WO2023131303A1 - 数字孪生网络的编排方法、数字孪生网络、介质和程序 - Google Patents

数字孪生网络的编排方法、数字孪生网络、介质和程序 Download PDF

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WO2023131303A1
WO2023131303A1 PCT/CN2023/071032 CN2023071032W WO2023131303A1 WO 2023131303 A1 WO2023131303 A1 WO 2023131303A1 CN 2023071032 W CN2023071032 W CN 2023071032W WO 2023131303 A1 WO2023131303 A1 WO 2023131303A1
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sublayer
model
kth
network
topology
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PCT/CN2023/071032
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English (en)
French (fr)
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朱艳宏
周铖
杨红伟
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中国移动通信有限公司研究院
中国移动通信集团有限公司
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Publication of WO2023131303A1 publication Critical patent/WO2023131303A1/zh

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/04—Network management architectures or arrangements
    • H04L41/044—Network management architectures or arrangements comprising hierarchical management structures
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/12—Discovery or management of network topologies
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/34—Signalling channels for network management communication
    • H04L41/342—Signalling channels for network management communication between virtual entities, e.g. orchestrators, SDN or NFV entities

Definitions

  • This application relates to the field of network technology, involving but not limited to a digital twin network (Digital Twin Network, DTN) arrangement method, DTN, media and programs.
  • DTN Digital Twin Network
  • DTN combined with digital twin technology is a network system that can realize mutual mapping between physical network entities and virtual twins. Its core value lies in real-time closed-loop control with the network, low-cost trial and error, and a full life cycle from design to networking. management and network visualization.
  • the embodiment of the present application provides a DTN orchestration method, DTN, medium and program.
  • the twin network sublayer of the DTN receives the scenario simulation sent by the functional model sublayer If necessary, analyzing the scene simulation requirements can determine the analysis results, and obtain the monomer model and topology model corresponding to the analysis results from the basic model sub-layer, and then arrange the monomer models based on the topology model to obtain the orchestration results, so that Realized DTN's network element arrangement for scenario simulation requirements.
  • the embodiment of the present application provides a DTN orchestration method, the twin network layer of the DTN includes a functional model sublayer, a twin network sublayer and a basic model sublayer; the method includes:
  • the twin network sublayer When the twin network sublayer receives the kth scenario simulation requirement information sent by the functional model sublayer, it analyzes the kth scenario simulation requirement information, and determines the kth analysis result; wherein, the first interface For the data transmission interface between the twin network sublayer and the functional model sublayer of the DTN; k is an integer greater than or equal to 1;
  • the twin network sublayer obtains a monomer model and a topology model corresponding to the kth analysis result from the basic model sublayer; wherein, the monomer model includes a multivariate representation model of network elements; the topology model , including topological relationship information between at least two network elements;
  • the twin network sublayer arranges the single model corresponding to the kth analysis result based on the topology model corresponding to the kth analysis result to obtain the kth arrangement result.
  • the embodiment of the present application also provides a DTN, the twin network layer of the DTN includes a functional model sublayer, a twin network sublayer and a functional model sublayer; wherein:
  • the functional model sublayer is configured to determine the kth scenario simulation requirement information, and send the kth scenario simulation requirement information to the twin network sublayer;
  • the twin network sublayer is configured to analyze the kth scenario simulation requirement information and determine the kth analysis result when receiving the kth scenario simulation requirement information; where k is greater than or equal to 1 an integer of
  • the twin network sublayer is further configured to obtain the monomer model and topology model corresponding to the k-th analysis result from the basic model sub-layer, and based on the topology model corresponding to the k-th analysis result, Arranging the monomer model corresponding to the kth analysis result to obtain the kth orchestration result; wherein, the monomer model includes a multi-element representation model of a network element; the topology model includes a network element between at least two network elements topological relationship information.
  • the embodiment of the present application also provides another DTN, the DTN includes a processor and a memory, wherein a computer program is stored in the memory; when the computer program is executed by the processor, it can realize any of the preceding Describe the arrangement method of DTN
  • the embodiment of the present application also provides a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and when the computer program is executed by the processor of the electronic device, it can realize the DTN as described in the previous one. arrangement method.
  • the embodiment of the present application also provides a computer program, which, when running on a computer, causes the computer to execute any one of the DTN orchestration methods described above in the embodiment of the present application.
  • the twin network sublayer of DTN receives the kth scenario simulation requirement information sent by the functional model sublayer and analyzes it to determine the kth analysis result
  • the monomer model and topology model corresponding to the k-th analysis result can be obtained from the basic model sub-layer of DTN, and then based on the topology model corresponding to the k-th analysis result, the monomer model corresponding to the k-th analysis result is arranged to obtain
  • the k-th orchestration result realizes network element orchestration of DTN for specific scenario simulation requirement information.
  • FIG. 1 is a schematic diagram of the architecture of DTN in the related art
  • FIG. 2A is a first schematic flow diagram of a DTN arrangement method provided by an embodiment of the present application.
  • FIG. 2B is a schematic flow diagram of obtaining a monomer model and a topology model corresponding to the kth analysis result by the twin network sublayer provided by the embodiment of the present application;
  • FIG. 2C is a schematic flow diagram of the simulation verification of the kth arrangement result provided by the embodiment of the present application.
  • FIG. 3 is a second schematic flow chart of the DTN programming method provided by the embodiment of the present application.
  • FIG. 4 is a schematic diagram of the first structure of the DTN provided by the embodiment of the present application.
  • FIG. 5 is a schematic diagram of a second structure of the DTN provided by the embodiment of the present application.
  • FIG. 6 is a schematic diagram of a third structure of a DTN provided by an embodiment of the present application.
  • DTN is a network system that has physical network entities and virtual twins, and can realize real-time mutual mapping between physical network entities and virtual twins.
  • the core value of DTN lies in real-time closed-loop network control, low-cost trial and error, full lifecycle management from design to networking, and network visualization.
  • the construction of DTN is still limited to the research and exploration of network equipment and topology visualization, and the visualization modeling method cannot realize the functions of DTN network virtual mapping, low-cost trial and error, and internal and external closed-loop control. It is also impossible to realize the arrangement and simulation of network elements in specific scenarios.
