Method for measuring toughness of high-speed railway transportation network

Abstract

The invention discloses a method for measuring the toughness of a high-speed railway transportation network, and relates to the technical field of railway transportation networks. Comprising the following steps: respectively establishing a high-speed railway tense service network in a normal state and a high-speed railway tense service network under interference, and determining the instantaneous performance of the high-speed railway transportation network in the normal state and the instantaneous performance of the high-speed railway transportation network in the interference state; and determining the optimal network toughness of the high-speed railway transportation network according to the instantaneous performance in the normal state and the instantaneous performance in the interference state. According to the method, the instantaneous performance is measured and calculated on the smaller time granularity, the change rule of the instantaneous performance along with time can be effectively observed, the measurement and calculation precision of the network toughness is improved, and the method has more guiding significance for formulating a recovery strategy.

Description

Technical Field

[0001] The present invention relates to the technical field of railway transportation networks, and in particular to a method for measuring the resilience of a high-speed railway transportation network. Background Art

[0002] After years of planning and construction, high-speed rail has formed a huge and complex high-speed rail transportation network. The expansion of the network scale has greatly improved the spatial accessibility of cities and provided a strong impetus for economic development. With the acceleration of the networking process, the system presents complex characteristics of comprehensive, dynamic and open, making high-speed rail more vulnerable to sudden incidents, causing large-scale train delays or suspensions, and greatly reducing the quality of transportation services. Due to the unpredictability of emergencies, it is difficult for railway operating departments to grasp their occurrence patterns and deploy protective measures in advance. Therefore, effectively quantifying the performance loss of high-speed railway networks under emergencies and adopting scientific strategies for rapid recovery are the core contents of high-speed railway resilience research and are also practical problems that need to be solved in the operation and maintenance stage.

[0003] In the prior art, the high-speed railway transportation network is measured through a physical network model. The specific process includes: constructing a physical network model of the high-speed railway transportation network, and clarifying the nodes and connections in the physical network model; setting parameters such as node weights and connection capacity according to actual data; using simulation software or tools to simulate the performance changes of the physical network model after an emergency event; analyzing the performance change curve of the physical network model after the attack, and evaluating the resilience level of the high-speed railway transportation network through the performance response function.

[0004] The defects of the above-mentioned existing technologies are: the existing technologies consider the performance changes of the physical network model of the high-speed railway transportation network after an emergency event, and do not capture the changes in the network service level over time, resulting in the performance response function lacking expression at the time information level. The resulting resilience deviates greatly from the actual situation, which affects the formulation of the recovery strategy. Summary of the invention

[0005] Based on this, it is necessary to provide a high-speed railway transportation network resilience measurement method to address the above technical issues.

[0006] An embodiment of the present invention provides a high-speed railway transportation network resilience measurement method, including:

[0007] Obtain the train operation paths of each train in the entire train timetable of the high-speed railway transportation network, build a high-speed railway temporal service network under normal conditions based on the train operation paths, and obtain the instantaneous performance of the high-speed railway transportation network under normal conditions

[0008] A high-speed railway disturbance event is applied to the high-speed railway temporal service network under normal conditions to obtain the fault section and interruption time range of the high-speed railway temporal service network under normal conditions; the train operation paths corresponding to the affected trains passing through the fault section within the interruption time range are screened out from the entire railway train timetable to obtain a processed timetable; and the high-speed railway temporal service network under disturbance is constructed according to the train operation paths in the processed timetable;

[0009] In the high-speed railway temporal service network under interference, different recovery schemes x are used to restore the high-speed railway temporal service network under interference to a periodic state, and the travel time is minimized. i To the second site v j The shortest temporal path of the high-speed railway transportation network under interference conditions is obtained Among them, x is the recovery plan, a is the disturbance event, and t is the time;

[0010] According to the instantaneous performance under normal conditions and transient performance under interference conditions Determining the optimal network resilience of a high-speed rail transportation network.

[0011] Optionally, obtaining the train running path of each train in the entire train timetable on the high-speed railway specifically includes:

[0012] Obtain the high-speed train classification code in the railway train timetable, select the train operation data starting with G, D, and C to build the service network as the first input data set;

[0013] The data of lines and stations that have been put into operation in the high-speed railway network of the whole railway are obtained, and the lines that have lost their transportation functions and redundant stations are eliminated. The physical network representing the topological network structure of the high-speed railway lines of the whole railway is constructed by using the Space L spatial construction method as the second input data set.

