Communication network simulation method based on digital twin technology

Abstract

The invention discloses a communication network simulation method based on a digital twinborn technology, which relates to the technical field of communication network simulation and comprises the following steps of: importing structural data and network data in an actual communication environment into digital twinborn models of various topological structures for simulation; calculating a conventional evaluation coefficient of each node and a conventional evaluation coefficient of each topological structure; calculating a limit evaluation coefficient of each node and a limit evaluation coefficient of each topological structure; and calculating to obtain a comprehensive evaluation index of each topological structure, determining an optimal topological structure, calculating a comprehensive evaluation index of each node in the corresponding topological structure, and determining an optimization priority sequence of each node. According to the method, the topological structure most suitable for specific application requirements can be selected, so that the overall network performance and efficiency are improved, the optimization priority is determined according to the comprehensive evaluation index of each node, the nodes with poor optimization performance can be concentrated, and the overall performance of the network is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of communication network simulation, and specifically to a communication network simulation method based on digital twin technology. Background Art

[0002] As the complexity and scale of communication networks increase, traditional test and analysis methods often cannot meet the needs of network performance and management. Digital twin technology has gradually become an effective means to solve the challenges in real networks because of its ability to perform large-scale, high-fidelity simulations in virtual environments. Digital twin refers to a virtual entity created through mathematical models, real-time data and simulation technology, whose behavior and state can accurately reflect physical objects or systems in the real world. In the field of communication networks, digital twin technology is used to build a virtual model of the network to simulate and predict the behavior, performance and changes of the network.

[0003] In the Chinese invention application with application publication number CN114095375A, a network topology algorithm, industrial control safety simulation method and system are disclosed, including judging the attributes of the parent node model, obtaining a set of pre-generated positions, traversing the remaining positions, judging the expansion direction, creating a new model at the pre-generated position, and establishing a parent-child node model relationship; judging whether the size of the network topology is appropriate, and if not, adjusting the size of the network topology; judging whether the position of the network topology is appropriate, and if not, adjusting the position of the network topology; creating lines to connect node models with the same parent node.

[0004] In the above invention application, the network topology is customized and generated according to the commonly used model in discrete manufacturing industry, and the network topology can be adaptive, but the parent-child node model relationship is established according to the parent node model, and the impact of the entire network topology system structure on network performance is not analyzed. Potential bottlenecks or performance bottlenecks in the network may not be identified and resolved in time. These problems may include node overload, unreasonable topology structure, too long transmission path, etc., which affect the overall performance indicators such as bandwidth utilization, delay and throughput.

[0005] To this end, the present invention provides a communication network simulation method based on digital twin technology. Summary of the invention

[0006] 1. Technical issues to be solved

[0007] In view of the shortcomings of the prior art, the present invention provides a communication network simulation method based on digital twin technology. The present invention imports the structural data and network data in the actual communication environment into the digital twin model of various topological structures for simulation, and calculates the conventional evaluation coefficient of each node. and the conventional evaluation coefficient Ck for each topology a, can evaluate the performance of different topologies when carrying average traffic, and calculate the limit evaluation coefficient of each node And the limit evaluation coefficient Xk of each topology a , we can understand the performance of different topologies under maximum flow and calculate the comprehensive evaluation index Zkp of each topology a , determine the optimal topology structure, and calculate the comprehensive evaluation index Jzp of each node in the optimal topology structure i , determine the optimization priority ranking of each node, and select the topology structure that best suits specific application requirements, thereby improving the overall network performance and efficiency. Determine the optimization priority based on the comprehensive evaluation index of each node, and focus on optimizing nodes with poor performance and improving the overall performance of the network, thereby solving the technical problems recorded in the background technology.

[0008] (II) Technical solution

[0009] To achieve the above objectives, the present invention is implemented through the following technical solutions: A communication network simulation method based on digital twin technology comprises the following steps:

[0010] Use network analysis tools to obtain structural data and network data in the actual communication environment, and use communication network simulation tools to establish digital twin models of various topological structures. Import the structural data and network data in the actual communication environment into the digital twin models of various topological structures for simulation, and obtain the network data of each node under average flow and maximum flow;

[0011] Get the average bandwidth utilization of each node under average traffic Average latency Average jitter Average throughput and average packet loss rate Calculate the general evaluation coefficient for each node And based on the conventional evaluation coefficient of each node Calculate the general evaluation coefficient Ck for each topology a ;

[0012] Get the maximum bandwidth utilization of each node under maximum traffic Maximum delay Maximum jitter Maximum throughput Maximum packet loss rate Calculate the limit evaluation coefficient of each node And according to the limit evaluation coefficient of each node Calculate the limit evaluation coefficient Xk for each topology a ;

[0013] Get the general evaluation coefficient Ck for each topology a and the limit evaluation coefficient Xk a , calculate and obtain the comprehensive evaluation index Zkp of each topological structure a , determine the optimal topology, and convert the conventional evaluation coefficient of each node of the corresponding topology into and the limit evaluation coefficient Calculate the comprehensive evaluation index Jzp of each node i , determine the optimization priority ranking of each node.

