A complex mobile communication network modeling method and simulation system

Abstract

This invention provides a modeling method and simulation system for complex mobile communication networks, belonging to the field of communication network simulation technology. The system includes a communication network model initialization module, a scenario data parsing module, a communication network state update module, a communication network model data management module, a communication link connectivity calculation module, a communication link seed edge selection module, a classical graph theory algorithm module, and an information communication performance calculation module. This invention first uses a seed edge algorithm to select the primary link, reducing redundant communication links to backup links. Then, it uses a classical graph theory algorithm to abstract the communication network in operation into a weighted undirected graph, thus simplifying, abstracting, and modeling complex mobile communication networks, reducing the complexity of topology analysis, and simplifying communication network solution. This invention supports communication performance calculation during the simulation process, effectively meeting the needs of communication network models in the simulation field.

Description

Technical Field

[0001] This invention belongs to the field of communication network simulation technology, and relates to a modeling method for complex mobile communication networks. Specifically, it relates to a method and simulation system for modeling mobile communication networks in complex battlefield environments based on seed edge optimization and classical graph theory. Background Technology

[0002] Classical graph theory is commonly used to model and describe communication networks. Graph theory originated from the mathematician Euler's study of the Seven Bridges of Königsberg problem. It is a branch of mathematics that studies graphs. A graph in graph theory consists of a set of given points and lines connecting the points. Points represent objects, and lines represent the relationships between objects. A communication network is a collection of communication nodes and communication links. Graph theory can clearly represent the connections between communication networks. By representing the communication link relationships between combat entities as lines in a graph, and transforming real battlefield combat entities into abstract graph nodes, a graph model is formed, laying the foundation for subsequent communication network analysis.

[0003] Complex mobile communication networks are collections of nodes connected using multiple communication methods to achieve interconnectivity of command, intelligence, and situational awareness information. Tactical military communications require speed, accuracy, and security, emphasizing the overall functional characteristics of the communication network. With the development of the network-centric warfare concept, integrated military communication networks adapted to modern warfare and centered on information have become the development direction of tactical communications. An integrated military communication network is based on an integrated and comprehensive switching protocol system, employing multiple advanced communication technologies to integrate different communication systems. Integrated military communication networks are characterized by multiple communication methods, complex communication nodes, and time-varying network topology.

[0004] In complex and ever-changing battlefield environments, mobile communication networks typically employ redundant communication methods to ensure their resilience and anti-jamming capabilities, supporting the accurate and timely transmission of command information. Each communication node is equipped with multiple communication methods, such as fiber optics, shortwave, ultra-shortwave, and satellite. Consequently, when modeling tactical communication networks using classical graph theory, the topological nodes of the graph will have multiple edges, making it difficult to adequately model and describe such communication networks using only classical graph theory.

[0005] In the field of simulation, communication models are typically communication transmission equipment models or communication switching equipment models. These models are mostly used to verify communication protocols or to provide the performance of a single communication device. They cannot describe the mobile communication network as a whole and cannot meet the simulation requirements of upper-level combat applications for communication networks. Summary of the Invention

[0006] To address the technical challenges of modeling mobile communication networks in complex battlefield environments, this invention proposes a modeling method and simulation system for complex mobile communication networks. This invention employs a seed edge optimization method combined with classical graph theory to analyze and describe the mobile communication network, thereby reducing the degree of nodes in the graph. This not only narrows the solution space of the network topology and reduces the complexity of topology analysis but also better aligns with the practical applications of mobile communication networks, enabling a more accurate description of complex mobile communication networks.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0008] A method for modeling complex tactical communication networks includes the following steps:

[0009] Step 1: At the start of the simulation, parse the scenario data input to the communication network model according to the agreed data format; after completing the scenario data parsing, obtain the initial position information of the communication nodes, the topology information of all communication subnets, and the parameter information of all communication subnets, and store them in local memory;

[0010] Step 2: For every two members in each communication subnet, use the initial position of the communication node to calculate the communication link connectivity and obtain the initial connectivity state of all communication subnets.

[0011] Step 3: For all communication links in all communication subnets, perform seed edge optimization, select the primary links in all communication subnets as seed edges, and save the other communication links as a set of backup links.

[0012] Step 4: Treat all communication nodes as points in graph theory and all primary links of subnets as edges in graph theory. Using classical graph theory algorithms, form a weighted undirected graph as the communication network model, thereby completing the initial construction and configuration of the communication network.

