Optical network routing planning method and device, storage medium and electronic device

Abstract

Embodiments of the present application provide an optical network routing planning method, device and storage medium, and an electronic device. The optical network routing planning method comprises: initializing an original optical network topology graph as an initial planning matrix; determining K candidate paths from a source node to a target node in the initial planning matrix by using a branch and bound method, wherein the K candidate paths comprise one shortest path and K-1 second shortest paths; respectively calculating path coincidence degrees of the K-1 second shortest paths and the shortest path, to obtain path evaluation values corresponding to the K candidate paths respectively; integrating the K candidate paths according to the path evaluation values corresponding to the K candidate paths respectively, to obtain K optimal paths; and performing routing planning on each layer of the original optical network according to each segment path formed by splitting the K optimal paths. Through the present application, the problem of improper selection of optical network routing in the related art is solved, and the technical effect of determining optimal optical network routing and improving the selection efficiency of network routing is achieved.

Description

Technical Field

[0001] This application relates to the field of communications, and more specifically, to an optical network routing planning method, apparatus, storage medium, and electronic device. Background Technology

[0002] An Optical Transport Network (OTN) is a transport network that integrates synchronous digital hierarchies (SDH) and wavelength division multiplexing (WDM) in the optical domain to achieve the transmission, multiplexing, routing, and monitoring of service signals. OTN technology combines SDH and WDM technologies, overcoming their shortcomings and representing a novel transmission technology. OTN networks have a layered structure, enabling service scheduling and protection at both the optical and electrical layers. Therefore, OTN networks require multiple layers of protection for critical services. Accurately assessing the links and their value within the optical transport network and selecting the most suitable network links is therefore crucial. Summary of the Invention

[0003] This application provides an optical network routing planning method, apparatus, storage medium, and electronic device to at least solve the problem of inappropriate optical network routing selection in related technologies.

[0004] According to one embodiment of this application, an optical network routing planning method is provided, comprising: initializing an original optical network topology map into an initial planning matrix, wherein the initial planning matrix indicates the network parameters and routing planning parameters of each layer in the original optical network topology map; determining K candidate paths from a source node to a target node in the initial planning matrix using the branch and bound method, wherein the K candidate paths include one shortest path and K-1 second shortest paths; calculating the path overlap between the K-1 second shortest paths and the shortest path respectively to obtain the path evaluation value corresponding to each of the K candidate paths; integrating the K candidate paths according to the path evaluation values ​​corresponding to each of the K candidate paths to obtain K optimal paths; and performing routing planning on each layer of the original optical network according to the path segments formed by splitting the K optimal paths.

[0005] According to another embodiment of this application, an optical network routing planning apparatus is provided, comprising: an initialization module for initializing an original optical network topology map into an initial planning matrix, wherein the initial planning matrix indicates the network parameters and routing planning parameters of each layer in the original optical network topology map; a determination module for determining K candidate paths from a source node to a target node in the initial planning matrix using the branch and bound method, wherein the K candidate paths include one shortest path and K-1 second shortest paths; a path evaluation module for calculating the path overlap between the K-1 second shortest paths and the shortest path respectively, to obtain the path evaluation value corresponding to each of the K candidate paths; an integration module for integrating the K candidate paths according to the path evaluation values ​​corresponding to each of the K candidate paths, to obtain K optimal paths; and a planning module for performing routing planning at each layer of the original optical network according to the path segments formed by splitting the K optimal paths.

[0006] According to yet another embodiment of this application, a computer-readable storage medium is also provided, wherein a computer program is stored therein, and the computer program is configured to perform the steps in any of the above method embodiments when it is run.

[0007] According to yet another embodiment of this application, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0008] This application proposes a threshold-based adjustment to the initialization matrix on top of the branch and bound algorithm. This allows for the filtering out of invalid paths with excessively high hop counts, thus accelerating path selection efficiency. Furthermore, by combining branch and bound with linear programming, the application uses the path evaluation value derived from multiple factors as a standard to search for network routes within the OTN that meet the requirements. This solves the problem of inappropriate optical network route selection in related technologies, achieving the technical effect of determining the optimal optical network route and improving network route selection efficiency. Attached Figure Description

[0009] Figure 1 This is a hardware structure block diagram of the optical network routing planning method according to an embodiment of this application;

[0010] Figure 2 This is a flowchart of an optical network routing planning method according to an embodiment of this application;

[0011] Figure 3 This is a flowchart of an optical network routing planning method according to an embodiment of this application;

[0012] Figure 4This is a structural block diagram of an optical network routing planning device according to an embodiment of this application. Detailed Implementation

[0013] The embodiments of this application will be described in detail below with reference to the accompanying drawings and examples.

