A method for constructing behavior-centered simulation models based on meta-models

Abstract

A method for constructing a behavior-centric simulation model based on a meta-model is presented. The method includes constructing a micro-behavioral meta-model, which comprises control-type meta-model elements and functional-type meta-model elements. Based on the functional-type and control-type meta-model elements in the micro-behavioral meta-model, a micro-behavioral model with three behavioral levels is constructed, and a simulation model centered on the micro-behavioral model is built. This method decouples the behavior in functional components from the entity model and utilizes meta-modeling technology to model the entire behavioral space in a low-code manner, improving the efficiency of behavior modeling. Simultaneously, by decoupling the function and control of behavior in the behavioral structure, it technically supports modeling behavior at any granularity and combinations of behaviors with different granularities, solving the problem of reusability of behaviors at different levels in the simulation model and improving the development efficiency of the simulation model.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method for constructing a behavior-centered simulation model based on a meta-model. Background Technology

[0002] Analyzing complex problems using simulation methods typically requires building massive, interdisciplinary, and cross-domain simulation models. Traditional modeling techniques usually employ inheritance and composition methods to construct simulation model frameworks, and then use these frameworks to reuse existing models and jointly develop new ones, thereby improving modeling efficiency.

[0003] The simulation model framework based on inheritance and composition belongs to the entity-centric model framework, mainly reflected in two aspects: First, the modeling process: first, the entity model is built, then the behavioral model is built, and finally the relationships between entity behaviors are set; second, the model structure: it is organized around the entity model, which provides simple behaviors by integrating functional components, and provides more complex behaviors by integrating behavioral models. This entity-centric model framework has two problems: first, the development efficiency of simple behaviors is low; second, the level of simple behaviors is uneven. These two problems restrict the reusability of the simulation model at different levels and also reduce the development efficiency of the simulation model. Summary of the Invention

[0004] Therefore, it is necessary to provide a method for constructing a behavior-centered simulation model based on a meta-model to address the aforementioned technical problems.

[0005] According to the first aspect of this application, a method for constructing a behavior-centered simulation model based on a meta-model is proposed, the method comprising:

[0006] Construct a micro-behavioral meta-model; the micro-behavior refers to the behavior of constructing the smallest identifiable entity associations of a complex system; the micro-behavioral meta-model includes control class meta-model elements and functional class meta-model elements;

[0007] Based on the functional meta-model elements and control meta-model elements in the micro-behavioral meta-model, a micro-behavioral model including three behavioral levels is constructed.

[0008] Construct a simulation model centered on the aforementioned microscopic behavior model.

[0009] In one implementation, the control meta-model elements include six control meta-model elements: start, end, sequence, parallel, selection, and convergence.

[0010] In one implementation, the functional meta-model elements include two types of functional meta-model elements: general functional meta-model elements and special functional meta-model elements.

[0011] In one implementation, the general-purpose functional meta-model elements define the loading, input, output, and invocation interfaces for general-purpose functions, formally described as follows:

[0012] ;

[0013] in, This represents the set of functional model interfaces specified by the elements of the general functional class metamodel. This indicates the loading interface, which, when used, loads the component's dynamic library into memory. This indicates the input interface. Each time this functional component is used, this interface must be called first to assign values ​​to the functional calculation parameters. This indicates that an interface is invoked after the input interface is invoked. It specifies the specific function in the dynamic library to be invoked for functional calculation to generate the required output. This represents the output interface. After each use of a functional component, the output result of the functional calculation is obtained by calling this interface.

[0014] In one implementation, the special functional class metamodel elements include six elements: loading function, assembly behavior, generating entity, unregistering entity, model output, and model interaction.

[0015] The formal description is as follows:

[0016] ;

[0017] in, This indicates the loading function, which means loading a dedicated functional model component from the model library; This indicates the assembly behavior, which involves loading a behavior from the model library and combining it with the behavior being edited. This indicates that an entity will be generated, that is, an entity of a specified type will be created and initial parameters will be assigned to it. This indicates the destruction of an entity, that is, deregistering a specified entity from the simulation engine and causing it to exit the simulation; This represents the model output, which is the latest state of the entity and the events generated by key state changes. This indicates model interaction, which means that this entity needs to exchange information with other entities or other components within the entity.

