Vehicle-mounted communication network simulation test method

Abstract

The invention discloses a simulation test method for a vehicle-mounted communication network. The method comprises the following steps: S1, initializing a vehicle-mounted communication environment; s2, generating a dynamic network topology; s3, environment adaptability dynamic interference is generated; s4, co-simulation of the dynamic network and interference; s5, testing and evaluating communication performance; and S6, visualizing and exporting a test result. According to the invention, the network topology reflecting the real-time motion state of the vehicle is dynamically generated, the communication change between the vehicles can be reflected in real time in different traffic environments, and the interference conditions in various complex environments are simulated in combination with the environment adaptability dynamic interference generation module, so that the performance of the vehicle-mounted communication network can be comprehensively evaluated.

Description

Technical Field

[0001] The present invention relates to the technical field of simulation testing, and in particular to a vehicle-mounted communication network simulation testing method. Background Art

[0002] With the rapid development of intelligent transportation systems (ITS) and vehicle-to-everything (V2X) technologies, vehicle-to-vehicle communication networks are increasingly being used in areas such as traffic management, driver assistance, and autonomous driving. As a key infrastructure that connects vehicles to vehicles (V2V), vehicles to infrastructure (V2I), and vehicles to networks (V2N), the communication performance and stability of vehicle-to-vehicle communication networks directly affect the safety and efficiency of road traffic. However, due to the dynamics and complexity of vehicle-to-vehicle communication networks and the need for real-time communication in high-density vehicle scenarios, how to ensure the efficiency, reliability, and low latency of communication links has become an important issue.

[0003] Traditional vehicle communication network simulation methods mostly focus on performance evaluation in static network environments, and fail to fully consider dynamic changes between vehicles, complex traffic scenarios, and changing communication environments. Especially in complex road environments such as highways and urban roads, the communication links between vehicles will be affected by factors such as signal interference, link attenuation, and changes in vehicle density. These factors may lead to communication interruptions, network congestion, or data transmission delays, greatly affecting the stability and reliability of the vehicle communication system. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a vehicle communication network simulation test method, comprising the following steps:

[0006] S1: Initialization of the vehicle communication environment;

[0007] S2: Dynamic network topology generation;

[0008] Dynamic network topology generation includes the following steps:

[0009] S21: Using the vehicle attributes and real-time simulation data in S1, the vehicle's mobility status, including current position, speed, acceleration, and direction, is collected as input parameters for generating a dynamic network topology;

[0010] S22: Based on the relative distance and communication range between vehicles, the connection relationship between vehicles is calculated in real time to generate a dynamic network topology;

[0011] S23: Dynamically adjust network topology nodes and edges to simulate scenario changes when vehicles enter or exit the communication range;

[0012] S24: output network topology data structure, including node identification, connection relationship and real-time updated link quality parameters, to provide input data for subsequent interference signal generation and collaborative simulation;

[0013] S3: Environmentally adaptive dynamic interference generation;

[0014] Environmentally adaptive dynamic interference generation includes the following steps:

[0015] S31: extract interference model parameters of the corresponding scenario according to the environment type set in S1, and simulate the signal interference situation in the actual scenario in combination with the topology data generated by S2;

[0016] S32: Dynamically generate environmental interference signals and simulate multipath reflection, obstruction and path loss of signals based on vehicle position, building distribution and terrain features to enhance the realistic reproduction of complex scenes;

[0017] S33: adjusting the range and strength of the interference signal in real time according to the movement state of the vehicle and the interference strength, and outputting interference signal parameters for use in collaborative simulation;

[0018] S4: Dynamic network and interference co-simulation;

[0019] S5: Communication performance testing and evaluation;

[0020] S6: Test result visualization and export.

[0021] As a preferred solution of the vehicle communication network simulation test method of the present invention, the initialization of the vehicle communication environment in S1 includes the following steps:

[0022] S11: According to the test requirements, set the geographical information and traffic scenario parameters of the simulation area, including urban, highway and mountainous traffic environments;

[0023] S12: Configure vehicle attribute parameters, including the number of vehicles, initial position, speed, acceleration, direction and path planning information;

[0024] S13: Import communication protocol parameters, including network communication protocol stack configuration of DSRC, C-V2X or 5G.

[0025] As a preferred solution of the vehicle communication network simulation test method of the present invention, the generating of dynamic network topology comprises the following steps:

[0026] S221: Vehicle motion parameter collection and preprocessing

[0027] Real-time collection of vehicle motion parameters, including the vehicle's three-dimensional coordinate position, velocity vector, acceleration vector and driving direction;

[0028] Through sensor fusion technology, the error of positioning data is corrected to ensure the high accuracy of vehicle location information;

[0029] Based on time series analysis, predict the short-term movement trend of vehicles and provide data support for subsequent dynamic topology adjustment;

[0030] S222: Dynamic adjacency matrix construction

[0031] Generate a real-time adjacency matrix based on the Euclidean distance between vehicles and the signal coverage of the communication equipment. The matrix elements represent the connection status between vehicles.

[0032] The communication link weight model is introduced to comprehensively consider signal strength, bandwidth, delay and link stability, and weights are assigned to each link to form a weighted adjacency matrix.

[0033] Generate dynamic network topology based on real-time updated adjacency matrix to reflect the dynamic changes of connection status and communication links between vehicles;

[0034] Design an adaptive threshold algorithm to dynamically adjust the node connection threshold according to the complexity of the environment and communication requirements to ensure the flexibility and adaptability of the topology;

[0035] S223: Node Clustering and Communication Optimization

[0036] Based on the real-time location of vehicles and traffic scenarios, a clustering algorithm is used to group network nodes into regions to reduce computational complexity.

