CN117354229A

CN117354229A - Network path determining method, device, computer equipment and storage medium

Info

Publication number: CN117354229A
Application number: CN202311271264.6A
Authority: CN
Inventors: 黎懿根; 许健荣; 杨刚刚; 荆忠凯
Original assignee: China Telecom Technology Innovation Center; China Telecom Corp Ltd
Current assignee: China Telecom Technology Innovation Center; China Telecom Corp Ltd
Priority date: 2023-09-28
Filing date: 2023-09-28
Publication date: 2024-01-05

Abstract

The application relates to a network path determination method, a network path determination device, computer equipment and a storage medium. The method comprises the following steps: acquiring a first path between each pair of adjacent nodes of the network and a weight corresponding to the first path; determining shortest paths from a source node to a target node of a network according to the first paths and the weight of the first paths, and setting each intermediate node in the shortest paths as a weight-reducing node, wherein the shortest paths are paths with minimum total weight, and the intermediate nodes are nodes except the source node and the sink node; performing path search in a network, and performing weight reduction processing for reducing the weight of a second path passing through any weight reduction node to obtain the comprehensive weight of the second path, wherein the second path comprises at least one first path; and determining a plurality of second paths with minimum comprehensive weights from the source node to the target node as target paths according to the second paths and the comprehensive weights of the second paths. By adopting the method, the efficiency of determining the topN path of the network can be improved.

Description

Network path determining method, device, computer equipment and storage medium

Technical Field

The present invention relates to the field of communications technologies, and in particular, to a network path determining method, an apparatus, a computer device, and a storage medium.

Background

With the development of communication technology, in a communication network service deployment scenario, in order to provide a better communication service, for example, to increase a network broadband speed, reduce a network delay, and the like, multiple shortest paths are required to be screened from deployable network paths from a source node to a destination node, and network deployment is performed according to the screened shortest paths, which may be referred to as topN shortest path calculation.

However, the topN shortest path algorithm commonly used at present, such as the Yen algorithm, other modified Dijkstra algorithm, and the like, all have the defect of higher time complexity and space complexity, and the efficiency of determining the shortest path in the scene of multiple paths and complex network environment is lower.

Disclosure of Invention

In view of the foregoing, it is desirable to provide a network path determination method, apparatus, computer device, computer-readable storage medium, and computer program product that can improve the efficiency of determining a topN path of a network.

In a first aspect, the present application provides a network path determining method, including:

Acquiring a first path between each pair of adjacent nodes of a network and a weight corresponding to the first path;

determining shortest paths from a source node to a target node of the network according to the first paths and the weight of the first paths, and setting each intermediate node in the shortest paths as a weight-reducing node, wherein the shortest paths are paths with minimum total weight, and the intermediate nodes are nodes except the source node and the sink node;

performing path searching in the network, and performing weight reduction processing for reducing the weight of a second path passing through any weight reduction node to obtain the comprehensive weight of the second path, wherein the second path comprises at least one first path;

and determining a plurality of second paths with minimum comprehensive weights from the source node to the target node as target paths according to the second paths and the comprehensive weights of the second paths.

In one embodiment, the weight reduction processing for reducing the weight of the second path passing through any weight reduction node, to obtain the comprehensive weight of the second path, includes:

calculating the sum of the weights of the first paths contained in the second paths to obtain the total weight of the second paths;

And under the condition that the second path passes through the weight reduction node, acquiring a difference value of the total weight and the weight reduction weight corresponding to the second path as a comprehensive weight of the second path.

In one embodiment, before the obtaining the difference between the total weight and the weight of the second path, the method further includes:

acquiring a total weight from the source node to a target weight-reduction node as a weight corresponding to the second path, wherein the target weight-reduction node is the weight-reduction node through which the second path passes;

when the source node does not pass through the target weight-reducing node, the weight-reducing weight value is zero.

In one embodiment, the performing path searching in the network includes:

and performing path searching by taking the source node as an expansion starting point and the target node as an expansion end point, wherein the path searching mode is a hop-by-hop expansion mode, and selecting an expansion path with the minimum comprehensive weight as a basic path of the next hop path searching aiming at the expansion path obtained by each path searching.

In one embodiment, the performing the path search with the source node as an extension start point and the target node as an extension end point includes:

After finishing one-hop path searching, taking the current end point of the basic path as the expansion start point, and executing the path searching of the next hop to realize the hop-by-hop expansion of the basic path;

and when the end point of the extended path or the end point of the basic path is the target node, obtaining a target path of the current round of path search.

In one embodiment, the method further comprises:

and storing at least one expansion path which is not selected in expansion paths obtained by searching in any hop as a candidate expansion path into a buffer queue for searching in any hop, so as to determine a basic path of the next round of path searching based on the candidate expansion path buffered in the buffer queue when the next round of path searching is executed.

