A method and device for evaluating the resilience of mountain highway networks in a multi-hazard environment

Abstract

The present invention provides a method and device for evaluating the resilience of mountain highway networks in a multi-disaster environment, belonging to the field of highway technology. The present invention can quantitatively evaluate the resilience of mountain highway networks. First, the satisfaction degree of urban connectivity demand and the urban connectivity efficiency of each section are calculated through the travel time and travel cost, and the resilience of the road network is determined by the slope magnitude of the change line graph of the satisfaction degree of urban connectivity demand and the urban connectivity efficiency between each section. At the same time, the key local road networks are selected through the slopes between each section, and the single-section and multi-section blocking analyses are carried out on the sections in the key local road networks to determine the sections that have the greatest impact on the resilience of the road network, so as to take more targeted measures for the sections at a limited cost, such as effectively improving the functional resilience of the road network by taking measures such as adding redundant routes or improving the disaster resistance ability of the sections.

Description

Technical Field

[0001] The present invention relates to the technical field of highways, and particularly to a method and device for evaluating the resilience of mountain highway networks in a multi-hazard environment. Background Art

[0002] Highway networks are important transportation infrastructures for economic and social exchanges between regions. Road blockages caused by disasters can affect the normal operation of the network functions. Especially with the intensification of extreme weather and seismic activities, the number of road blockage events is increasing continuously, the impact degree is aggravating, endangering the sustainable development of mountain highway transportation. Mountainous areas such as the western Sichuan Plateau and the Yunnan-Guizhou Plateau are located in the transition section between the Qinghai-Tibet Plateau and the plain, with complex topography and geology. Under the combined action of multiple factors such as heavy precipitation, earthquakes, and human activities, various natural disasters occur frequently, easily causing damage to transportation infrastructure and traffic blockages. At the same time, the characteristics of large altitude changes, strong seasonal rainfall, and sparse vegetation along mountain highways make some low-altitude sections vulnerable to slope disasters such as debris flows and landslides in summer, and high-altitude sections are prone to icing and snow accumulation in winter.

[0003] Once an important section is buried by a landslide or the road is washed away by a debris flow, it may even lead to traffic paralysis and it is difficult to carry out road rescue. The highway network system is complex, there are many evaluation indicators for the resilience of the highway network, and the indicators affect each other, resulting in the inability to evaluate the resilience of the highway network.

[0004] Therefore, there is an urgent need for a method for evaluating the resilience of mountain highway networks in a multi-hazard environment to provide a corresponding theoretical basis for highway management departments to cope with natural disasters and take necessary disaster prevention and mitigation measures. Summary of the Invention

[0005] This disclosure is provided to introduce concepts in a brief form, which will be described in detail in the following Detailed Description section. This disclosure is not intended to identify the target features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0006] The present invention provides a method for evaluating the resilience of mountain highway networks in a multi-hazard environment, including the following steps:

[0007] S1. Obtain the location information of each section in the target road network and the urban points in the road network. Take the intersections between each section and the urban points as nodes, the sections between each node as edges, and weight them with the generalized connectivity cost to construct a road network model;

[0008] S2. Based on complex networks, take each section as a node and the connection relationship between each section as an edge to determine the importance of each section;

[0009] S3. Construct a generalized connectivity cost model between each urban node based on travel time and travel cost, and set a generalized connectivity cost threshold based on the nodes to determine whether there is a reasonable path between the two urban nodes. If the generalized connectivity cost of a path between two urban nodes is greater than the threshold, it indicates that the path between the two nodes is an unreasonable path; when the generalized connectivity cost of a path between two urban nodes is less than or equal to the threshold, it indicates that the path between the two nodes is a reasonable path.

[0010] S4. Simulate the sections in the road network model in descending order of section importance, and record the number of reasonable paths between the node and all other nodes before and after the interruption simulation in sequence. Use the ratio of the number of reasonable paths before and after the disaster interruption simulation as the satisfaction degree of the connectivity demand of this node. The satisfaction degree of the connectivity demand of the road network is the sum of the satisfaction degrees of the connectivity demands of all nodes.

