A key link identification method based on a multi-modal network

Abstract

The application relates to the technical field of communication, and particularly discloses a key link identification method based on a multi-modal network, which comprises the following steps: dividing links into multiple levels and mapping the multiple levels into multiple modes; calculating the minimum path set between all source-destination node pairs, calculating the transmission reliability of the network according to the minimum path set and collecting the original transmission reliability matrix; changing the mode of the link and updating the probability distribution of the mode of the link to obtain a new transmission reliability matrix; converting the original reliability matrix into a first matrix, converting the first matrix into a target vector; combining all the target vectors into a second matrix; calculating the weight vector of the influence of the mode change of the link on the maximum feasible flow of different source-destination node pairs; normalizing the elements in the second matrix to obtain a third matrix; solving the maximum Mahalanobis distance and the minimum Mahalanobis distance according to the third matrix and the weight vector, and determining the link importance, so as to identify the key link according to the link importance.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of communication technology, in particular to a key link identification method based on a multi-modal network. BACKGROUND

[0002] The reliability and efficiency requirements of the power system are increasing in modern society. As an important part of the power system, the optimization of the reliability and transmission capacity of the power communication network is particularly critical. In the power communication network, the reliability of the link is directly related to the transmission performance of the entire network. Therefore, how to effectively identify the key link and improve the reliability of the network has become a problem to be solved.

[0003] Traditional key link identification methods are mostly based on a single parameter or static network structure, which is difficult to adapt to the actual complex environment in the power communication network. In the face of changing link states and diversified business needs, traditional methods often cannot accurately reflect the influence of the link on the overall network reliability. Therefore, relying on traditional methods to identify key links is insufficient to meet the needs of the power communication network, and thus the identification of key links in multi-modal flow networks is imminent. SUMMARY

[0004] To solve the above problems, the purpose of the present application is to provide a key link identification method based on a multi-modal network, which can accurately quantify and identify the key links of the multi-modal network, and further evaluate the link importance of the complex multi-modal flow network.

[0005] The present application provides a key link identification method based on a multi-modal network, comprising:

[0006] Step S1, according to the residual bandwidth of the link in the power communication network, the link is divided into multiple levels, and the multiple levels are mapped to multiple modes of the link in the multi-modal network;

[0007] Step S2, the minimum path set between all source-destination node pairs in the multi-modal network is calculated by the feasible cycle method, the transmission reliability of the network is calculated according to the minimum path set, and the original transmission reliability matrix is obtained by summarizing the transmission reliability; the transmission reliability is the probability of the flow from the source node to the destination node in the multi-modal network;

[0008] Step S3, changing the mode of the link and updating the probability distribution of the mode of the link, obtaining a new transmission reliability matrix corresponding to the changed link;

[0009] Step S4, the original reliability matrix is converted into a first matrix with the same number of rows and columns as the new transmission reliability matrix by the zero padding method, and the first matrix is converted into a target vector;

[0010] Step S5, obtaining target vectors corresponding to all links in the network, and combining all the target vectors into a second matrix;

[0011] Step S6, using the CRITIC method to calculate a weight vector of the influence of the mode change of the link on the maximum feasible flow of different source-destination node pairs;

[0012] Step S7, performing standardization processing on elements in the second matrix to obtain a third matrix;

[0013] Step S8, solving the maximum Mahalanobis distance and the minimum Mahalanobis distance according to the third matrix and the weight vector, and determining the link importance according to the maximum Mahalanobis distance and the minimum Mahalanobis distance, so as to identify the key link in the network according to the link importance.

[0014] In a possible implementation, the S2 includes:

[0015] The transmission reliability R of the network is calculated according to the following formula d :

[0016]

[0017] In the formula, R d is the probability that the link state vector is greater than or equal to the union of each minimum path vector, L is the number of minimum path vectors, S u is the sum of the probabilities that the link state vector is greater than or equal to any u minimum path vectors.

