Semi-physical simulation experiment system and method based on monte carlo equipment experiment platform

Abstract

The application discloses a kind of based on Monte Carlo equipment experimental platform's semi-physical simulation experiment system and method, belong to digital simulation technical field.The system includes Monte Carlo equipment experimental platform, for configuring experimental parameter, providing digital simulation key sample data and generating random interference parameter;Semi-physical simulation integrated management system is used for scheduling management to semi-physical simulation system;According to experimental planning, semi-physical simulation experiment is carried out on semi-physical simulation system using digital simulation key sample data;And load random interference parameter in experimental operation process;Digital simulation system is used to realize the visualization of experimental process.The application realizes the seamless integration of experimental equipment key parameter module, aerodynamic analysis module and equipment simulation model in environment, and develops and designs Monte Carlo equipment test platform to complete equipment Monte Carlo experiment rapid design, and then completes systematized large sample simulation test.

Description

[0001] Divisional application

[0002] This application is a divisional application of Chinese invention patent application filed on March 18, 2025 [Application No.: 2025103150795] [Title: An Online Verification System and Method for Hardware-in-the-Box Simulation Experiment]. Technical Field

[0003] This invention belongs to the field of digital simulation technology, and in particular relates to a hardware-in-the-loop simulation experimental system and method based on the Monte Carlo equipment experimental platform. Background Technology

[0004] Hardware-in-the-loop (HIL) simulation is heavily constrained by simulation hardware and the availability of digital models, leading to complex simulation experiment organization and insufficient flexibility. To address this issue, domestic efforts primarily focus on improving the scheduling and management efficiency of HIL models through intelligent transformation of simulation framework software and the development of generalized modeling techniques. From the mid-to-late 1990s to the present, driven by the development of various new aircraft, a number of high-level and large-scale HIL laboratories have conducted equipment application simulation experiments based on HIL technology, achieving breakthroughs in a large number of key technologies. Advanced simulation technologies such as distributed interactive simulation and virtual reality have been widely applied. Machine learning provides analytical tools for data analysis techniques, primarily studying the theory and algorithms for extracting patterns and models from data—i.e., learning algorithms—and is an inevitable product of the development of artificial intelligence to a certain stage. Many methods in data mining originate from machine learning. Traditional machine learning algorithms such as linear discriminant analysis, logistic regression, decision trees, random forests, neural networks, and support vector machines have evolved into algorithms such as deep learning, reinforcement learning, and deep reinforcement learning.

[0005] The prior art, Chinese patent application CN202410186768.6, discloses a virtual-real interaction system based on Unreal Engine and hardware-in-the-loop simulation, including Unreal Engine and hardware-in-the-loop simulation system; the hardware-in-the-loop simulation system includes a controller, a communication middleware, and a hardware-in-the-loop simulation model; Unreal Engine interacts with the hardware-in-the-loop simulation model through the communication middleware; Unreal Engine issues simulation control commands through the controller, which are sent to the hardware-in-the-loop simulation model through the communication middleware; the hardware-in-the-loop simulation model sends the model data generated during the simulation process through the communication middleware.

[0006] Existing technologies have the following drawbacks: 1. The simulation verification parameters of key parameters in existing pure digital models of equipment are usually fixed values, without considering the actual application scenarios, resulting in low environmental and model accuracy. 2. Traditional semi-physical simulation models are limited by the access to physical objects during construction, and are usually structurally rigid and functionally singular, making it impossible to conduct large-scale, multi-functional simulation experiments. 3. Large-sample pure digital experimental simulations involve a large number of experimental factors, multiple dimensions, and complex interrelationships. Accurate qualitative evaluation of key experimental samples requires on-site equipment verification experiments, which results in long evaluation cycles and significant difficulties.

[0007] In addition, to obtain real-world data on equipment experiments under random interference factors, two common experimental methods are used: real-world equipment testing and computer-simulated equipment testing. Obtaining data through actual field testing requires extensive experimentation, significantly increasing the development cost of experimental equipment and extending the design cycle. With the continuous development of computer technology, simulation technology has gradually become an indispensable tool 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 minimal cost, providing methods and basis for equipment R&D design and performance verification. In Monte Carlo equipment testing, designers are required to write corresponding simulation analysis modules and data interfaces between modules, and develop corresponding equipment simulation model programs to conduct numerous Monte Carlo verification experiments. The design task is heavy and prone to errors. Furthermore, due to the often poor interface compatibility and data interaction difficulties between different simulation modules or software tools, the scalability and reusability of the model and code are greatly limited, further increasing the preliminary development workload of Monte Carlo simulation tests for experimental equipment. Summary of the Invention

[0008] The purpose of this invention is to provide a hardware-in-the-loop simulation experimental system and method based on a Monte Carlo equipment experimental platform, which partially solves or alleviates the above-mentioned deficiencies in the prior art, develops and designs a Monte Carlo equipment experimental platform to complete the rapid design of equipment Monte Carlo experiments, and then completes a systematic large-sample simulation test.

[0009] To solve the aforementioned technical problems, the present invention specifically adopts the following technical solution:

[0010] A hardware-in-the-loop (HIL) simulation experimental system based on a Monte Carlo equipment experimental platform includes: a Monte Carlo equipment experimental platform for configuring experimental parameters, providing key digital simulation sample data, and generating random disturbance parameters; statistically analyzing the operating parameters and evaluation results of the experimental equipment to obtain statistical characteristic values ​​of key parameters of the experimental equipment; a hardware-in-the-loop simulation integrated management system for scheduling and managing the hardware-in-the-loop simulation system; the hardware-in-the-loop simulation system for establishing a hardware-in-the-loop simulation model of the experimental equipment based on its motion characteristics and motion laws; planning experiments based on the experimental parameters; conducting hardware-in-the-loop simulation experiments on the hardware-in-the-loop simulation system using key digital simulation sample data according to the experimental plan; and loading random disturbance parameters issued by the Monte Carlo equipment experimental platform during the experiment to obtain the disturbed operating parameters and evaluation results of the experimental equipment; and a digital simulation system for constructing a three-dimensional model of the experimental equipment and experimental scene based on the hardware-in-the-loop simulation model and data during the experiment to visualize the experimental process.

[0011] As an improvement, the Monte Carlo equipment experimental platform includes: an experimental parameter configuration module, 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; the experimental data includes the injection type and injection time of random interference; and an experimental data generation module, used to extract key digital simulation sample data from large-sample experiments for use in semi-physical simulation experiments, and generate random interference parameters.

[0012] As an improvement, the hardware-in-the-loop simulation system is a distributed system, including several hardware-in-the-loop simulation nodes; the three-dimensional model of the experimental equipment constructed by the digital simulation system corresponds one-to-one with the hardware-in-the-loop simulation nodes, and the digital simulation system and the hardware-in-the-loop simulation system are synchronized in time during the experiment.

[0013] As an improvement, the hardware-in-the-loop simulation nodes transmit data between each other via fiber optic reflection memory.

[0014] As an improvement, the hardware-in-the-loop simulation integrated management system includes: an experiment planning module, used to plan experiments according to experimental parameters and distribute experimental data and key digital simulation sample data to hardware-in-the-loop simulation nodes for experiments; and a node scheduling module, used to schedule corresponding hardware-in-the-loop simulation nodes to participate in experiments according to the number of threads set in the Monte Carlo equipment experimental platform.

[0015] As an improvement, the digital simulation system includes: a virtual engine for constructing a three-dimensional geometric model of the experimental equipment and creating a visualized experimental scene, thereby realizing the visualized reconstruction of the equipment's state; and a physics engine for simulating physical experimental scenes and providing physical interaction.

[0016] This invention also provides a hardware-in-the-loop simulation method based on a Monte Carlo equipment experimental platform, comprising:

[0017] Based on the motion characteristics and motion laws of the experimental equipment, a semi-physical simulation model of the experimental equipment is established.

