Vulnerability evaluation and vulnerability node identification method for regional bridge and tunnel network security operation and maintenance

Abstract

The invention provides a vulnerability assessment and vulnerability node identification method for regional bridge and tunnel network security operation and maintenance. The method comprises the following steps: 1, preprocessing and integrating basic information of regional bridges and tunnels, and establishing a regional bridge-tunnel network; 2, analyzing bridge-tunnel network characteristics based on a graph theory and a complex network theory to obtain key nodes of a bridge-tunnel network topological structure; 3, integrating the accessibility and the network efficiency, selecting a proper vulnerability index, adopting different node failure strategies, and calculating a corresponding relation between a node removal ratio and the vulnerability index; 4, calculating the overall vulnerability of the bridge and tunnel network, carrying out vulnerability analysis on the network, and recording the vulnerability of each bridge and tunnel node; and 5, adopting a Monte Carlo sampling method, and identifying the fragile nodes of the bridge-tunnel network by randomly calculating the fragility expectation of the bridge-tunnel nodes for multiple times. The method is suitable for topology analysis, vulnerability analysis and key vulnerable node identification processes of a general regional bridge and tunnel network.

Description

Technical Field

[0001] The present invention relates to the technical fields of complex networks, vulnerability analysis, and civil engineering infrastructure health monitoring, and in particular to a vulnerability assessment and vulnerable node identification method for regional bridge and tunnel network security operation and maintenance. Background Art

[0002] As key infrastructure in urban transportation networks, bridges and tunnels undertake important transportation functions. Once a bridge-tunnel node fails, it will seriously affect the connectivity of traffic flow and the stability of the regional transportation network, and even cause large-scale social and economic losses. Traditional transportation infrastructure maintenance mainly relies on periodic inspection and maintenance, but fails to make full use of modern technology to conduct a systematic analysis of the overall vulnerability of the network, and there are deficiencies in the identification method of key bridge-tunnel nodes. Therefore, using complex network theory to conduct vulnerability assessment of bridge-tunnel networks can provide a scientific basis for the safe operation and maintenance of bridges and tunnels, and improve the robustness and risk resistance of transportation networks.

[0003] Vulnerability analysis can be applied to various complex systems, especially network structures, infrastructure, and information systems. Vulnerability analysis determines which elements play a key role in the function of the system by evaluating the importance and failure probability of each element in the system. Once these key elements fail, it may cause the system to crash or seriously damage its functions. In the bridge-tunnel network, managers can use vulnerability analysis to discover weak links in the network in advance, optimize emergency response and resource allocation, and enhance the risk resistance of the bridge-tunnel network. By identifying and strengthening key vulnerable nodes through vulnerability analysis, traffic jams and delays caused by network node failure can be avoided, which can improve the operating efficiency of the bridge-tunnel network, improve the social travel experience, and ensure public safety. In the construction of smart bridge-tunnel systems, dynamic and real-time vulnerability assessments can provide optimization suggestions for traffic flow control, road load distribution, etc., so as to better serve the safe and efficient operation of regional bridge-tunnel networks.

[0004] Most current studies on the vulnerability of transportation networks focus on highway networks and intersections, focusing on the impact of road node failures on traffic flow. The application of complex network theory in the field of transportation is gradually increasing. Through graph theory methods, transportation networks are abstracted into a collection of nodes and edges, the role of different nodes in the network is analyzed, and the robustness of the overall network is evaluated. Typical research directions include identifying key nodes in transportation networks based on centrality indicators, analyzing the connectivity of network structures, and evaluating the fault propagation effects of transportation networks. These studies have laid a theoretical foundation for the assessment of transportation network vulnerability.

[0005] In the field of bridge and tunnel vulnerability analysis, existing research still has several deficiencies. First, the uniqueness of bridge and tunnel facilities is not considered enough. Unlike ordinary highway nodes, bridges and tunnels have more complex structural characteristics and higher construction costs, and their failure has a more significant impact on traffic flow and regional connectivity. Existing research has not deeply explored the role of bridge and tunnel facilities in the entire network, and uses the same analysis method as ordinary nodes, resulting in inaccurate assessment of their vulnerability. Secondly, the current dynamic research on vulnerability analysis is insufficient. Most vulnerability analyses are based on static network models, which makes it difficult to evaluate the dynamic adjustment and connectivity changes of the traffic network after node failure. However, in reality, traffic networks often encounter emergencies, such as natural disasters, bridge damage, traffic accidents, etc., which will lead to the redistribution of network structure and traffic. In addition, the research on cross-system coupling effects is also relatively limited. Most existing studies are limited to a single object and lack consideration of the coupling effects of multiple types of nodes. In summary, although the current research on traffic network vulnerability analysis provides a theoretical basis for the safety assessment of bridge and tunnel facilities, it has significant deficiencies in the particularity, dynamics, and multi-type coupling of bridge and tunnel networks.

[0006] In the actual needs of traffic management, traditional management and maintenance methods usually rely on regular inspections, and the target bridges and tunnels for inspection are judged through experience. Although this method can provide certain guarantees, it lacks scientific and systematic evaluation methods. Especially for key transportation facilities such as bridges and tunnels, a more accurate analysis tool is needed to quantitatively evaluate the vulnerability of bridge and tunnel facilities and identify the bridge and tunnel nodes that have the greatest impact on network robustness. Through this quantitative analysis method, traffic managers can not only optimize resource allocation and prioritize the reinforcement of high-risk nodes, but also provide reliable decision-making basis for emergency management and maintenance strategies, ensuring the risk resistance and safety of bridge and tunnel networks in emergencies.

