Knowledge graph-based simulation model encapsulation knowledge modeling method

Abstract

The application provides a simulation model encapsulation knowledge modeling method based on a knowledge graph, and relates to the technical field of simulation modeling. The method comprises the following steps: modeling encapsulation knowledge by using a graph structure element, obtaining a formal description of the encapsulation knowledge, dividing the encapsulation knowledge into nine encapsulation knowledge categories, formally describing the nine encapsulation knowledge categories, obtaining encapsulation knowledge connotation of the encapsulation knowledge, obtaining encapsulation ontology knowledge of the encapsulation knowledge, and obtaining encapsulation ontology relationship knowledge of the encapsulation knowledge. The method provides a standardized encapsulation framework and process, so that the simulation model to be integrated can be integrated into a simulation platform in a plug-and-play manner, and the difficulty and cost of integration are greatly reduced.

Description

Technical Field

[0001] This invention relates to the field of simulation modeling technology, and in particular to a knowledge graph-based method for encapsulating knowledge modeling in simulation models. Background Technology

[0002] Conducting scientific experiments using simulation methods requires full utilization of existing heterogeneous simulation model resources within the field. There are four approaches to addressing the reuse of simulation models: custom development, middleware-based integration, encapsulation, and assembly. Custom development is time- and manpower-intensive; middleware-based integration is unsuitable for the large-sample, ultra-real-time simulation needs of scientific experiments; and assembly is not applicable to heterogeneous simulation model resources. Currently, encapsulation is generally used for reusing simulation models.

[0003] However, traditional encapsulation methods require modification or adjustment of the model code to reuse the simulation model, which not only increases the complexity of integration but may also introduce new errors. Summary of the Invention

[0004] This invention provides a knowledge graph-based method for encapsulating knowledge modeling in simulation models. It addresses the shortcomings of traditional encapsulation methods that involve manually modifying the interface parameter code of the model to be integrated, which are complex and prone to errors. By providing a standardized encapsulation framework and process, an encapsulation layer can be added to the outside of the simulation model without modifying the simulation model to be integrated. This allows the model to be integrated into the simulation platform in a plug-and-play manner, greatly reducing the difficulty and cost of integration.

[0005] In the process of model encapsulation and integration, this invention focuses on solving the problem of encapsulation knowledge modeling, that is, what knowledge is needed and how to describe it when encapsulating heterogeneous simulation models. This invention provides a knowledge graph-based method for knowledge model encapsulation in simulation models, including the following steps.

[0006] The encapsulated knowledge is modeled using graph structure elements to obtain a formal description of the encapsulated knowledge. The graph structure elements include nodes, relation edges, and attributes. The nodes are used to describe the concept ontology in the encapsulated knowledge, the relation edges are used to describe the relationships between the concepts, and the attributes are used to describe the attributes of the nodes and / or the relation edges.

[0007] The encapsulated knowledge is divided into nine categories, and the nine categories are formally described to obtain the encapsulated knowledge connotation. The nine categories include entity metadata knowledge, entity category attribute knowledge, entity structure knowledge, behavior knowledge, standard interface parameter knowledge, unit conversion knowledge, standard object model knowledge, cross-resolution mapping knowledge, and encapsulation process knowledge.

[0008] The encapsulation ontology knowledge of the encapsulation knowledge and the encapsulation ontology relationship knowledge of the encapsulation knowledge are obtained. The encapsulation ontology knowledge is determined based on the subordinate ontology of the nine encapsulation knowledge categories, and the encapsulation ontology relationship knowledge is determined based on the relationship between the subordinate ontology of the nine encapsulation knowledge categories.

[0009] According to the knowledge graph-based simulation model encapsulation knowledge modeling method provided by the present invention, the attributes are further used to describe the connotation of the concept and the relationship, and the relationship edges are further used to describe the extension of the concept and the relationship.

[0010] According to the present invention, a knowledge graph-based simulation model encapsulation knowledge modeling method is provided, wherein the encapsulated knowledge is divided into nine encapsulated knowledge categories, and the nine encapsulated knowledge categories are formally described to obtain the encapsulated knowledge connotation, including:

[0011] Determine the target knowledge classification corresponding to the nine encapsulated knowledge categories, wherein the target knowledge classification is either declarative knowledge classification or procedural knowledge classification;

[0012] Based on the target knowledge classifications corresponding to the nine encapsulated knowledge categories, the nine encapsulated knowledge categories are described as a set of declarative knowledge classifications and a set of procedural knowledge classifications. The set of declarative knowledge classifications includes entity metadata knowledge, entity category attribute knowledge, entity structure knowledge, standard interface parameter knowledge, and standard object model knowledge. The set of procedural knowledge classifications includes encapsulated process knowledge, behavioral knowledge, cross-resolution mapping knowledge, and dimensional conversion knowledge.

[0013] According to the present invention, a knowledge graph-based simulation model encapsulation knowledge modeling method is provided, wherein obtaining the encapsulation ontology knowledge of the encapsulated knowledge includes:

[0014] Based on the subordinate ontology corresponding to each of the nine encapsulation knowledge categories, a formal description of each encapsulation knowledge category is performed to obtain the encapsulation ontology knowledge of each encapsulation knowledge category.

[0015] The encapsulation ontology knowledge of the encapsulation knowledge is determined based on the encapsulation ontology knowledge of each encapsulation knowledge category.

[0016] According to the present invention, a knowledge graph-based simulation model encapsulation knowledge modeling method is provided. The entity metadata knowledge includes entity morphology ontology, model quality ontology, coordinate system ontology, time progression mechanism ontology, and resolution ontology. The entity category attribute knowledge includes entity category ontology and entity attribute ontology. The entity structure knowledge includes container component ontology, physical component ontology, and behavioral component ontology. The standard interface parameter knowledge includes entry simulation interface set ontology, model input interface set ontology, time progression interface ontology, model output interface set ontology, and exit simulation interface ontology. The standard object model knowledge includes interaction mechanism ontology and interaction content ontology. The encapsulation process knowledge includes model description ontology, composability analysis ontology, encapsulation code generation ontology, and packaging into a library ontology. The cross-resolution mapping knowledge includes attribute mapping ontology and object model mapping ontology. The dimensional conversion knowledge includes source dimensional ontology, target dimensional ontology, and conversion formula ontology.

[0017] According to the knowledge graph-based simulation model encapsulation knowledge modeling method provided by the present invention, the step of obtaining the encapsulation ontology relation knowledge of the encapsulated knowledge includes:

[0018] Determine the relationships between subordinate entities in the encapsulated knowledge, wherein the relationships between subordinate entities correspond to at least one of the following relationships: composition relationship, behavior relationship, interaction relationship, inheritance relationship, triggering relationship, process relationship, and mapping relationship.

[0019] The relationships between the subordinate ontologies are formally described to obtain the encapsulated ontology relationship knowledge of the encapsulated knowledge.

