CN115884235A - 5G network digital twin modeling method and device, computer equipment and storage medium - Google Patents

Abstract

The invention relates to the field of computers, and provides a 5G network digital twin modeling method, device, equipment and medium. The method comprises the following steps: selecting a digital twin specification, wherein the digital twin specification comprises a data model, a geometric model, a digital model, a rule model, a control model and a feedback model and is used for describing a type of resource entity; selecting a data resource corresponding to the digital twin specification in a resource library to generate a digital twin instance; a digital twin scene is selected for the digital twin instance and the digital twin instance is run. The digital twinborn method disclosed by the invention realizes that the virtual entity model truly and objectively describes and describes the physical entity through integration and fusion of models with different dimensions, different spatial scales and different time scales; and monitoring the change of the physical object in the virtual model, predicting the potential risk, and reasonably and effectively planning or maintaining the related equipment.

Description

5G network digital twin modeling method and device, computer equipment and storage medium

Technical Field

The invention relates to the technical field of digital twinning, in particular to a 5G network digital twinning modeling method, a device, computer equipment and a storage medium.

Background

The digital twin technology is a technical means which integrates multi-physics, multi-scale and multi-disciplinary attributes, has the characteristics of real-time synchronization, faithful mapping and high fidelity, and can realize interaction and fusion of a physical world and an information world. Specifically, digital twinning is the addition or expansion of new capabilities to a physical entity by synchronously simulating the behavior of the physical entity in a real environment via a virtual model of the physical entity. The digital twin model is an important component of the digital twin, and the twin model digitally expresses physical entities in a virtual world and is the basis for realizing the digital twin technology. The digital twin is based on a data model, so that cross-professional/cross-regional/cross-level network resource (network operation data + resource data) data fusion and association are realized, full resource visualization and management of the cloud network are gradually realized, two-stage resource one-point scheduling is realized, and intensive management improves the cloud network service operation efficiency. And basic data support and service are provided for service scenes such as accurate marketing, accurate investment, speed and cost reduction, network optimization and data conversion, base station energy conservation, small mobile asset planning, iron tower assessment and the like.

The modeling dimension of the digital twin can be expanded along with the depth of a twin application scene, and the existing digital twin is only a twin with low maturity, so that the application requirements of rapid deployment, fault early warning and network scheduling of a 5G network digital twin model in a real scene are difficult to meet.

Disclosure of Invention

In view of the above, the invention provides a 5G network digital twin modeling method, device, equipment and medium, wherein the 5G network digital twin modeling method provided by the invention is based on the existing resource center cloud network resource fusion data; a metadata design system and a digital twin modeling methodology are taken as guidance; and digitally constructing the information of the composition, characteristics, functions, performance and the like of each entity in the real physical network to form a unique cloud network digital twin for telecommunication. The service requirements of the application scene on real cloud network real depiction, network virtual-real interaction, network intelligent simulation deduction, intention management network and the like are gradually realized.

Based on the above objects, an aspect of embodiments of the present invention provides a 5G network digital twin modeling method, including the steps of: selecting a digital twin specification, wherein the digital twin specification comprises a data model, a geometric model, a digital model, a rule model, a control model and a feedback model and is used for describing a class of resource entities; selecting a data resource corresponding to the digital twin specification in a resource library to generate a digital twin instance; a digital twin scene is selected for the digital twin instance and the digital twin instance is run.

In some embodiments, the storage type of the geometric model is an ER model plus a three-dimensional data model, and the geometric model adopts a two-dimensional plus three-dimensional visualization expression mode according to complexity, fineness and simulation degree.

In some embodiments, the rule model is used to describe internal rules of the resource facility device.

In some embodiments, the digital twin body operates a mathematical model according to different parameters to analyze the internal operation rule of the digital twin body, and the digital model obtains a corresponding operation rule through calculation according to operation related data and outputs the result.

In some embodiments, the visual definition is performed by a plurality of dimensions such as color, brightness, animation, geometric change and the like according to the operation result of the digital model.

In some embodiments, the control model is used to define control information that a digital twin can apply, the control information being the sum of actions that a digital twin can receive in the digital world.

In some embodiments, the feedback module is configured to provide message feedback for the digital twin, obtain a result of physical entity feedback in the real world, and implement message feedback through the digital twin.