[0018] Extracting key sample data from large-sample experiments for digital simulation;

[0019] The random interference factors and their distribution patterns during the operation of the experimental equipment are determined, and random interference parameters are generated by the platform based on the random interference factors and their distribution patterns.

[0020] Configure experimental parameters, plan experiments based on experimental parameters, conduct hardware-in-the-loop simulation experiments on the hardware-in-the-loop simulation system using key sample data from digital simulation, and load random disturbance parameters during the experiment.

[0021] Based on the hardware-in-the-loop simulation model and the data from the experiment, the experimental equipment and experimental scene are constructed in three dimensions to visualize the experimental process.

[0022] Obtain the experimental equipment's operating parameters and equipment evaluation results after the disturbance;

[0023] The operating parameters of the experimental equipment and the evaluation results of the equipment are statistically analyzed to obtain the statistical characteristic values ​​of the key parameters of the experimental equipment.

[0024] As an improvement, a hardware-in-the-loop simulation model of the experimental equipment is established using a hardware-in-the-loop simulation system, and a hardware-in-the-loop simulation experiment is conducted based on key sample data from digital simulation. With time synchronization with the hardware-in-the-loop simulation system, the digital simulation system constructs the experimental equipment and experimental scene in three dimensions based on the hardware-in-the-loop simulation model and data during the experiment.

[0025] As an improvement, the steps for time synchronization between digital simulation systems and hardware-in-the-loop simulation systems include:

[0026] The S101 hardware-in-the-loop simulation system synchronizes clocks among its internal simulation nodes.

[0027] S102 uses a UTC clock source to align the start time of the digital simulation system and the hardware-in-the-loop simulation system.

[0028] S103 performs clock synchronization based on step size verification when the simulation step size of the digital simulation system is consistent with that of the hardware-in-the-loop simulation system. Specifically, this includes:

[0029] S1031 initialization, setting the synchronization period, setting both the digital simulation system synchronization flag and synchronization flag to 0, and resetting the simulation step counter to zero;

[0030] When S1032 receives the pushback clock synchronization information from the hardware-in-the-loop simulation system, with both the synchronization preparation flag and the synchronization flag being 0, it parses the simulation number step size value and assigns it to the step size counter, and then sets the synchronization preparation flag to 1.

[0031] When the synchronization flag is 1 and the synchronization flag is 0, the simulation step size number is parsed and compared with the current value of the step size counter. If they are equal, the synchronization flag is set to 1, the synchronization flag is set to 0, and step S1033 is executed. If they are not equal, the synchronization flag is set to 0 and step S1032 is executed repeatedly.

[0032] S1033 waits until the synchronization cycle ends;

[0033] S1034 uses a UTC clock source to align the current time of the digital simulation system and the hardware-in-the-loop simulation system.

[0034] S1035 enters the next synchronization cycle, and steps S1032 to S1035 are executed cyclically.

[0035] As an improvement, the steps for time synchronization between digital simulation systems and hardware-in-the-loop simulation systems include:

[0036] S111 uses the clock of the hardware-in-the-loop simulation system as the master clock and the clock of the digital simulation system as the slave clock, and sets the calibration period.

[0037] The S112 digital simulation system receives clock synchronization messages from the hardware-in-the-loop simulation system, extracts the master clock time of the synchronization message sent by the hardware-in-the-loop simulation system from the clock synchronization message, and records it as T1.

[0038] The S113 digital simulation system records the arrival time T2 of the clock synchronization message from the clock.

[0039] The S114 digital simulation system sends a message, setting an indicator to start transmitting error measurement within the message, and recording the transmission time T3 from the clock.

[0040] After receiving a message with a start transmission error measurement flag, the S115 hardware-in-the-loop simulation system records the delivery time T4 in its master clock; and sets a flag representing the completion of transmission error measurement in the message pushed back to the digital simulation system, and records the delivery time T4 in the pushed-back message.

[0041] After receiving a message with a completion transmission error measurement flag, the S116 digital simulation system calculates the clock face deviation and time delay of the master and slave clocks based on T1, T2, T3, and T4, and adjusts the slave clock of the digital simulation system according to the clock face deviation and time delay.

[0042] Beneficial effects:

[0043] 1. Highly efficient system integration and workflow seamlessness. This invention achieves seamless integration of the key parameter module, aerodynamic analysis module, and equipment simulation model within the environment. Data interaction and collaborative work between different modules are smoother, avoiding inefficiencies caused by compatibility issues or poor data transmission. For example, the results of the aerodynamic analysis module can be directly used as input parameters for 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, generation of random configuration parameters, rapid model construction to statistical analysis of test results, a full-process simulation is achieved. This means that all aspects of the experiment can be completed within a single system, eliminating the need to switch between multiple different systems or tools, significantly shortening the experimental cycle. For instance, the Monte Carlo equipment testing platform can quickly generate random configuration parameters for the experimental equipment and directly apply them to model construction and experimental operation in the hardware-in-the-loop simulation system, achieving a seamless process and improving overall experimental efficiency.

[0044] 2. Rapid Design and Systematic Large-Sample Simulation. The Monte Carlo equipment testing platform developed in this invention enables rapid design of Monte Carlo experiments for equipment. This platform adopts a hierarchical modular design concept, with clearly defined functions for different levels of modules, including experimental parameter configuration, data generation, and process control. Experimenters can quickly set the number of experimental tasks, threads, and various experimental data, such as the injection type and time of random interference, through this platform, thereby quickly starting the experiment and saving preparation time. This invention can complete systematic large-sample simulation experiments, verifying the performance and reliability of the experimental equipment through a large amount of experimental data. The Monte Carlo equipment testing platform extracts key digital simulation sample data from large-sample experiments. This data is more representative and realistic, and can more accurately reflect the actual operation of the experimental equipment. Experiments are conducted on a semi-physical simulation system using this key sample data, and random interference parameters are loaded to simulate various complex real-world scenarios, thereby obtaining more comprehensive and reliable experimental results.

[0045] 3. Advanced Technical Support and Optimization. Multiple clock synchronization methods are employed between the digital simulation system and the hardware-in-the-loop simulation system, such as step-size verification and satellite clock cycle alignment, and reflection memory and master clock verification. High-precision clock synchronization ensures time consistency between the two systems during the experiment, avoiding experimental errors and data inconsistencies caused by time asynchrony, thus improving the reliability of experimental results. For example, during real-time simulation, each simulation node can interact and perform calculations under the same time reference, guaranteeing the accuracy of the experiment.

[0046] The system features optimized design in communication architecture, data integration standards, and data storage. In communication, the Monte Carlo equipment experimental platform and the hardware-in-the-loop (HIL) simulation integrated management system interact via the DDS communication protocol. A unified HIL framework integration standard is used for data transmission and reception in JSON string format, ensuring efficient and accurate data transmission. Regarding data integration, clearly defined interaction interfaces and processes ensure smooth data exchange between different systems. For data storage, based on database design patterns and transaction mechanisms, concurrent memory access control for large data samples is implemented, effectively controlling the load on CPU and I / O resources. Simultaneously, data visualization tools provide data support for experiments, facilitating data management and analysis by experimental personnel.

[0047] The hardware-in-the-loop simulation system employs a reflective memory network and an RTX real-time system, solving the real-time performance and clock stability issues inherent in traditional Ethernet and Windows systems. The reflective memory network offers strict determinism and predictability in transmission, with low data transmission latency, high speed, and large capacity, meeting the real-time performance and large-volume data transmission requirements of distributed real-time simulation systems. The RTX real-time system provides excellent real-time controllability, scalability, and stability, ensuring the reliability and accuracy of the simulation process.

