Dynamically editable two-layer topology modeling method for sparse road networks under disaster events

Abstract

The present invention discloses a method for dynamically editing a double-layer topological structure modeling of a sparse road network under the influence of a disaster event, comprising a road section element information table, a node element information table, and a disaster event element information table; constructing a sparse road network trunk layer topological structure model GC; constructing a sparse road network local layer road network topological structure model GL; constructing a sparse road network double-layer topological structure model G; establishing editing rules for the sparse road network double-layer topological structure model under the influence of a typical disaster event; the present invention provides a refined road network description by decomposing the road network into a trunk layer and a local layer, and clarifying the definitions of key nodes and edges, combining complex network directed graph theory and attribute-topological association methods, and is particularly suitable for the structural characteristics of sparse road networks; this method can more accurately reflect the characteristics of low road density and wide distribution of key nodes, improves the accuracy and effectiveness of emergency rescue path planning, and is conducive to playing an important role in disaster emergency rescue in areas with sparse road networks.

Description

Technical Field

[0001] The present invention relates to the technical field of digital transportation system, and in particular to a method for dynamically editing a double-layer topological structure modeling of a sparse road network under the influence of a disaster event. Background Art

[0002] Against the backdrop of intensified climate change, the uncertainties and unknown risks faced by the transportation system are increasing. Especially in remote and rural areas, due to geological and climatic conditions, the road network is usually sparse (small traffic volume, low road density, wide distribution of key nodes, etc.), and this sparse road network is the only way to achieve regional connectivity, provide emergency services and support economic activities. At the same time, due to bad weather (dense fog, heavy snow, icy roads), natural disasters (such as mudslides, landslides, earthquakes, etc.), etc., the road sections / key nodes in the sparse road network will be randomly destroyed, and there will be complete interruptions and one-way traffic. The road network topology is quite different from that of conventional sections. At the same time, it is often necessary to carry out traffic control or even road closures within a certain range and for a certain period of time for the temporary state caused by sudden disasters, and traffic will be restored after the temporary state ends, that is, the road network topology often changes in a local range and for a limited period of time. Therefore, it is urgent to propose a road network model that can accurately reflect the structural characteristics of sparse road networks and the dynamic changes of road network status under disaster events, and provide a model basis for sparse road network traffic status assessment, emergency rescue path planning, and traffic control measures, thereby improving the timeliness and accuracy of emergency rescue in sparse road networks under the influence of disaster events.

[0003] In patent CN202010437381.5 "A method and system for topological modeling of a traffic road network", the key element information database is formed by obtaining the road information of all roads; the road information is sorted using preset coding rules to create intersection information and section information. Finally, the topological model of the traffic road network is established using intersection information, section information and a recursive algorithm. This method is mainly aimed at the modeling needs of complex urban road networks, and fails to fully consider the characteristics of low road density and wide distribution of key nodes in sparse road networks. In addition, the use of static coding and recursive algorithms lacks the ability to respond to dynamic changes in the road network under the influence of sudden disaster events, and it is difficult to adapt to emergencies in emergency rescue of sparse road networks.

[0004] In patent CN201910034685.4 "A self-evolving traffic network topology modeling method", a traffic network model is established, represented by the node degree and adjacency matrix; the Markov model is used to calculate and predict traffic flow, and finally the node importance (node ​​degree) and the connection relationship between nodes (adjacency matrix) in the topological structure are updated according to the prediction results. However, this method is based on the dynamic update of traffic flow in the existing static road network structure, and lacks consideration of the dynamic changes in the physical structure of the road network under the influence of sudden disaster events, such as the addition, deletion or restoration of roads or nodes.

[0005] In patent CN202010028429.7 "A method and device for constructing a road network topology structure", multiple road network nodes in a preset area are obtained, and based on the static attribute information of each road network node and the upstream and downstream road network node information, attribute topology information is established, and an attribute list and a shape list are established, and the spatiotemporal road network topology structure of the preset area is further established to reflect the changes in the road network topology over time. However, this method reflects the dynamic characteristics of the road network structure through the changes in attribute topology information and shape information. This information includes the road grade, length, coordinates, shape category of the road network nodes, and their connection relationship in different time periods. The impact of disaster events on the road network topology structure has not been considered, and there is a lack of consideration for the low road network density, long road sections, wide distribution of key nodes, and dense road networks in local towns in sparse road networks. It is impossible to achieve dynamic updates of sparse road networks under the influence of disaster events.