[0011] S5. Based on the generalized connectivity cost model, calculate the satisfaction degree of the connectivity efficiency of the node. The connectivity efficiency of an unreasonable path between two nodes is zero, and the connectivity efficiency of a reasonable path is inversely proportional to the traffic connection strength index of the node. The connectivity efficiency of this node is the sum of the connectivity efficiencies with all other nodes. Record the connectivity efficiency of the node before and after the disaster interruption simulation in sequence, and use the ratio of the connectivity efficiency before and after the disaster interruption simulation as the satisfaction degree of the connectivity efficiency of this node. The satisfaction degree of the connectivity efficiency of the road network is the sum of the satisfaction degrees of the connectivity efficiencies of all nodes.

[0012] S6. Determine the resilience value of the road network based on the change relationship between the satisfaction degree of the connectivity demand and the satisfaction degree of the connectivity efficiency of the road network.

[0013] Furthermore, in the step S2, the calculation method for calculating the importance of each section is as follows: calculate the four indicators of the node degree, betweenness centrality, closeness centrality, and clustering coefficient of each section respectively, and use the entropy weight method to assign weights to the four indicators to determine the weights of the four indicators. The importance of this section is obtained by adding the weights of the four indicators.

[0014] Furthermore, the generalized connectivity cost is:

[0015]

[0016] In the formula: C ij represents the generalized connectivity cost between nodes i and j; O represents the set of sections passed by the path between nodes i and j. m1 represents the fuel consumption cost per unit length of the standard vehicle on section x, with the unit of yuan / km; m2 represents the toll per unit length of the standard vehicle on section x, with the unit of yuan / km. l x represents the total distance of section x. T x represents the travel time, and k1 and k2 represent conversion coefficients.

[0017] The method for setting the generalized connectivity cost threshold is as follows:

[0018]

[0019] In the formula, represents the lowest generalized connectivity cost between nodes i and j; μ represents the tolerance coefficient, and its value range is [0 - 1].

[0020] Furthermore, in step S5, the calculation method of the traffic connection intensity index includes:

[0021] S501. Obtain six parameters of the gross national product, regional resident population, proportion of the tertiary industry, proportion of the secondary industry, total tourism revenue, and total social consumer goods of each urban node in the target road network, and calculate the urban influence index of the urban node:

[0022]

[0023] In the formula, G represents the urban influence index, and W i represents the weight of index i, A i represents the magnitude of the corresponding parameter among the six parameters;

[0024] S502. Calculate the weights of the six parameters by the CRITIC method;

[0025] S503. Obtain the traffic connection intensity index according to the urban influence index:

[0026]

[0027] T ij represents the traffic connection intensity between nodes; K represents the proportionality constant of the model, which can be adjusted according to the actual situation; represents the urban influence index of node i and the urban influence index of node j; represents the actual distance between node i and node j; α, β, γ represent correction coefficients, and take α = β = 1, γ = 1.

[0028] Furthermore, in step S6, the method for determining the resilience value of the road network based on the change relationship between the connectivity demand satisfaction degree and the connectivity efficiency satisfaction degree of the road network is as follows: taking the number of disaster-blocked simulated road segments as the abscissa and the loss percentage of the connectivity demand satisfaction degree of the road network as the ordinate to establish a coordinate system, and determining the change slope vector of the loss percentage of the connectivity demand satisfaction degree between two road segments as the connectivity demand satisfaction degree resilience index between the road segments, then the connectivity demand satisfaction degree resilience index of the road network is the vector sum of the connectivity demand satisfaction degree resilience indexes between the road segments;

[0029] Taking the number of disaster-blocked simulation sections as the abscissa and the loss percentage of the satisfaction degree of the road network's connectivity efficiency as the ordinate to establish a coordinate system, determining the change slope vector of the loss percentage of the satisfaction degree of the connectivity efficiency between two sections as the resilience index of the satisfaction degree of the connectivity efficiency between sections, then the resilience index of the satisfaction degree of the road network's connectivity efficiency is the vector sum of the resilience indices of the satisfaction degree of the connectivity efficiency between sections;

[0030] The resilience value vector between sections is the sum of the resilience index vector of the satisfaction degree of the connectivity demand between sections and the resilience index vector of the satisfaction degree of the connectivity efficiency between sections. The resilience value between sections is the included angle value between the resilience value vector and the abscissa. The larger the included angle value, the lower the resilience value; the smaller the included angle value, the higher the resilience value;

[0031] The resilience value vector of the road network is the sum of the resilience index vector of the satisfaction degree of the connectivity demand of the road network and the resilience index vector of the satisfaction degree of the connectivity efficiency of the road network. The resilience value of the road network is the included angle value between the resilience value vector and the abscissa. The larger the included angle value, the lower the resilience value; the smaller the included angle value, the higher the resilience value.