[0048] 4. Visualization and Data Analysis Support. The digital simulation system uses a virtual engine and a physics engine to construct experimental equipment and scenarios in 3D, enabling visualization of the experimental process. Experimenters can intuitively observe the operating status and performance of the experimental equipment under different scenarios, promptly identify problems, and make adjustments. For example, in aircraft simulation experiments, experimenters can observe the aircraft's attitude, trajectory, and other parameters in real time through a 3D visualization interface, allowing for a more intuitive evaluation of the experimental results.

[0049] The Monte Carlo equipment testing platform statistically analyzes the operating parameters and evaluation results of experimental equipment to obtain statistical characteristic values ​​of key parameters. Through statistical analysis of a large amount of experimental data, it is possible to gain a deeper understanding of the performance and reliability of the experimental equipment under different random disturbances, providing a scientific basis for equipment research and development. For example, by calculating the mean, variance, and other statistical characteristic values ​​of key parameters of the experimental equipment, the stability and consistency of the equipment can be evaluated, allowing for targeted optimization and improvement. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. The elements or parts in the drawings are not necessarily drawn to scale. Obviously, the drawings described below are some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0051] Figure 1 This is a schematic diagram of the architecture of an online verification system for hardware-in-the-loop simulation experiments.

[0052] Figure 2 Schematic diagram of the Monte Carlo equipment experimental platform architecture;

[0053] Figure 3 Data interaction flowchart for the Monte Carlo equipment experimental platform;

[0054] Figure 4 This is a schematic diagram of the architecture of a hardware-in-the-loop simulation integrated management system.

[0055] Figure 5 This is a resource topology diagram of a hardware-in-the-loop simulation node system.

[0056] Figure 6 This is a flowchart of the data communication process for the online verification system of a hardware-in-the-loop simulation experiment.

[0057] Figure 7 This is a schematic diagram of the simulation time synchronization of the online verification system for hardware-in-the-loop simulation experiments.

[0058] Figure 8 A flowchart illustrating how a digital simulation system receives clock synchronization information from a hardware-in-the-loop simulation system to complete clock synchronization.

[0059] Figure 9 A flowchart of the synchronization process based on the IEEE 1588 protocol.

[0060] Figure 10 Data interaction diagram of the Monte Carlo equipment experimental platform and management software;

[0061] Figure 11 This is a schematic diagram of the simulation model integration principle for a semi-physical model-based integrated management system.

[0062] Figure 12 A roadmap for data storage technology of semi-physical large-sample simulation results;

[0063] Figure 13 This is an interactive link for the database of semi-physical large-sample simulation results. Detailed Implementation

[0064] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0065] In this document, suffixes such as "module," "component," or "unit" used to denote elements are used solely for the purpose of illustrative purposes 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," and "the other end," indicating orientation or positional relationships based on the orientation or positional relationships shown in the accompanying drawings, are used only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. In this document, unless otherwise expressly specified and limited, terms such as "installed," "equipped with," and "connected" should be interpreted broadly. 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; it can be a connection within two elements. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. In this document, "and / or" includes any and all combinations of one or more of the listed related items. "Multiple" in this document means two or more, i.e., it includes two, three, four, five, etc.

[0066] The basic architecture of the online verification system for hardware-in-the-loop simulation experiments provided by this invention consists of three parts: a user layer, a model layer, and a hardware layer. Figure 1As shown. The hardware layer, serving as the bottom layer of the environment, consists of high-performance servers or client PCs, network communication equipment, information security equipment, etc., forming the fundamental hardware environment supporting simulation implementation and model data interaction. The model layer mainly comprises simulation models of various subsystems (including equipment models, aerodynamic models, control models, and visualization models) and data modules, integrated and configured through a hardware-in-the-loop simulation management system. The top layer of the environment is the user layer, including the hardware-in-the-loop simulation management system, the Monte Carlo equipment experimental platform, and the digital simulation system. The hardware-in-the-loop simulation management system utilizes a heterogeneous communication network constructed using fiber optic reflective memory combined with Ethernet to connect with the Monte Carlo equipment experimental platform and the digital simulation system, enabling user interaction with the environment and visualization analysis of simulation results.

[0067] The Monte Carlo equipment experimental platform, the hardware-in-the-loop simulation integrated management system, and the digital simulation system are introduced below.

[0068] I. Monte Carlo Equipment Experimental Platform.

[0069] The Monte Carlo equipment testing platform is used to configure experimental parameters, provide key sample data for digital simulation, and generate random disturbance parameters; it also performs statistical analysis on the operating parameters of the experimental equipment and the equipment evaluation results to obtain statistical characteristic values ​​of the key parameters of the experimental equipment.

[0070] Specifically, the platform uses a layered modular design concept to divide the platform framework into functional parts, and rationally arranges different modules in their respective appropriate layers. Different layers construct different task modules, and the modules interact with each other using a specific structure. Its system architecture diagram is as follows: Figure 2 As shown, the lower layer provides services to the upper layer, and the upper layer completes its required functions by calling the lower layer's interfaces. The experimental platform interaction layer has functions such as configuring various experimental parameters, generating experimental data, controlling experimental processes, monitoring experimental progress, and managing experimental logs. The functional layer has functions such as thread management, data management, data encapsulation / parsing, data storage, and internal communication management to support the various functional requirements of the application layer. The general interface layer provides communication interfaces and interface protocols for external interaction, realizing the standardization of data interaction. Real-time communication between the experimental platform and the hardware-in-the-loop (HIL) integration framework is established.

[0071] 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.

[0072] The purpose of the experiment is to obtain 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 location, deployment location, and the injection type and injection time of random interference (including faults and deviations).

[0073] The experimental data generation function is implemented by the experimental data generation module. This module extracts key digital simulation data from large-sample experiments for use in hardware-in-the-loop (HIL) simulation experiments and generates random interference parameters. Since the large-sample experiments are digital simulations with randomly generated data, some of the data may not reflect reality. To improve the realism of the experiments, the experimental data generation module extracts suitable key digital simulation data from the data and injects this data into the HIL model of the experimental equipment for testing.

[0074] Parameters for random disturbances such as faults and deviations are also randomly generated by the experimental data generation module.

[0075] The process for starting the experiment on the Monte Carlo equipment experimental platform is as follows: Figure 3 As shown, first, start the equipment experimental platform, set the number of cyclic sample trials, and activate the random data generation button. After the data generation is complete, close the data generation process. Based on the generated data, refine the configuration for each task according to its actual situation. After the data configuration is complete, set the number of threads to be started, which will control the hardware-in-the-loop management system to schedule relevant simulation nodes to participate in the experiment.

[0076] Once the configuration is complete, the verification experiment can be started. The experimental platform will send DDS command data packets according to the parameter configuration of each experiment. Experimental task instructions are issued, and the hardware-in-the-loop simulation integrated system will call the key node resource models to complete the node tasks and report the task completion status. The equipment experimental platform subscribes to and parses the simulation result information. After all simulation nodes in the current task group have completed their tasks, the platform will send the next set of equipment experimental tasks. This process continues until the test progress completion rate reaches 100%, at which point the equipment experiment ends. If the experimental task data is abnormal, the experimental task sending thread can be manually stopped to end the experiment. After the experiment is completed, a batch of data can be visualized and analyzed. Large-sample Monte Carlo equipment test evaluation results are provided.

[0077] II. Hardware-in-the-loop simulation integrated management system.

[0078] The hardware-in-the-loop simulation integrated management system is used to schedule and manage the hardware-in-the-loop simulation system. The hardware-in-the-loop simulation system is used to establish a hardware-in-the-loop simulation model of the experimental equipment based on the motion characteristics and motion laws of the experimental equipment; to plan experiments based on the experimental parameters; to conduct hardware-in-the-loop simulation experiments on the hardware-in-the-loop simulation system using key sample data of digital simulation based on the experimental plan; and to load random disturbance parameters during the experimental operation to obtain the operating parameters of the experimental equipment after disturbance and the equipment evaluation results.