[0014] Furthermore, network analysis tools (such as Wireshark and tcpdump) are used to obtain nodes (such as routers, switches, terminal devices), connection methods (such as wired and wireless), network protocols (such as TCP / IP and UDP), and data transmission rates in the actual communication environment to build an actual communication structure database. Network analysis tools are also used to capture actual network traffic information in actual communications, that is, the average and maximum traffic carried by each node per unit time, to build an actual communication network database.

[0015] Furthermore, communication network simulation tools (such as NS-3, OMNeT++, QualNet / EXata, OpNet, GNS3) are used to establish digital twin models of various topological structures, and the nodes, connection methods, network protocols, data transmission rates, etc. of all topological digital twin models are consistent with the actual communication structure database.

[0016] Common network topologies include star topology, bus topology, ring topology, tree topology and mesh topology. In star topology, all devices are connected to a central node (such as a switch or hub), which is responsible for forwarding data packets to the target device; in bus topology, all devices are connected together through a single communication line (bus), and all devices can receive the sent data packets during data transmission; in ring topology, devices are connected in a ring manner, each device is connected to the adjacent device, and the data packet is passed on the ring until it reaches the target device; in tree topology, devices are connected in a hierarchical structure to form a tree network. It usually includes a root node, and the connections are made from the root node to the child nodes step by step; in mesh topology, all devices are directly connected to each other to form a mesh structure. This structure is often used to build highly reliable and fault-tolerant networks.

[0017] Furthermore, the actual communication network database is imported into the digital twin model of various topological structures for simulation. All nodes are simulated to run at the same time under the average traffic carried, and the average bandwidth utilization of each node under the average traffic is obtained. Average latency Average jitter Average throughput and average packet loss rate And simulate all nodes running at the same time under the maximum traffic carried, and obtain the maximum bandwidth utilization of each node under the maximum traffic Maximum delay Maximum jitter Maximum throughput Maximum packet loss rate

[0018] Furthermore, the average bandwidth utilization of each node under average traffic is obtained Average latency Average jitter Average throughput and average packet loss rate Calculate the general evaluation coefficient for each node

[0019] Wherein, i represents the sequential number of each node, i=1, 2, ..., n, and a represents the sequential number of each topological structure, a=1, 2, ..., m.

[0020] Further, obtain the conventional evaluation coefficient of each node Calculate the conventional evaluation coefficient ck for each topology a :

[0021]

[0022] Conventional evaluation coefficient ck for each topology a The calculation formula is as above.

[0023] Furthermore, the maximum bandwidth utilization of each node under the maximum flow is obtained Maximum delay Maximum jitter Maximum throughput Maximum packet loss rate Calculate the limit evaluation coefficient of each node

[0024] Limit evaluation coefficient of each node The calculation formula is as above.

[0025] Furthermore, the limit evaluation coefficient of each node is obtained Calculate the limit evaluation coefficient Xk for each topology a :

[0026]

[0027] Limit evaluation coefficient Xk for each topologya The calculation formula is as above.

[0028] Furthermore, the conventional evaluation coefficient ck of each topology is obtained a and the limit evaluation coefficient Xk a , calculate and obtain the comprehensive evaluation index Zkp of each topological structure a :

[0029]

[0030] Select max(Zkp a )The topology structure corresponding to number v is taken as the optimal topology structure of the communication network.

[0031] Furthermore, we extract max(Zkp a ) The conventional evaluation coefficient of each node of the topological structure corresponding to number v and the limit evaluation coefficient Calculate the comprehensive evaluation index Jzp of each node i :

[0032]

[0033] Where v is max(Zkp a ) corresponds to the number of a, 1≤v≤m.

[0034] Furthermore, the comprehensive evaluation index Jzp of each node is i The nodes are sorted from small to large and then output in order as the node optimization priority. The nodes that are ranked higher have higher optimization priority.

