Heterogeneous interoperation semantic system construction method

Abstract

The invention discloses a heterogeneous interoperation semantic system construction method, which comprises a first operating system and a second operating system, and comprises the following steps: respectively calling a first object model and a second object model to be combined in the first operating system and the second operating system; extracting meta-models in the first object model and the second object model to obtain a first meta-model and a second meta-model; performing element fusion according to the first meta-model and the second meta-model to obtain a meta-model of the semantic model; extracting attribute messages in the first object model and the second object model to obtain a first attribute set and a second attribute set; identifying an association relationship type based on the first attribute set and the second attribute set; carrying out attribute fusion according to the association relationship type and the meta-model to obtain an interoperation semantic model; according to the method, the semantic models can be constructed according to the heterogeneous systems, interconnection between the two heterogeneous models is realized under the action of the semantic models, and a joint test between the heterogeneous systems is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of joint tests, and more specifically, to a method for constructing a heterogeneous interoperable semantic system. Background Art

[0002] Currently, in the field of joint tests, a large number of simulation models in different professional fields have been accumulated to support the development of simulation tests. In order to more effectively share and reuse models and improve the integration efficiency of simulation systems, a variety of technical systems such as DIS, HLA, and TENA have emerged one after another. Each system has its specific object model expression method, and the models under each system have achieved sharing and reuse within their respective systems.

[0003] However, the current research on object models is mainly limited within a single technical system. For example, the iterative update of the TENA object model and the evolution of the HLA FOM are all carried out in their respective closed technical environments. Regarding how to integrate object models under different technical systems and establish an interoperable semantic model that can cover the characteristics of heterogeneous systems, there is currently a lack of systematic research and solutions. This lack of research has led to the interoperability between heterogeneous systems still relying on heavyweight conversion mechanisms such as gateways, which not only increases the system complexity but also affects the interoperability efficiency.

[0004] Therefore, how to achieve operational interconnection of heterogeneous systems is an urgent problem for those skilled in the art to solve. Summary of the Invention

[0005] In view of this, the present invention provides a method for constructing a heterogeneous interoperable semantic system, which can construct a semantic model according to heterogeneous systems, and under the action of the semantic model, achieve the interconnection between two heterogeneous models and realize the joint test between heterogeneous systems.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] As Figure 1 , a method for constructing a heterogeneous interoperable semantic system, including a first operating system and a second operating system, comprises the following steps:

[0008] S1: Retrieve the first object model and the second object model to be jointed in the first operating system and the second operating system respectively;

[0009] S2: Extract the meta-models in the first object model and the second object model to obtain a first meta-model and a second meta-model; perform element fusion according to the first meta-model and the second meta-model to obtain the meta-model of the semantic model;

[0010] S3: Extract the attribute messages in the first object model and the second object model to obtain the first attribute set and the second attribute set; identify the type of association relationship based on the first attribute set and the second attribute set;

[0011] S4: Perform attribute fusion according to the type of association relationship and the meta-model to obtain an interoperable semantic model.

[0012] Preferably, performing element fusion according to the first meta-model and the second meta-model specifically includes:

[0013] Extract the constituent elements in the first meta-model and the second meta-model respectively, and make a comparison according to the constituent elements. Fuse the elements with an association relationship and jointly form the meta-model of the semantic model with the elements without an association relationship.

[0014] Preferably, when making a comparison according to the constituent elements, divide the constituent elements into data type elements and structure type elements, and make a comparison for the data type elements and the structure type elements respectively.

[0015] Preferably, the S3 further includes:

[0016] Before identifying the type of association relationship, identify the object consistency of the first object model and the second object model. When the objects are consistent, perform the identification of the type of association relationship.

[0017] Preferably, the attribute fusion in the S4 according to the type of association relationship and the meta-model specifically includes:

[0018] Identify the physical meanings of the attributes in the first attribute set and the second attribute set respectively, and perform an association relationship judgment to identify the attribute pairs of different association relationship types;

[0019] The association relationship includes complete consistency, complete inconsistency, and inclusion relationship;

[0020] For the attribute pairs with complete consistency, define them using the specified naming method in the first object model or the second object model;

[0021] For the attribute pairs with complete inconsistency, add the corresponding two attributes to the interoperable semantic model respectively;

[0022] For the attribute pairs with an inclusion relationship, add the included attribute to the interoperable semantic model in an inward way or establish an extended relationship.

