Method and device for evaluating toughness of mountain road network in multi-disaster environment

Abstract

The invention provides a mountain road network toughness evaluation method and device in a multi-disaster environment, and belongs to the technical field of roads. According to the method, the toughness of the mountain road network can be quantitatively evaluated, and the city and town communication demand satisfaction degree and the city and town communication efficiency of each road section are calculated through the passing time and the passing cost; the toughness of the road network is determined according to the slope of the change broken line graph of the urban communication demand satisfaction degree and the urban communication efficiency between the road sections, and meanwhile, a key local road network is selected according to the slope between the road sections; single-double road section blocking analysis and multi-road section blocking analysis are carried out on the road sections in the key local road network to judge the road section which has the greatest influence on the road network toughness, so that more targeted measures are taken for the road sections under the limited cost; for example, the road network function toughness is effectively improved by adopting measures of increasing redundant routes or improving the anti-disaster capability of the road sections and the like.

Description

Technical Field

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

[0002] The highway network is an important transportation infrastructure for economic and social exchanges between regions, and road blockages caused by disasters will affect the normal operation of the road network. In particular, as extreme weather and earthquake activities intensify, the number of road blockages continues to increase, the impact is aggravated, and the sustainable development of mountain road transportation is endangered. The mountainous areas of the western Sichuan Plateau and the Yunnan-Guizhou Plateau are located in the transition section between the Qinghai-Tibet Plateau and the plains. The terrain and geology are complex. Under the coupling of multiple factors such as heavy rainfall, earthquakes, and human activities, various natural disasters occur frequently, which can easily cause 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 roads make some low-altitude sections vulnerable to slope disasters such as mudslides and landslides in summer, and high-altitude sections are prone to ice and snow in winter.

[0003] Once an important road section is buried by a landslide or cut off by a mudslide, it may even cause traffic paralysis and make road rescue difficult. The highway network system is complex, and there are many evaluation indicators for highway network resilience. The indicators affect each other, making it impossible to evaluate the resilience of the highway network.

[0004] Therefore, there is an urgent need for a method to evaluate the resilience of mountain highway networks under multi-hazard environments to provide a corresponding theoretical basis for highway management departments to respond to natural disasters and take necessary disaster prevention and mitigation measures. Summary of the invention

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

[0006] The present invention provides a method for evaluating the resilience of a mountain road network under a multi-hazard environment, comprising the following steps:

[0007] 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;

[0008] 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;

[0009] 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 the two town nodes. If the generalized connectivity cost of a path between the 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 the two town 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 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;

[0011] 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.

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

[0013] Furthermore, in step S2, the method for calculating the importance of each road section is: respectively calculate the four indicators of node degree, betweenness centrality, closeness centrality, and clustering coefficient of each road section and use the entropy weight method to weight the four indicators, determine the weights of the four indicators, and add the weights of the four indicators to obtain the importance of the road section.

[0014] Furthermore, the generalized connectivity cost is:

[0015]

[0016] 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;

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

[0018]

[0019] In the formula, represents the minimum generalized connectivity cost between nodes i and j; μ represents the tolerance coefficient, which ranges from [0-1].

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

[0021] 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, and calculate the town influence index of the town node:

[0022]

[0023] In the formula, G represents the town influence index, W i represents the weight of indicator i, A i Represents the size of the corresponding parameter among the six parameters;

[0024] S502, calculating the weights of six parameters by CRITIC method;

[0025] S503. Obtain the traffic connection intensity index based on the town influence index:

[0026]

[0027] 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, α=β=1, γ=1.

[0028] Furthermore, in the step S6, the method for determining the resilience 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 as follows: a coordinate system is established with the number of disaster blocking simulation sections as the horizontal coordinate and the loss percentage of the connectivity demand satisfaction of the road network as the vertical coordinate, and a slope vector of the change of the loss percentage of the connectivity demand satisfaction between two sections is determined as the connectivity demand satisfaction resilience index between the sections, and the connectivity demand satisfaction resilience index of the road network is the vector sum of the connectivity demand satisfaction resilience index between the sections;

[0029] 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.

[0030] 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.

[0031] 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.

[0032] A device comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor implements the above method when executing the computer program.