  • FIG. 1 is a schematic diagram of a DTN architecture in the related art.
  • DTN includes a network application layer 101, a twin network layer 102, and a physical network layer 103; among them, the network application layer 101 is used to realize network innovation technology verification, network visualization, intent verification, network management, and network maintenance and optimization and other functions.
  • Twin network layer 102 is the core part of DTN, which includes three parts: data sharing warehouse 1021 , service mapping model 1022 and network twin body management module 1023 .
  • the data sharing warehouse 1021 is used to implement functions such as data management, data service, data storage, and data collection.
  • the data in the data sharing warehouse 1021 involves user business, network configuration, and operating status;
  • the service mapping model 1022 acquires data from the data sharing warehouse 1021, it can realize the functional model and the basic model through iterative optimization and simulation verification.
  • the service mapping model 1022 is used for planning, construction, maintenance, optimization, and operation.
  • the network twin management module 1023 can realize model management, security management and topology management, and the network twin management module 1023 can also perform data interaction with the service mapping model 1022 .
  • the physical network layer 103 includes various physical network entities and network connection structures between various physical network entities.
  • the data sharing warehouse 1021 can collect various network data from the physical network layer 103 , and the service mapping model is used to issue control commands to the physical network layer 103 . Data interaction of capability invocation and intent translation is realized between the network application layer 101 and the twin network layer 102 .
  • the embodiment of the present application provides a DTN arrangement method, DTN and media.
  • the embodiment of the present application provides a DTN orchestration method, which can determine the corresponding monomer model and topology model according to the specific scene simulation requirements, and arrange the monomer model based on the topology model, so as to obtain the orchestration result, and then realize DTN for specific Orchestration and deployment of scenario simulation requirements.
  • the embodiment of the present application provides a DTN orchestration method, which can be implemented by a DTN processor.
  • processor can be an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a digital signal processor (Digital Signal Processor, DSP), a digital signal processing device (Digital Signal Processing Device, DSPD), a programmable At least one of logic device (Programmable Logic Device, PLD), field programmable logic gate array (Field Programmable Gate Array, FPGA), central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor kind.
  • ASIC Application Specific Integrated Circuit
  • DSP Digital Signal Processor
  • DSPD Digital Signal Processing Device
  • PLD Programmable Logic Device
  • FPGA Field Programmable Gate Array
  • CPU Central Processing Unit
  • the twin network layer of DTN includes a functional model sublayer, a twin network sublayer, and a basic model sublayer.
  • the function model sublayer may include some modules in the twin network layer 102 shown in FIG. 1; exemplary, the function model sublayer may include the A module for realizing functions such as planning, construction, maintenance, optimization, and operation; for example, the function model sublayer can realize the connection between DTN and the network application layer, which can receive the scene requirement information sent by the network application layer, and The requirement information is analyzed to determine the simulation requirement information of the kth scenario.
  • the twin network sublayer may include some modules in the twin network layer 102 shown in FIG. 1.
  • the twin network sublayer may include the service mapping model 1022 in FIG. 1; exemplary The twin network sublayer may include the service mapping model 1022 and the network twin management module 1023 in FIG. 1; exemplary, the twin network sublayer may include some submodules in the service mapping model 1022 and the network twin management module 1023 Part of the submodules in;
  • the submodules corresponding to all functions of the service mapping model 1022 and the network twin management module 1023 can be analyzed, and some submodules in the service mapping model 1022 and the network twin management module 1023 can be analyzed Divide into twin network sublayers.
  • the basic model sublayer can be a module that contains multiple monomer models and multiple topology models in DTN; exemplary, the basic model sublayer can be the service mapping model shown in Figure 1 1022 includes the module part of the network element model and the topology model.
  • FIG. 2A is a schematic flowchart of a first DTN orchestration method provided by an embodiment of the present application. As shown in Figure 1, the orchestration method may include steps 201 to 203:
  • Step 201 when the twin network sublayer receives the kth scenario simulation requirement information sent by the functional model sublayer, it analyzes the kth scenario simulation requirement information, and determines the kth analysis result.
  • k is an integer greater than or equal to 1.
  • the twin network sublayer may not perform the parsing operation.
  • the simulation requirement information of the k-th scenario may include at least one kind of information such as simulation time in at least one dimension, simulation environment, and an optimization target corresponding to the scenario.
  • the kth scenario simulation requirement information may be different from the k-1th scenario simulation requirement information.
  • the k-th scenario simulation requirement information may include requirement information for at least one scenario optimization among network layout optimization, data transmission efficiency optimization, network capacity optimization, and network energy consumption optimization.
  • any requirement in the simulation requirement information of the kth scenario can be satisfied by setting at least one network element in the deployed network, It is implemented by improving and optimizing at least one aspect of connection relationship, state control, power consumption management, and function realization.
  • the satisfaction of any requirement in the simulation requirement information of the kth scenario can be realized by implementing the overall function of the undeployed network.
  • the satisfaction of any requirement in the simulation requirement information of the kth scenario can be realized by optimizing and improving network element settings.
  • the kth analysis result may include the function, type, quantity, association relationship between network elements, and the strength of the association relationship between network elements required to meet the demand information of the kth scene , and at least one type of information in the multiplexing status of at least one network element.
  • the twin network sublayer analyzes the kth scenario simulation requirement information, and determines the kth analysis result, which can be achieved in any of the following ways:
  • the twin network sublayer analyzes the k-th scenario simulation demand information, divides the same type of demand information in the k-th scenario simulation demand information with a smaller granularity, and determines the division result as the k-th analysis result.
  • the twin network sublayer determines the analysis method according to the simulation requirement information of the k-th scenario, and then classifies and divides each scenario simulation requirement in the simulation requirement information of the k-th scenario according to the priority or importance according to the resolution method, and determines the classification result Parse the result for the kth.
  • Step 202 the twin network sublayer obtains the monomer model and topology model corresponding to the kth analysis result from the basic model sublayer.
  • the monomer model includes a multiple representation model of network elements; the topology model includes topological relationship information between at least two network elements.