[0023] Preferably, in the S3, use a trained object fusion model to identify the type of real-time association relationship.

[0024] Preferably, the first operating system and the second operating system are TENA architecture and HLA architecture respectively; the first object model is the WeaponFire object model under the TENA architecture; the second object model is the WeaponFire object model under the HLA architecture.

[0025] Preferably, the interoperability semantic model includes:

[0026] The model name of the interoperability semantic model is WeaponFire;

[0027] The attribute elements within the interoperability semantic model include one or more of the following attributes:

[0028] (1) Attributes with exactly the same association relationship type: Message ID, Shooter Platform ID, Target Platform ID, Firing Location, Munition Type, Warhead Type;

[0029] (2) Attributes with completely different: Exercise Force, Mission ID, Weapon Type, Firing Range, Maximum Range, Remaining Ammunition, Sending Time, Firing Control Range, Firing Control Mission ID, Initial Ammunition Velocity, Munition Type ID, Number of Ammunition Fired, and Number of Shells Fired per Minute;

[0030] Preferably, the definitions and data structures of each attribute element in the interoperability semantic model are specifically as follows:

[0031] The Message ID is defined as messageID or WeaponFireID; the data class is UniqueID;

[0032] The Shooter Platform ID is defined as shooterPlatformID or FiringObjectIdentifier; the data type is UniqueID;

[0033] The Target Platform ID is defined as TargetObjectIdentifier or targetPlatformID; the data type is UniqueID;

[0034] The Firing Location is defined as FiringLocation or tspiAtFire; the data type is TSPI

[0035] The Munition Type is defined as MunitionType or ammo; the data type is PlatformType

[0036] The Warhead Type is defined as WarheadType or warheadType; the data type is WarheadType;

[0037] The rehearsal force is defined as exerciseForce; the data type is ExerciseForce;

[0038] The mission ID is defined as missionID; the data type is UniqueID;

[0039] The weapon type is defined as weaponType; the data type is PlatformType;

[0040] The shooting distance is defined as rangeToTargetInMeters; the data type is float32;

[0041] The maximum range is defined as maxRangeOfWeaponInMeters; the data type is uint32; For the explosion, the explosion is defined as burst, and the data type is Burst;

[0042] The remaining ammunition is defined as roundsRemaining; the data type is uint16;

[0043] The sending time is defined as sendTime; the data type is Time;

[0044] The fire control solution range is defined as FireControlSolutionRange; the data type is float32;

[0045] The fire mission ID is defined as FireMissionIndex; the data type is uint32;

[0046] The initial velocity of the ammunition is defined as InitialVelocityVector; the data type is Velocity;

[0047] The object instance ID of the ammunition to be launched is defined as MunitionObjectIdentifier; the data type is UniqueID;

[0048] The quantity of ammunition fired is defined as QuantityFired; the data type is uint16;

[0049] The number of shells fired per minute is defined as RateOfFire; the data type is uint16.

[0050] As can be seen from the above technical solutions, compared with the prior art, the present invention discloses a method for constructing a heterogeneous interoperable semantic system, which can construct a semantic model according to heterogeneous systems, and under the action of the semantic model, realize the interconnection between two heterogeneous models and the joint test between heterogeneous systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0052] Figure 1 It is a schematic diagram of a method for constructing a heterogeneous interoperable semantic system provided in an embodiment of the present invention.

[0053] Figure 2 It is a schematic diagram of the attribute mapping method in an embodiment of the present invention;

[0054] Figure 3 It is a schematic diagram of the method for defining the data type result of the attribute mapping in an embodiment of the present invention;

[0055] Figure 4 It is a schematic diagram of the object model structure of the TENA architecture in an embodiment of the present invention.

[0056] Figure 5 It is a schematic diagram of the object model structure of the HLA architecture in an embodiment of the present invention.

