A Hardware-in-the-Loop Simulation Experiment Online Calibration System and Method

Abstract

The present invention discloses a semi-physical simulation experiment online calibration system and method, belonging to the field of digital simulation technology. The system includes a Monte Carlo equipment experiment platform for configuring experiment parameters, providing key sample data for digital simulation, and generating random interference parameters; a semi-physical simulation integrated management system for scheduling and managing the semi-physical simulation system, conducting semi-physical simulation experiments on the semi-physical simulation system using the key sample data for digital simulation according to the experiment plan, and loading random interference parameters during the experiment operation; and a digital simulation system for realizing the visualization of the experiment process. The present invention realizes the seamless integration of the key parameter module of the experimental equipment, the pneumatic analysis module, and the equipment simulation model in the environment, and develops and designs a Monte Carlo equipment test platform to complete the rapid design of the equipment Monte Carlo experiment and further complete the systematic large-sample simulation experiment.

Description

Technical Field

[0001] The present invention belongs to the technical field of digital simulation, and particularly relates to a semi-physical simulation experiment online verification system and method. Background Art

[0002] Semi-physical simulation is strongly restricted by simulation hardware and canned digital models, resulting in complex simulation experiment organization and insufficient flexibility. To address this issue, domestic efforts mainly focus on the intelligent transformation of simulation framework software and the generalization modeling technology of simulation models to improve the scheduling and management efficiency of semi-physical simulation models. Since the mid- to late 1990s, driven by the development of various new types of aircraft, a number of semi-physical simulation laboratories with relatively high levels and large scales have carried out equipment application simulation experiments based on semi-physical simulation technology, breaking through a large number of key technologies, and widely applying advanced simulation technologies such as distributed interactive simulation and virtual reality. Machine learning provides analysis means for data analysis technology, mainly studying the theory and algorithms of how to extract patterns and models from data, that is, learning algorithms, which is an inevitable product of the development of artificial intelligence to a certain stage. Many methods in data mining are derived from machine learning. Traditional machine learning algorithms such as linear discriminant analysis, logistic regression, decision trees, random forests, neural networks, support vector machines, etc. have evolved into algorithms such as deep learning, reinforcement learning, and deep reinforcement learning.

[0003] The prior art Chinese patent application CN202410186768.6 discloses a virtual-real interaction system based on Unreal Engine and semi-physical simulation, including an Unreal Engine and a semi-physical simulation system; the semi-physical simulation system includes a controller, a communication middleware, and a semi-physical simulation model; the Unreal Engine conducts data interaction with the semi-physical simulation model through the communication middleware; the Unreal Engine issues simulation control instructions through the controller and sends them to the semi-physical simulation model through the communication middleware; the semi-physical simulation model sends the model data generated during the simulation process through the communication middleware.

[0004] The prior art has the following disadvantages: 1. The simulation verification parameters of the key parameters of the pure digital model of existing equipment are usually set fixed values, without considering the actual situation of the application scenario, resulting in problems of low environmental and model accuracy. 2. During the construction of traditional semi-physical simulation models, restricted by the access to physical objects, the structure is usually fixed and the function is single, and large-scale multi-functional simulation tests cannot be carried out. 3. For large-sample pure digital experimental simulations, there are a large number of experimental factors, many dimensions, and complex interrelationships. The accurate qualitative evaluation of key experimental samples requires proof-of-concept field equipment verification experiments, and the acquisition period of evaluation results is long and the difficulty is high.

[0005] In addition, to obtain the actual equipment experiment data under random interference factors, there are two common test methods: actual equipment test experiment and computer simulation equipment test experiment. Obtaining data through actual field tests of equipment requires a large number of tests, which greatly increases the research and development cost of experimental equipment and prolongs the equipment design cycle. With the continuous development of computer technology, simulation technology has gradually become an indispensable means for the development and management of modern equipment systems. Using computer simulation technology to conduct simulation experiments on the developed equipment can quickly obtain the statistical results of the equipment test parameters required by designers at a minimal cost, providing methods and basis for equipment R & D design and performance verification. In Monte Carlo equipment experiment work, designers are required to write corresponding simulation analysis modules and data interfaces between modules, and develop corresponding equipment simulation model programs for a large number of Monte Carlo verification tests. The design tasks are heavy and error-prone. In addition, due to problems such as poor interface compatibility and difficult data interaction between different simulation modules or software tools, the scalability and reusability of models and codes are greatly restricted, further increasing the preliminary development workload of Monte Carlo simulation tests for experimental equipment. Summary of the Invention

[0006] The purpose of the present invention is to provide a semi-physical simulation experiment online calibration system and method, which partially solves or alleviates the above deficiencies in the prior art, develops and designs a Monte Carlo equipment test platform to complete the rapid design of Monte Carlo experiments on equipment, and then completes systematic large-sample simulation experiments.

[0007] To solve the above-mentioned technical problems, the present invention specifically adopts the following technical solutions:

[0008] A semi-physical simulation experiment online calibration system, comprising: a Monte Carlo equipment experiment platform, configured to configure experiment parameters, provide digital simulation key sample data, and generate random interference parameters; statistically analyze the operating parameters of the experimental equipment and the equipment evaluation results to obtain the statistical characteristic values of the key parameters of the experimental equipment; a semi-physical simulation integrated management system, configured to schedule and manage the semi-physical simulation system; the semi-physical simulation system is configured to establish a semi-physical simulation model of the experimental equipment according to the motion characteristics and motion laws of the experimental equipment; conduct experiment planning according to the experiment parameters; conduct semi-physical simulation experiments on the semi-physical simulation system using the digital simulation key sample data according to the experiment planning; and load the random interference parameters issued by the Monte Carlo equipment experiment platform during the experiment operation to obtain the perturbed operating parameters of the experimental equipment and the equipment evaluation results; a digital simulation system, configured to perform three-dimensional construction of the experimental equipment and the experimental scenario according to the semi-physical simulation model and the data during the experiment process to realize the visualization of the experiment process.

[0009] As an improvement, the Monte Carlo equipment experiment platform includes: an experimental parameter configuration module, which is used to configure the number of experimental tasks, set the number of threads to be started, and the experimental data to be configured for each task experiment; the experimental data includes the injection type and injection time of random interference; an experimental data generation module, which is used to extract key digital simulation sample data from large-sample experiments for semi-physical simulation experiments and generate random interference parameters.

[0010] As an improvement, the semi-physical simulation system is a distributed system, including several semi-physical simulation nodes; the three-dimensional experimental equipment model constructed by the digital simulation system corresponds one-to-one with the semi-physical simulation nodes. During the experiment, the digital simulation system and the semi-physical simulation system are time-synchronized.

[0011] As an improvement, data is transmitted between the semi-physical simulation nodes through fiber optic reflective memory.

[0012] As an improvement, the semi-physical simulation integrated management system includes: an experimental planning module, which is used to plan experiments according to experimental parameters and send experimental data and key digital simulation sample data to the semi-physical simulation nodes for experiments; a node scheduling module, which is used to schedule corresponding semi-physical simulation nodes to participate in experiments according to the number of threads set by the Monte Carlo equipment experiment platform.

[0013] As an improvement, the digital simulation system includes: a virtual engine, which is used to construct a three-dimensional geometric model of the experimental equipment and create a visual experimental scene, so as to realize the visual reconstruction of the equipment state; a physical engine, which is used to simulate the physical experimental scene and provide physical interaction.

[0014] The present invention also provides a method for online verification of semi-physical simulation experiments, including:

[0015] Establish a semi-physical simulation model of the experimental equipment according to the motion characteristics and motion laws of the experimental equipment;

[0016] Extract key digital simulation sample data from large-sample experiments;

[0017] Determine the random interference factors and their distribution laws suffered by the experimental equipment during operation, and generate random interference parameters according to the random interference factors and their distribution laws on the platform;

[0018] Configure experimental parameters, plan experiments according to the experimental parameters, conduct semi-physical simulation experiments on the semi-physical simulation system using key digital simulation sample data according to the experimental plan, and load random interference parameters during the experiment;

[0019] Conduct three-dimensional construction of the experimental equipment and the experimental scene according to the semi-physical simulation model and the data during the experiment to realize the visualization of the experimental process;

[0020] Obtain the operating parameters of the experimental equipment after perturbation and the equipment evaluation results;

[0021] Statistically analyze the operating parameters of the experimental equipment and the equipment evaluation results, so as to obtain the statistical characteristic values of the key parameters of the experimental equipment.