[0032] A device includes a memory, a processor, and a computer program stored on the memory and capable of running on the processor. When the processor executes the computer program, the above-mentioned method is implemented.

[0033] Advantages of the present invention:

[0034] The present invention can quantitatively evaluate the resilience of mountainous road networks. First, calculate the satisfaction degree of urban connectivity demand and urban connectivity efficiency of each section through travel time and travel cost, and determine the resilience of the road network by the slope magnitude of the change broken line graph of the satisfaction degree of urban connectivity demand and urban connectivity efficiency between each section. At the same time, select the key local road networks through the slopes between each section, and conduct single-section and multi-section blockage analysis on the sections in the key local road networks to determine the sections that have the greatest impact on the resilience of the road network, so as to take more targeted measures for the sections at a limited cost, such as effectively improving the functional resilience of the road network by taking measures such as adding redundant routes or improving the disaster resistance ability of sections. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a flow schematic diagram of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0036] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described below with reference to the accompanying drawings. In the description of the present invention, it should be understood that the orientation or positional relationships indicated by the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc. are based on the orientation or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation to the present invention.

[0037] Embodiment 1

[0038] As Figure 1 shown, a method for evaluating the resilience of mountain highway networks in a multi-hazard environment includes the following steps:

[0039] S1. Obtain the location information of each section in the target road network and the urban points in the road network. Use the intersections between each section and the urban points as nodes, the sections between each node as edges, and weighted by the generalized connectivity cost to construct a road network model;

[0040] S2. Based on complex networks, use each section as a node and the connection relationship between each section as an edge to determine the importance of each section;

[0041] S3. Construct a generalized connectivity cost model between each urban node based on the travel time and travel cost, and set a generalized connectivity cost threshold based on the node to determine whether there is a reasonable path between the two urban nodes. If the generalized connectivity cost of a path between two urban nodes is greater than the threshold, it indicates that the path between the two nodes is an unreasonable path; when the generalized connectivity cost of a path between two urban nodes is less than or equal to the threshold, it indicates that the path between the two nodes is a reasonable path;

[0042] S4. Sequentially simulate blocking the sections in the road network model from high to low according to the section importance, and sequentially record the number of reasonable paths before and after blocking between the node and all other nodes, and use the ratio of the number of reasonable paths before and after the disaster blocking simulation as the connectivity demand satisfaction degree of the node. The connectivity demand satisfaction degree of the road network is the sum of the connectivity demand satisfaction degrees of all nodes;

[0043] Among them, the calculation method of the reasonable path number of the node is:

[0044]

[0045] In the formula: CN r (i) represents the connectivity demand satisfaction degree of node i; CN ij ＇ represents the number of reasonable paths, C ij0represents the total reasonable paths between nodes i and j before disaster interruption; C ij represents the total reasonable paths between nodes i and j after disaster interruption. If there is no effective path between nodes i and j, it is regarded as not being able to be satisfied;

[0046] The satisfaction degree of urban connectivity demand for the overall road network is:

[0047]

[0048] In the formula: CN r represents the satisfaction degree of connectivity demand before disaster interruption for all urban nodes in the network, CN r0 (i) represents the satisfaction degree of connectivity demand after disaster interruption for all urban nodes in the network;

[0049] S5. Based on the generalized connectivity cost model, calculate the satisfaction degree of the connectivity efficiency of nodes. The connectivity efficiency of unreasonable paths between two nodes is zero, and the connectivity efficiency of reasonable paths is inversely proportional to the traffic connection intensity index of the nodes. The connectivity efficiency of this node is the sum of the connectivity efficiencies with all other nodes. Record the connectivity efficiencies of the nodes before and after the disaster interruption simulation in sequence, and use the ratio of the connectivity efficiencies before and after the disaster interruption simulation as the satisfaction degree of the connectivity efficiency of this node. The satisfaction degree of the connectivity efficiency of the road network is the sum of the satisfaction degrees of the connectivity efficiencies of all nodes.