[0057] Figure 6 It is a schematic diagram of the interoperable semantic model structure in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0058] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0059] Such as Figure 1 , the embodiments of the present invention disclose a method for constructing a heterogeneous interoperable semantic system, including the following steps:

[0060] S1: Retrieve the first object model and the second object model to be combined in the first operating system and the second operating system respectively.

[0061] S2: Extract the meta-models from the first object model and the second object model to obtain the first meta-model and the second meta-model; perform element fusion based on the first meta-model and the second meta-model to obtain the meta-model of the semantic model.

[0062] S3: Extract the attribute messages from the first object model and the second object model to obtain the first attribute set and the second attribute set; identify the type of association relationship based on the first attribute set and the second attribute set.

[0063] S4: Perform attribute fusion based on the type of association relationship and the meta-model to obtain the interoperable semantic model.

[0064] In this embodiment, in order to achieve interoperability between two object models in heterogeneous systems, the present invention performs fusion from two aspects of the meta-model and attribute elements, constructs a semantic model with unified description, and realizes interoperability between heterogeneous systems through the semantic model.

[0065] In one embodiment, the first meta-model and the second meta-model in S2 are respectively obtained by extraction from two object models to be combined. The fusion based on the two meta-models includes: respectively extracting the constituent elements in the first meta-model and the second meta-model, comparing according to the constituent elements, fusing the elements with an association relationship, and jointly constructing the meta-model of the semantic model with the elements without an association relationship.

[0066] Specifically, taking the object models under two different architectures, architecture A and architecture B, as an example, its meta-model is composed of multiple elements, such as different data types, type definitions, inheritance relationships, etc. The corresponding analysis methods for the meta-model expression ability include:

[0067] Data type expression ability: Analyze the support degrees of the two meta-models in aspects such as basic data types, composite data types, and custom types.

[0068] Structure expression ability: Compare the expression abilities of the two elements in structural features such as type definition, inheritance relationship, composition relationship, and association relationship.

[0069] After analyzing the expression ability, perform extension and update according to the relationship of the expression ability, that is, the association relationship, so that the meta-model in the interconnected semantic model can be compatible with both architectures at the same time. The extension and update methods include: (1) For elements with exactly the same meaning, the name is the same as that in architecture A; (2) For elements with completely different meanings, directly add them, and the name is the same as the original element; (3) For elements with an inclusion relationship in meaning, add the element with stronger expression ability; (4) For elements with partial overlap in meaning, extend the elements in architecture A, and the name is the same as that in architecture A.

[0070] In this embodiment, the extension and update are based on architecture A. In addition, it is also possible to use another architecture as the basis and perform the extension and update of elements in the same way. For the selection of the basic architecture, after obtaining the meta-model, the expression ability of the meta-model can be roughly estimated initially, and the meta-model with greater expression ability can be specified among them.

[0071] For example Figure 2 , in one embodiment, in S3, by analyzing the attribute elements under two different object models, the mapping between each object model and the interoperability semantic model is realized, so that the unified attribute description can be realized within the interoperability semantic model under the mapping of both parties. The specific steps of S3 include:

[0072] S31: Confirm the consistency of the name meanings according to the names of the first object model and the second object model in the heterogeneous system;

[0073] S32: When the name meanings are consistent, any one of the two object models can be used to specify the model name; when the name meanings are inconsistent, the original name is retained and the corresponding attributes are added to the interoperability semantic model;

[0074] S33: For the case where the name meanings are consistent, it is necessary to further judge the consistency of the physical meanings of the attributes;

[0075] S34: Perform corresponding attribute definition operations according to different consistency judgment results. For example, when the attribute meanings are exactly the same, the name of the corresponding attribute under any one of the two object models can be used; when they are completely inconsistent, the two attributes are added to the semantic model by adding new attributes; when the two attributes have an inclusion relationship, the included attribute is added to the data structure of the existing attribute by internalization, or an inheritance or composition relationship is established between the corresponding two attributes through an extension relationship.

[0076] In this embodiment, if a basic model has been specified before performing the attribute mapping, the structure of the specified basic model is first loaded into the semantic model and adjusted on this basis.