[0022] As an improvement, use a hardware-in-the-loop simulation system to establish a hardware-in-the-loop simulation model of the experimental equipment, and conduct a hardware-in-the-loop simulation experiment according to the key sample data of digital simulation; on the basis of time synchronization with the hardware-in-the-loop simulation system, the digital simulation system constructs a three-dimensional model of the experimental equipment and the experimental scenario according to the hardware-in-the-loop simulation model and the data during the experiment.

[0023] As an improvement, the steps of time synchronization between the digital simulation system and the hardware-in-the-loop simulation system include:

[0024] S101 Perform clock synchronization among the simulation nodes inside the hardware-in-the-loop simulation system;

[0025] S102 Align the start time of the digital simulation system and the hardware-in-the-loop simulation system using the UTC clock source;

[0026] S103 Under the condition that the simulation step size of the digital simulation system is consistent with that of the hardware-in-the-loop simulation system, perform clock synchronization based on step size verification, specifically including:

[0027] S1031 Initialize, set the synchronization period, set both the ready-to-synchronize flag and the synchronization flag of the digital simulation system to 0, and reset the simulation step size counter to zero;

[0028] S1032 When receiving the backward clock synchronization information of the hardware-in-the-loop simulation system, under the condition that both the ready-to-synchronize flag and the synchronization flag are 0, parse the simulation number step size value and assign it to the step size counter, and then set the ready-to-synchronize flag to 1;

[0029] Under the condition that the ready-to-synchronize flag is 1 and the synchronization flag is 0, after parsing the simulation step size number value, compare it with the current value of the step size counter. If they are equal, set the synchronization flag to 1, set the ready-to-synchronize flag to 0, and then execute step S1033; if they are not equal, set the ready-to-synchronize flag to 0 and loop to execute step S1032;

[0030] S1033 Wait until the synchronization period ends;

[0031] S1034 Align the current time of the digital simulation system and the hardware-in-the-loop simulation system using the UTC clock source;

[0032] S1035 Enter the next synchronization period, and loop to execute steps S1032 to S1035.

[0033] As an improvement, the steps for time synchronization between the digital simulation system and the hardware-in-the-loop simulation system include:

[0034] S111 Use the clock of the hardware-in-the-loop simulation system as the master clock, use the clock of the digital simulation system as the slave clock, and set the correction period;

[0035] S112 The digital simulation system receives the clock synchronization message sent by the hardware-in-the-loop simulation system, extracts the master clock time when the hardware-in-the-loop simulation system sends the synchronization message from the clock synchronization message and records it as T1;

[0036] S113 The slave clock of the digital simulation system records the arrival time T2 of the clock synchronization message;

[0037] S114 The digital simulation system sends a message, sets an identifier for starting the transmission error measurement in the message, and the slave clock records the sending time T3;

[0038] S115 After the hardware-in-the-loop simulation system receives the message with the identifier for starting the transmission error measurement, the master clock records the arrival time T4; and sets an identifier representing the completion of the transmission error measurement in the message pushed back to the digital simulation system, and records the arrival time T4 in the pushed-back message;

[0039] S116 After the digital simulation system receives the message with the identifier for completing the transmission error measurement, calculate the clock face deviation and time delay between the master and slave clocks according to T1, T2, T3, and T4, and adjust the slave clock of the digital simulation system according to the clock face deviation and time delay.

[0040] Beneficial effects:

[0041] 1. Efficient system integration and process connection. The present invention realizes the seamless integration of the key parameter module of the experimental equipment, the pneumatic analysis module and the equipment simulation model in the environment. The data interaction and collaborative work between different modules are smoother, avoiding the inefficiency caused by compatibility problems between modules or poor data transmission. For example, the results of the pneumatic analysis module can be directly used as the input parameters of the equipment simulation model, reducing manual intervention and data processing time, making the entire experimental process more compact and efficient. From the construction of the experimental environment of the experimental equipment, the generation of random configuration parameters, the rapid construction of the model to the statistical analysis of the test results, full-process simulation is realized. This means that all links of the experiment can be completed within one system without switching between multiple different systems or tools, greatly shortening the experimental cycle. For example, the Monte Carlo equipment test platform can quickly generate random configuration parameters of the experimental equipment and directly apply them to the model construction and experimental operation in the hardware-in-the-loop simulation system, achieving a seamless process and improving the overall experimental efficiency.

[0042] 2. Rapid Design and Systematic Large-Sample Simulation. The Monte Carlo equipment test platform developed and designed in the present invention can complete the rapid design of equipment Monte Carlo experiments. This platform adopts a hierarchical module design concept, where modules at different levels have clear division of labor and possess various functions such as experimental parameter configuration, data generation, and process control. Experimenters can quickly set the number of experimental tasks, the number of threads, and various experimental data, such as the injection type and injection time of random interference, etc., through this platform, thus quickly starting the experiment and saving the experimental preparation time. The present invention can complete systematic large-sample simulation tests, and verify the performance and reliability of experimental equipment through a large amount of experimental data. The Monte Carlo equipment test platform extracts key sample data of digital simulation from large-sample experiments. These data are more representative and authentic, and can more accurately reflect the situation of experimental equipment during actual operation. Experiments are carried out on the hardware-in-the-loop simulation system using these key sample data, and random interference parameters are loaded to simulate various complex actual scenarios, thereby obtaining more comprehensive and reliable experimental results.

[0043] 3. Advanced Technical Guarantee and Optimization. Multiple clock synchronization methods are adopted between the digital simulation system and the hardware-in-the-loop simulation system, such as step-size verification and satellite clock cycle alignment, reflection memory and master clock verification, etc. High-precision clock synchronization ensures the time consistency of the two systems during the experiment, avoids experimental errors and data inconsistency problems caused by time asynchronization, and improves the reliability of experimental results. For example, during real-time simulation, each simulation node can perform data interaction and calculation under the same time reference, ensuring the accuracy of the experiment.

[0044] The system has optimized designs in aspects such as communication architecture, data integration standard, and data storage. In terms of communication, the Monte Carlo equipment experiment platform and the hardware-in-the-loop simulation integrated management system conduct data interaction through the DDS communication protocol, and adopt a unified hardware-in-the-loop framework integration standard to send and receive data in the form of Json strings, ensuring the efficiency and accuracy of data transmission. In terms of data integration, clear interaction interfaces and processes are defined to ensure smooth data interaction between different systems. In terms of data storage, based on the database design pattern and transaction mechanism, concurrent access control of large data samples is realized, effectively controlling the load of CPU and I / O resources. At the same time, data visualization tools provide data support for the experiment, facilitating experimenters to manage and analyze experimental data.

[0045] The hardware-in-the-loop simulation system uses a reflective memory network and an RTX real-time system, solving the problems of traditional Ethernet and Windows systems in terms of real-time performance and clock stability. The reflective memory network has strict transmission determinacy and predictability, with small data transmission latency, high speed, and large capacity, capable of meeting the requirements of distributed real-time simulation systems for real-time performance and large-volume data transmission. The RTX real-time system has excellent real-time control, scalability, and stability, ensuring the reliability and accuracy of the simulation process.

[0046] 4. Visualization and data analysis support. The digital simulation system constructs a three-dimensional model of the experimental equipment and experimental scenarios through a virtual engine and a physics engine, realizing the visualization of the experimental process. Experimental personnel can intuitively observe the operating states and performance of the experimental equipment in different scenarios, promptly discover problems and make adjustments. For example, in the aircraft simulation experiment, experimental personnel can observe parameters such as the attitude and trajectory of the aircraft in real time through a three-dimensional visualization interface, more intuitively evaluating the experimental results.