  • the network elements may include actual physical network elements in the physical network layer shown in FIG. 1, such as switches and routers, and may also include virtual network elements in the physical network layer shown in FIG. 1, such as container node.
  • the multivariate representation model of the network element may be obtained by describing the network element in multiple dimensions.
  • the multivariate representation model of the network element may include at least two kinds of information of the name, type, function, and port quantity of the network element.
  • the topology model can represent the topology between at least two network elements contained in it in the form of topology, or the flow topology of data between network elements when it realizes the data processing function Structure;
  • the representation form of the topology structure may include a vector form, an image form, a semantic representation form, and a visual model file in the form of a topology map.
  • the topology relationship information may include information about whether and how to connect at least two network elements in the topology model.
  • the topology relationship information between at least two network elements included in the network structure corresponding to the topology model can be obtained through at least A formal representation of the topology between two monomer models.
  • the topological relationship information in the topology model can reflect the network structure corresponding to the topology model through the connection mode between at least two monomer models, the number of interfaces of the monomer models, and the signaling interaction mode between the monomer models. It includes the connection mode between at least two network elements, the number of interfaces of the network elements, and the signaling interaction mode between each network element.
  • Step 203 based on the topology model corresponding to the kth analysis result, the twin network sublayer arranges the monomer model corresponding to the kth analysis result to obtain the kth arrangement result.
  • the twin network sublayer arranges the monomer model corresponding to the kth analysis result based on the topology model corresponding to the kth analysis result, and obtains the kth arrangement result, which can be realized in the following manner:
  • the twin network sublayer performs multivariate sorting on the monomer model corresponding to the kth analysis result, that is, the multivariate representation model, to obtain the combing result, and then based on the topology model corresponding to the kth analysis result, the function and function of each network element in the multivariate combing result Ports, connection relationships, etc. are arranged to obtain the kth arrangement result.
  • the twin network sublayer when the twin network sublayer receives the kth scenario simulation requirement information sent by the functional model sublayer and analyzes it to determine the kth analysis result , the monomer model and topology model corresponding to the k-th analysis result can be obtained from the basic model sublayer, and then based on the topology model corresponding to the k-th analysis result, the monomer model corresponding to the k-th analysis result is arranged to obtain the k-th analysis result k arranges the results, thereby realizing the network element arrangement of DTN for specific scene simulation demand information.
  • the DTN also includes a network application layer
  • the DTN arrangement method provided in the embodiment of this application may also include the following operations:
  • the functional model sublayer receives the scenario requirement information sent by the network application layer, analyzes the scenario requirement information, determines the kth scenario simulation requirement information, and sends the kth scenario simulation requirement information to the twin network sublayer through the first interface.
  • the first interface is a data transmission interface between the twin network sublayer and the functional model sublayer of the DTN.
  • the network application layer may be network application layer 101 shown in FIG. At least one;
  • the scenario requirement information sent by the network application layer may be specific to the actual physical network, such as network delay optimization for some areas of the Internet of Things;
  • the scenario requirement information sent by the network application layer may be for the network to be deployed, for example, for the simulation verification of the capacity of at least one cell in the wireless communication network to be deployed in a certain area.
  • the number of scenario requirement information can be multiple, that is to say, the network application layer can send multiple scenario requirement information to the function model sublayer at one time, and the function model sublayer can analyze the plurality of scenario requirement information,
  • the simulation requirement information of the kth scenario is obtained.
  • the simulation requirement information of the kth scenario may include optimization requirement information for at least two dimensions of a network, or at least two application scenarios.
  • the scenario requirement information sent by the network application layer may be embodied in the form of a configuration file, wherein the organization form of various data in the configuration file may be based on the transmission protocol between the network application layer and the functional model sub-layer Sure.
  • the first interface may be an interface for transmitting scenario requirement information, feeding back scenario simulation results, and various control commands between the twin network sublayer and the functional model sublayer.
  • the functional model sublayer can analyze the scenario requirement information to determine the kth scenario simulation requirement information and send it to the twin network sublayer, thus, the functional model sublayer and The twin network sublayers are independent and interdependent. On the one hand, it can realize the efficient analysis of the scene demand information of the network application layer, and on the other hand, it can improve the orchestration efficiency of the twin network sublayers for specific application scenarios.
  • the twin network sublayer obtains the monomer model and topology model corresponding to the kth analysis result from the basic model sublayer, which can be realized through Figure 2B, which is the basic
  • the twin network sublayer provided in the embodiment of the application obtains a schematic flow diagram of the monomer model and the topology model corresponding to the k-th analysis result, as shown in FIG. 2B , the flow may include steps 202-1 to 202-3:
  • Step 202-1 the twin network sublayer processes the kth analysis result, obtains the kth monomer configuration information and the kth topology configuration information, and sends the kth monomer configuration information and the kth topology configuration information to Base model sublayer.
  • the second interface is a data transmission interface between the basic model sublayer and the twin network sublayer.
  • the configuration information of the kth monomer may include at least one of the functions required by the monomer model, the number of the monomer models, and the type of the monomer model.
  • the k-th topology configuration information may represent at least one type of information including the type and structure of the network topology, and the connection relationship between the monomer models of the network topology.
  • the second interface can implement data transmission between the twin network sublayer and the basic model sublayer, including monomer models, topology models, k-th monomer configuration information, and k-th topology configuration information.
  • Step 202-2 After receiving the k-th monomer configuration information and the k-th topology configuration information through the second interface, the basic model sublayer searches the monomer model library for the k-th resolution result corresponding to the k-th monomer configuration information. A single model, and searching for a topology model corresponding to the kth analysis result from the topology model library based on the kth topology configuration information.
  • the monomer model library may contain multiple monomer models. Exemplarily, the types, functions, port numbers, and names of these monomer models may be different.
  • the topology model library may contain multiple topology models, and the types, functions, and application scenarios of these topology models may be different; for example, the network elements or units contained in different topology models
  • the number of voxels as well as their functions may vary.