[0077] In addition, whenever a new attribute or an update of an attribute is completed, it is necessary to specify the data type for the corresponding attribute. For example Figure 3 , for the data type based on the structure type, that is, the existing data type, the following judgment is made: when the redundancy degree of the attribute and the existing data type is none or less, the existing data type is used; when there is partial overlap, the existing data type can be selected for extension; otherwise, the data type of the new attribute in the original architecture is used and the corresponding data type definition is added.

[0078] To further implement the above technical solution, the present invention trains a neural network model to achieve natural language understanding, identify the physical meanings of two attribute names, and further judge the consistency relationship between different physical meanings. For example, the relationship between "fruit" and "apple" is an inclusion relationship, the relationship between "fruit" and "animal" is a completely inconsistent relationship, and the relationship between "mouse" and "rat" is a completely consistent relationship.

[0079] Exemplarily, the BERT model can be adopted, and a fully connected layer is added on top of the output of the BERT model as a classification layer. Therefore, the improved BERT model can achieve the interpretation of the meaning of natural language and can also determine the consistency, i.e., the association relationship category, between different meanings.

[0080] Regarding the training data, pairs of attribute names with different consistency relationships can be extracted from the knowledge graph; or pairs of attribute names and their relationships can be mined from the text corpus through remote supervision. On this basis, manual annotation can be used to construct some data to ensure the accuracy and diversity of the data.

[0081] During the data processing, the BERT model will first tokenize the input text of the pair of attribute names through the pre-trained BertTokenizer and add special tokens (such as [CLS], [SEP], etc.) to convert the text into a sequence of word vectors. Then, the sequence of word vectors is input into a multi-layer bidirectional encoder based on the Transformer architecture. In the encoder, the self-attention mechanism will perform weighted calculations on the word vectors at each position in the sequence to fully capture the context information of the text, mine the semantic features of the attribute names, and then obtain a semantic representation that integrates the context information, i.e., the meaning, after multi-layer encoding. Further, this semantic representation is input into the added fully connected classification layer, and the probabilities of different categories (inclusion relationship, completely inconsistent relationship, completely consistent relationship, etc.) are calculated through the Softmax function, and finally the category is obtained.

[0082] In another embodiment, on the basis of using a neural network model to achieve meaning understanding, a large language model can be called, and the meaning understanding result is input into the large language model for judgment in combination with a preset prompt word template, so that the large language model outputs the corresponding category result.

[0083] In another embodiment, the present invention applies this method to the interoperability between the TENA architecture and the HLA architecture systems to achieve the interoperability of the object model of "WeaponFire" for weapon firing under the two military exercise systems. Among them, the WeaponFire object model under the TENA architecture and "WeaponFire" under the HLA architecture are respectively as Figure 4 and Figure 5 shown, Figure 6 which is the final interoperability semantic model. The specific construction method is as follows:

[0084] 1. Meta - model analysis phase:

[0085] For HLA, the meta - object model is the object model template, which clearly divides the content of the object model into "object classes" and "interaction classes". Table 1 shows the constituent elements of the TENA object model meta - model and the HLA object model template in terms of data type expression ability and structure expression ability.

[0086] Table 1 Constituent elements of the meta - model

[0087]

[0088] (1) Data type expression ability:

[0089] The meanings of the elements are exactly the same: Both the TENA and HLA meta - models have basic data types, enumerations, attributes, structures, and parameter elements;

[0090] The meanings of the elements have an inclusion relationship: The interaction class in HLA and the message in TENA both represent transient objects, but the TENA message can have methods while the HLA interaction class does not; The object class in HLA and the SDO in TENA both represent persistent objects, but the TENA SDO can have methods while the HLA object class does not; The complex data types in HLA can be represented by local classes, structures, or vectors in TENA;

[0091] The meanings of the elements partially overlap: None;

[0092] The meanings of the elements are completely different: The unique elements of the TENA meta - model are data flow, state distribution object pointer, and exception.

[0093] (2) Structure expression ability:

[0094] The meanings of the elements are exactly the same: Both the TENA and HLA meta - models have inheritance relationships;

[0095] The meanings of the elements have an inclusion relationship: None;

[0096] The meanings of the elements partially overlap: None;

[0097] The meanings of the elements are completely different: The unique elements of the TENA meta - model are operations, interfaces, implementations, dependencies, and aggregations.