[0037] For the node connections in each area, the minimum spanning tree algorithm is used to optimize the communication path, reduce redundant links and reduce the computing load;

[0038] Dynamically adjust node partitioning strategies based on real-time traffic scenarios;

[0039] S224: Dynamic topology update and stability maintenance

[0040] Combined with the dynamic motion state of the vehicle, when the vehicle enters or exits the communication range, the adjacency matrix is ​​updated in real time to reflect the changes of the vehicle entering and exiting the communication range;

[0041] Use time series data to predict possible link disruptions and adjust the topology in advance to reduce the impact of communication disruptions;

[0042] Dynamically adjust the node communication parameters of the topology according to the imported communication protocol to avoid overloading of key nodes or paths and affecting network stability;

[0043] The network topology generation module is introduced as the basic module in the system. It automatically adjusts and optimizes the network topology structure through real-time data and preset algorithms to ensure the stability and efficiency of the topology.

[0044] As a preferred solution of the vehicle communication network simulation test method of the present invention, the introduction of the communication link weight model includes the following steps:

[0045] Define the main performance indicators of the communication link as weight factors, including signal strength, bandwidth, delay and link stability;

[0046] Each weight factor is normalized to adjust its value to the same dimension range for unified calculation;

[0047] Assign weight coefficients to each weight factor based on communication requirements and scenario priorities;

[0048] Use a weighted formula to calculate the weight value of each communication link to comprehensively reflect the communication performance of the link;

[0049] Based on the real-time changes in vehicle motion status and communication environment, the link weight value is dynamically updated to ensure that the topology structure can promptly reflect changes in communication link performance;

[0050] According to the calculated link weight values, a weighted adjacency matrix is ​​constructed;

[0051] In the generated dynamic network topology, link weight values ​​are used for path selection, resource allocation, and communication optimization to verify the effect of the weight model on improving network performance.

[0052] As a preferred solution of the vehicle communication network simulation test method of the present invention, the mathematical formula of the adaptive threshold algorithm is as follows:

[0053]

[0054] in:

[0055] T ij : Adaptive connection threshold between node i and node j in dynamic network topology;

[0056] t0, t1: start time and end time of the time interval;

[0057] v i(t), v j (t): velocity vector of node i and node j at time t, representing the motion parameters of the vehicle;

[0058] ||v i (t)-v j (t)||: Euclidean norm of the velocity difference between nodes i and j, indicating the relative motion state of the two nodes;

[0059] σ: standard deviation in the Gaussian kernel function, which controls the influence of the speed difference between nodes on the connection strength;

[0060] λ k : The dynamic influence coefficient of node k, reflecting the weight of node k on the connection between other nodes;

[0061] d ik : The Euclidean distance between node i and its neighbor node k;

[0062] R ij : Link quality parameters between nodes i and j, such as signal strength or bandwidth;

[0063] α: link quality sensitivity adjustment parameter;

[0064] β: link quality benchmark value, used to measure whether the link is reliable;

[0065] represents the sum of the connection strengths of node i to all its neighbor nodes, based on distance and dynamic influence weights; N is the total number of neighbor nodes;

[0066] It refers to the Gaussian kernel function representation of the relative speed difference between two nodes, reflecting the impact of the motion state on the connection threshold;

[0067] The time integral represents the cumulative effect of the relative motion state of nodes i and j on the connection strength in the time interval [t0, t1];

[0068] Represents the sum of the dynamic connection strengths between node i and all its neighboring nodes;

[0069] It represents the normalized value of the relative connection strength between nodes i and j in the global neighbor environment, reflecting the relative importance of i and j in the dynamic network;

[0070] Indicates the influence of link quality on the connection threshold; when R ij >β, the link quality is high and the output tends to 1; when R ii When <β, the link quality is low and the output tends to 0;

[0071] Value range meaning:

[0072] T ii The value range of is (0, ∞), which represents the connection threshold between two nodes. The higher the value, the stronger the reliability of the connection between the two nodes.

[0073] As a preferred solution of the vehicle communication network simulation test method of the present invention, the dynamic adjustment of network topology nodes and edges includes the following steps:

[0074] S231: updating the position of each vehicle in real time, and then judging whether the vehicle enters or exits the communication range of other vehicles according to the current position of the vehicle and the signal coverage range of its communication device;

[0075] S232: recalculating the connection state based on the relative distance between the vehicles and link quality parameters (such as signal strength, delay, etc.), and determining a new connection or disconnection relationship;

[0076] S233: updating the adjacency matrix or the weighted adjacency matrix according to the new connection state, and adjusting the link weights to ensure the accuracy and real-time performance of the topology;

[0077] S234: Optimize connection paths through the shortest path or minimum spanning tree algorithm, remove redundant links, and improve network efficiency and stability;

[0078] S235: Dynamically adjust the topology structure to adapt to complex traffic environments based on vehicle behavior prediction and real-time traffic scene changes;

[0079] S236: Based on the load situation after the topology update, reallocate communication resources and optimize the load of key nodes and links to ensure efficient and stable operation of the network.