[0047] The Monte Carlo equipment experimental platform statistically analyzes the operating parameters of the experimental equipment and the equipment evaluation results, obtaining the statistical characteristic values of the key parameters of the experimental equipment. Through the statistical analysis of a large amount of experimental data, it is possible to deeply understand the performance and reliability of the experimental equipment under different random interferences, providing a scientific basis for the research and development design of the equipment. For example, by calculating statistical characteristic values such as the mean and variance of the key parameters of the experimental equipment, the stability and consistency of the equipment can be evaluated, and thus the equipment can be optimized and improved targeted. Brief Description of the Drawings

[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to actual scale. Obviously, the following-described drawings are some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0049] Figure 1 It is a schematic diagram of the architecture of the hardware-in-the-loop simulation experiment online verification system;

[0050] Figure 2 It is a schematic diagram of the architecture of the Monte Carlo equipment experimental platform;

[0051] Figure 3 It is a data interaction flowchart of the Monte Carlo equipment experimental platform;

[0052] Figure 4 It is a schematic diagram of the architecture of the hardware-in-the-loop simulation integrated management system;

[0053] Figure 5 It is the topology diagram of the system resources of the hardware-in-the-loop simulation node system;

[0054] Figure 6 It is the data communication flow chart of the online verification system for the hardware-in-the-loop simulation experiment;

[0055] Figure 7 It is the schematic diagram of the simulation time synchronization of the online verification system for the hardware-in-the-loop simulation experiment;

[0056] Figure 8 It is the flow chart for the digital simulation system to complete clock synchronization by receiving the clock synchronization information sent by the hardware-in-the-loop simulation system;

[0057] Figure 9 It is the synchronization flow chart based on the principle of the IEEE1588 protocol;

[0058] Figure 10 It is the data interaction diagram between the Monte Carlo equipment experiment platform and the management software;

[0059] Figure 11 It is the schematic diagram of the simulation model integration based on the hardware-in-the-loop model integration management system;

[0060] Figure 12 It is the technical roadmap for storing the data of the simulation results of the large sample of the hardware-in-the-loop simulation;

[0061] Figure 13 It is the interactive link of the database for the simulation results of the large sample of the hardware-in-the-loop simulation. Specific implementation manners

[0062] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0063] In this document, suffixes such as "module", "component", or "unit" used to represent elements are only for facilitating the description of the present invention and have no specific meaning in themselves. Therefore, "module", "component", or "unit" can be used interchangeably. In this document, terms such as "upper", "lower", "inner", "outer", "front", "rear", "one end", "the other end", etc. indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for facilitating the description of the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus cannot be construed as a limitation on the present invention. In addition, terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance. In this document, unless otherwise clearly specified and defined, terms such as "installed", "provided with", "connected", etc. shall be understood in a broad sense. For example, "connected" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, a direct connection, or an indirect connection through an intermediate medium, and can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. In this document, "and / or" includes any and all combinations of one or more of the listed related items. In this document, "a plurality" means two or more, that is, it includes two, three, four, five, etc.

[0064] The basic architecture of the hardware-in-the-loop simulation experiment online verification system provided by the present invention consists of three parts: a user layer, a model layer, and a hardware layer, as Figure 1 shown. Among them, as the underlying environment, the hardware layer consists of a high-performance server or a client PC, network communication devices, information security devices, etc., and is the hardware environment foundation that supports the realization of simulation and the interaction of model data. The model layer mainly consists of simulation models of each subsystem (including equipment models, pneumatic models, control models, and visualization models) and data modules, and the integration and simulation process construction are carried out through the hardware-in-the-loop simulation integrated management system. The top layer of the environment is the user layer, including the hardware-in-the-loop simulation integrated management system, the Monte Carlo equipment experiment platform, and the digital simulation system. Among them, the hardware-in-the-loop simulation integrated management system uses fiber optic reflective memory combined with Ethernet to build a heterogeneous communication network to realize the interaction between users and the environment and the visual analysis of simulation results with the Monte Carlo equipment experiment platform and the digital simulation system.

[0065] The Monte Carlo equipment experiment platform, the hardware-in-the-loop simulation integrated management system, and the digital simulation system are introduced separately below.

[0066] I. Monte Carlo equipment experiment platform.

[0067] The Monte Carlo equipment experimental platform is used to configure experimental parameters, provide key sample data for digital simulation, and generate random interference parameters; it statistically analyzes the operating parameters of the experimental equipment and the evaluation results of the equipment to obtain the statistical characteristic values of the key parameters of the experimental equipment.

[0068] Specifically, the platform completes the functional division of the platform framework with the concept of hierarchical module design, reasonably arranges different modules in their respective appropriate layers, constructs different task modules at different layers, and uses a specific structure for data interaction between modules. Its system architecture diagram is as Figure 2 shown. The lower layer provides services for the upper layer, and the upper layer completes the functions it needs to call by invoking the lower layer interfaces. The interaction layer of the experimental platform has functions such as various experimental parameter configurations, experimental data generation, experimental process control, test progress monitoring, and experimental log management. The function layer has functions such as thread management, data management, data encapsulation / parsing, data storage, and internal communication management to support various functional requirements of the application layer. The general interface layer provides communication interfaces and interface protocols for external interaction to standardize data interaction and establish real-time communication between the experimental platform and the hardware-in-the-loop integration framework.

[0069] More specifically, the experimental parameter configuration function is implemented by the experimental parameter configuration module, which is used to configure the number of experimental tasks, set the number of threads to be started, and the experimental data to be configured for each task experiment; the experimental data includes the injection type and injection time of random interference.

[0070] The purpose of the experiment is to obtain the data generated by the experimental equipment under various interferences. Therefore, in addition to some basic parameters such as the number of tasks and the number of threads, the experimental parameter configuration module also needs to set the target position, deployment position, and the injection type and injection time of random interferences (including faults and deviations).

[0071] The experimental data generation function is implemented by the experimental data generation module, which is used to extract key sample data for digital simulation from large-sample experiments for hardware-in-the-loop simulation experiments and generate random interference parameters. The large-sample experiment is a digital simulation experiment, and its data is randomly generated, so some of the data does not conform to reality. To improve the authenticity of the experiment, the experimental data generation module is responsible for extracting the required key sample data for digital simulation from the data and injecting the key sample data for digital simulation into the hardware-in-the-loop simulation model of the experimental equipment for experiments.

[0072] The parameters of random interferences such as faults and deviations are also randomly generated by the experimental data generation module.

[0073] The process of the Monte Carlo equipment experimental platform starting an experiment is as Figure 3As shown, first start the equipment experiment platform, set the number of loop sample tests, start the experimental data random generation button, and close the data generation after the data is generated. According to the generated data, refine the configuration of each task according to the actual situation of the task. After the data configuration is completed, set the number of threads to be started to control the semi-physical integrated management system to schedule relevant simulation nodes to participate in the experiment.

[0074] After the configuration is completed, the verification experiment can be started. The experiment platform will send DDS instruction data packets according to the parameter configuration of each experiment. Send the experimental task instructions, and the semi-physical simulation integration system will call the key node resource model according to the instructions to complete the node tasks and report the task completion status. After the equipment experiment platform subscribes to the simulation result information and parses it, after all simulation nodes of the current group of tasks are completed, the platform will send the next group of equipment experiment tasks. And so on, when the test progress completion rate reaches 100%, the equipment experiment ends. If the experimental task data is abnormal, the experimental task sending thread can also be manually stopped to end the experiment. After the test is completed, the data can be visually analyzed. Give the evaluation results of the large-sample Monte Carlo equipment test.

[0075] II. Semi-physical simulation integrated management system.

[0076] The semi-physical simulation integrated management system is used to schedule and manage the semi-physical simulation system; the semi-physical simulation system is used to establish a semi-physical simulation model of the experimental equipment according to the motion characteristics and motion laws of the experimental equipment; conduct experimental planning according to the experimental parameters; conduct semi-physical simulation experiments on the semi-physical simulation system using digital simulation key sample data according to the experimental planning; and load random interference parameters during the experiment operation to obtain the operation parameters of the experimental equipment after perturbation and the equipment evaluation results.