[0014] The train running path of each train is obtained according to the mapping relationship between the service network in the first input data set and the physical network in the second input data set.

[0015] Optionally, a processed schedule is obtained, specifically including:

[0016] Apply high-speed railway disturbance events according to actual interference scenarios. High-speed railway disturbance events include damaged railway lines, damage levels, and the number of repair resources during the emergency period. Estimate the interruption time range of railway lines based on the fault level and number of repair resources of high-speed railway disturbance events, and locate the fault interval based on the high-speed railway network topology structure.

[0017] According to the running path and interruption time range of each train, it is determined whether each train passes through the fault section within the interruption time range; if the train passes through the fault section within the interruption time range, the train running path of the train is screened out; if the train does not pass through the fault section within the interruption time range, the train running path of the train is retained to obtain the processed timetable.

[0018] Optionally, the high-speed railway temporal service network specifically includes:

[0019] The links between network nodes are connected through five-tuples (v i ,v j ,t d ,t a ,r) indicates that train r is at t d When from the first site v i Departure from t a Arrived at the second station v j Station, the nodes at both ends of the link are given the time attribute t d and t a ;

[0020] The network sites are expanded into a sequence of spatiotemporal nodes with different time attributes. The connection between two spatiotemporal nodes represents the train at t d Depart at t a When it arrives, the established connection is called a service edge;

[0021] Determine whether the space-time nodes of the same station meet the transfer time continuity relationship. The generated connection is called a transfer edge.

[0022] According to the service edges and transfer edges, the high-speed railway temporal service network is specifically a complex directed graph G T =(V T ,E T );

[0023] Among them, V T Represents a site collection, Indicates the first site v i The space-time node set of where {t1,t2,…,t m} is the first station v that each train arrives at i time series;

[0024] E T represents a set of directed edges including service edges and transfer edges, Indicates the first site v i and the second site v j The set of service edges between represents the set of transfer edges at the same station,

[0025] Optionally, the search starts from the first site v i To the second site v j The shortest temporal path includes:

[0026]

[0027] Among them, t represents the time, v represents the station, i, s, m and j are formal parameters, and n is a positive integer;

[0028] The temporal path includes: service edges between different sites Transfer edges between the same stations

[0029] Optionally, obtain the instantaneous performance of the high-speed railway transportation network under normal conditions The specific calculation formula is:

[0030]

[0031] Among them, P i,j (t) is the first site v at time t i and the second site v j The set of feasible temporal paths between i,j (p,t) is the first site v at time t i and the second site v j The travel time of the temporal path p between them.

[0032] Optionally, obtain the instantaneous performance of the high-speed railway transportation network under interference conditions The specific calculation formula is:

[0033]

[0034] Among them, when the recovery strategy x is adopted under the influence of disturbance event a, P i,j (x,t|a) is the first site v at time t i and the second site v j The set of feasible temporal paths between i,j (p,x,t|a) is the first site v at time t i and the second site v j The temporal path between i,j The travel time of (x,t|a).

[0035] Optionally, determining the optimal network resilience of the high-speed railway, specifically including:

[0036]

[0037] stx∈X

[0038] S(x)≤S max

[0039] Among them, x is the recovery plan, t r (x) is the network performance recovery time of solution x, X is the set of alternative solutions; S(x) is the number of repair resources corresponding to the solution, S max is the maximum resource amount, a is the disturbance event, and R is the network resilience.

[0040] Compared with the prior art, the above-mentioned high-speed railway transportation network resilience measurement method provided by the embodiment of the present invention has the following beneficial effects:

[0041] The invention constructs a high-speed railway temporal service network under normal conditions based on the whole railway train schedule, and constructs a high-speed railway temporal service network under interference by applying a high-speed railway disturbance event to the high-speed railway temporal service network under normal conditions; in the process of obtaining instantaneous performance under interference conditions, the process of using different recovery schemes x to completely restore the high-speed railway temporal service network is used as a cycle, and the travel time is minimized as the goal, and the high-speed railway temporal service network is searched from the first station v i To the second site v j The shortest temporal path takes into account the cumulative changes of instantaneous performance on the entire time scale and measures the instantaneous performance at a smaller time granularity. It can effectively observe the change pattern of instantaneous performance over time, improve the measurement accuracy of network resilience, and be more instructive for the formulation of recovery strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A schematic diagram of a process flow of a high-speed railway transport network resilience measurement method provided in an embodiment;

