Network connectivity evaluation method, electronic equipment and storage medium

Abstract

The invention aims to provide a network connectivity evaluation method, electronic equipment and a storage medium, and the method comprises the steps: converting a topological structure of a target network into a directed graph, carrying out the statistics of the number of redundant links between each node pair according to the directed edges between all node pairs in the directed graph, and carrying out the calculation of the number of redundant links between each node pair; and weighting and averaging the number of all redundant links, then quantifying the obtained weighted total value by adopting a natural connectivity scaling mode, calculating the network redundancy of the target network, taking the network redundancy as an evaluation index of the target network, and determining the current connectivity state of the target network through the evaluation index. On the basis of the mode, the redundancy of the edges in the network with the topological structure being the directed graph can be monotonically and sensitively reflected, so that the evaluation result is more accurate and effective, and technicians can further improve the toughness and load of the network according to the evaluation result.

Description

Technical Field

[0001] The present invention mainly relates to a network quality evaluation method, and in particular to a network connectivity evaluation method, electronic equipment and storage medium. Background Art

[0002] Natural connectivity is an evaluation index that can reflect network connectivity. It measures the redundancy of paths in the network by counting the number of closed loops in the network topology, thereby quantifying changes in network connectivity. However, for a network with a directed graph topology, when there is no loop in the directed graph, or when the deleted edge does not belong to the edge in the loop, the natural connectivity will remain unchanged, that is, the natural connectivity cannot sensitively reflect the connectivity in the directed graph; secondly, for a directed communication network, its connectivity and redundancy need to be reflected by the paths between node pairs, not closed loops. At the same time, natural connectivity does not take into account the problem of repeated path counting caused by short closed loops. Therefore, relying on natural connectivity to evaluate connectivity in a directed network will lead to deviations in the evaluation results and fail to reflect the true status of network connectivity. Summary of the invention

[0003] The purpose of the present application is to provide a network connectivity evaluation method, an electronic device and a storage medium, which are used to solve the above-mentioned problems in the prior art.

[0004] In a first aspect, the present application provides a network connectivity evaluation method, comprising:

[0005] Converting the topological structure of the target network into a directed graph, wherein the directed graph includes a plurality of nodes in the target network and directed edges between the plurality of nodes;

[0006] According to the directed edges between all node pairs in the directed graph, the number of redundant links between each node pair is counted, and the weighted average of all redundant links is taken. Then, the obtained weighted total value is quantified using the natural connectivity scaling method to calculate the network redundancy of the target network, where the network redundancy represents the weighted sum of the average number of links between all node pairs in the directed graph.

[0007] Use network redundancy as an evaluation index for the target network;

[0008] The current connectivity status of the target network is determined through evaluation indicators.

[0009] In some embodiments, the expression of network redundancy is as follows:

[0010]

[0011] Among them, NR is the network redundancy; i and j are the two subscripts of a node pair in the directed graph; N is the total number of nodes, trace() is the operation function used to calculate the trace of the matrix; A is the adjacency matrix corresponding to the topological structure; R is the intermediate matrix, and the expression of R is represents the number of redundant links between all node pairs, k is the path length, and K is the maximum path length.

[0012] In some embodiments, the current connectivity state of the target network includes a redundant state and a less-redundant state; wherein,

[0013] The redundant state corresponds to a scenario where the network redundancy is greater than or equal to 0, and the sub-redundant state corresponds to a scenario where the network redundancy is less than 0.

[0014] In some embodiments, after determining the current connectivity state of the target network through the connectivity evaluation index, the method further includes:

[0015] Adjust the gender ratio among multiple nodes according to network redundancy;

[0016] The gender ratio refers to the ratio of the number of odd nodes or even nodes in a plurality of nodes to the total number of nodes; each node pair consists of a corresponding odd node and an even node.

[0017] In some embodiments, adjusting the gender ratio among the plurality of nodes according to network redundancy includes:

[0018] Under the condition that the total number of nodes in the target network remains unchanged, the gender ratio between multiple nodes is adjusted to record the gender ratio-network redundancy change curve of the target network;

[0019] Determining the distribution interval of the gender ratio according to the gender ratio-network redundancy change curve, wherein the median value of the distribution interval corresponds to the maximum value of each recorded network redundancy;

[0020] Adjust the gender ratio among multiple nodes to be within the distribution range.

