Simulation model packaging knowledge modeling method based on knowledge graph

Abstract

The invention provides a simulation model packaging knowledge modeling method based on a knowledge graph, and relates to the technical field of simulation modeling. The method comprises the steps that modeling is conducted on packaging knowledge through graph structure elements, formalized description of the packaging knowledge is obtained, the packaging knowledge is divided into nine packaging knowledge categories, formalized description is conducted on the nine packaging knowledge categories, packaging knowledge connotation of the packaging knowledge is obtained, and the packaging knowledge connotation of the packaging knowledge is obtained. Packaging ontology knowledge of the packaging knowledge is obtained, and packaging ontology relation knowledge of the packaging knowledge is obtained. According to the method, a standardized packaging framework and process are provided, so that the simulation model to be integrated can be integrated into the simulation platform in a plug-and-play mode, and the integration difficulty and cost are greatly reduced.

Description

Technical Field

[0001] The present invention relates to the field of simulation modeling technology, and in particular to a simulation model encapsulation knowledge modeling method based on a knowledge graph. Background Art

[0002] To conduct scientific experiments in the field using simulation methods, it is necessary to make full use of the existing multi-form heterogeneous simulation model resources in the field. There are four methods for reusing simulation models in the field: customized development, middleware-based integration, encapsulation, and assembly. The customized development method has high time and labor costs, the middleware-based integration method is not suitable for the large-sample ultra-real-time simulation requirements of scientific experiments, and the assembly method is not suitable for heterogeneous simulation model resources. At present, the encapsulation method is generally used for simulation model reuse.

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

[0004] The present invention provides a knowledge graph-based simulation model encapsulation knowledge modeling method to solve the defects of the traditional manual encapsulation method of modifying the interface parameter code of the model to be integrated in the prior art, which is complicated and prone to errors. By providing a standardized encapsulation framework and process, an encapsulation layer is added to the outside of the simulation model through a knowledge-driven method without modifying the simulation model to be integrated, so that the model can be integrated into the simulation platform in a plug-and-play manner, which greatly reduces the difficulty and cost of integration.

[0005] In the process of model encapsulation and integration, the present invention focuses on solving the problem of encapsulation knowledge modeling, that is, what knowledge is needed for the encapsulation of heterogeneous simulation models and how to describe it. The present invention provides a simulation model encapsulation knowledge modeling method based on knowledge graph, which includes the following steps.

[0006] Modeling the encapsulated knowledge by using graph structure elements to obtain a formal description of the encapsulated knowledge, wherein the graph structure elements include nodes, relationship edges and attributes, wherein the nodes are used to describe the concept ontology in the encapsulated knowledge, the relationship 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 relationship edges; The encapsulation knowledge is divided into nine encapsulation knowledge categories, and the nine encapsulation knowledge categories are formally described to obtain the encapsulation knowledge connotation of the encapsulation knowledge. The nine encapsulation knowledge categories include entity metadata knowledge, entity category attribute knowledge, entity structure knowledge, behavior knowledge, standard interface parameter knowledge, dimension conversion knowledge, standard object model knowledge, cross-resolution mapping knowledge and encapsulation process knowledge; Acquire encapsulation ontology knowledge of the encapsulation knowledge, and acquire encapsulation ontology relationship knowledge of the encapsulation knowledge, wherein the encapsulation ontology knowledge is determined based on the subordinate ontologies of the nine encapsulation knowledge categories, and the encapsulation ontology relationship knowledge is determined based on the relationships between the subordinate ontologies of the nine encapsulation knowledge categories.

[0007] According to a simulation model encapsulation knowledge modeling method based on knowledge graph provided by the present invention, the attributes are also used to describe the connotations of the concepts and the relationships, and the relationship edges are also used to describe the extensions of the concepts and the relationships.

[0008] According to a simulation model packaging knowledge modeling method based on knowledge graph provided by the present invention, the packaging knowledge is divided into nine packaging knowledge categories, and the nine packaging knowledge categories are formally described to obtain the packaging knowledge connotation of the packaging knowledge, including: Determine target knowledge classifications corresponding to the nine encapsulated knowledge categories respectively, wherein the target knowledge classifications are declarative knowledge classifications or procedural knowledge classifications; According to the target knowledge classifications corresponding to the nine encapsulation knowledge categories respectively, the nine encapsulation knowledge categories are described as a declarative knowledge classification set and a procedural knowledge classification set. 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 behavior knowledge, the cross-resolution mapping knowledge and the dimensional conversion knowledge.

[0009] According to a method for modeling packaging knowledge of a simulation model based on a knowledge graph provided by the present invention, the step of obtaining packaging ontology knowledge of the packaging knowledge includes: According to the subordinate ontology corresponding to each of the nine encapsulation knowledge categories, each of the nine encapsulation knowledge categories is formally described to obtain encapsulation ontology knowledge of each of the nine encapsulation knowledge categories; The encapsulation ontology knowledge of the encapsulation knowledge is determined according to the encapsulation ontology knowledge of each encapsulation knowledge category.

[0010] According to a simulation model encapsulation knowledge modeling method based on knowledge graph provided by the present invention, the entity metadata knowledge includes entity morphology ontology, model quality ontology, coordinate system ontology, time advancement 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 behavior component ontology, the standard interface parameter knowledge includes entering simulation interface set ontology, model input interface set ontology, time advancement interface ontology, model output interface set ontology and exiting 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 warehousing ontology, the cross-resolution mapping knowledge includes attribute mapping ontology and object model mapping ontology, and the dimension conversion knowledge includes source dimension ontology, target dimension ontology and conversion formula ontology.

[0011] According to a simulation model packaging knowledge modeling method based on knowledge graph provided by the present invention, the step of obtaining packaging ontology relationship knowledge of the packaging knowledge includes: Determine the relationship between subordinate ontologies in the encapsulated knowledge, wherein the relationship between the subordinate ontologies corresponds to at least one relationship between the subordinate ontologies among a combination relationship, a behavior relationship, an interaction relationship, an inheritance relationship, a trigger relationship, a process relationship and a mapping relationship; The relationship between the subordinate ontologies is formally described to obtain encapsulated ontology relationship knowledge of the encapsulated knowledge.