[0098] In summary, since the TENA architecture meta - model has a stronger expression ability than the HLA architecture object model template and can cover it, the TENA architecture meta - model can be selected as the meta - model of the interoperability semantic model.

[0099] The composition of the interoperability semantic model meta-model includes: state distribution object, message, basic data type, local class, structure, vector, enumeration, inheritance, data stream, operation, interface, state distribution object pointer, exception, implementation, dependency, and aggregation.

[0100] 2. Object model mapping phase:

[0101] Since the physical meanings of the WeaponFire object models in the TENA architecture and the HLA architecture are the same, which belongs to the processing of object models with the same physical meaning, the name of the interoperability semantic model is WeaponFire (mainly based on the TENA architecture).

[0102] When the TENA object model is specified as the base model, the physical meanings of the attributes in the WeaponFire object model in HLA are compared with those in the TENA object model one by one. The specific comparison process and results are as follows:

[0103] (1) The physical meanings of the attribute elements are exactly the same

[0104] For the EventIdentifier in the HLA object model WeaponFire and the messageID in the TENA object model WeaponFire, their physical meanings are both message IDs. The name of the message ID attribute in the interoperability semantic model WeaponFire uses the messageID of the TENA attribute. For the data type, since the expression ability of the data structure UniqueID corresponding to the TENA attribute is greater than that of the EventIdentifier corresponding to the HLA attribute, the data type of the message ID attribute in the interoperability semantic model WeaponFire uses UniqueID.

[0105] For the FiringObjectIdentifier in the HLA object model WeaponFire and the shooterPlatformID in the TENA object model WeaponFire, their physical meanings are both shooting platform IDs. The name of the shooting ID attribute in the interoperability semantic model WeaponFire uses the shooterPlatformID of the TENA attribute. For the data type, since the expression ability of the data structure UniqueID corresponding to the TENA attribute is greater than that of the RTIobjectId corresponding to the HLA attribute, the data type of the shooterPlatformID attribute in the interoperability semantic model WeaponFire uses UniqueID.

[0106] For the HLA object model WeaponFire, the physical meaning of TargetObjectIdentifier and the TENA object model WeaponFire's targetPlatformID is both the target platform ID. For the name of the target ID in the interoperability semantic model WeaponFire's property, the TENA property's targetPlatformID is used. For the data type, since the expressive power of the data structure UniqueID corresponding to the TENA property is greater than that of the RTIobjectId corresponding to the HLA property, the data type of the interoperability semantic model WeaponFire's property targetPlatformID uses UniqueID.

[0107] For the HLA object model WeaponFire, the physical meaning of FiringLocation and the TENA object model WeaponFire's tspiAtFire is both the firing location. For the name of the firing location in the interoperability semantic model WeaponFire's property, the TENA property's tspiAtFire is used. For the data type, since the expressive power of the data structure TSPI corresponding to the TENA property is greater than that of the WorldLocationStruct corresponding to the HLA property, the data type of the interoperability semantic model WeaponFire's property tspiAtFire uses TSPI.

[0108] For the HLA object model WeaponFire, the physical meaning of MunitionType and the TENA object model WeaponFire's ammo is both the ammunition type. For the name of the ammunition type in the interoperability semantic model WeaponFire's property, the TENA property's ammo is used. For the data type, since the expressive power of the data structure PlatformType corresponding to the TENA property is greater than that of the EntityTypeStruct corresponding to the HLA property, the data type of the interoperability semantic model WeaponFire's property ammo uses PlatformType.

[0109] For the HLA object model WeaponFire, the physical meaning of FuseType and the TENA object model WeaponFire's fuse is both fuse type. The name of the interoperability semantic model WeaponFire's property ammunition type uses the TENA property's fuse. For data types, since the expression capabilities of the data structures FuseType corresponding to the TENA property and FuseTypeEnum16 corresponding to the HLA property are both enumeration types and there is no inclusion relationship in the representation ranges, the unique enumeration values of FuseTypeEnum16 are supplemented into the FuseType enumeration of TENA. The data type of the interoperability semantic model WeaponFire's property fuse uses the extended FuseType.