[0079] Specifically, with the goal of integrating hardware-in-the-loop simulation resources from the industrial sector and flexibly constructing hardware-in-the-loop simulation environments, this embodiment constructs... Figure 4 The hardware-in-the-loop simulation integrated management system shown above enables the scheduling and management of the hardware-in-the-loop simulation system through the interactive application layer and the functional layer. It enables data interaction with the Monte Carlo equipment experimental platform, the digital simulation system, and various hardware-in-the-loop simulation test systems through the general interface layer and the hardware interface layer. It provides a bridge for the simulation model integrated management system to schedule and control the participating hardware-in-the-loop simulation test systems, and supports the joint simulation experiments of diverse scenarios combining virtual and real elements.

[0080] In the design of the hardware-in-the-loop simulation integrated management system, a layered and modular design concept is adopted to complete the layering and division of the framework. Different modules are rationally arranged in their respective appropriate layers, with different functional modules within the same layer. Lower layers provide services to upper layers, and upper layers complete the functions required by their own layers by calling the interfaces of lower layers. The interactive application layer includes an experiment planning module, a node scheduling module, an experiment monitoring module, and an experiment node management module to realize experiment planning, node scheduling control, experiment monitoring, and experiment node management, thereby scheduling, controlling, and monitoring the overall operation flow of the hardware-in-the-loop simulation system.

[0081] For example, the implementation planning module is used to plan experiments based on experimental parameters (set by the Monte Carlo equipment experimental platform) and to send experimental data and key digital simulation sample data to the hardware-in-the-loop simulation node for experimentation. The Monte Carlo equipment experimental platform provides relevant experimental parameters, including the number of experiments.

[0082] The node scheduling module is used to schedule the corresponding hardware-in-the-loop simulation nodes to participate in the experiment based on the number of threads set in the Monte Carlo equipment experimental platform.

[0083] The functional layer provides functions such as clock synchronization management, internal communication management, data transmission and reception management, data storage management, thread management, and dynamic filling management, supporting various functional requirements of the application layer. The general interface layer provides interface protocols with digital simulation systems and Monte Carlo equipment experimental platforms, as well as with hardware-in-the-loop simulation experimental systems, achieving standardized data interaction. The hardware interface layer provides Ethernet, reflective memory network cards, and other general-purpose intelligent I / O devices, supporting the access of heterogeneous hardware and enabling diverse hardware-in-the-loop simulations.

[0084] In this embodiment, the hardware-in-the-loop simulation system used for hardware-in-the-loop simulation of experimental equipment is a distributed system. A distributed real-time simulation system is a complex simulation system composed of multiple subsystems (i.e., hardware-in-the-loop nodes). During the simulation process, each subsystem needs to exchange data in real time. Therefore, the real-time simulation system has strict requirements on the data exchange capabilities between the hardware-in-the-loop nodes. To obtain real-time simulation results, the real-time performance of data transmission during the simulation process is very high, and traditional Ethernet based on the TCP / IP protocol cannot meet these requirements. Compared to the previously used TCP / IP network, the real-time network based on optical front-reflection memory, in addition to having strict transmission determinism and predictability, also features low data transmission latency, high transmission speed, large data transmission volume, simple and easy-to-use communication protocol, light simulator load, strong adaptability to hardware and software platforms, reliable transmission error correction capabilities, and support for interrupt signal transmission. These advantages of the reflective memory network can meet the requirements of real-time performance and large-volume data transmission in a distributed real-time simulation system.

[0085] Furthermore, to address the issue of unstable Windows system clocks, which prevent their use in real-time simulation systems, a clock control mechanism based on the RTX real-time system is adopted. RTX is a general-purpose real-time extension system based on the Windows operating system. RTX boasts excellent real-time controllability, high scalability, and stability, making it the only software-based hard real-time solution on the Windows platform.

[0086] like Figure 5As shown, the hardware-in-the-loop (HIL) simulation system built based on the above technologies is a distributed simulation system composed of multiple HIL nodes. Each HIL node consists of a digital simulation system, a simulation management machine, a real-time simulator, a satellite navigation simulator, and general-purpose intelligent devices, communicating via Ethernet and a reflective memory network. The main control machine and computing node machines run a Windows+RTX system. In real-time simulation mode, the simulation model is compiled into an RTSS real-time executable program, which runs on the RTX real-time subsystem. Windows and the RTX real-time subsystem communicate using shared memory. Simulation nodes are divided into lightweight nodes and fully-fledged simulation nodes. The entire HIL integrated management system can dynamically schedule any simulation node to participate in the entire process of constructing the Monte Carlo equipment experiment according to the requirements of the desired scenario.

[0087] III. Digital Simulation System.

[0088] Digital simulation systems are used to construct three-dimensional models of experimental equipment and scenarios based on hardware-in-the-loop (HIL) models and data from the experimental process, thereby enabling visualization of the experimental process. Specifically, a digital simulation system includes: a virtual engine, used to construct three-dimensional geometric models of experimental equipment and create visualized experimental scenarios, thus achieving visualized reconstruction of equipment status; and a physics engine, used to simulate physical experimental scenarios and provide physical interaction.

[0089] More specifically, to further enhance the realism of the entire experiment, a digital virtual engine and a physics engine are combined to construct the experimental scenario. The entire hardware-in-the-loop simulation experimental scenario is visualized, and the real-time status of the equipment is more realistically reconstructed in a three-dimensional scene. Specifically, a hardware-in-the-loop simulation model of the experimental equipment is established using a hardware-in-the-loop simulation system, and a hardware-in-the-loop simulation experiment is conducted based on key sample data from the digital simulation. Synchronized with the hardware-in-the-loop simulation system in time, the digital simulation system constructs the experimental equipment and experimental scenario in three dimensions based on the hardware-in-the-loop simulation model and data from the experimental process.

[0090] In the experiment, the communication between the digital simulation system and the hardware-in-the-loop simulation system adopts a scheme combining DDS and fiber optics. To achieve communication between the two systems, a virtual-physical system data interaction scheme consisting of three communication types and two communication modules was designed based on the specific data transmission requirements of the experiment. The data interaction scheme is as follows: Figure 6 As shown.

[0091] The Monte Carlo equipment testing platform simultaneously controls the digital simulation system and the hardware-in-the-loop simulation integrated management system via DDS commands. The digital simulation system constructs a 3D geometric model of the equipment and a physical experimental scenario using a virtual engine and a physics engine. The data from the 3D equipment geometric model and the physical environment model of the experimental scenario interact with the Monte Carlo equipment testing platform and the hardware-in-the-loop simulation integrated management system through DDS communication plugins and fiber optic reflection memory communication plugins. The Monte Carlo equipment testing platform is responsible for configuring various experimental task parameters, including but not limited to key parameters of the experimental equipment, key parameters of the experimental environment, and key parameters for interference / fault injection. After configuring the relevant parameters, they are simultaneously distributed to the digital system and the hardware-in-the-loop simulation system via DDS commands. Upon receiving the relevant simulation experimental scenario configuration parameters, the digital system initializes the experimental scenario and the key parameters of the corresponding equipment geometric model to initiate the simulation experiment initialization phase. Similarly, upon receiving the relevant configuration parameters and fault / interference injection parameters for the simulation model, the hardware-in-the-loop simulation system initializes the corresponding hardware-in-the-loop simulation nodes.

[0092] The equipment geometric model and the hardware-in-the-loop simulation node are in one-to-one correspondence. Data interaction between them is achieved through real-time data sharing via a fiber optic reflection memory communication plugin. During the simulation experiment, the virtual / real system clocks of the virtual system and the hardware-in-the-loop simulation system are strictly synchronized using a distributed clock synchronization algorithm.

[0093] Simulation time synchronization within and between the hardware-in-the-loop (HILL) and digital simulation systems is a key technology for achieving co-simulation experiments. Figure 7 As shown.