[0012] The present invention also provides a knowledge modeling device for encapsulating simulation models based on knowledge graphs, comprising the following modules.

[0013] A knowledge description module, used to model the encapsulated knowledge using graph structure elements to obtain a formal description of the encapsulated knowledge, wherein the graph structure elements include nodes, relationship edges and attributes, wherein the nodes are used to describe the concept ontology in the encapsulated knowledge, the relationship 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 relationship edges; A knowledge connotation module is used to divide the encapsulation knowledge into nine encapsulation knowledge categories, and formally describe the nine encapsulation knowledge categories to obtain the encapsulation knowledge connotation of the encapsulation knowledge, wherein the nine encapsulation knowledge categories include entity metadata knowledge, entity category attribute knowledge, entity structure knowledge, behavior knowledge, standard interface parameter knowledge, dimension conversion knowledge, standard object model knowledge, cross-resolution mapping knowledge and encapsulation process knowledge; The ontology and relationship module is used to obtain the encapsulation ontology knowledge of the encapsulation knowledge, and to obtain the encapsulation ontology relationship knowledge of the encapsulation knowledge. The encapsulation ontology knowledge is determined based on the subordinate ontologies of the nine encapsulation knowledge categories, and the encapsulation ontology relationship knowledge is determined based on the relationships between the subordinate ontologies of the nine encapsulation knowledge categories.

[0014] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for encapsulating knowledge of a simulation model based on a knowledge graph as described above is implemented.

[0015] 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 any of the above-described simulation model encapsulation knowledge modeling methods based on knowledge graphs.

[0016] The present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned simulation model encapsulation knowledge modeling methods based on knowledge graphs.

[0017] The knowledge graph-based simulation model encapsulation knowledge modeling method provided by the present invention provides a standardized encapsulation framework and process, so that 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 It is a flow chart of the knowledge graph-based simulation model encapsulation knowledge modeling method provided by the present invention.

[0020] Figure 2 It is a schematic diagram of heterogeneity existing between simulation model resources of different forms provided by the present invention.

[0021] Figure 3 It is a flowchart of the method for obtaining the encapsulated knowledge connotation provided by the present invention.

[0022] Figure 4 It is a flowchart of the method for obtaining encapsulation ontology knowledge provided by the present invention.

[0023] Figure 5 It is a flowchart of the method for obtaining encapsulation ontology relationship knowledge provided by the present invention.

[0024] Figure 6 It is a structural schematic diagram of the knowledge modeling device for encapsulating simulation models based on knowledge graph provided by the present invention.

[0025] Figure 7 It is a schematic diagram of the physical structure of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] To conduct scientific experiments using simulation methods, it is necessary to make full use of the existing multi-form heterogeneous simulation model resources in the field. There are four methods for reusing simulation models in the field: customized development, middleware-based integration, encapsulation, and assembly. The customized development method has high time and labor costs, the middleware-based integration method is not suitable for the large-sample ultra-real-time simulation requirements of scientific experiments, and the assembly method is not suitable for heterogeneous simulation model resources. At present, the encapsulation method is generally used for simulation model reuse.

[0028] However, traditional packaging methods require modification or adjustment of the model code before simulation model reuse, which not only increases the complexity of integration but also may introduce new errors.

[0029] In view of this, an embodiment of the present invention provides a method for modeling packaging knowledge of a simulation model based on a knowledge graph, including modeling packaging knowledge using graph structure elements to obtain a formal description of the packaging knowledge, dividing the packaging knowledge into nine packaging knowledge categories, and formally describing the nine packaging knowledge categories to obtain the packaging knowledge connotation of the packaging knowledge, obtain the packaging ontology knowledge of the packaging knowledge, and obtain the packaging ontology relationship knowledge of the packaging knowledge. This method provides a standardized packaging framework and process, so that the simulation model to be integrated can be integrated into the simulation platform in a plug-and-play manner, greatly reducing the difficulty and cost of integration.

[0030] The technical solutions in the embodiments of the present invention will be described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0031] Figure 1It is a flow chart of the method for encapsulating knowledge modeling of simulation models based on knowledge graph provided by the present invention. The method for encapsulating knowledge modeling of simulation models based on knowledge graph can be applied to electronic devices, which can be various types of devices with information processing capabilities during implementation. For example, the electronic device can include a personal computer, a laptop computer, a PDA or a server, etc.; the electronic device can also be a mobile terminal, for example, the mobile terminal can include a mobile phone, a car computer, a tablet computer or a projector, etc. Figure 1 As shown, the method may include the following steps 101 to 103.

[0032] Step 101: Use graph structure elements to model the encapsulated knowledge to obtain a formal description of the encapsulated knowledge, wherein the graph structure elements include nodes, relationship edges and attributes, wherein the nodes are used to describe the concept ontology in the encapsulated knowledge, the relationship 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 relationship edges.

[0033] It should be noted that the encapsulated knowledge is modeled using graph structure elements such as nodes, edges, and attributes, which can be formally described as follows: , where EnvKnoStr represents the encapsulated knowledge description method, Concep represents the concept ontology in the encapsulated knowledge, and node description is used in the present invention, Rela represents the relationship between concepts in the encapsulated knowledge, and directed edges in the relationship edges can be used to describe it in the present invention, and Attr represents the attributes that describe the concept ontology nodes and relationship edges.

[0034] Furthermore, the attributes are also used to describe the connotations of the concepts and the relationships, and the relationship edges are also used to describe the extensions of the concepts and the relationships.

[0035] It can be seen that in formal description, attributes can also be used to describe the connotation of concepts and relationships, and edges can be used to describe the extension of concepts.