[0007] Therefore, starting from the complex network theory and combining the characteristics of bridges and tunnels, this paper proposes a practical bridge-tunnel network vulnerability assessment and key node identification method, and uses the Monte Carlo sampling method to improve the consistency and reliability of key vulnerable node identification. This paper can effectively make up for the shortcomings of existing research on the vulnerability analysis of bridge and tunnel infrastructure, provide reliable technical support for the safe operation and maintenance of regional bridge and tunnel networks, and has a wide range of engineering application value. Summary of the invention

[0008] The purpose of the present invention is to provide a vulnerability assessment and vulnerable node identification method for the secure operation and maintenance of regional bridge and tunnel networks, in order to address the problem of lack of systematic assessment of the vulnerability of bridge and tunnel facilities in traditional traffic management. This method uses complex network theory to perform topological analysis on the bridge and tunnel network, establishes vulnerability indicators, and combines the Monte Carlo sampling method to improve the consistency and reliability of vulnerable node identification. Its advantage is that it can accurately identify the bridge and tunnel nodes that have the greatest impact on the stable operation of the network, thereby providing a scientific basis for the maintenance, reinforcement and emergency management of regional bridge and tunnel facilities, and improving the overall robustness and security of the network.

[0009] The present invention is implemented by the following technical solution. The present invention proposes a vulnerability assessment and vulnerable node identification method for regional bridge and tunnel network security operation and maintenance, and the method comprises the following steps:

[0010] Step 1: Preprocess and integrate the information of regional bridges and tunnels to establish a regional bridge-tunnel network;

[0011] Step 2: Analyze the characteristics of the bridge-tunnel network based on graph theory and complex network theory to obtain the key nodes of the bridge-tunnel network topology structure;

[0012] Step 3: Considering the accessibility and network efficiency, select vulnerability indicators, adopt different node failure strategies, and calculate the corresponding relationship between the node removal ratio and the vulnerability indicator;

[0013] Step 4: Calculate the overall vulnerability of the bridge-tunnel network, perform vulnerability analysis on the network and record the vulnerability of each bridge-tunnel node;

[0014] Step 5: Use the Monte Carlo sampling method to identify the vulnerable nodes of the bridge-tunnel network by randomly calculating the expected vulnerability of the bridge-tunnel nodes multiple times.

[0015] Furthermore, the step 1 is specifically as follows:

[0016] The information of regional bridges and tunnels is preprocessed, and the geographical coordinates of bridges and tunnels WGS84 are converted into projection coordinates UTM using the Transverse Mercator projection. and longitude λ, the conversion formula is:

[0017] x=k0N(λ-λ0)+EastOffset

[0018]

[0019] Where k0 is the scaling factor; N is the latitude and longitude ellipsoid radius; λ0 is the longitude of the central meridian; EastOffset is the east offset; From the equator to the latitude The arc length of

[0020] The regional bridges and tunnels are divided into three types: bridges, viaducts and tunnels. Numbers, coordinates, connectivity and distance are extracted as the main features to establish a regional bridge and tunnel network.

[0021] Furthermore, the step 2 is specifically as follows:

[0022] Step 21: Perform a depth-first search (DFS) on the established bridge-tunnel network to obtain the connectivity and cut points of the network;

[0023] Step 2: Count the number of edges connected to each bridge and tunnel node to obtain the node degree and network degree distribution; analyze the connection characteristics between nodes, calculate the assortativity and average clustering coefficient of the network, and the calculation formula is:

[0024]

[0025] In the formula, R and C are the associativity coefficient and average clustering coefficient of the network respectively; i , k i They represent the degrees of the two endpoints of an edge respectively; M is the number of edges in the network; N is the number of nodes in the network, l i is the degree of node i; E i is the number of edges between neighboring nodes of node i;

[0026] Step 2: Analyze the path characteristics of the network, including the average path length a, network diameter l, eccentricity ρ and radius r, and search for the central node. The calculation formula is:

[0027]

[0028] r=minρ

[0029] Where d(i,j) is the shortest path between node i and node j; the node with eccentricity equal to the radius is the central node of the network;

[0030] Step 24: Analyze the network topology importance of each bridge and tunnel node, and calculate the degree centrality, eigenvector centrality, betweenness centrality and closeness centrality of the node respectively. The calculation formula is:

[0031]

[0032] In the formula, λ is the eigenvalue; a ij is an element of the adjacency matrix, indicating whether there is an edge between node i and node j; σ st is the total number of shortest paths between node s and node t; σ st (i) is the number of shortest paths passing through node i.

[0033] Furthermore, the step 21 is specifically as follows:

[0034] Step 2: Initialize a timestamp variable time to track the order of accessing nodes; initialize the node access time discovery and the earliest traceable time low;

[0035] Step 2-2: Select an initial node i, and set the access time dis[i] and the initial earliest backtracking time low[i] of the node to the current time;

[0036] Step 213: traverse all neighbors j of the node: if j has not been visited, continue to execute step 213 and update low[i]; if j has been visited and is not the parent node of i, update low[i] to min(low[i], dis[j]), indicating that j can be reached through the backtracking path;

[0037] After completing step 213, if there are still unvisited nodes, initialize the nodes again and repeat steps 211 to 213. Each time this is done, the number of connected components increases by one. For each node i, if i is a root node and there are two or more subtrees, then i is a cut point; if i is a non-root node, check whether low[j] is greater than or equal to dis[i], that is, if the child node j and its descendants cannot return to the ancestor of i through the backtracking path, then i is a cut point.