[0094] The running time inside the hardware-in-the-loop simulation system is set to time 0 when the model receives the running command from the control software, and a fixed time interval is used as the starting step for time verification.

[0095] A real-time fiber optic reflective memory network (FROM) 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 determinism and predictability, but also features high data transmission speed, simple communication protocol, light host load, and strong adaptability to hardware and software platforms. The latency of reflective memory communication nodes is less than 1µs; the fiber optic communication rate can reach 2.125Gbps; independent DMA channels with write speeds of no less than 100Mbps; the FROM consists of fiber optic interface boards plugged into the computer connected together via fiber optic cables to form a ring network. Each node's onboard memory has a copy of the shared data from other nodes. Logically, all nodes in the network share the same memory, with data written at one point and updated simultaneously at multiple points, achieving high-speed data transmission and sharing. The hardware-in-the-loop (HIL) system achieves clock synchronization internally through the reflective memory fiber optic network, with a clock error within 0.5ms. Therefore, the internal time of the HIL system can be considered precise.

[0096] The internal runtime of the digital system is defined as time 0, starting from the moment the runtime command data is sent to the DDS channel. It uses a tick time stepping method (which may result in a 4.99ms stepping size when a 5ms step is set). The internal time of the digital system is inaccurate, but the step size is controllable. Furthermore, the UE digital system acquires its time using the Windows computer system time. The Windows operating system uses a time-slice round-robin mechanism to schedule processes, and network imbalances are unpredictable and uncontrollable. The inability to control when the time synchronization process is scheduled and when it stops causes unpredictable delays in packet transmission over the network, affecting clock synchronization accuracy. Therefore, digital simulation systems under Windows cannot achieve millisecond-level precision synchronization. Thus, 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 crucial to the clock synchronization scheme of the entire virtual-physical co-simulation system.

[0097] Computer clocks typically cannot provide stable, high-precision step sizes for digital systems, nor can they achieve high-precision clock synchronization with the hardware clock of a hardware-in-the-loop (HIL) system. This solution, based on the principles of step size verification and satellite clock cycle alignment, designs a low-cost, high-precision virtual-physical member clock synchronization method. After the HIL system achieves fine synchronization, it outputs simulation step sizes and related verification information. The digital system receives the step size pulse information output by the HIL system as the simulation step size frequency of its logic clock. It also receives and parses related verification step sizes via a network interface as the simulation step size of its logic clock. Furthermore, it uses satellite clock cycles to verify the clocks of the HIL system and the digital simulation system. In this way, the logic clock of the virtual member and the hardware clock of the physical member have highly consistent simulation step sizes and values, achieving high-precision clock synchronization between the digital simulation system and the HIL system.

[0098] Based on the above ideas, such as Figure 8 As shown, the steps for time synchronization between the digital simulation system and the hardware-in-the-loop simulation system include:

[0099] The S101 hardware-in-the-loop (HIL) system achieves clock synchronization between simulation nodes based on the advantages of high transmission efficiency, low latency, and simple protocol of the reflective memory architecture. The reflective memory architecture offers these advantages. In the HIL system, each simulation node utilizes these characteristics to achieve clock synchronization. This step lays the foundation for high-precision clock synchronization of the entire system, ensuring that all parts within the HIL system maintain time consistency, much like soldiers in an army calibrating their time within their respective units to prepare for subsequent coordinated operations.

[0100] Before co-simulation begins, S102 uses a 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 it as a reference, the two systems can be on the same starting line at the initial moment, ensuring that the time difference is within a very small range, and providing initial consistency for subsequent accurate clock synchronization.

[0101] S103 performs clock synchronization based on step size verification when the simulation step size of the digital simulation system is consistent with that of the hardware-in-the-loop simulation system. Specifically, this includes:

[0102] S1031 initializes the system, sets the synchronization period, and sets both the digital simulation system's ready-to-synchronize flag and the synchronization flag to 0. The simulation step counter is then reset to zero. The ready-to-synchronize flag is a status flag indicating 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 synchronization-related critical processing is performed. When a specific hardware-in-the-loop (HIL) simulation system pushback clock synchronization information is received and certain conditions are met, the flag is set to 1, indicating that the system is starting to prepare for synchronization operations, preparing for subsequent critical synchronization operations such as comparing step size values.

[0103] The synchronization flag is also a status flag used to determine whether the clocks of the digital simulation system and the hardware-in-the-loop simulation system have been synchronized. An initial value of 0 indicates incomplete synchronization. With the synchronization flag set to 1, when the simulation step size number obtained by the digital simulation system from the hardware-in-the-loop simulation system matches the current value of its own step size counter, the synchronization flag is set to 1. This means that at the current moment, the digital simulation system, based on the clock synchronization information provided by the hardware-in-the-loop simulation system, confirms that the step sizes of the two systems match and completes one clock synchronization process.

[0104] The simulation step counter is a variable used in a digital simulation system to record and track the simulation step size value.

[0105] When S1032 receives the pushback clock synchronization information from the hardware-in-the-loop simulation system, with both the synchronization preparation flag and the synchronization flag being 0, it parses the simulation number step size value and assigns it to the step size counter, and then sets the synchronization preparation flag to 1.

[0106] When the synchronization flag is 1 and the synchronization flag is 0, the simulation step size number is parsed and compared with the current value of the step size counter. If they are equal, the synchronization flag is set to 1, the synchronization flag is set to 0, and step S1033 is executed. If they are not equal, the synchronization flag is set to 0 and step S1032 is executed.

[0107] S1033 Waits until the synchronization period ends. The system waits in this step until the preset synchronization period ends. The synchronization period is a time interval set for periodically performing clock synchronization checks. During this waiting period, the system maintains its current state to ensure that the relevant operations are completed within a full cycle.

[0108] The S1034 uses a UTC clock source to align the current time of the digital simulation system and the hardware-in-the-loop simulation system. After one synchronization cycle, the time of the two systems is recalibrated to eliminate any time discrepancies that may occur during the synchronization process, ensuring that the time of the two systems remains highly consistent.

[0109] S1035 enters the next synchronization cycle, repeatedly executing steps S1032 to S1035. Through continuous looping, clock synchronization verification and calibration are continuously performed on the digital simulation system and the hardware-in-the-loop simulation system, ensuring that the two systems maintain high-precision clock synchronization throughout the entire simulation process.

[0110] The aforementioned clock synchronization method based on step-size verification involves numerous system blocking and clock adjustments in the digital simulation system. Considering the instability of digital systems, this method is improved. Based on the verification principle of the IEEE 1588 protocol, a clock synchronization method based on reflective memory and master clock verification is proposed. A hardware-in-the-loop (HIL) system clock with high accuracy and stability is used as the master clock, its main task being to send time-related information as a reference for clock correction. The digital system, acting as the slave clock, returns information to the master clock after receiving messages. The deviation between the HIL and digital system clocks is determined by exchanging clock messages between them, and time correction is completed within the digital simulation system.

[0111] Based on the above ideas, such as Figure 9 As shown in the figure, this embodiment also proposes a method for time synchronization between a digital simulation system and a hardware-in-the-loop simulation system, including:

[0112] S111 uses the clock of the hardware-in-the-loop simulation system as the master clock and the clock of the digital simulation system as the slave clock, and sets a correction period. The correction period is set to periodically adjust the slave clock, ensuring that it remains synchronized with the master clock during long-term operation. The length of the correction period needs to be determined based on the specific requirements and stability of the system; a period that is too short will increase system overhead, while a period that is too long may lead to the accumulation of synchronization errors.

[0113] The S112 digital simulation system receives clock synchronization messages from the hardware-in-the-loop simulation system, extracts the master clock time of the synchronization message sent by the hardware-in-the-loop simulation system from the clock synchronization message, and records it as T1.

[0114] The S113 digital simulation system records the arrival time T2 of the clock synchronization message from the clock.