In one embodiment, the method further comprises:

acquiring the preset demand number of the target path;

and when the number of the target paths reaches the preset demand number, or the candidate expansion paths in the cache queue are all determined to be the basic paths, ending the execution of the path search.

In one embodiment, the determining the shortest path from the source node to the target node of the network according to the first path and the weight of the first path includes:

Constructing a hash table adjacency matrix of the network according to the first path and the weight of the first path;

constructing a topological graph of the network according to the hash table adjacency matrix;

and determining the shortest path according to the topological graph.

In a second aspect, the present application further provides a network path determining apparatus, the apparatus including:

the first path acquisition module is used for acquiring a first path between each pair of adjacent nodes of the network and a weight corresponding to the first path;

the weight-reducing node determining module is used for determining the shortest path from a source node to a target node of the network according to the first path and the weight of the first path, and setting each intermediate node in the shortest path as a weight-reducing node, wherein the shortest path is the path with the minimum total weight, and the intermediate nodes are nodes except the source node and the sink node;

the comprehensive weight calculation module is used for carrying out path search in the network, and carrying out weight reduction processing for reducing the weight of a second path passing through any weight reduction node to obtain the comprehensive weight of the second path, wherein the second path comprises at least one first path;

And the target path determining module is used for determining a plurality of second paths with minimum comprehensive weights from the source node to the target node as target paths according to the second paths and the comprehensive weights of the second paths.

In a third aspect, the present application also provides a computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:

In a fourth aspect, the present application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:

In a fifth aspect, the present application also provides a computer program product comprising a computer program which, when executed by a processor, performs the steps of:

The network path determining method, the network path determining device, the computer equipment, the storage medium and the computer program product acquire a first path between each pair of adjacent nodes of the network and a weight corresponding to the first path; determining shortest paths from a source node to a target node of the network according to the first paths and the weight of the first paths, and setting each intermediate node in the shortest paths as a weight-reducing node, wherein the shortest paths are paths with minimum total weight, and the intermediate nodes are nodes except the source node and the sink node; then, path searching is carried out in the network, weight reduction processing for reducing the weight is carried out on a second path passing through any weight reduction node, and the comprehensive weight of the second path is obtained, wherein the second path comprises at least one first path; and finally, determining a plurality of second paths with minimum comprehensive weights from the source node to the target node as target paths according to the second paths and the comprehensive weights of the second paths. According to the method, the weight reduction nodes of the network are determined, and weight reduction processing for reducing the weight of the path passing through any weight reduction node is carried out when path searching is carried out, so that the path passing through the weight reduction node or the path with the minimum comprehensive weight is preferentially selected in the path searching process, the path with the larger weight is prevented from being selected, the efficiency of determining the shortest path is improved, and the overall complexity of determining the shortest path is reduced.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the related art, the drawings that are required to be used in the embodiments or the related technical descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to the drawings without inventive effort for a person having ordinary skill in the art.

FIG. 1 is an application environment diagram of a network path determination method in one embodiment;

FIG. 2 is a flow diagram of a network path determination method in one embodiment;

FIG. 3 is a flowchart illustrating a step of calculating the comprehensive weights of the second path according to one embodiment;

FIG. 4 is a flow chart of the step of calculating the shortest path in one embodiment;

FIG. 5 is a schematic diagram of performing a path search in one embodiment;

FIG. 6 is a first schematic diagram of performing a path search in one embodiment;

FIG. 7 is a second schematic diagram of performing a path search in one embodiment;

FIG. 8 is a third schematic diagram of performing a path search in one embodiment;

FIG. 9 is a fourth schematic diagram of performing a path search in one embodiment;

FIG. 10 is a schematic diagram of a hash table adjacency matrix in one embodiment;

FIG. 11 is a schematic diagram of the effect of path selection using the method of the present application in one embodiment;

FIG. 12 is a block diagram of a network path determination device in one embodiment;

fig. 13 is an internal structural view of a computer device in one embodiment.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.

It will be appreciated that the most common topN shortest path algorithm at present is the Yen algorithm, which is centered on the use of the P that has been found, and other modified Dijkstra algorithms _k (kth short path) generation P _k+1 Each time find P _k+1 At the time, P _k All nodes except the endpoint are considered possible deviant nodes for calculation,find the shortest path as P _k+1 . The time complexity of the algorithm is O (Kn (n logn+m)), K is the number of schemes, n is the number of nodes, and m is the number of edges.

Other modifications Dijkstra algorithm: many researchers of KSP problem have proposed various different improved algorithms for solving the K shortest path based on Dij kstra algorithm, wherein the more excellent algorithm is a K-th optimal path algorithm based on a bi-directional search strategy proposed by Gao Song et al, and the time complexity is O (K) ² m+nlogn), K is the number of schemes, n is the number of nodes, and m is the number of edges.