[0018] In a possible implementation, the S3 includes:

[0019] The probability distribution of the mode of the link is updated according to the following formula

[0020]

[0021] In the formula, P is the probability that the link k is in mode number (m-θ), M * is the maximum mode value of the link k after the capacity is increased, M k is the maximum value of the mode of the link k.

[0022] In a possible implementation, the target vector is an N×1 column vector, N is the number of source-destination node pairs in the multi-mode network; and the S4 includes:

[0023] The value ΔR i k of the i-th row of the target vector is calculated according to the following formula

[0024] ΔR ik =∑ 0≤d≤Dk d×(st i k (d)-st i 0 (d))

[0025] Where d is the number of unit flow, D k is the maximum feasible flow of the multimodal network after changing the mode of link k, st i 0 is the vector corresponding to the i-th row of the original transmission reliability matrix R0, st i k is the new transmission reliability matrix R corresponding to link k k The vector corresponding to the i-th row of .

[0026] In a possible implementation, S6 includes:

[0027] Different source and sink nodes are regarded as different indicators, and the impact of each link on the maximum feasible flow of a certain source and sink node is regarded as the indicator value under the corresponding indicator;

[0028] The weight vector W of the impact of the link mode change on the maximum feasible flow of different source-destination node pairs is calculated according to the following formula:

[0029] W={w1,w2...w N}

[0030]

[0031] Among them, w n is the value in the weight vector W, n∈[1,N], E n is the amount of information contained in the nth indicator, and N is the number of source-destination node pairs in the multimodal network;

[0032] Calculate the information E contained in the nth indicator according to the following formula n :

[0033]

[0034] Among them, ξ n is the standard deviation of the nth indicator in the link mode change, c in is the correlation coefficient between the i-th indicator and the n-th indicator.

[0035] In a possible implementation, S6 further includes:

[0036] The standard deviation ξ of the nth indicator in the link mode change is calculated according to the following formula m :

[0037]

[0038] Where K is the number of links in the multimodal network, N is the number of source-destination node pairs in the multimodal network, is the second matrix R * The mean of the nth row elements, is the second matrix R * The element in the nth row and ith column of .

[0039] In a possible implementation, S6 further includes:

[0040] Calculate the correlation coefficient c between the i-th indicator and the n-th indicator according to the following formula in :

[0041]

[0042] Among them, ξ i is the standard deviation of the i-th indicator in the link mode change, ξ n is the standard deviation of the nth indicator in the link mode change, R * i is the second matrix R * The i-th row, R * n is the second matrix R * The nth row of .

[0043] In a possible implementation, the S7 includes:

[0044] The second matrix R is calculated according to the following formula * The elements in are normalized:

[0045]

[0046] Among them, z ik is the element in the third matrix Z, R ik is the second matrix R * The elements in , K is the number of links in the multimodal network, and k is the number of the link.

[0047] In a possible implementation, S8 includes:

[0048] The link importance S of link k is calculated according to the following formula k :

[0049]

[0050] in, is the maximum Minkowski distance of link k, is the minimum Minkowski distance of link k.

[0051] In a possible implementation, the S8 further includes:

[0052] The maximum Mahalanobis distance of the link k is calculated according to the following formula

[0053]

[0054] The minimum Mahalanobis distance of the link k is calculated according to the following formula

[0055]

[0056] wherein N is the number of source-destination node pairs in the multi-modal network, w i the value of the i-th index in the weight vector, is the maximum value of the elements in the third matrix, is the minimum value of the elements in the third matrix, z ik is the element in the third matrix, and p is the parameter of the Mahalanobis distance.

