A method for determining delayed transmission in time-varying networks using digital-real fusion testing

Abstract

The present invention discloses a method for determining delayed transmission in a time-varying network for digital-real fusion testing. The hardware part of the method is implemented using Xilinx's Artix7 chip, and the software part is implemented using a digital test system developed by Unity software. The method specifically includes: multi-dimensional test environment construction, specifically including building a full physical simulation field, setting multi-dimensional test targets, and configuring dynamic test data; dynamic congestion network construction, including a digital-real communication network composed of physical communication nodes built based on FPGA and digital twin nodes with data communication functions, while configuring the corresponding functions of the nodes and setting the status of the dynamic nodes; multi-agent model construction, specifically including a multi-agent network model, a multi-agent deep reinforcement learning algorithm model, and a traffic scheduling mechanism. The present invention can achieve stable delayed communication in the digital-real communication network and can effectively support the efficient operation of the digital-real fusion test.

Description

Technical Field

[0001] The present invention belongs to the technical field of digital-real fusion, and in particular relates to a method for determining delayed transmission in a time-varying network through digital-real fusion testing. Background Art

[0002] Equipment research and development is a comprehensive project involving multiple disciplines and fields. From design and manufacturing to testing and deployment, each link requires precise calculations and rigorous testing to ensure the performance and safety of the equipment. With the widespread application of digital and intelligent technologies, the combination of digital testing and physical testing has become an important trend. Based on semi-physical simulation, digital-physical fusion testing further emphasizes the integration of physical testing and digital testing, fully considering the influence of environmental factors, and constructing a comprehensive physical field testing environment and multi-dimensional precise scene-object model. During the testing process, emphasis is placed on the fusion analysis of physical test data and digital test data to improve the accuracy and credibility of the test.

[0003] Digital-physical fusion testing requires data communication between the digital domain and the physical domain, with digital communication nodes and physical communication nodes interconnected to form a digital-physical communication network. The prerequisite for implementing digital-physical fusion testing is that the digital domain and the physical domain must maintain a high degree of consistency, which requires stable information transmission with low latency between different nodes, that is, delayed deterministic communication. However, in the actual digital-physical fusion testing process, due to the large amount of test tasks and limited communication resources, there are congested nodes in the digital-physical communication network, and as the test objectives change, the congested nodes will change. Communication node congestion means that the amount of data transmitted at this time is greater than the bandwidth that the communication protocol or communication equipment can withstand, resulting in data queuing or even data loss, which will lead to inconsistency or even loss of multi-source test data.

[0004] The current solutions to this problem can be roughly divided into two categories. One is to classify the data that needs to be transmitted according to priority, and then allocate communication resources to ensure the transmission of high-priority data first, and then transmit low-priority data after the high-priority data transmission is completed. This type of method can achieve delayed deterministic transmission of high-priority data, but in the context of digital-physical fusion testing, almost all test data have strong consistency and real-time requirements. The second method is to plan another transmission path for the data that needs to be transmitted, and allocate the congested data flow to other relay nodes for transmission to the destination. This scheduling method achieves load balancing of each node in the communication network to a certain extent, but when scheduling traffic for congested nodes, it may affect the normal function of the scheduled nodes. In addition, different data are transmitted from different paths, and the delays in reaching the terminal are also different, which cannot effectively meet the requirements of multi-source data transmission consistency. Summary of the Invention

[0005] The technical problem to be solved by the present invention is: to provide a method for determining the delay transmission of a time-varying network in a digital-real fusion test, which can realize the delay deterministic transmission of a digital-real communication network in a digital-real fusion test.