[0014] It should be noted that the terms "first," "second," etc., in the specification, claims, and drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0015] The methods and embodiments provided in this application can be executed on a mobile terminal, computer terminal, or similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal for an optical network routing planning method according to an embodiment of this application. Figure 1 As shown, a mobile terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor (MCU) or a field-programmable gate array (FPGA)) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0016] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the optical network routing planning method in this embodiment. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thus implementing the above-described method. The memory 104 may include high-speed random access memory and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0017] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0018] This embodiment provides an optical network routing planning method. Figure 2 This is a flowchart of an optical network routing planning method according to an embodiment of this application, such as... Figure 2 As shown, the process includes, but is not limited to, the following steps:

[0019] Step S202: Initialize the original optical network topology map into an initial planning matrix, wherein the initial planning matrix indicates the network parameters and routing planning parameters of each layer in the original optical network topology map;

[0020] Step S204: Using the branch and bound method, determine K candidate paths from the source node to the target node in the initial planning matrix. The K candidate paths include one shortest path and K-1 second shortest paths.

[0021] Step S206: Calculate the path overlap between the K-1 second shortest paths and the shortest path respectively, and obtain the path evaluation value corresponding to each of the K candidate paths;

[0022] Step S208: Integrate the K candidate paths according to their respective path evaluation values ​​to obtain the K optimal paths;

[0023] Step S210: Perform routing planning at each layer of the original optical network according to the path segments formed by splitting the K optimal paths.

[0024] Branch and bound searches the solution space tree of a problem in a breadth-first or minimum-cost-first manner. In branch and bound, each live node has only one chance to become an expanded node. When a live node becomes an expanded node, all its child nodes are generated at once. Among these child nodes, those leading to infeasible or suboptimal solutions are discarded, and the remaining child nodes are added to the live node list. Then, a node is taken from the live node list to become the current expanded node, and the above node expansion process is repeated. This process continues until the required solution is found or the live node list is empty. During the node expansion process, once a node's lower bound is found to be no less than the currently found shortest path length, the algorithm prunes the subtree rooted at that node. The algorithm utilizes the control relationships between nodes for pruning.

[0025] The original optical network topology diagram is transformed into a two-dimensional model and initialized as a planning matrix. A branch and bound algorithm is then used to determine K candidate paths within this matrix. These K candidate paths are not limited to the K shortest paths; they specifically include one shortest path and K-1 second-shortest paths. K is a positive integer, and its specific value is not limited here. After determining the shortest path, the overlap between the K-1 second-shortest paths and the shortest path is calculated to obtain the path evaluation value for each of the K shortest paths. Based on these path evaluation values, the K shortest paths are integrated to obtain the K optimal paths. Routing planning and deployment are then performed in the optical network according to these K optimal paths.

[0026] This application proposes a threshold-based adjustment to the initialization matrix on top of the branch and bound algorithm. This allows for the filtering out of invalid paths with excessively high hop counts, thus accelerating path selection efficiency. Furthermore, by combining branch and bound with linear programming, the application uses the path evaluation value derived from multiple factors as a standard to search for network routes within the OTN that meet the requirements. This addresses the problem of inappropriate optical network route selection in related technologies, achieving the technical effect of determining the optimal optical network route and improving network route selection efficiency.

[0027] As an optional implementation, step S202 above initializes the original optical network topology graph into an initial planning matrix, including:

[0028] S202-2, Obtain the input matrix indicating network parameters, wherein the network parameters include the number of nodes, the number of links, the source node, and the target node of the original optical network;

[0029] S202-4, Obtain the connection matrix of the distance between every two connected nodes in the original optical network;

[0030] S202-6, Construct the initial planning matrix using the input matrix and the connection matrix.