[0057] The method for identifying key links based on a multi-modal network provided by the application divides the links in the power communication network into multiple levels according to the residual bandwidth of the links and maps the multiple levels into multiple modes of the links in the multi-modal flow network. The minimum path set is solved, the transmission reliability is calculated, the data dimensionality of the transmission reliability set is processed, the matrix is obtained, the matrix is mapped into the Mahalanobis space after the index weight is calculated, the Mahalanobis distance between the maximum value and the minimum value is calculated as the link importance of the link, the key links of the multi-modal network are accurately quantified and identified, the link importance of the links in the complex multi-modal flow network is evaluated, and the reliability of the power communication network is improved and the communication network structure is optimized, which has guiding significance. BRIEF DESCRIPTION OF DRAWINGS

[0058] Figure 1 The flowchart of the method for identifying key links provided by the embodiment of the application is shown in the figure;

[0059] Figure 2 The network topology diagram provided by the embodiment of the application is shown in the figure;

[0060] Figure 3 The schematic diagram of the link importance provided by the embodiment of the application is shown in the figure. DETAILED DESCRIPTION

[0061] The embodiments of the application are further described in detail below with reference to the accompanying drawings and embodiments. The detailed description of the following embodiments and the accompanying drawings are used to exemplarily illustrate the principles of the application, but cannot be used to limit the scope of the application, that is, the application is not limited to the preferred embodiments described, and the scope of the application is defined by the claims.

[0062] In the description of the present application, it should be noted that, unless otherwise specified, the meaning of "a plurality of" is two or more; the terms "first", "second", etc. are only for the purpose of description, and cannot be understood as indicating or implying relative importance; the above-mentioned terms can be understood in the specific meaning in the present application by the person skilled in the art according to the specific circumstances.

[0063] Figure 1 The flowchart of the key link identification method provided by the embodiment of the present application is shown in Figure 1 As shown, the present application provides a key link identification method based on a multi-modal network, which comprises:

[0064] Step S1, according to the residual bandwidth of the link in the power communication network, the link is divided into multiple levels, and the multiple levels are mapped into multiple modes of the link in the multi-modal network;

[0065] Among them, the greater the mode number, i.e. the mode value, the greater the corresponding residual bandwidth. Link k=1,2,3,…,K, K is the number of links in the multi-modal network, and the probability of link k in each mode is represented as M k is the maximum value of the mode of link k.

[0066] Step S2, the minimum path set d-MP(s,t) between all source-destination node pairs in the multi-modal network is calculated by the feasible circulation method, and the transmission reliability R d (s,t) of the network is calculated according to the minimum path set, and the original transmission reliability matrix

[0067] Among them, the transmission reliability R d (s,t) is the probability of the flow from the source node to the destination node in the multi-modal network, d=1,2,…,D0, D0 is the maximum feasible flow between all source-destination node pairs under the current link state distribution. The transmission reliability set R 0 is an N-row D0-column matrix, N is the number of source-destination node pairs in the multi-modal network.

[0068] The link state vector x is called the minimum path vector if it satisfies the following conditions: if and for any y x k >y k , and there is at least one link k such that x k >y k .

[0069] All L minimum path vectors are calculated as a priori, represented as z 1 ,z 2..., z L , then the reliability R d is the probability that the link state vector x is greater than or equal to the union of each minimum path vector.

[0070] is the structure function of the multi-state network, defined as the maximum feasible flow of the network with the current link state vector x. x = (x1, x2,..., x k ), represents the link state vector of the network, link k is in the mode x k , and the set of d-MP is called the minimum path set.

[0071] Let E j be the event that the link state vector x is greater than or equal to each minimum path vector, i.e., {x ≥ z j}, the union probability of the event E j is the transmission reliability R d of the network.

[0072] The probability that the link state vector is greater than or equal to the union of each minimum path vector is calculated according to the following formula

[0073]

[0074] where x is the link state vector, L is the number of minimum path vectors, and z L is the minimum path vector.

[0075] The inclusion-exclusion (IE) principle is used to calculate the union probability of all given d-MP. The IE method calculates the upper and lower bounds of the joint probability of events successively through the Bonferroni inequality, and finally converges to the accurate probability value.