[0043] Figure 2 A schematic diagram of a network structure of a high-speed railway transport network resilience measurement method provided in an embodiment;

[0044] Figure 3 An example diagram of the physical meaning of resilience of a high-speed railway transportation network resilience measurement method provided in one embodiment;

[0045] Figure 4 A resilience curve diagram of a high-speed railway transport network resilience measurement method provided in one embodiment;

[0046] Figure 5 An interference scenario diagram of a high-speed railway transport network resilience measurement method provided in an embodiment;

[0047] Figure 6 A real-time resilience curve diagram of a high-speed railway transport network resilience measurement method provided in an embodiment, Figure 6(a) is the real-time resilience curve of the interference scenario affecting area 1. Figure 6 (b) is a real-time resilience curve diagram of the interference scenario affecting area 2;

[0048] Figure 7 A network resilience value heat map of a high-speed railway transportation network resilience measurement method provided in an embodiment, Figure 7 (a) is the network resilience value heat map of the interference scenario affecting area 1. Figure 7 (b) in the figure is a heat map of the network resilience value for the interference scenario affecting area 2. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0050] In one embodiment, a high-speed railway transport network resilience measurement method is provided, such as Figure 1 As shown, the method includes:

[0051] Obtain the train operation paths of each train in the entire train timetable of the high-speed railway transportation network, build a high-speed railway temporal service network under normal conditions based on the train operation paths, and obtain the instantaneous performance of the high-speed railway transportation network under normal conditions

[0052] A high-speed railway disturbance event is applied to the high-speed railway temporal service network under normal conditions to obtain the fault section and interruption time range of the high-speed railway temporal service network under normal conditions; the train operation paths corresponding to the affected trains passing through the fault section within the interruption time range are screened out from the entire railway train timetable to obtain a processed timetable; and the high-speed railway temporal service network under disturbance is constructed according to the train operation paths in the processed timetable.

[0053] In the high-speed railway temporal service network under interference, different recovery schemes x are used to restore the high-speed railway temporal service network under interference to a periodic state, and the travel time is minimized. i To the second site v j The shortest temporal path of the high-speed railway transportation network under interference conditions is obtained Among them, x is the recovery plan, a is the disturbance event, and t is the time.

[0054] According to the instantaneous performance under normal conditions and transient performance under interference conditions Determining the optimal network resilience of a high-speed rail transportation network.

[0055] The specific process is:

[0056] 1. Set the scope of the study area, obtain the high-speed train classification code in the railway train timetable on a certain working day, and select the train operation data starting with G, D, and C to build the service network as the first input data set. Some of the data are shown in Table 1.

[0057] Table 1 High-speed train timetable dataset

[0058]

[0059] 2. Obtain the line data and station data of the high-speed railway network that has been opened for operation, eliminate the lines that have lost their transportation functions and redundant stations, and use the Space L spatial construction method to construct a physical network that represents the topological network structure of the high-speed railway lines of the entire railway as the second input data set.

[0060] 3. According to the mapping relationship between the service network in the first input data set and the physical network in the second input data set, the train operation path of each train is obtained.

[0061] 4. Apply high-speed railway disturbance events according to the actual interference scenario. High-speed railway disturbance events include damaged railway lines, degree of damage, and number of repair resources during the emergency period. Estimate the interruption time range of the railway line according to the fault degree and number of repair resources of the high-speed railway disturbance event, and locate the fault interval according to the high-speed railway network topology.

[0062] 5. According to the running path and interruption time range of each train, determine whether each train passes through the fault section within the interruption time range. If the train passes through the fault section within the interruption time range, the train running path of the train is screened out. If the train does not pass through the fault section within the interruption time range, the train running path of the train is retained to obtain the processed timetable.

[0063] 6. Construct a high-speed railway temporal service network under normal conditions based on the train operation path, and construct a high-speed railway temporal service network under interference based on the processed timetable.