[0031] As an optional implementation, S202-6 above constructs an initial planning matrix using the input matrix and the connection matrix, including:

[0032] S206-62, determine the path threshold set for the initial planning matrix, wherein the routing planning parameters include the path threshold;

[0033] S206-64, Remove the connection elements in the connection matrix whose distance between two connection nodes is greater than the path threshold to obtain the target connection matrix;

[0034] S206-66, construct the initial planning matrix using the input matrix and the target connection matrix.

[0035] Specifically, the existing network topology is initialized in the form of a planning matrix, and path thresholds are imposed. The planning matrix M is shown below:

[0036] M = [M1; M2] (1)

[0037] M1=[N_num,L_num,s_nodes,d_node] (2)

[0038] M2 = [n1, n2, vis1; ...; n x1 ,n x2 ,vis m (3)

[0039] Where M1 is the input matrix, N_num is the total number of nodes in the network topology, L_num is the total number of links in the network topology graph, s_nodes and d_node represent the source node and the destination node, respectively; M2 is the connection matrix, n1 and n2 represent two connected nodes, and vis1 represents the straight-line distance between the two nodes.

[0040] While initializing the planning matrix, the route selection threshold D is also set. max Configure settings to remove elements from the topology graph whose relative distance is greater than D. max The points are restricted to a route selection range of D ≤ D. max .

[0041] As an optional implementation, S204 above uses the branch and bound method to determine K candidate paths from the source node to the target node in the initial planning matrix, including:

[0042] S204-2, using the branch and bound method, find the length of each path segment at each node and accumulate them to determine the current shortest path, and remove other branch paths;

[0043] S204-4: Update the current shortest path for each path to obtain the shortest path among the K candidate paths;

[0044] S204-6 sets the target node in the shortest path to an unavailable state, and re-uses the branch and bound method to find the current second shortest path, obtaining K-1 second shortest paths from the K candidate paths.

[0045] Specifically, the process of determining K candidate paths in the programming matrix using the branch and bound method is as follows:

[0046] The first step is to find the length of each path segment by hop according to the branch and bound algorithm and sum the path lengths. Keep the minimum path length sum and remove other paths by pruning. Repeat this process segment by segment until the first shortest path (the first candidate path) is obtained.

[0047] The second step is to store the first shortest path obtained, set some nodes in the path as unreachable, and then initiate the route selection calculation again to obtain the first second shortest path (the second candidate path).

[0048] Third, repeat step two until K-1 second shortest paths (the Kth candidate path) are obtained.

[0049] After obtaining the shortest path and K-1 second-shortest paths, the path set L is not limited to storing the shortest path and K-1 second-shortest paths in a path storage matrix. save Represented as:

[0050] L save =[L min ,L1,L2,…,L K-1 (4)

[0051] Among them, L min Representing the shortest path, L1, L2, ..., L K-1 These represent K-1 sub-shortest paths.

[0052] As an optional implementation, S206 above calculates the path overlap between the K-1 second-shortest paths and the shortest path, including:

[0053] S206-2, extract the reference node set composed of the nodes traversed by the shortest path, and then extract the candidate node set composed of the nodes traversed by K-1 second shortest paths in turn.

[0054] S206-4 Calculate the Hamming distance between each candidate node set and the reference node set to determine the path overlap between the K-1 second shortest paths and the shortest path.

[0055] After obtaining the path set L save Next, path separation is calculated, and the path separation process uses Simhash Hamming distance to verify path overlap. The shortest path L is used as the basis for the calculation. minAs a reference path, extract the set of nodes LNodes that the shortest path traverses. min :

[0056] LNodes min =[N min1 N min2 ,…,N minm (5)

[0057] Extract the set of nodes LNodes for the K-1 second shortest paths. i :

[0058] LNodes i =[N i1 N i2 ,…,N im (6)

[0059] Calculate the Hamming distance Lsim between each of the second shortest paths and the shortest path. i The calculation formula is as follows:

[0060] Lsim i =LNodes i &&LNodes min ,1≤i≤K-1 (7)

[0061] Thus, we obtain the Hamming distance set Lsim for each of the K-1 second shortest paths, represented as:

[0062] Lsim = [Lsim1, Lsim2, ..., Lsim] i ,…,Lsim K-1 ], 1≤i≤K-1 (8)

[0063] For K-1 second-shortest paths, the larger the calculated Hamming distance value, the greater the overlap between the path and the shortest path; conversely, the smaller the calculated Hamming distance value, the less overlap between the path and the shortest path.