  • the management of the monomer model by the monomer model library and the management of the topology model by the topology model library can be achieved by adding monomer index information to the monomer model and adding topology index information to the topology model It is realized that in this way, the search efficiency of the monomer model as well as the topology model can be improved.
  • monomer index information may include at least one type of monomer model, function, port quantity, and application scenario;
  • topology index information may include topology model type, function, application scenario, and network At least one type of information in the metadata.
  • the basic model sublayer searches the monomer configuration library for the monomer model corresponding to the kth analysis result based on the configuration information of the kth monomer, which may be based on the monomer model in the configuration information of the kth monomer It is realized by matching at least one type of information such as the type, function, number of ports, and application scenarios with the information of the same dimension in the single index information.
  • the basic model sublayer searches the topology configuration library for a topology model corresponding to the kth analysis result based on the kth topology configuration information, which may be based on the type, function, or type of the topology model in the kth topology configuration information. It is realized by matching at least one kind of information such as the number of network elements and application scenarios with the information of the same dimension in the topology index information.
  • Step 202-3 the twin network sublayer obtains the monomer model and the topology model corresponding to the kth analysis result through the second interface.
  • the twin network sublayer determines the k-th monomer configuration information and the k-th topology configuration information, it can send these information to the basic model sub-layer, and in the basic model sub-layer
  • the layer determines the monomer model and topology model corresponding to the k-th analysis result based on the k-th monomer configuration information and the k-th topology configuration information, it can also send the monomer model and topology model corresponding to the k-th analysis result to the twin network Sub-layers, so as to realize the functional decoupling between each sub-layer in DTN, improve the data processing efficiency between each sub-layer, so as to improve the orchestration efficiency of twin network sub-layers for specific application scenarios.
  • the twin network sublayer of the DTN also includes a data collection and storage sublayer.
  • step B1 to step B2 can also be performed:
  • Step B1 the basic model sublayer acquires network data from the data collection and storage sublayer, performs multi-dimensional modeling on the network element data in the network data, and obtains a single model library.
  • the network data may include structured data such as device information, fault alarms, and key performance indicators (Key Performance Indicator, KPI), and at least one type of data such as topology information between network elements, link operating status, and the like.
  • structured data such as device information, fault alarms, and key performance indicators (Key Performance Indicator, KPI)
  • KPI Key Performance Indicator
  • the data collection and storage sublayer may be a module used in DTN to collect and organize data from the physical network layer.
  • the data collection and storage sublayer may be the service mapping shown in FIG. 1 Data sharing warehouse 1021 in model 102 .
  • performing multi-dimensional modeling and representation on the network element data may include acquiring multiple information representations of the network element data, and performing association modeling on the multiple information representations based on the functional characteristics of the network elements.
  • the single model library can be obtained after the basic model sub-layer receives at least one of the network data collected by the data collection and storage sub-layer and the stored network data, and then integrates and analyzes the network data .
  • the basic model sublayer can obtain network data from the data collection and storage sublayer through the third interface;
  • the third interface is a data transmission interface between the basic model sublayer and the data collection and storage sublayer.
  • the third interface can realize the transmission of data such as network data and network data collection instructions between the data collection and storage sublayer and the basic model sublayer.
  • the protocols adopted for data transmission by the first interface, the second interface, and the third interface may be changed according to the data transmission types of the first interface, the second interface, and the third interface. This embodiment of the present application does not limit it.
  • the single model library can analyze and classify the network data to obtain multiple It is obtained by integrating the data related to network element functions after the data related to network element functions.
  • Step B2 the basic model sub-layer analyzes network element associations in the network data to obtain a topology model library.
  • the topology model library may contain various topology models corresponding to the actual network deployment in the physical network layer, and may also include historical topology models corresponding to the historical arrangement results, that is, the arrangement provided by the embodiment of the present application
  • the intelligent and automatically generated topology model is obtained by the method.
  • the network element association relationship may include at least one of whether there is an association relationship between network elements, the strength of the association relationship, and whether the association relationship is unidirectional or bidirectional, which is not limited in this embodiment of the present application. .
  • the basic model sublayer can obtain network data from the data collection and storage sublayer in advance, and analyze the network data and network element associations in the network data,
  • the monomer model library and the topology model library are obtained, which lays the foundation for the twin network sublayer to analyze and further arrange the simulation demand information of the k-th scene, and improves the arrangement efficiency of DTN.
  • the topology model library includes network element association information and a visual model corresponding to the network element association information;
  • the single model library includes network element N-tuple information and N-tuple information corresponding visualization model.
  • the network element association information includes vector relationship information between network elements; N is an integer greater than or equal to 2.
  • the network element association information may include at least one of information on whether there is an association relationship between network elements, the strength of the association relationship between network elements, and the conditions for generating the association relationship between network elements .
  • the vector relationship information between network elements may include directionality of connections between network elements, such as unidirectional connection or bidirectional connection.
  • the vector relationship information between network elements may be represented in the form of a visual model, or in the form of semantic expression, which is not limited in this embodiment of the present application.
  • the visual model of network element association information may include a visual presentation model of legends, icons, images, and connection relationships between legend icon images, which may be more intuitive, in two-dimensional or three-dimensional form Displays association information between network elements.
  • the N-tuple information of a network element may include a multiple representation model of a network element, which may include network element attributes, network element types, data processing rules of network elements, relationships between network elements and other network elements, At least two of state switching conditions of network elements, and axioms related to network element data processing.
  • the network element attributes can include the characteristics, characteristics and parameters of the network element; the network element type can include the type, name and identification of the network element; the data processing rules of the network element can include the data forwarding logic of the network Transmission protocols, etc.; the relationship between NEs and other NEs, which may include the connection relationship between NEs and other NEs; the state switching conditions of NEs, which may include the attributes before and after the state switching of NEs, and their relationship with other NEs.
  • the change of the connection relationship between network elements; the axioms related to network element data processing may include the statement of the prior knowledge of network element data processing.
  • the multivariate representation model of the network element when the multivariate representation model of the network element includes information of N dimensions, it may be called N-tuple information of the network element.
  • the visual model corresponding to the N-tuple information may be a model that displays the N-tuple information of the network element in a visual combination form including images, diagrams, and legends.
