Implementation method of full-simulation experimental environment

Abstract

The invention relates to an implementation method of a full-simulation experiment environment, which belongs to the technical field of software intelligent manufacturing and comprises the following steps of: 1, constructing a unified basic framework of the full-simulation experiment environment; step 2, constructing a generality module of a unified basic framework of a full-simulation experiment environment, wherein the generality module comprises a workbench module, a tool integration module, a process automation module, a data intelligent management module, a work cooperation module and an engineering project management module; and 3, respectively constructing a numerical simulation subsystem, a system simulation subsystem and a model simulation subsystem based on the full simulation experiment environment. According to the method, a unified basic framework is adopted for tool integration and process construction, and the method can be applied to construction of a full-simulation design environment, a full-simulation experiment environment and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of software intelligent manufacturing, and in particular to a method for realizing a full simulation experiment environment. Background Art

[0002] The development of aviation, aerospace and atomic energy technology in the 1940s and 1950s promoted the advancement of simulation technology. The rapid development of computer technology in the 1960s provided advanced tools for simulation technology and accelerated its development.

[0003] Using computers to simulate the system is not only convenient and flexible, but also more economical. Therefore, computer simulation occupies an important position in simulation technology. In the early 1950s, most of the simulation research on continuous systems was carried out on analog computers. In the mid-1950s, people began to use digital computers to realize digital simulation. Computer simulation technology then developed in two directions: analog computer simulation and digital computer simulation. After adding logic control and analog storage functions to analog computer simulation, hybrid analog computer simulation and hybrid computer simulation that combined hybrid analog computers and digital computers appeared. In the process of developing simulation technology, a large number of simulation packages and simulation languages ​​have been developed. In the late 1970s, a dedicated all-digital parallel simulation computer was successfully developed.

[0004] The tools of simulation mainly refer to simulation hardware and simulation software. Simulation hardware includes computers and special physical simulators, such as motion simulators, target simulators, load simulators, and environmental simulators. Simulation software includes simulation programs, simulation packages, simulation languages, and simulation software systems with databases as the core. There are many types of simulation software, which are used in the engineering field for system performance evaluation, such as mechanism dynamics analysis, control mechanics analysis, structural analysis, thermal analysis, and processing simulation.

[0005] The simulation method mainly refers to the method of establishing simulation models and conducting simulation experiments, which can be divided into two categories: simulation methods for continuous systems and simulation methods for discrete event systems. People sometimes also include the method of establishing mathematical models in the simulation method. This is because although there is a set of theoretical modeling and experimental modeling methods for continuous systems, when simulating the system, an approximate model obtained through assumptions is often used to test whether the assumptions are correct. If necessary, the model is modified to make it closer to the real system. For discrete event systems, establishing its mathematical model is part of the simulation.

[0007] The wide variety of simulation software has promoted the demand for integrated simulation platforms. The current mainstream integrated simulation platforms include general simulation platforms, multidisciplinary simulation platforms and industry simulation platforms. General simulation platforms include Matlab, AMESim, etc.; multidisciplinary simulation platforms include LMS, etc.; industry simulation platforms include X-Plane, FlightGear, Gazebo, JMavSim, AirSim, etc. in the field of UAV development.

[0008] Most of the current mainstream integrated simulation platforms focus more on simulation algorithms, and provide less support for system simulation and model simulation other than numerical simulation. Summary of the invention

[0009] In order to solve the above-mentioned problems of the prior art, a simulation system can integrate application tools of different disciplines, and multiple simulation tools can be shared among different members. A method for realizing a simulation experiment environment for experimental tasks that require multiple positions, multiple roles, and multiple people to collaborate is provided.

[0010] A method for implementing a full simulation experiment environment, comprising:

[0011] Step 1: Define a unified basic framework for the full simulation experiment environment covering three directions: numerical simulation and emulation, system simulation and emulation, and model simulation and emulation;

[0012] Step 2: Construct common modules of the unified basic framework of the full simulation experiment environment, the common modules include a workbench module, a tool integration module, a process automation module, a data intelligent management module, a work collaboration module and an engineering project management module; the workbench module is used to perform visual operations of various simulation businesses; the tool integration module is used to integrate various simulation tools to realize the sharing of simulation tools; by establishing a collection of numerical simulation, system simulation and model simulation, and then establishing subsets of the components of each of the above three collections in a tree structure; the process automation module is used to realize the automated process control of process editing, process mining, event monitoring and message queue functions; the data intelligent management module is used to realize simulation process data collection, intelligent clustering and merging of simulation results and visual presentation of simulation conclusions; the work collaboration module is used to realize task allocation, joint fault handling, joint abnormality correction, result merging and zeroing; the engineering project management module is used to manage each full simulation experiment project;

[0013] Step 3: Use common modules to build numerical simulation subsystems, system simulation subsystems, and model simulation subsystems on the unified basic framework of the full simulation experiment environment.