[0021] In some embodiments, the median value of the allocation interval is 0.5.

[0022] In some embodiments, the evaluation index also includes any one or a combination of network efficiency, natural connectivity, edge connectivity, number of strongly connected components, algebraic connectivity, and vertex critical removal rate.

[0023] In a second aspect, the present application provides an electronic device, including a memory and a processor, wherein instructions are stored in the memory, and when the instructions are called by the processor, the processor executes any method as described in the first aspect above.

[0024] In a third aspect, the present application provides a storage medium comprising computer program instructions, which, when called by a processor of a computer, are used to enable the computer to implement any method of the first aspect described above.

[0025] In a fourth aspect, the present application provides a computer program product. When the computer program product is executed by a processor of a computer, the computer implements any method of the first aspect.

[0026] Compared with the prior art, the present invention has the following advantages:

[0027] The purpose of the present application is to provide a network connectivity evaluation method, electronic device and storage medium, including converting the topological structure of the target network into a directed graph, and according to the directed edges between all node pairs in the directed graph, counting the number of redundant links between each node pair, weighting and averaging the number of all redundant links, and then quantifying the obtained weighted total value using the natural connectivity scaling method, calculating the network redundancy of the target network, using the network redundancy as an evaluation index for the target network, and determining the current connectivity state of the target network through the evaluation index. Based on the above method, it is possible to monotonically and sensitively reflect the redundancy of the edges in a network whose topological structure is a directed graph, making the evaluation results more accurate and effective, and helping technicians to further improve the resilience and load of the network based on the evaluation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The accompanying drawings are included to provide a further understanding of the present application. They are included and constitute a part of the present application. The accompanying drawings illustrate embodiments of the present application and together with the present specification serve to explain the principles of the present application. In the accompanying drawings:

[0029] Figure 1 is a flow chart of a network connectivity evaluation method exemplarily provided by the present application;

[0030] Figure 2 This is one of the example graphs of a gender ratio-network redundancy change curve provided by the present application;

[0031] Figure 3 This is the second example diagram of a gender ratio-network redundancy change curve provided by the present application;

[0032] Figure 4 It is one of the topological diagrams of a target network provided exemplarily in this application;

[0033] Figure 5 This is the second topological diagram of a target network provided by the present application as an example;

[0034] Figure 6This is a third topology diagram of a target network provided by the present application as an example;

[0035] Figure 7 This is a fourth topology diagram of a target network provided by the present application as an example;

[0036] Figure 8 This is a schematic diagram of a time-network redundancy change curve provided by the present application as an example;

[0037] Fig. 9 This is a curve comparison diagram of connection probability-evaluation index provided by the present application as an example;

[0038] Fig.10 This is a curve comparison diagram of edge deletion quantity-evaluation index provided by the present application as an example;

[0039] Fig.11 This is a schematic diagram of an electronic device provided as an example in this application. DETAILED DESCRIPTION

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of the present application. For ordinary technicians in this field, the present application can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0041] As shown in this application and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0042] In a first aspect of this embodiment, a network connectivity evaluation method is provided. Figure 1 Describe. Figure 1 ,The network connectivity evaluation method includes the following steps:

[0043] S101, converting the topological structure of the target network into a directed graph, where the directed graph includes a plurality of nodes in the target network and directed edges between the plurality of nodes.

[0044] S102, based on the directed edges between all node pairs in the directed graph, the number of redundant links between each node pair is counted, and the number of all redundant links is weighted and averaged, and then the obtained weighted total value is quantified using the natural connectivity scaling method to calculate the network redundancy of the target network, wherein the network redundancy represents the weighted sum of the average number of links between all node pairs in the directed graph.

[0045] S103: Using network redundancy as an evaluation index for the target network.

[0046] S104, determining the current connectivity status of the target network through the evaluation index.