[0013] Step 5: During the simulation, receive and parse the simulation service data, obtain the location of each communication node or the status of the communication device, and then calculate the impact of changes in the location of the communication node or the status of the communication device on the communication network; if the current link is no longer available, select a link from the set of backup links for use;

[0014] Step 6: When the location of a communication node changes, recalculate the communication link connectivity, update the communication link state, re-optimize the seed edge of the communication link, select the seed edge of the communication link, update the data structure of the classical graph theory algorithm, and thus update the structural state of the communication network.

[0015] Furthermore, the communication link connectivity calculation employs the free space propagation model, the Okumura-Hata model, the Cost231 calculation model, and the ITU.P526 electromagnetic wave diffraction calculation model to determine whether the communication link between any two communication nodes is connected.

[0016] Furthermore, the seed edge optimization of the communication link is used to select the primary link when there are multiple communication links between two communication nodes, and the remaining communication links are stored in the local memory as backup links, and the primary link is used as the edge between the two communication nodes; when the primary link is interfered with or cannot be connected, the optimal link is selected from the backup links as the edge between the two communication nodes.

[0017] Furthermore, in step 4, a data structure based on classical graph theory is constructed at the start of the simulation using classical graph theory algorithms to generate a weighted undirected graph, which includes a set of vertices and a set of edges.

[0018] In step 6, the data structure based on classical graph theory is maintained and updated during the simulation, and the vertex set and edge set of the weighted undirected graph are updated.

[0019] Furthermore, during the simulation, a communication network model is used to calculate the transmission efficiency of information in the communication network, including information transmission delay, transmission delay of each hop communication link, and communication rate of each hop link, and the transmission efficiency is then passed to the upper-layer application.

[0020] A complex tactical communication network simulation system includes a CPU and a memory, wherein the CPU is used to implement the following software modules:

[0021] The communication network model initialization module is used to load other modules in the communication network model when the communication network model starts, and to complete the initial configuration of the communication link connectivity calculation module and the communication link seed edge selection module.

[0022] The scenario data parsing module is used to parse the scenario data input to the communication network model according to the agreed data format at the start of the simulation. After completing the scenario data parsing, it obtains the initial position information of the communication nodes, the topology information of all communication subnets, and the parameter information of all communication subnets, and stores them in the memory.

[0023] The communication network state update module is used to receive and parse simulation service data during the simulation process, obtain the location of each communication node or the state of the communication device, and then calculate the impact of changes in the location of the communication node or the state of the communication device on the communication network. When the location of the communication node changes, the communication link connectivity calculation module needs to be called again to update the communication link state, the communication link seed edge selection module needs to be called again to select the communication link seed edge, and the data structure of the classical graph theory algorithm module needs to be updated, thereby updating the structural state of the communication network.

[0024] The communication network model data maintenance module is used to maintain the data structure of the communication network model. This includes: receiving data parsed by the scenario data parsing module and the communication network state update module; calling the communication link connectivity calculation module and the communication link seed edge selection module to obtain the edge dataset and backup edge dataset in the communication network model data structure; calling the classical graph theory algorithm module to complete the construction of the communication network model data structure; and during the simulation process, receiving requests from the communication network state update module for communication performance calculation from the simulation service, calling the information communication performance calculation module to complete the communication performance calculation, and returning the result to the corresponding simulation service.

[0025] The communication link connectivity calculation module is used to calculate and determine whether the communication link between any two communication nodes is connected.

[0026] The communication link seed edge optimization module is used to select the primary link when there are multiple communication links between two communication nodes, store the remaining communication links as backup links in the memory, and use the primary link as the edge between the two communication nodes; when the primary link is interfered with or cannot be connected, the optimal link is selected from the set of backup links as the edge between the two communication nodes.

[0027] The classical graph theory algorithm module is used to construct the data structure of the communication network model based on classical graph theory at the beginning of the simulation, and return the vertex set and edge set of the generated weighted undirected graph to the communication network model data maintenance module; it is also used to maintain and update the data structure based on classical graph theory during the simulation, and update the vertex set and edge set and return it to the communication network model data maintenance module.

[0028] The information communication performance calculation module is used to calculate the transmission performance of information in the communication network using a communication network model, including information transmission delay, transmission delay of each hop of the communication link, and communication rate of each hop of the link.