[0020] The present invention also provides a knowledge modeling device for encapsulating simulation models based on knowledge graphs, comprising the following modules.

[0021] The knowledge description module is used to model encapsulated knowledge using graph structure elements to obtain a formal description of the encapsulated knowledge. The graph structure elements include nodes, relation edges, and attributes. The nodes are used to describe the concept ontology in the encapsulated knowledge, the relation edges are used to describe the relationships between the concepts, and the attributes are used to describe the attributes of the nodes and / or the relation edges.

[0022] The knowledge connotation module is used to divide the encapsulated knowledge into nine categories of encapsulated knowledge and to formally describe the nine categories of encapsulated knowledge to obtain the encapsulated knowledge connotation of the encapsulated knowledge. The nine categories of encapsulated knowledge include entity metadata knowledge, entity category attribute knowledge, entity structure knowledge, behavior knowledge, standard interface parameter knowledge, unit conversion knowledge, standard object model knowledge, cross-resolution mapping knowledge, and encapsulation process knowledge.

[0023] The ontology and relation module is used to obtain the encapsulation ontology knowledge of the encapsulation knowledge and the encapsulation ontology relation knowledge of the encapsulation knowledge. The encapsulation ontology knowledge is determined based on the subordinate ontology of the nine encapsulation knowledge categories, and the encapsulation ontology relation knowledge is determined based on the relationship between the subordinate ontology of the nine encapsulation knowledge categories.

[0024] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the knowledge graph-based simulation model encapsulation knowledge modeling method as described above.

[0025] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the knowledge graph-based simulation model encapsulation knowledge modeling method as described above.

[0026] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the knowledge graph-based simulation model encapsulation knowledge modeling method as described above.

[0027] The knowledge graph-based simulation model encapsulation knowledge modeling method provided by this invention provides a standardized encapsulation framework and process, enabling the simulation model to be integrated into the simulation platform in a plug-and-play manner, which greatly reduces the difficulty and cost of integration. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0029] Figure 1 This is a flowchart illustrating the knowledge modeling method for encapsulating simulation models based on knowledge graphs provided by this invention.

[0030] Figure 2 This is a schematic diagram illustrating the heterogeneity between different forms of simulation model resources provided by this invention.

[0031] Figure 3 This is a flowchart illustrating the method for obtaining encapsulated knowledge content provided by the present invention.

[0032] Figure 4 This is a flowchart illustrating the method for obtaining encapsulated ontology knowledge provided by the present invention.

[0033] Figure 5 This is a flowchart illustrating the method for obtaining encapsulated ontology relationship knowledge provided by the present invention.

[0034] Figure 6 This is a schematic diagram of the knowledge modeling device based on a knowledge graph simulation model provided by the present invention.

[0035] Figure 7 This is a schematic diagram of the physical structure of the electronic device provided by the present invention. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this 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 this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0037] Conducting scientific experiments using simulation methods requires full utilization of existing heterogeneous simulation model resources in the field. There are four methods for reusing simulation models within the field: custom development, middleware-based integration, encapsulation, and assembly. Custom development methods are time- and manpower-intensive; middleware-based integration methods are unsuitable for the large-sample, ultra-real-time simulation needs of scientific experiments; and assembly methods are not applicable to heterogeneous simulation model resources. Currently, encapsulation methods are generally used for reusing simulation models.

[0038] However, traditional encapsulation methods require modification or adjustment of the model code to reuse the simulation model, which not only increases the complexity of integration but may also introduce new errors.

[0039] In view of this, embodiments of the present invention provide a method for modeling knowledge encapsulation in simulation models based on knowledge graphs. This method includes modeling the encapsulated knowledge using graph structure elements to obtain a formal description of the encapsulated knowledge; dividing the encapsulated knowledge into nine categories and formally describing each of these categories to obtain the encapsulated knowledge connotation; acquiring the encapsulated ontology knowledge of the encapsulated knowledge; and acquiring the encapsulated ontology relationship knowledge of the encapsulated knowledge. This method provides a standardized encapsulation framework and process, enabling the simulation model to be integrated into the simulation platform in a plug-and-play manner, significantly reducing the difficulty and cost of integration.

[0040] The technical solutions of the present invention will now be described with reference to the accompanying drawings in the embodiments of the present invention.

[0041] Figure 1This is a flowchart illustrating the knowledge graph-based simulation model encapsulation knowledge modeling method provided by the present invention. The knowledge graph-based simulation model encapsulation knowledge modeling method can be applied to electronic devices, which can be various types of devices with information processing capabilities during implementation. For example, the electronic device may include a personal computer, laptop, PDA, or server; the electronic device may also be a mobile terminal, such as a mobile phone, in-vehicle computer, tablet computer, or projector. Figure 1 As shown, the method may include steps 101 to 103.

[0042] Step 101: Model the encapsulated knowledge using graph structure elements to obtain a formal description of the encapsulated knowledge. The graph structure elements include nodes, relation edges, and attributes. The nodes are used to describe the concept ontology in the encapsulated knowledge, the relation edges are used to describe the relationships between the concepts, and the attributes are used to describe the attributes of the nodes and / or the relation edges.

[0043] It should be noted that using graph structure elements such as nodes, edges, and attributes to model encapsulated knowledge can be formally described as follows:

[0044] In this invention, EnvKnoStr represents the encapsulated knowledge description method, Concep represents the concept ontology in the encapsulated knowledge, which is described using nodes, Rela represents the relationship between concepts in the encapsulated knowledge, which can be described using directed edges in the relationship edges, and Attr represents the attribute describing the concept ontology nodes and relationship edges.

[0045] Furthermore, the attributes are also used to describe the connotation of the concept and the relationship, and the relationship edges are also used to describe the extension of the concept and the relationship.

[0046] It is evident that formal descriptions can also use attributes to describe the connotation of concepts and relationships, and edges to describe the extension of concepts.

[0047] Step 102: Divide the encapsulated knowledge into nine categories and formally describe the nine categories to obtain the encapsulated knowledge connotation. The nine categories include entity metadata knowledge, entity category attribute knowledge, entity structure knowledge, behavior knowledge, standard interface parameter knowledge, unit conversion knowledge, standard object model knowledge, cross-resolution mapping knowledge, and encapsulation process knowledge.

[0048] It should be noted that encapsulation knowledge is used to support intelligent encapsulation of simulation models. Encapsulation knowledge can include nine categories, specifically entity metadata knowledge, entity category attribute knowledge, entity structure knowledge, behavioral knowledge, standard interface parameter knowledge, unit conversion knowledge, standard object model knowledge, cross-resolution mapping knowledge, and encapsulation process knowledge.

[0049] By categorizing encapsulated knowledge into nine types and formally describing them, we can obtain the essence of encapsulated knowledge. Subdividing encapsulated knowledge into nine categories improves the organization and systematic nature of knowledge, facilitates its acquisition and application, and promotes its updating and expansion.

