Vehicle-mounted time-sensitive network simulation modeling method and system based on digital twinning

Abstract

The invention discloses a vehicle-mounted time-sensitive network simulation modeling method and system based on digital twinning, and the method comprises the steps: extracting key nodes in a vehicle-mounted communication network topology, building a network topology model, and building a corresponding vehicle-mounted communication network model in OMNeT + + according to the network topology model; performing priority grading on different types of information flows in the vehicle-mounted communication network model; setting various parameters of the information flow in the vehicle-mounted communication network model; selecting a scheduling algorithm to be evaluated, and generating a gating list information file corresponding to each algorithm; building a digital twinborn simulation model of an actual vehicle-mounted network by utilizing a gating list GCL; and collecting a simulation result of each information stream output by the OMNeT + +, and evaluating the traffic scheduling algorithm according to each statistical result. The method provided by the invention has higher evaluation efficiency for the traffic scheduling algorithm.

Description

Technical Field

[0001] The present application relates to a simulation modeling method for in-vehicle time-sensitive network based on digital twin, belonging to the technical field of digital twin network. Background Art

[0002] With the rapid development of modern automotive intelligent technologies represented by autonomous driving, the automotive electronic and electrical architecture is transforming from the traditional distributed mode to the domain centralized architecture, which poses higher requirements for the bandwidth, latency, and transmission stability of in-vehicle communication networks. To meet the real-time requirements of intelligent vehicle communication networks, in-vehicle Ethernet urgently needs to transmit data with bounded latency and low jitter. For this purpose, the IEEE 802.1 TSN working group has developed the time-sensitive network (TSN) standard for the IEEE 802.3 network, allowing deterministic real-time communication through standard Ethernet.

[0003] In the field of in-vehicle TSN simulation modeling, although the work of Park et al. focused on the traffic characteristic analysis in the audio-video bridging network, discussing audio-video bridging traffic, best-effort traffic, and scheduled traffic, it did not directly address the scheduling of TSN traffic. Heise et al. proposed a simulation framework based on the frame preemption mechanism. However, this framework lacks consideration in time synchronization, limiting its applicability in the TSN environment and only focusing on the exploration of non-time-sensitive functions. Steinbach et al. extended the INET framework based on the OMNeT++ simulation platform and incorporated the time-triggered Ethernet model. Although their results were verified under the CoRE4INET architecture, it only achieved specific extensions of time-triggered Ethernet and did not fully meet the standard requirements of TSN. Currently, the key issue in TSN research is traffic scheduling. Although existing TSN simulation modeling research has achieved some results, there are still obvious limitations and deficiencies, including the failure to fully cover all standard requirements of TSN, the inability to flexibly and conveniently switch traffic scheduling algorithms, complex modeling means, low reusability, etc. Summary of the Invention

[0004] The technical problem to be solved by the present invention: Existing TSN simulation modeling methods cannot fully cover all standard requirements of TSN and cannot flexibly and conveniently switch traffic scheduling algorithms.

[0005] To solve the above technical problems, the present invention provides a simulation modeling method for in-vehicle time-sensitive network based on digital twin, including the following steps:

[0006] Step 1, according to the actual in-vehicle communication network scenario, extract the key nodes in the in-vehicle communication network topology, establish a network topology model, and establish a corresponding in-vehicle communication network model in OMNeT++ according to the network topology model;

[0007] Step 2: Use a time-aware shaping mechanism to prioritize different types of information flows in the vehicular communication network model;

[0008] Step 3: Set the parameters of the information flows in the vehicular communication network model; Write the TSN network topology information into a topo.csv file, and then write the parameters of the information flows into a task.csv file; The parameters include period, deadline, and packet size;

[0009] Step 4: Select the scheduling algorithm to be evaluated. After compiling and running the topo.csv and task.csv files, use the TSNKit solver to generate a gating list information file corresponding to each algorithm;

[0010] Step 5: Write the obtained gating list GCL information into an.xml configuration file of the OMNeT++ simulation platform, modify and save the routing and simulation running time limit of OMNeT++, build a digital twin simulation model of the actual vehicular network, and use the digital twin simulation model to monitor the TSN communication network in real time;

[0011] Step 6: Collect the simulation results of each information flow output by OMNeT++, including end-to-end delay and packet loss rate, calculate the average delay and packet loss situation, draw a statistical graph, and evaluate the traffic scheduling algorithm according to the statistical results of the statistical graph.