[0077] Specifically, aiming at integrating the semi-physical simulation resources of the industrial department and flexibly constructing a semi-physical simulation environment, this embodiment constructs Figure 4 the semi-physical simulation integrated management system shown. It realizes the scheduling and management of the semi-physical simulation system through the interactive application layer and the function layer, and realizes data interaction with the Monte Carlo equipment experiment platform, the digital simulation system, and each semi-physical simulation test system through the general interface layer and the hardware interface layer, providing a bridge for the simulation model integrated management system to schedule and control the participating semi-physical simulation test systems, and supporting the development of diverse scenario joint simulation experiments combining virtual and real.

[0078] In the design of the hardware-in-the-loop simulation integrated management system, the layering and partitioning of the framework are completed with the concept of hierarchical modular design, and different modules are reasonably arranged in their respective appropriate layers. There are different functional modules in the same layer. The lower layer provides services for the upper layer, and the upper layer completes the functions required at this layer by calling the interfaces of the lower layer. The interactive application layer includes an experiment planning module, a node scheduling and control module, an experiment monitoring module, and an experiment node management module to implement experiment planning, node scheduling and control, experiment monitoring, and experiment node management, etc., for the scheduling control and monitoring of the overall operation process of the hardware-in-the-loop simulation system.

[0079] For example, the implementation planning module is used to plan experiments according to experiment parameters (set by the Monte Carlo equipment experiment platform), and send experiment data and key sample data of digital simulation to the hardware-in-the-loop simulation nodes for experiments. The Monte Carlo equipment experiment platform provides relevant experiment parameters including the number of experiments

[0080] The node scheduling module is used to schedule the corresponding hardware-in-the-loop simulation nodes to participate in experiments according to the number of threads set by the Monte Carlo equipment experiment platform.

[0081] The function layer has functions such as clock synchronization management, internal communication management, data sending and receiving management, data storage management, thread management, and dynamic filling management, etc., to support various functional requirements of the application layer. The general interface layer provides interface protocols with the digital simulation system and the Monte Carlo equipment experiment platform, as well as interface protocols with the hardware-in-the-loop simulation test system, to realize the standardization of data interaction. The hardware interface layer provides Ethernet, reflective memory network cards, and other general intelligent I / O devices to support the access of heterogeneous hardware and realize diversified hardware-in-the-loop simulation.

[0082] In this embodiment, the hardware-in-the-loop simulation system for performing hardware-in-the-loop simulation on experimental equipment is a distributed system. The distributed real-time simulation system is a complex simulation system composed of multiple subsystems (i.e., hardware-in-the-loop simulation nodes). During the simulation process, data needs to be exchanged in real time between the subsystems. Therefore, the real-time simulation system has relatively strict requirements for the data exchange ability between the hardware-in-the-loop simulation nodes. In order to obtain real-time simulation effects, the real-time requirement for data transmission is very high during the simulation process. The traditional Ethernet based on the TCP / IP protocol cannot meet the requirements. Compared with the previously used TCP / IP network, the real-time network based on optical front reflective memory not only has strict transmission certainty and predictability, but also has the characteristics of small data transmission delay, fast transmission speed, large amount of transmitted data, simple and easy-to-use communication protocol, light load of the simulator, strong adaptability of the software and hardware platform, reliable transmission error correction ability, support for interrupt signal transmission, etc. These advantages of the reflective memory network can meet the requirements for real-time performance and large amount of data transmission in the distributed real-time simulation system.

[0083] In addition, aiming at the problem that the Windows system clock is unstable and cannot be used in real-time simulation systems, a clock control mechanism based on the RTX real-time system is adopted. RTX is a general real-time extension system based on the Windows operating system. RTX has excellent real-time control, high scalability and stability, and is the only software-based hard real-time solution on the Windows platform.

[0084] As Figure 5 shown, the hardware-in-the-loop simulation system constructed based on the above technologies is a distributed simulation system composed of multiple hardware-in-the-loop simulation nodes. The hardware-in-the-loop simulation nodes are composed of a digital simulation system, a simulation management machine, a real-time simulation machine, a satellite navigation simulator, general intelligent devices, etc., and communicate through Ethernet and reflective memory networks. The main control machine and the computing node machine run the Windows + RTX system. In the real-time simulation mode, the simulation model is compiled into a RTSS real-time executable program and runs in the RTX real-time subsystem. Windows and the RTX real-time subsystem communicate using shared memory. The simulation nodes are divided into lightweight nodes and full-scale simulation nodes. The entire hardware-in-the-loop integrated management system can dynamically schedule any simulation node to fully participate in the construction of the Monte Carlo equipment experiment according to the scenario requirements.

[0085] III. Digital simulation system.

[0086] The digital simulation system is used to perform three-dimensional construction of experimental equipment and experimental scenarios based on the hardware-in-the-loop simulation model and the data during the experiment to achieve the visualization of the experimental process. Specifically, the digital simulation system further includes: a virtual engine, which is used to construct the three-dimensional geometric model of the experimental equipment and create a visual experimental scenario, thereby realizing the visual reconstruction of the equipment state; a physical engine, which is used to simulate the physical experimental scenario and provide physical interactions.

[0087] More specifically, to further increase the authenticity of the entire experiment, the experimental scenario is constructed by combining the digital virtual engine and the physical engine to visualize the entire hardware-in-the-loop simulation experimental scenario and reconstruct the real-time state of the equipment more realistically in the three-dimensional scenario. That is, a hardware-in-the-loop simulation model of the experimental equipment is established using the hardware-in-the-loop simulation system, and a hardware-in-the-loop simulation experiment is carried out according to the key sample data of the digital simulation; on the basis of time synchronization with the hardware-in-the-loop simulation system, the digital simulation system performs three-dimensional construction of the experimental equipment and experimental scenarios according to the hardware-in-the-loop simulation model and the data during the experiment.

[0088] In the experiment, the communication between the digital simulation system and the hardware-in-the-loop simulation system adopts a scheme combining DDS and optical fiber. In order to realize the communication function between the two types of systems, according to the specific requirements of data transmission in the experiment, a data interaction scheme for the virtual-real system composed of three communication classes and two communication modules is designed. The data interaction scheme is as Figure 6As shown

[0089] The Monte Carlo equipment experimental platform controls the digital simulation system and the hardware-in-the-loop simulation integrated management system simultaneously through DDS instructions. Among them, the digital simulation system constructs the three-dimensional geometric model of the equipment and the physical experiment scene through the virtual engine and the physical engine. The data of the three-dimensional equipment geometric model and the physical environment model of the experiment scene are interacted with the Monte Carlo equipment experimental platform and the hardware-in-the-loop simulation integrated management system through the DDS communication plug-in and the fiber optic reflective memory communication plug-in. The Monte Carlo equipment experimental platform is responsible for configuring various experimental task parameters. The experimental tasks include, but are not limited to, the key parameters of the experimental equipment, the key parameters of the experimental environment, and the key parameters of interference / fault injection. After configuring the relevant parameters, they are distributed to the digital system and the hardware-in-the-loop simulation system simultaneously in the form of DDS instructions. After receiving the relevant simulation experiment scene configuration parameters, the digital system initializes the experiment scene and the key parameters of the corresponding equipment geometric model for the simulation experiment initialization stage. After receiving the configuration parameters related to the relevant simulation model and the fault / interference injection parameters, the hardware-in-the-loop simulation system also initializes the corresponding hardware-in-the-loop simulation nodes.

[0090] Among them, the equipment geometric model and the hardware-in-the-loop simulation nodes are in one-to-one correspondence. The data interaction between them realizes real-time data sharing through the fiber optic reflective memory communication plug-in. During the simulation experiment, the virtual / real system clocks of the virtual system and the hardware-in-the-loop simulation system will be strictly time-synchronized through the distributed clock synchronization algorithm.

[0091] The simulation time synchronization inside the hardware-in-the-loop simulation system and between the digital simulation system and the two systems is a key technology for realizing the virtual-real combined simulation experiment. The overall simulation time synchronization scheme of the digital simulation system and the hardware-in-the-loop simulation system is as Figure 7 shown

[0092] For the running time inside the hardware-in-the-loop simulation system, the moment when the model receives the running instruction issued by the management and control software is taken as the 0 moment, and a fixed time interval is used as the start step for time verification.