[0050] Step 103: Obtain the encapsulation ontology knowledge of the encapsulation knowledge, and obtain the encapsulation ontology relationship knowledge of the encapsulation knowledge. The encapsulation ontology knowledge is determined based on the subordinate ontology of the nine encapsulation knowledge categories, and the encapsulation ontology relationship knowledge is determined based on the relationship between the subordinate ontology of the nine encapsulation knowledge categories.

[0051] It should be noted that after acquiring the encapsulation knowledge content, for each of the nine encapsulation knowledge categories, it is also necessary to acquire the corresponding encapsulation ontology knowledge and encapsulation ontology relationship knowledge. The corresponding encapsulation ontology knowledge for each encapsulation knowledge category is determined based on the subordinate ontologies of that category, and the corresponding grouping ontology relationship knowledge is determined based on the relationships between the subordinate ontologies.

[0052] This invention addresses the encapsulation needs of diverse and heterogeneous simulation model resources within a specific domain. Utilizing knowledge graph technology, it provides a knowledge graph-based method for model encapsulating simulation model knowledge. This method comprises four steps: acquiring the encapsulated knowledge description method, the encapsulated knowledge connotation, the encapsulated ontology knowledge, and the encapsulated ontology relationship knowledge. This technology can resolve issues related to the encapsulation of knowledge connotation, boundaries, and construction methods for diverse and heterogeneous simulation model resources within a specific domain. It enables the integration of simulation models into a simulation platform without modifying the code of the model to be integrated.

[0053] The simulation model can be divided into three stages according to the modeling process: conceptual model, mathematical logic model, and program model. The method provided by this invention can be used to solve the problem of encapsulating multi-form heterogeneous program models in the third stage.

[0054] Multi-form refers to the form in which simulation model resources are used. Based on the degree of physical involvement in the simulation, simulation model resources are divided into three forms: digital, semi-physical, and physical. Digital simulation models are further divided into two forms based on their coupling with the simulation system: system-embedded and system-independent. System-embedded simulation models cannot be separated from the simulation system and must be used as a system. System-independent simulation models can be used independently of the simulation system. System-independent simulation model resources can be further divided into two forms based on modeling technology: component and entity. Component-based simulation model resources are constructed using componentization technology, while entity-based simulation model resources are not constructed using componentization technology. Based on this classification method, simulation model resources are summarized into four forms: component, entity, system, and physical / semi-physical.

[0055] Figure 2 This is a schematic diagram illustrating the heterogeneity between different forms of simulation model resources provided by this invention. For example... Figure 2 As shown, heterogeneity refers to the differences in syntax, semantics, and pragmatics among the four different types of simulation model resources. Syntacticity, semantics, and pragmatics are hierarchical classifications of language's descriptive capabilities in cognitive linguistics. Language is the means of interaction between simulation model resources, and integration aims to solve the heterogeneity problem among these resources at these three levels. Syntactic heterogeneity refers to the differences between the simulation model to be integrated and the simulation system in terms of development language, operating environment, time stepping mechanism, program interface, and parameter types. Semantic heterogeneity refers to the differences in functionality and logic of the behavior generated by the simulation model based on control commands. Pragmatic heterogeneity refers to the differences in the military problems addressed by the simulation model, the resolution assumptions made during the modeling process, simplifications, and constraints on inter-model relationships—in other words, the application context heterogeneity problem. Because simulation modeling usually follows the principle of simplicity, abstracting and making assumptions about the real world, the application context becomes a crucial factor affecting model reuse.

[0056] Figure 3 This is a flowchart illustrating the method for obtaining encapsulated knowledge content provided by the present invention. For example... Figure 3 As shown, step 102, which involves dividing the encapsulation knowledge into nine categories and formally describing these nine categories to obtain the encapsulation knowledge connotation, may include:

[0057] Step 201: Determine the target knowledge classification corresponding to the nine encapsulated knowledge categories, wherein the target knowledge classification is either declarative knowledge classification or procedural knowledge classification.

[0058] Step 202: Based on the target knowledge classifications corresponding to the nine encapsulated knowledge categories, describe the nine encapsulated knowledge categories as a set of declarative knowledge classifications and a set of procedural knowledge classifications. The set of declarative knowledge classifications includes entity metadata knowledge, entity category attribute knowledge, entity structure knowledge, standard interface parameter knowledge, and standard object model knowledge. The set of procedural knowledge classifications includes encapsulated process knowledge, behavioral knowledge, cross-resolution mapping knowledge, and dimensional conversion knowledge.

[0059] It should be noted that encapsulation knowledge is used to support the intelligent encapsulation of simulation models. Encapsulation knowledge includes nine categories: entity metadata knowledge, entity category attribute knowledge, entity structure knowledge, behavioral knowledge, standard interface parameter knowledge, dimensional conversion knowledge, standard object model knowledge, cross-resolution mapping knowledge, and encapsulation process knowledge. From a cognitive psychology perspective, these nine categories are further divided into procedural and declarative knowledge categories, formally described as follows:

[0060] In this context, Pres represents declarative knowledge, and Proc represents procedural knowledge.

[0061] Among them, declarative knowledge Pres includes five encapsulated knowledge categories: entity metadata knowledge, entity category attribute knowledge, entity structure knowledge, standard interface parameter knowledge, and standard object model knowledge. Formalized as follows:

[0062] Wherein, EntMetaD represents entity metadata knowledge, EntCatAtr represents entity category attribute knowledge, EntStut represents entity structure knowledge, StIntPar represents standard interface parameter knowledge, and StObMod represents standard object model knowledge.

[0063] Procedural knowledge (Proc) includes four categories of encapsulated knowledge: encapsulated process knowledge, behavioral knowledge, cross-resolution mapping knowledge, and dimensional conversion knowledge. Formal descriptions are as follows:

[0064] In this context, EnvStp represents encapsulation process knowledge, OpBeh represents behavioral knowledge, DRMap represents cross-resolution mapping knowledge, and DimTran represents unit conversion knowledge.

[0065] Understandably, further categorizing the nine types of encapsulated knowledge into two knowledge classifications enhances the logic and coherence of knowledge, simplifies the learning and understanding process, and supports the flexible application and innovation of knowledge.

[0066] Figure 4 This is a flowchart illustrating the method for acquiring encapsulated ontology knowledge provided by the present invention. For example... Figure 4 As shown, the encapsulation ontology knowledge for obtaining the encapsulation knowledge may include:

[0067] Step 301: Based on the subordinate ontology corresponding to each of the nine encapsulation knowledge categories, perform a formal description of each encapsulation knowledge category to obtain the encapsulation ontology knowledge of each encapsulation knowledge category;

[0068] Step 302: Determine the encapsulation ontology knowledge of the encapsulation knowledge based on the encapsulation ontology knowledge of each encapsulation knowledge category.