[0029] The beneficial effects of this invention are as follows:

[0030] 1. This invention first uses the seed edge algorithm of communication links to select the primary link and downgrades redundant communication links to backup links. Then, it uses classical graph theory algorithms to abstract the communication network in operation into a weighted undirected graph in graph theory to calculate the performance problem of communication networks of interest in the field of military simulation technology, thus solving the problem that the functional characteristics of complex tactical communication networks are difficult to describe.

[0031] 2. This invention uses a seed edge optimization algorithm and a classical graph theory algorithm to describe the communication network model, thereby simplifying, abstracting and modeling complex mobile communication networks, reducing the complexity of topology analysis and simplifying the solution of communication networks.

[0032] 3. This invention supports the selection of information transmission paths and the calculation of communication performance such as information transmission delay during the simulation process, which can well meet the needs of communication network models in the simulation field. Attached Figure Description

[0033] Figure 1 This is a structural block diagram of the communication network simulation system in this embodiment of the invention.

[0034] Figure 2 This is a hierarchical diagram of the communication network model in the simulation application of this invention.

[0035] Figure 3 This is a flowchart of the communication network modeling method in an embodiment of the present invention.

[0036] Figure 4 This is a schematic diagram illustrating the principle of multi-link optimization in an embodiment of the present invention.

[0037] Figure 5 This is a schematic diagram illustrating the principle of Dijkstra's shortest path algorithm in an embodiment of the present invention. Detailed Implementation

[0038] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0039] A method for modeling complex tactical communication networks includes the following steps:

[0040] Step 1: At the start of the simulation, parse the scenario data input to the communication network model according to the agreed data format; after completing the scenario data parsing, obtain the initial position information of the communication nodes, the topology information of all communication subnets, and the parameter information of all communication subnets, and store them in local memory;

[0041] Step 2: For every two members in each communication subnet, use the initial position of the communication node to calculate the communication link connectivity and obtain the initial connectivity state of all communication subnets.

[0042] Step 3: For all communication links in all communication subnets, perform seed edge optimization, select the primary links in all communication subnets as seed edges, and save the other communication links as a set of backup links.

[0043] Step 4: Treat all communication nodes as points in graph theory and all primary links of subnets as edges in graph theory. Using classical graph theory algorithms, form a weighted undirected graph as the communication network model, thereby completing the initial construction and configuration of the communication network.

[0044] Step 5: During the simulation, receive and parse the simulation service data, obtain the location of each communication node or the status of the communication device, and then calculate the impact of changes in the location of the communication node or the status of the communication device on the communication network; if the current link is no longer available, select a link from the set of backup links for use;

[0045] Step 6: When the location of a communication node changes, recalculate the communication link connectivity, update the communication link state, re-optimize the seed edge of the communication link, select the seed edge of the communication link, update the data structure of the classical graph theory algorithm, and thus update the structural state of the communication network.

[0046] Furthermore, the communication link connectivity calculation employs the free space propagation model, the Okumura-Hata model, the Cost231 calculation model, and the ITU.P526 electromagnetic wave diffraction calculation model to determine whether the communication link between any two communication nodes is connected.

[0047] Furthermore, the seed edge optimization of the communication link is used to select the primary link when there are multiple communication links between two communication nodes, and the remaining communication links are stored in the local memory as backup links, and the primary link is used as the edge between the two communication nodes; when the primary link is interfered with or cannot be connected, the optimal link is selected from the backup links as the edge between the two communication nodes.

[0048] Furthermore, in step 4, a data structure based on classical graph theory is constructed at the start of the simulation using classical graph theory algorithms to generate a weighted undirected graph, which includes a set of vertices and a set of edges.

[0049] In step 6, the data structure based on classical graph theory is maintained and updated during the simulation, and the vertex set and edge set of the weighted undirected graph are updated.

[0050] Furthermore, during the simulation, a communication network model is used to calculate the transmission efficiency of information in the communication network, including information transmission delay, transmission delay of each hop communication link, and communication rate of each hop link, and the transmission efficiency is then passed to the upper-layer application.

[0051] like Figure 1As shown, a communication network simulation system includes: a communication network model initialization module, a scenario data parsing module, a communication network state update module, a communication network model data management module, a communication link connectivity calculation module, a communication link seed edge selection module, a classical graph theory algorithm module, and an information communication efficiency calculation module.