  • the single model library includes the N-tuple information of the network element and the visualization model corresponding to the N-tuple information
  • the topology model library includes the network element association relationship and the network element association relationship.
  • the visual model corresponding to the relationship that is to say, the monomer model library and the topology model library contain multi-dimensional information of network elements and network structures in the physical network layer, so that the monomer model based on the monomer model library And when the topology model in the topology model library is arranged, the efficiency of the arrangement can be effectively improved, the arrangement time can be shortened, and the arrangement process can be optimized.
  • the DTN arrangement method provided by the embodiment of the present application may also include a simulation verification operation for the kth arrangement result.
  • FIG. 2C is a schematic flowchart of the simulation verification process for the kth arrangement result provided by the embodiment of the present application. As shown in Figure 2C, the process may include steps 204 to 205:
  • Step 204 the twin network sublayer performs simulation verification on the kth orchestration result, and obtains the kth verification result.
  • the kth verification result may include the kth arrangement result and at least one dimension of information representing the data processing capability of the kth arrangement result obtained by performing simulation verification on the kth arrangement result;
  • the information characterizing the data processing capability of the kth arrangement result may include at least one of the data processing delay, data processing stability, data throughput, and data concurrency capability of the kth arrangement result.
  • the twin network sublayer performs simulation verification on the kth orchestration result to obtain the kth verification result, which can be achieved by any of the following methods:
  • the twin network sublayer obtains the default simulation verification environment, and sets the default simulation verification conditions, and then based on the default simulation verification conditions, in the default simulation verification environment, the kth The arrangement results are simulated and verified, and the kth verification result is obtained.
  • the twin network sublayer obtains the simulation verification environment and the simulation verification conditions from the k-th scenario simulation requirement information, and performs simulation in the simulation verification environment based on the simulation verification conditions Verify to get the kth verification result.
  • the simulation verification environment includes the operating system for performing the simulation verification and the simulation software used, etc.; the simulation verification condition may include at least one of the data on which the simulation verification is based, the execution time of the simulation verification, and the timing of triggering the simulation verification data.
  • Step 205 when the kth verification result does not match the target verification result, the twin network sublayer sends the kth verification result to the functional model sublayer; when the kth verification result matches the target verification result, the twin network sublayer The layer determines the kth arrangement result as the final arrangement result.
  • the target verification result may be sent by the functional model sublayer to the twin network sublayer, or may be obtained by the twin network sublayer from the kth scenario simulation requirement information sent by the functional model sublayer, This embodiment of the present application does not limit it.
  • the twin network sublayer determines that the kth arrangement result is the final arrangement result, it can also send the kth verification result to the functional model through the first interface
  • the function model sublayer receives the kth verification result, it can output the kth verification result to the network application layer.
  • the twin network sublayer can send the kth verification result to the functional model sublayer through the first interface, so that the functional model sublayer can send the kth verification result based on the kth verification result Further programming instructions.
  • the arrangement method provided by the embodiment of the present application can determine whether the kth arrangement result is the final arrangement result according to the matching relationship between the kth verification result and the target verification result after the twin network sublayer obtains the kth verification result , and, in a case where it is determined that the kth arrangement result is not the final arrangement result, the kth verification result can also be sent to the functional model sublayer. That is to say, in the embodiment of the present application, regardless of whether the kth arrangement result is the final arrangement result, DTN can perform all-round judgment and processing on the kth arrangement result, thereby further improving the efficiency of DTN arrangement and improving DTN arrangement stability and comprehensiveness.
  • the level of intelligence can also reduce the manual intervention in the whole process and improve the efficiency and accuracy of analysis and arrangement.
  • the functional model sublayer determines the k+1th scenario simulation requirement information, and sends the k+1th scenario simulation requirement information to the twin network sublayer.
  • the functional model sublayer can analyze the kth verification result, determine the reason why the kth orchestration result cannot meet the target verification result, and determine the k+1th scenario simulation based on the reason and the scenario requirement information. demand information, and then send the k+1th scenario simulation demand information to the twin network sublayer.
  • the twin network sublayer when it receives the k+1th scene simulation requirement information, it can analyze the k+1th scenario simulation requirement information to obtain the k+1th analysis result, and then obtain the k+1th analysis result from the basic Obtain the topology model and monomer model corresponding to the k+1th analysis result in the model sublayer, and arrange the monomer model corresponding to the k+1th analysis result based on the topology model corresponding to the k+1th analysis result, Get the k+1th arrangement result, so as to realize the iterative arrangement operation of DTN.
  • the functional model sublayer after the functional model sublayer receives the kth verification result, it can determine the k+1th scenario simulation requirement information based on the kth verification result, and send the k+1th scenario
  • the simulation demand information is sent to the twin network sublayer, so that the iterative optimization of the orchestration operation is completed through the mutual cooperation between the functional model sublayer and the twin network sublayer, thereby improving the automation and intelligence of the orchestration operation.
  • Fig. 3 is a second schematic flowchart of the DTN scheduling method provided by the embodiment of the present application.
  • twin network layer 301 of DTN30 may include functional model sublayer 3011 , twin network sublayer 3012 , basic model sublayer 3013 and data collection and storage sublayer 3014 . It should be noted that each step in FIG. 3 may be implemented by the processor of DTN30. As shown in Figure 3, the process may include the following steps:
  • Step 1 Synchronize physical network data in real time.
  • real-time synchronization of physical network data may be an operation actively initiated by the data collection and storage sublayer 3014; exemplary, the data collection and storage sublayer 3014 can acquire network data in the physical network layer 103 in real time.
  • Step 2. Perform operations such as processing, storage, and service on the network data.
  • the data collection and storage sublayer 3014 may also divide the network data into data related to network elements and data related to topology.
  • Step 3 Obtain network element data and topology data.
  • the basic model sublayer 3013 may send an instruction to acquire network element data and topology data to the data collection and storage sublayer 3014 .
  • Step 4. Send network element data and topology data.
  • the data collection and storage sublayer 3014 may send the network element data and topology data to the basic model sublayer 3013 after receiving the instruction to acquire the network element data and topology data.
