Circuit multi-path carrying analysis method and system, electronic device, and storage medium

Abstract

This application provides a circuit multi-route bearer analysis method, system, electronic device, and storage medium, belonging to the field of communication technology. The method utilizes neural network technology to identify the actual transmission network topology map and obtain computer-processable transmission network topology data. For a set of target relay service data including multiple relay circuits, where each relay circuit has the same source and destination sites and relay type, the method queries the transmission network topology data based on the source and destination sites to obtain multiple second route information that can carry the corresponding circuits. By comparing and analyzing the first route information and multiple second route information of the multiple relay circuits in the target relay service data, the circuit multi-route bearer analysis result is obtained. The multi-route bearer analysis result includes one of the following: route construction requirements, reasonable route bearer configuration, or route bearer adjustment scheme. This application can automatically manage and reasonably allocate the transmission network bearer routes of relay circuits, reducing service failure rates.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a circuit multi-route bearer analysis method, system, electronic device and storage medium. Background Technology

[0002] As more and more important trunk circuits are carried on transmission networks (such as optical transmission networks OTN), such as CN2 trunk, IPTV trunk, IPRAN trunk, IDC trunk, etc., various trunk circuits are not configured with protection routes when they are opened on OTN networks. Therefore, when a single trunk circuit is interrupted, its traffic will be switched to other trunk circuits.

[0003] Currently, with the increasing volume of traffic transmitted and the growing number of trunk circuits, manual management of OTN trunk circuits makes it difficult to allocate trunk circuit routes reasonably when there are many trunk circuits in operation. This leads to uneven load distribution among the circuits, and when a local trunk circuit fails, other trunk circuits are prone to traffic congestion and service interruption, posing a risk to the service. Summary of the Invention

[0004] The main objective of this application is to propose a circuit multi-route bearer analysis method, system, electronic device, and storage medium, which aims to automatically manage and rationally allocate the transmission network bearer routes of relay circuits, thereby reducing the service failure rate.

[0005] To achieve the above objectives, one aspect of this application proposes a circuit multi-route bearer analysis method, comprising the following steps:

[0006] The transmission network topology data is obtained by using neural network technology to identify the transmission network topology map, and the transmission network topology data includes the stations of the transmission network and the connection relationships between the stations;

[0007] Acquire target relay service data, wherein the target relay service data includes multiple relay circuits, each relay circuit having the same source and destination sites and relay type, and each relay circuit including first routing information and bearer rate;

[0008] Multiple second routing information entries are obtained by querying the transmission network topology data based on the source and destination sites;

[0009] The first routing information and the second routing information of multiple trunk circuits in the target trunk service data are compared and analyzed to obtain the circuit multi-route bearer analysis result. The multi-route bearer analysis result includes one of the following: route construction requirements, reasonable route bearer, or route bearer adjustment scheme.

[0010] In some embodiments, the transmission network topology data is obtained through the following steps:

[0011] Obtain the transmission network topology;

[0012] A trained convolutional neural network is used to identify the site features in the transmission network topology map to obtain a site information set, wherein the site information set includes site coordinates, site visual connectivity features, and site area visual features;

[0013] Construct a graph structure based on the site information set;

[0014] A trained graph neural network is used to identify the station connection relationships in the graph structure to obtain the station connection data in the transmission network topology graph;

[0015] The transmission network topology data is determined based on the site connection data and the site information set.

[0016] In some embodiments, determining the transport network topology data based on the site connection data and the site information set includes the following steps:

[0017] The site type of each site is determined based on the site information set, wherein the site type is either a single-route site or a multi-route multiplexing site;

[0018] Based on the site connection data, determine the site with the most connections in the transmission network topology diagram, and determine the site as the traversal starting point;

[0019] Based on the site connection data, target sites that are connected to the traversal starting point are determined, and the connection relationship between the target sites and the traversal starting point is marked with an expected character, wherein the expected character represents the site type of the target site that has a connection relationship;

[0020] The target site is determined as the new traversal starting point, and the steps of determining the target site connected to the traversal starting point based on the site connection data and marking the connection between the target site and the traversal starting point using the expected character are repeated to obtain the transmission network topology data, wherein the target site was not used as the traversal starting point in the previous traversal rounds.

[0021] In some embodiments, the first routing information of the trunk circuit in the target trunk service data is obtained through the following steps:

[0022] Query the corresponding medium routing text based on the circuit name of the relay circuit in the target relay service data;

[0023] Regular expressions are used to extract site information from the media routing text;

[0024] The first routing information of the relay circuit is determined based on the site information.

[0025] In some embodiments, the circuit multi-route bearer analysis method further includes the following steps:

[0026] Based on the first routing information, multiple trunk circuits with the same first routing information in the target trunk service data are merged into one trunk circuit;

[0027] The sum of the carrying rates of multiple relay circuits with the same first routing information is determined as the carrying rate of the merged relay circuit.

[0028] In some embodiments, obtaining multiple second routing information by querying the transmission network topology data based on the source and destination sites includes the following steps:

[0029] Based on the source and destination sites, traverse the transmission network topology data to determine the path sites from the source to the destination.

[0030] Multiple second-path information is determined based on the path stations and the station types of the path stations between the source and destination.