[0006] The present invention solves the technical problem by adopting the following technical solution: a method for determining delayed transmission in a time-varying network by using digital-real fusion testing, comprising:

[0007] Step 1: Build a full physical simulation environment, including:

[0008] a. Based on the actual operating scenario of the equipment to be tested, a full physical simulation field environment is constructed using a digital simulation system;

[0009] b. Systematically configure the various test targets for the equipment under test, including: configuring the mechanism model of the test function to support the needs of different test targets; clarifying the test scenarios, including environmental conditions, operating procedures, and potential interference factors; formulating detailed test requirements covering performance indicators, durability testing, and reliability assessment; and establishing evaluation criteria for test results. c. After completing the configuration of the full physical simulation field environment and various test targets, construct dynamic test data, including: generating corresponding test data for different test targets to ensure coverage of all possible operating conditions and extreme conditions; then generating different volumes of test data based on the test granularity and precision requirements; and dynamically adjusting test parameters through real-time data monitoring and feedback mechanisms.

[0010] Step 2: Build a dynamic congestion network, including:

[0011] a. Build a digital-physical communication network, which consists of digital nodes, physical nodes, and corresponding communication protocols and clock synchronization protocols;

[0012] b. Node Function Configuration: Based on the design specifications and actual operating conditions of the equipment under test, the functional characteristics of each node are analyzed one by one. Using digital simulation technology, the functional modules of each node are reconstructed to ensure that they accurately reflect actual operating behavior in the simulation environment. For each functional node, the communication resources consumed, such as bandwidth requirements, are analyzed when performing specific tasks. Subsequently, a node evaluation model is established to analyze resource utilization and node function execution.

[0013] c. Congestion node setting: Dynamically generate congestion nodes in the digital communication network constructed above according to the test content and objectives;

[0014] Step 3: Build a multi-agent model, including:

[0015] For the digital nodes and physical nodes in the digital-physical communication network, corresponding agent models are constructed. The function of this agent model is to ensure the normal operation of the node test function and participate in the decision-making of the network traffic scheduling plan; multiple agent models are interconnected, and then a multi-agent deep reinforcement learning model is constructed to train the digital-physical communication network to realize the data traffic scheduling function of congested nodes without affecting the operation of node functions.

[0016] The present invention is implemented using Xilinx's Artix7 chip and Unity software.

[0017] The beneficial effects of the present invention compared with the prior art are:

[0018] (1) By constructing a multi-dimensional digital-physical communication network in digital-physical fusion testing, a comprehensive digital test scenario is constructed by combining multi-target test scenarios and full physical field models, so that the results of digital testing have a high degree of credibility.

[0019] (2) A traffic scheduling method based on multi-agent reinforcement learning is proposed. When facing network congestion, traffic scheduling can be performed and delayed deterministic transmission can be achieved without affecting the normal operation of nodes.

[0020] (3) A traffic scheduling method is proposed when the network faces dynamic congested nodes, which improves the universality and reliability of digital communication networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is an architectural diagram of a method for determining delayed transmission in a time-varying network using digital-real fusion testing according to the present invention. DETAILED DESCRIPTION

[0022] The present invention will be described in further detail below with reference to the accompanying drawings.

[0023] This invention relates to a method for deterministic time-delay transmission in a time-varying network for digital-physical fusion testing. This method, implemented using Xilinx's Artix7 FPGA chip and Unity software, includes the construction of a multidimensional test environment, a dynamically congested network, and a multi-agent model. This method addresses deterministic time-delay transmission in digital-physical communication networks for digital-physical fusion testing of complex equipment in various fields. This method enables deterministic time-delay transmission in digital-physical communication networks under dynamically congested nodes.

[0024] Figure 1 This is a diagram of the architecture of a method for determining delayed transmission in a time-varying network using digital-real fusion testing. Figure 1 Describe the transmission method. The transmission method includes:

[0025] Step 1: Build a full physical simulation environment. The details are as follows:

[0026] a. Based on the actual operating scenarios of the equipment under test, a fully physical simulation environment is constructed using a digital simulation system. This process involves configuring relevant mechanism models to reproduce the physical test scenario as realistically as possible. For example, for aircraft testing, an aerodynamic model can be used to simulate the effects of airflow during flight. By accurately simulating various physical factors in the environment, including temperature, pressure, and humidity, and using high-precision sensor data and historical operational data to adjust model parameters, the simulation results are made more reliable and effective.