Therefore, determining the shortest path of the network with the algorithms of the prior art has the drawbacks of high algorithm complexity and low efficiency in determining the shortest path.

The network path determining method provided by the embodiment of the application can be applied to an application environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. The data storage system may store data that the server 104 needs to process. The data storage system may be integrated on the server 104 or may be located on a cloud or other network server. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things devices, and portable wearable devices, where the internet of things devices may be smart speakers, smart televisions, smart air conditioners, smart vehicle devices, and the like. The portable wearable device may be a smart watch, smart bracelet, headset, or the like. The server 104 may be implemented as a stand-alone server or as a server cluster of multiple servers. By the network path determining method, a plurality of shortest paths available when the communication network of the terminal 102 and the server 104 is deployed can be determined quickly and efficiently.

In an application scenario of the present application, a terminal 102 obtains a first path between each pair of adjacent nodes of a network, and a weight corresponding to the first path; determining shortest paths from a source node to a target node of a network according to the first paths and the weight of the first paths, and setting each intermediate node in the shortest paths as a weight-reducing node, wherein the shortest paths are paths with minimum total weight, and the intermediate nodes are nodes except the source node and the sink node; performing path search in a network, and performing weight reduction processing for reducing the weight of a second path passing through any weight reduction node to obtain the comprehensive weight of the second path, wherein the second path comprises at least one first path; and determining a plurality of second paths with minimum comprehensive weights from the source node to the target node as target paths according to the second paths and the comprehensive weights of the second paths.

In an exemplary embodiment, as shown in fig. 2, a network path determining method is provided, which is illustrated by using the method applied to the terminal 102 in fig. 1 as an example, and includes the following steps 202 to 208, where:

step S202, a first path between each pair of adjacent nodes of the network and a weight corresponding to the first path are obtained.

The network path determining method can be used for shortest path calculation in various scenes, for example, in the communication field, operators can use the method to calculate a plurality of shortest paths to plan and deploy a communication network so as to determine the optimal signal transmission path, and the effects of reducing signal transmission delay and improving network performance are achieved. For example, in the field of electronic map navigation, the method can be used for calculating a plurality of shortest paths to provide a plurality of selectable navigation routes for users, and the weight of each path can be set by considering factors such as traffic jam, road type, distance, charge and the like so as to help the users to select the optimal path.

Wherein, a node may be a specific location, device, resource or entity in the network, and the type of the node depends on the scenario in which it is located. For example, in a communication network deployment scenario, a node may be a communication device such as a router, switch, etc. in a network, as well as a network component such as a communication tower, data center, etc. For example, in an electronic map path planning scenario, the nodes may be traffic lights, buildings, GPS coordinate points, etc., displayed in the electronic map network.

Referring to FIG. 5, a schematic diagram of performing a path search in one embodiment is shown. As shown in fig. 5, a network topology map including nodes and paths between nodes may be constructed. In the network topology diagram, N1 to N13 are nodes, each node edge is a path between two nodes, an arrow points to a direction of the path, and a number corresponding to the arrow is a weight of the path.

In a specific implementation, the data of all nodes and node edges of the designated network may be acquired by the data acquisition module 211 of the composition module 21 in fig. 12 through a cache, and if the cached data is out of date, for example, survives for more than 1 hour, the above data may be retrieved from the database and written into the cache. Further, in an exemplary embodiment, the acquired data of the nodes and node edges may be processed by the data transformation patterning module 212 of the patterning module 21 in fig. 12 to generate a corresponding hash table adjacency matrix.

The weight may be a mapping value of the path distance, and the smaller the weight is, the shorter the path distance is. It should be noted that the weight may be an actual distance of the path or a value obtained by proportionally processing the actual distance, for example, the path distance is 1km, and the corresponding weight may be 1000 or 1. In one embodiment, the weight may also be a virtual value corresponding to the path distance affected by different factors, for example, the weight may be directly set to 1, 2, 3, etc.

The first path may be a path between each pair of adjacent nodes, for example, paths N1-N2 where N1 points to N2 in fig. 5, paths N2-N4 where N2 points to N4, and so on are all first paths.

Step S204, determining the shortest path from the source node to the target node of the network according to the first path and the weight of the first path, and setting each intermediate node in the shortest path as a weight-reducing node.

The shortest path is the path with the minimum total weight, and the intermediate node is a node except the source node and the destination node. The target node is a destination node corresponding to the shortest path planning, and is generally a destination node of the network, so that the whole network is conveniently deployed and planned through the shortest path.