[0115] The S114 digital simulation system sends a message, setting an indicator to start transmitting error measurement within the message, and recording the transmission time T3 from the clock.

[0116] After receiving a message with a start transmission error measurement flag, the S115 hardware-in-the-loop simulation system records the delivery time T4 in its master clock; and sets a flag representing the completion of transmission error measurement in the message pushed back to the digital simulation system, and records the delivery time T4 in the pushed-back message.

[0117] After receiving a message with a completion transmission error measurement flag, the S116 digital simulation system calculates the clock face deviation and time delay of the master and slave times based on T1, T2, T3, and T4, and adjusts the slave clock of the digital simulation system according to the clock face deviation and time delay.

[0118] The deviation between T2 and T1 is the sum of the clock face deviation and time delay in both the physical and digital systems, i.e. ;

[0119] Offset is the clock face deviation between the master and slave clocks, and Delay is the time delay.

[0120] The hardware-in-the-loop simulation system receives a 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 the time delay.

[0121] The deviation between T4 and T3 is the difference between the clock face deviation and time delay in the physical and digital systems, i.e. ;

[0122] Combining the above formulas, we get:

[0123] ;

[0124] ;

[0125] This allows us to obtain the clock face deviation and time delay between the master and slave clocks.

[0126] IV. Online Verification System for Hardware-in-the-Loop Simulation Experiments. The online verification system for hardware-in-the-loop simulation experiments achieves seamless integration of key parameter modules, aerodynamic analysis modules, and equipment simulation models within the environment. It also develops and designs a Monte Carlo equipment testing platform to facilitate rapid design of Monte Carlo experiments and subsequently complete systematic large-sample simulation tests. This enables full-process simulation from the construction of the experimental environment, the random generation of random configuration parameters, the rapid construction of the experimental equipment model, to the statistical analysis of test results, accelerating the efficiency and quality of equipment testing and significantly improving the efficiency and quality of equipment research and development. The following section introduces the system from aspects such as communication infrastructure, data integration standards, experimental method integration, and data storage.

[0127] 1. Communication architecture.

[0128] The primary task of the Monte Carlo equipment testing platform is to extract key sample data from large-scale experiments for use as data for sample-level simulation experiments in a hardware-in-the-loop (HIL) system. By configuring the sample-level experimental situational data and introducing relevant environmental, system bias, and system failure factors through the Monte Carlo platform, multiple batches of HIL system combat scenario verifications can be conducted. This further enhances the realism of the experimental equipment in digital scenarios.

[0129] The hardware-in-the-loop (HIL) simulation integrated management system is a software and hardware system that uses HIL simulation model management software to schedule and manage various heterogeneous hardware-in-the-loop models. To conduct joint simulation experiments, the Monte Carlo equipment experimental platform needs to interact with the HIL simulation integrated management system via data and commands, and report model data, status, and events to the experimental platform, thus enabling data interaction between the experimental task platform and the HIL simulation nodes within the integrated framework.

[0130] The Monta Carlo equipment experimental platform uses DDS communication to communicate with the hardware-in-the-loop integrated management system. Data communication is conducted using a unified hardware-in-the-loop framework integration standard, sending and receiving data in JSON string format. The specific data communication logic is as follows: Figure 10 As shown. First, the number of experimental tasks, the number of threads to be started, and the relevant data to be configured for each task are set through the Monta Carlo equipment experimental platform. The data includes target location, deployment location, fault, deviation injection type, and injection time.

[0131] The Monte Carlo equipment experimental platform sends configured experimental tasks in batches to the hardware-in-the-loop integrated management system via DDS. The system schedules resources and subdivides tasks according to the experimental requirements. Each hardware-in-the-loop simulation node reports its task completion results to the system upon completion. The management software then reports the task completion status back to the Monte Carlo experimental platform via DDS. Based on the progress of each task, the platform initiates the next experimental task. The main task of the Monte Carlo equipment experimental platform is to parse each configured experimental task, transforming the data into a specific data structure. Further, it encapsulates the key configuration data within the data structure into a hardware-in-the-loop integrated framework communication standard and sends 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 task completion, the management software encapsulates the task results into a standard hardware-in-the-loop string and sends the data back to the Monte Carlo equipment experimental platform via DDS, thus completing the closed-loop data communication link for the entire sample experiment.

[0132] 2. Data integration standards

[0133] The semi-physical model integrated management system communicates with the Monte Carlo equipment experimental platform through the DDS communication mechanism. Based on predefined topic names, communication channels, and data standards, high-quality data interaction between the experimental platform and the semi-physical integrated management system can be achieved.

[0134] The hardware-in-the-loop model integrated management system uses control software to manage and schedule each hardware-in-the-loop simulation node. Taking an aircraft as an example, the hardware-in-the-loop model integrated management system and the Monte Carlo equipment experimental platform interact with each other via the DDS communication protocol. The interaction interface and process are as follows:

[0135] ①Interface

[0136] Table 1 Interaction Interface

[0137]

[0138] ② Interaction Flow

[0139] Interactive Implementation Process: After the experimental data configuration of the Monte Carlo equipment experimental platform is completed, an initialization data packet is sent to the hardware-integrated management software via the DDS sending interface function. The specific data content is described as follows:

[0140] Table 2 Initialization Data Packet

[0141]

[0142] After receiving the initialization JSON data packet, the management software parses the JSON data packet, sends the parameters to the real-time simulator to perform model initialization operations, and reports an initialization success event packet. The content of the event packet is described as follows:

[0143] Table 3 Initialization Event Feedback

[0144]

[0145] After receiving the initialization success event packet, the digital agent model sends a feedback to the digital integration framework, waiting to receive and issue a run instruction packet to the hardware-integrated model integration framework. The content of the event packet is described as follows:

[0146] Table 4 Launch Event Packet

[0147]

[0148] The management software receives the launch command packet, parses it, and issues a model execution command to the hardware-in-the-loop model. The model returns a success event and begins real-time simulation. The content of the return event packet is described as follows:

[0149] Table 5 Launch Event Feedback Packet

[0150]

[0151] Subsequently, the semi-physical digital agent model will send JSON data packets for semi-physical model execution in real time via time-stepped Tick data packets, as described below:

[0152] Table 6 Tick Run Data Package

[0153]

[0154] After receiving the JSON data packet, the management software parses it and sends the parameters to the real-time simulator for real-time calculation. The control software then receives the latest model status calculated by the real-time simulator, the details of which are described below:

[0155] Table 7 Tick Reported Data Packets

[0156]

[0157] If the model task is completed or the equipment malfunctions, the control software will report a task completion / malfunction event. After receiving the hit event, the equipment test platform will determine whether the test task is completed based on the task event, as described below:

[0158] Table 8 Task Completion / Fault Event Reporting Data Packet

[0159]

[0160] If it is necessary to inject deviations or faults into the model, the equipment experimental platform sends a fault data packet to the hardware-integrated management software via the DDS sending interface function. The content of the data packet is described as follows:

[0161] Table 9 Fault Injection Event Run Data Packet

[0162]

[0163] 3. Integration of Monte Carlo testing methods

[0164] During actual operation, experimental equipment exhibits deviations in attitude, trajectory, and control parameters from ideal parameters due to environmental factors and system errors. Monte Carlo equipment testing aims to analyze various random disturbances experienced by special equipment in real-world scenarios, providing methods for handling these disturbances and establishing a six-degree-of-freedom Monte Carlo equipment simulation model. Extensive simulation experiments were conducted using a hardware-in-the-loop online verification system to study the accuracy of key parameter data for the experimental equipment, verifying the accuracy of the simulation model and the effectiveness of the simulation methods.

[0165] In this embodiment, the online verification method for hardware-in-the-loop simulation experiments using the Monte Carlo method includes the following steps:

[0166] S1. Based on the motion characteristics and motion laws of the experimental equipment, establish a semi-physical simulation model of the experimental equipment.