[0038] Furthermore, the step three is specifically as follows:

[0039] Step 31: Select the relative size of the largest connected subgraph and network efficiency in the bridge-tunnel network characteristics as the network vulnerability index, and the calculation formula is:

[0040]

[0041] Where LCC(G) is the relative size of the largest connected subgraph; E(G) is the network efficiency; N is the total number of bridges and tunnels; n lc is the number of nodes in the maximum connected component; is the penalty term for the connected nodes of the network efficiency, where n c is the total number of connected nodes in the network, and n is the number of nodes in the current network;

[0042] Step 32: Sort the bridge and tunnel nodes by degree centrality, weighted degree centrality, betweenness centrality and weighted betweenness centrality to simulate a static failure order; remove the node with the largest weighted betweenness centrality in the graph, and then recalculate the weighted betweenness centrality of each point. Repeat this step until all nodes are removed to obtain a dynamic sorting of weighted betweenness centrality to simulate a dynamic failure order; randomly sort the nodes to simulate a random failure order;

[0043] Step 33: According to the different failure sequences obtained in step 32, node removal strategies are implemented for the bridge-tunnel network. Each time a node is removed, the network damage ratio is counted and the corresponding network vulnerability index is recorded.

[0044] Furthermore, the step three-three is specifically as follows:

[0045] Step 331: Input the initialized bridge-tunnel network, calculate the network efficiency and number of nodes in the initial state, and select a removal strategy for the bridge-tunnel nodes;

[0046] Step 332: select a bridge-tunnel node according to the corresponding node sorting, remove it from the network to simulate the damage of the bridge-tunnel, and calculate the network efficiency after the damage and the relative size of the largest connected subgraph;

[0047] Step 333: Count the proportion of damaged nodes and determine whether all nodes have been traversed. If not, continue to execute step 332 until all nodes are removed and output the vulnerability indicator statistics.

[0048] Furthermore, the step 4 is specifically as follows:

[0049] Step 41: Based on the relative size of the largest connected subgraph of the network and the network efficiency, a unified index for quantifying the vulnerability of the bridge-tunnel network is proposed. The calculation formula is:

[0050]

[0051] Where SV(G') is the static vulnerability of the network; ΔE(G') is the change in network efficiency; α is the connectivity importance coefficient;

[0052] Set the vulnerability threshold of network failure to 1. When the network vulnerability reaches the threshold, E(G') is 0 or LCC(G') is 1%, which can be used as the trigger condition for network collapse.

[0053] Step 42: Calculate the change in network vulnerability caused by each node removal, match the vulnerability to the node one by one according to the node removal order recorded in step 33, and calculate the vulnerability of each bridge and tunnel node.

[0054] Furthermore, the step five is specifically as follows:

[0055] Step 51: adopt a random failure sequence, obtain the vulnerability analysis results of the bridge-tunnel network under random node failure according to step 33, and record the vulnerability of each bridge-tunnel node;

[0056] Step 52: Based on the Monte Carlo principle, repeat step 51, sample a large number of times in the random failure strategy, and estimate the expected vulnerability of each bridge-tunnel node as its overall vulnerability; identify the vulnerable nodes of the bridge-tunnel network according to the overall vulnerability, and mark and visualize them in combination with the bridge-tunnel network topology map.

[0057] The present invention also proposes an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of a vulnerability assessment and vulnerable node identification method for secure operation and maintenance of regional bridge and tunnel networks are implemented.

[0058] The present invention also proposes a computer-readable storage medium for storing computer instructions, which, when executed by a processor, implement the steps of a vulnerability assessment and vulnerable node identification method for secure operation and maintenance of regional bridge and tunnel networks.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] 1. Quantitative vulnerability assessment. The proposed method establishes a quantitative calculation model for the vulnerability of bridge-tunnel networks and provides an accurate assessment standard. Compared with the traditional assessment method that relies on experience or static models, the method of the present invention provides a scientific basis for vulnerability analysis and can quantify the potential risks of bridge-tunnel facilities through data.

[0061] 2. Accurately identify key vulnerable nodes. Combining complex network theory with Monte Carlo sampling methods, the present invention can accurately identify key vulnerable nodes that have the greatest impact on the connectivity and overall stability of the bridge-tunnel network. Traditional methods are susceptible to random factors when identifying key nodes, while the method described in the present invention improves the consistency and credibility of the identification results through repeated analysis and simulation.

[0062] 3. Improve monitoring and maintenance efficiency. By accurately identifying vulnerable nodes, traffic managers can prioritize monitoring or maintenance of key bridge and tunnel facilities based on vulnerability analysis results. This targeted monitoring and maintenance strategy can significantly improve resource utilization efficiency, save operation and maintenance costs, and ensure the overall security and stability of the network.