In another aspect of the embodiments of the present invention, there is also provided a 5G network digital twin modeling apparatus, including: the system comprises a first module, a second module and a third module, wherein the first module is used for selecting a digital twin specification, and the digital twin specification comprises a data model, a geometric model, a digital model, a rule model, a control model and a feedback model and is used for describing a class of resource entities; a second module for selecting a data resource in a resource repository corresponding to the digital twin specification to generate a digital twin instance; a third module to select a digital twin scene for the digital twin instance and to run the digital twin instance.

In another aspect of the embodiments of the present invention, there is also provided a computer device, including at least one processor; and a memory storing computer instructions executable on the processor, the instructions when executed by the processor implementing the steps of any of the methods described above.

In another aspect of the embodiments of the present invention, a computer-readable storage medium is also provided, in which a computer program for implementing any one of the above method steps is stored when the computer program is executed by a processor.

The invention has at least the following beneficial effects: the invention provides a 5G network digital twin modeling method, a device, equipment and a medium, wherein the 5G network digital twin modeling method provided by the invention,

the digital twins realize the real and objective description and depiction of physical entities by the virtual entity model through the integration and fusion of models with different dimensions, different spatial scales and different time scales; the method is based on a technology of fusing mirror images of a physical entity space and a virtual space, integrates the technologies of Artificial Intelligence (AI), machine Learning (ML) and the like, combines data, algorithm and decision analysis together, establishes simulation, namely virtual mapping of a physical object, finds a problem before the problem occurs, monitors the change of the physical object in a virtual model, diagnoses the complex processing and abnormal analysis of multi-dimensional data based on artificial intelligence, predicts the potential risk, and reasonably and effectively plans or maintains related equipment; the past and existing behaviors or processes of a certain physical entity are dynamically presented in a digital form, so that the enterprise performance is promoted; the real-time interaction, virtual-real bidirectional connection, consistency and synchronism of the information space and the physical space are realized through the fusion of the information data and the physical data, real-time process simulation and optimization are carried out, various intelligent services used according to needs are provided, and more real-time and accurate application services are provided; and a corresponding digital twin model is created according to different application objects and service requirements, so that the problem that the physical entity space technology is difficult to touch, control and monitor in real time is solved.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other embodiments can be obtained according to the drawings without creative efforts.

FIG. 1 is a schematic diagram of a digital twin modeling method for a 5G network in some embodiments of the present application;

FIG. 2 is a schematic structural view of a digital twin gauge in some embodiments of the present application;

FIG. 3 is a schematic illustration of a digital twin instance versus resource data relationship mapping in some embodiments of the present application;

FIG. 4 is a schematic diagram of a 5G network digital twin data application in some embodiments of the present application;

FIG. 5 is a schematic diagram of the structure of 5G network digital twin data in some embodiments of the present application;

FIG. 6 is a schematic flow chart of a 5G network digital twin modeling method in some embodiments of the present application;

FIG. 7 is a diagram illustrating a digital twinning specification in some embodiments of the present application;

FIG. 8 is a diagram illustrating information descriptive of a data model of a digital twin specification in some embodiments of the present application;

FIG. 9 is a schematic illustration of a geometric model definition of a digital twinning specification in some embodiments of the present application;

FIG. 10 is a schematic illustration of a method of digital model representation of a digital twinning specification in some embodiments of the present application;

FIG. 11 is a schematic illustration of a representation of a plausibility law for a digital model of a digital twin specification in some embodiments of the present application;

FIG. 12 is a schematic illustration of a method of regular model representation of a digital twin specification in some embodiments of the present application;

FIG. 13 is a schematic illustration of a control model representation of a digital twin specification in some embodiments of the present application;

FIG. 14 is a schematic illustration of a feedback model representation of a digital twin specification in some embodiments of the present application;

FIG. 15 is a schematic illustration of a two-dimensional vector, three-dimensional vector representation of a digital twin specification spatial geographic location model in some embodiments of the present application;

figure 16 is a schematic diagram of a 5GC network capability scheduling subsystem in some embodiments of the present application;

FIG. 17 is a schematic diagram of digital twin modeling of a 5G network provided by the present invention;

FIG. 18 is a schematic diagram of an embodiment of a computer device provided by the invention;

FIG. 19 is a schematic diagram of an embodiment of a computer-readable storage medium.