[0033] Beneficial effects of the present invention:

[0034] The present invention can quantitatively evaluate the resilience of mountain highway networks. First, the degree of satisfaction of urban connectivity needs and the efficiency of urban connectivity of each road section are calculated through travel time and travel cost. The resilience of the road network is determined by the slope of the line graph of changes in the degree of satisfaction of urban connectivity needs and the efficiency of urban connectivity between each road section. At the same time, the key local road network is selected through the slope between each road section. Single and double section blocking analysis and multi-section blocking analysis are performed on the sections in the key local road network to determine the sections with the greatest impact on the resilience of the road network, so that more targeted measures can be taken 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 of sections. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention is further described below in conjunction with the accompanying drawings. In the description of the present invention, it should be understood that the terms "upper", "lower", "front", "back", "left", "right", "top", "bottom", "inside", "outside" and the like indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings, which are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention.

[0037] Example 1

[0038] like Figure 1 As shown, a method for evaluating the resilience of a mountain road network under a multi-hazard environment includes the following steps:

[0039] 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;

[0040] 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;

[0041] 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 the two town nodes. If the generalized connectivity cost of a path between the 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 the two town nodes is less than or equal to the threshold, it indicates that the path between the two nodes is a reasonable path;

[0042] 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;

[0043] The calculation method of the reasonable path number of a node is:

[0044]

[0045] Where: CN r (i) represents the connectivity requirement satisfaction of node i; CN ij ＇ represents the number of reasonable paths, C ij0represents the sum of reasonable paths between nodes i and j before the disaster is blocked; C ij Represents the sum of reasonable paths between nodes i and j after the disaster is blocked. If there is no valid path between nodes i and j, it is considered unsatisfactory.

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

[0047]

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

[0049] S5. Based on the generalized connectivity cost model, the connectivity efficiency satisfaction of the node is calculated. 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 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. The connectivity efficiency of the node before and after the disaster blocking simulation is recorded in turn, and the ratio of the connectivity efficiency before and after the disaster blocking simulation is taken as the connectivity efficiency satisfaction of the node. The connectivity efficiency satisfaction of the road network is the sum of the connectivity efficiency satisfactions of all nodes.

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

[0051]

[0052] Where: N r represents the efficiency satisfaction of road network connectivity before and after disaster blocking, T ij Represents the traffic connection intensity between nodes, and inf means infinity.

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

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

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

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

[0057]

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

[0059] Betweenness centrality represents the number of shortest paths from any node to another node (not i) in the network that pass through node i. The shortest path between nodes refers to the minimum number of edges required to pass through. The more shortest paths that pass through a node, the more important the node is in the network. The betweenness centrality b i The calculation formula is as follows:

[0060]

[0061] Where N jl represents the number of shortest paths between nodes j and l; N jl (i) represents the number of nodes i that the shortest path between nodes j and l passes through;

[0062] Closeness centrality means that the shortest distance between nodes is the minimum number of nodes required to connect two points. The reciprocal of the sum of the distances between a node and other nodes in the network 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 more important the node is 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] The clustering coefficient can reflect the degree of clustering of nodes in a local road network. The higher the node clustering coefficient, the higher the degree of clustering of 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 the k nodes. The clustering coefficient c of node i is i It is the ratio of the number of edges connecting these k nodes in practice to the maximum number of edges that can be connected in theory. iThe calculation formula is as follows:

[0066]

[0067] Where: M represents the number of edges 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 discrete degree of each evaluation index. If the information entropy value of a certain evaluation index is smaller, the discrete degree of information in the index is greater, and its impact on the comprehensive evaluation is greater, and the corresponding weight of the index is greater; otherwise, the weight is smaller; for mountain road networks, mountain road networks have the characteristics of fewer traffic data observation points and larger node spacing, and there are fewer parameters and other problems. Data acquisition is inconvenient. In the absence of data, such as the average speed of vehicles on highway sections and the traffic flow of 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 inaccurate. Through the four indicators, the importance of each section in the road network can be quickly and accurately calculated in the absence of observation data.

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

[0070]

[0071] 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;

[0072] For mountain road networks, the travel time and travel cost of a road section are relatively easy to obtain data. The travel time can be obtained by recording the entry time and exit time at the two nodes of the road section respectively, and the travel costs of the section between the two nodes, such as tolls and fuel costs, can also be obtained through simple statistics. There is no need to monitor mountain road sections in real time to obtain data with large changes, such as vehicle flow and speed. The data obtained with relatively fixed tolls is accurate, and the generalized connectivity cost data obtained is also relatively accurate. Therefore, by using the generalized connectivity cost as the resilience calculation of the road network, predictions can be made even without a base year OD, which meets the characteristics of mountain road networks such as fewer traffic data observation points and larger node spacing.