[0064] The temporal service network is a complex directed graph G T =(V T ,E T ), V T Represents a collection of nodes. Represents a set of directed edges connecting nodes v i ∈V T and node v j ∈V T / {v i} has a directed edge e i ∈E T Represented as a five-tuple Indicates v i With v j The start time of the connection event is End time is w i,j is the weight of the directed edge. Different from the paths in static networks, there are multiple temporal edges between two nodes in temporal networks. m The temporal path p is represented as p(v1,v m )= <v1,e1,v2,…,v m-1 ,e m-1 ,v m >, where <v1,v2,…,v m-1 ,v m > is a node sequence, <e1,e2,…,e m-1 > is a temporal edge sequence, where Make Then the length of the temporal path p is

[0065] Based on the above definition, the temporal service network of high-speed railway is G T =(V T ,E T ), V T Represents a site collection, For the first site v i The space-time node set of where {t1,t2,…,t m} is the first station v that each train arrives at i Time series. T Represents a set of directed edges including service edges and transfer edges. For the first site v i and the second site v j The set of service edges between is the set of transfer edges at the same station, that is

[0066] 7. Search from the first site v i To the second site v j The shortest temporal path includes:

[0067]

[0068] Among them, t represents the time, v represents the site, i, s, m and j are formal parameters, and n is a positive integer.

[0069] The temporal path includes: service edges between different sites Transfer edges between the same stations

[0070] 8. Establish the instantaneous performance response function. The travel time of the trip chain is the most concerned issue for travelers. The travel time T includes the waiting time t w , transfer time t t and the pure running time of the train t v The waiting time is related to the frequency of train operation, the transfer time depends on the coordination of the transfer train schedule, and the running time is related to the speed level and stop mode of the train. Therefore, travel time can be regarded as a comprehensive indicator reflecting the level of transportation service.

[0071] Network performance at time t is calculated by transport service efficiency To quantitatively calculate, the instantaneous performance of the high-speed railway transportation network under normal conditions is:

[0072]

[0073] Among them, P i,j (t) is the first site v at time t i and the second site v j The set of feasible temporal paths between i,j (p,t) is the first site v at time t i and the second site v j The travel time of the temporal path p between them.

[0074] In the high-speed railway temporal service network under interference, different recovery schemes x are used to restore the high-speed railway temporal service network under interference to a periodic state, and the travel time is minimized. i To the second site v j The shortest temporal path of the high-speed railway transportation network under interference conditions is obtained It is expressed as:

[0075]

[0076] Among them, when the recovery strategy x is adopted under the influence of disturbance event a, P i,j (x,t|a) is the first site v at time t i and the second site v j The set of feasible temporal paths between i,j (p,x,t|a) is the first site v at time t i and the second site v j The temporal path between i,j The travel time of (x,t|a).

[0077] 9. Based on the instantaneous performance under normal conditions and transient performance under interference conditions Determining the optimal network resilience of a high-speed rail transportation network.

[0078] The interference occurs at the time t a To the complete recovery time t r The entire process of (x) is the research cycle. Taking into account the overall performance of the system in the disturbance impact stage, the network resilience R is reflected by the ratio of the cumulative performance in the disturbance impact stage to the cumulative performance in the normal state, such as Figure 3 As shown, its physical meaning is the ratio of the area of ​​the shaded part S1 to the oblique line part S2. The toughness optimization model is:

[0079]

[0080] stx∈X

[0081] S(x)≤S max

[0082] In the formula, x is the recovery plan, t r (x) is the network performance recovery time of solution x, X is the set of alternative solutions; S(x) is the number of repair resources corresponding to the solution, S max is the maximum resource amount, a is the disturbance event, and R is the network resilience.

[0083] 10. Calculate the network performance of different recovery schemes x in the alternative scheme set X under disturbance event a. According to the model in step 9, obtain the resilience value R(x|a) of the network under different repair strategies. Select the recovery scheme x with the minimum network performance loss and the maximum resilience. * ∈X as the recommended solution, and draw the toughness curve, such as Figure 4 shown.

[0084] Exemplarily, the present invention is implemented according to the following steps:

[0085] Step 1: Based on the daily train timetable data published in the ticket system, the train classification codes G, D, and C are used as the screening basis to collect the train operation data of China's high-speed railway on a certain working day. The data set includes the train number code and the departure and arrival time information of the starting, ending, and stopping points of each train. The departure and arrival times are converted into minutes to build a service network as the first input data set.