[0167] 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 moves linearly, curvilinearly, or rotationally) and its motion laws (such as the dynamic equations it follows and the periodicity of its motion). 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. It's like building a "stage" to simulate the operation of actual equipment, and all subsequent experimental operations will be based on this model.

[0168] S2 extracts key sample data for digital simulation from large-sample experiments.

[0169] After conducting numerous digital simulation experiments (i.e., large-sample experiments), the Monte Carlo equipment testing platform selected key sample data that best reflected real-world conditions from a vast amount of data. This key sample data is representative and reflects the typical operational states of the experimental equipment under different circumstances. For example, in a large-sample experiment simulating aircraft flight, data from key phases such as takeoff, cruise, and landing might be selected as key sample data. This data is of significant guiding importance for experiments conducted on a hardware-in-the-loop simulation system, helping to more accurately simulate actual conditions.

[0170] S3, 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.

[0171] This step is also performed by the Monte Carlo equipment testing platform. First, it's crucial to identify the random disturbances that will affect the experimental equipment during actual operation. For example, outdoor equipment may be affected by weather factors (such as wind speed, wind direction, and temperature changes) and electromagnetic interference. Then, the distribution patterns of these disturbances are determined; for instance, wind speed may follow a certain probability distribution. Based on these distribution patterns, corresponding random disturbance parameters are generated using a specialized platform. These parameters will be loaded into the hardware-in-the-loop simulation system in subsequent experiments to simulate the random disturbances experienced by the experimental equipment in a real environment, making the experiments more closely resemble real-world conditions.

[0172] S4. Configure experimental parameters, plan the experiment according to the experimental parameters, conduct a hardware-in-the-loop simulation experiment on the hardware-in-the-loop simulation system using key sample data of digital simulation according to the experimental plan, and load random interference parameters during the experiment.

[0173] The Monte Carlo equipment testing platform configures various experimental parameters according to the experimental objectives and requirements, such as the number of experiments, the duration of each experiment, and the initial state of the experimental equipment. The hardware-in-the-loop integrated management system then uses these parameters to conduct comprehensive experimental planning, determining the experimental process and steps. Subsequently, on the hardware-in-the-loop simulation system, hardware-in-the-loop simulation experiments are carried out using previously extracted key digital simulation sample data. During the experimental operation, random disturbance parameters are loaded according to a pre-defined method, enabling the experiment to simulate the interference experienced by the experimental equipment in a real environment, thereby observing and studying the operational performance of the experimental equipment under complex conditions.

[0174] S5 uses a semi-physical simulation model and data from the experiment to construct the experimental equipment and scene in three dimensions, thereby enabling visualization of the experimental process.

[0175] Using a hardware-in-the-loop (HIL) model and data generated during experiments, the digital simulation system creates 3D models of the experimental equipment and scenarios. This 3D modeling allows for the visualization of the experimental process by presenting the equipment's appearance, motion, and surrounding environment in intuitive 3D graphics. This helps researchers observe the equipment's operation more clearly and identify potential problems promptly, such as whether the equipment's motion conforms to expectations or whether the impact of interference factors in the experimental environment is readily apparent. This provides a more intuitive basis for experimental analysis.

[0176] S6, obtain the experimental equipment operating parameters and equipment evaluation results after the disturbance.

[0177] Under the influence of random disturbance parameters, the experimental equipment operates in a hardware-in-the-loop simulation system. This step aims to acquire various operational parameters of the experimental equipment after being disturbed, such as attitude parameters (pitch angle, yaw angle, roll angle, etc.), trajectory parameters (position coordinates, velocity, etc.), and control parameters (throttle opening, servo angle, etc.). Simultaneously, the operational performance of the experimental equipment under this disturbance condition is evaluated according to certain evaluation criteria, yielding equipment evaluation results. These results and parameters are crucial for studying the performance of the experimental equipment in real-world complex environments.

[0178] S7, statistical analysis of experimental equipment operating parameters and equipment evaluation results, thereby obtaining statistical characteristic values ​​of key parameters of the experimental equipment.

[0179] After the data is aggregated at the Monte Carlo equipment testing platform, statistical analysis is performed on the operating parameters and evaluation results of the experimental equipment obtained from multiple experiments. Statistical methods are used to calculate the statistical characteristic values ​​of the key parameters of the experimental equipment, such as mean, variance, and standard deviation. The mean reflects the average level of the key parameters, while the variance and standard deviation reflect the dispersion of the parameters. These statistical characteristic values ​​help to gain a deeper understanding of the overall characteristics of the key parameters of the experimental equipment, evaluate the stability and reliability of the experimental equipment under different random disturbances, and provide data support for further optimization of the experimental equipment or improvement of simulation methods.

[0180] The Monte Carlo method is an experimental mathematical approach that uses random numbers for statistical testing to obtain statistical characteristics (such as mean, variance, and probability) as numerical solutions to the problem. It employs probabilistic mathematical models and the statistical characteristics of the physical processes of the studied problem to reproduce the process. The Monte Carlo equipment experimental platform is based on a hardware-in-the-loop (HIL) framework and is developed in C++. It serves as a unified management and analysis tool for experimental task data configuration, control command generation, result reporting and parsing, and statistical analysis of experimental results within the HIL management system. The platform interacts with the HIL framework via the DDS communication protocol, enabling the management and scheduling of simulation models at various simulation nodes to complete the simulation experiment.

[0181] The online verification system for hardware-in-the-loop simulation experiments provides a complete method for integrating simulation models based on a hardware-in-the-loop integration framework. Its basic principles are as follows: Figure 11 As shown, the Monte Carlo equipment testing platform uses random data generation technology to encapsulate the equipment model input data into a tabular ASCII file. First, the platform parses the input parameters from the input file and sends the input data as instructions to the hardware-in-the-loop simulation integrated management system via the DDS protocol. The integrated management system dynamically schedules the simulation nodes to be started. The simulation nodes execute the simulation model program for analysis and calculation, and finally return the analysis results to the hardware-in-the-loop integrated management system. The system then reports the results to the Monte Carlo equipment testing platform for data statistics. Finally, the input and output data are stored in a designated file, and the input and output parameters during the simulation experiment are obtained by parsing the output file.

[0182] 4. Data storage.

[0183] To address the data storage problem of large-sample semi-physical simulation data, this paper establishes a data abstraction link based on database design patterns and target data objects, and implements a database table design to reflect large-sample semi-physical simulation data. Based on a transaction mechanism, concurrent memory access control for large data samples is implemented, effectively controlling CPU and I / O resource load while ensuring throughput. Data visualization provides data support for the validation of large-sample semi-physical models, such as... Figure 12 As shown.

[0184] like Figure 13 As shown, in the semi-physical large-sample simulation experiment, the semi-physical simulation system obtains data from multiple data sources (model parameters returned by the simulation model, and heartbeat monitoring of simulation nodes), calls the database driver in the form of a dynamic link library (DLL), and inserts data into the transaction queue through the API interface, waiting for concurrent execution of transactions. The interface accepts SQL execution statements and data. It interacts with the server through the visualization tool Navicat and the model validation platform to query and obtain relevant data. Data can also be exported through Navicat and provided to the model validation platform.