[0076] The sum S n of the probabilities that the link state vector is greater than or equal to any n minimum path vectors is calculated according to the following formula

[0077]

[0078] The transmission reliability R d of the network is calculated according to the following formula

[0079]

[0080] where R d (s, t) is the reliability R d of the given source-destination node pair (s, t), is the link state vector the probability that the link state vector is greater than or equal to the union of each of the minimum path vectors, the number of minimum path vectors, S u is the sum of the probabilities that the link state vector is greater than or equal to any u minimum path vectors.

[0081] Step S3, change the mode of the link and update the probability distribution of the mode of the link to obtain a new transmission reliability matrix corresponding to the changed link;

[0082] wherein changing the mode of the link k means increasing the capacity of the link k.

[0083] The probability distribution of the mode of the link is updated according to the following formula

[0084]

[0085] wherein, is the probability that the link k is in the mode numbered (m-θ), M * is the maximum mode value after the capacity of the link k is increased, M k is the maximum value of the mode of the link k.

[0086] The transmission reliability set R k = {R d (s,t) | the mode of the link k is changed}, d = 1, 2, …, D k , D k is the maximum feasible flow of the network after the mode of the link k is changed, the transmission reliability set R k is an N-row and D k column matrix.

[0087] Step S4, convert the original reliability matrix into a first matrix with the same number of rows and columns as the new transmission reliability matrix by the zero padding method, and convert the first matrix into a target vector; the number of rows and columns of the transmission reliability set and matrix are the same;

[0088] In a possible implementation, the target vector is an N x 1 column vector, and N is the number of source-destination node pairs in the multi-mode network; S4 includes: calculating the value of the i-th row of the target vector ΔR i k :

[0089]

[0090] wherein d is the number of unit flows, D k is the maximum feasible flow of the multi-mode network after the mode of the link k is changed, st i 0 is the i-th row of the original transmission reliability matrix R0corresponding to the vector, st i kThe vector corresponding to the i-th row of the new transmission reliability matrix R corresponding to link k k .

[0091] Step S5, traverse all links in the network to obtain the target vectors corresponding to each link, and combine all target vectors into a second matrix;

[0092] In one possible implementation, the target vectors ΔR k corresponding to all links in the multi-modal network are combined into a second matrix R * with N rows and K columns.

[0093] Step S6, calculate the weight vector of the influence of the modal change of the link on the maximum feasible flow of different source-destination node pairs by using the Criteria Importance Through Intercriteria Correlation (CRITIC) method;

[0094] In one possible implementation, different source-destination nodes are regarded as different indexes, and the influence of each link on the maximum feasible flow of a source-destination node pair is regarded as the index value under the corresponding index; that is, each row of the second matrix corresponds to the index value under each index;

[0095] The weight vector W of the influence of the modal change of the link on the maximum feasible flow of different source-destination node (s, t) pairs is calculated according to the following formula:

[0096] W = {w1, w2…wn} N}

[0097]

[0098] wherein w n is the value in the weight vector W, n ∈ [1, N], E n is the amount of information contained in the n-th index, and N is the number of source-destination node pairs in the multi-modal network.

[0099] The amount of information E n contained in the n-th index is calculated according to the following formula:

[0100]

[0101] wherein ξ n is the standard deviation of the n-th index in the modal change of the link, and c in is the correlation coefficient between the i-th index and the n-th index.

[0102] The standard deviation ξ n of the n-th index in the modal change of the link is calculated according to the following formula:

[0103]

[0104] Where K is the number of links in the multimodal network, N is the number of source-destination node pairs in the multimodal network, is the second matrix R * The mean of the nth row elements, is the second matrix R * The element in the nth row and ith column of .

[0105] Calculate the correlation coefficient c between the i-th indicator and the n-th indicator according to the following formula in :

[0106]

[0107] Among them, ξ i is the standard deviation of the i-th indicator in the link mode change, ξ n is the standard deviation of the nth indicator in the link mode change, R * i is the second matrix R * The i-th row, R * n is the second matrix R * The nth row of .

