Communication Network Simulation Method Based on Digital Twin Technology

Abstract

The present invention discloses a communication network simulation method based on digital twin technology, which relates to the technical field of communication network simulation, and includes importing the structural data and network data in the actual communication environment into digital twin models with various topological structures for simulation; calculating the conventional evaluation coefficients of each node and the conventional evaluation coefficients of each topological structure; calculating the limit evaluation coefficients of each node and the limit evaluation coefficients of each topological structure; calculating the comprehensive evaluation index of each topological structure, determining the optimal topological structure, and calculating the comprehensive evaluation index of each node in the corresponding topological structure to determine the optimization priority ranking of each node. The topological structure most suitable for specific application requirements can be selected, thereby improving the overall network performance and efficiency. By determining the optimization priority according to the comprehensive evaluation index of each node, efforts can be concentrated on optimizing the nodes with poor performance, improving the overall performance of the network.

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 Technique

[0002] With the increasing complexity and scale of communication networks, traditional test and analysis methods often fail to meet the requirements for network performance and management. Digital twin technology has gradually become an effective means to solve the challenges in real networks because it can perform large-scale and high-fidelity simulations in a virtual environment. A 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 virtual models of networks to simulate and predict the behavior, performance, and their changes of the networks.

[0003] In a Chinese invention application with the publication number CN114095375A, a network topology algorithm, an industrial control security simulation method and system are disclosed, including judging the model attributes of the parent node, 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 the relationship between the parent and child node models; 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 connections to connect the node models with the same parent node.

[0004] In the above invention application, the network topology is custom-generated according to the commonly used models in discrete manufacturing, and the network topology can be self-adaptive. However, when establishing the relationship between the parent and child node models based on the parent node model, the impact of the overall network topology system structure on network performance is not analyzed, and potential bottlenecks or performance bottlenecks in the network may not be identified and solved in a timely manner. These problems may include node overload, unreasonable topology structure, too long transmission path, etc., which affect performance indicators such as overall bandwidth utilization, latency, and throughput.

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

[0006] (I) Technical Problems to be Solved

[0007] Aiming at the deficiencies of the prior art, the present invention provides a communication network simulation method based on digital twin technology. The present invention imports the structure data and network data in the actual communication environment into digital twin models with various topological structures for simulation, and calculates the conventional evaluation coefficient of each node and the conventional evaluation coefficient Ck of each topological structure a, the performance advantages and disadvantages of different topologies under the average traffic load can be evaluated, and the limit evaluation coefficient of each node can be calculated. and the limit evaluation coefficient Xk of each topology a , the performance of different topologies under the maximum traffic can be understood, and the comprehensive evaluation index Zkp of each topology can be calculated. a , the optimal topology can be determined, and the comprehensive evaluation index Jzp of each node of the optimal topology can be calculated. i , the optimization priority ranking of each node can be determined, and the topology most suitable for specific application requirements can be selected, thereby improving the overall network performance and efficiency. By determining the optimization priority according to the comprehensive evaluation index of each node, efforts can be concentrated on optimizing the nodes with poor performance, improving the overall performance of the network, and thus solving the technical problems recorded in the background art.

[0008] (2) Technical solution

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

[0010] Use a network analysis tool to obtain the structural data and network data in the actual communication environment, and use a communication network simulation tool to establish digital twin models of various topologies. Import the structural data and network data in the actual communication environment into the digital twin models of various topologies for simulation to obtain the network data of each node under the average traffic and the network data under the maximum traffic.

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

[0012] Obtain the maximum bandwidth utilization rate of each node under the 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 of each topology a ;

[0013] Obtain the conventional evaluation coefficient Ck for each topology structure a and the limit evaluation coefficient Xk a , calculate and obtain the comprehensive evaluation index Zkp for each topology structure a , determine the optimal topology structure, and for each node of the corresponding topology structure, the conventional evaluation coefficient and the limit evaluation coefficient are calculated to form the comprehensive evaluation index Jzp for each node i , and determine the optimized priority ranking for each node

[0014] Furthermore, 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 transfer rates in the actual communication environment, and construct an actual communication structure database. And 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, and construct an actual communication network database

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

[0016] Common network topology structures 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), and the central node 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 when data is transmitted, all devices can receive the transmitted data packets; in ring topology, devices are connected in a ring, each device is connected to the adjacent device, and data packets are transmitted on the ring until they reach the target device; in tree topology, devices are connected in a hierarchical structure to form a tree-like network. Usually, it includes a root node, and is connected step by step from the root node to the sub-nodes; in mesh topology, all devices are directly connected to each other to form a mesh structure. This structure is usually used to build highly reliable and fault-tolerant networks