[0031] In some embodiments, comparing and analyzing the first routing information and the second routing information of multiple trunk circuits in the target trunk service data to obtain the circuit multi-route bearer analysis result includes the following steps:

[0032] Determine whether the quantity of the second routing information is greater than a preset quantity value;

[0033] If the quantity of the second routing information is less than a preset quantity value, then the multi-route bearer analysis result is determined to be a routing construction requirement;

[0034] If the quantity of the second routing information is greater than or equal to a preset quantity value, then determine whether the quantity of the first routing information and the quantity of the second routing information are the same;

[0035] When the number of the first routing information and the number of the second routing information are the same, it is determined whether the rate load is balanced based on the load rate of multiple trunk circuits in the target trunk service data. If the rate load is balanced, the multi-route load analysis result is determined to be reasonable. If the rate load is unbalanced, the multi-route load analysis result is determined to be a route load adjustment scheme, and the route load adjustment scheme includes adjusting the load rate of the route.

[0036] When the number of the first routing information is less than the number of the second routing information, the multi-route bearer analysis result is determined to be a route bearer adjustment scheme, wherein the route bearer adjustment scheme includes adjusting the relay circuit to the route without a bearer circuit.

[0037] To achieve the above objectives, another aspect of this application proposes a circuit multi-route bearer analysis system, comprising:

[0038] The first module is used to acquire transmission network topology data, wherein the transmission network topology data is obtained by identifying the transmission network topology map using neural network technology, and the transmission network topology data includes the stations of the transmission network and the connection relationships between the stations;

[0039] The second module is used to acquire target relay service data, wherein the target relay service data includes multiple relay circuits, each relay circuit has the same source and destination sites and relay type, and each relay circuit includes first routing information and bearer rate;

[0040] The third module is used to query the transmission network topology data based on the source and destination sites to obtain multiple second routing information;

[0041] The fourth module is used to compare and analyze the first routing information and the second routing information of multiple trunk circuits in the target trunk service data to obtain the circuit multi-route bearer analysis result. The multi-route bearer analysis result includes one of the following: route construction requirements, reasonable route bearer, or route bearer adjustment scheme.

[0042] To achieve the above objectives, another aspect of the present application provides an electronic device, which includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for enabling communication between the processor and the memory. When the program is executed by the processor, it implements the method described in the above embodiments.

[0043] To achieve the above objectives, another aspect of the embodiments of this application proposes a storage medium, which is a computer-readable storage medium for computer-readable storage. The storage medium stores one or more programs that can be executed by one or more processors to implement the methods described in the above embodiments.

[0044] This application proposes a circuit multi-route bearer analysis method, system, electronic device, and storage medium. It utilizes neural network technology to identify the actual transmission network topology map and obtain transmission network topology data, which includes the stations of the transmission network and the connections between them. For a set of target relay service data comprising multiple relay circuits, each with the same source and destination stations and relay type, the transmission network topology data is queried based on the source and destination stations to obtain multiple second route information that can carry the corresponding circuits. The first route information and multiple second route information of the multiple relay circuits in the target relay service data are compared and analyzed to obtain the circuit multi-route bearer analysis result. The multi-route bearer analysis result includes one of the following: route construction requirements, reasonable route bearer configuration, or route bearer adjustment scheme. This application uses neural network technology to identify the actual transmission network topology map and obtain transmission network topology data. Then, based on the transmission network topology data, it automatically compares and analyzes the relay circuits of the target relay service data to determine the circuit multi-route bearer analysis result. This enables automatic management and reasonable allocation of transmission network bearer routes for relay circuits, reducing service failure rates. Attached Figure Description

[0045] Figure 1 This is a flowchart of the circuit multi-route bearer analysis method provided in the embodiments of this application;

[0046] Figure 2 yes Figure 1 The flowchart of the method for acquiring transmission network topology data in step S101;

[0047] Figure 3 yes Figure 2 The flowchart of step S205 in the text;

[0048] Figure 4 yes Figure 1 The flowchart of step S102 in the document;

[0049] Figure 5 This is a flowchart of a circuit multi-route bearer analysis method provided in another embodiment of this application;

[0050] Figure 6 yes Figure 1 The flowchart of step S103 in the process;

[0051] Figure 7 This is a schematic diagram illustrating the logic of comparing and analyzing resource-side routing and network management-side routing provided in an embodiment of this application;

[0052] Figure 8 This is a schematic diagram of a circuit multi-route bearer analysis system provided in an embodiment of this application;

[0053] Figure 9 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application;

[0054] Figure 10 This is a schematic diagram of the transmission network topology provided in an embodiment of this application;

[0055] Figure 11 This is a schematic diagram of the transmission network topology identification logic provided in the embodiments of this application;

[0056] Figure 12 This is a schematic diagram of the media routing text obtained from the query provided in the embodiments of this application. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0058] It should be noted that although the system is divided into functional modules and the flowchart shows a logical order, in some cases, the steps shown or described may be executed in a different order than the module division in the system or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0059] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0060] First, let's analyze some of the terms used in this application:

[0061] Convolutional Neural Networks (CNNs) are a type of feedforward neural network that includes convolutional computations and has a deep structure. They can perform supervised learning using labeled training data to accomplish tasks such as visual image recognition and object detection.

[0062] Graph data is a data structure composed of nodes and edges. In graph data, nodes represent entities, and edges represent relationships between entities. For example, in a social network, nodes can represent users, and edges can represent friend relationships between users.

[0063] Graph Neural Networks (GNNs) are deep learning models that learn and reason on graph-structured data. They can handle nodes, edges, and the complex relationships between them. The core idea of ​​GNNs is to use information about a node's neighbors to update the node's state, thereby capturing structural information in the graph and enabling the prediction of connectivity relationships.