[0063] 4. Wide application value in engineering. The present invention is not only applicable to a single bridge-tunnel network, but also to regional transportation networks of various scales and complexities, and has strong adaptability and versatility. Whether in daily maintenance or emergency response, the method of the present invention can provide strong support for the management of transportation facilities and ensure the continuous and stable operation of the network. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0065] Figure 1 Provide a flow chart for vulnerability analysis and vulnerable node identification;

[0066] Figure 2 It is a diagram of the bridge-tunnel nodes and the bridge-tunnel network structure in a certain area;

[0067] Figure 3 is the node degree distribution diagram of the regional bridge-tunnel network;

[0068] Figure 4 The visualization diagram of the node centrality of the regional bridge-tunnel network; (a) is the degree centrality; (b) is the betweenness centrality; (c) is the closeness centrality; (d) is the eigenvector centrality;

[0069] Figure 5 It is a curve diagram showing the change of the relative size of the largest connected component of the network under different failure modes;

[0070] Figure 6 It is the variation curve of network efficiency and weighted network efficiency under different failure modes; Figure 6 Left: relative network efficiency; Figure 6 The right side shows the path-weighted relative network efficiency;

[0071] Figure 7 It is the vulnerability curve of the bridge-tunnel network under different failure modes;

[0072] Figure 8 Schematic diagram of the change in fragility caused by each node removal;

[0073] Fig. 9 Schematic diagram of the identification results of key vulnerable nodes in the bridge-tunnel network in this area. DETAILED DESCRIPTION

[0074] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0075] Combination Figure 1-Figure 9 The present invention proposes a vulnerability assessment and vulnerable node identification method for regional bridge and tunnel network security operation and maintenance, the method comprising the following steps:

[0076] Step 1: Preprocess and integrate the information of regional bridges and tunnels to establish a regional bridge-tunnel network;

[0077] The step 1 is specifically as follows:

[0078] The information of regional bridges and tunnels is preprocessed, and the geographical coordinates of bridges and tunnels WGS84 are converted into projection coordinates UTM using the Transverse Mercator projection. and longitude λ, the conversion formula is:

[0079] x=k0N(λ-λ0)+EastOffset

[0080]

[0081] Where k0 is the scaling factor, which is 0.9996; N is the radius of the longitude and latitude ellipsoid; λ0 is the longitude of the central meridian; EastOffset is the east offset, which is 500000 meters; From the equator to the latitude The arc length of

[0082] The regional bridges and tunnels are divided into three types: bridges, viaducts and tunnels. Numbers, coordinates, connectivity and distance are extracted as the main features to establish a regional bridge and tunnel network.

[0083] Step 2: Analyze the characteristics of the bridge-tunnel network based on graph theory and complex network theory to obtain the key nodes of the bridge-tunnel network topology structure;

[0084] The step 2 is specifically as follows:

[0085] Step 21: Perform a depth-first search (DFS) on the established bridge-tunnel network to obtain the connectivity and cut points of the network;

[0086] The step 21 is specifically as follows:

[0087] Step 2: Initialize a timestamp variable time to track the order of accessing nodes; initialize the node access time discovery and the earliest traceable time low;

[0088] Step 2-2: Select an initial node i, and set the access time dis[i] and the initial earliest backtracking time low[i] of the node to the current time;

[0089] Step 213: traverse all neighbors j of the node: if j has not been visited, continue to execute step 213 and update low[i]; if j has been visited and is not the parent node of i, update low[i] to min(low[i], dis[j]), indicating that j can be reached through the backtracking path;

[0090] After completing step 213, if there are still unvisited nodes, initialize the nodes again and repeat steps 211 to 213. Each time this is done, the number of connected components increases by one. For each node i, if i is a root node and there are two or more subtrees, then i is a cut point; if i is a non-root node, check whether low[j] is greater than or equal to dis[i], that is, if the child node j and its descendants cannot return to the ancestor of i through the backtracking path, then i is a cut point.

[0091] Step 2: Count the number of edges connected to each bridge and tunnel node to obtain the node degree and network degree distribution; analyze the connection characteristics between nodes, calculate the assortativity and average clustering coefficient of the network, and the calculation formula is:

[0092]

[0093] In the formula, R and C are the associativity coefficient and average clustering coefficient of the network respectively; i , k i They represent the degrees of the two endpoints of an edge respectively; M is the number of edges in the network; N is the number of nodes in the network, l i is the degree of node i; E i is the number of edges between neighboring nodes of node i;

[0094] Step 2: Analyze the path characteristics of the network, including the average path length a, network diameter l, eccentricity ρ and radius r, and search for the central node. The calculation formula is:

[0095]

[0096] r=minρ

[0097] Where d(i,j) is the shortest path between node i and node j; the node with eccentricity equal to the radius is the central node of the network;

[0098] Step 24: Analyze the network topology importance of each bridge and tunnel node, and calculate the degree centrality, eigenvector centrality, betweenness centrality and closeness centrality of the node respectively. The calculation formula is:

[0099]

[0100] In the formula, λ is the eigenvalue; a ij is an element of the adjacency matrix, indicating whether there is an edge between node i and node j; σ st is the total number of shortest paths between node s and node t; σ st (i) is the number of shortest paths passing through node i.