[0036] Step 102: Divide the encapsulation knowledge into nine encapsulation knowledge categories, and formally describe the nine encapsulation knowledge categories to obtain the encapsulation knowledge connotation of the encapsulation knowledge. The nine encapsulation knowledge categories include entity metadata knowledge, entity category attribute knowledge, entity structure knowledge, behavior knowledge, standard interface parameter knowledge, dimension conversion knowledge, standard object model knowledge, cross-resolution mapping knowledge and encapsulation process knowledge.

[0037] It should be noted that encapsulation knowledge is used to support intelligent encapsulation of simulation models. Encapsulation knowledge can include nine types of encapsulation knowledge categories, namely entity metadata knowledge, entity category attribute knowledge, entity structure knowledge, behavior knowledge, standard interface parameter knowledge, dimensional conversion knowledge, standard object model knowledge, cross-resolution mapping knowledge, and encapsulation process knowledge.

[0038] By dividing encapsulation knowledge into nine categories and formally describing them, we can get the connotation of encapsulation knowledge. Subdividing encapsulation knowledge into nine categories improves the organization and systematicness of knowledge, facilitates the acquisition and application of knowledge, and promotes the updating and expansion of knowledge.

[0039] Step 103: Obtain encapsulation ontology knowledge of the encapsulation knowledge, and obtain encapsulation ontology relationship knowledge of the encapsulation knowledge, wherein the encapsulation ontology knowledge is determined based on the subordinate ontologies of the nine encapsulation knowledge categories, and the encapsulation ontology relationship knowledge is determined based on the relationships between the subordinate ontologies of the nine encapsulation knowledge categories.

[0040] It should be noted that after obtaining the encapsulation knowledge connotation, the corresponding encapsulation ontology knowledge and encapsulation ontology relationship knowledge need to be obtained for the nine encapsulation knowledge categories. The corresponding encapsulation ontology knowledge for each encapsulation knowledge category is determined according to the subordinate ontology corresponding to each encapsulation knowledge category, and the corresponding grouping ontology relationship knowledge is determined according to the relationship between the subordinate ontologies.

[0041] Aiming at the encapsulation demand of multi-form heterogeneous simulation model resources in the field, the present invention uses knowledge graph technology to provide a simulation model encapsulation knowledge modeling method based on knowledge graph, which includes four steps of obtaining encapsulation knowledge description method, encapsulation knowledge connotation, encapsulation ontology knowledge and encapsulation ontology relationship knowledge. This technology can solve the problems of knowledge connotation, boundary and construction path of encapsulation of multi-form and heterogeneous simulation model resources in the field. It can realize the integration of the simulation model to be integrated into the simulation platform without modifying the code of the simulation model to be integrated.

[0042] Among them, the simulation model can be divided into three stages of products according to the modeling process: conceptual model, mathematical logic model, and program model. The method provided by the present invention can be used to solve the problem of encapsulating multi-modal heterogeneous program models in the third stage.

[0043] Multi-modality refers to the form of simulation model resources when they are used. According to the degree of physical participation in simulation, simulation model resources are divided into three forms: digital, semi-physical, and physical. Digital simulation models are divided into two forms: system embedded and system independent according to the degree of coupling with the simulation system. System embedded means that the simulation model cannot be separated from the simulation system, and the simulation model resources must be used in the form of the system. System independence refers to a form in which the simulation model can be separated from the simulation system. System independent simulation model resources can be further divided into two forms: component and entity according to modeling technology. Component form refers to simulation model resources built using component technology, and entity form refers to simulation model resources that are not built using component technology. According to this classification method, simulation model resources are summarized into four forms: component, entity, system, and physical / semi-physical.

[0044] Figure 2 This is a schematic diagram of the heterogeneity between the simulation model resources of different forms provided by the present invention. Figure 2 As shown in the figure, heterogeneity refers to the differences in morphology, semantics, and pragmatics between the above four different forms of simulation model resources. Morphology, semantics, and pragmatics are the hierarchical divisions of language description capabilities in cognitive linguistics. Language is the means of interaction between simulation model resources. Integration is to solve the heterogeneity problems between these resources at the three levels of morphology, semantics, and pragmatics. Morphological layer 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, parameter type, etc. Semantic layer heterogeneity refers to the differences in functions and logic of the simulation model in generating behaviors according to control instructions. Pragmatic layer heterogeneity refers to the differences in resolution assumptions, simplifications, and constraints on relationships between models made during the modeling process for the military problems targeted by the simulation model, that is, the heterogeneity of application scenarios (Context). Since simulation modeling usually follows the principle of simplicity and abstracts and assumes the real world, this makes the application scenario an important factor affecting model reuse.

[0045] Figure 3 FIG. 1 is a flow chart of the method for obtaining the encapsulation knowledge content provided by the present invention. Figure 3 As shown, in step 102, the encapsulation knowledge is divided into nine encapsulation knowledge categories, and the nine encapsulation knowledge categories are formally described to obtain the encapsulation knowledge connotation of the encapsulation knowledge, which may include: Step 201: Determine target knowledge classifications corresponding to the nine encapsulated knowledge categories respectively, wherein the target knowledge classifications are declarative knowledge classifications or procedural knowledge classifications.

[0046] Step 202: According to the target knowledge classifications corresponding to the nine encapsulation knowledge categories respectively, the nine encapsulation knowledge categories are described as a declarative knowledge classification set and a procedural knowledge classification set, 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, and the procedural knowledge classification set includes the encapsulation process knowledge, the behavior knowledge, the cross-resolution mapping knowledge and the dimensional conversion knowledge.

[0047] It should be noted that encapsulation knowledge is used to support intelligent encapsulation of simulation models. Encapsulation knowledge includes nine types of encapsulation knowledge: entity metadata knowledge, entity category attribute knowledge, entity structure knowledge, behavior knowledge, standard interface parameter knowledge, dimension conversion knowledge, standard object model knowledge, cross-resolution mapping knowledge, and encapsulation process knowledge. From the perspective of cognitive psychology, these nine types of encapsulation knowledge are divided into two types of knowledge: procedural and declarative. The formal description is as follows: , where Pres represents declarative knowledge and Proc represents procedural knowledge.