[0069] It is understandable that by formally describing the subordinate ontologies corresponding to the encapsulated knowledge categories and determining the encapsulated ontology knowledge, the structure and expressibility of encapsulated knowledge are significantly enhanced, promoting a unified understanding and exchange of knowledge, and improving the predictability and controllability of the encapsulation process.

[0070] Furthermore, the entity metadata knowledge includes entity morphology ontology, model quality ontology, coordinate system ontology, time progression mechanism ontology, and resolution ontology; the entity category attribute knowledge includes entity category ontology and entity attribute ontology; the entity structure knowledge includes container component ontology, physical component ontology, and behavioral component ontology; the standard interface parameter knowledge includes simulation interface set ontology, model input interface set ontology, time progression interface ontology, model output interface set ontology, and simulation exit interface ontology; the standard object model knowledge includes interaction mechanism ontology and interaction content ontology; the encapsulation process knowledge includes model description ontology, composability analysis ontology, encapsulation code generation ontology, and packaging into the library ontology; the cross-resolution mapping knowledge includes attribute mapping ontology and object model mapping ontology; and the dimensional conversion knowledge includes source dimensional ontology, target dimensional ontology, and conversion formula ontology.

[0071] It should be noted that each of the nine encapsulation knowledge categories includes a corresponding subordinate ontology, which will be described in detail below.

[0072] 1. Entity metadata knowledge includes five sub-ontologies: form, quality, coordinate system, time progression mechanism, and resolution. Formal description:

[0073] ,

[0074] Among them, EntMorph represents the entity morphology ontology, used to describe the morphology of the simulation model; EntQua represents the model quality ontology, used to describe the confidence level of the simulation model; EntCoo represents the coordinate system ontology, used to describe the spatial coordinate system assumptions adopted by the state variables and parameters of the simulation model, including CGCS2000, WGS84, Mercator, Descartes, etc.; EntTStp represents the simulation time progression mechanism of the simulation model; and EntRsl represents the resolution ontology, used to describe the modeling granularity of the simulation model.

[0075] Furthermore, the entity ontology EntMorph comprises four sub-ontologies: digital components, digital entities, digital systems, and physical / semi-physical objects, formally described as follows:

[0076] ,

[0077] Among them, EMDCom represents the digital component form ontology, EMDEnt represents the digital entity form ontology, EMDSys represents the digital system form ontology, and EMPhy represents the physical / semi-physical form ontology.

[0078] Furthermore, the model quality ontology EntQua is divided into four subordinate ontology from high to low, and is formally described as follows:

[0079] ,

[0080] Q1 represents a high-quality model ontology, meaning the model has been verified with real data, or comes from an authoritative department, or has been verified by authoritative personnel; Q2 represents a medium-to-high-quality model ontology, meaning the model is already widely used in the field; Q3 represents a medium-to-low-quality model ontology, meaning the model has been verified and has been used no more than 3 times; Q4 represents a poor-quality model ontology, meaning the model has just been developed, has only been verified, and its operational stability and reliability can be guaranteed at the software level.

[0081] Furthermore, the time-progression mechanism ontology EntTStp includes three subordinate ontology types: discrete time, discrete events, and hybrid, which are formally described as follows:

[0082] ,

[0083] Among them, DisTime represents the discrete-time propagation ontology, DisEv represents the discrete-event propagation ontology, and Hybrid represents the hybrid propagation ontology.

[0084] Furthermore, the resolution ontology EntRsl contains two subordinate ontology, namely entity resolution and behavior resolution, which are formally described as follows:

[0085] ,

[0086] Where ERsl represents the entity resolution ontology, and BRsl represents the behavior resolution.

[0087] Furthermore, the entity resolution ontology ERsl includes four subordinate ontology types: task, performance, function, and engineering, which are formally described as follows:

[0088] ,

[0089] Among them, TLR represents mission-level resolution ontology, which is usually the mission-level modeling granularity when modeling formations, such as small squads; EfLR represents effectiveness-level resolution ontology, which is usually the resolution of the model when using effectiveness indices, Monte Carlo methods, etc. to model equipment, such as radar detection range, and Monte Carlo methods such as radar detection probability; FLR represents function-level resolution ontology, which is usually the resolution of the model when using mathematical model fitting to model equipment, such as fitting radar detection function with radar equations; EnLR represents engineering-level resolution ontology, which is usually the resolution of the model when modeling the physical effects of equipment or equipment components, such as describing the aircraft's air maneuvering function with aerodynamic structure, dynamic equations, and flight control.

[0090] Furthermore, the behavioral resolution BRsl includes five sub-ontologies: mission, task, action, equipment operation, and equipment function, which are formally described as follows:

[0091] ,

[0092] Among them, OpMsnLR represents the behavioral ontology of the mission level model, OpTskLR represents the behavioral ontology of the task level model, TaActLR represents the behavioral ontology of the action level model, EqOpLR represents the behavioral ontology of the equipment operation level model, and EqFunLR represents the behavioral ontology of the equipment function level model.

[0093] 2. Entity category attribute knowledge includes two ontology components: entity category and entity attributes. Formal description:

[0094] ,

[0095] Wherein, EntCatStut represents the entity category attribute knowledge ontology, ECat represents the entity category ontology, and EAttr represents the entity attribute ontology.

[0096] Furthermore, the entity category ontology includes four subordinate ontology types: facilities, resources, units, and agents, which are formally described as follows:

[0097] ,

[0098] Among them, EFac represents the facility entity, EMat represents the material entity, EOUnit represents the unit entity, and EAgt represents the agent entity.

[0099] The resource entity EMat comprises two sub-entities: consumable resources and non-consumable resources, formally described as follows:

[0100] ,

[0101] Here, ConMat represents the consumable material entity, and UconMat represents the non-consumable material entity.

[0102] The consumable resources entity comprises three sub-entities: ammunition, water and food, and fuel. Formal description:

[0103] ,

[0104] Among them, Ammo represents the ammunition body, WaF represents the water and food body, and Fuel represents the fuel body.

[0105] The non-consumable resources entity comprises five subordinate entities: land, sea, air, space, and cyberspace, formally described as follows:

[0106] ,

[0107] Among them, LandUM represents the non-consumable material body used in the land area, SeaUM represents the non-consumable material body used in the sea area, AirUM represents the non-consumable material body used in the air area, SpaceUM represents the non-consumable material body used in the space area, and ElecUM represents the non-consumable material body used in the electromagnetic field.

[0108] Furthermore, the entity attribute knowledge EATtr contains several attributes, formally described as follows:

[0109] ,

[0110] Where EAi represents the i-th attribute of the entity, and n represents the number of entity attributes.

[0111] 3. The entity structure ontology EStut contains three subordinate ontology components: container component, physical component, and behavioral component. Its formal description is as follows:

[0112] ,

[0113] Where ECtnCom represents the container component ontology, the container component is used to assemble physical components and behavioral components into an entity model, the container component ontology is used to describe the functionality and behavioral semantics of the container component, EPhyCom represents the physical component ontology, and EBehCom represents the behavioral component ontology.