[0050] Among them, the satisfaction degree of the connectivity efficiency of the road network is:

[0051]

[0052] In the formula: N r represents the satisfaction degree of the connectivity efficiency of the road network before and after disaster interruption, T ij represents the traffic connection intensity between nodes, and inf represents infinity.

[0053] S6. Determine the resilience value of the road network based on the change relationship between the satisfaction degree of the connectivity demand and the satisfaction degree of the connectivity efficiency of the road network.

[0054] In the road network, when a section is interrupted by a disaster, the greater the changes in the satisfaction degree of the connectivity demand and the satisfaction degree of the connectivity efficiency of this section, the weaker its ability to cope with disaster interruption, and the corresponding resilience index is smaller. The overall resilience of the road network is the sum of the resilience of each section in the road network.

[0055] Specifically, the calculation method for calculating the importance of each section in step S2 includes: calculating the four indicators of the node degree, betweenness centrality, closeness centrality, and clustering coefficient of each section respectively, and using the entropy weight method to assign weights to the four indicators to determine the weights of the four indicators, and adding the four indicators according to the weights to obtain the importance of the section.

[0056] Among them, the node degree represents the degree value k of node ii It reflects that section i is directly connected to other k sections. The more sections a section is directly connected to, the higher the degree value of the node, and the greater its role in the road network. Node degree k i The calculation formula is as follows:

[0057]

[0058] In the formula, α ij represents the element in the network adjacency matrix. If there is an edge connection between i and j, then α ij = 1; if not connected, α ij = 0;

[0059] Betweenness centrality represents the number of shortest paths passing through node i among all shortest paths from any node in the network to another node (neither of which is i). The shortest path between nodes refers to the minimum number of edges required to pass through. The more shortest paths passing through a certain node, the more important the role of the node in the network. Betweenness centrality b i The calculation formula is as follows:

[0060]

[0061] In the formula, N jl represents the number of shortest paths between nodes j and l; N jl (i) represents the number of shortest paths between nodes j and l passing through node i;

[0062] Closeness centrality indicates that the shortest distance between nodes is the minimum number of nodes required to connect between two points. The reciprocal of the sum of the distances between a certain node in the network and other nodes is defined as the closeness centrality of the node. The greater the closeness centrality of a node, the shorter the distance between the node and other nodes in the network, and the higher the importance of the node in the network. The calculation formula of closeness centrality C(i) is as follows:

[0063]

[0064] In the formula, n represents the number of nodes in the network; d ij represents the shortest path distance from node i to other nodes in the network;

[0065] Clustering coefficient can reflect the aggregation degree of each node in the local road network. The higher the clustering coefficient of a node, the higher the aggregation degree of the road sections in the road network. In a complex network, when node i is directly connected to other k nodes, there are at most k(k - 1) / 2 edges connecting among the k nodes. Then the clustering coefficient c i of node i is the ratio of the number of actually connected edges among these k nodes to the maximum possible number of connected edges in theory. Clustering coefficient c iThe calculation formula is as follows:

[0066]

[0067] Where: M represents the number of edges connected between the k nodes connected to node i;

[0068] The entropy weight method is used to assign weights to each evaluation index. It uses the entropy value to judge the dispersion degree of each evaluation index. If the information entropy value of a certain evaluation index is smaller, the dispersion degree of the information in the index is larger, then its influence on the comprehensive evaluation is greater, and the corresponding weight of this index is larger; otherwise, the weight is smaller; for the mountainous road network, the mountainous highway network has characteristics such as fewer traffic data observation points and larger node spacing, and there are problems such as fewer parameters. It is more inconvenient to obtain data. In the case of lack of data, such as data on the average speed of vehicles on highway sections and traffic flow on highway sections, it is necessary to monitor the road network in real time to obtain more accurate data. Therefore, fewer data parameters are obtained, and the data accuracy is relatively low. It is difficult to determine the importance of the target section, or the importance ranking is determined inaccurately. Through four indicators, the importance of each section in the road network can be quickly and accurately calculated in the case of lack of observation data.