[0047] Specifically, the expression of the directed graph can be shown as follows (1):

[0048] G=(V,E) (1)

[0049] Where V represents the set of nodes, expressed as formula (2):

[0050] V = {1, 2, 3, ..., N} (2)

[0051] E represents the edge set between nodes, expressed as formula (3):

[0052]

[0053] Among them, an edge in the edge set E is (i, j)∈E, which means that node i can send information to node j, that is, node j can receive the message of node i. At the same time, node i is called the incoming neighbor of node j, and node j is the outgoing neighbor of node i;

[0054] The set of all incoming neighbors of node i is expressed as (4)

[0055]

[0056] The set of all outgoing neighbors of node i is expressed as formula (5):

[0057]

[0058] The adjacency matrix corresponding to the target network is represented as A, and λ is used to represent the diagonal elements of the adjacency matrix A.

[0059] In a directed graph, if there is a path between any two distinct nodes, the graph is said to be strongly connected.

[0060] The essence of network redundancy is to count the number of redundant links between all node pairs. Specifically, using the properties of the k-th power of the adjacency matrix, we can Represents the number of redundant links between all node pairs, and sets the weight of each element. The result is shown in the following formula (6):

[0061]

[0062] The element r in the R matrix ij represents the weighted sum of the number of links of different lengths from node i to node j. Since the number of loops does not need to be counted in a directed graph, the diagonal elements in R are removed, as shown in the following equation (7):

[0063]

[0064] Because there is a A short self-loop and using So the weight of the number of paths that a short self-loop repeatedly counts between adjacent nodes i and j is Similarly, the number of short self-loop paths from j to i will be counted again, so the weighted value of the number of short self-loop paths that need to be removed is It is worth noting that the impact of short self-loops also exists in the statistics of paths between other node pairs. However, since the repeated statistical paths caused by short self-loops on other node pairs are at least 4 hops, the denominator weight k! is large, and the impact on the total weighted value of the number of links is small, so it is ignored, and then formula (8) is obtained:

[0065] The result is quantified by this method, and the expression of network redundancy is obtained as shown in formula (9):

[0067]

[0068] Among them, NR is the network redundancy; i and j are the two subscripts of a node pair in the directed graph; N is the total number of nodes, trace() is the operation function used to calculate the trace of the matrix; A is the adjacency matrix corresponding to the topological structure; R is the intermediate matrix, and the expression of R is represents the number of redundant links between all node pairs, k is the path length, and K is the maximum path length.

[0069] When there is no link in the network, the network redundancy is equal to its lower limit, which is -∞; when all elements in the adjacency matrix A are set to 1, the network redundancy is equal to its upper limit, as shown in the following formula (10):

[0070]

[0071] It can be seen from formula (1) to formula (10) that the value range of network redundancy is [-∞, NR max], which increases monotonically with the improvement of the connectivity of the target network. For the target network with a directed graph topology, the network redundancy calculated by counting the number of redundant links between all node pairs basically eliminates the influence of the short self-loop problem. Compared with the natural connectivity, the network redundancy has the ability to monotonically and sensitively reflect the redundancy of the edges in the network with a directed graph topology, making the evaluation results more accurate and effective.

[0072] In some embodiments, the current connectivity state of the target network includes a redundant state and a sub-redundant state; wherein the redundant state corresponds to a scenario where the network redundancy is greater than or equal to 0, and the sub-redundant state corresponds to a scenario where the network redundancy is less than 0. In this way, by reading the network redundancy, the redundancy status of the target network can be quickly confirmed, thereby achieving an accurate evaluation of the network connectivity.

[0073] In some embodiments, the gender of nodes is divided into odd nodes and even nodes in the topology of the target network. The network redundancy can be used as an evaluation index to determine the current connectivity state of the target network, and the gender ratio between multiple nodes can be further adjusted according to the network redundancy; wherein the gender ratio refers to the ratio of the number of odd nodes or even nodes in the multiple nodes to the total number of nodes; each node pair consists of a corresponding odd node and an even node.

[0074] For example, when the target network needs to meet a high connectivity requirement, the network redundancy of the target network can be calculated to determine whether its current gender ratio meets the connectivity requirement.