[0064] As an optional implementation, S206-4 above calculates the Hamming distance between each candidate node set and the reference node set, and determines the path overlap between the K-1 second-shortest paths and the shortest path, including:

[0065] S206-42, Sort the candidate node sets corresponding to each of the K-1 second shortest paths according to their respective Hamming distances to obtain the modified Hamming distance set;

[0066] S206-44, perform path integration on K candidate paths according to the path overlap degree indicated by the modified Hamming distance set in descending order.

[0067] Use the Hamming distance set Lsim and the path set L save Both are sorted in ascending order based on the Hamming distance values, and the modified Hamming distance set Lsim_z and modified L are calculated. save _z, obtains the K shortest paths sorted from low to high path overlap.

[0068] As an optional implementation, S206 above calculates the path overlap between the K-1 second-shortest paths and the shortest path to obtain the path evaluation value corresponding to each of the K candidate paths, including:

[0069] S206-6, Based on the integrated K candidate paths, obtain the evaluation parameters and evaluation weights of each candidate path;

[0070] S206-8, the evaluation parameters of each candidate path are weighted and summed according to their evaluation weights to obtain the path evaluation value of each candidate path.

[0071] After integrating the K shortest paths, a comprehensive evaluation of these paths is required. Evaluation parameters include, but are not limited to, path length, hop count, latency, and bandwidth overhead. Each evaluation parameter corresponds to its own evaluation weight, and the sum of all evaluation weights is 1. The path evaluation value L is calculated using methods other than linear programming. Evaluei The formula for calculation using the weighted polynomial method is shown below:

[0072]

[0073] Where 1≤i≤K-1, L_dis i α1 and α2 represent the path length and length weight, respectively, and L_jum i α and α2 represent the hop count and hop weight of the path, respectively, and L_tdelay i α and α3 represent the request latency and latency weight of this path, respectively, and L_bdwidth_ne i α and α4 represent the request bandwidth and bandwidth weight of this path, respectively.

[0074] It should be noted that the specific values ​​of the four evaluation weights can be adjusted according to different needs, and no specific value is limited here.

[0075] The path evaluation value set L_Evalue is not limited to being represented as:

[0076]

[0077] As an optional implementation, S208 above integrates the K candidate paths according to their respective path evaluation values ​​to obtain the K optimal paths, including: integrating the K candidate paths in descending order of their path evaluation values ​​to obtain the K optimal paths.

[0078] The shortest paths are then reorganized according to their path evaluation values ​​from highest to lowest, transforming the path evaluation value set L_Evalue into a path transmission quality set L_Trans_value. Simultaneously, the set of K shortest paths L... save Transform into a set of K optimal paths Lfinal save .

[0079] After obtaining K optimal paths, the electrical layer bandwidth is allocated segment by segment according to the requested bandwidth of each segment in the path, and the wavelength is allocated segment by segment according to the principle of wavelength consistency between two points.

[0080] The specific process of optical network routing planning is not limited to, for example Figure 3 As shown. S1, the topology of the optical network is converted into a two-dimensional model, and a planning matrix is ​​generated. S2, the branch and bound method is used to find K shortest paths. S3, the Simhash algorithm is used to verify the path overlap. S4, it is determined whether the path overlap meets the requirements. If not, return to S2. If yes, proceed to S5, the path evaluation value is determined by the linear weighted polynomial. S6, the paths are sorted in descending order of evaluation value to obtain multiple optimal paths. S7, electrical layer bandwidth is allocated to each path. S8, it is determined whether wavelength consistency is met. If not, the path is deleted. If yes, proceed to S9, wavelength is allocated. After wavelength allocation, proceed to S10, and the routing results are output.

[0081] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory / random access memory (ROM / RAM), magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0082] This embodiment also provides an optical network routing planning device for implementing the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0083] Figure 4 This is a structural block diagram of an optical network routing planning device according to an embodiment of this application, such as... Figure 4 As shown, the device includes:

[0084] Initialization module 42 is used to initialize the original optical network topology map into an initial planning matrix, wherein the initial planning matrix indicates the network parameters and routing planning parameters of each layer in the original optical network topology map;

[0085] The determination module 44 is used to determine K candidate paths from the source node to the target node in the initial planning matrix using the branch and bound method. The K candidate paths include one shortest path and K-1 second shortest paths.