[0101] Step 3: Considering the accessibility and network efficiency, select vulnerability indicators, adopt different node failure strategies, and calculate the corresponding relationship between the node removal ratio and the vulnerability indicator;

[0102] The step three is specifically as follows:

[0103] Step 31: Select the relative size of the largest connected subgraph and network efficiency in the bridge-tunnel network characteristics as the network vulnerability index, and the calculation formula is:

[0104]

[0105] Where LCC(G) is the relative size of the largest connected subgraph; E(G) is the network efficiency; N is the total number of bridges and tunnels; n lc is the number of nodes in the maximum connected component; is the penalty term for the connected nodes of the network efficiency, where n c is the total number of connected nodes in the network, and n is the number of nodes in the current network;

[0106] Step 32: Sort the bridge and tunnel nodes by degree centrality, weighted degree centrality, betweenness centrality and weighted betweenness centrality to simulate a static failure order; remove the node with the largest weighted betweenness centrality in the graph, and then recalculate the weighted betweenness centrality of each point. Repeat this step until all nodes are removed to obtain a dynamic sorting of weighted betweenness centrality to simulate a dynamic failure order; randomly sort the nodes to simulate a random failure order;

[0107] Step 33: According to the different failure sequences obtained in step 32, node removal strategies are implemented for the bridge-tunnel network. Each time a node is removed, the network damage ratio is counted and the corresponding network vulnerability index is recorded.

[0108] The step three is specifically as follows:

[0109] Step 331: Input the initialized bridge-tunnel network, calculate the network efficiency and number of nodes in the initial state, and select a removal strategy for the bridge-tunnel nodes;

[0110] Step 332: select a bridge-tunnel node according to the corresponding node sorting, remove it from the network to simulate the damage of the bridge-tunnel, and calculate the network efficiency after the damage and the relative size of the largest connected subgraph;

[0111] Step 333: Count the proportion of damaged nodes and determine whether all nodes have been traversed. If not, continue to execute step 332 until all nodes are removed and output the vulnerability indicator statistics.

[0112] Step 4: Calculate the overall vulnerability of the bridge-tunnel network, perform vulnerability analysis on the network and record the vulnerability of each bridge-tunnel node;

[0113] The step 4 is specifically as follows:

[0114] Step 41: Based on the relative size of the largest connected subgraph of the network and the network efficiency, a unified index for quantifying the vulnerability of the bridge-tunnel network is proposed. The calculation formula is:

[0115]

[0116] Where SV(G') is the static vulnerability of the network; ΔE(G') is the change in network efficiency; α is the connectivity importance coefficient;

[0117] Set the vulnerability threshold of network failure to 1. When the network vulnerability reaches the threshold, E(G') is 0 or LCC(G') is 1%, which can be used as the trigger condition for network collapse.

[0118] Step 42: Calculate the change in network vulnerability caused by each node removal, match the vulnerability to the node one by one according to the node removal order recorded in step 33, and calculate the vulnerability of each bridge and tunnel node.

[0119] Step 5: Use the Monte Carlo sampling method to identify the vulnerable nodes of the bridge-tunnel network by randomly calculating the expected vulnerability of the bridge-tunnel nodes multiple times.

[0120] The step five is specifically as follows:

[0121] Step 51: adopt a random failure sequence, obtain the vulnerability analysis results of the bridge-tunnel network under random node failure according to step 33, and record the vulnerability of each bridge-tunnel node;

[0122] Step 52: Based on the Monte Carlo principle, repeat step 51, sample a large number of times in the random failure strategy, and estimate the expected vulnerability of each bridge-tunnel node as its overall vulnerability; identify the vulnerable nodes of the bridge-tunnel network according to the overall vulnerability, and mark and visualize them in combination with the bridge-tunnel network topology map.

[0123] The purpose of this embodiment is to propose a bridge-tunnel network vulnerability assessment and vulnerable node identification method for application in the vulnerability analysis and safe operation and maintenance of regional bridge-tunnel networks.

[0124] The core of the present invention is to analyze the vulnerability of the regional bridge and tunnel network based on complex network theory through topological analysis and vulnerability index modeling, and accurately identify the key vulnerable nodes of the network based on Monte Carlo sampling. The method is mainly divided into three steps. The first step is the preprocessing of bridge and tunnel information, constructing a parameterized graph model and performing centrality feature analysis on the topological structure of the network to identify potential key nodes; the second step is to select the relative size of the largest connected component and the network efficiency as vulnerability indicators, and establish a quantitative calculation formula for network vulnerability to evaluate the impact of node failure on the overall network; the third step is to apply the Monte Carlo sampling method for multiple random simulations to evaluate the changes in the network under different circumstances, and ensure that the identification results of vulnerable nodes have high consistency and reliability. Traffic managers can make scientific decisions based on the analysis results to optimize resource allocation and emergency management strategies.

[0125] Example

[0126] This embodiment is implemented based on the above specific implementation methods and a computer program written in Python. The hardware environment implemented is shown in Table 1. The implementation target is a bridge-tunnel group in a certain area. The effects of the present invention are described by the following specific examples.

[0127] Table 1 Hardware environment parameters

[0128]

[0129] The basic information of the regional bridge-tunnel group was collected and preprocessed, including latitude and longitude coordinates, bridge-tunnel types and connectivity, etc. There are 681 bridges and tunnels in the regional bridge-tunnel group, including 186 elevated bridges, 57 tunnels and 438 bridges.

[0130] The transverse Mercator projection is used, and the parameters are set to the default values, including k0 of 0.9996 and EastOffset of 500000m, to convert the longitude and latitude geographic coordinate information into projection coordinates. The regional bridges and tunnels are divided into three types: bridges, viaducts and tunnels. The bridge and tunnel numbers, horizontal and vertical coordinates, relative distances and connectivity are extracted as the main features to establish a regional bridge and tunnel network. The regional bridge and tunnel network structure is as follows: Figure 2 shown.