[0006] At present, the research on the construction of road network topology model still has the following shortcomings:

[0007] (1) Most of the research on the construction of road network topology model is mainly focused on dense road network areas such as urban traffic networks, highway traffic networks and public transportation networks. There is a lack of research considering the unique conditions of sparse road networks (such as low overall road density, high local road network density, and wide distribution of key nodes). The existing road network topology model construction methods are difficult to adapt to the structural characteristics of sparse road networks.

[0008] (2) Most of the road network topology models focus on single-layer topology structure micro models based on graph theory, which are abstracted as road intersections in the road network as points and road sections in the road network as edges. When faced with large-scale sparse road network simulations, their computational efficiency is low and cannot meet the timeliness and real-time requirements of emergency rescue.

[0009] (3) Most applications of road network topology models are limited to static or preset scenarios. They lack the ability to respond to real-time changes in road network status under the influence of sudden disaster events and are unable to dynamically characterize traffic conditions and emergency situations in sparse road networks. Summary of the invention

[0010] The purpose of the present invention is to propose a method for modeling a dynamically editable double-layer topological structure of a sparse road network under the influence of a disaster event, so as to solve the problem that the prior art does not take into account the structural attributes of the sparse road network, such as a wide coverage area, low overall regional road network density, high local regional road network density, wide distribution of key nodes, and long length of road sections between nodes; at the same time, it solves the problem of dynamic update of the road network structure under the influence of sudden disaster events.

[0011] To achieve the above object, the technical solution provided by the present invention is: a method for modeling a dynamically editable double-layer topological structure of a sparse road network under the influence of a disaster event, comprising the following steps:

[0012] Step 1: Obtain all road information, key node information, and disaster event information in the target area, and use the information to form a road section element information table, a node element information table, and a disaster event element information table;

[0013] Step 2: Decompose the sparse road network into a two-layer network consisting of a backbone layer and a local layer. Based on the principle of sparse road network simplification and combined with complex network directed graph theory, a sparse road network backbone layer topological structure model G is constructed. C ;

[0014] Step 3: Enlarge the node elements and edge elements of the sparse road network backbone layer topological structure model, and use directed graph theory to construct the sparse road network local layer topological structure model G from a micro perspective L , including the node local topology model G LN and the local topological structure model G of the road segment LE ;

[0015] Step 4: Based on the backbone layer topology model G C and the local layer topology model G L , through the attribute-topology association method, the road network topology structure and road network traffic attributes are associated to build a sparse road network double-layer topology structure model G;

[0016] Step 5: Establish editing rules for the two-layer topological structure model of the sparse road network under the influence of typical disaster events, further formulate update rules for the sparse road network under the influence of disaster events, and update the two-layer topological structure of the sparse road network according to the disaster event element table and the sparse road network update rules.

[0017] The advantages of the present invention compared with the prior art are:

[0018] (1) The sparse road network double-layer topology modeling method proposed in the present invention provides a refined road network description by decomposing the road network into a trunk layer and a local layer, and clarifying the definitions of key nodes and edges, combined with complex network directed graph theory and attribute-topology association methods, which is particularly suitable for the structural characteristics of sparse road networks. This method can more accurately reflect the characteristics of low road density and wide distribution of key nodes, improve the accuracy and effectiveness of emergency rescue path planning, and is conducive to playing an important role in disaster emergency rescue in remote and rural areas with sparse road networks.

[0019] (2) The present invention has strong dynamic response capabilities, especially the ability to dynamically update the road network structure in conjunction with disaster events. By establishing multi-type disaster event processing rules, it ensures that the model can quickly and dynamically adjust the road network structure according to real-time data and the impact of disaster events, thereby improving the adaptability and flexibility of the road network; secondly, it provides a comprehensive disaster response strategy, providing strong data support and technical guarantees for traffic management and emergency response, and has significant technical advantages and application prospects. For example, when natural disasters such as mudslides and landslides occur, the present invention can quickly update the road network structure of the affected area, re-plan emergency rescue routes, ensure that rescue vehicles can avoid the disaster area in time, and improve the efficiency and accuracy of emergency response.

[0020] (3) The present invention uses an innovative two-layer topology model to approximate the regional road network as nodes, significantly improving the simulation running speed. This method effectively reduces the computational load, enables the optimal path to be quickly solved in emergency rescue missions, shortens the emergency response time, and ensures that rescue vehicles can arrive at the disaster site in a timely manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 The present invention is a flowchart of a method for dynamically editing a double-layer topological structure modeling of a sparse road network under the influence of a disaster event.

[0022] Figure 2 It is a schematic diagram of a double-layer network of a sparse road network in the method for dynamically editing a double-layer topological structure modeling of a sparse road network under the influence of a disaster event of the present invention.