Detailed Description

Embodiments of the invention are described below. However, it is to be understood that the disclosed embodiments are merely examples and that other embodiments may take various and alternative forms.

In addition, it should be noted that all expressions using "first" and "second" in the embodiments of the present invention are used for distinguishing two entities with the same name but different names or different parameters, and it should be noted that "first" and "second" are only used for convenience of expression and should not be construed as a limitation to the embodiments of the present invention, and the descriptions thereof in the following embodiments are omitted. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.

One or more embodiments of the present application will be described below in conjunction with the following drawings.

Fig. 1 is a schematic diagram illustrating a digital twin modeling method for a 5G network according to some embodiments of the present application. As shown in fig. 1, a digital twin modeling method for a 5G network according to an embodiment of the present invention includes the following steps:

selecting a digital twin specification, wherein the digital twin specification comprises a data model, a geometric model, a digital model, a rule model, a control model and a feedback model and is used for describing a class of resource entities;

selecting a data resource corresponding to the digital twin specification in a resource library to generate a digital twin instance;

a digital twin scene is selected for the digital twin instance and the digital twin instance is run.

In the application, a digital twin specification is selected first, and then corresponding resource data is selected in a resource library. FIG. 2 is a schematic structural view of a digital twin gauge in some embodiments of the present application; as shown in fig. 2, the digital twin specification is metadata of the digital twin, and fig. 3 is a schematic diagram of a digital twin example and resource data relationship mapping in some embodiments of the present application; as shown in fig. 2-3, the specification describes a class of resource entities, and by correlating with specific resource data of the resource entities, corresponding digital twin instances are generated to operate in a digital twin scenario. FIG. 4 is a schematic diagram of a 5G network digital twin data application in some embodiments of the present application; as shown in fig. 4, the digital twin specification of the present invention mainly includes the construction of six models, which are a data model, a geometric model, a digital model, a rule model, a control model and a feedback model, and can be further expanded according to the application requirements. The functions of twin visualization, virtual-real interaction, intelligent decision, intention driving and the like can be realized through the twin service, and the aims of network planning, network construction, network operation and maintenance, network optimization, implementation control and offline analysis of the conventional cloud resource data sharing platform are realized through the functions. Taking the virtual-real interaction function as an example, the real-time or delayed feedback of the operation on the digital twin body is fed back on the physical entity, and the change on the physical entity can also be fed back through the virtual-real interaction and displayed through the twin visual real-time or delayed display.

Fig. 5 is a schematic structural diagram of digital twin data of a 5G network in some embodiments of the present application, fig. 6 is a schematic flow diagram of a digital twin modeling method of a 5G network in some embodiments of the present application, and fig. 7 is a schematic diagram of a digital twin specification in some embodiments of the present application; as shown in fig. 5-7, the digital twin specification required for digital twin modeling mainly includes the construction of six models, namely a data model, a geometric model, a digital model, a rule model, a control model and a feedback model. 5-6, the 5G network digital twin data is centered on the design of the twin specification and the twin data, including a native layer, a twin layer and an application layer. The native layer forms data which can be received by the twin layer through operations of collecting, converting, comparing, checking and the like on the native data, the twin layer comprises a digital twin specification, and the digital twin specification and the processed original data are combined to form twin data. The twin layer also comprises twin configuration and running data, and the twin data is combined with the configuration and running data and further combined with specific scene data of the application layer to form a running example of the twin.

The management objects of the data model are mostly descriptive information, for example: code, name, factory, etc. Compared with the existing model association, the data model basically exists in the existing metadata model, and can be obtained from the existing model in most cases, and the management and maintenance mode is not much different from the existing model. FIG. 8 is a graphical representation of descriptive information for a data model of a digital twin specification in some embodiments of the present application; as shown in fig. 8, the visual definition of the data model is often expressed in the form of a graph or characters for descriptive information. The storage type of the data model is an ER model.