[0027] b. Systematically configure the various test functions of the equipment to be tested. This process specifically involves: configuring the mechanism model of the test function to support the needs of different test objectives. For example, for performance testing, a dynamic model can be configured, while for durability testing, a material fatigue model may be required; clarifying the test scenario, including environmental conditions (such as temperature range, humidity level), operating procedures (such as specific steps for starting, running, and stopping), and potential interference factors (such as electromagnetic interference, vibration, etc.), to ensure that all factors that may affect the test results are taken into account; formulating detailed test requirements covering performance indicators, durability testing, and reliability evaluation; establishing evaluation criteria for test results, clarifying the performance indicators of the equipment to be tested under different working conditions, such as response time, output power, stability, etc., to ensure that the test results are comparable and repeatable, providing a solid foundation for subsequent analysis.

[0028] c. After completing the configuration of the full physical simulation field environment and various test targets, dynamic test data is constructed. This process includes: generating corresponding test data for different test targets to ensure coverage of all possible operating conditions and extreme conditions, such as high temperatures and strong magnetic field interference for aerospace equipment; then, based on the test granularity and precision requirements, generating different volumes of test data to facilitate in-depth analysis and comparison; and dynamically adjusting test parameters through real-time data monitoring and feedback mechanisms to improve test accuracy and comprehensiveness.

[0029] Step 2: Build a dynamic congestion network. The specific process is as follows:

[0030] a. Build a digital-physical communication network. Build a digital-physical communication network in the full physical field simulation environment described in step 1 above. This network can also be called a digital-physical fusion test synchronization network, which consists of digital nodes, physical nodes, and corresponding communication protocols and clock synchronization protocols. Physical nodes are communication units based on FPGA technology that support TCP / UDP protocols for data transmission; digital nodes are virtual communication units created in Unity software that have communication capabilities and simulate the communication resource status and functions of physical nodes. Physical nodes and digital nodes are interconnected to form a complete digital-physical communication network.

[0031] b. Node Function Configuration. Based on the design specifications and actual operational performance of the equipment under test, the functional characteristics of each node are analyzed individually. Using digital simulation technology, the functional modules of each node are reconstructed to ensure that they accurately reflect actual operational behavior in the simulation environment. For each node, the communication resources consumed, such as bandwidth requirements, are analyzed when performing specific tasks. A node evaluation model is then established to analyze resource utilization and the performance of node functions.

[0032] c. Congestion node configuration. Congestion nodes are dynamically generated in the aforementioned digital communication network based on the test content and objectives. These nodes are configured to transmit large amounts of test data, causing data from other nodes to experience data congestion when passing through them, resulting in significant communication delays and packet loss.

[0033] Step 3: Build a multi-agent model. The details are as follows:

[0034] A multi-agent model (MAM) is a distributed artificial intelligence model composed of multiple intelligent agents, each capable of autonomous decision-making and interaction. They can collaborate, compete, or negotiate with each other to complete common or individual tasks. For digital and physical nodes in a digital-to-physical communication network, corresponding agent models are constructed. These agent models ensure the proper functioning of node testing functions and monitor their operational status in real time. For example, the agent models can track every step of data collection, processing, and transmission to promptly identify potential problems. Furthermore, the agent models can participate in network traffic scheduling decisions: by analyzing the current network status, they can rationally allocate communication resources and optimize data transmission paths. Multiple agent models are interconnected, and inter-agent communication mechanisms are designed to enable them to share information, such as node load status and data transmission latency. Through collaboration, the agent models can collectively make more effective scheduling decisions. Subsequently, a multi-agent deep reinforcement learning model is constructed. The input of the model is the full physical field simulation environment state, the digital-physical communication network topology structure, the digital-physical communication network traffic data state, the test target, etc. Then, the full physical field simulation environment is used as the training environment for reinforcement learning. By dynamically setting the data traffic, the digital-physical communication network is trained to realize the data traffic scheduling function of congested nodes without affecting the functional operation of the node. The trained multi-agent deep reinforcement learning model is used as the output. The trained multi-agent deep reinforcement learning model is used to perform traffic scheduling for dynamic congested nodes during the actual digital-physical fusion test, thereby reducing data transmission delay and packet loss rate, and achieving a certain communication delay.