[0086] The path evaluation module 46 is used to calculate the path overlap between the K-1 second shortest paths and the shortest path, and obtain the path evaluation value corresponding to each of the K candidate paths.

[0087] Integration module 48 is used to integrate the K candidate paths according to their respective path evaluation values ​​to obtain the K optimal paths;

[0088] The planning module 50 is used to perform route planning at each layer of the original optical network according to the segments formed by splitting the K optimal paths.

[0089] Optionally, the initialization module 42 further includes: obtaining an input matrix indicating network parameters, wherein the network parameters include the number of nodes, the number of links, and the source and target nodes of the original optical network; obtaining a connection matrix of the distance between every two connected nodes in the original optical network; and constructing an initial planning matrix using the input matrix and the connection matrix.

[0090] Optionally, the initialization module 42 described above constructs an initial planning matrix using the input matrix and the connection matrix, including: determining a path threshold set for the initial planning matrix, wherein the routing planning parameters include the path threshold; removing connection elements in the connection matrix whose distance between two connection nodes is greater than the path threshold to obtain the target connection matrix; and constructing the initial planning matrix using the input matrix and the target connection matrix.

[0091] Optionally, the determination module 44 further includes: using the branch and bound method to find the length of each path segment at each node and accumulate it to determine the current shortest path and prune other branch paths; updating the current shortest path for each path to obtain the shortest path among the K candidate paths; setting the target node in the shortest path to an unavailable state, and reusing the branch and bound method to find the current second shortest path to obtain the K-1 second shortest paths among the K candidate paths.

[0092] Optionally, the path evaluation module 46 further includes: extracting a reference node set consisting of the nodes traversed by the shortest path, and sequentially extracting a candidate node set consisting of the nodes traversed by K-1 secondary shortest paths; calculating the Hamming distance between each candidate node set and the reference node set, and determining the path overlap between the K-1 secondary shortest paths and the shortest path.

[0093] Optionally, the path evaluation module 46 further includes: sorting the candidate node sets corresponding to the K-1 second shortest paths according to their respective Hamming distances to obtain a modified Hamming distance set; and integrating the K candidate paths according to the path overlap degree indicated by the modified Hamming distance set in descending order.

[0094] Optionally, the path evaluation module 46 further includes: based on the integrated K candidate paths, obtaining the evaluation parameters corresponding to each candidate path and the evaluation weight of each evaluation parameter; and weighting and summing the evaluation parameters of each candidate path according to the evaluation weight to obtain the path evaluation value of each candidate path.

[0095] Optionally, the integration module 48 further includes: integrating the K candidate paths in descending order of their path evaluation values ​​to obtain the K optimal paths.

[0096] It should be noted that the above modules can be implemented by software or hardware. For the latter, they can be implemented in the following ways, but are not limited to: all the above modules are located in the same processor; or, the above modules are located in different processors in any combination.

[0097] Embodiments of this application also provide a computer-readable storage medium storing a computer program, wherein the computer program is configured to execute the steps in any of the above method embodiments when run.

[0098] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard disk, magnetic disk, or optical disk.

[0099] Embodiments of this application also provide an electronic device including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0100] In one exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0101] Specific examples in this embodiment can be found in the examples described in the above embodiments and exemplary implementations, and will not be repeated here.

[0102] Obviously, those skilled in the art should understand that the modules or steps of this application described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented here, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, this application is not limited to any particular combination of hardware and software.