Taking fig. 5 as an example, assuming that the source node is N1, the sink node (also referred to as the destination node) is N13, the total weight of the paths N1-N4-N7-N10-N13 is 2+2+2+2=10, and it can be determined that the shortest path is N1-N4-N7-N10-N13, the weight subtracting nodes are N4, N7, and N10.

In one embodiment, each weight may also have a respective corresponding sub-weight, such that the total weight of the corresponding path may also be calculated by weighted summing the weights of each first path.

In one embodiment, in order to improve the efficiency of determining the shortest path, a hash table adjacency matrix can be constructed through a network topology graph, and then the hash table adjacency matrix is combined to calculate the shortest path of the network by adopting Dijkstra algorithm, so that the weight-reduced node of the network is determined, and then subsequent path searching is executed.

In a specific implementation, a Dijkstra algorithm can be adopted to calculate the shortest path from the source node to the destination node by combining with the hash table adjacency matrix. Further, in an exemplary embodiment, a maintainable pre-node array P may be obtained from a sink node P _n Begin to acquire P through P array _n Front node P of (1) _n-1 Obtaining P through the P array _n-1 Front node P of (1) _n-2 And the whole-course node of the shortest path is obtained by pushing until the front node is the source node P0 in the reverse direction. Further, in an exemplary embodiment, all intermediate nodes of the shortest path may be defined as weight-subtracting nodes.

Step S206, searching paths in the network, and performing weight reduction processing for reducing the weight of the second path passing through any weight reduction node to obtain the comprehensive weight of the second path.

Wherein the second path may include at least one first path. It is understood that the second path may be a path formed by connecting a plurality of first paths, or may be one first path. Taking fig. 5 as an example, the second path may be the first paths N1-N4, or may be N1-N4-N7 including two first path connections N1-N4 and N4-7.

It will be appreciated that in conventional path searches, it may be necessary to traverse a large number of nodes and edges to find the shortest path. In this embodiment, the second path passing through any weight reduction node obtains a corresponding comprehensive weight after weight reduction processing, and the weight reduction node is the middle node of the shortest path, so the more the number of weight reduction nodes passing through the second path is, the more weight the weight reduction processing is reduced by the second path. After the weight reduction nodes are introduced, the comprehensive weight of the second path is determined in the mode, the path search can be more intensively performed around the important nodes, the paths passing through more weight reduction nodes are rapidly and effectively determined in the process of determining the topN shortest path, and the paths passing through fewer weight reduction nodes or not passing through the weight reduction nodes are eliminated, so that the complexity of the search is reduced, and the efficiency of determining the topN shortest path can be improved.

Step S208, determining a plurality of second paths with minimum comprehensive weights from the source node to the target node as target paths according to the second paths and the comprehensive weights of the second paths.

It can be appreciated that, since the second path does not pass through the weight subtracting node, the comprehensive weight of the second path is its total weight; when the second path passes through the weight reduction node, the comprehensive weight of the second path is the difference value between the total weight and the weight reduction weight, so that the smaller the comprehensive weight, the smaller the total weight of the second path or the more the number of the weight reduction nodes, therefore, the efficiency and the accuracy of determining the topN shortest path can be improved by determining a plurality of second paths with the minimum comprehensive weight as target paths. The network path determining method, the network path determining device, the computer equipment, the storage medium and the computer program product acquire a first path between each pair of adjacent nodes of the network and a weight corresponding to the first path; determining shortest paths from a source node to a target node of the network according to the first paths and the weight of the first paths, and setting each intermediate node in the shortest paths as a weight-reducing node, wherein the shortest paths are paths with minimum total weight, and the intermediate nodes are nodes except the source node and the sink node; then, path searching is carried out in the network, weight reduction processing for reducing the weight is carried out on a second path passing through any weight reduction node, and the comprehensive weight of the second path is obtained, wherein the second path comprises at least one first path; and finally, determining a plurality of second paths with minimum comprehensive weights from the source node to the target node as target paths according to the second paths and the comprehensive weights of the second paths. According to the method, the weight reduction nodes of the network are determined, and weight reduction processing for reducing the weight of the path passing through any weight reduction node is carried out when path searching is carried out, so that the path passing through the weight reduction node or the path with the minimum comprehensive weight is preferentially selected in the path searching process, the path with the larger weight is prevented from being selected, the efficiency of determining the shortest path is improved, and the overall complexity of determining the shortest path is reduced.

In an exemplary embodiment, as shown in fig. 3, which is a schematic flow chart of the step of calculating the comprehensive weight of the second path in one embodiment, step S206 includes the following steps:

step S302, calculating the sum of the weights of the first paths contained in the second paths to obtain the total weight of the second paths.