[0014] Furthermore, the construction of the numerical simulation subsystem is specifically as follows:

[0015] Customize the block diagram and parameters of each module, build the overall block diagram according to the numerical simulation task, and clarify the location and parameter settings of each module;

[0016] Call the system's own tools or integrated external tools to perform numerical simulation calculations;

[0017] Obtain the various results output by the tool, explain these results, and summarize them to obtain numerical simulation results;

[0018] Based on the analysis of numerical simulation results, corresponding experimental conclusions are drawn, and further optimization and improvement plans are proposed.

[0019] Furthermore, the various results output by the tool include simulation data, graphs and tables.

[0020] Furthermore, the construction system simulation subsystem is specifically:

[0021] Establish or confirm system simulation objectives;

[0022] Collect basic data required for system simulation;

[0023] Import or establish system simulation models;

[0024] Based on the system simulation model, the discrete event simulation engine is used to perform simulation operations and output the operation results of the simulation model;

[0025] Conduct system simulation analysis and optimization.

[0026] Furthermore, the system simulation objectives include:

[0027] Descriptive analysis: Through simulation analysis of running data and calculation of statistical values, charts are drawn and quantitative indicators of various performances are obtained. Descriptive analysis is used to simulate and verify existing solutions.

[0028] Diagnostic analysis: Based on the descriptive analysis results, analyze the performance bottlenecks and find the causes, and find a better solution by running simulations multiple times;

[0029] Predictive analysis: simulate various random factors, design a large number of simulation experiments, analyze the trend of system performance changes under different parameters, and establish the relationship between response variables and input variables through predictive analysis, as well as the possible results after the input variables are changed;

[0030] Guidance analysis: Combine simulation with operations research methodology to provide suggestions for the optimal design and operation plans of the system, and provide improvement plans through guidance analysis.

[0031] Furthermore, the system simulation goal is selected to achieve one or more of the four goals simultaneously.

[0032] Furthermore, the establishment of the system simulation model specifically includes: creating various objects and describing their relationships; setting the attributes and behaviors of the objects; importing basic data and establishing the relationship between objects and data; defining business processes; defining user, role and resource models and their access mechanisms; and defining various constraints and restrictions.

[0033] Furthermore, the model building simulation subsystem is specifically:

[0034] Establish or confirm model simulation objectives;

[0035] Collect basic data required for model simulation;

[0036] Use discrete event simulation engine to perform simulation operation and output the simulation operation results of the model;

[0037] Conduct model simulation analysis and optimization.

[0038] Furthermore, the model simulation objectives include:

[0039] Descriptive analysis: By simulating and analyzing running data and calculating statistical values, drawing charts, and obtaining quantitative indicators of various performances; descriptive analysis is used to simulate and verify existing models;

[0040] Diagnostic analysis: Based on the descriptive analysis results, analyze the bottlenecks of the model and find the causes, and run the simulation multiple times to find a better solution;

[0041] Predictive analysis: simulate various random factors, design a large number of simulation experiments, and analyze the changing trends of model availability and reliability under different parameters. Through predictive analysis, establish the relationship between response variables and input variables, and the possible results after the input variables are changed;

[0042] Guidance analysis: Combine simulation with operations research methodology to provide recommendations on the optimal design and evolution of the model, and provide improvement plans through guidance analysis.

[0043] Furthermore, the model simulation target is selected to achieve one or more of the four targets simultaneously.

[0044] Beneficial effects of the present invention:

[0045] 1. Through a unified basic framework, tool integration and process building can be applied to the construction of full simulation design environment, full simulation experiment environment, etc. The unified framework enables complex system engineering to be presented simply and intuitively.

[0046] 2. Adopted external tool integration strategy. For tools that have not been integrated, code calling, script programming, parameter passing and calling are provided for integration; for external tools that have been integrated, they are used through buttons, drag and drop, commands, etc.

[0047] 3. Provide a process engine and multiple process operation modes, allowing tools and workflows to automatically execute, automatically correct, and automatically repair, thereby achieving flexible and controllable processes.

[0048] 4. In the complex design and experimental process, multiple people, multiple equipment and multiple tools are often involved. This solution optimizes the business logic of distributed collaboration to achieve cross-domain and cross-platform work collaboration.