[0048] Among them, declarative knowledge Pres includes five types of encapsulated knowledge categories: entity metadata knowledge, entity category attribute knowledge, entity structure knowledge, standard interface parameter knowledge, and standard object model knowledge. The formal description is: , where 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.

[0049] Procedural knowledge Proc includes four types of encapsulation knowledge: encapsulation process knowledge, behavior knowledge, cross-resolution mapping knowledge, and dimension conversion knowledge. The formal description is: , where EnvStp represents encapsulation process knowledge, OpBeh represents behavioral knowledge, DRMap represents cross-resolution mapping knowledge, and DimTran represents dimensional conversion knowledge.

[0050] It can be understood that further summarizing the nine encapsulated knowledge categories into two knowledge classifications enhances the logic and coherence of knowledge, simplifies the process of learning and understanding, and supports the flexible application and innovation of knowledge.

[0051] Figure 4 FIG. 1 is a flow chart of the method for obtaining encapsulation ontology knowledge provided by the present invention. Figure 4 As shown, the obtaining of the encapsulation ontology knowledge of the encapsulation knowledge may include: Step 301: According to the subordinate ontology corresponding to each of the nine encapsulation knowledge categories, formally describe each of the nine encapsulation knowledge categories to obtain encapsulation ontology knowledge of each of the nine encapsulation knowledge categories; Step 302: Determine the encapsulation ontology knowledge of the encapsulation knowledge according to the encapsulation ontology knowledge of each encapsulation knowledge category.

[0052] It can be understood that by formally describing the subordinate ontologies corresponding to the encapsulation knowledge categories and determining the encapsulation ontology knowledge, the structuring and expressibility of the encapsulation knowledge are significantly enhanced, the unified understanding and communication of knowledge are promoted, and the predictability and controllability of the encapsulation process are improved.

[0053] Furthermore, the entity metadata knowledge includes entity morphology ontology, model quality ontology, coordinate system ontology, time advancement 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 behavior component ontology, the standard interface parameter knowledge includes entry simulation interface set ontology, model input interface set ontology, time advancement 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 warehousing ontology, the cross-resolution mapping knowledge includes attribute mapping ontology and object model mapping ontology, and the dimension conversion knowledge includes source dimension ontology, target dimension ontology and conversion formula ontology.

[0054] It should be noted that each of the nine encapsulation knowledge categories includes corresponding subordinate ontologies. The subordinate ontologies included in the nine encapsulation knowledge categories will be specifically introduced below.

[0055] 1. Entity metadata knowledge includes five subordinate ontologies: morphology, quality, coordinate system, time advancement mechanism, and resolution. The formal description is: , Among them, EntMorph represents the entity morphology ontology, which is used to describe the morphology of the simulation model; EntQua represents the model quality ontology, which is used to describe the confidence of the simulation model; EntCoo represents the coordinate system ontology, which is 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 advancement mechanism of the simulation model; EntRsl represents the resolution ontology, which is used to describe the modeling granularity of the simulation model.

[0056] Furthermore, the entity morphology ontology EntMorph contains four subordinate ontologies: digital components, digital entities, digital systems, and physical / semi-physical objects, which are formally described as follows: , Among them, EMDCom represents the digital component morphology ontology, EMDEnt represents the digital entity morphology ontology, EMDSys represents the digital system morphology ontology, and EMPhy represents the physical / semi-physical morphology ontology.

[0057] Furthermore, the model quality ontology EntQua contains four subordinate ontologies from high to low, which are formally described as follows: , Among them, Q1 represents high-quality model ontology, that is, the model has been verified by real data, or comes from an authoritative department, or has been verified by authoritative personnel; Q2 represents medium-to-high quality model ontology, that is, the model has been widely used in the field; Q3 represents medium-to-low quality model ontology, that is, the model has been calibrated and has been used no more than 3 times; Q4 represents poor quality model ontology, that is, the model has just been developed and has only been calibrated, and its operation stability and reliability can be guaranteed from the software level.

[0058] Furthermore, the time advancement mechanism ontology EntTStp includes three subordinate ontologies: discrete time, discrete event, and hybrid, which are formally described as follows: , Among them, DisTime represents discrete time propulsion ontology, DisEv represents discrete event propulsion ontology, and Hybrid represents hybrid propulsion ontology.

[0059] Furthermore, the resolution ontology EntRsl contains two subordinate ontologies, entity resolution and behavior resolution, which are formally described as follows: , Among them, ERsl represents entity resolution ontology and BRsl represents behavior resolution.

[0060] Furthermore, the entity resolution ontology ERsl includes four subordinate ontologies: task, performance, function, and engineering, which are formally described as follows: , Among them, TLR represents the task-level resolution ontology. When modeling a formation, the modeling granularity is usually the task-level, such as a squad; EfLR represents the effectiveness-level resolution ontology. When using effectiveness indicators, Monte Carlo and other methods to model equipment, the resolution of the model is usually the effectiveness level. Effectiveness indicators include radar detection range, and Monte Carlo methods include radar detection probability; FLR represents the function-level resolution ontology. When mathematical model fitting is used to model equipment, the resolution of the model is usually the function level, such as using radar equations to fit the detection function of the radar; EnLR represents the engineering-level resolution ontology. When modeling the physical effects of equipment or equipment components, the resolution of the model is usually the engineering level, such as using aerodynamic structures, dynamic equations, flight control, etc. to describe the aircraft's air maneuverability function.

[0061] Furthermore, the behavior resolution BRsl includes five subordinate ontologies: mission, task, action, equipment operation, and equipment function, which are formally described as follows: , Among them, OpMsnLR represents the mission level model behavior ontology, OpTskLR represents the task level model behavior ontology, TaActLR represents the action level model behavior ontology, EqOpLR represents the equipment operation level model behavior ontology, and EqFunLR represents the equipment function level model behavior ontology.