[0069] Specifically, in step S3, the generalized connectivity cost is the sum of the cost cost and the travel time of the section:

[0070]

[0071] Where: C ij represents the generalized connectivity cost between nodes i and j; O represents the set of sections passed by the path between nodes i and j, m1 represents the fuel consumption cost per unit length of the standard vehicle on section x, with the unit of yuan / km; m2 represents the toll per unit length of the standard vehicle on section x, with the unit of yuan / km, l x represents the total distance of section x, T x represents the travel time, and k1 and k2 represent conversion coefficients;

[0072] For the mountainous road network, the travel time and travel cost of the section are both relatively easy-to-obtain data. The travel time can be obtained by simply recording the entry time and the exit time at the two nodes of the section respectively, and the travel cost between the two nodes, such as tolls and fuel costs, can also be obtained through simple statistics, without the need to monitor the mountainous section in real time to obtain data with large changes such as vehicle flow and speed. The obtained travel cost data is relatively fixed and accurate, and the obtained generalized connectivity cost data is also relatively accurate; therefore, by using the generalized connectivity cost as the calculation of the road network resilience, it can be predicted even without the base-year OD, which meets the characteristic requirements of the mountainous highway network such as fewer traffic data observation points and larger node spacing.

[0073] Specifically, in step S3, in the actual road network, there are many paths between two nodes in the road network, and a traveler will choose one of the paths to achieve the purpose of passing through; however, when choosing a path between nodes, the traveler often does not consider all the connected paths between nodes, but chooses a reasonable and feasible path, such as the path with the shortest passing distance, the shortest passing time, or the lowest passing cost, that is, the path with the lowest generalized connected cost. Of course, due to other considerations, the traveler may not necessarily choose the path with the lowest connected cost, but choose other reasonable paths, but will also consider a certain passing time and passing cost. Therefore, the traveler has a tolerance coefficient for the generalized connected cost of traveling between these two nodes. A threshold of the generalized connected cost is set. When the path between nodes is not blocked by disasters, the generalized connected cost between nodes must be less than the threshold; if the nodes on the path are not reused, that is, there is no loop in the network, then it is considered that there is an effective path between these nodes. The method for setting the threshold of the generalized connected cost is as follows:

[0074]

[0075] In the formula, represents the lowest generalized connected cost between nodes i and j; μ represents the tolerance coefficient, and the value range is [0 - 1]. That is, when the generalized passing cost of the y path does not exceed (1 + μ) times the minimum passing cost between nodes, the traveler can accept this path and consider it a reasonable path.

[0076] Specifically, in the traditional method, the traffic influence between towns is represented by the total regional population, and the impedance is represented by the distance between towns. However, the mountainous highway network has characteristics such as fewer traffic data observation points and larger node spacing, resulting in fewer parameters, and even no base-year OD (traffic volume) data, such as vehicle average speed, traffic flow, etc. In modern society, the traffic influence of towns is composed of multiple factors such as the level of economic and social development, development quality, and social and economic activity. Among them, the gross national product and population are the basic indicators of the region, reflecting the level of economic and social development of the region; the output values of the secondary and tertiary industries reflect the economic activity differences and industrial structures of different regions; the total retail sales of social consumer goods and the total tourism revenue reflect the intensity of economic and social activity of the town. The more active the social and economic activities are, the greater the external attraction is. Therefore, combining the economic and social development indicators of each town, using the actual travel distance between each node as the impedance, the data is easy to obtain and relatively accurate. The traffic connection intensity indicators between urban nodes in step S5 include:

[0077] S501. Obtain the gross national product, regional resident population, proportion of the tertiary industry, proportion of the secondary industry, total tourism revenue, and total retail sales of social consumer goods of each urban node in the target road network, and calculate the urban influence index of the urban node:

[0078]

[0079] Wherein, G represents the urban influence index, and W i represents the weight of index i, A i represents the magnitude of the corresponding parameter among the six parameters;

[0080] S502. Calculate the weights of the six parameters by the CRITIC method;

[0081] S503. Obtain the traffic connection strength index according to the urban influence index:

[0082]

[0083] T ij represents the traffic connection strength between nodes; K represents the proportionality constant of the model, which can be adjusted according to the actual situation; represents the urban influence index of node i and the urban influence index of node j; represents the actual distance between node i and node j; α, β, γ represent correction coefficients, and take α = β = 1, γ = 1.