[0093] The real-time fiber optic reflective memory network is a real-time network based on high-speed fiber optic network shared storage technology. Compared with traditional networking technologies, it not only has strict transmission determinacy and predictability, but also has characteristics such as high data transmission speed, simple communication protocol, light host load, and strong adaptability to software and hardware platforms. The reflection memory communication node latency is less than 1 us; the fiber optic communication rate can reach 2.125 Gbps; there is an independent DMA channel, and the write speed is not less than 100 Mbps; the real-time fiber optic reflective memory network is formed by fiber optic interface boards inserted in computers connected together by fiber optic cables to form a ring network. The on-board memory of the fiber optic interface board of each node has a copy of the shared data of other nodes. Logically, all nodes in the whole network share the same memory. Data is written at one point and updated simultaneously at multiple points, achieving high-speed data transmission and sharing in this way. The hardware-in-the-loop simulation system completes clock synchronization through the reflective memory fiber optic network inside the system, and the clock error is within 0.5 ms. Therefore, we can regard the internal time of the hardware-in-the-loop simulation system as accurate time.

[0094] The running time inside the digital system takes the moment when the running instruction data starts to be sent to the DDS channel as the 0 moment, and adopts the Tick time stepping method (there will be a situation where the set step size is 5 ms, but the actual step size is 4.99 ms). The internal time of the digital system is inaccurate and step-size controllable time. And the time acquisition method of the UE digital system is the windows computer system time. The Windows operating system uses the time slice rotation mechanism to schedule processes. The imbalance of the network is uncertain and uncontrollable. It is impossible to control when the time synchronization process is scheduled and when it stops, resulting in unpredictable delays in the network transmission of information packets, which affects the accuracy of clock synchronization. Therefore, the digital simulation system under the windows operating system cannot achieve synchronization with millisecond-level accuracy. Therefore, the simulation time synchronization between the digital simulation system and the hardware-in-the-loop simulation system is the bridge connecting the physical hardware clock and the virtual logic clock, and is the key to the clock synchronization scheme of the entire virtual-real combined simulation system.

[0095] The computer's own clock usually cannot provide a stable and highly accurate step division for the digital system, nor is it easy to achieve high-precision clock synchronization with the hardware clock of the hardware-in-the-loop simulation system. Based on the ideas of step verification and satellite clock cycle alignment, this solution designs a low-cost and high-precision virtual-real member clock synchronization method. After the hardware-in-the-loop simulation system completes fine synchronization, it outputs clock synchronization information such as simulation steps and related verification information. The digital system receives the step pulse information output by the hardware-in-the-loop as the simulation step frequency of the digital system's logic clock, receives and parses the relevant verification steps through the network interface as the simulation step of the digital system's logic clock, and uses the satellite clock cycle to verify the clocks of the hardware-in-the-loop simulation system and the digital simulation system. In this way, the logic clocks of the virtual members and the hardware clocks of the physical members have highly consistent simulation steps and values, that is, the digital simulation system and the hardware-in-the-loop simulation system complete high-precision clock synchronization.

[0096] Based on the above ideas, as Figure 8 shown, the steps for time synchronization between the digital simulation system and the hardware-in-the-loop simulation system include:

[0097] S101 The internal simulation nodes of the hardware-in-the-loop simulation system achieve clock synchronization based on the advantages of high transmission efficiency, low latency, and simple protocol of the reflective memory system. The reflective memory system has the advantages of high transmission efficiency, low latency, and simple protocol. In the hardware-in-the-loop simulation system, each simulation node uses these characteristics to achieve clock synchronization. This step lays the foundation for the high-precision clock synchronization of the entire system, ensuring that all parts within the hardware-in-the-loop simulation system are consistent in time, just like soldiers in the army first calibrate the time within their respective teams to prepare for subsequent coordinated actions.

[0098] S102 Before starting the co-simulation, use the UTC clock source to align the start times of the digital simulation system and the hardware-in-the-loop simulation system. UTC (Coordinated Universal Time) is a high-precision time standard. By using this as a reference, the two systems can be on the same time starting line at the initial moment, ensuring that the time difference is within a very small range, providing initial consistency for subsequent precise clock synchronization.

[0099] S103 In the situation where the simulation step of the digital simulation system is consistent with the simulation step of the hardware-in-the-loop simulation system, perform clock synchronization based on step verification, specifically including:

[0100] S1031 Initialization: Set the synchronization period, set both the digital simulation system's ready-to-synchronize flag and the synchronization flag to 0, and reset the simulation step counter to zero. The ready-to-synchronize flag is a status flag bit used to indicate whether the digital simulation system is ready for clock synchronization. When the ready-to-synchronize flag is 0, it indicates that the system is not ready and no key synchronization-related processing is performed. When specific hardware-in-the-loop simulation system back-pushed clock synchronization information is received and certain conditions are met, it is set to 1, indicating that the system starts to prepare for synchronization operations and prepares for key synchronization operations such as comparing step length values later.

[0101] The synchronization flag is also a status flag bit used to clarify whether the clocks of the digital simulation system and the hardware-in-the-loop simulation system have been synchronized. An initial value of 0 represents that the synchronization has not been completed. On the premise that the ready-to-synchronize flag is 1, when the numerical value of the simulation step number of the hardware-in-the-loop simulation system parsed by the digital simulation system is equal to the current value of its own step counter, the synchronization flag is set to 1, meaning that at the current moment, the digital simulation system confirms that the step lengths of the two match based on the clock synchronization information provided by the hardware-in-the-loop simulation system, and a clock synchronization process is completed.

[0102] The simulation step counter is a variable in the digital simulation system used to record and track the simulation step values.

[0103] S1032 When receiving the hardware-in-the-loop simulation system back-pushed clock synchronization information, in the case where both the ready-to-synchronize flag and the synchronization flag are 0, parse the simulation number step value and assign it to the step counter, and then set the ready-to-synchronize flag to 1;

[0104] In the case where the ready-to-synchronize flag is 1 and the synchronization flag is 0, after parsing the simulation step number value, compare it with the current value of the step counter. If they are equal, set the synchronization flag to 1, set the ready-to-synchronize flag to 0, and then execute step S1033; if they are not equal, set the ready-to-synchronize flag to 0 and then execute step S1032;

[0105] S1033 Wait until the synchronization period ends. The system waits at this step until the preset synchronization period ends. The synchronization period is a time interval set for periodically performing clock synchronization verification. During this waiting process, the system maintains its current state to ensure that relevant operations are completed within a complete cycle.

[0106] S1034 Align the current time of the digital simulation system and the hardware-in-the-loop simulation system using the UTC clock source. After a synchronization period ends, calibrate the time of the two systems again to eliminate possible time deviations during the synchronization process and keep the time of the two systems highly consistent at all times.

[0107] S1035 Enter the next synchronization cycle, and loop through steps S1032 to S1035. Through continuous looping, continuously perform clock synchronization verification and calibration on the digital simulation system and the hardware-in-the-loop simulation system to ensure that the two systems maintain high-precision clock synchronization throughout the simulation process.

[0108] In the above clock synchronization method based on step size verification, the digital simulation system needs to perform more system blocking and clock adjustment behaviors. Considering the instability of the digital system, the above method is improved, and based on the verification principle of the IEEE1588 protocol, a clock synchronization method based on reflection memory and master clock verification is proposed. The clock of the hardware-in-the-loop simulation system with higher accuracy and stability is used as the master clock, and its main task is to send time-related information, which is used as the benchmark for clock correction. The digital system is used as the slave clock and returns the information to the master clock after receiving the message. The deviation between the clock of the hardware-in-the-loop simulation system and the clock of the digital system is determined by exchanging clock messages between the two, and the time correction is completed in the digital simulation system.