[0114] 4. Standard interface parameter knowledge includes five sub-entities: the set of interfaces for entering simulation, the set of interfaces for model input, the time progression interface, the set of interfaces for model output, and the set of interfaces for exiting simulation. Formal description:

[0115] ,

[0116] Among them, MEntSim represents the main body of the simulation interface set, MInput represents the main body of the model input interface set, MStep represents the main body of the time advancement interface, MOutput represents the main body of the model output interface set, and MExitSim represents the main body of the simulation interface set. The simulation interface set is used to clear the memory before the model exits.

[0117] Furthermore, the simulation interface set ontology MEntSim contains two subordinate ontology types: instantiation interface and initialization interface, formally described as follows:

[0118] ,

[0119] Here, MInst represents the model instantiation interface ontology, and MInit represents the model initialization interface ontology. The model instantiation interface ontology includes the model model, model ID, model parameter data, and model scenario data parameter attributes, formally described as:

[0120] ,

[0121] Among them, MinstPara represents the set of input parameters for the model instantiation function interface, MType represents the model type attribute, the model type is the unique identifier of the model in the model library, MID represents the model identifier attribute, the model identifier is the unique identifier of the simulation model instance in a simulation run, MPara represents the inherent parameter attribute of the model itself, and MScenario represents the determinate parameter attribute of the model.

[0122] The model initialization interface body MInit contains an initialization string parameter, which is formally described as follows:

[0123] ,

[0124] Here, MInitPara represents the set of parameters for the model initialization function interface, and MIStr represents the string attribute of the model initialization parameters.

[0125] The model input interface set ontology MInput includes two sub-ontologies: event input and model parameter input, which are formally described as follows:

[0126] ,

[0127] Where MPushEvent represents the event input interface body for each time step of the model, and MParaInput represents the parameter input interface body for each time step of the model.

[0128] The MPushEvent interface body contains an event queue parameter property, which is formally described as follows:

[0129] ,

[0130] Here, MPEPara represents the set of input parameters for the MPushEvent interface, and EvList represents the event queue parameter attributes.

[0131] The MParaInput interface body contains a list parameter property, formally described as follows:

[0132] ,

[0133] Here, MPIPara represents the parameter set of the MParaInput interface, and ParaList represents the list parameter attributes.

[0134] The time-step interface body MStep includes two parameter attributes: simulation time and simulation time step, which are formally described as follows:

[0135] ,

[0136] Where MSPara represents the set of input parameters for the MStep interface, CurSimTime represents the current relative simulation time attribute, and CurSTStep represents the current simulation time step attribute.

[0137] Furthermore, the model output interface set ontology MOutput includes two subordinate ontology types: state output interface and event output interface, which can be formally described as follows:

[0138] ,

[0139] Here, EvOutput represents the event output interface body, and StateOutput represents the state output interface body.

[0140] 5. Standard object model knowledge is used to describe the interaction knowledge of simulation models, including two sub-ontologies: interaction mechanism and interaction content. Its formal description is as follows:

[0141] ,

[0142] Here, SOMMec represents the interaction mechanism ontology, and SOMCtn represents the interaction content ontology.

[0143] The interaction mechanism ontology SOMMec contains two subordinate ontology components: events and shared memory. Its formal description is as follows:

[0144] ,

[0145] Among them, EvMec represents the event interaction mechanism ontology, which is applicable to simulation models of three forms: physical / semi-physical, digital system, and digital entity, as well as simulation model users and simulation models of different forms; MSMec represents the shared memory interaction mechanism ontology, which is applicable to simulation models of two forms: digital entity and digital component.

[0146] The interactive content ontology SOMCtn includes five sub-ontologies: scenario, input command, output state, output event report, and model interaction. Formal description:

[0147] ,

[0148] In this context, SOMScn represents the conceptual object model ontology, SOMIIns represents the input instruction object model ontology, SOMOSt represents the output state object model ontology, SOMOEv represents the output event report object model ontology, and SOMInt represents the model interaction object model ontology.

[0149] The formal description of the model interaction object model ontology is as follows:

[0150] ,

[0151] Where SOMIi represents the i-th interactive object model ontology, and m represents the number of interactive object models.

[0152] 6. Behavioral knowledge OpBeh is used to describe the behavioral logic of the model's generated behavior, and is formally described as follows:

[0153] ,

[0154] Where OBSi represents the semantic ontology of the i-th behavior type, n represents the number of behavior types, and the formal description of the behavior ontology contained in each behavior type ontology is as follows:

[0155] ,

[0156] Where OBSij is the ontology of the j-th behavior in the i-th class, and m represents the number of behaviors contained in the i-th behavior class.

[0157] 7. Encapsulation Process Knowledge: EnvStp includes four sub-entities: model description, composability analysis, encapsulation code generation, and packaging into the library. Its formal description is as follows:

[0158] ,

[0159] Among them, MDes represents the model description ontology, which is used to guide the model encapsulation of relevant information filled in by the user for the simulation model. The knowledge associated with the guidance includes entity metadata, entity category structure, standard interface parameters, behavioral semantics, and standard object model knowledge; ComAna represents the composability analysis ontology, which is used to guide the encapsulation algorithm to analyze the structural and behavioral integrity and composability of the encapsulated model; ECGen represents encapsulation code generation, which can generate encapsulation code for specific models based on template knowledge; PacIpt represents packaging into the library, which is used to package the encapsulated model into a dynamic library format that can be recognized by the simulation model library, and import the dynamic library and model parameters into the simulation model library.

[0160] 8. Cross-resolution mapping knowledge (DRMap) includes two parts: attribute mapping and object model mapping. Its formal description is as follows:

[0161] ,

[0162] AtrDRM stands for Attribute Mapping. This knowledge is needed when the structure of the model to be packaged is incomplete and other resolution attributes and mapping knowledge are required to infer the missing component attributes of the model to be packaged. ObMdDRM stands for Object Model Mapping. This knowledge is needed when the model to be packaged needs to interact with simulation models of other resolutions during runtime to perform cross-resolution mapping of the object model.

[0163] 9. Dimensional Conversion Knowledge: DimTran consists of source dimensions, target dimensions, and conversion formulas, and can be formally described as follows:

[0164] ,

[0165] Wherein, DSrc represents the source dimension, that is, the dimension that needs to be converted; DDest represents the target dimension, that is, the dimension that needs to be converted to; and TForm represents the conversion formula, that is, the mathematical formula for converting the source dimension into the target dimension.

[0166] Understandably, identifying the subordinate ontologies corresponding to the nine encapsulated knowledge categories and generating formal descriptions offers significant benefits in terms of the accuracy and completeness of encapsulated knowledge representation, the systematic and convenient nature of knowledge management, and the flexibility and scalability of knowledge application. These benefits contribute to improving the quality and efficiency of encapsulated knowledge and promote the development and application of intelligent encapsulation technology for simulation models.