[0035] In summary, this invention discloses a method for determining delayed transmission in a time-varying network for digital-physical fusion testing. The method involves constructing a multidimensional test environment, a dynamically congested network, and a multi-agent model. This method enables traffic scheduling for congested nodes in a digital-physical communication network under dynamic congestion conditions without interfering with the nodes' normal testing functions. This method can improve the consistency and reliability of digital-physical communication networks in complex dynamic environments, effectively supporting the results of digital-physical fusion testing.

[0036] The contents not described in detail in the specification of the present invention belong to the prior art known to those skilled in the art.

[0037] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for determining delayed transmission in a time-varying network by using digital-real fusion testing, characterized in that: The following steps are involved: Step 1: Build a full physical simulation environment, including: a. Based on the actual operating scenarios of the equipment to be tested, a full-physical simulation field environment is constructed using a digital simulation system. b. Systematically configure the various test targets of the equipment to be tested, including: configuring the mechanism model of the test function to support the needs of different test targets; clarifying the test scenarios, including environmental conditions, operating procedures, and potential interference factors; formulating detailed test requirements covering performance indicators, durability testing, and reliability assessment; and establishing evaluation criteria for test results. c. After completing the configuration of the full-physical simulation field environment and various test targets, dynamic test data is constructed, including: generating corresponding test data for different test targets to ensure coverage of all possible operating conditions and extreme conditions; then generating different volumes of test data based on the requirements of test granularity and precision; and dynamically adjusting test parameters through real-time data monitoring and feedback mechanisms. Step 2: Build a dynamic congestion network, including: a. Build a digital-physical communication network, which consists of digital nodes, physical nodes, and corresponding communication protocols and clock synchronization protocols; b. Node Function Configuration: Based on the design specifications and actual operating conditions of the equipment under test, the functional characteristics of each node are analyzed one by one. Using digital simulation technology, the functional modules of each node are reconstructed to ensure that they accurately reflect actual operating behavior in the simulation environment. For each functional node, the communication resources consumed, such as bandwidth requirements, are analyzed when performing specific tasks. Subsequently, a node evaluation model is established to analyze resource utilization and node function execution. c. Congestion node setting: Dynamically generate congestion nodes in the digital communication network constructed above according to the test content and objectives; Step 3: Build a multi-agent model, including: For the digital nodes and physical nodes in the digital-physical communication network, corresponding agent models are constructed. The function of this agent model is to ensure the normal operation of the node test function and participate in the decision-making of the network traffic scheduling plan; multiple agent models are interconnected, and then a multi-agent deep reinforcement learning model is constructed to train the digital-physical communication network to realize the data traffic scheduling function of congested nodes without affecting the operation of node functions.

2. The method for determining delayed transmission in a time-varying network by using digital-real fusion testing as claimed in claim 1, wherein: Step 1 includes: configuring relevant mechanism models to reproduce physical test scenarios; simulating various physical factors in the environment, including temperature, pressure, and humidity.

3. The method for determining delayed transmission in a time-varying network by using digital-real fusion testing as claimed in claim 1, wherein: The physical node is a communication unit based on FPGA technology that supports TCP / UDP protocol for data transmission; the digital node is a virtual communication unit created in Unity software that has communication capabilities and simulates the communication resource status and functions of the physical node. The physical node and the digital node are interconnected to form a complete digital-physical communication network.

4. The method for determining delayed transmission in a time-varying network using digital-real fusion testing according to claim 1, wherein: The method for determining delayed transmission in a time-varying network by using digital-real fusion testing is implemented using Xilinx's Artix7 chip and Unity software.

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