The ER model or E-R model, referred to herein as Entity-relationship model, or Entity-relationship schema (ERD) (English: entity-relationship model), is a data model or schema used for high-level description of conceptual data models. The constituent components of the E-R model are entity sets, attributes and contact sets, and the expression method is as follows: (1) The entity set is represented by a rectangular box, and entity names are written in the rectangular box. (2) The attribute of the entity is represented by an oval box, the attribute name is written in the box, and the entity is connected with the entity set by using an undirected edge. (3) The connections between entities are represented by diamonds, the connections are named with proper meanings, names are written in the diamonds, rectangular boxes of entities participating in the connections are connected with the diamonds respectively by undirected connections, and the types of the connections, namely 1-1, 1-N or M-N, are marked on the connections. The E-R graph model is composed of entities, attributes and relations. The entity is a user of data, which represents objectively existing life real objects in the software system, such as people, animals, objects, lists, departments, projects and the like, and the same entity class constitutes an entity set. The connotation of an entity is represented by an entity type. An entity type is a definition of an entity in an entity set. For example, a user has a name, a gender, an address, a phone number, etc. an "entity identifier" is an identifier that can uniquely represent an entity's attributes and set of attributes. The entity identifier is also the primary key of the entity. In the ER diagram, attributes corresponding to entities are represented by oval symbols, and underlined names are generally called identifiers. In the general living world, entities do not exist independently, and the entities and other entities have thousands of connections. For example, a person works in a certain department of a company, wherein the entities have "the person" and "the certain department of the company", and there are many contact links between them. ER models are commonly used in information system design; such as they are used during the conceptual structural design phase to describe information requirements and/or the type of information to be stored in the database. Data modeling techniques can be used to describe any ontology of a particular domain of discourse (i.e., a region of interest) (i.e., a summary and classification of terms used and their associations). In the case of database-based information system design, at a later stage (often called logical design), the conceptual model is mapped onto a logical model, such as a relational model; it is mapped onto the physical model, in turn, during physical design. Note that these two stages are sometimes referred to together as a "physical design".

The management objects of the geometric model are symbols, three-dimensional models, spatial positions (longitude and latitude), heights, widths and lengths. The three-dimensional model comprises models with different precisions, a white model and a high-precision model; the same model may include models of a variety of different textures; the geometric model also includes models of various originals. In the existing model, the content of the geometric model is not specially corresponding, and the geometric model with different capability levels is defined by carrying out hierarchical definition from different angles of the complexity, the fineness, the simulation degree and the like of the geometric model according to the capability of the geometric model from high to low. FIG. 9 is a schematic illustration of a geometric model definition of a digital twinning specification in some embodiments of the present application; as shown in fig. 9, the geometric model has a strong visualization expression capability, and can provide two-dimensional and three-dimensional visualization expression from the symbol-based model, and the visualization expression of other related models can be realized by the geometric model, so that the visualization capability of the digital twin in the digital twin scene is enriched. The storage type of the geometric model is ER model + three-dimensional data model.

And the digital model is used for describing an internal rule model of the resource facility equipment. Digital models include, but are not limited to: internal laws between different attributes of the digital twin data model (e.g., total capacity = row number column number; total capacity = error capacity + unused capacity + used capacity); the digital twin body operates a mathematical model (a flow simulation model, a path simulation model and a network quality analysis model) according to different parameters to analyze the internal operation rule of the digital twin body, and the digital model obtains corresponding operation rules through calculation according to operation related data and outputs results. In the existing model, the content which can specially correspond to the digital model of the application is not provided, the internal rules of the attributes can be carried in related services, and the internal rules are not extracted and defined from the dimensionality of the digital twin. And the digital model simulating the internal law according to different parameters is a blank part. FIG. 10 is a schematic illustration of a method of digital model representation of a digital twinning specification in some embodiments of the present application; FIG. 11 is a schematic illustration of a representation of a plausibility law for a digital model of a digital twin specification in some embodiments of the present application; as shown in fig. 10 to 11, the digital model is relatively complex in visual expression and is also content relatively central in the digital twin, and according to the operation result of the digital model, it is generally necessary to design the corresponding content as a part capable of expressing the visual ability of the digital twin, and the content is defined by a plurality of dimensions such as color, brightness, animation, and geometric change. The storage type of the digital model is binary data (long-run characters or program packages).