[0167] Figure 5 This is a flowchart illustrating the method for obtaining encapsulated ontology relationship knowledge provided by the present invention. For example... Figure 5As shown, the encapsulation ontology relationship knowledge for obtaining the encapsulation knowledge may include:

[0168] Step 401: Determine the relationships between subordinate ontologies in the encapsulated knowledge. The relationships between subordinate ontologies correspond to at least one of the following: composition relationship, behavior relationship, interaction relationship, inheritance relationship, triggering relationship, process relationship, and mapping relationship.

[0169] Step 402: Perform a formal description of the relationships between the subordinate ontologies to obtain the encapsulated ontology relationship knowledge of the encapsulated knowledge.

[0170] It should be noted that the encapsulated knowledge relationship Rela includes relationships between seven categories of ontologies: composition, behavior, interaction, inheritance, triggering, process, and mapping. Formalized as follows:

[0171] ,

[0172] Among them, CmpR represents the composition relationship edge between entity ontology and physical component ontology, used to describe the set of physical components contained in a specific type of entity, with the direction from physical component ontology to entity ontology; BehR represents the behavior relationship edge between entity and behavior ontology, used to describe the set of behaviors that entity can execute, with the direction from entity ontology to behavior ontology; IntR represents the interaction relationship edge between entities, used to describe the seven types of interaction relationships between entities: detection, command, intelligence, jamming, logistics support, destruction, and firepower, with the direction from interaction initiator to interaction receiver; InhR represents the inheritance relationship edge between upper-level knowledge ontology and lower-level knowledge ontology, with the direction from lower-level knowledge ontology to upper-level knowledge ontology; TrgR represents the trigger relationship edge, used to describe the trigger relationship between command interaction ontology and behavior ontology, with the direction from command interaction ontology to behavior ontology; FlwR represents the process relationship edge, used to describe model encapsulation process and interface call process knowledge, with the direction from MapR representing the mapping relationship, used to describe the mapping calculation knowledge between source and target dimensions, between object models of different resolutions, and between attributes of different resolutions.

[0173] It is understood that this invention encapsulates ontology relation knowledge through seven types of combination relation edges. That is, by providing structured knowledge representation, good scalability and flexibility, it facilitates automated processing and intelligent applications, thereby realizing the effective management and application of encapsulated ontology relation knowledge.

[0174] Based on the foregoing embodiments, this invention provides a knowledge modeling device for encapsulating simulation models based on knowledge graphs. The modules and units included in the device can be implemented by a processor; of course, they can also be implemented by specific logic circuits. In the implementation process, the processor can be a central processing unit (CPU), a microprocessor (MPU), a digital signal processor (DSP), or a field-programmable gate array (FPGA), etc.

[0175] The following describes the knowledge graph-based simulation model encapsulation knowledge modeling device provided by the present invention. The knowledge graph-based simulation model encapsulation knowledge modeling device described below and the knowledge graph-based simulation model encapsulation knowledge modeling method described above can be referred to in correspondence with each other.

[0176] Figure 6 This is a schematic diagram of the knowledge modeling device for encapsulating simulation models based on knowledge graphs provided by this invention. Figure 6 As shown, the device 500 includes a knowledge description module 501, a knowledge connotation module 502, and an ontology and relation module 503, wherein:

[0177] The knowledge description module 501 is used to model encapsulated knowledge using graph structure elements to obtain a formal description of the encapsulated knowledge. The graph structure elements include nodes, relation edges, and attributes. The nodes are used to describe the concept ontology in the encapsulated knowledge, the relation edges are used to describe the relationship between the concepts, and the attributes are used to describe the attributes of the nodes and / or the relation edges.

[0178] The knowledge connotation module 502 is used to divide the encapsulated knowledge into nine encapsulated knowledge categories and to formally describe the nine encapsulated knowledge categories to obtain the encapsulated knowledge connotation of the encapsulated knowledge. The nine encapsulated knowledge categories include entity metadata knowledge, entity category attribute knowledge, entity structure knowledge, behavior knowledge, standard interface parameter knowledge, unit conversion knowledge, standard object model knowledge, cross-resolution mapping knowledge, and encapsulation process knowledge.

[0179] The ontology and relation module 503 is used to obtain the encapsulation ontology knowledge of the encapsulation knowledge and the encapsulation ontology relation knowledge of the encapsulation knowledge. The encapsulation ontology knowledge is determined based on the subordinate ontology of the nine encapsulation knowledge categories, and the encapsulation ontology relation knowledge is determined based on the relationship between the subordinate ontology of the nine encapsulation knowledge categories.

[0180] In some embodiments, the attributes are further used to describe the connotation of the concept and the relationship, and the relationship edges are further used to describe the extension of the concept and the relationship.

[0181] In some embodiments, the knowledge connotation module 502 includes a classification determination unit and a connotation determination unit, wherein,

[0182] The classification determination unit is used to determine the target knowledge classification corresponding to the nine encapsulated knowledge categories, wherein the target knowledge classification is either declarative knowledge classification or procedural knowledge classification.

[0183] The connotation determination unit is used to describe the nine encapsulated knowledge categories as a declarative knowledge classification set and a procedural knowledge classification set according to the target knowledge classification corresponding to the nine encapsulated knowledge categories respectively. The declarative knowledge classification set includes the entity metadata knowledge, the entity category attribute knowledge, the entity structure knowledge, the standard interface parameter knowledge, and the standard object model knowledge. The procedural knowledge classification set includes the encapsulation process knowledge, the behavioral knowledge, the cross-resolution mapping knowledge, and the dimensional conversion knowledge.

[0184] In some embodiments, the ontology and relationship module 503 includes an ontology acquisition unit and an ontology knowledge unit, wherein,

[0185] The ontology acquisition unit is used to formally describe each encapsulation knowledge category based on the subordinate ontology corresponding to each of the nine encapsulation knowledge categories, and obtain the encapsulation ontology knowledge of each encapsulation knowledge category.

[0186] The ontology knowledge unit is used to determine the encapsulation ontology knowledge of the encapsulation knowledge based on the encapsulation ontology knowledge of each encapsulation knowledge category.

[0187] In some embodiments, the entity metadata knowledge includes an entity morphology ontology, a model quality ontology, a coordinate system ontology, a time progression mechanism ontology, and a resolution ontology; the entity category attribute knowledge includes an entity category ontology and an entity attribute ontology; the entity structure knowledge includes a container component ontology, a physical component ontology, and a behavioral component ontology; the standard interface parameter knowledge includes an entry simulation interface set ontology, a model input interface set ontology, a time progression interface ontology, a model output interface set ontology, and an exit simulation interface ontology; the standard object model knowledge includes an interaction mechanism ontology and an interaction content ontology; the encapsulation process knowledge includes a model description ontology, a composability analysis ontology, an encapsulation code generation ontology, and a packaging and library ontology; the cross-resolution mapping knowledge includes an attribute mapping ontology and an object model mapping ontology; and the dimensional conversion knowledge includes a source dimensional ontology, a target dimensional ontology, and a conversion formula ontology.