[0049] 5. During the experiment and analysis process, a large number of simulation tasks will generate a large amount of various data. FSE supports automatic collection and clustering of data, and provides it to business experts and experimental personnel in the form of groups, thereby avoiding the loss and distortion caused by data transcription to a certain extent, and also improving the efficiency of experimental result processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a flow chart of the full simulation experiment environment design method of the present invention;

[0051] Figure 2 is the external tool integration logic diagram of the present invention;

[0052] Figure 3 is a diagram of the external tool grouping of the present invention;

[0053] Figure 4 It is a local aerodynamic model diagram of the flight simulation system of the present invention. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] Example 1: Reference Figure 1 A method for realizing a full simulation experimental environment simultaneously supports numerical simulation, system simulation and model simulation, adopts an external tool integration strategy, supports process intelligence, supports work collaboration, supports intelligent data management, supports multiple simulation components, provides multiple experimental scenario designs and multidisciplinary integration optimization algorithms. In this embodiment, the process of establishing a full simulation experimental environment for a flight simulation system is taken as an example.

[0056] 1. Define a unified basic framework for the full simulation experimental environment of a flight simulation system. Cover the three main directions of full simulation: (1) numerical simulation and emulation; (2) system simulation and emulation; (3) model simulation and emulation.

[0057] 2. Construct the common modules of the unified basic framework of a full simulation experimental environment of a flight simulation system, including workbench module, tool integration module, process automation module, data intelligent management module, work collaboration module, engineering project management module, etc.

[0058] Build a workbench module. The workbench is a visual operation interface for executing various simulation businesses. The top is a menu bar and a toolbar; the upper left part is the project panel, which presents the relevant materials of the simulation project in the form of a resource tree; the lower left part is the simulation record panel, which presents the record entries of multiple simulations of a project; the upper middle part is the operation and display panel, which is used to design and modify the simulation process, steps, applied simulation tools, etc., as well as to present the simulation execution process and post-execution data, graphs, tables, etc.; the lower middle part is the timeline and log panel respectively; on the right is the quick grouping of commonly used functions and tools, such as information, preview, and change pattern / table, etc.

[0059] To build the tool integration module, first establish three sets of tool integration modules according to the three main directions of full simulation: 1. Numerical simulation; 2. System simulation; 3. Model simulation. Then establish subsets of the above three sets in a tree structure. Numerical simulation can establish subsets such as structural simulation components, fluid mechanics simulation components, thermal simulation components, electromagnetic simulation components, dynamics simulation components, general post-processing components, general computing components, etc.; system simulation can establish subsets such as system simulation components, process simulation components, failure simulation components, super-resolution simulation components, etc. Figure 2 shows the external tool integration logic of the present invention, Figure 3 The default grouping of external tools of the present invention is shown.

[0060] Build a process automation module, which includes process editing, process mining, event monitoring, message queue functions, etc. The process automation module focuses on the control flow of the automated process. It focuses on the sequence of tasks rather than a single task.

[0061] Build a data intelligent management module to realize tasks such as simulation process data collection, intelligent clustering and merging of simulation results, and visualization of simulation conclusions.

[0062] Build a work collaboration module to realize tasks such as task allocation, joint fault handling, joint anomaly correction, result merging, and zeroing.

[0063] Build engineering project management module.

[0064] 3. Construct the numerical simulation and simulation subsystem of a flight simulation system.

[0065] Complete the overall block diagram of a certain flight simulation system numerical simulation task, the location of each module and the description of parameter settings, the block diagram and parameters of the custom modules, etc.

[0066] Call the system's own tools or integrated external tools to perform numerical simulation calculations, and obtain stable values ​​after multiple simulation experiments.

[0067] Summarize simulation results. Intercept or export simulation data, graphs, tables, etc. output by simulation tools, and provide explanatory notes on these results.

[0068] Analysis and conclusion: Based on the numerical simulation results, corresponding experimental conclusions are drawn, and further optimization and improvement plans are proposed.

[0069] 4. Construct the system simulation and simulation subsystem of a flight simulation system.

[0070] Establish or confirm the simulation goal. Before executing the simulation program, you should confirm the goal of system simulation, that is, why the simulation is performed, what indicators to analyze, etc. The goals of system simulation mainly include the following four categories:

[0071] 1) Descriptive Analysis: The purpose is to obtain quantitative indicators of various performances through simulation analysis of running data, calculation of statistical values, and drawing of charts. Descriptive analysis is mostly used for simulation reproduction and verification of existing solutions.

[0072] 2) Diagnostic Analysis: The purpose is to analyze performance bottlenecks and find causes based on descriptive analysis results, and run simulations multiple times to find a better solution.