[0018] In one implementation, the construction of a micro-behavioral model comprising three behavioral levels based on the functional meta-model elements and control meta-model elements in the micro-behavioral meta-model includes:

[0019] Simulation behavior is divided into functional level behavior, action level behavior, and task level behavior. Functional level behavior refers to general or special functions, action level behavior refers to the minimum identifiable behavioral capabilities provided by a specific entity, and task level behavior refers to behaviors that can complete independent business capabilities.

[0020] Based on the elements of the dedicated function class meta-model, a dedicated function model is developed, and the dedicated functions corresponding to the dedicated function model exist in the form of a loadable dynamic library.

[0021] Based on the control class meta-model elements, special function models, and general function meta-model elements, behaviors of two levels, action-level behaviors and task-level behaviors, are constructed in a tree structure.

[0022] In one embodiment, the method further includes:

[0023] The micro-behavioral model is run based on a hierarchical finite state machine.

[0024] In one embodiment, the hierarchical finite state machine includes an upper-level behavioral logic control state machine and a lower-level functional state machine.

[0025] The operation of the micro-behavioral model based on a hierarchical finite state machine includes:

[0026] The upper-level behavioral logic control state machine is used to parse the behavioral logic of the micro-behavioral model and issue functional operation instructions to the lower-level state machine according to the behavioral logic sequence.

[0027] The lower-level functional state machine is used to load general functional class meta-model elements or special functional models and execute specified behaviors according to the control instructions issued by the upper-level behavioral logic control state machine.

[0028] In one implementation, the simulation model centered on the microscopic behavior model includes the following components:

[0029] The system comprises a logic control state machine, a functional state machine, a behavior container, an entity container, a function container, a component manager, a shared blackboard, behavior interaction, behavior output, behavior input, the micro-behavioral meta-model, and behavior data; wherein, the behavior container is used at least to load the logic control state machine; the function container is used at least to load the functional state machine; and the behavior data includes the micro-behavioral model and a dedicated function model dynamic library.

[0030] According to a second aspect of this application, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described in the first aspect above.

[0031] Based on the above-mentioned behavior-centric model construction method based on metamodel, the behavior in functional components is decoupled from the entity model. By using metamodel technology, the entire behavior space is modeled in a low-code manner, which improves the efficiency of behavior modeling. At the same time, the behavior runtime environment and behavior description are decoupled in the behavior structure, and the function and control of the behavior are decoupled. This can technically support the modeling of behavior at any granularity, as well as the combination of behaviors at different granularities. This solves the problem of reusability of behaviors at different levels in simulation models and also improves the development efficiency of simulation models. Attached Figure Description

[0032] Figure 1 This is a flowchart illustrating a behavior-centered simulation model construction method based on a meta-model, as provided in one embodiment.

[0033] Figure 2 This is a schematic diagram of a JSON-formatted micro-behavioral model provided in one embodiment;

[0034] Figure 3 This is a schematic diagram of a behavior-centered simulation model construction device based on a meta-model provided in one embodiment;

[0035] Figure 4 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0037] The simulation model framework based on inheritance and composition belongs to the entity-centric model framework, which is mainly reflected in two aspects: First, the modeling process: first, build the entity model, then build the behavior model, and finally set the entity behavior relationship; second, the model structure: organized around the entity model, the entity model provides simple behavior by integrating functional components, and provides more complex behavior by integrating the behavior model. This entity-centric model framework has two main problems: First, it suffers from low-code and no-code development efficiency for simple behaviors. While the framework provides low-code and no-code support for complex behavior models using behavior trees, state machines, and workflows, simple behaviors coupled within functional components require full-code development for initial development or subsequent adjustments. Furthermore, common functions such as line-of-sight computation (used by both communication and detection components) have low reusability, hindering overall model development efficiency. Second, the levels of simple behaviors are inconsistent. For example, in a commercial simulation system, functional components include behaviors such as returning to the airport, activating sensors, and maneuvering towards a target point. The modeling granularity of these three behaviors is not at the same level. Returning to the airport involves coordinated human and aircraft actions; activating sensors involves a simple power button; and maneuvering towards a target point is somewhere in between. This lack of uniformity in behavior levels restricts the reusability of entity and behavior models across simulation platforms. These two problems, at different levels, limit the reusability of simulation models and reduce their development efficiency.