[0109] Based on the above idea, as Figure 9 shown, this embodiment further proposes a method for time synchronization between a digital simulation system and a hardware-in-the-loop simulation system, including:

[0110] S111 Use the clock of the hardware-in-the-loop simulation system as the master clock, use the clock of the digital simulation system as the slave clock, and set a correction period. Set the correction period to periodically adjust the slave clock to ensure that the slave clock can continuously maintain synchronization with the master clock during long-term operation. The length of the correction period needs to be determined according to the specific requirements and stability of the system. If the period is too short, it will increase the system overhead, and if it is too long, it may lead to the accumulation of synchronization errors.

[0111] S112 The digital simulation system receives the clock synchronization message sent by the hardware-in-the-loop simulation system, extracts the master clock time when the hardware-in-the-loop simulation system sends the synchronization message from the slave clock synchronization message and records it as T1;

[0112] S113 The slave clock of the digital simulation system records the arrival time T2 of the clock synchronization message;

[0113] S114 The digital simulation system sends a message, sets an identifier for starting the transmission error measurement in the message, and the slave clock records the transmission time T3;

[0114] S115 After receiving the message with the identifier for starting the transmission error measurement, the master clock of the hardware-in-the-loop simulation system records the arrival time T4; and sets an identifier representing the completion of the transmission error measurement in the message pushed back to the digital simulation system, and records the arrival time T4 in the pushed-back message;

[0115] After the S116 digital simulation system receives the message with the completed transmission error measurement identifier, it calculates the clock face deviation and time delay of the master and slave times according to T1, T2, T3, and T4, and adjusts the slave clock of the digital simulation system according to the clock face deviation and time delay.

[0116] The deviation between T2 and T1 is the sum of the clock face deviation and time delay of the hardware-in-the-loop and digital systems, that is ;

[0117] where Offset is the clock face deviation between the master and slave clocks, and Delay is the time delay.

[0118] The hardware-in-the-loop simulation system receives the delay signal from the digital simulation system at each simulation step. The system delay signal includes the transmission time T3 of the delay signal; the master clock records the arrival time T4 of the system delay signal. The deviation between T4 and T3 is the difference between the clock face deviation and time delay.

[0119] The deviation between T4 and T3 is the difference between the clock face deviation and time delay of the hardware-in-the-loop and digital systems, that is ;

[0120] Integrating the above formulas, we can get:

[0121] ;

[0122] ;

[0123] Thus, the clock face deviation and time delay between the master and slave clocks are obtained.

[0124] IV. Online calibration system for hardware-in-the-loop simulation experiments. The online calibration system for hardware-in-the-loop simulation experiments realizes the seamless integration of the key parameter module of the experimental equipment, the pneumatic analysis module, and the equipment simulation model in the environment, and develops and designs a Monte Carlo equipment test platform to complete the rapid design of the equipment Monte Carlo experiment and then complete the systematic large-sample simulation experiment. To achieve the full-process simulation from the construction of the experimental equipment experimental environment, the random generation of random configuration parameters of the experimental equipment, the rapid construction of the experimental equipment model, to the statistical analysis of the experimental equipment test results, to accelerate the efficiency and quality of the equipment experiment and test, and significantly improve the efficiency and quality of the equipment R & D and design. The following introduces this system from aspects such as communication construction, data integration standard, experimental method integration, data storage, etc.

[0125] 1. Communication architecture.

[0126] The main task of the Monte Carlo equipment experimental platform is to extract the key sample data of digital simulation from large-sample experiments and use it as the data for the sample-level simulation experiment of the hardware-in-the-loop simulation system. By configuring the sample-level experimental situation data through the Monte Carlo equipment experimental platform and introducing relevant factors such as the environment, system deviation, and system failure, the combat scenario verification of the hardware-in-the-loop simulation system can be carried out in multiple batches. Further improve the fidelity of the experimental equipment in the digital scenario.

[0127] The hardware-in-the-loop simulation integrated management system, in the form of software and hardware, relies on the hardware-in-the-loop simulation model management software to schedule and manage various heterogeneous hardware devices of the hardware-in-the-loop simulation models. In order to carry out joint simulation experiments, the Monte Carlo equipment experimental platform needs to interact with the hardware-in-the-loop simulation integrated management system in terms of data and instructions, and report the model data, status, and events to the experimental platform to achieve data interaction between the experimental task platform and the hardware-in-the-loop simulation nodes in the hardware-in-the-loop integration framework.

[0128] The Monte Carlo equipment experimental platform uses the DDS communication method to communicate with the hardware-in-the-loop integration management system. The data communication method sends and receives data in the form of Json strings through the unified hardware-in-the-loop framework integration standard. The specific data communication logic is as Figure 10 shown. First, configure the number of experimental tasks, set the number of threads to be started, and the relevant data to be configured for each task experiment through the Monte Carlo equipment experimental platform. The data includes the target location, deployment location, fault, deviation injection type, and injection time.

[0129] The Monte Carlo equipment experimental platform sends the configured experimental tasks to the hardware-in-the-loop model integration management system in batches through DDS. The hardware-in-the-loop model integration management system performs resource scheduling and task subdivision according to the experimental tasks. After each hardware-in-the-loop simulation node completes the task, it reports the result of the task execution to the hardware-in-the-loop model integration management system. The management software then reports the task completion status to the Monte Carlo experimental platform through DDS. According to the progress of the experimental task completion, the experimental platform starts the next experimental task. The main task of the Monte Carlo equipment experimental platform is to parse each configured experimental task, convert the data into a specific data structure, and further encapsulate the key configuration data in the data structure into the communication standard of the hardware-in-the-loop integration framework, and send the data to the management software using the DDS communication protocol. The management software parses the protocol, subdivides the tasks, and sends them to each simulation node. After completing the task, the management software encapsulates the task result into a string of the hardware-in-the-loop integration standard and then uses DDS to send the data back to the Monte Carlo equipment experimental platform. Complete the closed-loop data communication link for the entire sample experiment.

[0130] 2. Data Integration Standard

[0131] The semi-physical model integrated management system communicates with the Monte Carlo equipment experiment platform through the DDS communication mechanism. Based on the predefined topic names, communication channels, and data standards, high-quality data interaction between the experiment platform and the semi-physical integrated management system can be achieved.

[0132] The semi-physical model integrated management system is managed and scheduled by the control software for each semi-physical simulation node. Taking the aircraft as an example, the semi-physical model integrated management system and the Monte Carlo equipment experiment platform conduct data interaction through the DDS communication protocol. The interaction interfaces and processes are as follows:

[0133] ① Interfaces

[0134] Table 1 Interaction Interfaces

[0135]

[0136] ② Interaction Process

[0137] Interaction implementation process: After the experimental data of the Monte Carlo equipment experiment platform is configured, an initialization data packet is sent to the semi-physical integrated management software through the DDS sending interface function. The specific description of its data content is as follows:

[0138] Table 2 Initialization Data Packet

[0139]

[0140] After the management software receives the initialization JSON data packet, it parses the JSON data packet, sends the parameters to the real-time simulator for model initialization operations, and reports an initialization success event packet. The description of its event packet content is as follows:

[0141] Table 3 Initialization Event Feedback

[0142]

[0143] After the digital agent model receives the initialization success event packet, it feeds back to the digital integration framework, waits to receive and issue a running instruction packet to the semi-physical model integration framework. The description of its event packet content is as follows:

[0144] Table 4 Launch Event Packet

[0145]

[0146] After the management software receives the launch instruction packet, it parses and issues a model running instruction to the semi-physical model. The model returns a running success event and starts real-time simulation. The description of the returned event packet content is as follows:

[0147] Table 5 Launch Event Feedback Packet

[0148]

[0149] Subsequently, the hardware-in-the-loop digital proxy model will send the JSON data packet of the hardware-in-the-loop model operation in real time through the time-step Tick data packet, and its description is as follows:

[0150] Table 6 Tick operation data packet

[0151]

[0152] After the management software receives the operation JSON data packet, it parses the JSON data packet and sends the parameters to the real-time simulator to participate in the real-time solution. The control software receives the latest state of the model after the real-time simulator calculates, and its specific content is described as follows:

[0153] Table 7 Tick reporting data packet

[0154]

[0155] If the model task is completed / equipment fails, the control software will report the task completion / failure event. After the equipment experiment platform receives the hit event, it will determine whether the current experiment task is completed according to the task event, and its content is described as follows:

[0156] Table 8 Task completion / failure event reporting data packet

[0157]

[0158] If it is necessary to inject deviations or faults into the model, the equipment experiment platform sends the fault data packet to the hardware-in-the-loop integrated management software through the DDS sending interface function, and its content is described as follows:

[0159] Table 9 Fault injection event operation data packet

[0160]

[0161] 3. Integration of Monte Carlo test method

[0162] During the actual operation of the experimental equipment, due to the influence of environmental factors and system errors, there are deviations between its attitude, trajectory and control parameters and the ideal attitude, trajectory and control parameters. Monte Carlo equipment tests aim to analyze various random interference factors suffered by special equipment in practice, give the processing methods of various interference factors, and establish a six-degree-of-freedom Monte Carlo equipment simulation model. Through a large number of simulation experiments on the online calibration system of the hardware-in-the-loop simulation experiment, the accuracy of the key parameter data of the experimental equipment is studied, and the accuracy of the simulation model and the effectiveness of the simulation method are verified.