[0023] Figure 3 It is a schematic diagram of the main layer road network topology structure model of a sparse road network in the method for dynamically editing a double-layer topology structure modeling of a sparse road network under the influence of a disaster event of the present invention.

[0024] Figure 4 It is a schematic diagram of the local layer node topology structure model of a sparse road network in the method for dynamically editing a double-layer topology structure modeling of a sparse road network under the influence of a disaster event of the present invention.

[0025] Figure 5It is a schematic diagram of the topological structure model of the local layer section of a sparse road network in the method for dynamically editing the double-layer topological structure modeling of a sparse road network under the influence of a disaster event of the present invention.

[0026] Figure 6 This is a schematic diagram of the macroscopic topological structure model of the sparse road network in Tibet and neighboring areas in Example 1.

[0027] Figure 7 This is a schematic diagram of the updated sparse road network macro-topology model under disaster events in Tibet and neighboring areas in Example 1. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0029] Embodiment 1:

[0030] Combined with Figure 1-7 , this embodiment provides an editable double-layer topology modeling method for sparse road networks under the influence of disaster events, such as Figure 1 As shown, the modeling method includes the following steps:

[0031] Step 1: Obtain all road information, key node information, and disaster event information in the target area, and use the information to form a road segment element information table, a node element information table, and a disaster event element information table.

[0032] Here, the target area refers to the area where the sparse road network double-layer editable topology structure is to be established. This area has the characteristics of sparse road network nodes, long road section length, low network density, and local road network density. The Tibet Autonomous Region is located in the southwestern border of China. With the continuous investment in infrastructure construction, the road transportation network in Tibet is becoming more and more complete. At present, Tibet and its adjacent areas have formed a road network with four national highways, Sichuan-Tibet, Qinghai-Tibet, Xinjiang-Tibet, and Yunnan-Tibet, connected by trunks and branches. The road network structure in Tibet is relatively sparse. Among them, the Sichuan-Tibet Highway (G318) starts from Chengdu in the east and ends in Lhasa in the west, with a total length of about 2,149 km. The main line includes the Sichuan-Tibet North Line (G317 National Highway-G109 National Highway) and the Sichuan-Tibet South Line (G318 National Highway). The Xinjiang-Tibet Highway (G219) starts from Yecheng County, Kashgar Prefecture, Xinjiang in the north and ends at Chawu Township, Lazi County, Shigatse City, Tibet in the south, with a total length of about 2,143 km; the road is long, and the Xinjiang-Tibet Highway has extreme geographical and climatic conditions, so the traffic volume is small and the road network density is extremely low. The Yunnan-Tibet Highway starts from Kunming, Yunnan and ends in Lhasa, Tibet, with a total length of 2,317 km. Some sections share the same line as the Sichuan-Tibet Highway. The Qinghai-Tibet Highway starts from Xining, Qinghai Province in the east and ends in Lhasa, Tibet in the west, with a total length of 1,937 km.

[0033] It is found that the road network in Tibet and its neighboring areas is mainly composed of national highways, provincial highways and county highways. Due to the limitations of geographical environment and economic development level, the development of road facilities is relatively lagging. The road network density is low, the road section length is long, the connection nodes are few, and the road linear conditions within the road section vary greatly, and the types of geological disasters are different; at the same time, affected by the topographic and geological conditions, geological disaster events in Tibet include landslides, water damage, mudslides, rockfalls, etc. Therefore, the road network in Tibet and its adjacent areas with sparse road networks is selected, specifically with Ganzi, Sichuan, Lijiang, Yunnan, Golmud, Qinghai, and Kashgar, Xinjiang as the starting point, and Lhasa as the end point to constitute the implementation target area, and the road network implementation examples in this area are analyzed.

[0034] As a preferred implementation scheme of this embodiment, the road information described in step 1 includes road geometry attributes and static traffic attribute data, the road geometry attribute data includes road number Edge_ID, road name Edge_Name, node number FNode_ID at one end of the road, node number TNode_ID at the other end of the road, road length Edge__Length, and node number array EndNodes at both ends of the road, where EndNodes = [FNode_ID, TNode_ID]; the static traffic attribute data includes lane width Wide_path, number of lanes Num_path, road capacity Cap, lane flow Flow, and travel time T. Among them, the road number Edge_ID and the node number array EndNodes at both ends of the road are set as table row indexes. Based on this, a road segment element information table is formed. Based on the road network structure data of the open source platform OSM (OpenStreetMap), combined with actual surveys to obtain geographic information data and static traffic data in Tibet and neighboring areas, a sparse road network segment element information table for Tibet and neighboring areas is established. Some road segment element information is shown in Table 1.