[0080] As a preferred solution of the vehicle communication network simulation test method of the present invention, the dynamically generating environmental interference signal includes the following steps:

[0081] S321: Extract physical scene elements that may affect the signal based on vehicle location, building distribution and terrain features, and provide basic data for subsequent interference models;

[0082] S322: generating a mathematical model of signal interference in combination with the extracted scene features to simulate basic interference conditions;

[0083] S323: Based on the real-time speed, direction and acceleration of the vehicle, the propagation path and range of the signal interference are dynamically calculated to enhance the adaptability to mobile scenarios;

[0084] S324: Using building and terrain distribution information, dynamically generate multipath reflection signals and shielding effects to ensure that the interference signal is consistent with the real scene;

[0085] S325: According to the relative distance between vehicles, communication range and dynamic changes in the environment, the intensity and range of the interference signal are adjusted in real time to enhance the realism of the signal;

[0086] S326: Generate a parameter file including interference signal strength, range, and multipath delay characteristics as input data for subsequent simulations.

[0087] As a preferred solution of the vehicle communication network simulation test method of the present invention, the dynamic network and interference collaborative simulation in S4 includes the following steps:

[0088] S41: superimpose the dynamic network topology generated by S2 and the environmental interference signal generated by S3 to form a joint simulation scenario to simulate the communication behavior of vehicles under different environmental conditions;

[0089] S42: During the simulation, dynamically adjust the network link parameters so that they are affected by the interference signal and updated in real time;

[0090] S43: Through the feedback mechanism, the data on the impact of interference on the network topology is transmitted to the network topology generation module, and the topology update algorithm is optimized to make the simulation closer to the actual communication scenario.

[0091] As a preferred solution of the vehicle communication network simulation test method of the present invention, the communication performance test and evaluation in S5 includes the following steps:

[0092] S51: Based on the collaborative simulation scenario of S4, set the communication performance test indicators and conduct a comprehensive evaluation of the communication performance of the vehicle network;

[0093] S52: Output inter-vehicle communication performance data according to the test results and generate an analysis report, which details the changing trend of network performance and key influencing factors under different environmental conditions;

[0094] S53: Compare the performance of different communication protocols and interference conditions to provide data support for the optimization of vehicle communication systems.

[0095] As a preferred solution of the vehicle communication network simulation test method of the present invention, the visualization and export of the test results in S6 includes the following steps:

[0096] S61: Using simulation software, the dynamic changes of network topology of S2 and the distribution of interference signals of S3 are displayed in real time in a graphical manner, making the simulation process more intuitive;

[0097] S62: Export all data generated during the simulation process, including dynamic topology data, interference signal parameters, and communication performance test indicators, and generate a standardized test report.

[0098] Beneficial effects of the present invention:

[0099] 1. The present invention dynamically generates a network topology that reflects the real-time movement status of the vehicle, which can reflect the communication changes between vehicles in real time under different traffic environments. It also combines the environmental adaptability dynamic interference generation module to simulate the interference conditions in various complex environments, thereby comprehensively evaluating the performance of the vehicle communication network.

[0100] 2. The present invention can simulate different traffic environments and different types of interference signals, and accurately test the performance of the vehicle communication network in the actual environment. This makes the simulation test closer to the real environment, thereby providing more accurate and effective performance evaluation, and providing strong support for the optimization and decision-making of the vehicle communication network.

[0101] 3. The present invention can comprehensively test various performance indicators of the vehicle-mounted communication network and provide data support for the optimized design of the vehicle-mounted communication network, especially the performance under different traffic environments and interference conditions, which can provide guidance for the deployment and optimization of the vehicle-mounted network and ensure the efficient and stable operation of the network. BRIEF DESCRIPTION OF THE DRAWINGS

[0102] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0103] Figure 1 The present invention is a flowchart of a vehicle communication network simulation test method.

[0104] Figure 2 The present invention is a flowchart of a dynamic network topology generation step of a vehicle communication network simulation test method.

[0105] Figure 3 The present invention is a flowchart of a step of generating dynamic interference for environmental adaptability of a vehicle communication network simulation test method. DETAILED DESCRIPTION

[0106] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0107] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0108] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0109] Secondly, the present invention is described in detail with reference to the schematic diagram. When describing the embodiments of the present invention in detail, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0110] Example 1

[0111] Reference Figure 1-3 , which is a first embodiment of the present invention, provides a vehicle communication network simulation test method, comprising the following steps:

[0112] S1: Initialization of the vehicle communication environment

[0113] S11: According to the test requirements, set the geographical information and traffic scenario parameters of the simulation area, including urban, highway and mountainous traffic environments, to provide basic data support for subsequent dynamic network topology generation and interference signal simulation.

[0114] S12: Configure vehicle attribute parameters, including the number of vehicles, initial position, speed, acceleration, direction, and path planning information, to lay the foundation for the dynamic generation of network topology.

[0115] S13: Import communication protocol parameters, including DSRC, C-V2X or 5G network communication protocol stack configuration. Ensure that the simulation environment is consistent with the actual vehicle communication system, providing the necessary conditions for communication performance evaluation.

[0116] S2: Dynamic network topology generation

[0117] S21: Using the vehicle attributes and real-time simulation data in S1, the vehicle's mobility status, including current position, speed, acceleration, and direction, is collected as input parameters for generating a dynamic network topology.

[0118] S22: Based on the relative distance and communication range between vehicles, the connection relationship between vehicles is calculated in real time to generate a dynamic network topology.

[0119] S23: Dynamically adjust network topology nodes and edges to simulate scenario changes when vehicles enter or exit the communication range.