[0064] Optical transmission technology: a technology that transmits optical signals between a sender and a receiver.

[0065] Optical Transport Network (OTN) is a transport network based on wavelength division multiplexing (WDM) technology and organized at the optical layer. It is the next-generation backbone transport network.

[0066] Optical transmission network topology: In an optical transmission network, the site equipment is abstracted as a point, and the transmission optical cable medium is abstracted as a line. The geometric figure composed of points and lines forms the topology of the optical transmission network.

[0067] As traffic volume increases, the bandwidth of a single or even a few trunk circuits becomes insufficient to meet the demand. Therefore, the number of trunk circuits opened for each type can exceed ten. The more trunk circuits opened, the more difficult it becomes to manually count them. It's impossible to evenly distribute the opened trunk circuits across all reachable routes. When some transmission routes are interrupted, traffic switches to other trunk circuits, causing overload and congestion, thus hindering services. Alternatively, even if multiple routes are available for trunk circuit opening, the long time interval between openings makes it easy to overlook the routing of already opened trunks. This can lead to newly opened trunks sharing the same transmission routes as previously opened trunks. If a fault occurs on that route, all trunks will be interrupted, resulting in complete service disruption and incalculable losses. It can be seen that when the transmission network carries relay circuits manually, it is impossible to reasonably allocate the relay circuit routes when there are many relay circuits in operation. The load on each circuit is uneven. When a local relay circuit fails, other relay circuits are prone to traffic congestion and service interruption, which leads to service risks.

[0068] Based on this, embodiments of this application provide a circuit multi-route bearer analysis method, system, electronic device, and storage medium, which aim to automatically manage and rationally allocate the transmission network bearer routes of relay circuits to reduce service failure rates.

[0069] The circuit multi-route bearer analysis method, system, electronic device and storage medium provided in the embodiments of this application are specifically described through the following embodiments. First, the circuit multi-route bearer analysis method in the embodiments of this application is described.

[0070] The circuit multi-route bearer analysis method provided in this application relates to the field of artificial intelligence technology. This method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application implementing the circuit multi-route bearer analysis method, but is not limited to the above forms.

[0071] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0072] Figure 1 This is an optional flowchart of the circuit multi-route bearer analysis method provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S101 to S104.

[0073] Step S101: Obtain transmission network topology data, wherein the transmission network topology data is obtained by using neural network technology to identify the transmission network topology map, and the transmission network topology data includes the stations of the transmission network and the connection relationships between the stations;

[0074] Step S102: Obtain target trunk service data, wherein the target trunk service data includes multiple trunk circuits, each trunk circuit has the same source and destination sites and trunk type, and each trunk circuit includes first routing information and bearer rate;

[0075] Step S103: Obtain multiple second route information by querying the transmission network topology data based on the source and destination sites;

[0076] Step S104: Compare and analyze the first routing information and the second routing information of multiple trunk circuits in the target trunk service data to obtain the circuit multi-route bearer analysis results. The multi-route bearer analysis results include one of the following: route construction requirements, reasonable route bearer, or route bearer adjustment scheme.

[0077] Steps S101 to S104 of this embodiment involve using neural network technology to identify the actual transmission network topology map and obtain transmission network topology data. This transmission network topology data includes the stations of the transmission network and the connections between them. For a target relay service data set comprising multiple relay circuits, where each relay circuit has the same source and destination stations and relay type, the transmission network topology data is queried based on the source and destination stations to obtain multiple second routing information lines capable of carrying the corresponding circuits. The first routing information and multiple second routing information lines of the multiple relay circuits in the target relay service data are compared and analyzed to obtain a circuit multi-route bearer analysis result. This multi-route bearer analysis result includes one of the following: route construction requirements, reasonable route bearer configuration, or route bearer adjustment scheme. This embodiment of the application uses neural network technology to identify the actual transmission network topology map and obtain transmission network topology data. Then, based on the transmission network topology data, it automatically compares and analyzes the relay circuits of the target relay service data to determine the circuit multi-route bearer analysis result. This allows for automatic management and reasonable allocation of transmission network bearer routes for relay circuits, reducing service failure rates.

[0078] In step S101 of some embodiments, the transmission network topology data is descriptive data of the transmission network topology map obtained by recognizing the transmission network topology map using neural network technology. The transmission network topology data records the stations and connections between stations in the transmission network in matrix or tabular form. A station in the transmission network represents the location of a device with routing capabilities, and the connection relationship between stations represents the communication relationship between routes, i.e., whether data can be transmitted or not between routes. The transmission network topology map can be stored on the network management side, depicting the deployment of network devices and routing relationships between devices in a real-world environment. In this embodiment, the transmission network can be an optical transmission network, the transmission network topology map can be an optical transmission network topology map, and the transmission network topology data can be optical transmission network topology data. This embodiment converts the transmission network topology map, which can only be recognized manually, into a data format that can be recognized by a computer, so that when subsequently inputting source and destination stations, it is convenient to quickly identify all reachable routing information between two stations by traversing the graph.

[0079] Please see Figure 2 In some embodiments, the transmission network topology data in step S101 can be obtained through steps S201 to S205, including but not limited to:

[0080] Step S201: Obtain the transmission network topology diagram;

[0081] Step S202: The trained convolutional neural network is used to identify the station features in the transmission network topology map to obtain a station information set, wherein the station information set includes station coordinates, station visual connectivity features and station area visual features.