The rule model is used for defining the association relationship between the digital twin and other external entities, and unlike the digital model, the rule model defines the relationship between the digital twin and the external entities, and the relationship is closer to the definition of the topological relationship. By defining a link relationship, an inclusion relationship, etc., to indicate the external other entity to which it relates, the relationship between it and the external other entity is constrained while the external entity that contacts it is built. In the existing metadata model, part of entity association rule data exists, and twin modeling can be refined and defined by referring to the dimension of the rule model of the digital twin. FIG. 12 is a schematic illustration of a method of representation of a rule model of a digital twin specification in some embodiments of the present application; as shown in fig. 12, the visual definition of the rule model is often defined by using a topology-related expression method, and defines the visual expression content of the relationship between the rule model and the associated digital twin, including but not limited to connecting lines, dynamic lines, and the like. The storage type of the rule model is an ER model.

The control model is control information that can be applied to the digital twin, in the world of digital twins, essentially all of the behavior of the digital twin is initiated because the control model inputs corresponding control information to the digital twin. The control model defines control information that the digital twin can apply, and the control model can define operation information for equipment, such as starting and closing, and the like, and can also be similar to operation related to services, such as capacity expansion, dismantling and opening. In general, control information refers to the sum of actions a digital twin can receive in the digital world. In the existing model, the content which can correspond to the control model is not specially available, the content related to the control model can be carried in related services, and the content is not abstracted and defined from the dimension of the digital twin. The control model is essentially the digital twin model with its control network morphology entry defined. FIG. 13 is a schematic illustration of a control model representation of a digital twin specification in some embodiments of the present application; as shown in fig. 13, the control model visually defines the source, mainly because the input of the control information causes the relevant morphological change of the digital twin, because the corresponding visual content needs to be defined for the morphological change. The storage type of the control model is an ER model (control function name, input parameters and operation identification).

The feedback model is information which can be sent out by a defined digital twin, namely 'feedback' of the digital twin itself, and the external of the digital twin can be understood. The content of the feedback model definition mainly comprises: attributes, i.e. static information about the digital twinner, such as code, name, etc., fed back externally by the digital twinner; the digital twin can externally feed back a relevant result after the digital model operates according to the operation result of the digital model; feedback information related to the intelligent device, and a feedback model obtains a result fed back by a physical entity in the real world and is directly realized based on the digital twin. Taking the alarm information as an example, the model may first feed back alarm content, such as capacity, usage rate and other related information, through static attribute information; secondly, the feedback model can perform alarm feedback according to the operation result of the digital model, and if the operation result of the digital model triggers an alarm once, the feedback model feeds back corresponding alarm information; and finally, the alarm information fed back by the intelligent equipment, real-time alarm data and service and historical alarm data and service are complicatedly butted by a feedback model, and the alarm information is fed back based on the digital twin. In the existing resource model, there is no feedback model specially corresponding to the network operation condition, the content of the feedback model may be carried in the related service (for example, alarm, performance, etc.), and it is not abstracted and defined from the dimension of the digital twin. The nature of the feedback model is to provide message feedback for digital twins. FIG. 14 is a schematic illustration of a feedback model representation of a digital twin specification in some embodiments of the present application; as shown in fig. 14, the visual definition of the feedback model may be defined for the specific content of the feedback, and the visual form definition of the digital twin may be expressed for the part where the feedback information can cause the form of the digital twin. The storage type of the feedback model is an ER model (name of a feedback function, input parameters, output parameters, and the like).

The spatial model mainly manages the definition and content of the digital twin with respect to spatial location. The spatial model can be basically extracted and arranged from the existing data model. FIG. 15 is a schematic illustration of a two-dimensional vector, three-dimensional vector representation of a digital twin specification spatial geographic location model in some embodiments of the present application; as shown in fig. 15, the visual definition of the spatial model may be expressed based on the positional relationship of the digital twins of the geographic information system or the corresponding scene to each other. The storage type of the spatial model is an ER model (spatial field).