[0108] Step S7, normalizing the elements in the second matrix to obtain a third matrix;

[0109] In a possible implementation, S7 includes:

[0110] The second matrix R is calculated according to the following formula * The elements in are normalized:

[0111]

[0112] Among them, z ik is the element in the third matrix Z, R ik is the second matrix R * The elements in , K is the number of links in the multimodal network, and k is the number of the link.

[0113] Define the maximum value Z + The following formula:

[0114] Z + ={max(z 11 ,z 12 ,…,z 1K ),max(z 21 ,z 22 ,…,z 2K ),…,max(z N1 ,z N2..., z NK )

[0115] Definition of minimum value Z - The following formula:

[0116] Z - = {min(z 11 ,z 12 ..., z 1K ), min(z 21 ,z 22 ..., z 2K ),..., min(z N1 ,z N2 ..., z NK )

[0117] Step S8, solve the maximum Mahalanobis distance and the minimum Mahalanobis distance according to the third matrix and the weight vector, and determine the link importance according to the maximum Mahalanobis distance and the minimum Mahalanobis distance, so as to identify the key link in the network according to the link importance.

[0118] In one possible implementation, the maximum Mahalanobis distance of the link k is calculated according to the following formula

[0119]

[0120] The minimum Mahalanobis distance of the link k is calculated according to the following formula

[0121]

[0122] Wherein, N is the number of source and sink node pairs in the multi-modal network, w i The value of the i-th index in the weight vector, is the maximum value of the elements in the third matrix, is the minimum value of the elements in the third matrix, z ik is an element in the third matrix, and p is a parameter of the Mahalanobis distance.

[0123] The link importance S of the link k is calculated according to the following formula k :

[0124]

[0125] Wherein, is the maximum Mahalanobis distance of the link k, is the minimum Mahalanobis distance of the link k.

[0126] The method of the application will be further illustrated below by applying it to a local network of a provincial power communication network.

[0127] Figure 2 The network topology graph provided for the embodiments of the present application contains 14 nodes and 16 links, and the nodes (1, 2, 5, 7, 13) in the graph are source-destination node pairs in the multi-modal network.

[0128] Step 1, according to the residual bandwidth of the 16 links in the power communication network, the links are divided into 3 levels, and are mapped into 3 modes of the links in the multi-modal flow network. The probability distribution of the probability of each link being in each mode is the same, i.e. p k = (0.2, 0.4, 0.4 | k = 1, 2, 3, …, 16).

[0129] Step 2, the minimum path set d-MP (s,t (s, t) of transmitting d units of flow between the specified source node s and the destination node t is calculated by the feasible circulation method, and the corresponding transmission reliability R d (s, t) is calculated. All source-destination node pairs in the network are traversed to obtain the original transmission reliability matrix R of the network. 0 The original transmission reliability set R k is a 10-row 4-column matrix, where the number of source-destination node pairs in the network is 10, and the maximum feasible flow between all source-destination node pairs under the current link state distribution is 4, i.e. dmax is 4.

[0130] Step 3, the capacity of link k is increased, i.e. the mode of link k in the multi-modal network is changed, and the probability distribution of the mode of link k is updated to obtain a new transmission reliability matrix of 10 rows and D k columns. After the capacity of link k is increased, its maximum mode value is 3.

[0131] Step 4, the transmission reliability set is obtained by the zero padding method to obtain a 10x1 column target vector ΔR k .

[0132] Step 5, all links in the network are traversed, and according to steps 3 and 4, the target vectors ΔR k corresponding to all links in the network are obtained, which are combined into a second matrix R * of 10 rows and 16 columns, where R * (i, k) represents the ΔR i of the i-th source-destination node pair after the mode of link k is changed. k .