[0075] In some embodiments, adjusting the gender ratio among the plurality of nodes according to network redundancy includes:

[0076] Under the condition that the total number of nodes in the target network remains unchanged, the gender ratio between multiple nodes is adjusted to record the gender ratio-network redundancy change curve of the target network;

[0077] Determining the distribution interval of the gender ratio according to the gender ratio-network redundancy change curve, wherein the median value of the distribution interval corresponds to the maximum value of each recorded network redundancy;

[0078] Adjust the gender ratio among multiple nodes to be within the distribution range.

[0079] Specifically, refer to Figure 2 , Figure 3 , this application provides two example graphs of the gender ratio-network redundancy change curve under the total number of nodes. Among them, Figure 2 The total number of nodes in the target network is set to 50, the ratio between odd nodes and the total number of nodes is adjusted, and the changes in the corresponding network redundancy are recorded in real time. Figure 3The total number of nodes in the target network is set to 100, and the ratio between the odd nodes and the total number of nodes is adjusted, and the changes in the corresponding network redundancy are recorded in real time. Figure 2 , Figure 3 It can be seen that when the total number of nodes is 50, the network redundancy reaches the highest when the gender ratio is about 50%, and when the total number of nodes is 100, the network redundancy reaches the highest in the range of 30%-70% gender ratio. Therefore, when the network redundancy is the highest, the median of the gender ratio distribution interval is roughly 0.5. In actual situations, the appropriate distribution interval can be flexibly set according to this median, such as [0.45, 0.55], or [0.3, 0.7], etc., without restriction.

[0080] In addition, in addition to directed graphs, the above evaluation indicators can also be used for other types of target networks, such as undirected graphs. In some embodiments, the evaluation indicators also include indicators of conventional dimensions such as network efficiency. Alternatively, it also includes and network redundancy as an evaluation indicator to evaluate the connectivity of the target network. For example, the evaluation indicators can further include: any one or combination of natural connectivity, edge connectivity, number of strongly connected components, algebraic connectivity, and vertex critical removal rate, and this application does not limit this.

[0081] The following simulation examples are provided to verify the technical effect.

[0082] In this example, the target network is a distributed network with a directed graph topology. The gender of the nodes in the target network is divided into odd nodes and even nodes, and the total number of nodes is 100. The gender ratio of the odd nodes in the target network is updated in the time period of 0-200. When the system time is 0, the target network includes 100 odd nodes and 0 even nodes.

[0083] Further, refer to Figure 4 , providing a topology diagram of the target network at system time 1, which shows that there are still no even nodes in the target network at system time 1. Figure 5 , providing a topology diagram of the target network at system time 5, which shows that even nodes begin to appear in the target network at system time 5. Figure 6 , providing a topology diagram of the target network at system time 10, which shows that the number of even nodes in the target network continues to increase at system time 10. Figure 7 , providing a topology diagram of the target network at system time 15, which shows that the number of even nodes in the target network tends to be stable at system time 15.

[0084] At the same time, reference Figure 8 , record the time-network redundancy change curve of the target network in the time period of 0-200. Figure 8It can be seen that the network redundancy increases rapidly over time in the approximate time period of 0-15, and remains stable in the subsequent time periods. Figure 4-Figure 7 The time of change in the gender ratio shown is basically consistent, which can reflect the monotonicity and sensitivity of network redundancy when used as an evaluation indicator of the target network, making the evaluation results accurate and effective, and helping technicians to improve the resilience and load of the network based on this evaluation result.

[0085] Further, refer to Fig. 9 , Fig.10 , randomly generate a directed graph with 100 nodes, and control the number of edges in the directed graph by the connection probability and the number of edges to be deleted. Fig. 9 , the horizontal axis represents the connection probability, and the vertical axis represents the corresponding value. Looking down from the horizontal axis of 0.8, the four curves correspond to the natural connectivity, edge connectivity, network redundancy, and the number of strongly connected components. Fig. 9 It can be seen that as the number of edges in the directed graph increases, the natural connectivity and network redundancy can show a monotonically increasing trend as a whole, and because the influence of short self-loops is taken into account, as the connection probability increases, the more duplicate paths are eliminated by network redundancy, and the greater the difference with the natural connectivity. Fig.10 , the horizontal axis represents the number of grid edges, and the vertical axis represents the corresponding value. Looking down from the horizontal axis of 600, the four curves correspond to the number of strongly connected components, natural connectivity, edge connectivity, and network redundancy. Fig.10 It can be seen that as the number of deleted edges increases, the network redundancy can maintain a continuous monotonically decreasing trend, while the natural connectivity first decreases and then remains unchanged. By analyzing and comparing the principles of natural connectivity and network redundancy, it is believed that when the number of deleted edges is large, there is no closed loop in the entire network, so the natural connectivity will fail at this time, while the network redundancy will not be affected.