[0035] Table 1 Information table of road elements in Tibet and neighboring areas (example)

[0036]

[0037]

[0038] As a preferred implementation scheme of this embodiment, the key node information in step 1 includes the node number Node_ID, the node name Node_Name, the node connection number DON, the node longitude Lng, and the node latitude Lat. Among them, the node number is set as the list row index. Based on this, a node element information table is formed. The key node element information table of the sparse road network in Tibet and neighboring areas is shown in Table 2.

[0039] Table 2 Node element information table of Tibet and neighboring areas (example)

[0040] Node_ID Node_Name OD DON lng lat 1 Awati Interchange 1 1 77.53671 39.06813 2 Liuyuan Toll Station 1 1 82.84 35.71 3 Xiaochaidan Interchange 1 1 95.45249 37.44401 4 S228 / Lushui (South) / Manlai Exit 1 1 98.87695 25.60563 8 Yecheng West Interchange 0 2 77.3897 37.96375 9 Gaize County People's Government 1 2 84.06319 32.30263 10 Cheddar Village 0 2 84.75499 32.10586 11 Xiongmei Town Middle Bridge 0 2 89.01055 31.47182 12 Nagqu Bus Terminal 0 4 92.06081 31.47807 13 Yushu Municipal People's Government 1 4 97.00918 32.99293 14 Saga County People's Government 0 2 85.23294 29.32882 15 Lhaze County People's Government 1 2 87.63674 29.08209 …… …… …… …… …… ……

[0041] As a preferred implementation scheme of this embodiment, the disaster event information described in step 1 includes the disaster occurrence time Time, the disaster event category Event, the road section number Edge_ID where the disaster event is located, the number of lane closures LnNum, the starting position node number TNode_ID of the road section where the disaster event is located, the ending node number TNode_ID of the road section where the disaster event is located, the distance TDistance from the starting point of the disaster event to the starting point TNode_ID of the road section where the disaster event is located, and the length of the disaster event Length. Based on this, a disaster event element information table is formed. Assuming that the sparse road network in Tibet and neighboring areas is affected by geological disaster events, the disaster event element table is constructed as shown in Table 3.

[0042] Table 3 Disaster event element information table in Tibet and neighboring areas (example)

[0043] Time Event Edge_ID LnNum TNode ENode TDistance length April 13, 2024 20:10:00 0 nan nan nan nan nan length April 13, 2024 20:15:00 1 20 1 11 10 100412 300

[0044] Step 2: Decompose the sparse road network into a two-layer network consisting of a backbone layer and a local layer. Based on the principle of sparse road network simplification and combined with complex network directed graph theory, a sparse road network backbone layer topological structure model G is constructed. C .

[0045] As a preferred implementation of this embodiment, the sparse road network double-layer network in step 2 includes a sparse road network backbone layer topology structure G C and the local layer road network topology structure G of the sparse road network L ,like Figure 2 As shown in the figure, the sparse road network system is regarded as a multi-layer network. In order to distinguish the heterogeneity between different layers, the double-layer network theory is used to represent the trunk layer road network and the local layer road network of the sparse road network. The upper layer is the trunk layer road network, and the lower layer is the local layer road network. The switching between the trunk road network and the town road network is realized through the intersection of the local road network and the trunk road network.

[0046] As a preferred implementation scheme of this embodiment, the sparse backbone layer road network topology structure in step 2 is composed of basic modeling units simplified from the sparse road network;

[0047] The simplification principles described in step 2 include:

[0048] (1) Delete urban roads in the target area and retain expressways, national roads, and provincial roads;

[0049] (2) Delete the internal nodes of the road and merge multiple roads that are connected in topological structure and have no branches;

[0050] (3) Removing short auxiliary roads that do not change the overall connectivity of the trunk road network;

[0051] (4) Merge the internal road nodes in cities (prefectures), counties (districts), and townships (towns) within the target area into one node;