[0188] In some embodiments, the ontology and relationship module 503 includes a relationship acquisition unit and a relationship knowledge unit, wherein,

[0189] The relationship acquisition unit is used to determine the relationship between subordinate entities in the encapsulated knowledge. The relationship between subordinate entities corresponds to at least one relationship between subordinate entities among composition relationship, behavior relationship, interaction relationship, inheritance relationship, trigger relationship, process relationship and mapping relationship.

[0190] The relational knowledge unit is used to formally describe the relationships between the subordinate ontologies to obtain the encapsulated ontology relational knowledge of the encapsulated knowledge.

[0191] In this embodiment of the invention, by providing a standardized encapsulation framework and process, the simulation model to be integrated can be integrated into the simulation platform in a plug-and-play manner, which greatly reduces the difficulty and cost of integration.

[0192] Figure 7 This is a schematic diagram of the physical structure of the electronic device provided by the present invention. For example... Figure 7 As shown, the electronic device may include: a processor 610, a communications interface 620, a memory 630, and a communications bus 640, wherein the processor 610, the communications interface 620, and the memory 630 communicate with each other through the communications bus 640. The processor 610 can call logic instructions in the memory 630 to execute a knowledge graph-based simulation model encapsulation knowledge modeling method. This method includes: modeling the encapsulated knowledge using graph structure elements to obtain a formal description of the encapsulated knowledge. The graph structure elements include nodes, relation edges, and attributes. The nodes describe the concept ontology in the encapsulated knowledge, the relation edges describe the relationships between the concepts, and the attributes describe the attributes of the nodes and / or the relation edges; dividing the encapsulated knowledge into nine categories and formally describing these nine categories to obtain the encapsulated knowledge connotation. The nine categories include entity metadata knowledge, entity category attribute knowledge, entity structure knowledge, behavioral knowledge, standard interface parameter knowledge, dimensional conversion knowledge, standard object model knowledge, cross-resolution mapping knowledge, and encapsulation process knowledge; obtaining the encapsulation ontology knowledge and the encapsulation ontology relationship knowledge of the encapsulated knowledge. The encapsulation ontology knowledge is determined based on the subordinate ontology of the nine encapsulated knowledge categories, and the encapsulation ontology relationship knowledge is determined based on the relationships between the subordinate ontology of the nine encapsulated knowledge categories.

[0193] Furthermore, the logical instructions in the aforementioned memory 630 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0194] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the knowledge graph-based simulation model encapsulation knowledge modeling method provided by the above methods. This method includes: modeling the encapsulated knowledge using graph structure elements to obtain a formal description of the encapsulated knowledge. The graph structure elements include nodes, relation edges, and attributes. The nodes are used to describe the concept ontology in the encapsulated knowledge, the relation edges are used to describe the relationships between the concepts, and the attributes are used to describe the attributes of the nodes and / or the relation edges; and then... Encapsulated knowledge is divided into nine categories, and these nine categories are formally described to obtain the encapsulated knowledge connotation. The nine categories include entity metadata knowledge, entity category attribute knowledge, entity structure knowledge, behavioral knowledge, standard interface parameter knowledge, dimensional conversion knowledge, standard object model knowledge, cross-resolution mapping knowledge, and encapsulation process knowledge. The encapsulation ontology knowledge and the encapsulation ontology relationship knowledge of the encapsulated knowledge are obtained. The encapsulation ontology knowledge is determined based on the subordinate ontology of the nine encapsulation knowledge categories, and the encapsulation ontology relationship knowledge is determined based on the relationships between the subordinate ontology of the nine encapsulation knowledge categories.

[0195] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., a solid-state disk (SSD)).

[0196] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the knowledge graph-based simulation model encapsulation knowledge modeling method provided by the above methods. This method includes: modeling the encapsulated knowledge using graph structure elements to obtain a formal description of the encapsulated knowledge; the graph structure elements include nodes, relation edges, and attributes; the nodes are used to describe the concept ontology in the encapsulated knowledge; the relation edges are used to describe the relationships between the concepts; and the attributes are used to describe the attributes of the nodes and / or the relation edges; and classifying the encapsulated knowledge into nine encapsulated knowledge categories. The nine encapsulation knowledge categories are formally described to obtain the encapsulation knowledge connotation. These nine categories include entity metadata knowledge, entity category attribute knowledge, entity structure knowledge, behavioral knowledge, standard interface parameter knowledge, dimensional conversion knowledge, standard object model knowledge, cross-resolution mapping knowledge, and encapsulation process knowledge. The encapsulation ontology knowledge and encapsulation ontology relationship knowledge are also obtained. The encapsulation ontology knowledge is determined based on the subordinate ontology of the nine encapsulation knowledge categories, and the encapsulation ontology relationship knowledge is determined based on the relationships between the subordinate ontology of the nine encapsulation knowledge categories.

[0197] The aforementioned computer-readable storage medium may be any combination of one or more computer-readable media. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) or flash memory, optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0198] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including—but not limited to—electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0199] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including—but not limited to—wireless, wire, optical fiber, radio frequency (RF), etc., or any suitable combination thereof.

[0200] Computer program code for performing the operations described herein can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as "C" or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0201] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0202] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0203] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A knowledge graph-based simulation model encapsulation knowledge modeling method, characterized in that, include: The encapsulated knowledge is modeled using graph structure elements to obtain a formal description of the encapsulated knowledge. The graph structure elements include nodes, relation edges, and attributes. The nodes are used to describe the concept ontology in the encapsulated knowledge, the relation edges are used to describe the relationships between the concepts, and the attributes are used to describe the attributes of the nodes and / or the relation edges. The encapsulated knowledge is divided into nine categories, and the nine categories are formally described to obtain the encapsulated knowledge connotation. The nine categories include entity metadata knowledge, entity category attribute knowledge, entity structure knowledge, behavior knowledge, standard interface parameter knowledge, unit conversion knowledge, standard object model knowledge, cross-resolution mapping knowledge, and encapsulation process knowledge. The encapsulation ontology knowledge of the encapsulation knowledge and the encapsulation ontology relationship knowledge of the encapsulation knowledge are obtained. The encapsulation ontology knowledge is determined based on the subordinate ontology of the nine encapsulation knowledge categories, and the encapsulation ontology relationship knowledge is determined based on the relationship between the subordinate ontology of the nine encapsulation knowledge categories. The encapsulation knowledge is used to support intelligent encapsulation of simulation models; The encapsulation process knowledge includes four sub-entities: model description, composability analysis, encapsulation code generation, and packaging into the library. Formal description is as follows: EnvStp={MDes,ComAna,ECGen,PacIpt}, Among them, MDes represents the model description ontology, which is used to guide the model encapsulation of relevant information filled in by the user in the simulation model. The knowledge associated with the guidance includes entity metadata, entity category structure, standard interface parameters, behavioral semantics, and standard object model knowledge; ComAna represents the composability analysis ontology, which is used to guide the encapsulation algorithm to analyze the structural and behavioral integrity and composability of the encapsulated model; ECGen represents encapsulation code generation, which generates encapsulation code for a specific model based on template knowledge; PacIpt represents packaging into the library, which is used to package the encapsulated model into a dynamic library format that can be recognized by the simulation model library, and import the dynamic library and model parameters into the simulation model library; The cross-resolution mapping knowledge includes two parts: attribute mapping and object model mapping. When the structure of the model to be packaged is incomplete and it is necessary to use other resolution attributes and mapping knowledge to infer the missing component attributes of the model to be packaged, the attribute mapping is used. When the model to be packaged is running and needs to interact with other resolution simulation models, the object model mapping is needed to perform cross-resolution mapping of the object model.