[0038] To address the aforementioned problems, this invention provides a behavior-centric model construction method based on a meta-model. The behavior-centric approach is reflected in three aspects: first, the modeling process, which constructs behaviors first, then entities; second, the model structure, which organizes the model structure around behaviors, decoupling behaviors in functional components from the entity model, and using meta-model technology to improve the efficiency of behavior modeling by modeling the entire behavior space in a low-code manner; and third, the behavior structure, which decouples the behavior runtime environment from the behavior description, implementing different levels of behavior operation through hierarchical finite state machines, decoupling the function and control of behaviors, and technically supporting behavior modeling at any granularity, as well as combinations of behaviors at different granularities, thus solving the problem of reusing behaviors at different levels.

[0039] Specifically, such as Figure 1 As shown, a method for constructing a behavior-centric model based on a meta-model includes:

[0040] S101, Construct a micro-behavioral meta-model; the micro-behavior refers to the behavior of constructing the smallest identifiable entity associations of a complex system; the micro-behavioral meta-model includes control class meta-model elements and functional class meta-model elements;

[0041] S102, Based on the functional meta-model elements and control meta-model elements in the micro-behavioral meta-model, construct a micro-behavioral model including three behavioral levels;

[0042] S103, Construct a simulation model centered on the microscopic behavior model.

[0043] The micro-behavioral meta-model in S101 above will be introduced below.

[0044] Micro-behavior refers to the behavior of constructing the smallest identifiable entity relationships in a complex system. The micro-behavior metamodel includes elements of the control class metamodel and elements of the functional class metamodel. Formal description:

[0045] ;

[0046] in, This represents a meta-model of micro-behavior. Represents the elements of the control class meta-model. Represents the functional class meta-model elements.

[0047] The control class metamodel elements include six types: start, end, sequence, parallelism, selection, and merging. Formal description:

[0048] ;

[0049] in, The term "start" indicates the beginning of the metamodel, i.e., the start of a behavioral logic or sub-behavioral logic; "End" indicates the end of the metamodel, i.e., the end of a behavioral logic or sub-behavioral logic. This represents a sequential metamodel, where there is a one-to-one relationship between the preceding and following behaviors, and the execution logic is to execute the preceding behavior first, followed by the following behavior. This represents a parallel meta-model, where there is a one-to-many relationship between pre-processing and post-processing behaviors. The execution logic is to execute the pre-processing behavior first, followed by the simultaneous execution of multiple parallel post-processing behaviors. This indicates a selection metamodel, where there is a one-to-many relationship between pre-actions and post-actions. The execution logic involves restricting the pre-action, and then selecting one post-action from multiple options based on its state value. This represents the convergence meta-model, indicating that the preceding and following behaviors have a many-to-one relationship, and the execution logic is that the following behavior is executed only after the preceding behavior is completed.

[0050] Functional metamodel elements include two types: general and specific. Their formal description is as follows:

[0051] ;

[0052] in, Represents the elements of the dedicated functional metamodel. This represents the elements of the general functional meta-model.

[0053] The elements of the general-purpose functional metamodel define the loading, input, output, and calling interfaces of general-purpose functions, and are formally described as follows:

[0054] ;

[0055] in, This represents the set of functional model interfaces specified by the elements of the general functional class metamodel; This represents the loading interface, which loads the component dynamic library into memory when used. The component dynamic library predefines general functional model components, such as line-of-sight calculation equations, radar equations, and six-degree-of-freedom kinematic equations.

[0056] This indicates the input interface. Each time this functional component is used, this interface must be called first to assign values ​​to the functional calculation parameters.

[0057] This indicates that an interface is invoked after the input interface is invoked. It specifies the specific function in the dynamic library to be invoked for functional calculation to generate the required output.

[0058] This represents the output interface. After each use of a functional component, the output result of the functional calculation is obtained by calling this interface.

[0059] The elements of the special function class metamodel include six elements: loading function, assembly behavior, entity generation, entity unregistration, model output, and model interaction.