[0052] Preferably, in step 2, based on the sparse road network simplification principle and combined with the directed graph theory of complex networks, a topological structure model G of the backbone layer of the sparse road network is constructed C , represented as a directed graph G C = {N C , E C , L C , C}; where N C is the set of all nodes in the backbone layer road network, N C = {N C1 , N C2 , …, N Ci …, N CI}, I is the total number of all nodes in the backbone layer road network, and the node N Ci includes highway entrance and exit toll stations, plane intersections of national highways and provincial highways, approximate nodes of local road networks in cities (prefectures), approximate nodes of local road networks in counties (districts), and approximate nodes of local road networks in townships (towns); E C is the set of all edges in the backbone layer road network, E C = {E C1 , E C2 , …, E Cm …, E CM}, M is the total number of all edges in the backbone layer road network, and the edge E Cm is a directed line segment between two adjacent nodes on the backbone layer road network, including roads with higher levels such as highways, national highways, and provincial highways within the target area; L C = {l cij ∣ i, j = 1, 2, …, I and N Cj ∈ N i}, l cij is the distance of the edge from node N Ci to node N Cj , and the value is the corresponding road section length; C is the set of node capacities of the backbone layer road network, C = {C i ∣ i = 1, 2, …, I}, where C i represents the approximate parking lot capacity of node N Ci . The schematic diagram of the topological structure model of the backbone layer of the sparse road network is as shown in Figure 3 . According to the construction method of the topological structure of the backbone layer of the sparse road network, the schematic diagram of the macroscopic topological structure model of the sparse road network in Tibet and its neighboring areas is as shown in Figure 6 .

[0053] Step 3: Enlarge the node elements and edge elements of the topological structure model of the backbone layer of the sparse road network, and construct a topological structure model G of the local layer of the sparse road network from a microscopic perspective using directed graph theoryL , including the node local topology model G LN and the local topological structure model G of the road segment LE .

[0054] As a preferred implementation of this embodiment, the sparse road network local layer road network topology structure model G described in step 3 L It is a local enlargement of the node elements and edge elements of the sparse road network backbone layer topological structure model, including the node local topological structure G LN and the local topological structure G of the road segment LE The node local topology structure is the topology structure of the backbone network node after enlargement, which refers to the local network with independent traffic attributes inside the backbone network node, including more nodes and edges. LN Represented as a directed graph G LN = {N LN ,E LN}; where N LN is the set of all nodes in the local layer network, N LN = {N LN1 ,N LN2 ,…,N LNq …,N LNQ}, Q is the total number of all nodes in the local layer network, node N LNq Including road intersections within the local road network of cities (states), counties (districts), and townships (towns); E LN is the set of all edges in the local layer network, E LN ={E LN1 ,E LN2 ,…,E LNr …,E LNR}, R is the total number of all edges in the local layer network, edge E LNr It is the road connection in the local area, including county roads, streets, lanes, etc. in the local area. The schematic diagram of the local topological structure model of sparse road network nodes is as follows: Figure 4 shown.

[0055] As a preferred implementation of this embodiment, the local topological structure G of the road section described in step 3 LE It is the topological structure of the enlarged road section of the backbone layer. The long road section in the sparse road network area is further divided into smaller units based on traffic events, traffic control or construction work requirements, and is represented as a directed graph G LE = {N LE ,E LE}; where N LE is the set of all connected points after the road section is further divided, N LE = {N LE1 ,N LE2 ,…,NLEd …,N LED}, Z is the total number of all nodes after the road segment is subdivided, and node N LEd Including bridges, tunnels, demarcation points of topographic features, road line condition conversion points, road section connection points caused by disaster events, and road section connection points caused by traffic control; E LE is the set of all edges between all connected points, E LE ={E LE1 ,E LE2 ,…,E LEf …,E LEF}, F is the total number of all edges in the road segment topology, edge E LEf It is the connecting road of the smaller unit in the road segment. The schematic diagram of the local topological structure model of the sparse road network segment is as follows: Figure 5 shown.

[0056] Step 4: Based on the backbone layer topology model G C and the local layer topology model G L , the road network topology structure and road network traffic attributes are associated through the attribute-topology association method, and a sparse road network two-layer topology structure model G is constructed.

[0057] As a preferred implementation scheme of this embodiment, the road network topology structure and the road network traffic attributes are associated by the attribute-topology association method described in step 4. The specific method is:

[0058] First, the adjacency matrix is ​​used to represent the connection relationship between sparse road network nodes. The adjacency matrix is ​​a two-dimensional array, in which if there is a road section directly connecting two nodes, the corresponding array element is 1, otherwise it is 0; the commonly used data structures for representing graphs include adjacency matrix, adjacency list, adjacency multi-list, etc. The time complexity of the three is of the same order of magnitude in common network analysis operations such as network traversal and shortest path search. Since the out-degree (or in-degree) of each node in the traffic network is mostly not greater than 4, if the adjacency matrix is ​​used to store the traffic network, a large number of elements in this matrix are 0, which is called a sparse matrix. The road network adjacency matrix is ​​expressed and stored in a sparse matrix storage method.

[0059] Then, based on the node attribute feature table and the road segment attribute feature table, the same-direction lanes are aggregated and associated to the topological structure through the road segment identifier EndNodes.