[0110] For the HLA object model WeaponFire, the physical meaning of WarheadType and the TENA object model WeaponFire's warheadType is both warhead type. The name of the interoperability semantic model WeaponFire's property warhead type uses the TENA property's warheadType. For data types, since the expression capabilities of the data structures WarheadType corresponding to the TENA property and WarheadTypeEnum16 corresponding to the HLA property are both enumeration types and there is no inclusion relationship in the representation ranges, the unique enumeration values of WarheadTypeEnum16 are supplemented into the WarheadType enumeration of TENA. The data type of the interoperability semantic model WeaponFire's property warhead type uses the extended WarheadType.

[0111] (2) Situations where the meanings of property elements are completely inconsistent:

[0112] The physical meaning of the property named exerciseForce in the TENA object model WeaponFire is exercise force, and there is no property in the HLA object model WeaponFire that represents this physical meaning. Therefore, the interoperability semantic model WeaponFire adds a property named exerciseForce. The original property data structure is ExerciseForce, and the existing data types include ExerciseForce. Therefore, the data structure corresponding to the property named rangeToTargetInMeters in the interoperability semantic model WeaponFire is ExerciseForce.

[0113] The physical meaning of the attribute named missionID in the TENA object model WeaponFire is mission ID. Since there is no attribute in the HLA object model WeaponFire representing this physical meaning, the interoperability semantic model WeaponFire adds an attribute named missionID. The original attribute data structure is UniqueID, and the existing data types include UniqueID. Therefore, the data structure corresponding to the attribute named missionID in the interoperability semantic model WeaponFire is UniqueID.

[0114] The physical meaning of the attribute named weaponType in the TENA object model WeaponFire is weapon type. Since there is no attribute in the HLA object model WeaponFire representing this physical meaning, the interoperability semantic model WeaponFire adds an attribute named weaponType. The original attribute data structure is PlatformType, and the existing data types include PlatformType. Therefore, the data structure corresponding to the attribute named weaponType in the interoperability semantic model WeaponFire is PlatformType.

[0115] The physical meaning of the attribute named rangeToTargetInMeters in the TENA object model WeaponFire is firing range. Since there is no attribute in the HLA object model WeaponFire representing this physical meaning, the interoperability semantic model WeaponFire adds an attribute named rangeToTargetInMeters. The original attribute data structure is float32, and the existing data types include float32. Therefore, the data structure corresponding to the attribute named rangeToTargetInMeters in the interoperability semantic model WeaponFire is float32.

[0116] The physical meaning of the attribute named maxRangeOfWeaponInMeters in the TENA object model WeaponFire is maximum range. Since there is no attribute in the HLA object model WeaponFire representing this physical meaning, the interoperability semantic model WeaponFire adds an attribute named maxRangeOfWeaponInMeters. The original attribute data structure is uint32, and the existing data types include uint32. Therefore, the data structure corresponding to the attribute named maxRangeOfWeaponInMeters in the interoperability semantic model WeaponFire is uint32.

[0117] The physical meaning of the attribute named "burst" in the TENA object model WeaponFire is explosion. Since there is no attribute in the HLA object model WeaponFire that represents this physical meaning, the interoperability semantic model WeaponFire adds an attribute named "burst". The original attribute data structure is Burst, and the existing data type includes Burst. Therefore, the data structure corresponding to the attribute named "burst" in the interoperability semantic model WeaponFire is Burst.

[0118] The physical meaning of the attribute named "roundsRemaining" in the TENA object model WeaponFire is remaining ammunition. Since there is no attribute in the HLA object model WeaponFire that represents this physical meaning, the interoperability semantic model WeaponFire adds an attribute named "roundsRemaining". The original attribute data structure is uint16, and the existing data type includes unit16. Therefore, the data structure corresponding to the attribute named "roundsRemaining" in the interoperability semantic model WeaponFire is uint16.

[0119] The physical meaning of the attribute named "sendTime" in the TENA object model WeaponFire is sending time. Since there is no attribute in the HLA object model WeaponFire that represents this physical meaning, the interoperability semantic model WeaponFire adds an attribute named "sendTime". The original attribute data structure is Time, and the existing data type Time can represent it. Therefore, the data structure corresponding to the attribute named "sendTime" in the interoperability semantic model WeaponFire is Time.