[0062] 2. Entity category attribute knowledge includes two ontologies: entity category and entity attribute. The formal description is: , Among them, EntCatStut represents entity category attribute knowledge ontology, ECat represents entity category ontology, and EAttr represents entity attribute ontology.

[0063] Furthermore, the entity category ontology contains four subordinate ontologies: facilities, materials, units, and agents, which are formally described as follows: , Among them, EFac represents the facility ontology, EMat represents the material ontology, EOUnit represents the unit ontology, and EAgt represents the agent ontology.

[0064] The material ontology EMat contains two subordinate ontologies: consumable materials and non-consumable materials. The formal description is: , Among them, ConMat represents the consumable material entity, and UconMat represents the non-consumable material entity.

[0065] The consumable material ontology includes three subordinate entities: ammunition, water and food, and fuel, which are formally described as follows: , Among them, Ammo represents the ammunition body, WaF represents the water and food body, and Fuel represents the fuel body.

[0066] The non-consumable material ontology includes five subordinate ontology: land, sea, air, space, and network and electricity, which can be formally described as follows: , Among them, LandUM represents the non-consumable material entity used for land, SeaUM represents the non-consumable material entity used for sea, AirUM represents the non-consumable material entity used for air, SpaceUM represents the non-consumable material entity used for space, and ElecUM represents the non-consumable material entity used for electromagnetic domain.

[0067] Furthermore, the entity attribute knowledge EAttr contains several attributes, which are formally described as follows: , Among them, EAi represents the i-th attribute of the entity, and n represents the number of entity attributes.

[0068] 3. The entity structure ontology EStut contains three subordinate ontologies: container component, physical component, and behavioral component. The formal description is: , Among them, ECtnCom represents the container component ontology, which is used to assemble physical components and behavioral components into an entity model. The container component ontology is used to describe the functions and behavioral semantics of the container component. EPhyCom represents the physical component ontology, and EBehCom represents the behavioral component ontology.

[0069] 4. Standard interface parameter knowledge includes five subordinate entities: entry simulation interface set, model input interface set, time advancement interface, model output interface set, and exit simulation interface. The formal description is: , Among them, MEntSim represents the entry simulation interface collection ontology, MInput represents the model input interface collection ontology, MStep represents the time advancement interface ontology, MOutput represents the model output interface collection ontology, and MExitSim represents the exit simulation interface ontology. The exit simulation interface is used to clear the memory before exiting the model.

[0070] Furthermore, the simulation interface collection ontology MEntSim contains two subordinate ontologies: instantiation interface and initialization interface. The formal description is: , Among them, MInst represents the model instantiation interface ontology, and MInit represents the model initialization interface ontology. The model instantiation interface ontology contains the model model, model ID, model parameter data, and model assumption data parameter attributes, which are formally described as follows: , Among them, MInstPara represents the input parameter set of the model instantiation function interface, MType represents the model model attribute, and the model model is the unique identifier of the model in the model library. MID represents the model identification attribute, and the model identification 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 assumed parameter attribute of the model.

[0071] The model initialization interface MInit contains an initialization string parameter, which is formally described as: , Among them, MInitPara represents the parameter set of the model initialization function interface, and MIStr represents the model initialization parameter string attribute.

[0072] The model input interface collection ontology MInput includes two subordinate ontologies: event input and model parameter input. The formal description is: , Among them, MPushEvent represents the event input interface ontology of each time step of the model, and MParaInput represents the parameter input interface ontology of each time step of the model.

[0073] The MPushEvent interface body contains an event queue parameter attribute, which is formally described as: , Among them, MPEPara represents the input parameter set of the MPushEvent interface, and EvList represents the event queue parameter attributes.

[0074] The MParaInput interface body contains a list parameter attribute, formally described as: , Among them, MPIPara represents the parameter set of the MParaInput interface, and ParaList represents the list parameter attribute.

[0075] The time advancement interface ontology MStep contains two parameter attributes: simulation time and simulation time step, which are formally described as: , Among them, MSPara represents the input parameter set of the MStep interface, CurSimTime represents the current relative simulation time attribute, and CurSTStep represents the current simulation time step attribute.

[0076] Furthermore, the model output interface collection ontology MOutput contains two subordinate ontologies: state output interface and event output interface, which are formally described as follows: , Among them, EvOutput represents the event output interface body, and StateOutput represents the state output interface body.

[0077] 5. Standard object model knowledge is used to describe the interaction knowledge of simulation models, including two subordinate ontologies: interaction mechanism and interaction content. The formal description is: , Among them, SOMMec represents the interaction mechanism ontology, and SOMCtn represents the interaction content ontology.

[0078] The interaction mechanism ontology SOMMec contains two subordinate ontologies: event and shared memory, which are formally described as follows: , Among them, EvMec represents the event interaction mechanism ontology, which is applicable to the three forms of simulation models: physical / semi-physical, digital system, and digital entity, as well as between simulation model users and simulation models of different forms; MSMec represents the shared memory interaction mechanism ontology, which is applicable to the two forms of simulation models: digital entity and digital component.

[0079] The interactive content ontology SOMCtn includes five subordinate ontologies: assumption, input command, output status, output event report, and model interaction, which are formally described as , Among them, SOMScn represents the assumption 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.

[0080] The model interaction object model ontology is formally described as: , Among them, SOMIi represents the i-th interactive object model ontology, and m represents the number of interactive object models.

[0081] 6. Behavioral knowledge OpBeh is used to describe the behavioral logic of model generation behavior, which is formally described as: , Among them, OBSi represents the semantic ontology of the i-th behavior type, n represents the number of behavior types, and the behavior ontology contained in each behavior type ontology is formally described as: , Among them, OBSij is the jth behavior ontology of the ith category, and m represents the number of behaviors contained in the ith category.