[0012] In the aforementioned method for simulating and modeling a vehicular time-sensitive network based on digital twin, in Step 1, the key nodes include various sensors, radars, domain controllers, and switches.

[0013] In the aforementioned method for simulating and modeling a vehicular time-sensitive network based on digital twin, in Step 1, the vehicular communication network topology adopts a TSN network topology structure. The TSN network architecture includes network nodes, TSN switches, and transmission links, and a ring-shaped TSN network topology is adopted.

[0014] In the aforementioned method for simulating and modeling a vehicular time-sensitive network based on digital twin, in Step 2, the different types of information flows include time-triggered traffic, priority traffic, and best-effort traffic.

[0015] In the aforementioned method for simulating and modeling a vehicular time-sensitive network based on digital twin, in Step 3, the parameters include period, deadline, and packet size.

[0016] In the aforementioned method for simulating and modeling a vehicular time-sensitive network based on digital twin, in Step 6, the statistical results include average delay and jitter duration.

[0017] For the foregoing method for simulating and modeling a vehicle-mounted time-sensitive network based on digital twin, in step 6, the method for calculating the end-to-end delay of data packets is as follows:

[0018]

[0019] Among them, 、 、 、 respectively represent the end-to-end delay, transmission delay, processing delay, and propagation delay.

[0020] For the foregoing method for simulating and modeling a vehicle-mounted time-sensitive network based on digital twin, in step 6, a statistical chart is drawn using the plotting method of Python or Matlab, and the statistical chart is a line chart.

[0021] A computer system includes a memory, a processor, and a computer program stored on the memory. The processor executes the computer program to implement the steps of the above method.

[0022] A computer-readable storage medium stores a computer program thereon. When the computer program is executed by a processor, the steps of the above method are implemented.

[0023] The beneficial effects achieved by the present invention: In the method of the present invention, digital twin is introduced into TSN modeling, which can make the simulation model adapt to various application scenarios, flexibly apply different traffic scheduling algorithms, can be used to compare and evaluate the performance of different traffic scheduling algorithms, and can monitor the information flow transmission process in real time and dynamically. And integrating TSNKit into the OMNeT++ simulation platform can not only give full play to the powerful solving function of TSNKit, but also make use of OMNeT++ to supplement the shortcomings of TSNKit in the incomplete evaluation of traffic scheduling algorithms and the inability to observe the information flow transmission process in real time and dynamically.

[0024] The method of the present invention can adapt to various application scenarios, flexibly apply different traffic scheduling algorithms, and can be used to compare and evaluate the performance of different traffic scheduling algorithms; there are many types of traffic scheduling algorithms that can be evaluated, including static routing scheduling algorithms, dynamic routing scheduling algorithms, wait-free transmission algorithms, etc. Description of the Drawings

[0025] Figure 1 is a flowchart of a method for simulating and modeling a vehicle-mounted time-sensitive network based on digital twin in Embodiment 1 of the present invention;

[0026] Figure 2 is a schematic diagram of a ring network topology structure in Embodiment 1 of the present invention;

[0027] Figure 3It is a schematic diagram of the in-vehicle communication network delay model in Embodiment 1 of the present invention;

[0028] Figure 4 It is the in-vehicle communication network topology diagram established in OMNeT++ in Embodiment 1 of the present invention;

[0029] Figure 5 It is the effect diagram of the end-to-end delay situation simulated by the HLS scheduling algorithm;

[0030] Figure 6 It is the effect diagram of the end-to-end delay situation simulated by the JRS-NW scheduling algorithm. Detailed implementation manners

[0031] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings of the specification.