[0103] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A method for optical network routing planning, characterized in that, include: The original optical network topology is initialized into an initial planning matrix, wherein the initial planning matrix indicates the network parameters and routing planning parameters of each layer in the original optical network topology; Using the branch and bound method, K candidate paths from the source node to the target node are determined in the initial planning matrix, wherein the K candidate paths include 1 shortest path and K-1 second shortest paths; Extract a reference node set consisting of the nodes traversed by the shortest path, and sequentially extract candidate node sets consisting of the nodes traversed by the K-1 second shortest paths; calculate the Hamming distance between each candidate node set and the reference node set, determine the path overlap between the K-1 second shortest paths and the shortest path, and obtain the path evaluation value corresponding to each of the K candidate paths. The K candidate paths are integrated based on their respective path evaluation values ​​to obtain the K optimal paths; The path segments formed by splitting the K optimal paths are routed at each layer of the original optical network. The method further includes: after obtaining K optimal paths, allocating the electrical layer bandwidth segment by segment according to the requested bandwidth of each segment of the path, and allocating the wavelength segment by segment according to the wavelength consistency principle between two points.

2. The method according to claim 1, characterized in that, The original optical network topology is initialized into an initial planning matrix, including: Obtain an input matrix indicating the network parameters, wherein the network parameters include the number of nodes and links of the original optical network, as well as the source node and the target node; Obtain the connection matrix of the distance between every two connected nodes in the original optical network; The initial planning matrix is ​​constructed using the input matrix and the connection matrix.

3. The method according to claim 2, characterized in that, Constructing the initial planning matrix using the input matrix and the connection matrix includes: Determine the path threshold set for the initial planning matrix, wherein the routing planning parameters include the path threshold; Remove the connection elements in the connection matrix whose distance between two connection nodes is greater than the path threshold to obtain the target connection matrix; The initial planning matrix is ​​constructed using the input matrix and the target connection matrix.

4. The method according to claim 1, characterized in that, Using the branch and bound method, K candidate paths from the source node to the target node are determined in the initial programming matrix, including: Using the branch and bound method, the length of each path segment is found and accumulated at each node to determine the current shortest path, and other branch paths are removed. Update the current shortest path for each path to obtain the shortest path among the K candidate paths; Set the target node in the shortest path to an unavailable state, and re-apply the branch and bound method to find the current second shortest path, thereby obtaining the K-1 second shortest paths from the K candidate paths.

5. The method according to claim 1, characterized in that, Calculate the Hamming distance between each candidate node set and the reference node set, and determine the path overlap between the K-1 second-shortest paths and the shortest path, including: Sort the candidate node sets corresponding to each of the K-1 second shortest paths according to their respective Hamming distances to obtain the modified Hamming distance set; Based on the path overlap degree indicated by the modified Hamming distance set in descending order, the K candidate paths are integrated.

6. The method according to claim 5, characterized in that, Calculate the path overlap between the K-1 second-shortest paths and the shortest path to obtain the path evaluation value for each of the K candidate paths, including: Based on the integrated K candidate paths, obtain the evaluation parameters and evaluation weights of each candidate path; The evaluation parameters of each candidate path are weighted and summed according to the evaluation weights to obtain the path evaluation value of each candidate path.

7. The method according to claim 1, characterized in that, The K candidate paths are integrated based on their respective path evaluation values ​​to obtain the K optimal paths, including: The K candidate paths are integrated in descending order of their path evaluation values ​​to obtain the K optimal paths.

8. An optical network routing planning device, characterized in that, include: An initialization module is used to initialize the original optical network topology map into an initial planning matrix, wherein the initial planning matrix indicates the network parameters and routing planning parameters of each layer in the original optical network topology map; The determination module is used to determine K candidate paths from the source node to the target node in the initial planning matrix using the branch and bound method, wherein the K candidate paths include 1 shortest path and K-1 second shortest paths; The path evaluation module is used to extract a reference node set consisting of the nodes traversed by the shortest path, and sequentially extract candidate node sets consisting of the nodes traversed by the K-1 second shortest paths; calculate the Hamming distance between each candidate node set and the reference node set, determine the path overlap between the K-1 second shortest paths and the shortest path, and obtain the path evaluation value corresponding to each of the K candidate paths. The integration module is used to integrate the K candidate paths according to their respective path evaluation values ​​to obtain K optimal paths; The planning module is used to perform route planning at each layer of the original optical network according to the path segments formed by splitting the K optimal paths. The optical network routing planning device is also used to allocate electrical layer bandwidth segment by segment according to the requested bandwidth of each segment of the path after obtaining K optimal paths, and to allocate wavelength segment by segment according to the wavelength consistency principle between two points.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the steps of the method described in any one of claims 1 to 7.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method described in any one of claims 1 to 7.

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