[0035] (III) Beneficial effects

[0036] The present invention provides a communication network simulation method based on digital twin technology, which has the following beneficial effects:

[0037] 1. Use network analysis tools to obtain structural data and network data in the actual communication environment, and use communication network simulation tools to establish digital twin models of various topological structures. Import the structural data and network data in the actual communication environment into the digital twin models of various topological structures for simulation, and obtain the network data of each node under average traffic and maximum traffic. This can help to better understand network performance, optimize network topology, improve network security and management efficiency, and reduce network costs.

[0038] 2. Get the average bandwidth utilization of each node under average traffic Average latency Average jitter Average throughput and average packet loss rate Calculate the general evaluation coefficient for each node And based on the conventional evaluation coefficient of each node Calculate the general evaluation coefficient Ck for each topology a , can evaluate the performance of different topologies when carrying average traffic, help to fully understand network performance, and provide valuable information for network design, optimization, resource allocation, and troubleshooting.

[0039] 3. Obtain the maximum bandwidth utilization of each node under maximum traffic Maximum delay Maximum jitter Maximum throughput Maximum packet loss rate Calculate the limit evaluation coefficient of each node And according to the limit evaluation coefficient of each node Calculate the limit evaluation coefficient Xk for each topology a , we can understand the performance of different topologies under maximum traffic, evaluate the capacity and carrying capacity of the network, and discover potential bottleneck nodes in the network for optimization and improvement.

[0040] 4. Obtain the conventional evaluation coefficient Ck for each topological structure a and the limit evaluation coefficient Xk a , calculate and obtain the comprehensive evaluation index Zkp of each topological structure a , determine the optimal topology, and convert the conventional evaluation coefficient of each node of the corresponding topology into and the limit evaluation coefficient Calculate the comprehensive evaluation index Jzp of each node i , determine the optimization priority ranking of each node, and select the topology that best suits specific application requirements, thereby improving the overall network performance and efficiency. Determine the optimization priority based on the comprehensive evaluation index of each node, and focus on optimizing nodes with poor performance to improve the overall performance of the network. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a flow chart of the communication network simulation method based on digital twin technology of the present invention. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] See also Figure 1 The present invention provides a communication network simulation method based on digital twin technology, comprising the following steps:

[0044] Step 1: Use network analysis tools to obtain structural data and network data in the actual communication environment, and use communication network simulation tools to establish digital twin models of various topological structures. Import the structural data and network data in the actual communication environment into the digital twin models of various topological structures for simulation, and obtain the network data of each node under average flow and maximum flow.

[0045] The step 1 includes the following contents:

[0046] Step 101: Use network analysis tools (such as Wireshark, tcpdump) to obtain nodes (such as routers, switches, terminal devices), connection methods (such as wired, wireless), network protocols (such as TCP / IP, UDP) and data transmission rates in the actual communication environment to build an actual communication structure database. Use network analysis tools to capture actual network traffic information in actual communication, that is, the average traffic and maximum traffic carried by each node per unit time, to build an actual communication network database.

[0047] Step 102: Use communication network simulation tools (such as NS-3, OMNeT++, QualNet / EXata, OpNet, GNS3) to establish digital twin models of various topological structures, and the nodes, connection methods, network protocols, data transmission rates, etc. of all topological digital twin models are consistent with the actual communication structure database.

[0048] Common network topologies include star topology, bus topology, ring topology, tree topology and mesh topology. In star topology, all devices are connected to a central node (such as a switch or hub), which is responsible for forwarding data packets to the target device; in bus topology, all devices are connected together through a single communication line (bus), and all devices can receive the sent data packets during data transmission; in ring topology, devices are connected in a ring manner, each device is connected to the adjacent device, and the data packet is passed on the ring until it reaches the target device; in tree topology, devices are connected in a hierarchical structure to form a tree network. It usually includes a root node, and the connections are made from the root node to the child nodes step by step; in mesh topology, all devices are directly connected to each other to form a mesh structure. This structure is often used to build highly reliable and fault-tolerant networks.

[0049] Step 103: Import the actual communication network database into the digital twin model of various topological structures for simulation, simulate all nodes running under the average traffic carried at the same time, and obtain the average bandwidth utilization of each node under the average traffic. Average latency Average jitter Average throughput and average packet loss rate And simulate all nodes running at the same time under the maximum traffic carried, and obtain the maximum bandwidth utilization of each node under the maximum traffic Maximum delay Maximum jitter Maximum throughput Maximum packet loss rate

[0050] When using, combine the contents in steps 101 to 103:

[0051] Use network analysis tools to obtain structural data and network data in the actual communication environment, and use communication network simulation tools to establish digital twin models of various topological structures. Import the structural data and network data in the actual communication environment into the digital twin models of various topological structures for simulation, and obtain the network data of each node under average traffic and maximum traffic. This can help to better understand network performance, optimize network topology, improve network security and management efficiency, and reduce network costs.