[0131] After establishing the graph structure of the bridge-tunnel network, a depth-first search is performed on the network to obtain the connectivity and cut points of the network. After searching, the bridge-tunnel network is connected, with a total of 26 cut points, numbered 84, 227, 148, 150, 491, 489, 492, 72, 377, 429, 310, 321, 330, 335, 349, 347, 356, 640, 650, 651, 652, 648, 649, 167, 232, 313. These cut points are distributed throughout the network. If the bridge and tunnel at the network cut point fails, the network will be interrupted, and some nodes will no longer be connected.

[0132] Count the number of edges connected to each bridge-tunnel node to obtain the node degree and the overall degree distribution of the network, such as Figure 3 As shown. Analyze the connection characteristics between nodes, calculate the network assortativity and average clustering coefficient; calculate the path characteristics of the network, including the average path length, network diameter, eccentricity and radius, and search for central nodes; the calculation methods of each parameter are shown in Table 2.

[0133] Table 2 Network feature calculation method

[0134]

[0135] After calculation, the degree of the bridge-tunnel network in this area is between 1 and 10, mostly distributed around 4, showing a certain Poisson distribution feature. Most bridge-tunnel nodes are connected to about 4 other nodes. The associativity coefficient of the network is 0.1398, between -1 and 1, indicating that the network has associativity, but the correlation is weak. The average clustering coefficient of the network is 0.132, between 0 and 1, and the clustering of the network is weak, that is, most high-degree nodes are connected to low-degree nodes. The average path length of the network is 12.24, the diameter is 39, and the radius is 21. Considering this The bridge-tunnel network has a total of 681 nodes, and its average path length is relatively short, reflecting certain small-world characteristics; the distance-weighted values ​​of the average path length, diameter and radius of the network are 73.28km, 231.87km and 116.16km respectively; the central nodes of the network are nodes 100, 112, 114, 115, 121, 136, 324, 326, 465, 468 and 471; the path-weighted central node is node 468. These nodes are distributed in the center of the network and have the shortest distance to the farthest node.

[0136] The topological importance of the bridge-tunnel nodes is ranked, and the degree centrality, eigenvector centrality, betweenness centrality and closeness centrality of the nodes are calculated respectively. The calculation method is shown in Table 3, and the calculation results are visualized as shown in Figure 4As shown in the figure, the darker nodes have higher centrality characteristics. Analyzing the degree centrality, most nodes in the network have similar degrees; analyzing the betweenness centrality, only a few nodes play a more important role in the shortest path of the network; analyzing the closeness centrality, most nodes are around the central node of the network; analyzing the eigenvector centrality, the network degree near node 342 is higher. This node centrality ranking reflects the importance of nodes in a static network, and it plays a more critical role in the network than cut points and central nodes.

[0137] Table 3 Node centrality calculation method

[0138]

[0139] After the static topological structure analysis of the bridge-tunnel network is completed, the vulnerability analysis process is carried out. First, the relative size of the largest connected subgraph and network efficiency are selected as vulnerability indicators for evaluating the network in the network characteristics. The calculation method is shown in Table 4.

[0140] Table 4 Network vulnerability index calculation method

[0141]

[0142] Taking into account the physical connectivity and efficiency of the network, the bridge and tunnel nodes are sorted by degree centrality, weighted degree centrality, betweenness centrality and weighted betweenness centrality to simulate the static failure order. Remove the node with the largest weighted betweenness centrality in the graph, and then recalculate the weighted betweenness centrality of each point. Perform this step in sequence until all nodes are removed to obtain a dynamic sorting of weighted betweenness centrality to simulate the dynamic failure order. In addition, the nodes are randomly sorted to simulate a random failure order. When conducting a node removal test on the bridge and tunnel network, first initialize the bridge and tunnel network, select the node removal strategy, and calculate the network efficiency and number of nodes in the initial state; then remove the nodes in the network in sequence according to the corresponding node ranking to simulate the damage of the bridge and tunnel. Each time a node is removed, the damaged proportion of the node is counted and the current vulnerability index of the network is calculated; repeat this process until all nodes in the network are removed. The vulnerability index change curves of the bridge and tunnel network under different strategies are obtained, as shown in Figure 5 and Figure 6 shown.

[0143] analyze Figure 5 It can be seen that the relative size of the maximum connected component of the bridge-tunnel network under dynamic failure decreases sharply with the proportion of node removal, and decreases to about 0.0 after removing about 20% of the nodes. The network is no longer connected and the transportation function fails. Under several other failure strategies, the network fails after removing about 70% of the nodes, indicating that the nodes ranked higher in the dynamic sorting are more important. Figure 6It can be seen that the network efficiency under dynamic failure also decreases the most, reaching a low point after removing about 40% of the nodes; in the network efficiency change curve, the value of the later network efficiency increases with the removal of nodes. This is because with the removal of nodes and edges, multiple independent nodes are formed in the network. These nodes are not helpful for network efficiency, so removing them will lead to an increase in efficiency. This phenomenon is more obvious in weighted network efficiency.

[0144] According to the recorded vulnerability index, the unified index of network vulnerability quantification is calculated, taking α = 100, and the calculation formula is:

[0145]

[0146] Draw the vulnerability curve of the bridge-tunnel network under different failure strategies. Figure 7 As shown. When the value of vulnerability is greater than 1, the network efficiency of the current network is approximately 0, or the relative size of the largest connected subgraph is about 1%. It can be considered that the bridge-tunnel network has lost its traffic capacity and the network is on the verge of collapse. Therefore, the vulnerability threshold of network failure is set to 1. In the process of removing nodes, each removal of a node will cause a change in the network vulnerability, and the change value is the vulnerability of the node. Calculate the vulnerability of each node removed before the network collapse, as shown in Figure 8 shown.