[0052] The initialization module is used to load other modules when the communication network model starts, and to complete the initial configuration of the communication link connectivity calculation module, the communication link seed edge selection module, and the information communication efficiency calculation module according to the XML format configuration file. The XML format configuration file is defined as follows:

[0053]

[0054]

[0055] The LinkComupterFactor configuration item describes the main geographical features of the current combat zone. The communication link connectivity calculation module reads this configuration and uses different formulas to calculate whether the communication link is connected in the current simulation task. The value of this configuration item ranges from 1 to 7, representing the geographical features of the combat zone as urban areas, suburbs, open areas, woodlands, hills, mountains, and oceans, respectively.

[0056] The configuration item SeedFactor describes the weighting values ​​of various communication methods in the communication network. The communication link seed edge selection module reads this configuration item during initialization and stores it in a data structure for calculation during simulation. This communication network model currently supports six communication methods: shortwave, VHF, microwave, satellite, covered wire, and fiber optic. The weighting values ​​for each method are shown in Table 1.

[0057] Table 1. Link Weight Definition Table for Communication Methods

[0058] Communication methods Link weight shortwave 100 ultrashort wave 40 microwave 10 satellite 4 Covering line 2 optical fiber 1

[0059] The LinkEfficiency configuration item defines the communication efficiency of each communication link. This system primarily uses communication rate to represent information transmission efficiency. The information communication efficiency calculation module reads this configuration item during initialization and saves it to a data structure for use when information transmission efficiency needs to be calculated. The communication rates of the six communication modes supported by this system are shown in Table 2.

[0060] Table 2 Rate Definition Table for Communication Methods

[0061]

[0062]

[0063] Both the scenario data parsing module and the state update module reside in the business interface layer. The business interface layer provides interfaces for connecting to other modules of the simulation business and provides communication network computing services for other simulation business models / modules.

[0064] The scenario data parsing module provides a service interface. At the start of the simulation, the simulation engine receives and transmits the simulation scenario data in JSON format to the communication network model by calling this interface. The scenario data parsing module parses the JSON stream and transmits the obtained data to the communication network model data maintenance module, completing the construction and initialization of the communication network model under this simulation scenario. The interface format of the scenario data parsing module is as follows:

[0065]

[0066] Among them, scenarioId is the identifier of the simulation task. After obtaining this information, a mapping relationship is established between the communication network model and the simulation task; platforms contains information about all communication nodes in this simulation task, including the identifier, initial location, and loaded communication method of the communication node; nets contains the planning information of the communication network in this simulation scenario, including how many communication subnets are in this simulation task, which communication nodes are included in each communication subnet, and the communication method and communication parameters used by each communication subnet.

[0067] The status update module provides two service interfaces: a communication node change interface and a communication network status reporting interface. During simulation, the simulation engine needs to call the communication node change interface when a new communication node is added, a communication node is destroyed, or the location of a communication node changes. After the communication network status changes, the communication network model pushes the changes to the simulation engine or other simulation modules through the status reporting interface. The communication status information includes the communication status of each communication node, the operating status of each communication device in the communication node, the on / off status of each communication link, the rate of each communication link, the bandwidth of each communication link, and the transmission status of each piece of information, etc.

[0068] The main function of the model data maintenance module is to maintain and manage the data structure of the communication network model. It receives data from the service interface layer and, based on the data parsing, determines whether to either call the classic graph theory algorithm module to update the communication network model data or call the information communication performance calculation module to calculate communication performance. This module maintains five data structures, as shown below:

[0069] a)List <commnode>currNodes: Information about all communication nodes at the current simulation moment;

[0070] b)List <link> currLinks: Information about all communication subnets at the current simulation moment;

[0071] c)List <node>dijkstraNodes: The set of points calculated by the classical graph theory algorithm module;

[0072] d)List <edge>dijkstraEdges: The set of current edges after computation by the classical graph theory algorithm module;

[0073] e)List <edge>backupEdges: The set of backup edges calculated by the classical graph theory algorithm module;

[0074] When the state of a communication node changes, the simulation engine calls the state update module to update the updated data of the communication node in the `currNodes` structure. Then, it calls the link connectivity calculation module to update the current communication link changes, and the calculation result updates the `currLinks` data structure. After the `currLinks` is optimized by the communication link seed edge optimization module, the set of primary communication links selected is set as `dijkstraEdges`, and the remaining communication links are stored in the `backupEdges` set so that the primary communication link can be replaced if it fails. The set of nodes in `currNodes` calculated by the classical graph theory algorithm module is stored in `dijkstraNodes`.