[0086] Further, if Fig.11 An exemplary embodiment of the present application further provides an electronic device, including a memory 1101 and a processor 1102, wherein the memory 1101 stores instructions, and when the instructions are executed by the processor 1102, the processor 1102 executes any method of the first aspect.

[0087] It should be understood that the processor mentioned in the embodiments of the present application may be a CPU, or other general-purpose processors, DSPs, ASICs, FPGAs or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0088] It should also be understood that the memory mentioned in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory, an erasable programmable read-only memory, an electrically erasable programmable read-only memory, or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory, dynamic random access memory, synchronous dynamic random access memory, double data rate synchronous dynamic random access memory, enhanced synchronous dynamic random access memory, synchronously connected dynamic random access memory, and direct memory bus random access memory.

[0089] The present application also provides a storage medium storing computer program instructions, which, when executed by a processor of a computer, enables the computer to execute the steps of any of the methods mentioned above.

[0090] A computer-readable medium may include a propagated data signal containing computer program code, such as in baseband or as part of a carrier wave. The propagated signal may have a variety of manifestations, including electromagnetic, optical, etc., or a suitable combination. A computer-readable medium may be any computer-readable medium other than a computer-readable storage medium, which may be connected to an instruction execution system, device or apparatus to communicate, propagate or transmit a program for use. The program code on the computer-readable medium may be propagated via any suitable medium, including radio, cable, fiber optic cable, radio frequency signal, or similar medium, or any combination of the above mediums.

[0091] Generally, various embodiments of the present application may be implemented in hardware or dedicated circuits, software, logic, or any combination thereof. Certain aspects may be implemented in hardware, while other aspects may be implemented in firmware or software, which may be executed by a controller, microprocessor, or other computing device. Although various aspects of the embodiments of the present disclosure are shown and described as block diagrams, flow charts, or using some other graphical representations, it should be understood that, as non-limiting examples, the blocks, devices, systems, techniques, or methods described herein may be implemented in hardware, software, firmware, dedicated circuits or logic, general hardware or controllers or other computing devices, or some combination thereof.

[0092] The present application also provides at least one computer program product tangibly stored on a computer storage medium. The computer program product includes computer executable instructions, such as instructions included in a program module, which are executed in a device on a target real or virtual processor. When the computer program product is executed by the processor of the computer, the computer implements the network connectivity evaluation method as described above. Generally, the program module includes routines, programs, libraries, objects, classes, components, data structures, etc. that perform specific tasks or implement specific abstract data types. In various embodiments, the functions of the program modules can be combined or separated between program modules as needed. The machine executable instructions for the program modules can be executed in local or distributed devices. In distributed devices, the program modules can be located in local and remote storage media.

[0093] The basic concepts have been described above. Obviously, for those skilled in the art, the above invention disclosure is only used as an example and does not constitute a limitation of the present application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements and amendments to the present application. Such modifications, improvements and amendments are suggested in the present application, so such modifications, improvements and amendments still belong to the spirit and scope of the exemplary embodiments of the present application.

[0094] At the same time, the present application uses specific words to describe the embodiments of the present application. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of the present application. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, some features, structures or characteristics in one or more embodiments of the present application can be appropriately combined.

[0095] Some aspects of the present application may be performed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software may be referred to as "data blocks", "modules", "engines", "units", "components" or "systems". The processor may be one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DAPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, or combinations thereof. In addition, various aspects of the present application may be expressed as computer products located in one or more computer-readable media, which include computer-readable program codes. For example, computer-readable media may include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, tapes ...), optical disks (e.g., compact disks CDs, digital versatile disks DVDs ...), smart cards, and flash memory devices (e.g., cards, sticks, key drives ...).