It will be appreciated that since the second path includes at least one first path, the weights of the plurality of first paths may be summed to calculate a total weight for the second path, which may be expressed as:

wherein G is the total weight of the second path, i is the number of the edges, each edge corresponds to a first path, n is the total number of the edges, W _i The weight of the ith edge.

As shown in fig. 5, taking the second paths N1-N4-N7 as an example, the total weight corresponding to the second paths is 2+2+2=6.

Step S304, under the condition that the second path passes through the weight subtracting node, the difference value of the total weight and the weight subtracting weight corresponding to the second path is obtained and is used as the comprehensive weight of the second path.

In one embodiment, the calculation formula of the comprehensive weight of the second path may be:

F＝G-H

wherein F is the comprehensive weight of the second path, G is the total weight of the second path, and the weight corresponding to the second path is H.

In an exemplary embodiment, before the step S304 of obtaining the difference between the total weight and the weight of the second path, the method further includes:

acquiring the total weight from the source node to the target weight-reducing node as the weight corresponding to the second path, wherein the target weight-reducing node is the last weight-reducing node passed by the second path; when the second path does not pass through any weight-reducing node, the weight-reducing weight has a value of zero.

In one embodiment, for a given second path, the total weight of the source node to the last (or unique) weight subtracting node on the path may be calculated first, including starting from the source node, and summing the weights across the various edges on the path. Accordingly, if there are paths from the source node to the destination weight subtracting node, each path has a corresponding total weight, then in this case, the path with the largest total weight is selected from the paths, and the selected total weight is taken as the weight subtracting weight corresponding to the second path. If the second path does not pass through any weight-reducing nodes, i.e. there are no weight-reducing nodes in the path, the weight-reducing value is zero.

Weight reduction weight: and taking the selected total weight as the weight of the second path. If the second path does not pass through any weight reduction nodes (i.e., no weight reduction nodes in the path), then the weight reduction weight has a value of zero

The target weight reduction node is the last weight reduction node passing through the second path. Still taking fig. 5 as an example, for the second paths N1-N4-N7, since N4 and N7 are weight reduction nodes, the second paths pass through two weight reduction nodes in total, N7 is a target weight reduction node, and then the weight reduction weight corresponding to the second paths is the total weight of the source node N1 to the target weight reduction node N7, that is, 4, and the total weight of the second paths is the difference between the total weight (4) and the weight reduction weight (4), that is, 0. Correspondingly, for the second paths N1-N4-N6, as N4 is a weight reduction node and N6 is not a weight reduction node, the second paths pass through one weight reduction node, N4 is a target weight reduction node, the weight reduction weight corresponding to the second paths is the total weight of the source nodes N1 to N4, namely 2, and the comprehensive weight of the second paths is the difference value between the total weight (5) and the weight reduction weight (2), namely 3.

In an exemplary embodiment, the path searching in the network in the step S206 includes: and performing path search by taking the source node as an expansion starting point and the target node as an expansion end point.

The method for searching the paths is a hop-by-hop expansion method, and aiming at the expansion paths obtained by each path search, the expansion path with the smallest comprehensive weight is selected as a basic path for searching the next hop path.

Wherein, the path searching refers to the operation of searching for a new path in the network based on the existing path. The hop-by-hop expansion refers to a path searching mode of jumping to a node adjacent to the end point by taking the end point of the previous path as a reference to obtain a new path.

Still taking fig. 5 as an example, a path search of one-hop by one-hop expansion is performed with the source node N1 as an expansion start point, so as to obtain expansion paths N1-N2, N1-N4 and N1-N3, and since the paths N1-N4 pass through the weight-reducing node N4, the comprehensive weight of the paths N1-N2 and N1-N4 are 0, and the comprehensive weight of the paths N1-N2 and N1-N4 are 1 and 3, respectively, the expansion paths N1-N4 can be selected as a base path of the next-hop path search.

In an exemplary embodiment, the step of performing path search with the source node as an extension start point and the target node as an extension end point includes:

after the one-hop path search is completed, the current end point of the basic path is used as an expansion start point, and the path search of the next hop is executed so as to realize the hop-by-hop expansion of the basic path; and when the end point of the extended path or the end point of the basic path is a target node, obtaining a target path of the current round of path search.

Please refer to fig. 6, which is another diagram illustrating a path search performed in one embodiment. As shown in FIG. 6, the next-hop path search is performed by taking the extended path N1-N4-N7-N10 as a basic path, and the extended paths N1-N4-N7-N10, N1-N4-N8, N1-N4-N7-N9 are obtained, because N10 is a weight reduction node and N10 is the last passing node in the extended paths N1-N4-N7-N10, so that the total weight of the extended paths is equal to the weight reduction weight, and the comprehensive weight of the extended paths N1-N4-N7-N10 is 0, and therefore the extended paths N1-N4-N7-N10 are selected as the basic path for the next-hop path search. Since N10 can reach the target node N13 directly, the target path of the current path search can be determined to be N1-N4-N7-N10-N13.