[0120] The physical meaning of the attribute named "FireControlSolutionRange" in the HLA object model WeaponFire is the fire controllable range. Since there is no attribute in the TENA object model WeaponFire that represents this physical meaning, the interoperability semantic model WeaponFire adds an attribute named "FireControlSolutionRange". The original attribute data structure is float32, and the existing data type includes float32. Therefore, the data structure corresponding to the attribute named "FireControlSolutionRange" in the interoperability semantic model WeaponFire is float32.

[0121] The physical meaning of the attribute named FireMissionIndex in the HLA object model WeaponFire is the firing mission ID. There is no attribute in the TENA object model WeaponFire that represents this physical meaning. Therefore, the interoperability semantic model WeaponFire adds an attribute named FireMissionIndex. The original attribute data structure is uint32, and the existing data types include uint32. Therefore, the data structure corresponding to the attribute named FireMissionIndex in the interoperability semantic model WeaponFire is uint32.

[0122] The physical meaning of the attribute named InitialVelocityVector in the HLA object model WeaponFire is the initial velocity of the ammunition. There is no attribute in the TENA object model WeaponFire that represents this physical meaning. Therefore, the interoperability semantic model WeaponFire adds an attribute named InitialVelocityVector. The original attribute data structure is VelocityVectorStruct, and the existing data type Velocity can represent it. Therefore, the data structure corresponding to the attribute named InitialVelocityVector in the interoperability semantic model WeaponFire is Velocity.

[0123] The physical meaning of the attribute named MunitionObjectIdentifier in the HLA object model WeaponFire is the object instance ID of the fired ammunition. There is no attribute in the TENA object model WeaponFire that represents this physical meaning. Therefore, the interoperability semantic model WeaponFire adds an attribute named MunitionObjectIdentifier. The original attribute data structure is RTIobjectId, and the existing data type UniqueID can represent it. Therefore, the data structure corresponding to the attribute named MunitionObjectIdentifier in the interoperability semantic model WeaponFire is UniqueID.

[0124] The physical meaning of the attribute named QuantityFired in the HLA object model WeaponFire is the quantity of ammunition fired. There is no attribute in the TENA object model WeaponFire that represents this physical meaning. Therefore, the interoperability semantic model WeaponFire adds an attribute named QuantityFired. The original attribute data structure is uint16, and the existing data types include uint16. Therefore, the data structure corresponding to the attribute named QuantityFired in the interoperability semantic model WeaponFire is uint16.

[0125] In the HLA object model WeaponFire, the physical meaning of the attribute named RateOfFire is the number of shells fired per minute. In the TENA object model WeaponFire, there is no attribute representing this physical meaning. Therefore, in the interoperability semantic model WeaponFire, a new attribute named RateOfFire is added. The original attribute data structure is uint16, and the existing data types include uint16. Therefore, the data structure corresponding to the attribute named RateOfFire in the interoperability semantic model WeaponFire is uint16.

[0126] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same or similar parts among the embodiments, reference can be made to each other. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple. For the relevant parts, reference can be made to the description in the method section.

[0127] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for constructing a heterogeneous interoperable semantic system, including a first operating system and a second operating system, characterized in that, It includes the following steps: S1: Retrieve the first object model and the second object model to be combined in the first operating system and the second operating system respectively; S2: Extract the meta-models in the first object model and the second object model to obtain a first meta-model and a second meta-model; perform element fusion based on the first meta-model and the second meta-model to obtain the meta-model of the semantic model; S3: Extract the attribute messages in the first object model and the second object model to obtain a first attribute set and the second attribute set; identify the type of association relationship based on the first attribute set and the second attribute set; S4: Perform attribute fusion based on the type of association relationship and the meta-model to obtain an interoperable semantic model.

2. The method for constructing a heterogeneous interoperability semantic system according to claim 1, wherein Performing element fusion based on the first meta-model and the second meta-model specifically includes: Extract the constituent elements in the first meta-model and the second meta-model respectively, and make comparisons based on the constituent elements, fuse the elements with an association relationship, and jointly form the meta-model of the semantic model with the elements without an association relationship.