[0086] Step 2: Obtain the line data and station data of the high-speed railway line network that has been opened for operation, eliminate the lines and redundant stations that are not in the research scope, and select the station data corresponding to 257 major cities at prefecture level and above. If a city contains multiple high-speed railway stations, they will be merged into one node. Use the Space L spatial construction method to construct a physical network that represents the topological network structure of the high-speed railway line of the whole railway as the second input data set.

[0087] Step 3: According to the mapping relationship between the service network in the first input data set and the physical network in the second input data set, the train operation path of each train is obtained.

[0088] Step 4: According to the actual interference scenario, input the emergency interference data, mainly including the damaged railway line, the degree of damage, and the number of emergency repair resources. Combined with the degree of damage and the number of repair resources, estimate the time required to repair the line and obtain the interruption time range of each damaged line.

[0089] Step 5: Based on the high-speed railway line network topology obtained in step 1 and combined with the interference data in step 4, locate the position of the fault section in the network, query the running path of each train obtained in step 3, and determine whether each train is originally scheduled to pass through the fault section within the interruption time range, so as to define the impact of the interference on the train operation, and determine the affected train data in combination with the timetable data.

[0090] Step 6: Based on the affected train data determined in step 5, the train data affected by the interference is removed from the whole railway train operation data set obtained in step 2, and the whole railway train operation data set is updated.

[0091] Step 7: Considering the time attribute of the service chain, the high-speed railway temporal service network under normal conditions is constructed based on the train operation data of the entire railway in step 2, and the high-speed railway temporal service network under interference is constructed based on the train operation data of the entire railway updated in step 6. The specific steps are as follows:

[0092] Step 7.1: Considering the heterogeneity of train flows at different times, the links between network nodes are connected through the five-tuple (v i ,v j ,t d ,t a ,r) indicates that train r is at t d Time from v i Departure from t a Arrived at v j Station, the nodes at both ends of the link are given the time attribute t d and t a .

[0093] Step 7.2: To express the connection relationship more clearly, the network sites are expanded into a sequence of spatiotemporal nodes with different time attributes. The connection between two spatiotemporal nodes represents the train at t d Departure at t a When it arrives, the connection established is called a service edge.

[0094] Step 7.3: Determine whether the space-time nodes of the same station generated in step 7.2 satisfy the transfer time continuity relationship. The generated connection is called a transfer edge.

[0095] Step 7.4: Based on 7.1-7.3 above, the high-speed railway temporal service network is specifically a complex directed graph G T =(V T ,E T );

[0096] Among them, V T Represents a site collection, For the first site v i The space-time node set of where {t1,t2,…,t m} is the first station v that each train arrives at i Time series. T Represents a set of directed edges, including service edges and transfer edges. For the first site v i and the second site v j The set of service edges between is the set of transfer edges at the same station, that is

[0097] Step 8: Based on the high-speed railway temporal service network under interference, with the goal of minimizing travel time, search for the i To site v j The shortest temporal path is:

[0098]

[0099] The temporal path includes service edges between different sites Transfer edges between the same stations

[0100] Step 9: Based on the normal high-speed railway temporal service network, search for the shortest temporal path between all stations in the network at time t1 and calculate the travel time. The instantaneous performance of the high-speed railway network at time t1 under normal conditions is Calculate as follows:

[0101]

[0102] Among them, P i,j (t) is the first site v at time t i and the second site v j The set of feasible temporal paths between i,j (p,t) is the first site v at time t i and the second site v j The travel time of the temporal path p between them.

[0103] Step 10: Based on the high-speed railway temporal service network under interference, when different restoration strategies x are adopted, the shortest temporal path between all stations in the network at time t1 is searched and the travel time is calculated. The instantaneous performance of the high-speed railway network at time t1 under interference is Calculate as follows:

[0104]

[0105] Among them, when the recovery strategy x is adopted under the influence of disturbance event a, P i,j (x,t|a) is the first site v at time t i and the second site v j The set of feasible temporal paths between i,j (p,x,t|a) is the first site v at time t i and the second site v j The temporal path between i,j The travel time of (x,t|a).