  • Step 5 Build a single model library and a topology model library.
  • the basic model sublayer 3013 can analyze the network element data and topology data in multiple dimensions, so as to obtain the monomer model and the topology model, and based on the multiple monomer models and the multiple topology models, the monomer Model library and topology model library.
  • Step 6 Send the scenario requirement information to the DTN.
  • Step 6 may be performed by the network application layer 101;
  • the network application layer 101 may send the scenario requirement information to the function model sub-layer 3011.
  • the scenario requirement information may be a configuration file including at least two scenario requirements.
  • Step 7 Analyzing the scene simulation requirement information to obtain the scene simulation strategy.
  • step 7 may be performed by the function model sublayer 3011; wherein, the scenario simulation strategy here may be the kth scenario simulation requirement information in the foregoing embodiment.
  • Step 8. Send the scenario simulation policy.
  • the function model sublayer 3011 may send the scenario simulation policy, that is, the kth scenario simulation requirement information, to the twin network sublayer 3012 through the first interface.
  • Step 9 Arranging volume analysis scenario simulation strategies to obtain analysis results.
  • the parsing result here may be the kth parsing result in the foregoing embodiment.
  • the orchestration body may be a module in the twin network sublayer 3012 for performing orchestration operations.
  • Step 10 the orchestrator in the twin network sublayer 3012 obtains the monomer model and the topology model according to the analysis result.
  • the orchestrator may send the kth monomer configuration information and the kth topology configuration information to the basic model sublayer 3013 through the second interface.
  • the monomer model and topology model here may be the monomer model and topology model corresponding to the k-th analysis result.
  • Step 11 the basic model sublayer 3013 sends the monomer model and topology model.
  • the basic model sublayer 3013 can search the monomer model and the topology model corresponding to the k-th analysis result in the monomer model library and the topology model library according to the k-th monomer configuration information and the k-th topology configuration information, After the search is completed, the monomer model and the topology model corresponding to the kth parsing result may be sent to the twin network sublayer 3012 through the second interface.
  • Step 12 the orchestrator arranges the single model according to the topology model, and performs scene simulation.
  • the choreographer can arrange the monomer model corresponding to the k-th parsing result according to the topology model corresponding to the k-th parsing result to obtain the k-th parsing result, and then perform scene simulation on the k-th parsing result, so that Get the kth simulation result.
  • Step 13 sending the simulation result.
  • the simulation result here may be the kth simulation result matching the target verification result in the foregoing embodiments.
  • the function model sublayer 3011 may present the kth compilation result in the kth simulation result to the network application layer 101 .
  • Step 14 sending the target simulation result.
  • the target simulation result may be the kth verification result matching the target verification result in the foregoing embodiments.
  • the function model sublayer 3011 sends the target simulation result to the network application layer 101 .
  • the network application layer 101 may visually present the kth arrangement result and the final arrangement result therein.
  • the basic model sublayer 3013 in the twin network layer 301 of the DTN30 can obtain network data in the data collection and storage sublayer 3014, and build a monomer model library, a topology model library, and a functional model in real time.
  • the sublayer 3011 can receive the scenario requirement information of the network application layer 101, and determine the kth scenario simulation requirement information based on the scenario requirement information, and send the information to the twin network sublayer 3012, so that the twin network sublayer 3012 can compare the kth scenario Analyze the simulation demand information to obtain the kth analysis result, and obtain the monomer model and topology model from the basic model sub-layer 3013 based on the kth analysis result, and then arrange and simulate the monomer model based on the topology model, so as to obtain the kth Validation results.
  • each sublayer in the DTN30 are independent and interrelated, which can improve the efficiency of analysis, arrangement and simulation, thus providing an efficient and reliable method for arrangement and analysis of the network structure.
  • the data collection and storage sublayer 3014 has already stored the monomer model and topology model, so that DTN has the ability of rapid deployment, dynamic adjustment, and reusability in the process of orchestration, and thus greatly It enhances its flexibility, scalability, and energy-saving performance, and can realize scenario-driven simulation, verification, etc.
  • the embodiment of the present application further provides a DTN30
  • FIG. 4 is a schematic diagram of a first structure of the DTN30 provided in the embodiment of the present application.
  • the twin network layer 301 of the DTN 30 may include a functional model sublayer 3011 , a twin network sublayer 3012 and a basic model sublayer 3013 .
  • the function model sublayer 3011 is configured to analyze the scenario requirement information when receiving the scenario requirement information sent by the network application layer, determine the kth scenario simulation requirement information, and send the kth scenario simulation requirement information through the first interface.
  • the k-scene simulation requirement information is sent to the twin network sublayer 3012; wherein, the first interface is a data transmission interface between the twin network sublayer and the function model sublayer.
  • the DTN30 also includes a network application layer;
  • the twin network sublayer 3012 is configured to process the k-th analysis result, obtain the k-th monomer configuration information and the k-th topology configuration information, and send them through the second interface
  • the kth monomer configuration information and the kth topology configuration information are sent to the basic model sublayer 3013; wherein, the second interface is a data transmission interface between the basic model sublayer and the twin network sublayer;
  • the basic model sublayer 3013 is configured to, after receiving the kth monomer configuration information and the kth topology configuration information through the second interface, search for the unit corresponding to the kth analysis result from the monomer model library based on the kth monomer configuration information. body model, and search the topology model corresponding to the kth analysis result from the topology model library based on the kth topology configuration information;
  • the twin network sublayer 3012 is further configured to obtain the monomer model and topology model corresponding to the kth analysis result from the basic model sublayer 3013 through the second interface.
  • the twin network layer 301 also includes a data collection and storage sublayer 3014; a basic model sublayer 3013 is configured to obtain network data from the data collection and storage sublayer 3014 of the DTN, and multiplex the network element data in the network data Dimensional modeling and characterization to obtain a single model library;
  • the basic model sublayer 3013 is also configured to analyze network element associations in network data to obtain a topology model library.
  • the topology model library includes network element association information and a visual model corresponding to the network element association information; wherein, the network element association information includes vector relationship information between network elements;
  • the single model library includes N-tuple information of network elements and a visual model corresponding to the N-tuple information; wherein, N is an integer greater than or equal to 2.