[0082] Step S203: Construct a graph structure based on the site information set;

[0083] Step S204: Use a trained graph neural network to identify the station connection relationships in the graph structure and obtain the station connection data in the transmission network topology graph;

[0084] Step S205: Determine the transmission network topology data based on the site connection data and the site information set.

[0085] In this embodiment, identifying stations and their connections in the transmission network topology and marking connected stations can be achieved using a combination of computer vision and graph theory methods. This embodiment uses a deep learning model combining convolutional neural networks (CNN) and graph neural networks (GNN) to implement two processes: station detection and connection identification, as detailed below:

[0086] First, a trained convolutional neural network is used to identify site features in the transmission network topology map, resulting in a set of site information. Specifically, the transmission network topology map is as follows: Figure 10 As shown, the transmission network topology map undergoes preprocessing, including standardization and resizing, before being input into a convolutional neural network (CNN). The CNN is then used to detect sites on the preprocessed topology map. CNNs excel at recognizing features and objects in images. Through training, they learn site features, including but not limited to visual features of site regions, visual connectivity features of sites, and pixel locations (i.e., site coordinates), thus obtaining a set of site information. This set includes the feature representations and names of each site. The CNN can be trained using an image dataset labeled with site shapes and connectivity markers. The labeled data includes the coordinates or bounding box information of the sites.

[0087] Secondly, a graph neural network (GNN) is used to identify the connections between stations, obtaining station connection data in the network topology graph. Specifically, a graph structure is constructed based on the detected station information set, with stations in the set serving as nodes. Edges are constructed based on the visual connection features of the stations (such as lines and paths). Feature vectors are assigned to each node (station) and edge; node features use the station's color and shape, while edge features use connecting lines. The constructed graph structure is processed using a GNN to learn the dependencies and global features between nodes. The GNN can propagate and aggregate information from neighboring nodes to determine whether connections exist between nodes. The output of the GNN model yields the probability or deterministic score of the connection between each pair of nodes. If the connection probability is higher than a threshold, a connection is considered to exist between the two nodes; if the connection probability is lower than the threshold, no connection is considered to exist between the two nodes, thus obtaining the station connection data.

[0088] Finally, the transmission network topology data is determined based on the site connection data and the site information set.

[0089] Please see Figure 3 In some embodiments, step S205, which involves determining the transmission network topology data based on the site connection data and the site information set, may include, but is not limited to, steps S301 to S304:

[0090] Step S301: Determine the site type of each site based on the site information set, wherein the site type is either a single-route site or a multi-route multiplexing site;

[0091] Step S302: Determine the station with the most connections in the transmission network topology diagram based on the station connection data, and determine the station as the starting point for traversal;

[0092] Step S303: Determine the target sites that are connected to the traversal starting point based on the site connection data, and use the expected character to mark the connection relationship between the target sites and the traversal starting point. The expected character represents the site type of the target site that has a connection relationship.

[0093] Step S304: Determine the target site as the new traversal starting point, and repeat the steps of determining the target site that is connected to the traversal starting point based on the site connection data, and marking the connection relationship between the target site and the traversal starting point using the expected character, to obtain the transmission network topology data, wherein the target site was not used as the traversal starting point in the previous traversal rounds.

[0094] In this embodiment, the site type of each site can be determined based on the site information set. The site type is either a single-route site or a multi-route multiplexing site. The site types are described below:

[0095] A single-route site (also known as an OA-type site) can appear in only one transport route and is unique. If an OA-type site appears in two transport routes, the two routes are considered to overlap and can only be counted as one transport route.

[0096] Multiplexed sites (also known as OTM sites) are sites that can appear in different transport routes, meaning they are not unique.

[0097] In one example, the transport network topology data can be obtained by traversing the site connection data; please refer to [reference needed]. Figure 11 The transmission network topology diagram identification logic diagram, combined with Figure 10 The following is an example of a transmission network topology diagram, in which, Figure 10 Square-shaped sites are described by T, triangular sites by A, and connected sites are represented by 1, otherwise by 0.

[0098] Based on the site connection data, determine site A, which has the most connections in the optical transmission topology diagram, such as... Figure 10 The station with the most connecting lines in the image is identified as 10011.

[0099] The traversal starts at the station with the most connecting lines. Target stations connected to the starting point are identified, and their connections are marked with a desired character. For example, using 10011 as the starting point, eight stations are identified as connected to 10011: 20008, 20016, 40007, 10010, 50013, 20014, 20015, and 50012. Therefore, the connection between 10011 and these eight stations is recorded as 1 (indicating a connection). Furthermore, connection relationships can be recorded based on station type: target stations of single-route stations are recorded with the desired character "1A," and target stations of multi-route composite stations are recorded with the desired character "1T."

[0100] Using the aforementioned target sites as new traversal starting points, we then redetermine target sites connected to these starting points. It's important to note that these new starting points were not used as starting points in previous traversal iterations, thus avoiding duplicate recording of connections between sites. For example, we identify adjacent sites of eight sites in this way. Sites already connected are not recorded. For instance, sites connected to 20008 are identified as 10011 and 30006. Since the connection between 10011 (which was used as a starting point in previous traversal iterations and is therefore not a target site for 20008) and 20008 is already stored, we only record the connection between 30006 and 20008 as 1. This process continues until all eight sites have been traversed. Then, using the column data 30006, 20005, 10010, and 10001 as centers, we identify sites related to them, recording connections as 1, thus gradually completing the identification of the transmission network topology.