[0106] Step 11: Take the interference occurrence time t a To the complete recovery time t r The whole process of (x) is the research cycle, and steps 9 and 10 are executed cyclically to calculate the normal state and the interference state at each moment t∈[t a ,t r (x)], taking into account the limit on the number of repair resources, the network resilience R is calculated, and a scientific recovery strategy is obtained based on the optimal resilience. The model is as follows:

[0107]

[0108] stx∈X

[0109] S(x)≤S max

[0110] In the formula, x is the recovery plan, t r (x) is the network performance recovery time of solution x, X is the set of alternative solutions; S(x) is the number of repair resources corresponding to the solution, S max is the maximum resource amount, a is the disturbance event, and R is the network resilience.

[0111] For example, the above steps 1 to 11 are applied to China's high-speed railway network. The disaster impact range is set to cover area 1 and area 2, causing the railway section in the hub to be interrupted. The damage degree of each section is set to be the same, that is, the repair time of each section is 2 hours, and two interference scenarios are generated, such as Figure 5 shown.

[0112] To compare the network resilience under different recovery strategies, three strategies, namely random recovery, train frequency recovery, and random recovery, are selected for comparison with the recovery strategy based on optimal resilience. The results are shown in Table 2. The network resilience curves corresponding to the four recovery strategies are shown in Table 2. Figure 6 shown.

[0113] Table 2 Comparison of different recovery strategies

[0114]

[0115] Different recovery strategies lead to different network resilience. In both scenarios, the optimal recovery can minimize the cumulative loss of network performance, and the maximum resilience values ​​are 0.9857 and 0.9089 respectively. In the area 1 interference scenario, the network resilience obtained by optimal recovery is 1.46%, 2.61% and 3.87% higher than that obtained by train frequency recovery, betweenness recovery and random recovery, respectively, and the duration of disturbance impact is shortened by 1h19min, 4h1min and 5h7min, respectively. In the area 2 interference scenario, the network resilience obtained by optimal recovery is 2.84%, 3.53% and 6.17% higher than that obtained by the above four recovery strategies, respectively, and the duration of disturbance impact is shortened by 13min, 19min and 40min, respectively.

[0116] By comparing the two scenarios, we find that both scenarios contain 6 interruption intervals, and the repair time of each interval is 2h. The two scenarios are the same in terms of the properties of the interference scenario itself, but it is obvious that the impact of interference occurring in area 2 on network resilience is significantly greater than that in area 1. Therefore, network resilience has obvious spatial distribution differences.

[0117] From the time dimension, the disturbance occurrence time and repair time are continuously taken in the range of [1:00, 24:00] and [1h, 6h] with a time step of 1h, forming 144 (6×24) different time periods. The above two interference scenarios are simulated to occur in 144 different time periods, and the resilience of the high-speed rail network is calculated. The obtained resilience value heat map is shown in Figure 7 shown.

[0118] Figure 7 It reflects the network resilience value under different interference times and different repair times. It is observed that, looking horizontally along the x-axis, as the required repair time increases, the overall network resilience level shows a downward trend. Looking vertically along the y-axis, regardless of how the repair time changes, the lowest points of network resilience are concentrated at 6:00 and 7:00 in the morning, indicating that interference occurring at 6:00 and 7:00 in the morning has the greatest impact on network performance. Therefore, even in the same interference area, there are obvious differences in the time distribution of high-speed rail network resilience.

[0119] The above-mentioned embodiments only express several implementation methods of the present invention, and the description is relatively specific and detailed, but it cannot be understood as limiting the scope of the invention patent. It should be pointed out that for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. A high-speed railway transport network resilience measurement method, characterized in that: include: Obtain the train operation paths of each train in the entire train timetable of the high-speed railway transportation network, build a high-speed railway temporal service network under normal conditions based on the train operation paths, and obtain the instantaneous performance of the high-speed railway transportation network under normal conditions A high-speed railway disturbance event is applied to the high-speed railway temporal service network under normal conditions to obtain the fault section and interruption time range of the high-speed railway temporal service network under normal conditions; the train operation paths corresponding to the affected trains passing through the fault section within the interruption time range are screened out from the entire railway train timetable to obtain a processed timetable; and the high-speed railway temporal service network under disturbance is constructed according to the train operation paths in the processed timetable; In the high-speed railway temporal service network under interference, different recovery schemes x are used to restore the high-speed railway temporal service network under interference to a periodic state, and the travel time is minimized. i To the second site v j The shortest temporal path of the high-speed railway transportation network under interference conditions is obtained Among them, x is the recovery plan, a is the disturbance event, and t is the time; According to the instantaneous performance under normal conditions and transient performance under interference conditions Determining the optimal network resilience of a high-speed rail transportation network.