The digital twins realize the real and objective description and depiction of physical entities by the virtual entity model through the integration and fusion of models with different dimensions, different spatial scales and different time scales; the method is based on a technology of fusing mirror images of a physical entity space and a virtual space, integrates the technologies of Artificial Intelligence (AI), machine Learning (ML) and the like, combines data, algorithm and decision analysis together, establishes simulation, namely virtual mapping of a physical object, finds a problem before the problem occurs, monitors the change of the physical object in a virtual model, diagnoses the complex processing and abnormal analysis of multi-dimensional data based on artificial intelligence, predicts the potential risk, and reasonably and effectively plans or maintains related equipment; the past and existing behaviors or processes of a certain physical entity are dynamically presented in a digital form, so that the enterprise performance is promoted; the real-time interaction, virtual-real bidirectional connection, consistency and synchronism of the information space and the physical space are realized through the fusion of the information data and the physical data, real-time process simulation and optimization are carried out, various intelligent services used according to needs are provided, and more real-time and accurate application services are provided; corresponding digital twin models are created according to different application objects and business requirements, and the problem that the physical entity space technology is difficult to touch control and monitoring in real time is solved.

Figure 16 is a schematic diagram of a 5GC network capability scheduling subsystem in some embodiments of the present application; the typical scene application of the method is to open visualization for a ToB service, and customize the visualization of network service flow and resources by the ToB; and (3) monitoring the service KPI: the method comprises the steps that a ToB enterprise client group is used as a monitoring object, the access service quality of enterprise users is focused, such as the attachment success rate and the 4/5G switching re-registration success rate, and the access quality service is analyzed in real time; and (3) service development depicting: the method comprises the steps that ToB enterprise customers are used as analysis objects, and business development information such as business volume and user online volume used by the enterprise customers in areas such as the whole network, province and local network is described in quasi-real time; and (4) providing quasi-real-time quality difference analysis for users with access failure of the 5G terminal by taking the ToB industry client as an analysis object. The big data lake acquires data such as DPI signaling plane/CGF ticket equal-width table data, B domain ToB enterprise information data, O domain 5GC business log data, equipment performance/alarm data and the like. Providing toB enterprise-related data: and providing basic information of the trial-run enterprise customized network, such as slice + DNN, static user subscription data and the like. And (3) data quality control: 1) 2B, customizing network resource data to check and manage; 2) 2B, checking and managing the operation data of the customized network service; 3) The UDM static full-table data and the sharing platform are synchronized periodically; and collecting the requirements of the line of production in province and the customer manager on the 5G intelligent operation and maintenance of the ToB customized network. The digital twin modeling dimension can be expanded along with the depth of a twin application scene, and the application requirements of rapid deployment, fault early warning and network scheduling of a 5G network digital twin model in a real scene are met by establishing a high-maturity digital twin.

Based on the same purpose, the second aspect of the embodiment of the invention provides a 5G network digital twin modeling device. Fig. 17 is a schematic diagram of an embodiment of a 5G network digital twin modeling apparatus in some embodiments of the present application. As shown in fig. 17, a 5G network digital twin modeling apparatus in some embodiments of the present application includes: a first module 011 for selecting a digital twin specification, the digital twin specification comprising a data model, a geometric model, a digital model, a rule model, a control model, and a feedback model, for describing a class of resource entities; a second module 012, configured to select a data resource corresponding to the digital twin specification in a resource library to generate a digital twin instance; a third module 013 for selecting a digital twin scene for the digital twin instance and running the digital twin instance.

In view of the above object, a third aspect of the embodiments of the present invention provides a computer device, and fig. 18 is a schematic diagram of an embodiment of a computer device in some embodiments of the present application. An embodiment of a computer device as in some embodiments of the application of FIG. 18, comprises the following modules: at least one processor 021; and a memory 022, the memory 022 storing computer instructions 023 executable on the processor 021, the computer instructions 023, when executed by the processor 021, implementing the steps of the method as described above.

The invention also provides a computer readable storage medium. FIG. 19 is a schematic diagram of an embodiment of a computer-readable storage medium in some embodiments of the present application. As in fig. 19, the computer readable storage medium 031 stores a computer program 032 which, when executed by a processor, performs the method as described above.

Finally, it should be noted that, as one of ordinary skill in the art can appreciate that all or part of the processes of the methods of the above embodiments can be implemented by a computer program to instruct related hardware, and the program of the method for setting system parameters can be stored in a computer readable storage medium, and when executed, the program can include the processes of the embodiments of the methods as described above. The storage medium of the program may be a magnetic disk, an optical disk, a Read Only Memory (ROM), a Random Access Memory (RAM), or the like. The embodiments of the computer program may achieve the same or similar effects as any of the above-described method embodiments.