[0017] Furthermore, import the actual communication network database into the digital twin models of various topology structures for simulation. Simulate all nodes running under the average traffic they carry, and obtain the average bandwidth utilization rate of each node under the average traffic Average delay Average jitter Average throughput and average packet loss rate And simulate that all nodes run under the maximum traffic carried, and obtain the maximum bandwidth utilization rate of each node under the maximum traffic Maximum delay Maximum jitter Maximum throughput Maximum packet loss rate

[0018] Furthermore, obtain the average bandwidth utilization rate of each node under the average traffic Average delay Average jitter Average throughput and average packet loss rate Calculate the conventional evaluation coefficient of 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] Furthermore, obtain the conventional evaluation coefficient of each node Calculate the conventional evaluation coefficient ck of each topological structure a :[[]]END]]

[0021]

[0022] The conventional evaluation coefficient ck of each topological structure a The calculation formula is as above.

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

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

[0025] Furthermore, obtain the limit evaluation coefficient of each node Calculate the limit evaluation coefficient Xk of each topological structure a :[[]]END]]

[0026]

[0027] The limit evaluation coefficient Xk of each topological structure a has the calculation formula as above.

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

[0029]

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

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

[0032]

[0033] where v is the number of a corresponding to max(Zkp a ), and 1 ≤ v ≤ m.

[0034] Furthermore, sort the comprehensive evaluation index Jzp of each node i from small to large and output them in sequence as the node optimization priority ranking. The higher the ranking, the higher the optimization priority of the node.

[0035] (III) Beneficial effects

[0036] The present invention provides a communication network simulation method based on digital twin technology, having 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 the average traffic and the network data under the maximum traffic, which can help better understand the network performance, optimize the network topological structure, improve the network security and management efficiency, and reduce the network cost.

[0038] 2. Obtain the average bandwidth utilization rate of each node under the average traffic average delay average jitter Average throughput and average packet loss rate Calculate the regular evaluation coefficient of each node And based on the regular evaluation coefficient of each node Calculate the regular evaluation coefficient Ck of each topology a , which can evaluate the performance advantages and disadvantages of different topologies when carrying the average traffic, help to comprehensively understand the network performance, and provide valuable information for network design, optimization, resource allocation, and troubleshooting, etc.

[0039] 3. Obtain the maximum bandwidth utilization rate of each node under the 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 of each topology a , which can understand the performance of different topologies under the 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 regular evaluation coefficient Ck of each topology a and the limit evaluation coefficient Xk a , calculate and obtain the comprehensive evaluation index Zkp of each topology a , determine the optimal topology, and calculate the regular evaluation coefficient of each node of the corresponding topology and the limit evaluation coefficient to calculate and form the comprehensive evaluation index Jzp of each node i , determine the optimization priority ranking of each node, and the topology that best suits specific application requirements can be selected, thereby improving the overall network performance and efficiency. By determining the optimization priority according to the comprehensive evaluation index of each node, efforts can be concentrated on optimizing the nodes with poor performance to improve the overall performance of the network. Description of the drawings

[0041] Figure 1 It is a schematic flowchart of the communication network simulation method based on digital twin technology of the present invention. Detailed implementation manners

[0042] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0043] Please refer to Figure 1 , the present invention provides a communication network simulation method based on digital twin technology, including the following steps:

[0044] Step 1: Use a network analysis tool to obtain the structural data and network data in the actual communication environment, and use a communication network simulation tool 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 the average traffic and the network data under the maximum traffic.

[0045] The said Step 1 includes the following contents:

[0046] Step 101: Use a network analysis tool (such as Wireshark, tcpdump) to obtain the 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, and construct an actual communication structure database. And use the network analysis tool to capture the actual network traffic information in the actual communication, that is, the average traffic and the maximum traffic carried by each node per unit time, and construct an actual communication network database.

[0047] Step 102: Use a communication network simulation tool (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 structure digital twin models are consistent with those in 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), and the central node is responsible for forwarding data packets to the target device. In bus topology, all devices are connected together through a single communication line (bus). When data is transmitted, all devices can receive the sent data packets. In ring topology, devices are connected in a ring, with each device connected to its adjacent device, and data packets are passed around the ring until they reach the target device. In tree topology, devices are connected in a hierarchical structure, forming a tree-like network, usually including a root node, with connections from the root node to child nodes step by step. In mesh topology, all devices are directly connected to each other, forming a mesh structure, which is usually used to build highly reliable and fault-tolerant networks.