[0032] Embodiment 1

[0033] As Figure 1 shown, this embodiment provides a simulation modeling method for an in-vehicle time-sensitive network based on digital twin, including the following steps:

[0034] Step 1, according to the actual in-vehicle communication network scenario, extract the key nodes in the in-vehicle communication network topology, including various sensors, radars, domain controllers, switches, etc., establish a network topology model, and establish a corresponding in-vehicle communication network model in OMNeT++ according to the network topology model, as Figure 4 shown;

[0035] The in-vehicle communication network topology adopts a TSN network topology structure. The TSN network architecture includes network nodes, TSN switches, and transmission links. A ring-shaped TSN network topology is adopted. In the ring-shaped topology structure, each network device is connected to two adjacent devices to form a ring-shaped topology. This layout not only realizes the two-way transmission of data packets but also opens up redundant paths for data transmission, significantly improving the fault tolerance of the network, as Figure 3 shown.

[0036] OMNeT++ is an open-source network simulation framework based on the C++ language, suitable for the construction, simulation, and analysis of various communication networks and distributed systems. The simulation model in the framework consists of multiple independent modules, and each module undertakes a specific function, enhancing the flexibility and reusability of the simulation model.

[0037] Step 2: According to the IEEE 802.1 Qbv standard and combined with the actual in-vehicle communication network scenario, a time-aware shaping mechanism is adopted to classify the priority levels of different types of information flows in the in-vehicle communication network model, so as to ensure the delay-sensitive characteristics of high-priority information flows and at the same time ensure the effective transmission of data of other priority information flows. The different types of information flows include time-triggered traffic (TT), scheduled traffic or credit-based shaper traffic (CBS), best effort traffic (BE), etc., as shown in Table 1.

[0038] Table 1 Classification of Information Flow Priority Levels

[0039]

[0040] An information flow is data that is periodically transmitted from one node to another node. The end-to-end delay refers to the time difference between when the data is sent from the source node and received by the terminal node. The end-to-end delay in the TSN data transmission process mainly consists of three parts: transmission delay, processing delay, and propagation delay. Among them, the propagation delay refers to the time required for the data to be transmitted on the link between different link devices, which is affected by factors such as the packet size, transmission link length, and transmission rate; the processing delay refers to the processing time of the TSN switch for the packet, and this delay is related to the hardware performance and is an inherent characteristic of the switch; the transmission delay involves the queuing and scheduling waiting time of the packet in the switch and is affected by factors such as the scheduling mechanism and the packet size. The present invention focuses on the end-to-end delay performance of various information flows under different scheduling mechanisms.

[0041] Step 3: Set the parameters of the information flows in the in-vehicle communication network model, as shown in Table 2, list 6 information flows, two pieces of data for each information flow, a total of 12 pieces of data;

[0042] Table 2 6 Information Flows and Corresponding Parameters

[0043] Write the TSN network topology information into a topo.csv file, and then write the parameters of the 12 information flows into a task.csv file; the parameters include period, deadline, packet size, etc.

[0044] Step 4: Select the scheduling algorithm to be evaluated. After compiling and running the topo.csv and task.csv files, use the TSNKit solver (Time-Sensitive Network Toolkit) to generate the gate control list information file corresponding to each algorithm. In this embodiment, two scheduling algorithms, namely heuristic list scheduler (HLS) and joint routing and scheduling-no waiting (JRS-NW), are exemplified for deployment.

[0045] Step 5: Write the obtained gate control list (GCL) information into the.xml configuration file of the OMNeT++ simulation platform, modify and save the configurations such as routing and simulation running time limit of OMNeT++, build a digital twin simulation model of the actual vehicular network, and use the digital twin simulation model to monitor the TSN communication network in real time;

[0046] The gate control list defines the opening and closing times of each queue's gate. In addition to the opening and closing states of the gate, it also includes the duration for which each gate's opening and closing state persists. The waiting time generated by the scheduling part composed of the gate and the traffic scheduling algorithm is the transmission delay of the data packet.