[0120] S24: Output the network topology data structure, including node identification, connection relationship and real-time updated link quality parameters, to provide input data for subsequent interference signal generation and collaborative simulation.

[0121] S3: Environmentally Adaptive Dynamic Interference Generation

[0122] S31: According to the environment type set in S1 (such as city, highway or mountainous area), extract the interference model parameters of the corresponding scene (such as multipath effect, signal attenuation, frequency interference, etc.), combine with the topology data generated by S2, and simulate the signal interference situation in the actual scene.

[0123] S32: Dynamically generates environmental interference signals and simulates multipath reflection, obstruction and path loss of signals based on vehicle position, building distribution and terrain features to enhance the realistic reproduction of complex scenes.

[0124] S33: According to the moving state (such as speed, direction) of the vehicle and the interference intensity, the scope and intensity of the interference signal are adjusted in real time, and the interference signal parameters are output for use in the collaborative simulation.

[0125] S4: Dynamic Network and Interference Co-Simulation

[0126] S41: The dynamic network topology generated by S2 is superimposed with the environmental interference signal generated by S3 to form a joint simulation scenario to simulate the communication behavior of the vehicle under different environmental conditions.

[0127] S42: During the simulation process, the network link parameters (such as signal strength, delay, and packet loss rate) are dynamically adjusted so that they are affected by interference signals and updated in real time.

[0128] S43: Through the feedback mechanism, the data on the impact of interference on the network topology is transmitted to the network topology generation module, and the topology update algorithm is optimized to make the simulation closer to the actual communication scenario.

[0129] S5: Communication performance testing and evaluation

[0130] S51: Based on the collaborative simulation scenario of S4, set communication performance test indicators (such as communication delay, throughput, packet loss rate and link stability) to comprehensively evaluate the communication performance of the vehicle network.

[0131] S52: Output inter-vehicle communication performance data based on the test results and generate an analysis report that details the changing trends and key influencing factors of network performance under different environmental conditions.

[0132] S53: Compare the performance of different communication protocols and interference conditions to provide data support for the optimization of vehicle communication systems.

[0133] S6: Test result visualization and export

[0134] S61: Using simulation software, the dynamic changes of network topology of S2 and the interference signal distribution of S3 are displayed in real time in a graphical manner, making the simulation process more intuitive.

[0135] S62: Export all data generated during the simulation process, including dynamic topology data, interference signal parameters, and communication performance test indicators, and generate standardized test reports to provide a detailed basis for subsequent research and system optimization.

[0136] In summary, the present invention can truly reflect the communication behavior of the vehicle in a moving state by dynamically simulating the changes in the topology of the vehicle communication network. The present invention can comprehensively evaluate the anti-interference performance of the vehicle network by automatically generating environmental interference signals and dynamically adjusting them. In addition, the dynamic network topology generation in the present invention focuses on reflecting the real-time communication changes of vehicles in different traffic environments, while the environmental adaptability dynamic interference generation module can simulate real interference conditions for these communication scenarios. After combining the two, the communication behavior of the vehicle network in a real complex environment can be more comprehensively reproduced.

[0137] Specifically, dynamically adjusting network topology nodes and edges includes the following steps:

[0138] S231: updating the position of each vehicle in real time, and then judging whether the vehicle enters or exits the communication range of other vehicles according to the current position of the vehicle and the signal coverage range of its communication device;

[0139] S232: recalculating the connection state based on the relative distance between the vehicles and link quality parameters (such as signal strength, delay, etc.), and determining a new connection or disconnection relationship;

[0140] S233: updating the adjacency matrix or the weighted adjacency matrix according to the new connection state, and adjusting the link weights to ensure the accuracy and real-time performance of the topology;

[0141] S234: Optimize connection paths through the shortest path or minimum spanning tree algorithm, remove redundant links, and improve network efficiency and stability;

[0142] S235: Dynamically adjust the topology structure to adapt to complex traffic environments based on vehicle behavior prediction and real-time traffic scene changes;

[0143] S236: Based on the load situation after the topology update, reallocate communication resources and optimize the load of key nodes and links to ensure efficient and stable operation of the network.

[0144] The above steps ensure the flexibility and stability of the network topology in a dynamic environment, and can be adjusted according to the real-time status of the vehicle and changes in external conditions to meet actual communication needs.

[0145] Specifically, the generation of a dynamic network topology includes the following steps:

[0146] S221: Vehicle motion parameter collection and preprocessing

[0147] The vehicle's motion parameters are collected in real time, including the vehicle's three-dimensional coordinate position, velocity vector, acceleration vector and driving direction.

[0148] Through sensor fusion technology (GPS, IMU, vehicle-mounted camera, etc.), the error of positioning data is corrected to ensure the high accuracy of vehicle location information.

[0149] Based on time series analysis, the short-term movement trend of vehicles is predicted to provide data support for subsequent dynamic topology adjustment.

[0150] S222: Dynamic adjacency matrix construction

[0151] According to the Euclidean distance between vehicles and the signal coverage of the communication equipment, a real-time adjacency matrix is ​​generated, and the matrix elements represent the connection status between vehicles (connected is 1, disconnected is 0).

[0152] The communication link weight model is introduced to comprehensively consider signal strength, bandwidth, delay and link stability, assign weights to each link, and form a weighted adjacency matrix.

[0153] Based on the real-time updated adjacency matrix, a dynamic network topology is generated to reflect the dynamic changes of the connection status and communication links between vehicles.

