After the path is generated, the rescue experts will determine whether the selected path meets the actual traffic requirements of the rescue vehicle. If not, the rescue path will be removed from the search and rescue road network results, and the error points will be removed from the fused road network.

[0051] 5. Output planning trajectory and visualization:

[0052] After the path planning is completed, the generated disaster relief path is output in a standardized format, including detailed node coordinates, path length, estimated travel time and other information. It is sent to the disaster relief command system to guide the driving of rescue vehicles. An example of the planned path is shown in the attached figure. Figure 2 shown.

[0053] When new disaster information is detected, the system will automatically replan the route and generate a new disaster relief trajectory to ensure the continuity and effectiveness of the rescue operation.

[0054] This paper realizes the intelligent and automated post-disaster rescue route planning by coupling the urban road network with the off-road scenario. This method has significant advantages in dealing with complex terrain and changing post-disaster environments:

[0055] 1. Improve rescue efficiency: By integrating off-road scenarios with urban road networks, the system can provide more path options, helping rescue vehicles reach disaster areas more quickly and shortening rescue time.

[0056] 2. Enhanced path safety: Taking into account the complexity of the off-road environment and road damage, the system can generate safe and reliable disaster relief paths to prevent vehicles from getting stuck in dangerous and inaccessible areas.

[0057] 3. Achieving rapid response: By utilizing the optimal path algorithm and the hybrid road network model, the system can quickly generate and output disaster relief paths, providing strong support for emergency response.

[0058] In summary, the hybrid road network planning method provided by the present disclosure has broad application prospects in post-disaster rescue scenarios and can significantly improve the efficiency and safety of rescue operations. Through this method, the rescue command center can dynamically adjust path planning according to real-time situations to ensure that rescue vehicles can reach the disaster area in the shortest time and complete rescue tasks.

[0059] The above technical solutions are only exemplary embodiments of the present invention. For those skilled in the art, based on the disclosed application methods and principles of the present invention, it is easy to make various types of improvements or deformations, not limited to the methods described in the above specific embodiments of the present invention. Therefore, the foregoing description is only preferred and does not have a restrictive meaning.

Claims

1. A hybrid road network planning method for post-disaster rescue scenarios, comprising the following steps: S1, Extracting the feasible urban road network: Extracting the feasible urban road network according to the road surface conditions in the post-disaster road network and the passability of rescue vehicles; S2, Extracting the feasible off-road road network: Manually defining the off-road range, using the grid method to map the off-road environment, and extracting the feasible off-road road network; S3, Integrating road network information: Setting a unified coordinate system and grid size, mapping the road network paths within the off-road range to virtual road network nodes and edges, and integrating the extracted off-road road network and urban road network data to form a hybrid road network information GraphML file; S4, Hybrid road network planning: Based on the constructed hybrid road network GraphML file, using the optimal path algorithm for path planning, and considering the impact of different ground types on the passing speed, dynamically adjusting the path weights to generate a dynamic planned rescue path.

2. The method according to claim 1, wherein The specific steps of step S1 include: Obtaining the coordinate information of the disaster relief location; Obtaining urban road network data and saving the data in the standardized GraphML file format, which contains road attribute information; According to the actual situation after the disaster, updating the road surface attributes of each road in the urban road network; Combined with the passability requirements corresponding to the rescue vehicle type, evaluating the passability of each road in the urban road network; Based on the above analysis and evaluation, extracting the roads that rescue vehicles can pass through in the current post-disaster situation to form feasible urban road network information.

3. The method according to claim 1, wherein The specific steps of step S2 include: Manually defining the range of off-road scenarios that may need to be considered by rescue experts; Using the rasterization method to map the selected off-road scenario area: Dividing the off-road area into multiple uniform grid cells, and each cell represents a surface area of a fixed size; In each grid cell, recording the elevation value and ground attribute information of the cell; According to the elevation and ground attribute information in the rasterization model, combined with the off-road ability of the rescue vehicle, excluding the areas that are not suitable for vehicle passage and only retaining the suitable driving paths as the feasible off-road paths.

4. The method according to claim 1, wherein The specific steps of step S3 include: Unifying the coordinate system and grid size of the urban road network and the off-road road network; Mapping the extracted off-road road network paths to virtual road network nodes and edges, and the attributes of the edges include road type, passing speed, and path length, and assigning corresponding passing costs to the edges according to different ground types; Integrating the generated off-road road network data with the extracted urban road network data to form complete hybrid road network information, where the integration process includes: Connecting the virtual nodes in the off-road road network with adjacent urban road network nodes, and retaining the attribute information of each path during the connection process; Saving the integrated hybrid road network data as a new GraphML file.

5. The method according to claim 1, characterized in that The specific steps of step S4 include: Adopting the optimal path planning algorithm based on Dijkstra; During the path planning process, dynamically adjusting the path weights according to the impact of the ground type of each path on the passing speed; Based on considering all path weights, using the optimal path planning algorithm to generate the optimal rescue path; After the path is generated, it is judged according to expert experience whether the selected path meets the actual passing requirements of the rescue vehicle. If not, the rescue path is removed from the search and rescue road network results, and the error points are removed from the integrated road network.

6. The method according to any one of claims 1-5, further comprising: Steps of outputting and visualizing the planned trajectory after verification and feasibility, and steps of re-planning and generating a new disaster relief trajectory when new disaster information is obtained.

7. A hybrid road network planning device for post-disaster rescue scenarios applying any of the methods described in claims 1-6, characterized in that, Comprising: Feasible urban road network extraction module: used to extract the feasible urban road network according to the road surface conditions in the post-disaster road network and the passability of the rescue vehicle; Feasible off-road road network extraction module: used to map the off-road environment based on the manually defined off-road range by using the grid method, and extract the feasible off-road road network; Road network information fusion module: used to map the road network paths within the off-road range into virtual road network nodes and edges based on a unified coordinate system and grid size, and fuse the extracted off-road road network and urban road network data to form a mixed road network information GraphML file; Mixed road network planning module: used to perform path planning by using the optimal path algorithm based on the constructed mixed road network GraphML file, and consider the influence of different ground types on the passing speed, and dynamically adjust the path weights to generate a dynamic planned disaster relief path.

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