[0073] 3) Predictive Analysis: Simulate various random factors, design a large number of simulation experiments, and combine causal analysis, regression analysis, correlation analysis and other methods to analyze the trend of system performance changes under different parameters. Through predictive analysis, the relationship between the response variable and the input variable is established, and the possible results after the input variable changes.

[0074] 4) Prescriptive Analysis: Combine simulation with operations research methodology to provide suggestions for the optimal design and operation plans of the system, and provide improvement plans through prescriptive analysis.

[0075] Basic data collection. System simulation requires the support of basic data. The comprehensiveness and authenticity of basic data have a direct impact on the feasibility of simulation results. These data include process data, resource data, order data, fault data, quality data, and other data.

[0076] Simulation modeling (skip this step if the system model has been established and imported). Use the system's own tools or external tools to establish a system simulation model. The general process is as follows:

[0077] 1) Create various objects and describe their relationships.

[0078] 2) Define object properties and behaviors.

[0079] 3) Import basic data and establish associations between objects and data.

[0080] 4) Define business processes.

[0081] 5) Define user, role and resource models and their access mechanisms.

[0082] 6) Define various constraints and restrictions.

[0083] Simulation, analysis and optimization. Based on the system simulation model, the discrete event simulation engine is used to perform simulation operations and output the operation results of the simulation model. The simulation process generally includes three stages: simulation analysis based on original conditions, simulation analysis of key parameter changes, and simulation analysis after optimization.

[0084] 5. Construct a flight simulation system model and simulation subsystem.

[0085] MBSE and SCBSE have transitioned from traditional document-based systems engineering to model-based systems engineering, which is enough to reflect the importance of models. As the cornerstone of MBSE and SCBSE engineering, the reliability and integrity of models also require simulation and emulation, in order to predict in advance the anomalies and problems that are difficult to find in the actual application of the model. FSE supports the simulation and emulation of models. Figure 4 A diagram of the local aerodynamic model of a flight simulation system is shown.

[0086] Establish or confirm the simulation goal. Before executing the simulation program, the goal of the model simulation should be confirmed, that is, why the simulation is performed, what indicators to analyze, etc. The goals of the model simulation mainly include the following four categories:

[0087] 1) Descriptive Analysis: The purpose is to obtain quantitative indicators of various performances by running data and calculating statistical values ​​through simulation analysis, drawing charts, etc. Descriptive analysis is mostly used for simulation reproduction and verification of existing models.

[0088] 2) Diagnostic Analysis: The purpose is to analyze the bottlenecks of the model and find the causes based on the descriptive analysis results, and run the simulation multiple times to find a better solution.

[0089] 3) Predictive Analysis: Simulate various random factors, design a large number of simulation experiments, and combine causal analysis, regression analysis, correlation analysis and other methods to analyze the model availability and reliability change trends under different parameters. Through predictive analysis, the relationship between the response variable and the input variable is established, and the possible results after the input variable changes.

[0090] 4) Prescriptive Analysis: Combine simulation with operations research methodology to provide suggestions for the optimal design and evolution of the model, and provide improvement plans through prescriptive analysis.

[0091] Basic data collection. Model simulation also requires the support of basic data. The comprehensiveness and authenticity of basic data have a direct impact on the feasibility of simulation results.

[0092] Execute model simulation. Use the discrete event simulation engine to perform simulation operations and output the simulation operation results of the model. The simulation process generally includes three stages: simulation analysis based on original conditions, simulation analysis with key parameter changes, and simulation analysis after optimization.

[0093] Simulation analysis and optimization.

[0094] In the description of the embodiments of the present invention, it should be understood that the terms "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "center", "top", "bottom", "top", "bottom", "inside", "outside", "inner side", "outer side" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. Among them, "inside" refers to an internal or enclosed area or space. "Periphery" refers to the area surrounding a specific component or a specific area.

[0095] In the description of the embodiments of the present invention, the terms "first", "second", "third", and "fourth" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", "third", and "fourth" may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "multiple" means two or more.

[0096] In the description of the embodiments of the present invention, specific features, structures, materials or characteristics may be combined in a suitable manner in any one or more embodiments or examples.

[0097] In the description of the embodiments of the present invention, the term "and / or" herein is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " herein generally indicates that the associated objects before and after are in an "or" relationship.