[0101] Furthermore, connection relationships can be recorded based on site type. The target site record for a single-route site is the expected character "1A", and the target site record for a multi-route composite site is the expected character "1T". The transmission network topology data obtained after traversal is shown in Table 1.

[0102] Table 1 Transmission Network Topology Data

[0103] Site Name 20008 20016 40007 10010 50013 20014 20015 50012 10011 1A 1A 1T 1T 1A 1A 1A 1A 30006 1A 0 0 0 0 0 0 0 20005 0 1A 0 0 0 0 0 0 10010 0 0 1T 1T 0 0 0 0 10001 0 0 0 1T 0 0 0 0

[0104] In this context, 0 represents an unreachable route (no connection between stations); 1A represents a reachable route (connection between stations) but the reachable station is an OA (Office Automation) station. In multi-route analysis, this station can only appear in one transmission route, thus possessing uniqueness. If an OA station appears in two transmission routes, these two routes are considered overlapping and can only be counted as one transmission route; 1T represents a reachable route and the reachable station is an OTM (Online to Mobile) station. In multi-route analysis, this station can appear in different routes, i.e., it is not unique; only adjacent stations are traversed and stored.

[0105] In step S102 of some embodiments, the network management side obtains the names and routing information of various service trunk circuits from the resource system. The obtained trunk circuits are then classified according to their source and destination sites and their type. Trunk circuits of the same type form trunk service data. The trunk service data currently analyzed by the network management side is the target trunk service data. In this embodiment, "source and destination sites" refers to the same sending site (source end) and receiving site (destination end) of the trunk circuit, and "type" refers to the same service type, such as fixed-line or broadband, of the trunk circuit. The target trunk service data consists of trunk circuits of the same type (same source and destination, same type) with at least three or more transmission routes enabled. For example, Chengdu-Luzhou 10GE0001 CN2, Chengdu-Luzhou 10GE0002CN2, and Chengdu-Luzhou 10GE0003CN2 are all circuits that run from Chengdu to Luzhou and are all of the same type CN2 service. These three circuits should be enabled on three different transmission routes from Chengdu to Luzhou to achieve route sharing. In the event of a single route failure, the traffic will be switched to the other two routes.

[0106] Using the circuit name as a unique identifier, the data of the relay circuits obtained from the resource system is processed to record the information of all stations along the path of each relay circuit, thus obtaining the first routing information for each relay circuit. Simultaneously, the carrying capacity of the relay circuit is queried based on its circuit name.

[0107] Please see Figure 4 In some embodiments, the first routing information of the trunk circuit in the target trunk service data in step S102 can be obtained through steps S401 to S403, including but not limited to:

[0108] Step S401: Query the corresponding medium routing text based on the circuit name of the trunk circuit in the target trunk service data;

[0109] Step S402: Use regular expressions to extract site information from the media routing text;

[0110] Step S403: Determine the first routing information of the relay circuit based on the site information.

[0111] For example, taking the query of the medium route of the 10GE0020CN2 relay circuit as an example, the obtained medium route text is as follows: Figure 12 As shown.

[0112] The exported Excel routing table of the relay circuit was read using the pandas library in Python. The circuit name and medium routing fields were extracted. The circuit speed, 10G, was obtained from the circuit name. Medium routing was performed using regular expressions. The starting station was matched for stations beginning with "Route:" or "Route 1:". The station name was matched before "<" or after ">" and excluding "{" or "}". Adjacent stations were deduplicated; if two adjacent stations had the same name, only one was retained. The relay route's path stations are as follows: 10001, 10004, 20009, 110017, 110018, 110019, 110020, 110019, 110021, 110022, 110023. Note that stations 10010 and 110024, being the starting stations, are not recorded in the path station list. The speed and route of all similar relay circuits are extracted and saved in the above manner to obtain the first routing information.

[0113] Please see Figure 5 In some embodiments, the circuit multi-route bearer analysis method of this application may also include, but is not limited to, steps S501 to S502:

[0114] Step S501: Based on the first routing information, merge multiple trunk circuits with the same first routing information in the target trunk service data into one trunk circuit;

[0115] Step S502: The sum of the carrying rates of multiple relay circuits with the same first routing information is determined as the carrying rate of the merged relay circuit.

[0116] In this embodiment, the routing information of similar relay circuits belonging to the same target relay service data, exported and filtered from the resource system, is parsed. Relay circuits with the same route through stations are grouped and merged, the number of routes is recounted, and the route information of each type of route (defined as resource-side routes) is recorded. The sum of the carrying rates of each type of route is calculated. The number of merged relay circuits (i.e., the first routing information) in the target relay service data is greater than a preset value, for example, 3. This indicates that the target service to be analyzed needs to be carried by more than three transmission routes. If it is less than 3, it means that this type of service does not need further analysis.

[0117] In step S103 of some embodiments, multiple second routing information are obtained by querying the transmission network topology data based on the source and destination sites of the target relay service data currently being analyzed.

[0118] Please see Figure 6 In some embodiments, step S103 includes, but is not limited to, steps S601 to S602:

[0119] Step S601: Based on the source and destination sites, traverse the transmission network topology data to determine the path sites from the source to the destination.

[0120] Step S602: Determine multiple second route information based on the path stations and the station types of the path stations between the source and destination.