[0047] Based on the network topology model and the information flow classification model, a digital twin network of the intelligent vehicle TSN was established on the OMNeT++ 5.6.1 simulation platform, as Figure 4 shown. Multiple key components are deployed in this digital twin network, including sensors, a domain control unit (ZCU), TSN switches, and functional domain control units (MDC, CDC). The main sensors include two millimeter-wave radars (radar1, radar2), two lidars (lidar1, lidar2), and two cameras (camera1, camera2), which are responsible for collecting real-time data of the vehicle's surrounding environment. These sensors are connected to ZCU1 and ZCU2 through TSN switches (switch1 to switch4) to form the backbone network for data transmission and processing. All information flow senders continuously send messages according to the periods in Table 1, and the ports of each TSN switch are opened or closed on time according to the GCL list calculated by the scheduling algorithm to achieve the scheduling of information flows with different priorities.

[0048] Step 6: Collect the simulation results of each information flow output by OMNeT++, including end-to-end delay and packet loss rate, etc., and calculate the average delay and packet loss situation, as shown in Table 3:

[0049] Table 3 shows the latency and jitter of 12 information flows when the HLS algorithm and the JRS-NW algorithm are deployed respectively.

[0050] Table 3 End-to-end latency of the TSN digital twin network simulation

[0051] Draw a line chart, as Figure 5 and Figure 6 shown. Evaluate the traffic scheduling algorithm according to the statistical results of the line chart. The statistical results include average latency and jitter duration.

[0052] Analyzing the above charts, it can be seen that in this embodiment, the average latency of the 12 information flows under the two algorithms is generally close. For the high-priority 1ms control information flow and 2ms control information flow, both algorithms can ensure zero jitter and low latency. For the entertainment-related information flows with lower priority and the camera information flow with the largest data packets, although the latency is significantly higher than that of the control information flow, stable and low latency can still be ensured. In terms of jitter, all the jitter phenomena of the information flows are concentrated in the initial stage of the simulation and tend to be stable after about 0.1s, and no more jitter occurs. The information flows under the HLS algorithm perform better. Especially for the millimeter-wave radar information flow, both the jitter magnitude and the jitter duration are smaller than those of the JRS-NW. In the control information flow and the camera information flow, the jitter performances of the two are basically the same.

[0053] As Figure 2 shown, to study the end-to-end latency performance of various information flows under different scheduling algorithms, the calculation method of the end-to-end latency of data packets is as follows:

[0054]

[0055] Among them, , , , respectively represent the end-to-end delay, transmission delay, processing delay, and propagation delay.

[0056] In this embodiment, TSNKit is integrated and applied to the OMNeT++ simulation platform, which can not only give full play to the powerful solving function of TSNKit, but also make up for the shortcomings of TSNKit in the incomplete evaluation of traffic scheduling algorithms and the inability to observe the information flow transmission process in real time and dynamically by using OMNeT++.

[0057] Example 2

[0058] A method for simulating and modeling a vehicle-mounted time-sensitive network based on digital twin, comprising the following steps:

[0059] Step 1, according to the actual vehicle-mounted communication network scenario, extract the key nodes in the vehicle-mounted communication network topology, establish a network topology model, and establish a corresponding vehicle-mounted communication network model in OMNeT++;

[0060] Step 2, use the time-aware shaping mechanism to classify the priority of different types of information flows in the vehicle-mounted communication network model;

[0061] Step 3, set the parameters of the information flows in the vehicle-mounted communication network model; write the TSN network topology information into a topo.csv file, and then write the parameters of the information flows into a task.csv file; the parameters include period, deadline, and packet size;

[0062] Step 4, select the scheduling algorithm to be evaluated. After compiling and running the topo.csv and task.csv files, generate the corresponding gate control list information file for each algorithm through the TSNKit solver;

[0063] Step 5, write the obtained gate control list GCL information into an.xml configuration file of the OMNeT++ simulation platform, modify and save the routing and simulation running time limit of OMNeT++, build a digital twin simulation model of the actual vehicle network, and use the digital twin simulation model to monitor the TSN communication network in real time;

[0064] Step 6, collect the simulation results of each information flow output by OMNeT++, including end-to-end delay and packet loss rate, calculate the average delay and packet loss situation, draw a statistical chart, evaluate the traffic scheduling algorithm according to the statistical results of the statistical chart, and the statistical results include average delay and jitter duration. The evaluation of the simulation results can be carried out by using methods such as Python and Matlab plotting.