Furthermore, the methods disclosed according to embodiments of the present invention may also be implemented as a computer program executed by a processor, which may be stored in a computer-readable storage medium. Which when executed by a processor performs the above-described functions defined in the methods disclosed in embodiments of the invention.

Further, the above method steps and system elements may also be implemented using a controller and a computer readable storage medium for storing a computer program for causing the controller to implement the functions of the above steps or elements.

Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the disclosure herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as software or hardware depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the disclosed embodiments of the present invention.

In one or more exemplary designs, the functions may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a general-purpose or special-purpose computer, or a general-purpose or special-purpose processor. Also, any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital Subscriber Line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, D0L, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. Disk and disc, as used herein, includes Compact Disc (CD), laser disc, optical disc, digital Versatile Disc (DVD), floppy disk, blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

The foregoing is an exemplary embodiment of the present disclosure, but it should be noted that various changes and modifications could be made herein without departing from the scope of the present disclosure as defined by the appended claims. The functions, steps and/or actions of the method claims in accordance with the disclosed embodiments described herein need not be performed in any particular order. Furthermore, although elements of the embodiments of the invention may be described or claimed in the singular, the plural is contemplated unless limitation to the singular is explicitly stated.

It should be understood that, as used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly supports the exception. It should also be understood that "and/or" as used herein is meant to include any and all possible combinations of one or more of the associated listed items.

The numbers of the embodiments disclosed in the above embodiments of the present invention are merely for description, and do not represent the advantages or disadvantages of the embodiments.

It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.

Those of ordinary skill in the art will understand that: the discussion of any embodiment above is meant to be exemplary only, and is not intended to intimate that the scope of the disclosure, including the claims, of embodiments of the invention is limited to these examples; within the idea of an embodiment of the invention, also combinations between technical features in the above embodiments or in different embodiments are possible, and there are many other variations of the different aspects of the embodiments of the invention as described above, which are not provided in detail for the sake of brevity. Therefore, any omissions, modifications, substitutions, improvements, and the like that may be made without departing from the spirit and principles of the embodiments of the present invention are intended to be included within the scope of the embodiments of the present invention.

Claims (10)

1. A5G network digital twin modeling method is characterized by comprising the following steps:

selecting a digital twin specification, wherein the digital twin specification comprises a data model, a geometric model, a digital model, a rule model, a control model and a feedback model and is used for describing a class of resource entities;

selecting a data resource corresponding to the digital twin specification in a resource library to generate a digital twin instance;

a digital twin scene is selected for the digital twin instance and the digital twin instance is run.

2. The method according to claim 1, wherein the storage type of the geometric model is an ER model plus a three-dimensional data model, and the geometric model adopts a two-dimensional plus three-dimensional visual expression mode according to complexity, fineness and simulation degree.

3. The method of claim 1, wherein the rule model is used to describe internal rules of a resource facility device.

4. The method as claimed in claim 3, characterized in that the digital twin body operates a mathematical model according to different parameters to analyze the internal operation rule of the digital twin body, and the digital model obtains the corresponding operation rule through calculation according to the operation related data and outputs the result.

5. The method of claim 4, wherein the visual definition is performed by dimensions including color, brightness, animation, and geometric change according to the result of the operation of the digital model.

6. The method of claim 1, wherein the control model is used to define control information that a digital twin can apply, the control information being a sum of actions that a digital twin can receive in the digital world.

7. The method according to claim 1, wherein the feedback module is configured to provide message feedback for the digital twin, obtain a result of physical entity feedback in the real world, and implement message feedback through the digital twin.

8. A digital twin modeling device for a 5G network, comprising:

the system comprises a first module, a second module and a third module, wherein the first module is used for selecting a digital twin specification, and the digital twin specification comprises a data model, a geometric model, a digital model, a rule model, a control model and a feedback model and is used for describing a class of resource entities;

a second module for selecting a data resource in a resource repository corresponding to the digital twin specification to generate a digital twin instance;

a third module to select a digital twin scene for the digital twin instance and to run the digital twin instance.

9. A computer device, comprising:

at least one processor; and

a memory storing computer instructions executable on the processor, the instructions when executed by the processor implementing the steps of the method of any one of claims 1 to 7.

10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.

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