In an exemplary embodiment, the performing the path search with the source node as the extension start point and the target node as the extension end point further includes:

and storing at least one expansion path which is not selected in the expansion paths obtained by any jump search as a candidate expansion path into a buffer queue for any jump path search, so as to determine a basic path of the next round of path search based on the candidate expansion paths buffered in the buffer queue when the next round of path search is executed.

Specifically, taking fig. 5 as an example, after the extended paths N1-N4 with the smallest comprehensive weights are selected, the paths N1-N2 and N1-N3 that are searched in the path searching process but are selected in the path searching process may be added to the cache queue as candidate extended paths.

It will be appreciated that by storing candidate extended paths in the cache queue, potentially useful paths previously searched for are avoided from being lost, so that paths generated by previous path searches are not discarded, but can be reused in subsequent path searches. The calculated path information can be fully utilized, unnecessary calculation repetition is reduced, and therefore the path searching efficiency is improved.

In addition, in the process of path searching, expensive calculation resources are required for calculating each optional extension path, and by selecting candidate extension paths from a cache queue, the information of the candidate extension paths can be obtained rapidly in a mode of reading cache data, and paths which are most likely to meet the target or condition can be calculated with high pertinence, so that the calculation cost of path searching is reduced.

acquiring a preset demand number of a target path; and when the number of the target paths reaches the preset demand number, or the candidate expansion paths in the cache queue are all determined to be basic paths, ending the execution of the path search.

In one embodiment, the preset number of demands may be set to an integer greater than 1, e.g., the preset number of demands may be 2, 3, 4, etc.

In one embodiment, the process of determining the 4 shortest paths is described assuming that the number of preset requirements for topN paths is 4, i.e., top4 shortest path calculation. Referring to fig. 7 to 9, fig. 7 is a second schematic diagram of performing a path search in one embodiment, fig. 8 is a third schematic diagram of performing a path search in one embodiment, and fig. 9 is a fourth schematic diagram of performing a path search in one embodiment.

From the foregoing fig. 5 and 6, a first shortest path N1-N4-N7-N10-N13 may be obtained, and a subsequent top4 shortest path search based on the shortest path. Specifically, as shown in fig. 7, in the first-hop path search of the second round, the candidate extended path N1-N2 in the buffer queue does not pass through the weight subtracting node, so that the comprehensive weight of the candidate extended path N1-N2 is 1, and in the buffer queue, the comprehensive weight of the candidate extended path N1-N2 is minimum, so that the next-hop path search is performed based on the candidate extended path N1-N2 to obtain extended paths N1-N2-N4, N1-N2-N5, and N4 is the target weight subtracting node, and N4 is the last node in the extended path N1-N2-N4, so that the comprehensive weight of the extended path is 0, and therefore, it is determined that N1-N2-N4 with a smaller comprehensive weight is used as the basic path for the next-hop path search, and the paths N1-N2-N5 are stored in the buffer queue for the next-round path search reference. Further, in an exemplary embodiment, a next-hop path search is performed on the base path N1-N2-N4, and since N4 may reach the target node N13 by subtracting nodes N7 and N10, the combined weight of this path is minimal, and thus the second shortest path N1-N2-N4-N7-N10-N13 may be determined.

Further, in an exemplary embodiment, as shown in fig. 8, among the candidate extended paths remaining in the buffer queue, the path N1-N2-N4-N7-N10-N12 has a comprehensive weight of 2, and the comprehensive weight is the smallest, so that it is used as a base path of the next hop to perform the path search. Since N12 can reach N13 directly, the third shortest path N1-N2-N4-N7-N10-N12-N13 for the round is determined.

Further, in an exemplary embodiment, as shown in fig. 9, among the candidate extended paths remaining in the cache queue, the path N1-N3 has a comprehensive weight of 3, and thus is used as a base path to perform path search to obtain paths N1-N3-N4 and N1-N3-N6, and since N4 is a weight-reducing node, a base path N1-N3-N4 with a smaller comprehensive weight is determined. Since N4 can reach the target node N13 through the weight reduction nodes N7 and N10, a fourth shortest path N1-N3-N4-N7-N10-N13 is determined to obtain 4 shortest paths with preset requirement numbers.

Referring to fig. 11, an effect of path selection using the method of the present application in one embodiment is illustrated. As shown in fig. 11, for a network with tens of thousands of nodes and tens of thousands of edges, by adopting the network path determining method of the present application, 100 shortest paths can be determined within hundreds of milliseconds, and the efficiency of determining the shortest paths is significantly improved.