[0096] A computer-readable medium may include a propagated data signal containing computer program code, such as in baseband or as part of a carrier wave. The propagated signal may have a variety of manifestations, including electromagnetic, optical, etc., or a suitable combination. A computer-readable medium may be any computer-readable medium other than a computer-readable storage medium, which may be connected to an instruction execution system, device or apparatus to communicate, propagate or transmit a program for use. The program code on the computer-readable medium may be propagated via any suitable medium, including radio, cable, fiber optic cable, radio frequency signal, or similar medium, or any combination of the above mediums.

[0097] Similarly, it should be noted that in order to simplify the description of the disclosure of this application and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this application, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the object of this application are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0098] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may change according to the required features of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of the present application are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0099] Although the present application has been described with reference to the current specific embodiments, ordinary technicians in this technical field should recognize that the above embodiments are only used to illustrate the present application, and various equivalent changes or substitutions may be made without departing from the spirit of the present application. Therefore, as long as the changes and modifications to the above embodiments are within the essential spirit of the present application, they will fall within the scope of the claims of the present application.

Claims

1. A network connectivity evaluation method, characterized in that: The method comprises: Converting a topological structure of a target network into a directed graph, wherein the directed graph includes a plurality of nodes in the target network and directed edges between the plurality of nodes; According to the directed edges between all the node pairs in the directed graph, the number of redundant links between each node pair is counted, and the number of all redundant links is weighted and averaged, and then the obtained weighted total value is quantified by natural connectivity scaling to calculate the network redundancy of the target network, wherein the network redundancy represents the weighted sum of the average number of links between all the node pairs in the directed graph; Using the network redundancy as an evaluation index for the target network; The current connectivity status of the target network is determined by the evaluation index.

2. The network connectivity evaluation method according to claim 1, characterized in that: The expression of the network redundancy is as follows: Wherein, NR is the network redundancy; i and j are the two subscripts of a node pair in the directed graph; N is the total number of nodes, trace() is an operation function used to calculate the trace of the matrix; A is the adjacency matrix corresponding to the topological structure; R is the intermediate matrix, and the expression of R is represents the number of redundant links between all node pairs, k is the path length, and K is the maximum path length.

3. The network connectivity evaluation method according to claim 1, characterized in that: The current connectivity state of the target network includes a redundant state and a less redundant state; wherein, The redundant state corresponds to a scenario where the network redundancy is greater than or equal to 0, and the sub-redundant state corresponds to a scenario where the network redundancy is less than 0.

4. The network connectivity evaluation method according to any one of claims 1 to 3, characterized in that: After determining the current connectivity state of the target network by using the connectivity evaluation index, the method further includes: adjusting the gender ratio among the plurality of nodes according to the network redundancy; The gender ratio refers to the ratio of the number of odd nodes or even nodes in the multiple nodes to the total number of nodes; each node pair consists of a corresponding odd node and an even node.

5. The network connectivity evaluation method according to claim 4, characterized in that: The adjusting the gender ratio among the plurality of nodes according to the network redundancy comprises: Under the condition that the total number of nodes in the target network remains unchanged, by adjusting the gender ratio between the multiple nodes, recording the gender ratio-network redundancy change curve of the target network; Determining a distribution interval of the gender ratio according to the gender ratio-network redundancy variation curve, wherein the median of the distribution interval corresponds to the maximum value of each recorded network redundancy; The gender ratio among the plurality of nodes is adjusted to be within the allocation interval.

6. The network connectivity evaluation method according to claim 5, characterized in that: The median value of the distribution interval is 0.

5.

7. The network connectivity evaluation method according to any one of claims 1 to 3, characterized in that: The evaluation index also includes any one or a combination of network efficiency, natural connectivity, edge connectivity, number of strongly connected components, algebraic connectivity, and vertex critical removal rate.

8. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores instructions, and when the instructions are called by the processor, the processor executes the method according to any one of claims 1 to 8.

9. A storage medium, characterized in that: The method comprises computer program instructions, which, when executed by a processor of a computer, are used to cause the computer to implement the method according to any one of claims 1 to 8.

10. A computer program product, characterized in that When the computer program product is executed by a processor of a computer, the computer is caused to implement the method according to any one of claims 1 to 8.

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