[0121] In this embodiment, the source and destination stations of this type of relay are used as the starting and ending stations. The network topology data that has been identified (which represents the actual connection relationship between stations) is traversed to automatically calculate the actual number of reachable routes from the source to the destination under this type of relay (OTM stations can be repeated, OA stations cannot be repeated). The path stations of each route are recorded (defined as network management side routes) to obtain multiple second route information.

[0122] In step S104 of some embodiments, the first routing information and multiple second routing information of multiple trunk circuits in the target trunk service data are compared and analyzed to obtain the circuit multi-route bearer analysis result. The multi-route bearer analysis result includes one of the following: route construction requirement, reasonable route bearer, or route bearer adjustment scheme. A route construction requirement indicates that the actual route cannot bear this type of trunk circuit service and actual route construction is required. A reasonable route bearer indicates that the current route can bear the trunk circuit service in a balanced manner. A route bearer adjustment scheme indicates that the actual route can bear this type of trunk circuit service, but the bearer route or route bearer rate of some trunk circuits needs to be adjusted.

[0123] In some embodiments, step S104 may include, but is not limited to, steps S701 to S705:

[0124] Step S701: Determine whether the number of second routing information is greater than a preset number value;

[0125] Step S702: When the number of second routing information is less than the preset number, the multi-route bearer analysis result is determined as the routing construction requirement;

[0126] Step S703: When the number of second routing information is greater than or equal to a preset number, determine whether the number of first routing information and the number of second routing information are the same.

[0127] Step S704: When the number of first routing information and the number of second routing information are the same, determine whether the rate load is balanced based on the load rate of multiple trunk circuits in the target trunk service data. If the rate load is balanced, determine that the multi-route load analysis result is reasonable; if the rate load is unbalanced, determine that the multi-route load analysis result is a route load adjustment scheme, which includes adjusting the load rate of the route.

[0128] Step S705: When the number of first routing information is less than the number of second routing information, the multi-route bearer analysis result is determined as a route bearer adjustment scheme, wherein the route bearer adjustment scheme includes adjusting the relay circuit to the route of the unbearable circuit.

[0129] In this embodiment, please refer to Figure 7 Python can be used to compare and analyze resource-side routes (i.e., multiple first-line route information) and network management-side routes (i.e., multiple second-line route information). The comparison includes whether the number of routes is greater than the preset value of 3, and whether the number of routes is consistent.

[0130] The following analysis suggestions are provided for the output:

[0131] 1. If the number of routes is the same and the rate distribution is balanced, it means that the current activation method of this type of trunk is reasonable; if the number of routes is the same but the rate distribution is uneven, some circuit routes should be adjusted to make each route bear the rate evenly.

[0132] 2. If the number of routes automatically identified on the transmission network topology is greater than the number of routes with already opened trunk circuits, it indicates that there are still physical routes in the transmission network management topology that do not carry services. The output route adjustment scheme is suggested to adjust some trunk circuits to routes that do not yet carry trunk circuits according to the preset calculation principles, so as to achieve balanced sharing of multiple routes.

[0133] 3. If the number of routes automatically identified on the transmission network topology is 2 or less (including 2), it means that this type of trunk circuit does not currently have the capacity to carry three or more routes and route expansion is required. This solution should be recommended to the relevant construction department for the construction of three routes.

[0134] In some embodiments, the specific scheme for multi-path balanced transport according to the preset calculation principle is as follows: Routes with the same configured circuit rate are integrated, sorted according to the configured circuit rate of each route, and the number of circuits to be configured for each type of route with the same circuit rate is calculated. Routes with the same circuit rate are calculated using an amortization principle. For example, the main channel rates currently configured in optical transmission networks are 10G, 40G, 100G, 200G, and 400G, with 10G and 100G being the most commonly used. These two rates are taken as examples. Assuming Route A is configured with a circuit speed of 100G and has 1 circuit, Route B is configured with a circuit speed of 10G and has 2 circuits, and Route C is configured with a circuit speed of 10G and has 5 circuits, the total number of routes is 3, of which 1 is 100G and 7 are 10G. Therefore, the number of circuits that should be configured for 100G is 100G / 1 = 100G, so Route A should be configured with 1 circuit. The number of circuits that should be configured for 10G is 70G / 2 = 35G, so Routers B and C should each be configured with 3-4 circuits. Currently, Route B is configured with 2 circuits, which is an unreasonable distribution. One route on Route C needs to be moved to Route B.

[0135] Based on some embodiments of this application, the multi-route circuit bearer analysis method of this application will be described in general as follows:

[0136] Save the optical transmission network topology map on the transmission network management system, and use CNN+GNN technology to identify all site information and connection relationships and store them in the database.

[0137] Export all circuit information from the resource system and filter by trunk circuit name suffixes such as ASON, CN2, EE, ELS, EB, IPTV, IDC, DC I, IPM, etc. The circuit type can be flexibly added.

[0138] After filtering by trunk type, further filtering is performed by source and destination stations in the circuit name to select multiple groups of the same type of trunk.

[0139] Use Python to identify the speed and route information (routing information) of all relay circuits in each group of similar relays.

[0140] Based on the source and destination site information in the same type of relay, the system automatically traverses the sites and their connection relationships in the transmission network topology data stored in the database, and automatically calculates all different actual routes with the same source and destination information.