[0075] The classical graph theory algorithm module uses a set of classical graph theory algorithms to model the structure of communication networks and provides routing algorithms widely used in routers and switches. Based on these algorithms, the optimal path is selected for information communication in the simulation service. This system mainly uses weighted undirected graphs based on classical graph theory algorithms to describe the communication network model, and uses Dijkstra's algorithm as the optimal path selection algorithm to provide communication path selection functionality for information transmission simulation. The Dijkstra algorithm routing process is as follows: Figure 5 As shown.

[0076] The communication link connectivity calculation module uses various electromagnetic wave transmission calculation formulas to calculate whether a communication link is connected or disconnected, and is used to simulate various communication links in a communication network.

[0077] The communication link seed edge selection module sorts and filters various communication methods between communication nodes based on the weight of the communication links. It selects the communication link with the smallest weight as the primary communication link between the communication nodes and stores it in the `dijkstraEdges` set. The other communication links are stored as backup links in the `backupEdges` set. The communication link seed edge selection process is as follows: Figure 4 As shown.

[0078] The information communication performance calculation module is used when the simulation service requests communication performance calculations from the communication network model described in this system. It provides quantifiable values ​​of communication performance within the communication network to meet these performance requirements. During the calculation, it first uses a classic graph theory algorithm module to obtain the shortest transmission path between the information source and destination, based on the source and destination of the information transmission service. Then, it calculates the performance of each communication link along that path. When calculating the performance of communication links, it queries the transmission rate of that type of communication link in the module's data structure and then calculates the information transmission delay of that link based on the packet size. Furthermore, it calculates the transmission delay of all communication links along the information transmission path, thereby obtaining the communication performance of the information transmission service.

[0079] Figure 2 The hierarchical structure of this system in simulation applications is presented. Figure 3 The simulation process of this system in simulation applications is presented. During the simulation, the action entity models outside the communication network model continuously generate state changes for action entity models such as armored vehicles, tanks, and aircraft. These state changes drive the communication network model to update by calling the network state update interface, and the results of the communication network changes and updates are pushed to the communication simulation situation module for display. During the simulation, the combat command simulation module continuously generates various decision information and command orders based on the development of the situation in the simulated battlefield. These decision information and command orders need to be transmitted to the action entity simulation module so that the action entity simulation module can execute the command orders. This creates communication transmission requirements for command-related services in the communication network model. When the communication efficiency requirement of the communication network model is triggered, the communication efficiency calculation module is called to obtain the transmission path and transmission delay of command information, i.e., the communication efficiency mentioned in this system. Then, based on this communication efficiency, the corresponding action entity simulation module is calculated at which simulation moment it can receive the information, and the result is returned to the corresponding action entity simulation module. Similarly, during the simulation operation, the reconnaissance simulation module outside the communication network model will continuously generate various intelligence information. This intelligence information needs to be transmitted to the combat command simulation module so that it can make command decisions. This generates communication transmission requirements for reconnaissance intelligence services, triggering the communication efficiency calculation module of the communication network model to determine the transmission path and transmission delay of reconnaissance intelligence service information. Then, based on this communication efficiency result, it calculates at which simulation moment the corresponding command simulation module can receive the reconnaissance intelligence information and returns it to the corresponding command simulation module at this simulation moment.

[0080] The above content is a further detailed description of the present invention in conjunction with specific preferred embodiments. It should not be considered that the specific implementation of the present invention is limited to this. For those skilled in the art, other modifications and changes can be made in some simulation applications without departing from the concept of the present invention, and all such modifications and changes should be considered within the scope of protection of the claims submitted by the present invention.< / edge> < / edge> < / node> < / commnode>