[0098] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for implementing a full simulation experiment environment, characterized in that: include: Step 1: Define a unified basic framework for the full simulation experiment environment covering three directions: numerical simulation and emulation, system simulation and emulation, and model simulation and emulation; Step 2: Construct the common modules of the unified basic framework of the full simulation experiment environment, which include the workbench module, tool integration module, process automation module, data intelligent management module, work collaboration module and engineering project management module; the workbench module is used to perform visual operations of various simulation businesses; the tool integration module is used to integrate various simulation tools to realize the sharing of simulation tools; by establishing a collection of numerical simulation, system simulation and model simulation, and then establishing subsets of the components of each of the above three collections in a tree structure; the process automation module is used to realize the automated process control of process editing, process mining, event monitoring and message queue functions; The data intelligent management module is used to realize simulation process data collection, intelligent clustering and merging of simulation results, and visualization of simulation conclusions; the work coordination module is used to realize task allocation, joint fault handling, joint abnormality correction, result merging and zeroing; the engineering project management module is used to manage each full simulation experiment project; Step 3: Use common modules to build numerical simulation subsystem, system simulation subsystem and model simulation subsystem on the unified basic framework of the whole simulation experiment environment.

2. The method for realizing a full simulation experiment environment according to claim 1, characterized in that: The construction of the numerical simulation subsystem is specifically as follows: Customize the block diagram and parameters of each module, build the overall block diagram according to the numerical simulation task, and clarify the location and parameter settings of each module; Call the system's own tools or integrated external tools to perform numerical simulation calculations; Obtain the various results output by the tool, explain these results, and summarize them to obtain numerical simulation results; Based on the analysis of numerical simulation results, corresponding experimental conclusions are drawn, and further optimization and improvement plans are proposed.

3. The method for realizing a full simulation experiment environment according to claim 2, characterized in that: The various results output by the tool include simulation data, graphs and tables.

4. The method for realizing a full simulation experiment environment according to claim 1, characterized in that: The construction system simulation subsystem is specifically: Establish or confirm system simulation objectives; Collect basic data required for system simulation; Import or establish system simulation models; Based on the system simulation model, the discrete event simulation engine is used to perform simulation operations and output the operation results of the simulation model; Conduct system simulation analysis and optimization.

5. The method for realizing a full simulation experiment environment according to claim 4, characterized in that: The system simulation objectives include: Descriptive analysis: By simulating and analyzing running data and calculating statistical values, drawing charts, and obtaining quantitative indicators of various performances, descriptive analysis is used to simulate and verify existing solutions; Diagnostic analysis: Based on the descriptive analysis results, analyze the performance bottlenecks and find the causes, and find a better solution by running simulations multiple times; Predictive analysis: simulate various random factors, design a large number of simulation experiments, analyze the trend of system performance changes under different parameters, and establish the relationship between response variables and input variables through predictive analysis, as well as the possible results after the input variables are changed; Guidance analysis: Combine simulation with operations research methodology to provide suggestions for the optimal design and operation plans of the system, and provide improvement plans through guidance analysis.

6. The method for realizing a full simulation experiment environment according to claim 5, characterized in that: The selection of the system simulation goal is to achieve one or more of the four goals at the same time.

7. The method for realizing a full simulation experiment environment according to claim 4, characterized in that: The establishment of the system simulation model specifically includes: creating various objects and describing their relationships; setting the attributes and behaviors of the objects; importing basic data and establishing the relationship between objects and data; defining business processes; defining users, roles and resource models and their access mechanisms; and defining various constraints and restrictions.

8. The method for realizing a full simulation experiment environment according to claim 1, characterized in that: The model building simulation subsystem is specifically: Establish or confirm model simulation objectives; Collect basic data required for model simulation; Use discrete event simulation engine to perform simulation operation and output the simulation operation results of the model; Conduct model simulation analysis and optimization.

9. The method for realizing a full simulation experiment environment according to claim 8, characterized in that: The model simulation objectives include: Descriptive analysis: By simulating and analyzing running data and calculating statistical values, drawing charts, and obtaining quantitative indicators of various performances; descriptive analysis is used to simulate and verify existing models; Diagnostic analysis: Based on the descriptive analysis results, analyze the bottlenecks of the model and find the causes, and run the simulation multiple times to find a better solution; Predictive analysis: simulate various random factors, design a large number of simulation experiments, and analyze the changing trends of model availability and reliability under different parameters; establish the relationship between response variables and input variables through predictive analysis, and the possible results after the input variables are changed; Guidance analysis: Combine simulation with operations research methodology to provide recommendations on the optimal design and evolution of the model, and provide improvement plans through guidance analysis.

10. The method for realizing a full simulation experiment environment according to claim 9, characterized in that: The model simulation goal is to achieve one or more of the four goals at the same time.

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