[0073] Specifically, in step S3, in an actual road network, there are many paths between two nodes in the road network, and travelers will choose one of the paths to achieve their travel purpose; but when selecting a path between nodes, they often do not consider all the connected paths between the nodes, but choose a reasonable and feasible path, such as the path with the shortest travel distance, the shortest travel time or the lowest toll, that is, the path with the lowest generalized connectivity cost. Of course, travelers may not necessarily choose the path with the lowest connectivity cost for other considerations, but choose other reasonable paths but will also consider a certain travel time and cost. Therefore, travelers have a tolerance coefficient for the generalized connectivity cost of traveling between the two nodes, and set a threshold of the generalized connectivity cost. When the path between the nodes is not blocked by disasters, the generalized connectivity cost between the 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, it is considered that there is a valid path between the nodes, and the method for setting the generalized connectivity cost threshold is:

[0074]

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

[0076] Specifically, in the traditional method, the traffic influence between towns is expressed by the total population of the region, and the impedance is the distance between towns. However, the mountain road network has the characteristics of fewer traffic data observation points and larger node spacing, fewer parameters, and even no base year OD (traffic travel volume) data, such as average vehicle speed, traffic volume and other data. In modern society, the traffic influence of towns is composed of multiple factors such as economic and social development level, development quality, and social and economic activity. Among them, gross national product and population are basic indicators of the region, reflecting the economic and social development level of the region; the output value of the secondary and tertiary industries reflects the differences in economic activities and the industrial structure of the region in different regions; the total social retail sales and total tourism income reflect the economic and social activity intensity of the town. The more active the social and economic activities, the greater its external attraction. Therefore, combined with the economic and social development indicators of each town, the actual travel distance between each node is used as impedance, and the data is easy to obtain and more accurate. The traffic connection intensity indicators between town nodes in step S5 include:

[0077] 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:

[0078]

[0079] In the formula, G represents the town influence index, W i represents the weight of indicator i, A i Represents the size of the corresponding parameter among the six parameters;

[0080] S502, calculating the weights of six parameters by CRITIC method;

[0081] S503. Obtain the traffic connection intensity index based on the town influence index:

[0082]

[0083] 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, α=β=1, γ=1.

[0084] Specifically, in step S6, the method for determining the resilience value of the road network as a whole includes: a method for determining the resilience 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 as follows: first, the road sections in the road network are arranged in order from high to low according to their importance, and the road sections are blocked successively from high to low in order, a coordinate system is established with the number of disaster-blocked simulated road sections as the horizontal coordinate and the percentage loss of the connectivity demand satisfaction of the road network as the vertical coordinate, a slope vector of the change of the percentage loss of the connectivity demand satisfaction between two road sections is determined as the connectivity demand satisfaction resilience index between the road sections, and 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;

[0085] 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.

[0086] 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.

[0087] 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.

[0088] Example 2

[0089] This embodiment provides a device, which includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the computer program, the method in the above-mentioned embodiment 1 is implemented.

[0090] Processors include, but are not limited to, personal computers, minicomputers, mainframes, workstations, networked or distributed computing environments, separate or integrated computer platforms. Aspects of the invention may be implemented as 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, and when the storage medium or device is read by the computer, can be used to configure and operate the computer to perform the processes described herein. In addition, the machine-readable code, or portions thereof, may be transmitted over a wired or wireless network.

[0091] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached 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 the two town nodes. If the generalized connectivity cost of a path between the 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 the 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 resilience value of the road network based on the changing relationship between the connectivity demand satisfaction and connectivity efficiency satisfaction of the road network.

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 weights of the four indicators.

3. The method for evaluating the resilience of a mountain road network under a multi-hazard environment according to claim 1, characterized in that: 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, and calculate the town influence index of the town node: In the formula, G represents the town influence index, W i 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 index 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, α=β=1, γ=1.

5. The method for evaluating the resilience of a mountain road network under a multi-hazard environment according to claim 1, characterized in that: In the step S6, the method for determining the resilience 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 as follows: a coordinate system is established with the number of disaster blocking simulation sections as the horizontal coordinate and the loss percentage of the connectivity demand satisfaction of the road network as the vertical coordinate, and the change slope vector of the loss percentage of the connectivity demand satisfaction between two sections is determined as the connectivity demand satisfaction resilience index between the sections, and the connectivity demand satisfaction resilience index of the road network is the vector sum of the connectivity demand satisfaction resilience index between the 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.

6. A 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 5 is implemented.

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