[0154] An adaptive threshold algorithm is designed to dynamically adjust the node connection threshold according to the environment complexity and communication requirements to ensure the flexibility and adaptability of the topology.

[0155] S223: Node Clustering and Communication Optimization

[0156] According to the real-time location of the vehicle and the traffic scenario, a clustering algorithm (such as K-Means or DBSCAN) is used to group the network nodes into regions to reduce the computational complexity.

[0157] For the node connections in each area, the minimum spanning tree algorithm is used to optimize the communication path, reduce redundant links and reduce the computing load.

[0158] Combined with real-time traffic scenarios, the node partitioning strategy is dynamically adjusted, such as grouping vehicles by lanes in highway scenarios and clustering them by intersections in urban scenarios.

[0159] S224: Dynamic topology update and stability maintenance

[0160] Combined with the dynamic motion state of the vehicle, when the vehicle enters or exits the communication range, the adjacency matrix is ​​updated in real time to reflect the changes of the vehicle entering and exiting the communication range.

[0161] Use time series data to predict possible link disruptions and adjust the topology in advance to reduce the impact of communication interruptions.

[0162] According to the imported communication protocol (such as DSRC, C-V2X or 5G), the node communication parameters of the topology are dynamically adjusted to avoid overload of key nodes or paths affecting network stability.

[0163] The network topology generation module is introduced as the basic module in the system. It automatically adjusts and optimizes the network topology structure through real-time data and preset algorithms to ensure the stability and efficiency of the topology.

[0164] Furthermore, introducing the communication link weight model includes the following steps:

[0165] The main performance indicators of the communication link are defined as weight factors, including signal strength, bandwidth, delay and link stability.

[0166] Each weight factor is normalized to adjust its value to the same dimension range (such as 0 to 1) so that it can be calculated uniformly.

[0167] According to the communication requirements and scenario priorities, a weight coefficient is assigned to each weight factor, for example:

[0168] W=α·S+β·B+γ·D+δ·L

[0169] Among them, W is the comprehensive weight value, S is the signal strength, B is the bandwidth, D is the delay, L is the link stability, and α, β, γ, and δ are the corresponding weight coefficients.

[0170] The weight value of each communication link is calculated using a weighted formula to comprehensively reflect the communication performance of the link.

[0171] W ij =α·F ij +β·B ij +γ·(1 / D ij )+δ·L ij

[0172] Among them, i and j are the nodes at both ends of the link, 1 / D ijThe inverse of the delay is taken to reflect that the smaller the delay, the higher the weight.

[0173] Based on the real-time changes in vehicle motion status and communication environment, the link weight value is dynamically updated to ensure that the topology structure can promptly reflect changes in communication link performance;

[0174] According to the calculated link weight values, a weighted adjacency matrix A = [W ij ], where the matrix elements represent the comprehensive weights of the links between nodes.

[0175] In the generated dynamic network topology, link weight values ​​are used for path selection, resource allocation, and communication optimization to verify the effect of the weight model on improving network performance.

[0176] The above steps are intended to comprehensively reflect the performance indicators of the communication link, ensure that the generated dynamic network topology has efficient and stable communication capabilities, and can flexibly adapt to changes in vehicle movement and communication environment.

[0177] Furthermore, the mathematical formula of the adaptive threshold algorithm is as follows:

[0178]

[0179] T ij : Adaptive connection threshold between node i and node j in dynamic network topology;

[0180] t0, t1: start time and end time of the time interval;

[0181] v i (t), v j (t): velocity vector of node i and node j at time t, representing the motion parameters of the vehicle;

[0182] ||v i (t)-v j (t)||: Euclidean norm of the velocity difference between nodes i and j, indicating the relative motion state of the two nodes;

[0183] σ: standard deviation in the Gaussian kernel function, which controls the influence of the speed difference between nodes on the connection strength;

[0184] λ k : The dynamic influence coefficient of node k, reflecting the weight of node k on the connection between other nodes;

[0185] d ik : The Euclidean distance between node i and its neighbor node k;

[0186] R ij : Link quality parameters between nodes i and j, such as signal strength or bandwidth;

[0187] α: link quality sensitivity adjustment parameter;

[0188] β: link quality benchmark value, used to measure whether the link is reliable;

[0189] represents the sum of the connection strengths of node i to all its neighbor nodes, based on distance and dynamic influence weights; N is the total number of neighbor nodes;

[0190] It refers to the Gaussian kernel function representation of the relative speed difference between two nodes, reflecting the impact of the motion state on the connection threshold;

[0191] The time integral represents the cumulative effect of the relative motion state of nodes i and j on the connection strength in the time interval [t0, t1];

[0192] Represents the sum of the dynamic connection strengths between node i and all its neighboring nodes;

[0193] It represents the normalized value of the relative connection strength between nodes i and j in the global neighbor environment, reflecting the relative importance of i and j in the dynamic network;

[0194] Indicates the influence of link quality on the connection threshold; when R ij >β, the link quality is high and the output tends to 1; when R ij When <β, the link quality is low and the output tends to 0;

[0195] Value range meaning:

[0196] T ii The value range of is (0, ∞), which represents the connection threshold between two nodes. The higher the value, the stronger the reliability of the connection between the two nodes.

[0197] The above formula comprehensively considers the relative movement between nodes and the quality of the communication link, dynamically adjusts the connection threshold, and adapts to complex environmental changes.

[0198] Specifically, dynamically generating an environmental interference signal includes the following steps:

[0199] S321: Extract physical scene elements that may affect the signal based on vehicle location, building distribution and terrain features, and provide basic data for subsequent interference models.