It will be appreciated that when the number of target paths reaches the preset number of requirements means that the number of target paths currently determined has reached the requirements, so that it is not necessary to perform the path search again. Accordingly, when the candidate extended paths in the buffer queue are all determined as the base paths, it means that all the selectable paths in the network have been used in the current path search, so that the path search need not be performed again in this case.

In a specific implementation, a candidate expansion path L with the smallest comprehensive weight previously cached can be taken out of the cache queue B for path searching, and if a new expansion path cannot be searched out from the path L, the path L is deleted from the cache queue B; if expansion can be continued, the searched multiple new expansion paths are expanded: if the extension end point is the target node, the current extension path is added into the result queue R, otherwise, the current extension path is added into the buffer queue B. The steps are iteratively executed until the number of the result queues R reaches a preset requirement number N (indicating that the calculation is successful), or the buffer queues B are empty (indicating that the calculation is finished) or overtime (indicating that the calculation is failed or partially failed), and the execution of the path search is finished.

It can be understood that, in the calculation of the time complexity, the time of the candidate extended path L with the minimum comprehensive weight is taken from the buffer queue B, the total number of paths buffered in the queue B is assumed to be p, the heap priority queue is adopted, the cost of taking L each time is O (lovp), the time of extending the selected path with the minimum comprehensive weight is assumed to be 10 edges on average, and the cost of extending each time is O (10). In summary, the total time complexity is: o (nlogn+m+p (lovp+10)), i.e., O (nlogn+m+plogp). Where n is the total node number, m is the total edge number, and p is the size of the cache queue B. For a general depth or breadth path search, the final p value will typically be greater than the n value.

In an exemplary embodiment, as shown in fig. 4, which is a schematic flow chart of the step of calculating the shortest path in one embodiment, step S204 includes the steps of:

step S402, constructing a hash table adjacency matrix of the network according to the first path and the weight of the first path.

Referring to FIG. 10, a schematic diagram of a hash table adjacency matrix in one embodiment. In a specific implementation, a hash table adjacency matrix of the network as shown in fig. 10 can be constructed according to the obtained information of the nodes, the node edges and the weights. The hash table adjacency matrix comprises keys and value values, wherein the keys are all nodes in a network, the value is the weight value from the node corresponding to the key to other adjacent nodes, and the weight value corresponding to all the value of the node corresponding to each key is obtained, so that the complete hash table adjacency matrix of the network is constructed.

Step 404, constructing a topology graph of the network according to the hash table adjacency matrix.

In a specific implementation, the node and edge information may be extracted from a hash table adjacency matrix, for example, a data structure of the hash table is defined as: map < K1, map < K2, V > >, wherein, key K1 in the hash table is the A end node number of the edge, K2 is the Z end node number of the edge, and V is the weight of the edge K1-K2. Further, the nodes in the graph may be created from keys in the hash table, and the edges in the topology graph may be created from values in the hash table adjacency matrix, i.e., the weights of the edges.

Step 406, determining the shortest path according to the topology map.

In a specific implementation, according to the constructed topological graph, a Dijkstra algorithm can be adopted to calculate the shortest path of the network, namely the top1 shortest path of the network.

It can be understood that by performing the weight reduction processing on the second path passing through the weight reduction node, the method can guide the path search, reduce unnecessary path selection and improve the algorithm efficiency. In the conventional KSP (K-short Paths) algorithm, a plurality of candidate extension Paths are generally considered, so that the time complexity is high, and after the weight reduction process of the weight reduction node is introduced, the number of candidate extension Paths can be selectively reduced in the search process, so that the time complexity can be reduced from O (Kn (nlogn+m)) to O (nlogn+m). The path search becomes more efficient, and is particularly suitable for top N shortest path calculation of a large-scale network.

It will be appreciated that in the conventional Dijkstra algorithm, a two-dimensional adjacency matrix is typically used to represent the structure of the graph, where the size of the matrix is n×n, where n is the number of nodes. Each element in this matrix represents the weight of an edge between nodes, which is typically infinite if there are no directly connected edges. Thus, the spatial complexity of the two-dimensional adjacency matrix is O (n ² ) Because it is necessary to store information of possible edges between all nodes, including edges that do not exist. In contrast, when the hash table adjacency matrix constructed by the method is used, only the information of the actually existing edges is needed to be stored, and the possible edges among all nodes are not needed to be stored. The adjacency information for each node is stored in the hash table, so the spatial complexity depends on the number of edges m, instead of the number of nodes n, so the spatial complexity of the hash table adjacency matrix is O (m).

Therefore, by using the hash table adjacency matrix, the memory occupation can be greatly reduced while the correctness of the Dijkstra algorithm is maintained, and the method is particularly suitable for processing the condition of calculating the shortest path of a large-scale network topological graph.