[0082] 7. Encapsulation process knowledge EnvStp includes four subordinate ontologies: model description, composability analysis, encapsulation code generation, and packaging storage. The formal description is: , Among them, MDes represents the model description ontology, which is used to guide the model encapsulation user to fill in the relevant information of the simulation model. The associated knowledge 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 encapsulation model; ECGen represents encapsulation code generation, which can generate 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.

[0083] 8. Cross-resolution mapping knowledge DRMap includes attribute mapping and object model mapping, which can be formally described as follows: , Among them, AtrDRM represents attribute mapping. When the structure of the model to be encapsulated is incomplete, this knowledge is needed to infer the missing component attributes of the model to be encapsulated by using other resolution attributes and mapping knowledge; ObMdDRM represents object model mapping. When the model to be encapsulated is running and needs to interact with other resolution simulation models, this knowledge is needed to perform cross-resolution mapping of the object model.

[0084] 9. Dimension conversion knowledge DimTran consists of source dimension, target dimension and conversion formula, which can be formally described as: , Among them, 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; TForm represents the conversion formula, that is, the mathematical formula for converting the source dimension into the target dimension.

[0085] It can be understood that by determining the subordinate ontologies corresponding to the nine packaging knowledge categories and generating formal descriptions, there are significant benefits in terms of the accuracy and completeness of packaging knowledge expression, the systematicness and convenience of knowledge management, and the flexibility and scalability of knowledge application. These benefits help improve the quality and efficiency of packaging knowledge and promote the development and application of simulation model intelligent packaging technology.

[0086] Figure 5 FIG. 1 is a flow chart of the method for obtaining encapsulation ontology relationship knowledge provided by the present invention. Figure 5 As shown, the obtaining of the encapsulation ontology relationship knowledge of the encapsulation knowledge may include: Step 401: determining the relationship between subordinate ontologies in the encapsulated knowledge, wherein the relationship between the subordinate ontologies corresponds to at least one relationship between subordinate ontologies selected from the group consisting of a combination relationship, a behavior relationship, an interaction relationship, an inheritance relationship, a trigger relationship, a process relationship and a mapping relationship; Step 402: Formalize the relationship between the subordinate ontologies to obtain encapsulation ontology relationship knowledge of the encapsulation knowledge.

[0087] It should be noted that the encapsulated knowledge relationship Rela includes the relationship between seven types of subordinate ontologies: combination, behavior, interaction, inheritance, triggering, process, and mapping, which can be formally described as follows: , Among them, CmpR represents the combination relationship edge between the entity ontology and the physical component ontology, which is used to describe the set of physical components contained in a specific model entity, and the direction is from the physical component ontology to the entity ontology; BehR represents the behavior relationship edge between the entity and the behavior ontology, which is used to describe the set of behaviors that the entity can execute, and the direction is from the entity ontology to the behavior ontology; IntR represents the interaction relationship edge between entities, which is used to describe the seven types of interaction relationships between entities, including detection, command, intelligence, interference, logistics support, damage, and firepower, and the direction is from the interaction initiator to the interaction receiver; InhR represents the inheritance relationship edge between the upper knowledge ontology and the subordinate knowledge ontology, and the direction is from the subordinate knowledge ontology to the upper knowledge ontology; TrgR represents the trigger relationship edge, which is used to describe the trigger relationship between the command interaction ontology and the behavior ontology, and the direction is from the command interaction ontology to the behavior ontology; FlwR represents the process relationship edge, which is used to describe the model encapsulation process and interface call process knowledge, and the direction is from MapR to represent the mapping relationship, which is used to describe the mapping calculation knowledge between the source dimension and the target dimension, between object models of different resolutions, and between attributes of different resolutions.

[0088] It can be understood that the present invention encapsulates ontological relationship knowledge through seven combined relationship edge descriptions, that is, by providing structured knowledge representation, good scalability and flexibility, it is easy to carry out automated processing and intelligent application, thereby achieving effective management and application of encapsulated ontological relationship knowledge.

[0089] Based on the foregoing embodiments, an embodiment of the present invention provides a knowledge modeling device for encapsulating a simulation model based on a knowledge graph. The modules included in the device and the units included in the modules can be implemented by a processor; of course, they can also be implemented by a specific logic circuit; 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.

[0090] The simulation model encapsulation knowledge modeling device based on knowledge graph provided by the present invention is described below. The simulation model encapsulation knowledge modeling device based on knowledge graph described below and the simulation model encapsulation knowledge modeling method based on knowledge graph described above can be referenced to each other.

[0091] Figure 6 Schematic diagram of the structure of the knowledge modeling device for encapsulating simulation models based on knowledge graph provided by the present invention. Figure 6 As shown, the device 500 includes a knowledge description module 501, a knowledge connotation module 502 and an ontology and relationship module 503, wherein: The knowledge description module 501 is used to model the encapsulated knowledge using graph structure elements to obtain a formal description of the encapsulated knowledge, wherein the graph structure elements include nodes, relationship edges and attributes, wherein the nodes are used to describe the concept ontology in the encapsulated knowledge, the relationship 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 relationship edges; The knowledge connotation module 502 is used to divide the encapsulation knowledge into nine encapsulation knowledge categories, and formally describe the nine encapsulation knowledge categories to obtain the encapsulation knowledge connotation of the encapsulation knowledge, wherein the nine encapsulation knowledge categories include entity metadata knowledge, entity category attribute knowledge, entity structure knowledge, behavior knowledge, standard interface parameter knowledge, dimension conversion knowledge, standard object model knowledge, cross-resolution mapping knowledge and encapsulation process knowledge; The ontology and relationship module 503 is used to obtain the encapsulation ontology knowledge of the encapsulation knowledge, and to obtain the encapsulation ontology relationship knowledge of the encapsulation knowledge. The encapsulation ontology knowledge is determined based on the subordinate ontologies of the nine encapsulation knowledge categories, and the encapsulation ontology relationship knowledge is determined based on the relationships between the subordinate ontologies of the nine encapsulation knowledge categories.