[0060] Finally, by traversing the column index in the adjacency matrix, the node attributes and road segment attributes are quickly queried. The specified row index is used to store the topological structure data, and the attribute data of the nodes and road segments are linked to the topological structure of the road network to form a sparse road network double-layer topological structure model G. When the scale of the matrix is ​​large, the road network topological structure is expressed by storing the road segment table and the node table, and a road network model is established in which the topological structure and road network data are physically separated and logically combined. This model has low data redundancy and high network analysis efficiency.

[0061] Step 5: Establish editing rules for the two-layer topological structure model of the sparse road network under the influence of typical disaster events, further formulate update rules for the sparse road network under the influence of disaster events, and update the two-layer topological structure of the sparse road network according to the disaster event element table and the sparse road network update rules.

[0062] As a preferred implementation scheme of this embodiment, the typical disaster events described in step 5 include three categories, specifically: the first category is the newly built roads, the addition of detour routes after the failure of local road sections, and the event category Event=1; the second category is the failure of road section functions or traffic control, and the removal of temporary detour routes due to disaster events, and the event category Event=2; the third category is the update of road network attribute data in the scenario of road network traffic facility reconstruction and expansion, and the event category Event=3;

[0063] Due to its special geographical and climatic conditions, Tibet and its adjacent areas are prone to frequent geological disasters, such as debris flow, landslide, and roadbed collapse. At the same time, the road network in the region is relatively sparse and complex, and many places rely on only a single or limited channel connection. Therefore, it is very important to develop a road network simulation model with dynamic editing function to ensure that the road network status can be quickly adjusted when a disaster occurs, reflect the real road network status under disaster events, and enhance the ability of Tibet and its adjacent areas to cope with geological disasters. In general, the factors that cause changes in the sparse road network structure in Tibet and its adjacent areas include occasional geological disaster events, traffic events (short-term traffic environment changes caused by traffic accidents or other environmental factors), and planned traffic behaviors that affect the network status, such as temporary control of disaster sections, road construction operations, and the opening of new roads in the road network.

[0064] As a preferred implementation scheme of this embodiment, the editing rules for establishing the sparse road network double-layer topology structure model described in step 5 are specifically:

[0065] (1) According to the types of disasters that may occur in sparse road networks, the basic contents of dynamic editing under three common interruption scenarios of sparse road networks are summarized, including adding nodes / road sections, deleting nodes / road sections, and updating nodes / road sections;

[0066] (2) For the scenario of adding a new node / road segment (Event = 1), first traverse the node table row index to find the maximum node number MaxID; then, add two rows to the node table, with row index identifiers MaxID+1 and MaxID+2; and add one row to the road segment table, with row index identifiers [MaxID+1, MaxID+2].

[0067] (3) For the node / road segment deletion scenario (Event=2), the road segment table row index is traversed to find the road segment identifier to be deleted and then deleted; no operation is performed on the node table.

[0068] (4) For the node / road segment update scenario (Event=3), traverse the road segment table row index, find the road segment identifier that needs to be updated, and reassign the attribute.

[0069] As a preferred implementation scheme of this embodiment, the sparse road network update rules under the influence of disaster events described in step 5 are specifically as follows:

[0070] (1) Input the basic road network model as the initial road network G0;

[0071] (2) Extract the data before and after the disaster section G_before and G_after according to the disaster event elements;

[0072] (3) According to the event identification identifier event in the disaster event element table, the corresponding editing rules are called to generate the key location topology structure by adding / deleting nodes and adding / deleting road sections;

[0073] (4) associate and expand the attribute data of the topological structure generated to generate G_append;

[0074] (5) Combine the disaster section and the before and after data to form a new road network model graph G_new and output it. G_new = G_before +

[0075] G_after+G_append.

[0076] According to the disaster event element table, a disaster event occurred on the sparse road network section 20 in Tibet and neighboring areas. According to the above update rules, the schematic diagram of the updated sparse road network macro topology structure model under the disaster event in Tibet and neighboring areas is as follows: Figure 7 shown.