2. A high-speed railway transport network resilience measurement method as claimed in claim 1, characterized in that: The obtaining of the train running path of each train in the entire train timetable on the high-speed railway specifically includes: Obtain the high-speed train classification code in the railway train timetable, select the train operation data starting with G, D, and C to build the service network as the first input data set; The data of lines and stations that have been put into operation in the high-speed railway network of the whole railway are obtained, and the lines that have lost their transportation functions and redundant stations are eliminated. The physical network representing the topological network structure of the high-speed railway lines of the whole railway is constructed by using the Space L spatial construction method as the second input data set. The train running path of each train is obtained according to the mapping relationship between the service network in the first input data set and the physical network in the second input data set.

3. A high-speed railway transport network resilience measurement method as claimed in claim 1, characterized in that: The processed timetable specifically includes: Apply a high-speed railway disturbance event according to the actual interference scenario, wherein the high-speed railway disturbance event includes the damaged railway line, the degree of damage and the number of repair resources during the emergency period; estimate the interruption time range of the railway line according to the fault degree and the number of repair resources of the high-speed railway disturbance event, and locate the fault interval according to the high-speed railway network topology structure; According to the running path and interruption time range of each train, it is determined whether each train passes through the fault section within the interruption time range; if the train passes through the fault section within the interruption time range, the train running path of the train is screened out; if the train does not pass through the fault section within the interruption time range, the train running path of the train is retained to obtain the processed timetable.

4. A high-speed railway transport network resilience measurement method as claimed in claim 1, characterized in that: The high-speed railway temporal service network specifically includes: The links between network nodes are connected through five-tuples (v i ,v j ,t d ,t a ,r) indicates that train r is at t d When from the first site v i Departure from t a Arrived at the second station v j Station, the nodes at both ends of the link are given the time attribute t d and t a ; The network sites are expanded into a sequence of spatiotemporal nodes with different time attributes. The connection between two spatiotemporal nodes represents the train at t d Depart at t a When it arrives, the connection established is called a service edge; Determine whether the space-time nodes of the same station meet the transfer time continuity relationship. The generated connection is called a transfer edge. According to the service edges and transfer edges, the high-speed railway temporal service network is specifically a complex directed graph G T =(V T ,E T ); Among them, V T Represents a site collection, Indicates the first site v i The space-time node set of where {t1,t2,…,t m } is the first station v that each train arrives at i time series; E T represents a set of directed edges including service edges and transfer edges, Indicates the first site v i and the second site v j The set of service edges between represents the set of transfer edges at the same station, 5. A high-speed railway transport network resilience measurement method as claimed in claim 1, characterized in that: The search starts from the first site v i To the second site v j The shortest temporal path includes: Among them, t represents the time, v represents the station, i, s, m and j are formal parameters, and n is a positive integer; The temporal path includes: service edges between different sites Transfer edges between the same stations 6. A high-speed railway transport network resilience measurement method as claimed in claim 1, characterized in that: The instantaneous performance of the high-speed railway transportation network under normal conditions is obtained The specific calculation formula is: Among them, P i,j (t) is the first site v at time t i and the second site v j The set of feasible temporal paths between i,j (p,t) is the first site v at time t i and the second site v j The travel time of the temporal path p between them.

7. A high-speed railway transport network resilience measurement method as claimed in claim 1, characterized in that: The instantaneous performance of the high-speed railway transportation network under interference conditions is obtained The specific calculation formula is: Among them, when the recovery strategy x is adopted under the influence of disturbance event a, P i,j (x,t|a) is the first site v at time t i and the second site v j The set of feasible temporal paths between i,j (p,x,t|a) is the first site v at time t i and the second site v j The temporal path between p∈P i,j The travel time of (x,t|a).

8. A high-speed railway transport network resilience measurement method as claimed in claim 1, characterized in that: Determining the optimal network resilience of the high-speed railway specifically includes: stx∈X S(x)≤S max Among them, x is the recovery plan, t r (x) is the network performance recovery time of solution x, X is the set of alternative solutions; S(x) is the number of repair resources corresponding to the solution, S max is the maximum resource amount, a is the disturbance event, and R is the network resilience.

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