[0200] S322: Generate a mathematical model of signal interference based on the extracted scene features (such as multipath reflection, occlusion, and path loss) to simulate basic interference conditions.

[0201] S323: Based on the real-time speed, direction and acceleration of the vehicle, dynamically calculate the propagation path and range of the signal interference to enhance adaptability to mobile scenarios.

[0202] S324: Utilize building and terrain distribution information to dynamically generate multipath reflection signals and shielding effects to ensure that interference signals are consistent with real scenarios.

[0203] S325: According to the relative distance between vehicles, communication range and dynamic changes in the environment (such as weather or other interference sources), the intensity and range of the interference signal are adjusted in real time to enhance the realism of the signal.

[0204] S326: Generate a parameter file including interference signal strength, range, and multipath delay characteristics as input data for subsequent simulations.

[0205] The above steps start from analyzing the characteristics of vehicles and the environment, gradually establish interference models, and then dynamically adjust interference signals according to vehicle movement status and environmental changes to ensure the authenticity and dynamics of interference signals. Finally, interference signal parameters are used for collaborative simulation to complete the overall closed loop.

[0206] In summary, the present invention can reflect the communication changes between vehicles in real time under different traffic environments by dynamically generating a network topology that reflects the real-time motion state of the vehicle, and can combine the environmental adaptability dynamic interference generation module to simulate the interference conditions in various complex environments, so as to comprehensively evaluate the performance of the vehicle-mounted communication network. The present invention can simulate different traffic environments and different types of interference signals, and accurately test the performance of the vehicle-mounted communication network in the actual environment. This makes the simulation test closer to the real environment, thereby providing a more accurate and effective performance evaluation, and providing strong support for the optimization and decision-making of the vehicle-mounted communication network. The present invention can comprehensively test the various performance indicators of the vehicle-mounted communication network, provide data support for the optimal design of the vehicle-mounted communication network, especially the performance under different traffic environments and interference conditions, and can provide guidance for the deployment and optimization of the vehicle-mounted network, ensuring the efficient and stable operation of the network.

[0207] Example 2

[0208] Referring to Table 1, which is a second embodiment of the present invention, this embodiment is different from the first embodiment in that, in order to verify its beneficial effects, operating data and related instructions of the present invention in an actual environment are provided.

[0209] Implementation steps:

[0210] 1. Experimental environment construction

[0211] A high-performance simulation platform (such as NS-3 or SUMO) is used to build a dynamic traffic environment model, including three typical scenarios: highways, urban roads, and rural roads.

[0212] Combine vehicle dynamic motion data with the simulation environment to simulate the vehicle communication network under different traffic flows and speeds.

[0213] 2. Dynamic network topology generation

[0214] The real-time position, speed and direction of the vehicle are obtained through sensor fusion technology (such as GPS, IMU, and on-board cameras).

[0215] Generate a dynamic network topology to reflect the communication connection status and link weight between vehicles. The link weight includes signal strength, latency, bandwidth, and stability.

[0216] 3. Environmentally adaptive dynamic interference generation

[0217] Simulate different interference signals, including physical interference (such as signal attenuation, multipath effect) and network interference (such as congestion, attack).

[0218] The interference intensity is divided into three levels: low, medium and high, generating dynamic interference scenarios.

[0219] 4. Simulation test indicators

[0220] Throughput: The amount of data successfully transmitted per unit time.

[0221] Latency: The average time it takes for data to be sent and received.

[0222] Packet loss rate: the proportion of data packets lost.

[0223] Link stability: The average time that a communication link is maintained.

[0224] 5. Experimental data recording and analysis

[0225] Under different traffic environments and interference conditions, the simulation test results are recorded and the experimental data are presented in tabular form.

[0226] Experimental data:

[0227] Test scenario Interference intensity Throughput (Mbps) Average delay (ms) Packet loss rate (%) Link stability(s) highway Low 85.2 12.3 0.5 120.5 highway middle 70.8 20.5 1.2 95.3 highway high 50.4 35.7 3.8 60.2 Urban roads Low 80.3 15.2 0.7 105.6 Urban roads middle 60.7 28.4 2.3 80.4 Urban roads high 42.1 45.8 5.1 55.9 Country Road Low 88.5 10.8 0.3 140.8 Country Road middle 75.9 18.9 1.0 110.2 Country Road high 55.3 32.6 3.4 78.5

[0228] Table 1

[0229] in conclusion:

[0230] The data shows that when the interference intensity increases from low to high, the throughput of each scenario shows a significant downward trend. At the same time, the average delay and packet loss rate increase significantly. This shows that the interference intensity directly affects the quality of the communication link, especially in high-density network scenarios, where the network performance decline is particularly obvious.

[0231] The experimental results show that different traffic scenarios have different impacts on the performance of vehicle communication networks:

[0232] The throughput of the rural road scenario reaches a maximum of 88.5Mbps under low interference intensity, and the packet loss rate is only 0.3%, showing good adaptability to the communication environment.

[0233] Urban road scenarios are greatly affected by interference. Under high interference conditions, the throughput is only 42.1Mbps, the packet loss rate increases to 5.1%, and the link stability also drops to 55.9s, reflecting that the complex traffic environment has a great impact on communications.

[0234] As can be seen from Table 1, by dynamically generating the network topology, the impact of interference on network performance can be alleviated to a certain extent. For example, on urban roads, the throughput under low interference is 80.3Mbps, indicating that the topology update strategy can adapt to the dynamic distribution of vehicles.