[0092] In some embodiments, the attributes are further used to describe the connotations of the concepts and the relationships, and the relationship edges are further used to describe the extensions of the concepts and the relationships.

[0093] In some embodiments, the knowledge connotation module 502 includes a classification determination unit and a connotation determination unit, wherein: The classification determination unit is used to determine the target knowledge classifications corresponding to the nine encapsulated knowledge categories respectively, wherein the target knowledge classifications are declarative knowledge classifications or procedural knowledge classifications; The connotation determination unit is used to describe the nine encapsulation knowledge categories as a declarative knowledge classification set and a procedural knowledge classification set according to the target knowledge classifications respectively corresponding to the nine encapsulation knowledge categories. 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 behavior knowledge, the cross-resolution mapping knowledge and the dimensional conversion knowledge.

[0094] In some embodiments, the ontology and relationship module 503 includes an ontology acquisition unit and an ontology knowledge unit, wherein: The ontology acquisition unit is used to formally describe each of the nine encapsulation knowledge categories according to the subordinate ontology corresponding to each of the nine encapsulation knowledge categories, so as to obtain encapsulation ontology knowledge of each of the nine encapsulation knowledge categories; The ontology knowledge unit is used to determine the encapsulation ontology knowledge of the encapsulation knowledge according to the encapsulation ontology knowledge of each encapsulation knowledge category.

[0095] In some embodiments, the entity metadata knowledge includes entity morphology ontology, model quality ontology, coordinate system ontology, time advancement 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 behavior component ontology, the standard interface parameter knowledge includes entry simulation interface collection ontology, model input interface collection ontology, time advancement interface ontology, model output interface collection 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 warehousing ontology, the cross-resolution mapping knowledge includes attribute mapping ontology and object model mapping ontology, and the dimension conversion knowledge includes source dimension ontology, target dimension ontology and conversion formula ontology.

[0096] In some embodiments, the ontology and relationship module 503 includes a relationship acquisition unit and a relationship knowledge unit, wherein: The relationship acquisition unit is used to determine the relationship between the subordinate ontologies in the encapsulated knowledge, wherein the relationship between the subordinate ontologies corresponds to at least one relationship between the subordinate ontologies among a combination relationship, a behavior relationship, an interaction relationship, an inheritance relationship, a trigger relationship, a process relationship and a mapping relationship; The relationship knowledge unit is used to formally describe the relationship between the subordinate ontologies to obtain the encapsulated ontology relationship knowledge of the encapsulated knowledge.

[0097] In the embodiment of the present invention, by providing a standardized packaging 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.

[0098] Figure 7 Schematic diagram of the physical structure of the electronic device provided by the present invention. Figure 7 As shown, the electronic device may include: a processor (processor) 610 , a communication interface (Communications Interface) 620 , a memory (memory) 630 and a communication bus 640 , wherein the processor 610 , the communication interface 620 , and the memory 630 communicate with each other through the communication bus 640 . The processor 610 can call the logic instructions in the memory 630 to execute the simulation model encapsulation knowledge modeling method based on the knowledge graph, the method comprising: using graph structure elements to model the encapsulation knowledge to obtain a formal description of the encapsulation knowledge, the graph structure elements comprising nodes, relationship edges and attributes, the nodes being used to describe the concept ontology in the encapsulation knowledge, the relationship edges being used to describe the relationship between the concepts, and the attributes being used to describe the attributes of the nodes and / or the relationship edges; dividing the encapsulation knowledge into nine encapsulation knowledge categories, and formally describing the nine encapsulation knowledge categories to obtain the encapsulation knowledge connotation of the encapsulation knowledge, the nine encapsulation knowledge categories comprising entity metadata knowledge, entity category attribute knowledge, entity structure knowledge, behavior knowledge, standard interface parameter knowledge, dimension conversion knowledge, standard object model knowledge, cross-resolution mapping knowledge and encapsulation process knowledge; obtaining the encapsulation ontology knowledge of the encapsulation knowledge, and obtaining the encapsulation ontology relationship knowledge of the encapsulation knowledge, the encapsulation ontology knowledge being determined based on the subordinate ontologies of the nine encapsulation knowledge categories, and the encapsulation ontology relationship knowledge being determined based on the relationship between the subordinate ontologies of the nine encapsulation knowledge categories.

[0099] In addition, the logic instructions in the above-mentioned memory 630 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0100] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the simulation model encapsulation knowledge modeling method based on the knowledge graph provided by the above methods, and the method includes: using graph structure elements to model the encapsulation knowledge to obtain a formal description of the encapsulation knowledge, the graph structure elements include nodes, relationship edges and attributes, the nodes are used to describe the concept ontology in the encapsulation knowledge, the relationship 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 relationship edges; the Encapsulation knowledge is divided into nine encapsulation knowledge categories, and the nine encapsulation knowledge categories are formally described to obtain the encapsulation knowledge connotation of the encapsulation knowledge, the nine encapsulation knowledge categories include entity metadata knowledge, entity category attribute knowledge, entity structure knowledge, behavior knowledge, standard interface parameter knowledge, dimension conversion knowledge, standard object model knowledge, cross-resolution mapping knowledge and encapsulation process knowledge; the encapsulation ontology knowledge of the encapsulation knowledge is obtained, and the encapsulation ontology relationship knowledge of the encapsulation knowledge is obtained, the encapsulation ontology knowledge is determined according to the subordinate ontologies of the nine encapsulation knowledge categories, and the encapsulation ontology relationship knowledge is determined according to the relationship between the subordinate ontologies of the nine encapsulation knowledge categories.

[0101] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. 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 computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wired (e.g., coaxial cable, optical fiber, 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 includes one or more available media integrated therein. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive Solid State Disk (SSD)), etc.