[0185] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, 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 to cause a computer terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0186] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A hardware-in-the-loop simulation experimental system based on a Monte Carlo equipment experimental platform, characterized in that... include: The Monte Carlo equipment experimental platform is used to configure experimental parameters, provide key sample data for digital simulation, and generate random interference parameters. Statistical analysis was conducted on 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. A hardware-in-the-loop simulation integrated management system is used for scheduling and managing a hardware-in-the-loop simulation system. The hardware-in-the-loop simulation system is used to establish a hardware-in-the-loop simulation model of the experimental equipment based on its motion characteristics and motion laws; to plan experiments based on the experimental parameters; and to conduct hardware-in-the-loop simulation experiments on the hardware-in-the-loop simulation system using key sample data from digital simulation based on the experimental plan. During the experimental operation, random interference parameters issued by the Monte Carlo equipment experimental platform are loaded to obtain the experimental equipment operation parameters and equipment evaluation results after disturbance. The Monte Carlo equipment experimental platform uses DDS communication to communicate with the semi-physical integrated management system. The data communication method uses a unified semi-physical framework integration standard to send and receive data in JSON string format. Digital simulation systems are used to construct three-dimensional models of experimental equipment and scenarios based on semi-physical simulation models and data from the experimental process, thereby enabling visualization of the experimental process. The digital simulation system constructs a three-dimensional geometric model of the equipment and a physical experimental scene through a virtual engine and a physics engine. The data of the three-dimensional equipment geometric model and the physical environment model of the experimental scene are exchanged 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 reflection memory communication plug-in. During the simulation experiment, the digital simulation system and the hardware-in-the-loop (HIL) simulation system synchronize their time. Specifically, the HIL simulation system synchronizes its clock internally via a reflective memory fiber optic network. After synchronizing, the HIL simulation system outputs clock synchronization information, which includes step pulse information and related verification step size. The digital simulation system receives the step pulse information output by the HIL simulation system as the digital system's logic clock simulation step size frequency, receives and parses the related verification step size through a network interface as the digital simulation system's logic clock simulation step size, and uses a satellite clock cycle to verify the clocks of the HIL simulation system and the digital simulation system.

2. The hardware-in-the-loop simulation experimental system based on the Monte Carlo equipment experimental platform according to claim 1, 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; 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 for digital simulation from large-sample experiments for use in hardware-in-the-loop simulation experiments, and to generate random interference parameters.

3. The hardware-in-the-loop simulation experimental system based on the Monte Carlo equipment experimental platform according to claim 1, characterized in that: The hardware-in-the-loop simulation system is a distributed system, comprising several hardware-in-the-loop simulation nodes; the three-dimensional model of the experimental equipment constructed by the digital simulation system corresponds one-to-one with the hardware-in-the-loop simulation nodes; the hardware-in-the-loop simulation nodes include lightweight nodes and fully-equipped simulation nodes.

4. The hardware-in-the-loop simulation experimental system based on the Monte Carlo equipment experimental platform according to claim 2, characterized in that: The hardware-in-the-loop (HIL) nodes transmit data between each other via optical fiber reflection memory.

5. A hardware-in-the-loop simulation experimental system based on a Monte Carlo equipment experimental platform according to claim 1, characterized in that... The hardware-in-the-loop simulation integrated management system includes: The experiment planning module is used to plan experiments based on experimental parameters and send experimental data and key sample data of digital simulation to the hardware-in-the-loop simulation node for experimentation. The node scheduling module is used to schedule the corresponding hardware-in-the-loop simulation nodes to participate in the experiment based on the number of threads set in the Monte Carlo equipment experimental platform.

6. A hardware-in-the-loop simulation experimental method based on a Monte Carlo equipment experimental platform, characterized in that... Based on the hardware-in-the-loop simulation experimental system according to any one of claims 1 to 5, specifically, the hardware-in-the-loop simulation experimental method includes: S1. Based on the motion characteristics and motion laws of the experimental equipment, establish a semi-physical simulation model of the experimental equipment. S2, extract key sample data for digital simulation from large-sample experiments; S3, 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; S4. The experimental parameters are configured using the Monte Carlo equipment experimental platform, and the experimental planning is carried out using the hardware-in-the-loop simulation integrated management system based on the experimental parameters. According to the experimental planning, the hardware-in-the-loop simulation experiment is carried out using digital simulation key sample data on the hardware-in-the-loop simulation system, and random interference parameters are loaded during the experimental operation. S5. The digital simulation system synchronizes time with the hardware-in-the-loop simulation system, and uses the digital simulation system to construct the experimental equipment and experimental scene in three dimensions based on the hardware-in-the-loop simulation model and data during the experiment, so as to realize the visualization of the experimental process. Specifically, the time synchronization between the digital simulation system and the hardware-in-the-loop simulation system first uses the hardware-in-the-loop simulation system to complete clock synchronization within the system through a reflective memory fiber optic network. After the hardware-in-the-loop simulation system completes synchronization, it outputs clock synchronization information, which includes step pulse information and related verification step size. The digital simulation system receives the step pulse information output by the hardware-in-the-loop simulation system as the logic clock simulation step size frequency of the digital system, receives and parses the related verification step size through the network interface as the logic clock simulation step size of the digital simulation system, and uses satellite clock period to verify the clocks of the hardware-in-the-loop simulation system and the digital simulation system. S6, obtain the experimental equipment operating parameters and equipment evaluation results after the disturbance; S7, statistical analysis of experimental equipment operating parameters and equipment evaluation results, thereby obtaining statistical characteristic values ​​of key parameters of the experimental equipment.

7. The hardware-in-the-loop simulation method based on a Monte Carlo equipment experimental platform according to claim 6, characterized in that: A hardware-in-the-loop simulation model of the experimental equipment was established using a hardware-in-the-loop simulation system, and a hardware-in-the-loop simulation experiment was conducted based on key sample data from digital simulation.

8. A hardware-in-the-loop simulation experimental method based on a Monte Carlo equipment experimental platform according to claim 7, characterized in that... The steps for time synchronization between digital simulation systems and hardware-in-the-loop simulation systems include: The S101 hardware-in-the-loop simulation system synchronizes clocks among its internal simulation nodes. S102 uses a UTC clock source to align the start time of the digital simulation system and the hardware-in-the-loop simulation system. S103 performs clock synchronization based on step size verification when the simulation step size of the digital simulation system is consistent with that of the hardware-in-the-loop simulation system. Specifically, this includes: S1031 initialization, setting the synchronization period, setting both the digital simulation system synchronization flag and synchronization flag to 0, and resetting the simulation step counter to zero; When S1032 receives the pushback clock synchronization information from the hardware-in-the-loop simulation system, with both the synchronization preparation flag and the synchronization flag being 0, it parses the simulation number step size value and assigns it to the step size counter, and then sets the synchronization preparation flag to 1. When the synchronization flag is 1 and the synchronization flag is 0, the simulation step size number is parsed and compared with the current value of the step size counter. If they are equal, the synchronization flag is set to 1, the synchronization flag is set to 0, and step S1033 is executed. If they are not equal, the 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 the current time of the digital simulation system and the hardware-in-the-loop simulation system. S1035 enters the next synchronization cycle, and steps S1032 to S1035 are executed cyclically.

9. A hardware-in-the-loop simulation method based on a Monte Carlo equipment experimental platform according to claim 7, characterized in that... The steps for time synchronization between digital simulation systems and hardware-in-the-loop simulation systems include: S111 uses the clock of the hardware-in-the-loop simulation system as the master clock and the clock of the digital simulation system as the slave clock, and sets the calibration period. The S112 digital simulation system receives clock synchronization messages from the hardware-in-the-loop simulation system, extracts the master clock time of the synchronization message sent by the hardware-in-the-loop simulation system from the clock synchronization message, and records it as T1. The S113 digital simulation system records the arrival time T2 of the clock synchronization message from the clock. The S114 digital simulation system sends a message, setting an indicator to start transmitting error measurement within the message, and recording the transmission time T3 from the clock. After receiving a message with a start transmission error measurement flag, the S115 hardware-in-the-loop simulation system records the delivery time T4 in its master clock; and sets a flag representing the completion of 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 a message with a completion transmission error measurement flag, the S116 digital simulation system calculates the clock face deviation and time delay of the master and slave clocks based on T1, T2, T3, and T4, and adjusts the slave clock of the digital simulation system according to the clock face deviation and time delay.

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