It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.

Based on the same inventive concept, the embodiment of the application also provides a network path determining device for implementing the above-mentioned network path determining method. The implementation of the solution provided by the apparatus is similar to the implementation described in the above method, so the specific limitation in the embodiments of the network path determining apparatus provided below may be referred to the limitation of the network path determining method hereinabove, and will not be described herein.

In an exemplary embodiment, as shown in fig. 12, there is provided a network path determining apparatus, including: a first path acquisition module 610, a weight-reduction node determination module 620, a comprehensive weight calculation module 630, and a target path determination module 640, wherein:

a first path acquisition module 610 (may also be referred to as a data acquisition module) configured to acquire a first path between each pair of adjacent nodes of the network, and a weight corresponding to the first path;

the weight-subtracted node determining module 620 is configured to determine, according to the first path and the weight of the first path, a shortest path from a source node to a target node of the network, and set each intermediate node in the shortest path as a weight-subtracted node, where the shortest path is a path with a minimum total weight, and the intermediate nodes are nodes other than the source node and the sink node;

The comprehensive weight calculation module 630 (may also be referred to as a path search module) is configured to perform path search in the network, perform weight reduction processing for reducing the weight on a second path passing through any weight reduction node, and obtain a comprehensive weight of the second path, where the second path includes at least one first path;

the target path determining module 640 is configured to determine, as the target paths, a plurality of second paths with minimum comprehensive weights from the source node to the target node according to the second paths and the comprehensive weights of the second paths.

In one embodiment, the weight-reduction node determining module 620 includes a minimum weight calculating module, a path back-pushing module and a weight-reducer defining module, where the minimum weight calculating module is configured to determine a shortest path from a source node to a target node of the network according to the first path and the weight of the first path, and the path back-pushing module and the weight-reducer defining module are configured to set each intermediate node in the shortest path as a weight-reduction node.

In one embodiment, the comprehensive weight calculation module 630 is further specifically configured to:

and under the condition that the second path passes through the weight reduction node, acquiring a difference value of the total weight and the weight reduction weight corresponding to the second path as the comprehensive weight of the second path.

In one embodiment, the apparatus further comprises a data conversion patterning module for:

from the topology map, the shortest path is determined.

The respective modules in the above-described network path determination apparatus may be implemented in whole or in part by software, hardware, and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.

Fig. 13 in an exemplary embodiment, a computer device, which may be a terminal, is provided, and an internal structure diagram thereof may be as shown in fig. 13. The computer device includes a processor, a memory, an input/output interface, a communication interface, a display unit, and an input means. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a network path determination method. The display unit of the computer device is used for forming a visual picture, and can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be a key, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.

It will be appreciated by those skilled in the art that the structure shown in fig. 13 is merely a block diagram of a portion of the structure associated with the present application and is not limiting of the computer device to which the present application applies, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.

In an exemplary embodiment, a computer device is provided, comprising a memory and a processor, the memory having stored therein a computer program, the processor performing the steps of the method embodiments described above when the computer program is executed.

In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, implements the steps of the method embodiments described above.

In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the method embodiments described above.

It should be noted that, the user information (including, but not limited to, user equipment information, user personal information, etc.) and the data (including, but not limited to, data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party, and the collection, use, and processing of the related data are required to meet the related regulations.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRA M), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Cha nge Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.

The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.

The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.

Claims

1. A method of network path determination, the method comprising:

2. The method of claim 1, wherein the performing weight reduction processing on the second path passing through any weight reduction node to obtain the comprehensive weight of the second path includes:

3. The method of claim 2, further comprising, prior to said obtaining the difference in the total weight and the weight corresponding to the second path:

4. The method of claim 1, wherein said performing a path search in said network comprises:

5. The method of claim 4, wherein the performing a path search with the source node as an extension start point and the target node as an extension end point comprises:

6. The method according to claim 4, wherein the method further comprises:

and storing at least one expansion path which is not selected in expansion paths obtained by searching in any hop as a candidate expansion path into a cache queue for searching in any hop, so as to determine a basic path of the next round of path searching based on the candidate expansion path cached in the cache queue when the next round of path searching is executed.

7. The method of claim 6, wherein the method further comprises:

acquiring the preset demand number of the target path;

8. The method according to any of claims 1-7, wherein the determining a shortest path from a source node to a target node of the network according to the first path and the weight of the first path comprises:

and determining the shortest path according to the topological graph.

9. A network path determination apparatus, the apparatus comprising:

10. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any one of claims 1 to 8 when the computer program is executed.

11. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 8.

12. A computer program product comprising a computer program, characterized in that the computer program, when executed by a processor, implements the steps of the method of any one of claims 1 to 8.