[0065] Example 3

[0066] A computer system, comprising a memory, a processor, and a computer program stored on the memory, and the processor executes the computer program to implement the steps of the method in Example 1.

[0067] Example 4

[0068] A computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method in Example 1 are implemented.

[0069] A computer program product includes a computer program, characterized in that when the computer program is executed by a processor, it implements the steps of the method described in Embodiment 1.

[0070] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0071] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implements the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0072] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Therefore, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0073] The above is only the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and modifications can be made, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. A vehicle-mounted time-sensitive network simulation modeling method based on digital twins, characterized in that: The following steps are involved: Step 1: According to the actual vehicle communication network scenario, extract the key nodes in the vehicle communication network topology, establish a network topology model, and establish the corresponding vehicle communication network model in OMNeT++ according to the network topology model; Step 2: Use a time-aware shaping mechanism to prioritize different types of information flows in the vehicle communication network model; Step 3: Set various parameters of the information flow in the vehicle communication network model; write the TSN network topology information into a topo.csv file, and then write the parameters of the information flow into a task.csv file; Step 4: Select the scheduling algorithm to be evaluated, compile and run the topo.csv and task.csv files, and use the TSNKit solver to generate the gating list information file corresponding to each algorithm. Step 5: Write the obtained gated list GCL information into the .xml configuration file of the OMNeT++ simulation platform, modify and save the routing and simulation runtime of OMNeT++, build a digital twin simulation model of the actual vehicle network, and use the digital twin simulation model to monitor the TSN communication network in real time; Step 6: Collect the simulation results of each information flow output by OMNeT++, including end-to-end delay and packet loss rate, calculate the average delay and packet loss, draw statistical graphs, and evaluate the traffic scheduling algorithm based on the statistical results of the statistical graphs.

2. According to the digital twin-based vehicle-mounted time-sensitive network simulation modeling method of claim 1, it is characterized in that: In step 1, the key nodes include various sensors, radars, domain controllers and switches.

3. According to the digital twin-based vehicle-mounted time-sensitive network simulation modeling method of claim 1, it is characterized in that: In step 1, the vehicle communication network topology adopts the TSN network topology structure. The TSN network architecture includes network nodes, TSN switches and transmission links, and adopts a ring TSN network topology.

4. The vehicle-mounted time-sensitive network simulation modeling method based on digital twin according to claim 1 is characterized in that: In step 2, the different types of information flows include time-triggered traffic, priority traffic, and best-effort traffic.

5. The vehicle-mounted time-sensitive network simulation modeling method based on digital twin according to claim 1 is characterized in that: In step 3, the parameters include period, deadline, and data packet size.

6. The vehicle-mounted time-sensitive network simulation modeling method based on digital twin according to claim 1 is characterized in that: In step 6, the statistical results include average delay and jitter duration.

7. The vehicle-mounted time-sensitive network simulation modeling method based on digital twin according to claim 1 is characterized in that: In step 6, the end-to-end delay of the data packet is calculated as follows: ； in, , , , They represent end-to-end delay, transmission delay, processing delay, and propagation delay respectively.

8. The vehicle-mounted time-sensitive network simulation modeling method based on digital twin according to claim 1 is characterized in that: In step 6, a statistical graph is drawn using Python or Matlab drawing method, and the statistical graph is a line statistical graph.

9. A computer system comprising a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method according to any one of claims 1 to 7.

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