[0133] Step 6, the CRITIC method is used to calculate the weight vector W = (4.273, 0.912, 0.523, 1..192, 0.738, 0.340, 3.934, 2.838, 0.405, 0.213) of the influence of the change of the mode of the link on the maximum feasible flow of different source-destination node pairs (s, t).

[0134] Step 7, the elements in the matrix R * are normalized to obtain a third matrix Z according to the following formula.

[0135] The maximum value Z + in the normalized matrix Z.

[0136] Z +

[0137] The minimum value Z - in the normalized matrix Z.

[0138] Z - = (1.61, 0.37, 0.35, 0.41, 0, 1.69, 0.41, 0.83, 0.35, 0.70, 0.64, 0.70, 0.83, 0.37, 0.37, 0)

[0139] Step 8, the maximum and minimum Mahalanobis distances are solved according to the third matrix and the weight vector, and the link importance is determined according to the maximum and minimum Mahalanobis distances, so as to identify the key link in the network according to the link importance.

[0140] The parameter p of the Mahalanobis distance in the embodiment is 3. The link importance S k of the link k is as follows:

[0141]

[0142] Figure 3 The schematic diagram of the link importance provided by the embodiment of the application is shown in the figure, so that the key link can be clearly seen.

[0143] In summary, the key link identification method based on the multi-modal network provided by the application first divides the links in the power communication network into multiple levels according to the residual bandwidth of the links, and maps the multiple modes of the links in the multi-modal flow network. Then, the minimum path set is calculated by the feasible cycle method, and the corresponding transmission reliability R d (s, t) is calculated, the original transmission reliability matrix R 0 of the network is obtained by traversing all source and sink node pairs in the network. The mode of the link in the multi-modal network is changed, the original transmission reliability matrix R 0 is converted into a first matrix R k which has the same number of rows and columns as the new transmission reliability set, and is converted into a target vector ΔR k . All target vectors ΔR k corresponding to all links are obtained by traversing all links in the network, and are combined into a second matrix R *Then, the CRI TIC method is used to calculate the weight vector of the influence of the link mode change on the maximum feasible flow of different source and sink nodes, and the link importance of the link is obtained through standardization processing and calculation of the Mahalanobis distance. Finally, according to the link importance of the link, the key links in the network are identified as edge 1 and edge 6.

[0144] The method of the application solves the problem that the traditional method cannot accurately quantify and identify the key links of the multi-modal network by changing the link mode in the multi-modal flow network, comprehensively considering the influence of different source and sink nodes on the maximum feasible flow, accurately quantifying and identifying the key links of the multi-modal network, and evaluating the link importance of the link in the complex multi-modal flow network, which has guiding significance for improving the reliability of the power communication network and optimizing the communication network structure.

[0145] The above is only a specific embodiment of the application, but the protection scope of the application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the application, which should be covered within the protection scope of the application. Therefore, the protection scope of the application should be subject to the protection scope of the claims.

Claims

1. A key link identification method based on a multimodal network, characterized in that: include: Step S1: classifying the links in the power communication network into multiple levels according to the remaining bandwidth of the links, and mapping the multiple levels to multiple modes of the links in the multimodal network; Step S2, calculating a minimum path set between all source-destination node pairs in the multimodal network by a feasible round-robin method, calculating the transmission reliability of the network based on the minimum path set, and summarizing the transmission reliability to obtain an original transmission reliability matrix; The transmission reliability is the probability of traffic being transmitted from a source node to a sink node in a multimodal network; Step S3, changing the mode of the link and updating the probability distribution of the mode of the link, to obtain a new transmission reliability matrix corresponding to the changed link; Step S4, converting the original reliability matrix into a first matrix having the same number of rows and columns as the new transmission reliability matrix by a zero-padding method, and converting the first matrix into a target vector; Step S5, traversing all links in the network to obtain the target vector corresponding to each link, and combining all the target vectors into a second matrix; Step S6, using the objective weighting method CRITIC to calculate the weight vector of the impact of the modal change of the link on the maximum feasible flow of different source-destination node pairs; Step S7, normalizing the elements in the second matrix to obtain a third matrix; Step S8, solving the maximum Minkowski distance and the minimum Minkowski distance according to the third matrix and the weight vector, and determining the link importance according to the maximum Minkowski distance and the minimum Minkowski distance, so as to identify the key links in the network according to the link importance; The S8 includes: The link importance S of link k is calculated according to the following formula k : in, is the maximum Minkowski distance of link k, is the minimum Minkowski distance of link k; The maximum Minkowski distance of link k is calculated according to the following formula The minimum Minkowski distance of link k is calculated according to the following formula Where N is the number of source-destination node pairs in the multimodal network, w i The value of the i-th indicator in the weight vector, is the maximum value of the elements in the third matrix, is the minimum value of the elements in the third matrix, z ik is the element in the third matrix, and p is the parameter of Minkowski distance.