[0163] In this embodiment, the online verification method for the hardware-in-the-loop simulation experiment using the Monte Carlo method includes the following steps:

[0164] S1 Establish a hardware-in-the-loop simulation model of the experimental equipment according to the motion characteristics and motion laws of the experimental equipment.

[0165] The hardware-in-the-loop simulation system constructs a hardware-in-the-loop simulation model based on the motion characteristics of the experimental equipment (such as whether the equipment is in linear motion, curvilinear motion, or rotational motion, etc.) and motion laws (such as the dynamic equations followed, the periodicity of motion, etc.). This model will simulate the motion of the experimental equipment in the actual environment as realistically as possible, providing an operable virtual object for subsequent experiments, just like building a "stage" to simulate the operation of the actual equipment, and various subsequent experimental operations will be carried out based on this model.

[0166] S2 Extract key sample data for digital simulation from large-sample experiments.

[0167] After conducting a large number of digital simulation experiments (i.e., large-sample experiments), the Monte Carlo equipment experimental platform selects key sample data that are more in line with the actual situation from numerous data. These key sample data are representative and can reflect the typical operating states of the experimental equipment under different conditions. For example, in large-sample experiments simulating the flight of an aircraft, data at key stages such as takeoff, cruise, and landing of the aircraft may be selected as key sample data. These data are of great guiding significance for experiments on the hardware-in-the-loop simulation system and can help simulate the actual situation more accurately.

[0168] S3 Determine the random interference factors and their distribution laws during the operation of the experimental equipment, and generate random interference parameters according to the random interference factors and their distribution laws on the platform.

[0169] This step is also executed by the Monte Carlo equipment experimental platform. First, it is necessary to clarify which random interference factors the experimental equipment will be affected by during actual operation. For example, experimental equipment operating outdoors may be affected by weather factors (such as wind speed, wind direction, temperature changes, etc.), electromagnetic environment interference, etc. Then, determine the distribution laws of these interference factors. For example, the wind speed may conform to a certain probability distribution. According to these distribution laws, use a dedicated platform to generate corresponding random interference parameters. These parameters will be loaded into the hardware-in-the-loop simulation system in subsequent experiments to simulate the random interference received by the experimental equipment in the actual environment, making the experiment closer to the real situation.

[0170] S4 Configure experimental parameters, plan experiments according to the experimental parameters, conduct hardware-in-the-loop simulation experiments on the hardware-in-the-loop simulation system using key sample data for digital simulation according to the experimental plan, and load random interference parameters during the experiment operation.

[0171] The Monte Carlo equipment experimental platform configures various experimental parameters according to the experimental purposes and requirements, such as the number of experiments, the duration of each experiment, the initial state of the experimental equipment, etc. The hardware-in-the-loop model integrated management system conducts a comprehensive experimental plan based on these experimental parameters to determine the experimental process and steps. Then, on the hardware-in-the-loop simulation system, the hardware-in-the-loop simulation experiment is carried out using the key digital simulation sample data extracted previously. During the experiment operation, random interference parameters are loaded in the set manner so that the experiment can simulate the situation where the experimental equipment is interfered in the actual environment, thereby observing and studying the operation performance of the experimental equipment under complex conditions.

[0172] S5 Constructs the experimental equipment and the experimental scenario in three dimensions according to the hardware-in-the-loop simulation model and the data during the experiment process to achieve the visualization of the experimental process.

[0173] With the help of the hardware-in-the-loop simulation model and the data generated during the experiment process, the digital simulation system conducts three-dimensional modeling of the experimental equipment and the experimental scenario. Through the three-dimensional construction, the appearance, motion state of the experimental equipment, and the experimental scenario where it is located can be presented in an intuitive three-dimensional graphic manner, realizing the visualization of the experimental process. This helps the experimental personnel observe the operation of the experimental equipment more clearly and discover possible problems in a timely manner, such as whether the motion posture of the equipment meets the expectations, whether the influence of the interference factors in the experimental scenario on the equipment is intuitively visible, etc., providing a more intuitive basis for experimental analysis.

[0174] S6 Obtains the operation parameters of the experimental equipment after perturbation and the equipment evaluation results.

[0175] Under the condition of loading random interference parameters, the experimental equipment runs in the hardware-in-the-loop simulation system. This step aims to obtain various operation parameters of the experimental equipment after being perturbed, such as attitude parameters (pitch angle, yaw angle, roll angle, etc.), trajectory parameters (position coordinates, speed, etc.), and control parameters (such as throttle opening, servo angle, etc.). At the same time, the operation performance of the experimental equipment under this perturbation condition is evaluated according to certain evaluation criteria to obtain the equipment evaluation results. These results and parameters are crucial for studying the performance of the experimental equipment in the actual complex environment.

[0176] S7 Statistically analyzes the operation parameters of the experimental equipment and the equipment evaluation results to obtain the statistical characteristic values of the key parameters of the experimental equipment.

[0177] After the data is aggregated to the Monte Carlo equipment experiment platform, it statistically analyzes the operating parameters of the experimental equipment and the equipment evaluation results obtained from multiple experiments. Through statistical methods, the statistical characteristic values of the key parameters of the experimental equipment are calculated, such as the mean, variance, standard deviation, etc. The mean can reflect the average level of the key parameters, while the variance and standard deviation can reflect the degree of dispersion of the parameters. These statistical characteristic values help to deeply understand the overall characteristics of the key parameters of the experimental equipment, evaluate the stability and reliability of the experimental equipment under different random interferences, and provide data support for further optimizing the experimental equipment or improving the simulation method.

[0178] The Monte Carlo method is a test mathematical method that uses random numbers for statistical tests and takes the obtained statistical characteristic values (such as the mean, variance, probability, etc.) as the numerical solutions to the problems to be solved. It is a method that reproduces the physical process by means of a probabilistic mathematical model and the statistical characteristics of the physical process of the actual problem under study. The Monte Carlo equipment experiment platform is based on a semi-physical integration framework as the foundation, and develops a Monte Carlo equipment test platform based on C++ as an integrated management system for experimental tasks of semi-physical integration, including data configuration, control instruction generation, result reporting and parsing, and unified management and analysis of experimental results. The platform exchanges data with the semi-physical integration framework through the DDS communication protocol, manages and schedules the simulation models of each simulation node, and then completes the simulation experiment.

[0179] The semi-physical simulation experiment online verification system provides a complete set of simulation model integration methods based on the semi-physical integration framework, and its basic principle is as Figure 11 shown. The Monte Carlo equipment test platform uses data random generation technology to encapsulate the input data of the equipment model into an ASCII file in the form of a table. First, the platform parses the input parameters from the input file and sends the input data to the semi-physical simulation integration management system in the form of instructions through the DDS protocol. The integration management system dynamically schedules the simulation nodes that need to be started, and the simulation nodes execute the simulation model program for analysis and calculation. Finally, the analysis results are returned to the semi-physical integration management system, and the semi-physical integration management system reports the result data to the Monte Carlo equipment test platform for data statistics. Finally, the input and output data are stored in the specified location file, and the input and output parameters during the simulation experiment are obtained by parsing the output file.