[0060] The formal description is as follows:

[0061] ;

[0062] in, This indicates the loading function, which means loading a dedicated functional model component from the model library; This indicates the assembly behavior, which involves loading a behavior from the model library and combining it with the behavior being edited. This indicates that an entity will be generated, that is, an entity of a specified type will be created and initial parameters will be assigned to it. This indicates the destruction of an entity, that is, deregistering a specified entity from the simulation engine and causing it to exit the simulation; This represents the model output, which is the latest state of the entity and the events generated by key state changes. This indicates model interaction, which means that this entity needs to exchange information with other entities or other components within the entity.

[0063] After constructing the micro-behavioral meta-model that includes functional meta-model elements and control meta-model elements, a micro-behavioral model including three behavioral levels can be constructed based on the micro-behavioral meta-model.

[0064] Specifically, the above S102 can be implemented in the following way.

[0065] Simulation behavior is divided into functional level behavior, action level behavior, and task level behavior. Functional level behavior refers to general or special functions, action level behavior refers to the minimum identifiable behavioral capabilities provided by a specific entity, and task level behavior refers to behaviors that can complete independent business capabilities.

[0066] Based on the elements of the dedicated function class meta-model, a dedicated function model is developed, and the dedicated functions corresponding to the dedicated function model exist in the form of a loadable dynamic library.

[0067] Based on the control class meta-model elements, special function models, and general function meta-model elements, behaviors of two levels, action-level behaviors and task-level behaviors, are constructed in a tree structure.

[0068] Specifically, simulation behavior is divided into functional-level behavior, action-level behavior, and task-level behavior, which are formally described as follows:

[0069] ;

[0070] in, Represents a set of behavioral hierarchies. This represents the behavior at the functional level, referring to the behavior implemented by elements of the general functional meta-model, such as line-of-sight computing, radar equations, and six-degree-of-freedom maneuvering. This represents the action-level behavior, that is, the minimum identifiable behavioral capability provided by a specific entity, such as sensor area search, aircraft air patrol, etc. This represents task-level behavior, which is the behavior that can complete independent business operations. It is usually achieved by combining control class meta-model elements, functional class meta-model elements, and functional level behaviors as needed, such as air cargo transportation and facility and equipment maintenance.

[0071] When developing a dedicated functional model based on the elements of a dedicated functional meta-model, simulation modeling users can develop a dedicated functional model based on the interface requirements of the dedicated functional meta-model and form a loadable dynamic library.

[0072] When constructing actions and tasks based on elements of the control meta-model, specialized function models, and general function meta-model, the constructed behaviors are described using a tree structure. The constructed tree-structured behavior model is stored in the database in JSON format, as detailed below. Figure 2As shown, it is used for the construction and running of subsequent simulation models.

[0073] After constructing the micro-behavioral model, it can be run. Specifically, this application proposes to run the micro-behavioral model based on a hierarchical finite state machine to decouple the function and control of the behavior. The hierarchical finite state machine includes an upper-level behavioral logic control state machine and a lower-level functional state machine. The upper-level behavioral logic control state machine is embedded in a behavior container to parse the behavioral logic of the micro-behavioral model and issue functional operation instructions to the lower-level state machine according to the sequence of behavioral logic.

[0074] The lower-level functional state machine is embedded in the functional container to load general functional class meta-model elements or special functional models and execute specified behaviors according to the control instructions issued by the upper-level behavioral logic control state machine.

[0075] The logical control state machine and the functional state machine have a one-to-many relationship, meaning that one logical control state machine can control the operation of multiple functional state machines. The logical control state machine manages the functional state machines by querying the model structure data in the entity container and issues functional operation instructions to the functional state machines through the interactive blackboard. Functional states exist in the form of components, supporting one component for one function or several related functions to build one component. The functional state machine loads and calls the functional function according to the interface specified by the functional meta-model and obtains the output.

[0076] In S103 above, after constructing the microscopic behavior model, a simulation model centered on the microscopic behavior model can be constructed, wherein the simulation model includes:

[0077] The logic control state machine, functional state machine, behavior container, entity container, component manager, shared blackboard, functional container, behavior interaction, behavior output, behavior input, behavior metamodel, and behavior data are 12 components, formally described as follows:

[0078] SMFBM={LFSM, FFSM, BehCon, EntCon, FunCon, ComMgr, ShrBB, BehItr,BehIpt, BehOpt, BehuMM, BehDat}

[0079] SMFBM represents the framework structure of a microscopic behavior simulation model based on a meta-model.