[0235] Optimization suggestions:

[0236] For high-interference scenarios, it is recommended to adopt an adaptive communication parameter adjustment mechanism to dynamically allocate bandwidth and optimize link selection strategies.

[0237] In complex traffic scenarios, the communication load can be reduced and link stability and throughput can be improved by combining regional clustering with link optimization.

[0238] In low-density scenarios such as rural roads, it is recommended to further reduce the frequency of topology updates to save computing resources and maintain efficient and stable network performance.

[0239] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A vehicle communication network simulation test method, characterized in that: The following steps are involved: S1: Initialization of the vehicle communication environment; S2: Dynamic network topology generation; Dynamic network topology generation includes the following steps: S21: Using the vehicle attributes and real-time simulation data in S1, the vehicle's mobility status, including current position, speed, acceleration, and direction, is collected as input parameters for generating a dynamic network topology; S22: Based on the relative distance and communication range between vehicles, the connection relationship between vehicles is calculated in real time to generate a dynamic network topology; S23: Dynamically adjust network topology nodes and edges to simulate scenario changes when vehicles enter or exit the communication range; S24: output network topology data structure, including node identification, connection relationship and real-time updated link quality parameters, to provide input data for subsequent interference signal generation and collaborative simulation; S3: Environmentally adaptive dynamic interference generation; Environmentally adaptive dynamic interference generation includes the following steps: S31: extract interference model parameters of the corresponding scenario according to the environment type set in S1, and simulate the signal interference situation in the actual scenario in combination with the topology data generated by S2; S32: Dynamically generate environmental interference signals and simulate multipath reflection, obstruction and path loss of signals based on vehicle position, building distribution and terrain features to enhance the realistic reproduction of complex scenes; S33: adjusting the range and strength of the interference signal in real time according to the movement state of the vehicle and the interference strength, and outputting interference signal parameters for use in collaborative simulation; S4: Dynamic network and interference co-simulation; S5: Communication performance testing and evaluation; S6: Test result visualization and export.

2. The vehicle communication network simulation test method according to claim 1, characterized in that: The initialization of the vehicle communication environment in S1 includes the following steps: S11: According to the test requirements, set the geographical information and traffic scenario parameters of the simulation area, including urban, highway and mountainous traffic environments; S12: Configure vehicle attribute parameters, including the number of vehicles, initial position, speed, acceleration, direction and path planning information; S13: Import communication protocol parameters, including network communication protocol stack configuration of DSRC, C-V2X or 5G.

3. The vehicle communication network simulation test method according to claim 1, characterized in that: Generating a dynamic network topology comprises the following steps: S221: Vehicle motion parameter collection and preprocessing Real-time collection of vehicle motion parameters, including the vehicle's three-dimensional coordinate position, velocity vector, acceleration vector and driving direction; Through sensor fusion technology, the error of positioning data is corrected to ensure the high accuracy of vehicle location information; Based on time series analysis, predict the short-term movement trend of vehicles and provide data support for subsequent dynamic topology adjustment; S222: Dynamic adjacency matrix construction Generate a real-time adjacency matrix based on the Euclidean distance between vehicles and the signal coverage of the communication equipment. The matrix elements represent the connection status between vehicles. The communication link weight model is introduced to comprehensively consider signal strength, bandwidth, delay and link stability, and weights are assigned to each link to form a weighted adjacency matrix. Generate dynamic network topology based on real-time updated adjacency matrix to reflect the dynamic changes of connection status and communication links between vehicles; Design an adaptive threshold algorithm to dynamically adjust the node connection threshold according to the complexity of the environment and communication requirements to ensure the flexibility and adaptability of the topology; S223: Node Clustering and Communication Optimization Based on the real-time location of vehicles and traffic scenarios, a clustering algorithm is used to group network nodes into regions to reduce computational complexity. For the node connections in each area, the minimum spanning tree algorithm is used to optimize the communication path, reduce redundant links and reduce the computing load; Dynamically adjust node partitioning strategies based on real-time traffic scenarios; S224: Dynamic topology update and stability maintenance Combined with the dynamic motion state of the vehicle, when the vehicle enters or exits the communication range, the adjacency matrix is ​​updated in real time to reflect the changes of the vehicle entering and exiting the communication range; Use time series data to predict possible link disruptions and adjust the topology in advance to reduce the impact of communication disruptions; Dynamically adjust the node communication parameters of the topology according to the imported communication protocol to avoid overloading of key nodes or paths and affecting network stability; The network topology generation module is introduced as the basic module in the system. It automatically adjusts and optimizes the network topology structure through real-time data and preset algorithms to ensure the stability and efficiency of the topology.

4. The vehicle communication network simulation test method according to claim 3, characterized in that: The introducing of the communication link weight model comprises the following steps: Define the main performance indicators of the communication link as weight factors, including signal strength, bandwidth, delay and link stability; Each weight factor is normalized to adjust its value to the same dimension range for unified calculation; Assign weight coefficients to each weight factor based on communication requirements and scenario priorities; Use a weighted formula to calculate the weight value of each communication link to comprehensively reflect the communication performance of the link; Based on the real-time changes in vehicle motion status and communication environment, the link weight value is dynamically updated to ensure that the topology structure can promptly reflect changes in communication link performance; According to the calculated link weight values, a weighted adjacency matrix is ​​constructed; In the generated dynamic network topology, link weight values ​​are used for path selection, resource allocation, and communication optimization to verify the effect of the weight model on improving network performance.