[0084] Specifically, in the step S6, the method for determining the resilience value of the overall road network includes: the method for determining the resilience value of the road network based on the variation relationship between the connectivity demand satisfaction degree and the connectivity efficiency satisfaction degree of the road network is as follows: First, arrange the road segments in the road network in descending order of their importance, and continuously block the road segments in descending order. Take the number of disaster-blocked simulated road segments as the abscissa and the loss percentage of the connectivity demand satisfaction degree of the road network as the ordinate to establish a coordinate system. Determine the change slope vector of the loss percentage of the connectivity demand satisfaction degree between two road segments as the connectivity demand satisfaction degree resilience index between the road segments. Then, the connectivity demand satisfaction degree resilience index of the road network is the vector sum of the connectivity demand satisfaction degree resilience indices between the road segments;

[0085] Take the number of disaster-blocked simulated road segments as the abscissa and the loss percentage of the connectivity efficiency satisfaction degree of the road network as the ordinate to establish a coordinate system. Determine the change slope vector of the loss percentage of the connectivity efficiency satisfaction degree between two road segments as the connectivity efficiency satisfaction degree resilience index between the road segments. Then, the connectivity efficiency satisfaction degree resilience index of the road network is the vector sum of the connectivity efficiency satisfaction degree resilience indices between the road segments;

[0086] The resilience value vector between road segments is the sum of the connectivity demand satisfaction degree resilience index vector and the connectivity efficiency satisfaction degree resilience index vector between the road segments. The resilience value between road segments is the included angle value between the resilience value vector and the abscissa. The larger the included angle value, the lower the resilience value; the smaller the included angle value, the higher the resilience value;

[0087] The resilience value vector of the road network is the sum of the resilience index vector of the connectivity demand satisfaction degree of the road network and the resilience index vector of the connectivity efficiency satisfaction degree. The resilience value of the road network is the included angle value between the resilience value vector and the abscissa. The larger the included angle value, the lower the resilience value; the smaller the included angle value, the higher the resilience value.

[0088] Embodiment 2

[0089] The device provided in this embodiment includes a memory, a processor, and a computer program stored on the memory and capable of running on the processor. When the processor executes the computer program, the method in Embodiment 1 above is implemented.

[0090] The processor includes, but is not limited to, personal computers, minicomputers, mainframes, workstations, network or distributed computing environments, separate or integrated computer platforms. Aspects of the present invention can be implemented in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, optical read and / or write storage medium, RAM, ROM, etc., such that it can be read by a programmable computer. When the storage medium or device is read by the computer, it can be used to configure and operate the computer to execute the processes described herein. In addition, the machine-readable code, or portions thereof, can be transmitted via wired or wireless networks.