[0080] LFSM stands for Logic Control State Machine, which is used to load the behavioral logic (one type of micro-behavioral model data) of a micro-behavioral model and, based on the behavioral logic, issue instructions to functional state machines to control the operation of one or more functional state machines.

[0081] FFSM stands for Functional State Machine. Based on the instructions issued by the logic state machine, it loads and runs general functional class meta-model elements or special functional models.

[0082] BehCon represents the behavior container, which is used to load two components: the logic control state machine and the behavior interaction. It supports reading and writing the input and output of behavior from the entity component through the interaction component, and controls the functional state machine in the functional container through the interaction component.

[0083] EntCon represents the entity container, which is used to load four components: shared blackboard, component manager, behavior input, and behavior output. The component manager enables the assembly of simulation entities and the loading of behaviors. The behavior input and behavior output encapsulate the input and output interfaces of the engine to achieve standardized input and output of the simulation model.

[0084] FunCon represents a function container, used to load the function state machine;

[0085] ComMgr stands for Component Manager, which is used to register, unregister, and read entity and behavior components loaded inside the simulation model;

[0086] ShrBB stands for Shared Blackboard, which supports the storage, reading and writing of dynamic state data and static parameter data of the model, and supports write mutex locks;

[0087] BehItr represents a behavioral interaction component that supports data interaction between entities and between different components within an entity.

[0088] BehIpt represents behavioral input, used to receive three types of input data from the simulation model: control commands, instantiation, and interaction. It supports loading corresponding behavioral data according to control commands, generating micro-simulation behaviors using behavioral containers, creating a simulation model instance based on instantiation data, and parsing the semantics of interaction data between models and between components within the model.

[0089] BehOpt represents behavior output, used to implement model state, events, interactions, and creation / deregistration entity outputs. State output refers to recording the model's running process and supporting the model's state for data analysis. Events refer to key model state changes. Interactions refer to the interaction semantics parsed from the behavior input, using interaction components to achieve detection, communication, and other interactions between models and between components within a model. Creation / deregistration entity outputs refer to the creation of new models and the deregistration of existing models generated during the execution of behaviors, such as a plane taking off at an airport and landing after reaching its destination.

[0090] BehMM represents a micro-behavioral meta-model, which includes functional meta-model elements and control meta-model elements, as detailed above.

[0091] BehDat represents behavioral data, which includes three types of data: model parameters, micro-behavioral models, and dynamic libraries of dedicated functional models.

[0092] The micro-behavioral model and micro-behavioral meta-model to be constructed will build the simulation model in the form of components. Meta-model technology can be used to model the entire behavioral space in a low-code manner, which improves the efficiency of behavioral modeling. Furthermore, the common functional meta-model elements in the micro-behavioral meta-model can be directly used to reuse common functions without having to redevelop common functions for each simulation model. In addition, by designing the functional meta-model elements and control meta-model elements in the micro-behavioral meta-model, the function and control of behavior are decoupled, which can technically support the modeling of behavior at any granularity and the combination of different granularities of behavior. This solves the problem of reusing behaviors at different levels and greatly improves the development efficiency and reusability of simulation models.

[0093] Based on the same inventive concept, such as Figure 3 This application also proposes a behavior-centered simulation model construction device based on a meta-model, the device comprising:

[0094] Metamodel construction module 110 is used to construct a micro-behavioral metamodel; the micro-behavior refers to the behavior of constructing the minimum identifiable entity associations of a complex system; the micro-behavioral metamodel includes control class metamodel elements and functional class metamodel elements.

[0095] The behavior model construction module 120 is used to construct a micro-behavioral model including three behavioral levels based on the functional meta-model elements and control meta-model elements in the micro-behavioral meta-model.

[0096] The simulation model building module, 130, is used to build a simulation model centered on the microscopic behavior model.

[0097] For specific limitations regarding the device, please refer to the limitations of the method above, which will not be repeated here. Each module in the above device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0098] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 4As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a Chinese event relationship extraction model construction method or a Chinese event relationship extraction method. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0099] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0100] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the behavior-centered simulation model construction method based on meta-model as described in any of the above embodiments.

[0101] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the behavior-centered simulation model construction method based on meta-model as described in any of the above embodiments.