[0147] analyze Figure 7 and Figure 8 It can be seen that the failure rate of the bridge-tunnel network under dynamic failure is the fastest, and it is on the verge of collapse after removing about 18% of the nodes; the failure rate of the bridge-tunnel network under the failure strategy based on betweenness centrality is slightly faster than that based on degree centrality failure strategy, because the ranking of betweenness centrality is related to the shortest path between nodes; the fragility curve of the bridge-tunnel network under random failure is roughly consistent with other static removal strategies, and it collapses after about 60% of the nodes fail. Figure 8 It can be seen that the changes in network vulnerability caused by each node removal are different. There are key vulnerable nodes in the bridge-tunnel network. The increase in network vulnerability caused by the failure of these nodes is much greater than that of other general nodes.

[0148] Based on the Monte Carlo sampling method, for a complex random process or system, a single random sampling has a large uncertainty. Therefore, the random node failure strategy is repeated many times and the vulnerability of each test node is recorded. After 2000 samplings, a unified vulnerable node identification result is obtained, such as Fig. 9As shown. Among them, the numbers of the 10 nodes with the highest vulnerability are: 72, 210, 213, 246, 468, 474, 481, 483, 572, 576. Their failure will bring a stronger impact to the overall bridge-tunnel network in more cases. In order to effectively reduce potential risks, it is recommended that transportation network managers give priority to the status of these highly vulnerable nodes, implement targeted monitoring and maintenance measures, and ensure regular inspections and necessary reinforcement of these key nodes by optimizing resource layout and allocation, which can significantly improve the overall stability and security of the network. In addition, managers should formulate emergency plans to deal with the impact of the failure of these nodes, ensure that traffic flow can be quickly restored in emergencies, and reduce the negative impact on regional economic and social life.

[0149] The present invention combines complex network theory with the Monte Carlo method, which not only provides a quantitative, scientific and computable evaluation standard for the vulnerability analysis of regional bridge and tunnel networks, but also improves the consistency and credibility of the key vulnerable node identification results. It can help traffic managers optimize the monitoring, detection, maintenance and emergency strategies of bridge and tunnel facilities, rationally allocate resources, and enhance the risk resistance of regional bridge and tunnel networks.

[0150] The present invention also proposes an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of a vulnerability assessment and vulnerable node identification method for secure operation and maintenance of regional bridge and tunnel networks are implemented.

[0151] The present invention also proposes a computer-readable storage medium for storing computer instructions, which, when executed by a processor, implement the steps of a vulnerability assessment and vulnerable node identification method for secure operation and maintenance of regional bridge and tunnel networks.

[0152] The memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM bus RAM (DRRAM). It should be noted that the memory of the method described in the present invention is intended to include, but is not limited to, these and any other suitable types of memory.

[0153] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a high-density digital video disc (DVD)), or a semiconductor medium (eg, a solid state disc (SSD)).

[0154] In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in a processor or an instruction in the form of software. The steps of the method disclosed in conjunction with the embodiment of the present application can be directly embodied as a hardware processor for execution, or a combination of hardware and software modules in a processor for execution. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in conjunction with its hardware. To avoid repetition, it is not described in detail here.

[0155] It should be noted that the processor in the embodiment of the present application can be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method embodiment can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiment of the present application can be directly embodied as a hardware decoding processor to perform, or the hardware and software modules in the decoding processor can be combined and performed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0156] The above is a detailed introduction to the vulnerability assessment and vulnerable node identification method for regional bridge and tunnel network security operation and maintenance proposed in the present invention. This article uses specific examples to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A vulnerability assessment and vulnerable node identification method for regional bridge and tunnel network security operation and maintenance, characterized in that: The method comprises the following steps: Step 1: Preprocess and integrate the information of regional bridges and tunnels to establish a regional bridge-tunnel network; Step 2: Analyze the characteristics of the bridge-tunnel network based on graph theory and complex network theory to obtain the key nodes of the bridge-tunnel network topology structure; Step 3: Considering the accessibility and network efficiency, select vulnerability indicators, adopt different node failure strategies, and calculate the corresponding relationship between the node removal ratio and the vulnerability indicator; Step 4: Calculate the overall vulnerability of the bridge-tunnel network, perform vulnerability analysis on the network and record the vulnerability of each bridge-tunnel node; Step 5: Use the Monte Carlo sampling method to identify the vulnerable nodes of the bridge-tunnel network by randomly calculating the expected vulnerability of the bridge-tunnel nodes multiple times.

2. The method according to claim 1, characterized in that The step 1 is specifically as follows: The information of regional bridges and tunnels is preprocessed, and the geographical coordinates of bridges and tunnels WGS84 are converted into projection coordinates UTM using the Transverse Mercator projection. and longitude λ, the conversion formula is: x=k0N(λ-λ0)+EastOffset Where k0 is the scaling factor; N is the latitude and longitude ellipsoid radius; λ0 is the longitude of the central meridian; EastOffset is the east offset; From the equator to the latitude The arc length of The regional bridges and tunnels are divided into three types: bridges, viaducts and tunnels. Numbers, coordinates, connectivity and distance are extracted as the main features to establish a regional bridge and tunnel network.