  • the twin network sublayer 3012 is configured to simulate and verify the kth orchestration result to obtain the kth verification result; if the kth verification result does not match the target verification result, the twin network sublayer sends The kth verification result to the function model sublayer 3011; in the case that the kth verification result matches the target verification result, the twin network sublayer 3012 determines the kth arrangement result as the final arrangement result.
  • the function model sublayer 3011 is configured to determine the k+1th scenario simulation requirement information based on the kth verification result, and send the k+1th scenario simulation requirement information to the twin network sublayer 3012 .
  • the embodiment of the present application also provides another DTN30
  • FIG. 5 is a second structural schematic diagram of the DTN30 provided in the embodiment of the present application.
  • the twin network layer of DTN30 may include functional model sublayer 3011 , twin network sublayer 3012 , basic model sublayer 3013 and data acquisition and storage sublayer 3014 ; DTN30 may also include network application layer 101 .
  • the function model sublayer 3011 can receive the scenario requirement information sent by the network application layer 101, and analyze the scenario requirement information to obtain the kth scenario simulation requirement information, which may include traffic simulation, resource allocation, Resource balance scheduling and new device deployment, etc.; then send the kth scenario simulation requirement information to the twin network sublayer 3012 through the first interface between the function model sublayer 3011 and the twin network sublayer 3012.
  • the scenario requirement information sent by the network application layer 101
  • the kth scenario simulation requirement information which may include traffic simulation, resource allocation, Resource balance scheduling and new device deployment, etc.
  • the twin network sublayer 3012 includes an intelligent orchestration body 30121, which is configured to analyze the simulation demand information of the kth scenario, obtain the kth analysis result, and acquire and The monomer model and topology model corresponding to the k-th analysis result, and then based on the topology model corresponding to the k-th analysis result, arrange the monomer model corresponding to the k-th analysis result to obtain the k-th arrangement result, for example, the k-th analysis result
  • the orchestration result may include data connections (Data Communication, DC) between multiple virtual machines (Virtual Machine, VM) and multiple switches (Switch, SW).
  • the functional model sublayer 3011 can determine the k+1th scenario simulation requirement information based on the kth verification result, and send it to the twin network sublayer 3012, thereby starting the orchestration operation.
  • An iterative optimization process when the kth verification result matches the target verification result, determine the kth arrangement result as the final arrangement result.
  • the basic model sublayer 3013 may include a single model library 30131 and a topology model library 30132; for example, the single model library 30131 may be the first model library in the foregoing embodiments; the topology model library 30132 may be the The second model library.
  • the monomer model library 30131 can include multiple monomer models such as twin switches, twin servers, and twin routers; the topology model library can include real topology models and intelligently generated topology models, where the real topology model can be integrated with the physical network layer 103 Corresponding to the network topology in the network, the topology model is intelligently generated, which can be the final target orchestration result.
  • the data collection and storage sublayer 3014 can realize collection and measurement, operation control, data processing and data service functions.
  • the data collection and storage sublayer 3014 can send the network data collected from the physical network layer, including VM, SW, and DC, to the basic model sublayer 3013, so that it can update the monomer model library 30131 and the topology model library 30132.
  • DTN30 may include a processor 601 and a memory 602 , wherein, a computer program is stored in the memory 602, and when the processor 601 executes the computer program, the DTN orchestration method as described in any previous embodiment can be realized.
  • the aforementioned processor 601 may be at least one of ASIC, DSP, DSPD, PLD, FPGA, CPU, controller, microcontroller, and microprocessor.
  • memory 602 can be volatile memory (volatile memory), such as random access memory (Random Access Memory, RAM); Or non-volatile memory (non-volatile memory), such as read-only memory (Read-Only Memory) , ROM), flash memory, hard disk drive (Hard Disk Drive, HDD) or solid state disk (Solid State Disk, SSD); or a combination of the above-mentioned types of memory, and provide instructions and data to the processor 601.
  • this embodiment of the present application also provides a computer-readable storage medium, in which a computer program is stored, and when the computer program is executed by a processor of an electronic device, it can realize any of the previous implementations.
  • a computer program product containing instructions, which, when run on a computer, causes the computer to execute the orchestration method described in any of the preceding embodiments.
  • the above-mentioned computer-readable storage medium may be ROM, Programmable Read-Only Memory (Programmable Read-Only Memory, PROM), Erasable Programmable Read-Only Memory (Erasable Programmable Read-Only Memory, EPROM), electronic Erasable Programmable Read-Only Memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), Magnetic Random Access Memory (Ferromagnetic Random Access Memory, FRAM), Flash Memory (Flash Memory), Magnetic Surface Memory, Optical Disk, or CD-ROM (Compact Disc Read-Only Memory, CD-ROM) and other memories; it can also be various electronic devices including one or any combination of the above-mentioned memories, such as mobile phones, computers, tablet devices, personal digital assistants, etc.
  • PROM Programmable Read-Only Memory
  • EPROM Erasable Programmable Read-Only Memory
  • EPROM Erasable Programmable Read-Only Memory
  • EEPROM Electrical Erasable Programmable Read-Only Memory
  • the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware nodes, and of course also by hardware, but in many cases the former is better implementation.
  • the technical solution of the present application can be embodied in the form of a software product in essence or the part that contributes to the prior art, and the computer software product is stored in a storage medium (such as ROM/RAM, disk, CD) contains several instructions to make a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) execute the methods described in various embodiments of the present application.
  • These computer program instructions may be provided to a general purpose computer, special purpose computer, embedded processor, or processor of other programmable data processing equipment to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing equipment produce a An apparatus for realizing at least one of the functions specified by the flow of the flowchart and the functions specified by the blocks of the block diagram.
  • These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to operate in a specific manner, such that the instructions stored in the computer-readable memory produce an article of manufacture comprising instruction means, the instructions
  • the device realizes at least one of the functions specified by the flow of the flowchart and the functions specified by the blocks of the block diagram.
  • These computer program instructions can also be loaded onto a computer or other programmable data processing device, causing a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby
  • the instructions provide for implementing at least one of the functions specified by the flow of the flowchart and the functions specified by the blocks of the block diagram.