[0102] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

Claims

1. A method for constructing a behavior-centered simulation model based on a meta-model, characterized in that, The method includes: Construct a micro-behavioral meta-model; the micro-behavior refers to the behavior of constructing the smallest identifiable entity associations of a complex system; the micro-behavioral meta-model includes control class meta-model elements and functional class meta-model elements to decouple the function and control of behavior during modeling and support the reuse of behavior at different levels; the functional class meta-model elements include two types of functional meta-model elements: general functional class meta-model elements and special functional class meta-model elements. Based on the functional meta-model elements and control meta-model elements in the micro-behavioral meta-model, a micro-behavioral model including three behavioral levels is constructed to improve the efficiency of behavioral modeling by using meta-model technology to model the entire behavioral space in a low-code manner. The micro-behavioral model is run based on a hierarchical finite state machine to achieve decoupling of the function and control of the behavior at runtime. The hierarchical finite state machine includes an upper-level behavioral logic control state machine and a lower-level functional state machine; The operation of the micro-behavioral model based on a hierarchical finite state machine includes: The upper-level behavioral logic control state machine is used to parse the behavioral logic of the micro-behavioral model and issue functional operation instructions to the lower-level state machine according to the behavioral logic sequence. The lower-level functional state machine is used to load general functional class meta-model elements or special functional models according to the control instructions issued by the upper-level behavioral logic control state machine, and execute the specified behavior; Construct a simulation model centered on the aforementioned microscopic behavior model; Based on the functional meta-model elements and control meta-model elements in the micro-behavioral meta-model, a micro-behavioral model comprising three behavioral levels is constructed, including: Simulation behavior is divided into functional level behavior, action level behavior, and task level behavior. Functional level behavior refers to general or special functions, action level behavior refers to the minimum identifiable behavioral capabilities provided by a specific entity, and task level behavior refers to behaviors that can complete independent business capabilities. Based on the elements of the dedicated function class meta-model, a dedicated function model is developed, and the dedicated functions corresponding to the dedicated function model exist in the form of a loadable dynamic library. Based on the control class meta-model elements, special function models, and general function meta-model elements, behaviors of two levels, action level behavior and task level behavior, are constructed in a tree structure.

2. The method according to claim 1, characterized in that, The control meta-model elements include six types: start, end, sequence, parallel, selection, and convergence.

3. The method according to claim 1, characterized in that, The general-purpose functional meta-model elements define the loading, input, output, and calling interfaces for general-purpose functions, and are formally described as follows: ； Among them, SpcFBMitf represents the set of functional model interfaces specified by the general functional class meta-model elements; ComLod represents the loading interface, which loads the component dynamic library into memory when used; ComInput represents the input interface, which needs to be called before each use of the functional component to assign values ​​to the functional calculation parameters; ComFunCall represents the calling interface, which is called after calling the input interface to specify the specific functional function in the component dynamic library for functional calculation to generate the required output; and ComOutput represents the output interface, which is called after each use of the functional component to obtain the functional calculation output results.

4. The method according to claim 3, characterized in that, The special functional meta-model elements include six elements: loading function, assembly behavior, entity generation, entity cancellation, model output, and model interaction. The formal description is as follows: ； Among them, LoadFun represents the loading function, which loads a dedicated functional model component from the model library; AsmBeh represents the assembly behavior, which loads a behavior from the model library and combines it with the behavior being edited; CrtEnt represents the entity generation, which creates an entity of a specified type and assigns it initial parameters; DstEnt represents the entity destruction, which unregisters a specified entity from the simulation engine and causes it to exit the simulation; MdlOutput represents the model output, which outputs the latest state of this entity and the events generated by key state changes; MdlItr represents the model interaction, which is the information exchange that this entity needs to perform with other entities or other components within the entity.

5. The method according to claim 1, characterized in that, The simulation model centered on the microscopic behavior model includes the following components: The system comprises a logic control state machine, a functional state machine, a behavior container, an entity container, a function container, a component manager, a shared blackboard, behavior interaction, behavior output, behavior input, the micro-behavioral meta-model, and behavior data; wherein, the behavior container is used at least to load the logic control state machine; the function container is used at least to load the functional state machine; and the behavior data includes the micro-behavioral model and a dedicated function model dynamic library.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

Images