[0091] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art of this industry should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and all these changes and improvements fall within the scope of the present invention claimed. The scope of the present invention claimed is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating the resilience of a mountain highway network under a multi-hazard environment, characterized by: The following steps are involved: S1. Obtain the location information of each road section in the target road network and the town points in the road network, take the intersection points between each road section and the town points as nodes, take the road sections between each node as edges, and weight them with generalized connectivity cost to construct a road network model; S2. Based on the complex network, each road section is regarded as a node and the connection relationship between the road sections is regarded as an edge to determine the importance of each road section; S3. A generalized connectivity cost model between each town node is constructed based on the travel time and travel cost, and a generalized connectivity cost threshold is set based on the node to determine whether there is a reasonable path between two town nodes. If the generalized connectivity cost of a path between two town nodes is greater than the threshold, it indicates that the path between the two nodes is an unreasonable path; when the generalized connectivity cost of a path between two town nodes is less than or equal to the threshold, it indicates that the path between the two nodes is a reasonable path; S4. Simulate the blocked sections in the road network model in order from high to low importance, and record the number of reasonable paths between the node and all other nodes before and after the blocking simulation, and use the ratio of the number of reasonable paths before and after the disaster blocking simulation as the connectivity demand satisfaction of the node. The connectivity demand satisfaction of the road network is the sum of the connectivity demand satisfaction of all nodes; S5. Based on the generalized connectivity cost model, calculate the connectivity efficiency satisfaction of the node. The connectivity efficiency of an unreasonable path between two nodes is zero. The connectivity efficiency of a reasonable path is inversely proportional to the node's traffic connection intensity index. The connectivity efficiency of the node is the sum of the connectivity efficiencies between the node and all other nodes. Record the connectivity efficiency of the node before and after the disaster blocking simulation in turn, and take the ratio of the connectivity efficiency before and after the disaster blocking simulation as the connectivity efficiency satisfaction of the node. The connectivity efficiency satisfaction of the road network is the sum of the connectivity efficiency satisfaction of all nodes. S6. Determine the toughness value of the road network based on the changing relationship between the connectivity demand satisfaction and the connectivity efficiency satisfaction of the road network. The method for determining the toughness value of the road network based on the changing relationship between the connectivity demand satisfaction and the connectivity efficiency satisfaction of the road network is: A coordinate system is established with the number of simulated road sections blocked by disasters as the horizontal coordinate and the percentage loss of connectivity demand satisfaction of the road network as the vertical coordinate. The slope vector of the percentage loss of connectivity demand satisfaction between two road sections is determined as the connectivity demand satisfaction resilience index between the road sections. The connectivity demand satisfaction resilience index of the road network is the vector sum of the connectivity demand satisfaction resilience index between the road sections. A coordinate system is established with the number of simulated road sections blocked by disasters as the horizontal coordinate and the percentage loss of the connectivity efficiency satisfaction of the road network as the vertical coordinate. The slope vector of the change of the percentage loss of the connectivity efficiency satisfaction between two road sections is determined as the connectivity efficiency satisfaction resilience index between the road sections. The connectivity efficiency satisfaction resilience index of the road network is the vector sum of the connectivity efficiency satisfaction resilience indexes between the road sections. The toughness value vector between road sections is the sum of the toughness index vector of the connectivity demand satisfaction and the toughness index vector of the connectivity efficiency satisfaction between road sections. The toughness value between road sections is the angle between the toughness value vector and the horizontal axis. The larger the angle value, the lower the toughness value, and the smaller the angle value, the higher the toughness value. The resilience value vector of the road network is the sum of the connectivity demand satisfaction resilience index vector and the connectivity efficiency satisfaction resilience index vector. The resilience value of the road network is the angle between the resilience value vector and the horizontal axis. The larger the angle value, the lower the resilience value, and the smaller the angle value, the higher the resilience value.

2. The method for evaluating the resilience of a mountain road network under a multi-hazard environment according to claim 1, characterized in that: In step S2, the method for calculating the importance of each road section is as follows: four indicators, namely, node degree, betweenness centrality, closeness centrality, and clustering coefficient, are calculated for each road section respectively, and the four indicators are weighted by the entropy weight method, the weights of the four indicators are determined, and the importance of the road section is obtained by adding the four indicators according to the weights.

3. The method for evaluating the resilience of a mountain road network under a multi-hazard environment according to claim 1 is characterized by: The generalized connectivity cost is: Where: C ij represents the generalized connectivity cost between nodes i and j; O represents the set of road segments between nodes i and j; m1 represents the fuel consumption per unit length of a standard vehicle on road segment x, in units of RMB / km; m2 represents the toll per unit length of a standard vehicle on road segment x, in units of RMB / km; l x represents the total distance of road segment x, T x represents the travel time, k1 and k2 represent the conversion coefficients; The method for setting the generalized connectivity cost threshold is: In the formula, represents the minimum generalized connectivity cost between nodes i and j; μ represents the tolerance coefficient, which ranges from [0-1].

4. The method for evaluating the resilience of a mountain road network under a multi-hazard environment according to claim 1, characterized in that: In step S5, the method for calculating the traffic connection intensity index includes: S501. Obtain the gross national product, regional permanent population, proportion of the tertiary industry, proportion of the secondary industry, total tourism income, and total social consumer goods of each town node in the target road network to calculate the town influence index of the town node: In the formula, G represents the town influence index, represents the weight of indicator i, ; A i Represents the size of the corresponding parameter among the six parameters; S502, calculating the weights of six parameters by CRITIC method; S503. Obtain the traffic connection intensity index based on the town influence index: T ij represents the traffic connection intensity between nodes; K represents the proportional constant of the model, which can be adjusted according to actual conditions; represents the town influence index of node i and the town influence index of node j; represents the actual distance between node i and node j; α, β, γ represent correction coefficients, and α=β=1, γ=1.

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 4 is implemented.

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