[0052] Step 2: Get the average bandwidth utilization of each node under average traffic Average latency Average jitter Average throughput and average packet loss rate Calculate the general evaluation coefficient for each node And based on the conventional evaluation coefficient of each node Calculate the general evaluation coefficient Ck for each topology a .

[0053] The step 2 includes the following contents:

[0054] Step 201: Obtain the average bandwidth utilization of each node under average traffic Average latency Average jitter Average throughput and average packet loss rate Calculate the general evaluation coefficient for each node

[0055] Wherein, i represents the sequential number of each node, i=1, 2, ..., n, and a represents the sequential number of each topological structure, a=1, 2, ..., m.

[0056] Step 202: Obtain the conventional evaluation coefficient of each node Calculate the general evaluation coefficient Ck for each topology a :

[0057]

[0058] Conventional evaluation coefficient Ck for each topology a The calculation formula is as above.

[0059] When using, combine the contents in steps 201 and 202:

[0060] Get the average bandwidth utilization of each node under average traffic Average latency Average jitter Average throughput and average packet loss rate Calculate the general evaluation coefficient for each node And based on the conventional evaluation coefficient of each node Calculate the general evaluation coefficient Ck for each topology a , can evaluate the performance of different topologies when carrying average traffic, help to fully understand network performance, and provide valuable information for network design, optimization, resource allocation, and troubleshooting.

[0061] Step 3: Obtain the maximum bandwidth utilization of each node under maximum traffic Maximum delay Maximum jitter Maximum throughput Maximum packet loss rate Calculate the limit evaluation coefficient of each node And according to the limit evaluation coefficient of each node Calculate the limit evaluation coefficient Xk for each topology a .

[0062] The step three comprises the following steps:

[0063] Step 301: Obtain the maximum bandwidth utilization of each node under maximum traffic Maximum delay Maximum jitter Maximum throughput Maximum packet loss rate Calculate the limit evaluation coefficient of each node

[0064] Limit evaluation coefficient of each node The calculation formula is as above.

[0065] Step 302: Obtain the limit evaluation coefficient of each node Calculate the limit evaluation coefficient Xk for each topology a :

[0066]

[0067] Limit evaluation coefficient Xk for each topology a The calculation formula is as above.

[0068] When using, combine the contents in steps 301 and 302:

[0069] Get the maximum bandwidth utilization of each node under maximum traffic Maximum delay Maximum jitter Maximum throughput Maximum packet loss rate Calculate the limit evaluation coefficient of each node And according to the limit evaluation coefficient of each node Calculate the limit evaluation coefficient Xk for each topology a , we can understand the performance of different topologies under maximum traffic, evaluate the capacity and carrying capacity of the network, and discover potential bottleneck nodes in the network for optimization and improvement.

[0070] Step 4: Obtain the conventional evaluation coefficient Ck for each topology a and the limit evaluation coefficient Xk a , calculate and obtain the comprehensive evaluation index Zkp of each topological structure a , determine the optimal topology, and convert the conventional evaluation coefficient of each node of the corresponding topology into and the limit evaluation coefficient Calculate the comprehensive evaluation index Jzp of each node i , determine the optimization priority ranking of each node.

[0071] The step 4 comprises the following steps:

[0072] Step 401: Obtain the conventional evaluation coefficient Ck for each topological structure a and the limit evaluation coefficient Xk a , calculate and obtain the comprehensive evaluation index Zkp of each topological structure a :

[0073]

[0074] Select max(Zkp a )The topology structure corresponding to number v is taken as the optimal topology structure of the communication network.

[0075] Step 402: Extract max(Zkp a ) The conventional evaluation coefficient of each node of the topological structure corresponding to number v and the limit evaluation coefficient Calculate the comprehensive evaluation index Jzp of each node i :

[0076]

[0077] Where v is max(Zkp a ) corresponds to the number of a, 1≤v≤m.

[0078] Step 403: The comprehensive evaluation index Jzp of each node i The nodes are sorted from small to large and then output in order as the node optimization priority. The nodes that are ranked higher have higher optimization priority.