[0180] 4. Data storage.

[0181] Regarding the problem of storing semi-physical large-sample simulation data, based on the database design pattern and the target data object, a data abstraction link is established to realize the database table design reflecting the semi-physical large-sample simulation data. Based on the transaction mechanism, concurrent access control of large data samples is realized, effectively controlling the load of CPU and I / O resources while ensuring the throughput. Based on data visualization, it provides data support for the verification of large-sample semi-physical models, such asFigure 12 as shown

[0182] As Figure 13 shown, in the hardware-in-the-loop large-sample simulation experiment work, the hardware-in-the-loop simulation system obtains data from multiple data sources (model parameters transmitted back by the simulation model, heartbeat monitoring of simulation nodes), calls the database driver in the form of a dynamic link library (DLL), inserts the data into the transaction queue through the API interface, and waits for the concurrent execution of transactions. The interface accepts SQL execution statements and data. Interacts with the server through the visualization tool Navicat and the model verification platform to query and obtain the corresponding data. It is also possible to export the corresponding data through Navicat and provide it to the model verification platform.

[0183] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions for causing a computer terminal (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in various embodiments of the present invention.

[0184] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make many forms without departing from the spirit and scope protected by the claims of the present invention. All of these are within the protection scope of the present invention.

Claims

1. A semi-physical simulation experiment online verification system, characterized in that include: Monte Carlo equipment experimental platform, used to configure experimental parameters, provide key sample data for digital simulation and generate random interference parameters; Conduct statistical analysis on the experimental equipment operating parameters and equipment evaluation results to obtain the statistical characteristic values ​​of the key parameters of the experimental equipment; A semi-physical simulation integrated management system is used to schedule and manage the semi-physical simulation system; the semi-physical simulation system is used to establish a semi-physical simulation model of the experimental equipment according to the motion characteristics and motion laws of the experimental equipment; to plan the experiment according to the experimental parameters; and to perform a semi-physical simulation experiment on the semi-physical simulation system using digital simulation key sample data according to the experimental plan; And during the experiment operation, the random interference parameters issued by the Monte Carlo equipment experiment platform are loaded, so as to obtain the experimental equipment operation parameters and equipment evaluation results after the disturbance; Digital simulation system, used to construct the experimental equipment and experimental scene in three dimensions according to the semi-physical simulation model and the data in the experimental process, so as to realize the visualization of the experimental process; The steps for time synchronization between the digital simulation system and the semi-physical simulation system include: Clock synchronization between simulation nodes within the S101 semi-physical simulation system; S102 uses a UTC clock source to align the start time of the digital simulation system and the semi-physical simulation system; S103 performs clock synchronization based on step length verification when the simulation step length of the digital simulation system is consistent with the simulation step length of the semi-physical simulation system, specifically including: S1031 initialization, setting the synchronization period, setting the digital simulation system preparation synchronization flag and the synchronization flag to 0, and returning the simulation step counter to zero; When receiving the clock synchronization information pushed back by the semi-physical simulation system, S1032 parses the simulation number step value and assigns it to the step counter, and then sets the synchronization preparation flag to 1, if both the synchronization preparation flag and the synchronization preparation flag are 0; When the preparation synchronization flag is 1 and the synchronization flag is 0, the simulation step number value is parsed and compared with the current value of the step counter. If they are equal, the synchronization flag is set to 1, the preparation synchronization flag is set to 0, and step S1033 is executed; if they are not equal, the preparation synchronization flag is set to 0 and step S1032 is executed; S1033 waits until the synchronization cycle ends; S1034 uses a UTC clock source to align current times of the digital simulation system and the semi-physical simulation system; S1035 enters the next synchronization cycle and executes steps S1032 to S1035 in a loop; Alternatively, the steps of synchronizing the time of the digital simulation system and the semi-physical simulation system include: S111 uses the clock of the semi-physical simulation system as the master clock, uses the clock of the digital simulation system as the slave clock, and sets a correction period; The S112 digital simulation system receives the clock synchronization message sent from the semi-physical simulation system, extracts the master clock time of the semi-physical simulation system sending the synchronization message from the clock synchronization message and records it as T1; S113 digital simulation system records the arrival time T2 of the clock synchronization message from the clock; S114 The digital simulation system sends a message, sets a flag in the message to start transmission error measurement, and records the sending time T3 from the clock; S115 After the semi-physical simulation system receives the message with the start transmission error measurement mark, the master clock records the delivery time T4; and sets the mark representing the completion of the transmission error measurement in the message pushed back to the digital simulation system, and records the delivery time T4 in the pushed back message; After receiving the message with the transmission error measurement completion mark, the S116 digital simulation system calculates the clock deviation and time delay of the master and slave clocks according to T1, T2, T3, and T4, and adjusts the digital simulation system slave clock according to the clock deviation and time delay.

2. According to claim 1, a semi-physical simulation experiment online verification system is characterized in that The Monte Carlo equipment experimental platform includes: The experimental parameter configuration module is used to configure the number of experimental tasks, set the number of threads to be started, and the experimental data to be configured for each task experiment; the experimental data includes the injection type and injection time of random interference; The experimental data generation module is used to extract key sample data of digital simulation from large sample experiments for semi-physical simulation experiments and generate random interference parameters.

3. The online verification system for semi-physical simulation experiment according to claim 1 is characterized in that: The semi-physical simulation system is a distributed system, including several semi-physical simulation nodes; the three-dimensional model of the experimental equipment constructed by the digital simulation system corresponds one to one with the semi-physical simulation nodes, and during the experiment, the digital simulation system and the semi-physical simulation system are synchronized in time.

4. The online verification system for semi-physical simulation experiment according to claim 2 is characterized in that: The semi-physical simulation nodes transmit data via optical fiber reflection memory.

5. The online verification system for semi-physical simulation experiment according to claim 1 is characterized in that The semi-physical simulation integrated management system includes: The experiment planning module is used to plan the experiment according to the experimental parameters and send the experimental data and key sample data of digital simulation to the semi-physical simulation node for experiment; The node scheduling module is used to schedule the corresponding semi-physical simulation nodes to participate in the experiment according to the number of threads set in the Monte Carlo equipment experiment platform.

6. The online verification system for semi-physical simulation experiment according to claim 1 is characterized in that The digital simulation system comprises: Virtual engine, used to construct 3D geometric models of experimental equipment and create visual experimental scenes, thus realizing visual reconstruction of equipment status; Physics engine, used to simulate physical experiment scenes and provide physical interaction.

7. A method for online verification of a semi-physical simulation experiment, applied to the online verification system of a semi-physical simulation experiment as claimed in any one of claims 1 to 6, characterized in that include: According to the motion characteristics and motion laws of the experimental equipment, a semi-physical simulation model of the experimental equipment is established; Extract key sample data of digital simulation from large sample experiments; Determine the random interference factors and their distribution patterns during the operation of the experimental equipment, and generate random interference parameters based on the random interference factors and their distribution patterns; Configure experimental parameters, plan experiments according to the experimental parameters, conduct semi-physical simulation experiments on the semi-physical simulation system using key digital simulation sample data according to the experimental plan, and load random interference parameters during the experiment; The experimental equipment and experimental scene are constructed in three dimensions according to the semi-physical simulation model and the data in the experimental process to realize the visualization of the experimental process; Obtain the experimental equipment operating parameters and equipment evaluation results after disturbance; Statistics are collected on the operating parameters of the experimental equipment and the equipment evaluation results to obtain the statistical characteristic values ​​of the key parameters of the experimental equipment.

8. The online verification method for a semi-physical simulation experiment according to claim 7 is characterized in that: A semi-physical simulation model of the experimental equipment is established using the semi-physical simulation system, and semi-physical simulation experiments are carried out based on key sample data of the digital simulation. Based on time synchronization with the semi-physical simulation system, the digital simulation system performs three-dimensional construction of the experimental equipment and experimental scene based on the semi-physical simulation model and data from the experimental process.

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