3. A method for constructing a heterogeneous interoperability semantic system according to claim 2, characterized in that, When making comparisons based on the constituent elements, the constituent elements are divided into data type elements and structure type elements, and the data type elements and the structure type elements are compared separately.

4. A method for constructing a heterogeneous interoperability semantic system according to claim 1, characterized in that, S3 further includes: Before identifying the type of association relationship, identify the object consistency of the first object model and the second object model. When the objects are consistent, perform the identification of the type of association relationship.

5. A method for constructing a heterogeneous interoperability semantic system according to claim 1 or 4, characterized in that In S4, performing attribute fusion based on the type of association relationship and the meta-model specifically includes: Identify the physical meanings of the attributes in the first attribute set and the second attribute set respectively, make an association relationship judgment, and identify the attribute pairs of different association relationship types; The association relationships include complete consistency, complete inconsistency, and inclusion relationship; For the attribute pairs with complete consistency, define them using the specified naming method in the first object model or the second object model; For the attribute pairs with complete inconsistency, add the corresponding two attributes to the interoperable semantic model respectively; For the attribute pairs with an inclusion relationship, add the included attributes to the interoperable semantic model in an inward way or establish an extended relationship.

6. A method for constructing a heterogeneous interoperability semantic system according to claim 1, characterized in that, In S3, a trained object fusion model is used to identify the type of real-time association relationship.

7. A method for constructing a heterogeneous interoperable semantic system according to claim 1, characterized in that: The first operating system and the second operating system are TENA architecture and HLA architecture respectively; the first object model is the WeaponFire object model under the TENA architecture; the second object model is the WeaponFire object model under the HLA architecture.

8. A method for constructing a heterogeneous interoperability semantic system according to claim 8, characterized in that, The interoperable semantic model includes: The model name of the interoperable semantic model is WeaponFire; The attribute elements in the interoperable semantic model include one or more of the following attributes: (1) Attributes with an association relationship type of complete consistency: message ID, shooting platform ID, target platform ID, firing position, object instance ID of the launched ammunition, warhead type; (2) Completely inconsistent attributes: exercise force, mission ID, weapon type, firing range, maximum range, remaining ammunition, sending time, fire control range, fire control mission ID, initial ammunition velocity, ammunition type ID, number of ammunition fired, and number of rounds fired per minute.

9. A method for constructing a heterogeneous interoperable semantic system according to claim 8, characterized in that: The definitions and data structures of each attribute element in the interoperability semantic model are specifically as follows: The message ID is defined as messageID or WeaponFireID; the data class is UniqueID; The shooter platform ID is defined as shooterPlatformID or FiringObjectIdentifier; the data type is UniqueID; The target platform ID is defined as TargetObjectIdentifier or targetPlatformID; The data type is UniqueID; The firing location is defined as FiringLocation or tspiAtFire; the data type is TSPI The ammunition type is defined as MunitionType or ammo; The data type is PlatformType The warhead type is defined as WarheadType or warheadType; the data type is WarheadType; The exercise force is defined as exerciseForce; the data type is ExerciseForce; The mission ID is defined as missionID; The data type is UniqueID; The weapon type is defined as weaponType; The data type is PlatformType; The firing range is defined as rangeToTargetInMeters; The data type is float32; The maximum range of the weapon is defined as maxRangeOfWeaponInMeters; the data type is uint32; explosion, the explosion is defined as burst, and the data type is Burst; The remaining ammunition is defined as roundsRemaining; the data type is uint16; The sending time is defined as sendTime; The data type is Time; The fire control range is defined as FireControlSolutionRange; the data type is float32; The fire mission ID is defined as FireMissionIndex; the data type is uint32; The initial ammunition velocity is defined as InitialVelocityVector; the data type is Velocity; The object instance ID of the ammunition fired is defined as MunitionObjectIdentifier; the data type is UniqueID; The number of ammunition fired is defined as QuantityFired; the data type is uint16; The number of rounds fired per minute is defined as RateOfFire; the data type is uint16.

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