3. The method according to claim 2, characterized in that The step 2 is specifically as follows: Step 21: Perform a depth-first search (DFS) on the established bridge-tunnel network to obtain the connectivity and cut points of the network; Step 2: Count the number of edges connected to each bridge and tunnel node to obtain the node degree and network degree distribution; analyze the connection characteristics between nodes, calculate the assortativity and average clustering coefficient of the network, and the calculation formula is: In the formula, R and C are the associativity coefficient and average clustering coefficient of the network respectively; i , k i They represent the degrees of the two endpoints of an edge respectively; M is the number of edges in the network; N is the number of nodes in the network, l i is the degree of node i; E i is the number of edges between neighboring nodes of node i; Step 2: Analyze the path characteristics of the network, including the average path length a, network diameter l, eccentricity ρ and radius r, and search for the central node. The calculation formula is: r=minρ Where d(i,j) is the shortest path between node i and node j; the node with eccentricity equal to the radius is the central node of the network; Step 24: Analyze the network topology importance of each bridge and tunnel node, and calculate the degree centrality, eigenvector centrality, betweenness centrality and closeness centrality of the node respectively. The calculation formula is: In the formula, λ is the eigenvalue; a ij is an element of the adjacency matrix, indicating whether there is an edge between node i and node j; σ st is the total number of shortest paths between node s and node t; σ st (i) is the number of shortest paths passing through node i.

4. The method according to claim 3, characterized in that The step 21 is specifically as follows: Step 2: Initialize a timestamp variable time to track the order of accessing nodes; initialize the node access time discovery and the earliest traceable time low; Step 2-2: Select an initial node i, and set the access time dis[i] and the initial earliest backtracking time low[i] of the node to the current time; Step 213: traverse all neighbors j of the node: if j has not been visited, continue to execute step 213 and update low[i]; if j has been visited and is not the parent node of i, update low[i] to min(low[i], dis[j]), indicating that j can be reached through the backtracking path; After completing step 213, if there are still unvisited nodes, initialize the nodes again and repeat steps 211 to 213. Each time this is done, the number of connected components increases by one. For each node i, if i is a root node and there are two or more subtrees, then i is a cut point; if i is a non-root node, check whether low[j] is greater than or equal to dis[i], that is, if the child node j and its descendants cannot return to the ancestor of i through the backtracking path, then i is a cut point.

5. The method according to claim 4, characterized in that The step three is specifically as follows: Step 31: Select the relative size of the largest connected subgraph and network efficiency in the bridge-tunnel network characteristics as the network vulnerability index, and the calculation formula is: Where LCC(G) is the relative size of the largest connected subgraph; E(G) is the network efficiency; N is the total number of bridges and tunnels; n lc is the number of nodes in the maximum connected component; is the penalty term for the connected nodes of the network efficiency, where n c is the total number of connected nodes in the network, and n is the number of nodes in the current network; Step 32: Sort the bridge and tunnel nodes by degree centrality, weighted degree centrality, betweenness centrality, and weighted betweenness centrality to simulate the static failure order; Remove the node with the largest weighted betweenness centrality in the graph, and then recalculate the weighted betweenness centrality of each point. Repeat this step until all nodes are removed, and obtain the dynamic sorting of weighted betweenness centrality to simulate the dynamic failure order; randomly sort the nodes to simulate the random failure order; Step 33: According to the different failure sequences obtained in step 32, node removal strategies are implemented for the bridge-tunnel network. Each time a node is removed, the network damage ratio is counted and the corresponding network vulnerability index is recorded.

6. The method according to claim 5, characterized in that The step three is specifically as follows: Step 331: Input the initialized bridge-tunnel network, calculate the network efficiency and number of nodes in the initial state, and select a removal strategy for the bridge-tunnel nodes; Step 332: select a bridge-tunnel node according to the corresponding node sorting, remove it from the network to simulate the damage of the bridge-tunnel, and calculate the network efficiency after the damage and the relative size of the largest connected subgraph; Step 333: Count the proportion of damaged nodes and determine whether all nodes have been traversed. If not, continue to execute step 332 until all nodes are removed and output the vulnerability indicator statistics.

7. The method according to claim 6, characterized in that The step 4 is specifically as follows: Step 41: Based on the relative size of the largest connected subgraph of the network and the network efficiency, a unified index for quantifying the vulnerability of the bridge-tunnel network is proposed. The calculation formula is: Where SV(G') is the static vulnerability of the network; ΔE(G') is the change in network efficiency; α is the connectivity importance coefficient; Set the vulnerability threshold of network failure to 1. When the network vulnerability reaches the threshold, E(G') is 0 or LCC(G') is 1%, which can be used as the trigger condition for network collapse. Step 42: Calculate the change in network vulnerability caused by each node removal, match the vulnerability to the node one by one according to the node removal order recorded in step 33, and calculate the vulnerability of each bridge and tunnel node.

8. The method according to claim 7, characterized in that The step five is specifically as follows: Step 51: adopt a random failure sequence, obtain the vulnerability analysis results of the bridge-tunnel network under random node failure according to step 33, and record the vulnerability of each bridge-tunnel node; Step 52: Based on the Monte Carlo principle, repeat step 51, sample a large number of times in the random failure strategy, and estimate the expected vulnerability of each bridge-tunnel node as its overall vulnerability; identify the vulnerable nodes of the bridge-tunnel network according to the overall vulnerability, and mark and visualize them in combination with the bridge-tunnel network topology map.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium for storing computer instructions, characterized in that: When the computer instructions are executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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