5. The vehicle communication network simulation test method according to claim 3, characterized in that: The mathematical formula of the adaptive threshold algorithm is as follows: in: T ij : Adaptive connection threshold between node i and node j in dynamic network topology; t0, t1: start time and end time of the time interval; v i (t), v j (t): velocity vector of node i and node j at time t, representing the motion parameters of the vehicle; ||v i (t)-v j (t)||: Euclidean norm of the velocity difference between nodes i and j, indicating the relative motion state of the two nodes; σ: standard deviation in the Gaussian kernel function, which controls the influence of the speed difference between nodes on the connection strength; λ k : The dynamic influence coefficient of node k, reflecting the weight of node k on the connection between other nodes; d ik : The Euclidean distance between node i and its neighbor node k; R ij : Link quality parameters between nodes i and j, such as signal strength or bandwidth; α: link quality sensitivity adjustment parameter; β: link quality benchmark value, used to measure whether the link is reliable; represents the sum of the connection strengths of node i to all its neighbor nodes, based on distance and dynamic influence weights; N is the total number of neighbor nodes; It refers to the Gaussian kernel function representation of the relative speed difference between two nodes, reflecting the impact of the motion state on the connection threshold; The time integral represents the cumulative effect of the relative motion state of nodes o and j on the connection strength in the time interval [t0, t1]; Represents the sum of the dynamic connection strengths between node i and all its neighboring nodes; It represents the normalized value of the relative connection strength between nodes i and j in the global neighbor environment, reflecting the relative importance of i and j in the dynamic network; Indicates the influence of link quality on the connection threshold; when R ij >β, the link quality is high and the output tends to 1; when R ij When <β, the link quality is low and the output tends to 0; Value range meaning: T ij The value range of is (0,∞), which represents the connection threshold between two nodes. The higher the value, the stronger the reliability of the connection between the two nodes.

6. The vehicle communication network simulation test method according to claim 1, characterized in that: The dynamic adjustment of network topology nodes and edges comprises the following steps: S231: updating the position of each vehicle in real time, and then judging whether the vehicle enters or exits the communication range of other vehicles according to the current position of the vehicle and the signal coverage range of its communication device; S232: recalculating the connection state based on the relative distance between the vehicles and link quality parameters (such as signal strength, delay, etc.), and determining a new connection or disconnection relationship; S233: updating the adjacency matrix or the weighted adjacency matrix according to the new connection state, and adjusting the link weights to ensure the accuracy and real-time performance of the topology; S234: Optimize connection paths through the shortest path or minimum spanning tree algorithm, remove redundant links, and improve network efficiency and stability; S235: Dynamically adjust the topology structure to adapt to complex traffic environments based on vehicle behavior prediction and real-time traffic scene changes; S236: Based on the load situation after the topology update, reallocate communication resources and optimize the load of key nodes and links to ensure efficient and stable operation of the network.

7. The vehicle communication network simulation test method according to claim 1, characterized in that: The dynamically generating environmental interference signal comprises the following steps: S321: Extract physical scene elements that may affect the signal based on vehicle location, building distribution and terrain features, and provide basic data for subsequent interference models; S322: generating a mathematical model of signal interference in combination with the extracted scene features to simulate basic interference conditions; S323: Based on the real-time speed, direction and acceleration of the vehicle, the propagation path and range of the signal interference are dynamically calculated to enhance the adaptability to mobile scenarios; S324: Using building and terrain distribution information, dynamically generate multipath reflection signals and shielding effects to ensure that the interference signal is consistent with the real scene; S325: According to the relative distance between vehicles, communication range and dynamic changes in the environment, the intensity and range of the interference signal are adjusted in real time to enhance the realism of the signal; S326: Generate a parameter file including interference signal strength, range, and multipath delay characteristics as input data for subsequent simulations.

8. The vehicle communication network simulation test method according to claim 1, characterized in that: The dynamic network and interference collaborative simulation in S4 includes the following steps: S41: superimpose the dynamic network topology generated by S2 and the environmental interference signal generated by S3 to form a joint simulation scenario to simulate the communication behavior of vehicles under different environmental conditions; S42: During the simulation, dynamically adjust the network link parameters so that they are affected by the interference signal and updated in real time; S43: Through the feedback mechanism, the data on the impact of interference on the network topology is transmitted to the network topology generation module, and the topology update algorithm is optimized to make the simulation closer to the actual communication scenario.

9. The vehicle communication network simulation test method according to claim 1, characterized in that: The communication performance test and evaluation in S5 includes the following steps: S51: Based on the collaborative simulation scenario of S4, set the communication performance test indicators and conduct a comprehensive evaluation of the communication performance of the vehicle network; S52: Output inter-vehicle communication performance data according to the test results and generate an analysis report, which details the changing trend of network performance and key influencing factors under different environmental conditions; S53: Compare the performance of different communication protocols and interference conditions to provide data support for the optimization of vehicle communication systems.

10. The vehicle communication network simulation test method according to claim 1, characterized in that: The test result visualization and export in S6 includes the following steps: S61: Using simulation software, the dynamic changes of network topology of S2 and the distribution of interference signals of S3 are displayed in real time in a graphical manner, making the simulation process more intuitive; S62: Export all data generated during the simulation process, including dynamic topology data, interference signal parameters, and communication performance test indicators, and generate a standardized test report.

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