[0079] When used, combine the contents in steps 401 to 403:

[0080] Get the general evaluation coefficient Ck for each topology a and the limit evaluation coefficient Xk a , calculate and obtain the comprehensive evaluation index Zkp of each topological structure a , determine the optimal topology, and convert the conventional evaluation coefficient of each node of the corresponding topology into and the limit evaluation coefficient Calculate the comprehensive evaluation index Jzp of each node i , determine the optimization priority ranking of each node, and select the topology that best suits specific application requirements, thereby improving the overall network performance and efficiency. Determine the optimization priority based on the comprehensive evaluation index of each node, and focus on optimizing nodes with poor performance to improve the overall performance of the network.

[0081] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0082] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0083] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A communication network simulation method based on digital twin technology, characterized in that: The steps include: Use network analysis tools to obtain structural data and network data in the actual communication environment, and use communication network simulation tools to establish digital twin models of various topological structures. Import the structural data and network data in the actual communication environment into the digital twin models of various topological structures for simulation, and obtain the network data of each node under average flow and maximum flow; Get the average bandwidth utilization of each node under average traffic Average latency Average jitter Average throughput and average packet loss rate Calculate the general evaluation coefficient for each node And based on the conventional evaluation coefficient of each node Calculate the general evaluation coefficient Ck for each topology a ; Get the maximum bandwidth utilization of each node under maximum traffic Maximum delay Maximum jitter Maximum throughput Maximum packet loss rate Calculate the limit evaluation coefficient of each node And based on the limit evaluation coefficient of each node Calculate the limit evaluation coefficient Xk for each topology a ; Get the general evaluation coefficient Ck for each topology a and the limit evaluation coefficient Xk a , calculate and obtain the comprehensive evaluation index Zkp of each topological structure a , determine the optimal topology, and convert the conventional evaluation coefficient of each node of the corresponding topology into and the limit evaluation coefficient Calculate the comprehensive evaluation index Jzp of each node i , determine the optimization priority ranking of each node.

2. The communication network simulation method based on digital twin technology according to claim 1 is characterized in that: The actual communication network database is imported into the digital twin model of various topological structures for simulation. All nodes are simulated to run under the average traffic at the same time, and the average bandwidth utilization of each node under the average traffic is obtained. Average latency Average jitter Average throughput and average packet loss rate And simulate all nodes running at the same time under the maximum traffic carried, and obtain the maximum bandwidth utilization of each node under the maximum traffic Maximum delay Maximum jitter Maximum throughput Maximum packet loss rate 3. The communication network simulation method based on digital twin technology according to claim 2 is characterized in that: Get the average bandwidth utilization of each node under average traffic Average latency Average jitter Average throughput and average packet loss rate Calculate the general evaluation coefficient for each node Wherein, i represents the sequential number of each node, i=1, 2, ..., n, and a represents the sequential number of each topological structure, a=1, 2, ..., m.

4. The communication network simulation method based on digital twin technology according to claim 3 is characterized in that: Get the general evaluation coefficient of each node Calculate the general evaluation coefficient Ck for each topology a : Conventional evaluation coefficient Ck for each topology a The calculation formula is as above.

5. The communication network simulation method based on digital twin technology according to claim 1 is characterized in that: Get the maximum bandwidth utilization of each node under maximum traffic Maximum delay Maximum jitter Maximum throughput Maximum packet loss rate Calculate the limit evaluation coefficient of each node Limit evaluation coefficient of each node The calculation formula is as above.

6. The communication network simulation method based on digital twin technology according to claim 5 is characterized in that: Get the limit evaluation coefficient of each node Calculate the limit evaluation coefficient Xk for each topology a : Limit evaluation coefficient Xk for each topology a The calculation formula is as above.

7. The communication network simulation method based on digital twin technology according to claim 6 is characterized in that: Get the general evaluation coefficient Ck for each topology a and the limit evaluation coefficient Xk a , calculate and obtain the comprehensive evaluation index Zkp of each topological structure a : Select max(Zkp a )The topology structure corresponding to number v is taken as the optimal topology structure of the communication network.

8. The communication network simulation method based on digital twin technology according to claim 6 is characterized in that: Extract max(Zkp a ) The conventional evaluation coefficient of each node of the topological structure corresponding to number v and the limit evaluation coefficient Calculate the comprehensive evaluation index Jzp of each node i : Where v is max(Zkp a ) corresponds to the number of a, 1≤v≤m.

9. The communication network simulation method based on digital twin technology according to claim 8 is characterized in that: The comprehensive evaluation index Jzp of each node i The nodes are sorted from small to large and then output in order as the node optimization priority. The nodes that are ranked higher have higher optimization priority.

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