[0141] The system analyzes and compares the first routing information of the relay circuit with the second routing information matched with the transmission network topology data. If the routes are the same and the rate distribution is balanced, it indicates that the relay opening method is reasonable and no adjustment is needed. If the number of automatically calculated network management routes is greater than (3 or more) the number of routes obtained from the resources, a scheme to adjust some relay circuits to routes that have not yet carried relay circuits is output according to the principle of rate balance. If the number of automatically calculated network management routes is less than 2, it indicates that the relay circuit opened with this source and destination node does not have the conditions for carrying three routes, and an expansion suggestion is output.

[0142] After the relevant parts are completed, the topology image is re-identified, the stations and the connection relationships between the stations are re-stored, and then the analysis is carried out again.

[0143] For example, export the Chengdu-Deyang IP circuits in the resource system. Assume there are 4 circuits, all of which are 100G, with no other speeds. Use Python tools to identify the routing information of the 4 100G circuits allocated in the resource system as follows: Route 1: Chengdu-AB-Deyang (2 100G circuits are activated); Route 2: Chengdu-CD-Deyang; Route 3: Chengdu-DE-Deyang.

[0144] Identify the reachable route between Chengdu and Deyang on the network management system. This involves using a neural network-based network topology routing table storing the sites and their connections, starting from Chengdu and Deyang, and traversing the table to find the route. The resulting routes are: Route 1: Chengdu-AB-Deyang; Route 2: Chengdu-CD-Deyang; Route 3: Chengdu-DE-Deyang; Route 4: Chengdu-FG-Deyang. Confirm whether the type of station D in the actual reachable route is A or T (A represents an OA station, T represents an OTM station). If station D is an OA station, it indicates a potential co-routing issue between routes 2 and 3, and they should only be counted as one route. If station D is an OTM station, it indicates that routes 2 and 3 are different routes, and they should be counted as two routes.

[0145] Comparative analysis shows that the number of routes identified by the network management side is greater than 3, so no new routes need to be added. The number of routes on the network management side is 4, which is greater than the number of routes identified by the resource side (3). According to the comparative analysis, the information of the first three routes is consistent between the resource side and the network management side, but route 4 does not carry any circuits. Therefore, according to the principle of route sharing, the output adjustment suggestion is to transfer one of the two 100G circuits carried by route 1 to route 4.

[0146] Please see Figure 8 This application also provides a circuit multi-route bearer analysis system, including:

[0147] The first module is used to acquire transmission network topology data, which is obtained by using neural network technology to identify the transmission network topology map. The transmission network topology data includes the stations of the transmission network and the connection relationships between the stations.

[0148] The second module is used to acquire target trunk service data, which includes multiple trunk circuits. Each trunk circuit has the same source and destination sites and trunk type. Each trunk circuit includes first routing information and bearer rate.

[0149] The third module is used to query the transmission network topology data based on the source and destination sites to obtain multiple second routing information;

[0150] The fourth module is used to compare and analyze the first routing information and the second routing information of multiple trunk circuits in the target trunk service data to obtain the circuit multi-route bearer analysis results. The multi-route bearer analysis results include one of the following: route construction requirements, reasonable route bearer, or route bearer adjustment scheme.

[0151] It is understood that the content of the above-described circuit multi-route bearer analysis method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above-described circuit multi-route bearer analysis method embodiments, and the beneficial effects achieved are also the same as those achieved in the above-described circuit multi-route bearer analysis method embodiments.

[0152] This application also provides an electronic device, which includes: a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for communication between the processor and the memory. When the program is executed by the processor, it implements the aforementioned circuit multi-route bearer analysis method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0153] Please see Figure 9 , Figure 9 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0154] The processor 901 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0155] The memory 902 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and is called and executed by the processor 901 using the circuit multi-route bearer analysis method of the embodiments of this application.

[0156] The 903 input / output interface is used to implement information input and output.

[0157] The communication interface 904 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0158] Bus 905 transmits information between various components of the device (e.g., processor 901, memory 902, input / output interface 903, and communication interface 904);

[0159] The processor 901, memory 902, input / output interface 903, and communication interface 904 are connected to each other within the device via bus 905.

[0160] This application embodiment also provides a storage medium, which is a computer-readable storage medium for computer-readable storage. The storage medium stores one or more programs, which can be executed by one or more processors to implement the above-described circuit multi-route bearer analysis method.

[0161] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0162] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0163] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0164] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0165] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0166] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0167] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0168] In the embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between systems or units may be electrical, mechanical, or other forms.

[0169] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0170] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0171] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0172] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for analyzing the multi-route bearer in a circuit, characterized in that, Includes the following steps: The transmission network topology data is obtained by using neural network technology to identify the transmission network topology map, and the transmission network topology data includes the stations of the transmission network and the connection relationships between the stations; Acquire target relay service data, wherein the target relay service data includes multiple relay circuits, each relay circuit having the same source and destination sites and relay type, and each relay circuit including first routing information and bearer rate; Multiple second routing information entries are obtained by querying the transmission network topology data based on the source and destination sites; The first routing information and the second routing information of multiple trunk circuits in the target trunk service data are compared and analyzed to obtain the circuit multi-route bearer analysis result. The multi-route bearer analysis result includes one of the following: route construction requirements, reasonable route bearer, or route bearer adjustment scheme. The step of comparing and analyzing the first routing information and the second routing information of multiple trunk circuits in the target trunk service data to obtain the circuit multi-route bearer analysis result includes the following steps: Determine whether the quantity of the second routing information is greater than a preset quantity value; If the quantity of the second routing information is less than a preset quantity value, then the multi-route bearer analysis result is determined to be a routing construction requirement; If the quantity of the second routing information is greater than or equal to a preset quantity value, then determine whether the quantity of the first routing information and the quantity of the second routing information are the same; When the number of the first routing information and the number of the second routing information are the same, it is determined whether the rate load is balanced based on the load rate of multiple trunk circuits in the target trunk service data. If the rate load is balanced, the multi-route load analysis result is determined to be reasonable. If the rate load is unbalanced, the multi-route load analysis result is determined to be a route load adjustment scheme, and the route load adjustment scheme includes adjusting the load rate of the route. When the number of the first routing information is less than the number of the second routing information, the multi-route bearer analysis result is determined to be a route bearer adjustment scheme, wherein the route bearer adjustment scheme includes adjusting the relay circuit to the route without a bearer circuit.