[0077] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. A method for dynamically editing a two-layer topological structure modeling of a sparse road network under the influence of disaster events, characterized in that: The following steps are involved: Step 1: Obtain all road information, key node information, and disaster event information in the target area, and use the information to form a road section element information table, a node element information table, and a disaster event element information table; Step 2: Decompose the sparse road network into a two-layer network consisting of a backbone layer and a local layer. Based on the principle of sparse road network simplification and combined with complex network directed graph theory, a sparse road network backbone layer topological structure model G is constructed. C ; Step 3: Enlarge the node elements and edge elements of the sparse road network backbone layer topological structure model, and use directed graph theory to construct the sparse road network local layer topological structure model G from a micro perspective L , including the node local topology model G LN and the local topological structure model G of the road segment LE ; Step 4: Based on the backbone layer topology model G C and the local layer topology model G L , through the attribute-topology association method, the road network topology structure and road network traffic attributes are associated to build a sparse road network double-layer topology structure model G; Step 5: Establish editing rules for the double-layer topological structure model of the sparse road network under the influence of typical disaster events, further formulate updating rules for the sparse road network under the influence of disaster events, and update the double-layer topological structure of the sparse road network according to the disaster event element table and the sparse road network updating rules; The typical disaster events described in step 5 include three categories, specifically: the first category is the newly built roads, the addition of detour routes after the failure of local road sections, and the event category Event=1; the second category is the failure of road section functions or traffic control, and the removal of temporary detour routes due to disaster events, and the event category Event=2; the third category is the update of road network attribute data in the scenario of road network traffic facility reconstruction and expansion, and the event category Event=3; The editing rules for establishing the sparse road network double-layer topology structure model described in step 5 are specifically as follows: (1) According to the types of disasters that may occur in sparse road networks, the basic contents of dynamic editing under three common interruption scenarios of sparse road networks are summarized, including adding nodes / road sections, deleting nodes / road sections, and updating nodes / road sections; (2) For the scenario of adding a new node / road section, Event = 1, first traverse the node table row index to find the maximum node number MaxID; Then, two rows are added to the node table, with row index identifiers MaxID+1 and MaxID+2; one row is added to the link table, with row index identifiers [MaxID+1, MaxID+2]; (3) For the node / road segment deletion scenario, Event = 2, the road segment table row index is traversed to find the road segment identifier to be deleted and then deleted; no operation is performed on the node table; (4) For the node / segment update scenario, Event = 3, traverse the segment table row index, find the segment identifier that needs to be updated, and reassign the attribute.

2. The method for modeling a dynamically editable double-layer topological structure of a sparse road network under the influence of a disaster event according to claim 1, characterized in that: The road information described in step 1 includes road geometric attributes and static traffic attribute data, wherein the road geometric attribute data includes road number Edge_ID, road name Edge_Name, node number FNode_ID at one end of the road, node number TNode_ID at the other end of the road, road length Edge__Length, and node number array EndNodes at both ends of the road, where EndNodes = [FNode_ID, TNode_ID]; Static traffic attribute data includes lane width Wide_path, number of lanes Num_path, road capacity Cap, lane flow Flow and travel time T; Among them, the road number Edge_ID and the node number array EndNodes at both ends of the road are set as the list row index; based on this, a road section feature information table is formed.

3. The method for modeling a dynamically editable double-layer topological structure of a sparse road network under the influence of a disaster event according to claim 1, characterized in that: The key node information in step 1 includes the node number Node_ID, the node name Node_Name, the node connection number DON, the node longitude Lng and the node latitude Lat; wherein the node number is set as the list row index; based on this, a node element information table is formed.

4. The method for modeling a dynamically editable double-layer topological structure of a sparse road network under the influence of a disaster event according to claim 1, characterized in that: The disaster event information described in step 1 includes the disaster occurrence time Time, the disaster event category Event, the road section number Edge_ID where the disaster event is located, the number of lane closures LnNum, the starting node number FNode_ID of the road section where the disaster event is located, the ending node number TNode_ID of the road section where the disaster event is located, the distance TDistance from the starting point of the disaster event to the starting point FNode_ID of the road section where the disaster event is located, and the length Length of the disaster event; based on this, a disaster event element information table is formed.

5. The method for modeling a dynamically editable double-layer topological structure of a sparse road network under the influence of a disaster event according to claim 1, characterized in that: The sparse road network double-layer network in step 2 includes a sparse road network backbone layer topology structure G C and the local layer road network topology structure G of the sparse road network L , where the sparse backbone layer road network topology is composed of simplified basic modeling units of the sparse road network; The simplification principles described in step 2 include: (1) Delete urban roads in the target area and retain expressways, national roads, and provincial roads; (2) Delete the internal nodes of the road and merge multiple roads that are connected in topological structure and have no branches; (3) Removing short auxiliary roads that do not change the overall connectivity of the trunk road network; (4) Merge the internal road nodes of cities, counties, and townships within the target area into one node.