2. The key link identification method according to claim 1, characterized in that: The S2 includes: The transmission reliability R of the network is calculated according to the following formula: d : Where R d is the probability that the link state vector is greater than or equal to the union of each minimum path vector, L is the number of minimum path vectors, S u is the sum of the probabilities that the link state vector is greater than or equal to any u minimum path vectors.

3. The key link identification method according to claim 1, characterized in that: The S3 includes: Update the probability distribution of the link mode according to the following formula in, is the probability that link k is in mode number (m-θ), M * is the maximum modal value after the capacity of link k is increased, M k is the maximum value of the link k mode.

4. The key link identification method according to claim 1, characterized in that: The target vector is an N×1 column vector, where N is the number of source-destination node pairs in the multimodal network; S4 includes: The value ΔR of the i-th row of the target vector is calculated according to the following formula i k : Where d is the number of unit flow, D k is the maximum feasible flow of the multimodal network after changing the mode of link k, st i 0 is the vector corresponding to the i-th row of the original transmission reliability matrix R0, st i k is the new transmission reliability matrix R corresponding to link k k The vector corresponding to the i-th row of .

5. The key link identification method according to claim 1, characterized in that: The S6 includes: Different source and sink nodes are regarded as different indicators, and the impact of each link on the maximum feasible flow of a certain source and sink node is regarded as the indicator value under the corresponding indicator; The weight vector W of the impact of the link mode change on the maximum feasible flow of different source-destination node pairs is calculated according to the following formula: W={w1,w2…w N } Among them, w n is the value in the weight vector W, n∈[1,N], E n is the amount of information contained in the nth indicator, and N is the number of source-destination node pairs in the multimodal network; Calculate the information E contained in the nth indicator according to the following formula n : Among them, ξ n is the standard deviation of the nth indicator in the link mode change, c in is the correlation coefficient between the i-th indicator and the n-th indicator.

6. The key link identification method according to claim 5, characterized in that: The S6 further includes: The standard deviation ξ of the nth indicator in the link mode change is calculated according to the following formula n : Where K is the number of links in the multimodal network, N is the number of source-destination node pairs in the multimodal network, is the second matrix R * The mean of the nth row elements, is the second matrix R * The element in the nth row and ith column of .

7. The key link identification method according to claim 5, characterized in that: The S6 further includes: Calculate the correlation coefficient c between the i-th indicator and the n-th indicator according to the following formula in : Among them, ξ i is the standard deviation of the i-th indicator in the link mode change, ξ n is the standard deviation of the nth indicator in the link mode change, R * i is the second matrix R * The i-th row, R * n is the second matrix R * The nth row of .

8. The key link identification method according to claim 1, characterized in that: The S7 includes: The second matrix R is calculated according to the following formula * The elements in are normalized: Among them, z ik is the element in the third matrix Z, R ik is the second matrix R * The elements in , K is the number of links in the multimodal network, and k is the number of the link.

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