Claims

1. A method for modeling complex tactical communication networks, characterized in that, Includes the following steps: Step 1: At the start of the simulation, parse the scenario data input to the communication network model according to the agreed data format; after completing the scenario data parsing, obtain the initial position information of the communication nodes, the topology information of all communication subnets, and the parameter information of all communication subnets, and store them in local memory; Step 2: For every two members in each communication subnet, use the initial position of the communication node to calculate the communication link connectivity and obtain the initial connectivity state of all communication subnets. Step 3: For all communication links in all communication subnets, perform seed edge selection for communication links. Select the primary links in all communication subnets as seed edges, and save the other communication links as a set of backup links. Seed edge selection is used to select the primary link when there are multiple communication links between two communication nodes. The remaining communication links are stored as backup links in local memory, and the primary link is used as the edge between the two communication nodes. If the primary link is interfered with or cannot be connected, the optimal link from the backup links is selected as the edge between the two communication nodes. Step 4: Treat all communication nodes as vertices in graph theory and all primary links of subnets as edges in graph theory. Using classical graph theory algorithms, form a weighted undirected graph as the communication network model, thus completing the initial construction and configuration of the communication network. Specifically, at the beginning of the simulation, a data structure based on classical graph theory is constructed using classical graph theory algorithms to generate a weighted undirected graph, which includes a set of vertices and a set of edges. Step 5: During the simulation, receive and parse the simulation service data, obtain the location of each communication node or the status of the communication device, and then calculate the impact of changes in the location of the communication node or the status of the communication device on the communication network; if the current link is no longer available, select a link from the set of backup links as the main link. Step 6: When the location of a communication node changes, recalculate the communication link connectivity, update the communication link state, re-optimize the seed edge of the communication link, select the seed edge of the communication link, update the data structure of the classical graph theory algorithm, and thus update the structural state of the communication network. In particular, during the simulation, maintain and update the data structure based on classical graph theory, and update the vertex set and edge set of the weighted undirected graph.

2. The method for modeling complex tactical communication networks according to claim 1, characterized in that, The communication link connectivity calculation employs the free space propagation model, the Okumura-Hata model, the Cost231 calculation model, and the ITU.P526 electromagnetic wave diffraction calculation model to determine whether the communication link between any two communication nodes is connected.

3. The method for modeling complex tactical communication networks according to claim 1, characterized in that, During the simulation, a communication network model is used to calculate the transmission efficiency of information in the communication network, including information transmission delay, transmission delay of each hop communication link, and communication rate of each hop link, and the transmission efficiency is then passed to the upper-layer application.

4. A simulation system for a complex tactical communication network, comprising a CPU and a memory, characterized in that, The CPU is used to implement the following software modules: The communication network model initialization module is used to load other modules in the communication network model when the communication network model starts, and to complete the initial configuration of the communication link connectivity calculation module and the communication link seed edge selection module. The scenario data parsing module is used to parse the scenario data input to the communication network model according to the agreed data format at the start of the simulation. After completing the scenario data parsing, it obtains the initial position information of the communication nodes, the topology information of all communication subnets, and the parameter information of all communication subnets, and stores them in the memory. The communication network state update module is used to receive and parse simulation service data during the simulation process, obtain the location of each communication node or the state of the communication device, and then calculate the impact of changes in the location of the communication node or the state of the communication device on the communication network. When the location of the communication node changes, the communication link connectivity calculation module needs to be called again to update the communication link state, the communication link seed edge selection module needs to be called again to select the communication link seed edge, and the data structure of the classical graph theory algorithm module needs to be updated, thereby updating the structural state of the communication network. The communication network model data maintenance module is used to maintain the data structure of the communication network model. This includes: receiving data parsed by the scenario data parsing module and the communication network state update module; calling the communication link connectivity calculation module and the communication link seed edge selection module to obtain the edge dataset and backup edge dataset in the communication network model data structure; calling the classical graph theory algorithm module to complete the construction of the communication network model data structure; and during the simulation process, receiving requests from the communication network state update module for communication performance calculation from the simulation service, calling the information communication performance calculation module to complete the communication performance calculation, and returning the result to the corresponding simulation service. The communication link connectivity calculation module is used to calculate and determine whether the communication link between any two communication nodes is connected. The communication link seed edge optimization module is used to select the primary link when there are multiple communication links between two communication nodes, store the remaining communication links as backup links in the memory, and use the primary link as the edge between the two communication nodes; when the primary link is interfered with or cannot be connected, the optimal link is selected from the set of backup links as the edge between the two communication nodes. The classical graph theory algorithm module is used to construct the data structure of the communication network model based on classical graph theory at the beginning of the simulation, and return the vertex set and edge set of the generated weighted undirected graph to the communication network model data maintenance module; it is also used to maintain and update the data structure based on classical graph theory during the simulation, and update the vertex set and edge set and return it to the communication network model data maintenance module. The information communication performance calculation module is used to calculate the transmission performance of information in the communication network using a communication network model, including information transmission delay, transmission delay of each hop of the communication link, and communication rate of each hop of the link.

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