6. The method for modeling a dynamically editable double-layer topological structure of a sparse road network under the influence of a disaster event according to claim 1, characterized in that: In step 2, based on the principle of sparse road network simplification and combined with complex network directed graph theory, a sparse road network backbone layer topology structure model G is constructed. C , represented as a directed graph G C = {N C ,E C ,L C ,C}; Among them, N C is the set of all nodes in the backbone network, N C = {N C1 ,N C2 ,…,N Ci …,N CI }, I is the total number of all nodes in the backbone network, node N Ci Including expressway entrance and exit toll stations, intersections between national and provincial roads, approximate nodes of local road networks in cities, approximate nodes of local road networks in counties, and approximate nodes of local road networks in townships; E C is the set of all edges in the backbone network, E C ={E C1 ,E C2 ,…,E Cm …,E CM }, M is the total number of edges in the backbone network, edge E Cm It is a directed line segment between two adjacent nodes on the backbone road network, including highways, national roads, and provincial roads of higher levels in the target area; L C = {lcij|i,j=1,2,…,I and N C j∈Ni}, lcij is the node N C i to node N C The distance of the edge of j is the length of the corresponding road segment; C is the backbone network node capacity set, C = {Ci|i = 1, 2, ..., I}, where Ci represents the node N C Approximate parking capacity of i.

7. The method for modeling a dynamically editable double-layer topological structure of a sparse road network under the influence of a disaster event according to claim 1, characterized in that: The sparse road network local layer road network topology structure model G described in step 3 L It is a local enlargement of the node elements and edge elements of the sparse road network backbone layer topological structure model, including the node local topological structure G LN and the local topological structure G of the road segment LE ; The node local topology structure is the topology structure of the backbone layer road network node after enlargement, which refers to the local network with independent traffic attributes inside the backbone layer road network node, including more nodes and edges; Node local topology G LN Represented as a directed graph G LN = {N LN ,E LN }; Among them, N LN is the set of all nodes in the local layer network, N LN = {N LN1 ,N LN2 ,…,N LNq …,N LNQ }, Q is the total number of all nodes in the local layer network, node N LNq Including road intersections within the local road network of cities, counties and townships; E LN is the set of all edges in the local layer road network, E LN ={E LN1 ,E LN2 ,…,E LNr …,E LNR }, R is the total number of all edges in the local layer network, edge E LNr It is the road connection within a local area, including county roads, streets, and alleys within the local area.

8. The method for modeling a dynamically editable double-layer topological structure of a sparse road network under the influence of a disaster event according to claim 1, characterized in that: The local topological structure G of the road segment described in step 3 LE It is the topological structure of the enlarged road section of the backbone layer. The long road section in the sparse road network area is further divided into smaller units based on traffic events, traffic control or construction work requirements, which is represented as a directed graph G LE = {N LE ,E LE }; Among them, N LE is the set of all connected points after the road section is further divided, N LE = {N LE1 ,N LE2 ,…,N LEd …,N LED }, D is the total number of all nodes after the road segment is subdivided, node N LEd Including bridges, tunnels, demarcation points of topographic features, road alignment condition conversion points, road section connection points caused by disaster events, and road section connection points caused by traffic control; E LE is the set of all edges between all connected points, E LE ={E LE1 ,E LE2 ,…,E LEf …,E LEF }, F is the total number of all edges in the road segment topology, edge E LEf It is a connecting road for smaller units within a road section.

9. The method for modeling a dynamically editable double-layer topological structure of a sparse road network under the influence of a disaster event according to claim 1, characterized in that: In step 4, the road network topology structure and the road network traffic attributes are associated through the attribute-topology association method. The specific method is as follows: (1) First, the adjacency matrix is ​​used to represent the connection relationship between the nodes of the sparse road network. The adjacency matrix is ​​a two-dimensional array, in which if there is a road section directly connected between two nodes, the corresponding array element is 1, otherwise it is 0; (2) Then, based on the node attribute feature table and the road segment attribute feature table, the same-direction lanes are aggregated and associated to the topological structure through the road segment identifier EndNodes; (3) Finally, the node attributes and road segment attributes are quickly queried by traversing the column indexes in the adjacency matrix; The specified row index is used to store the topological structure data, and the attribute data of nodes and road segments are linked to the topological structure of the road network to form a sparse road network double-layer topological structure model G.

10. The method for modeling a dynamically editable double-layer topological structure of a sparse road network under the influence of a disaster event according to claim 1, characterized in that: Step 5 describes the formulation of sparse road network update rules under the influence of disaster events, specifically: (1) Input the basic road network model as the initial road network G0; (2) Extract the data before and after the disaster section G_before and G_after according to the disaster event elements; (3) According to the event identification identifier Event in the disaster event element table, the corresponding editing rules are called to generate the key location topology structure by adding / deleting nodes and adding / deleting road sections; (4) associate and expand the attribute data of the topological structure generated to generate G_append; (5) Combine the disaster section and the previous and next data to form a new road network model graph G_new and output it; G_new = G_before + G_after + G_append.

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