2. The method according to claim 1, characterized in that, The attributes are also used to describe the connotation of the concept and the relationship, and the relationship edges are also used to describe the extension of the concept and the relationship.

3. The method according to claim 1, characterized in that, The process involves classifying the encapsulation knowledge into nine categories and formally describing these nine categories to obtain the encapsulation knowledge connotation, including: Determine the target knowledge classification corresponding to the nine encapsulated knowledge categories, wherein the target knowledge classification is either declarative knowledge classification or procedural knowledge classification; Based on the target knowledge classifications corresponding to the nine encapsulated knowledge categories, the nine encapsulated knowledge categories are described as a set of declarative knowledge classifications and a set of procedural knowledge classifications. The set of declarative knowledge classifications includes entity metadata knowledge, entity category attribute knowledge, entity structure knowledge, standard interface parameter knowledge, and standard object model knowledge. The set of procedural knowledge classifications includes encapsulated process knowledge, behavioral knowledge, cross-resolution mapping knowledge, and dimensional conversion knowledge.

4. The method according to claim 1, characterized in that, The encapsulation ontology knowledge for acquiring the encapsulation knowledge includes: Based on the subordinate ontology corresponding to each of the nine encapsulation knowledge categories, a formal description of each encapsulation knowledge category is performed to obtain the encapsulation ontology knowledge of each encapsulation knowledge category. The encapsulation ontology knowledge of the encapsulation knowledge is determined based on the encapsulation ontology knowledge of each encapsulation knowledge category.

5. The method according to claim 4, characterized in that, The entity metadata knowledge includes entity morphology ontology, model quality ontology, coordinate system ontology, time progression mechanism ontology, and resolution ontology; the entity category attribute knowledge includes entity category ontology and entity attribute ontology; the entity structure knowledge includes container component ontology, physical component ontology, and behavioral component ontology; the standard interface parameter knowledge includes simulation interface set ontology, model input interface set ontology, time progression interface ontology, model output interface set ontology, and simulation exit interface ontology; the standard object model knowledge includes interaction mechanism ontology and interaction content ontology; the encapsulation process knowledge includes model description ontology, composability analysis ontology, encapsulation code generation ontology, and packaging into the library ontology; the cross-resolution mapping knowledge includes attribute mapping ontology and object model mapping ontology; and the dimensional conversion knowledge includes source dimensional ontology, target dimensional ontology, and conversion formula ontology.

6. The method according to claim 1, characterized in that, The encapsulation ontology relationship knowledge for obtaining the encapsulation knowledge includes: Determine the relationships between subordinate entities in the encapsulated knowledge, wherein the relationships between subordinate entities correspond to at least one of the following relationships: composition relationship, behavior relationship, interaction relationship, inheritance relationship, triggering relationship, process relationship, and mapping relationship. The relationships between the subordinate ontologies are formally described to obtain the encapsulated ontology relationship knowledge of the encapsulated knowledge.

7. A knowledge graph-based simulation model encapsulation knowledge modeling device, characterized in that, include: The knowledge description module is used to model encapsulated knowledge using graph structure elements to obtain a formal description of the encapsulated knowledge. The graph structure elements include nodes, relation edges, and attributes. The nodes are used to describe the concept ontology in the encapsulated knowledge, the relation edges are used to describe the relationships between the concepts, and the attributes are used to describe the attributes of the nodes and / or the relation edges. The knowledge connotation module is used to divide the encapsulated knowledge into nine categories of encapsulated knowledge and to formally describe the nine categories of encapsulated knowledge to obtain the encapsulated knowledge connotation of the encapsulated knowledge. The nine categories of encapsulated knowledge include entity metadata knowledge, entity category attribute knowledge, entity structure knowledge, behavior knowledge, standard interface parameter knowledge, unit conversion knowledge, standard object model knowledge, cross-resolution mapping knowledge, and encapsulation process knowledge. The ontology and relation module is used to obtain the encapsulation ontology knowledge of the encapsulation knowledge and the encapsulation ontology relation knowledge of the encapsulation knowledge. The encapsulation ontology knowledge is determined based on the subordinate ontology of the nine encapsulation knowledge categories, and the encapsulation ontology relation knowledge is determined based on the relationship between the subordinate ontology of the nine encapsulation knowledge categories. The encapsulation knowledge is used to support intelligent encapsulation of simulation models; The encapsulation process knowledge includes four sub-entities: model description, composability analysis, encapsulation code generation, and packaging into the library. Formal description is as follows: EnvStp={MDes,ComAna,ECGen,PacIpt}, Among them, MDes represents the model description ontology, which is used to guide the model encapsulation of relevant information filled in by the user in the simulation model. The knowledge associated with the guidance includes entity metadata, entity category structure, standard interface parameters, behavioral semantics, and standard object model knowledge; ComAna represents the composability analysis ontology, which is used to guide the encapsulation algorithm to analyze the structural and behavioral integrity and composability of the encapsulated model; ECGen represents encapsulation code generation, which generates encapsulation code for a specific model based on template knowledge; PacIpt represents packaging into the library, which is used to package the encapsulated model into a dynamic library format that can be recognized by the simulation model library, and import the dynamic library and model parameters into the simulation model library; The cross-resolution mapping knowledge includes two parts: attribute mapping and object model mapping. When the structure of the model to be packaged is incomplete and it is necessary to use other resolution attributes and mapping knowledge to infer the missing component attributes of the model to be packaged, the attribute mapping is used. When the model to be packaged is running and needs to interact with other resolution simulation models, the object model mapping is needed to perform cross-resolution mapping of the object model.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the knowledge graph-based simulation model encapsulation knowledge modeling method as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the knowledge graph-based simulation model encapsulation knowledge modeling method as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the knowledge graph-based simulation model encapsulation knowledge modeling method as described in any one of claims 1 to 6.

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