2. The circuit multi-route bearer analysis method according to claim 1, characterized in that, The transmission network topology data is obtained through the following steps: Obtain the transmission network topology; A trained convolutional neural network is used to identify the site features in the transmission network topology map to obtain a site information set, wherein the site information set includes site coordinates, site visual connectivity features, and site area visual features; Construct a graph structure based on the site information set; A trained graph neural network is used to identify the station connection relationships in the graph structure to obtain the station connection data in the transmission network topology graph; The transmission network topology data is determined based on the site connection data and the site information set.

3. The circuit multi-route bearer analysis method according to claim 2, characterized in that, Determining the transmission network topology data based on the site connection data and the site information set includes the following steps: The site type of each site is determined based on the site information set, wherein the site type is either a single-route site or a multi-route multiplexing site; Based on the site connection data, determine the site with the most connections in the transmission network topology diagram, and determine the site as the traversal starting point; Based on the site connection data, target sites that are connected to the traversal starting point are determined, and the connection relationship between the target sites and the traversal starting point is marked with an expected character, wherein the expected character represents the site type of the target site that has a connection relationship; The target site is determined as the new traversal starting point, and the steps of determining the target site connected to the traversal starting point based on the site connection data and marking the connection between the target site and the traversal starting point using the expected character are repeated to obtain the transmission network topology data, wherein the target site was not used as the traversal starting point in the previous traversal rounds.

4. The circuit multi-route bearer analysis method according to claim 1, characterized in that, The first routing information of the trunk circuit in the target trunk service data is obtained through the following steps: Query the corresponding medium routing text based on the circuit name of the relay circuit in the target relay service data; Regular expressions are used to extract site information from the media routing text; The first routing information of the relay circuit is determined based on the site information.

5. The circuit multi-route bearer analysis method according to claim 1, characterized in that, The circuit multi-route bearer analysis method also includes the following steps: Based on the first routing information, multiple trunk circuits with the same first routing information in the target trunk service data are merged into one trunk circuit; The sum of the carrying rates of multiple relay circuits with the same first routing information is determined as the carrying rate of the merged relay circuit.

6. The circuit multi-route bearer analysis method according to claim 1, characterized in that, The process of obtaining multiple second routing information by querying the transmission network topology data based on the source and destination sites includes the following steps: Based on the source and destination sites, traverse the transmission network topology data to determine the path sites from the source to the destination. Multiple second-path information is determined based on the path stations and the station types of the path stations between the source and destination.

7. A circuit multi-route bearer analysis system, characterized in that, include: The first module is used to acquire transmission network topology data, wherein the transmission network topology data is obtained by identifying the transmission network topology map using neural network technology, and the transmission network topology data includes the stations of the transmission network and the connection relationships between the stations; The second module is used to acquire target relay service data, wherein the target relay service data includes multiple relay circuits, each relay circuit has the same source and destination sites and relay type, and each relay circuit includes first routing information and bearer rate; The third module is used to query the transmission network topology data based on the source and destination sites to obtain multiple second routing information; The fourth module is used to compare and analyze the first routing information and the second routing information of multiple trunk circuits in the target trunk service data to obtain the circuit multi-route bearer analysis result. The multi-route bearer analysis result includes one of the following: route construction requirements, reasonable route bearer, or route bearer adjustment scheme. The fourth module is specifically used to perform the following steps: Determine whether the quantity of the second routing information is greater than a preset quantity value; If the quantity of the second routing information is less than a preset quantity value, then the multi-route bearer analysis result is determined to be a routing construction requirement; If the quantity of the second routing information is greater than or equal to a preset quantity value, then determine whether the quantity of the first routing information and the quantity of the second routing information are the same; When the number of the first routing information and the number of the second routing information are the same, it is determined whether the rate load is balanced based on the load rate of multiple trunk circuits in the target trunk service data. If the rate load is balanced, the multi-route load analysis result is determined to be reasonable. If the rate load is unbalanced, the multi-route load analysis result is determined to be a route load adjustment scheme, and the route load adjustment scheme includes adjusting the load rate of the route. When the number of the first routing information is less than the number of the second routing information, the multi-route bearer analysis result is determined to be a route bearer adjustment scheme, wherein the route bearer adjustment scheme includes adjusting the relay circuit to the route without a bearer circuit.

8. An electronic device, characterized in that, The electronic device includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for enabling communication between the processor and the memory, wherein the program, when executed by the processor, implements the steps of the method as described in any one of claims 1 to 6.

9. A storage medium, said storage medium being a computer-readable storage medium for computer-readable storage, characterized in that, The storage medium stores one or more programs, which can be executed by one or more processors to implement the steps of the method according to any one of claims 1 to 6.

Images