[0049] Step 103: Import the actual communication network database into the digital twin models of various topologies for simulation. Simulate all nodes running under the average traffic load they bear to obtain the average bandwidth utilization of each node under the average traffic load. Average delay Average jitter Average throughput And average packet loss rate And simulate all nodes running under the maximum traffic load they bear to obtain the maximum bandwidth utilization of each node under the maximum traffic load. Maximum delay Maximum jitter Maximum throughput Maximum packet loss rate

[0050] When in use, combine the content in Steps 101 to 103:

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

[0052] Step Two: Obtain the average bandwidth utilization of each node under the average traffic load Average delay Average jitter Average throughput And average packet loss rate Calculate the regular evaluation coefficient of each node And based on the regular evaluation coefficient of each node Calculate the conventional evaluation coefficient Ck for each topology structure a .

[0053] The second step includes the following content:

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

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

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

[0057]

[0058] The conventional evaluation coefficient Ck of each topology structure a has the above calculation formula.

[0059] When in use, combine the content in Steps 201 and 202:

[0060] Obtain the average bandwidth utilization rate of each node under the average traffic Average delay Average jitter Average throughput and average packet loss rate Calculate the conventional evaluation coefficient of each node and calculate the conventional evaluation coefficient Ck of each topology structure based on the conventional evaluation coefficient of each node Calculate the conventional evaluation coefficient Ck for each topology structure a , which can evaluate the performance advantages and disadvantages of different topology structures when carrying average traffic, help to comprehensively understand the network performance, and provide valuable information for network design, optimization, resource allocation, and troubleshooting, etc.

[0061] Step three: Obtain the maximum bandwidth utilization rate of each node under the maximum traffic Maximum delay Maximum jitter Maximum throughput Maximum packet loss rate Calculate the limit evaluation coefficient of each node and calculate the limit evaluation coefficient of each node Calculate the limit evaluation coefficient Xk of each topology a .

[0062] The third step includes the following steps:

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

[0064] The limit evaluation coefficient of each node is calculated as above.

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

[0066]

[0067] The limit evaluation coefficient Xk of each topology a is calculated as above.

[0068] In use, combine the content in Steps 301 and 302:

[0069] Obtain the maximum bandwidth utilization rate maximum delay maximum jitter maximum throughput maximum packet loss rate of each node under the maximum traffic, and calculate the limit evaluation coefficient of each node and calculate the limit evaluation coefficient Xk of each topology based on the limit evaluation coefficient of each node a , so as to understand the performance of different topologies under the 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 Four: Obtain the conventional evaluation coefficient Ck a and the limit evaluation coefficient Xk a of each topology, and calculate the comprehensive evaluation index Zkp a, determine the optimal topological structure, and the conventional evaluation coefficients of each node of the corresponding topological structure and the limit evaluation coefficients to calculate the comprehensive evaluation index Jzp of each node i , and determine the optimization priority ranking of each node.

[0071] Step 4 includes the following steps:

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

[0073]

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

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

[0076]

[0077] where v is the number of a corresponding to max(Zkp a ), and 1 ≤ v ≤ m.

[0078] Step 403: Sort the comprehensive evaluation index Jzp of each node i from small to large and output them in sequence as the node optimization priority ranking. The higher the ranking, the higher the optimization priority of the node.

[0079] When in use, combine the contents in Steps 401 to 403:

[0080] Obtain the conventional evaluation coefficient Ck of each topological structure a and the limit evaluation coefficient Xk a , calculate the comprehensive evaluation index Zkp of each topological structure a , determine the optimal topological structure, and the conventional evaluation coefficient of each node of the corresponding topological structure and the limit evaluation coefficient i to calculate and form the comprehensive evaluation index Jzp of each node, by determining the optimization priority ranking of each node, the most suitable topology for specific application requirements can be selected, thereby improving the overall network performance and efficiency. By determining the optimization priority based on the comprehensive evaluation index of each node, efforts can be concentrated on optimizing the nodes with relatively poor performance, enhancing the overall performance of the network.

[0081] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those of ordinary skill in the art will realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are executed 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 to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0083] As described above, the above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should be covered within 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 ; 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; 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 corresponding to the optimal 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; Where v is max(Zkp a ) corresponds to the number of a, 1≤v≤m.

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 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 :

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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