[0102] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the simulation model encapsulation knowledge modeling method based on the knowledge graph provided by the above-mentioned methods, the method comprising: using graph structure elements to model the encapsulation knowledge to obtain a formal description of the encapsulation knowledge, the graph structure elements comprising nodes, relationship edges and attributes, the nodes being used to describe the concept ontology in the encapsulation knowledge, the relationship edges being used to describe the relationship between the concepts, and the attributes being used to describe the attributes of the nodes and / or the relationship edges; dividing the encapsulation knowledge into nine encapsulation knowledge categories , and formally describe the nine encapsulation knowledge categories to obtain the encapsulation knowledge connotation of the encapsulation knowledge, the nine encapsulation knowledge categories include entity metadata knowledge, entity category attribute knowledge, entity structure knowledge, behavior knowledge, standard interface parameter knowledge, dimension conversion knowledge, standard object model knowledge, cross-resolution mapping knowledge and encapsulation process knowledge; 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 according to the subordinate ontologies of the nine encapsulation knowledge categories, and the encapsulation ontology relationship knowledge is determined according to the relationship between the subordinate ontologies of the nine encapsulation knowledge categories.

[0103] The above-mentioned computer-readable storage medium can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, - but not limited to - an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM) or flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0104] Computer-readable signal media may include a data signal propagated in baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0105] The program code embodied on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wire, optical cable, radio frequency (RF), etc., or any suitable combination of the foregoing.

[0106] Computer program code for performing the operations of the present specification may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0107] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0108] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0109] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A simulation model encapsulation knowledge modeling method based on knowledge graph, characterized in that: include: Modeling the encapsulated knowledge by using graph structure elements to obtain a formal description of the encapsulated knowledge, wherein the graph structure elements include nodes, relationship edges and attributes, wherein the nodes are used to describe the concept ontology in the encapsulated knowledge, the relationship 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 relationship edges; The encapsulation knowledge is divided into nine encapsulation knowledge categories, and the nine encapsulation knowledge categories are formally described to obtain the encapsulation knowledge connotation of the encapsulation knowledge. The nine encapsulation knowledge categories include entity metadata knowledge, entity category attribute knowledge, entity structure knowledge, behavior knowledge, standard interface parameter knowledge, dimension conversion knowledge, standard object model knowledge, cross-resolution mapping knowledge and encapsulation process knowledge; Acquire encapsulation ontology knowledge of the encapsulation knowledge, and acquire encapsulation ontology relationship knowledge of the encapsulation knowledge, wherein the encapsulation ontology knowledge is determined based on the subordinate ontologies of the nine encapsulation knowledge categories, and the encapsulation ontology relationship knowledge is determined based on the relationships between the subordinate ontologies of the nine encapsulation knowledge categories.

2. The method according to claim 1, characterized in that The attributes are also used to describe the connotations of the concepts and the relationships, and the relationship edges are also used to describe the extensions of the concepts and the relationships.

3. The method according to claim 1, characterized in that The encapsulation knowledge is divided into nine encapsulation knowledge categories, and the nine encapsulation knowledge categories are formally described to obtain the encapsulation knowledge connotation of the encapsulation knowledge, including: Determine target knowledge classifications corresponding to the nine encapsulated knowledge categories respectively, wherein the target knowledge classifications are declarative knowledge classifications or procedural knowledge classifications; According to the target knowledge classifications corresponding to the nine encapsulation knowledge categories respectively, the nine encapsulation knowledge categories are described as a declarative knowledge classification set and a procedural knowledge classification set. 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 behavior knowledge, the cross-resolution mapping knowledge and the dimensional conversion knowledge.

4. The method according to claim 1, characterized in that: The step of acquiring the encapsulation ontology knowledge of the encapsulation knowledge comprises: According to the subordinate ontology corresponding to each of the nine encapsulation knowledge categories, each of the nine encapsulation knowledge categories is formally described to obtain encapsulation ontology knowledge of each of the nine encapsulation knowledge categories; The encapsulation ontology knowledge of the encapsulation knowledge is determined according to 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 advancement 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 behavior component ontology, the standard interface parameter knowledge includes entry simulation interface set ontology, model input interface set ontology, time advancement 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 warehousing ontology, the cross-resolution mapping knowledge includes attribute mapping ontology and object model mapping ontology, the dimension conversion knowledge includes source dimension ontology, target dimension ontology and conversion formula ontology.

6. The method according to claim 1, characterized in that The step of acquiring the encapsulation ontology relationship knowledge of the encapsulation knowledge comprises: Determine the relationship between subordinate ontologies in the encapsulated knowledge, wherein the relationship between the subordinate ontologies corresponds to at least one relationship between the subordinate ontologies among a combination relationship, a behavior relationship, an interaction relationship, an inheritance relationship, a trigger relationship, a process relationship and a mapping relationship; The relationship between the subordinate ontologies is formally described to obtain encapsulated ontology relationship knowledge of the encapsulated knowledge.

7. A knowledge modeling device for encapsulating simulation models based on knowledge graph, characterized in that: include: A knowledge description module, used to model the encapsulated knowledge using graph structure elements to obtain a formal description of the encapsulated knowledge, wherein the graph structure elements include nodes, relationship edges and attributes, wherein the nodes are used to describe the concept ontology in the encapsulated knowledge, the relationship 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 relationship edges; A knowledge connotation module is used to divide the encapsulation knowledge into nine encapsulation knowledge categories, and formally describe the nine encapsulation knowledge categories to obtain the encapsulation knowledge connotation of the encapsulation knowledge, wherein the nine encapsulation knowledge categories include entity metadata knowledge, entity category attribute knowledge, entity structure knowledge, behavior knowledge, standard interface parameter knowledge, dimension conversion knowledge, standard object model knowledge, cross-resolution mapping knowledge and encapsulation process knowledge; The ontology and relationship module is used to obtain the encapsulation ontology knowledge of the encapsulation knowledge, and to obtain the encapsulation ontology relationship knowledge of the encapsulation knowledge. The encapsulation ontology knowledge is determined based on the subordinate ontologies of the nine encapsulation knowledge categories, and the encapsulation ontology relationship knowledge is determined based on the relationships between the subordinate ontologies of the nine encapsulation knowledge categories.

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

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

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