  • the embodiment of the present application provides a DTN arrangement method, DTN, medium and program.
  • the twin network layer of the DTN includes a functional model sublayer, a twin network sublayer and a basic model sublayer; when the twin network sublayer receives the kth scene simulation requirement information sent by the functional model sublayer, Analyzing the kth scenario simulation requirement information, and determining the kth analysis result; wherein, k is an integer greater than or equal to 1; the twin network sublayer obtains the kth analysis result from the basic model sublayer Corresponding monomer model and topology model; wherein, the monomer model includes a multivariate representation model of network elements; the topology model includes topological relationship information between at least two network elements; the twin network sub The layer arranges the monomer model corresponding to the kth analysis result based on the topology model corresponding to the kth analysis result to obtain the kth arrangement result.
  • the DTN orchestration method when the twin network sublayer of the DTN receives the kth scenario simulation requirement information sent by the functional model sublayer and analyzes it to determine the kth analysis result, it can be obtained from the DTN
  • the basic model sublayer obtains the monomer model and topology model corresponding to the k-th analysis result, and then arranges the monomer model corresponding to the k-th analysis result based on the topology model corresponding to the k-th analysis result to obtain the k-th arrangement result. In this way, the network element arrangement of DTN for specific scenario simulation requirement information is realized.

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Abstract

本申请实施例公开了一种数字孪生网络DTN的编排方法、数字孪生网络及介质，所述DTN的孪生网络层包括功能模型子层、孪生网络子层以及基础模型子层；所述孪生网络子层在接收到所述功能模型子层发送的第k场景仿真需求信息的情况下，对所述第k场景仿真需求信息进行解析，确定第k解析结果；其中，k为大于或等于1的整数；所述孪生网络子层从所述基础模型子层获取与所述第k解析结果对应的单体模型以及拓扑模型；其中，所述单体模型，包括网元的多元表示模型；所述拓扑模型，包括至少两个所述网元之间的拓扑关系信息；所述孪生网络子层基于与所述第k解析结果对应的拓扑模型，对与所述第k解析结果对应的单体模型进行编排，得到第k编排结果。

Description

数字孪生网络的编排方法、数字孪生网络、介质和程序
相关申请的交叉引用
本申请基于申请号为202210021707.5、申请日为2022年1月10日，名称为“数字孪生网络的编排方法、数字孪生网络及介质”的中国专利申请提出，并要求该中国专利申请的优先权，该中国专利申请的全部内容在此引入本申请作为参考。
技术领域
本申请涉及网络技术领域，涉及但不限于一种数字孪生网络(Digital Twin Network，DTN)的编排方法、DTN、介质和程序。
背景技术
结合数字孪生技术的DTN是一种能够实现物理网络实体与虚拟孪生体之间相互映射的网络系统，其核心价值在与网络实时闭环控制、低成本试错、从设计到组网的全生命周期管理以及网络可视化呈现。
发明内容
基于以上问题，本申请实施例提供了一种DTN的编排方法、DTN、介质和程序，通过本申请实施例提供的编排方法，DTN的孪生网络子层在接收到功能模型子层发送的场景仿真需求的情况下，对场景仿真需求进行解析能够确定解析结果，并从基础模型子层获取与解析结果对应的单体模型以及拓扑模型，然后基于拓扑模型对单体模型进行编排得到编排结果，从而实现了DTN针对场景仿真需求的网元编排。
本申请实施例提供的技术方案是这样的：
本申请实施例提供了一种DTN的编排方法，所述DTN的孪生网络层包括功能模型子层、孪生网络子层以及基础模型子层；所述方法包括：
所述孪生网络子层在接收到功能模型子层发送的第k场景仿真需求信息的情况下，对所述第k场景仿真需求信息进行解析，确定第k解析结果；其中，所述第一接口为所述孪生网络子层与所述DTN的功能模型子层之间的数据传输接口；k为大于或等于1的整数；
所述孪生网络子层从所述基础模型子层获取与所述第k解析结果对应的单体模型以及拓扑模型；其中，所述单体模型，包括网元的多元表示模型；所述拓扑模型，包括至少两个所述网元之间的拓扑关系信息；
所述孪生网络子层基于与所述第k解析结果对应的拓扑模型，对与所述第k解析结果对应的单体模型进行编排，得到第k编排结果。
本申请实施例还提供了一种DTN，所述DTN的孪生网络层包括功能模型子层、孪生网络子层以及功能模型子层；其中：
所述功能模型子层，配置为确定第k场景仿真需求信息，并发送所述第k场景仿真需求信息至所述孪生网络子层；
所述孪生网络子层，配置为在接收到所述第k场景仿真需求信息的情况下，对所述第k场景仿真需求信息进行解析，确定第k解析结果；其中，k为大于或等于1的整数；
所述孪生网络子层，还配置为从所述基础模型子层获取与所述第k解析结果对应的单体模型以及拓扑模型，并基于所述第k解析结果对应的拓扑模型，对与所述第k解析结果对应的单体模型进行编排，得到第k编排结果；其中，所述单体模型，包括网元的多元表示模型；所述拓扑模型，包括至少两个所述网元之间的拓扑关系信息。
本申请实施例还提供了另一种DTN，所述DTN包括处理器和存储器，其中，所述 存储器中存储有计算机程序；所述计算机程序被所述处理器执行时，能实现如前任一所述DTN的编排方法
本申请实施例还提供了一种计算机可读存储介质，所述计算机可读存储介质中存储有计算机程序，所述计算机程序被电子设备的处理器执行时，能够实现如前任一所述的DTN的编排方法。
本申请实施例还提供了一种计算机程序，当其在计算机上运行时，使得计算机执行本申请实施例的上述任一所述的DTN的编排方法。
由以上可以知道，本申请实施例提供的DTN的编排方法，在DTN的孪生网络子层接收到功能模型子层发送的第k场景仿真需求信息并对其进行解析确定第k解析结果的情况下，能够从DTN的基础模型子层获取与第k解析结果对应的单体模型以及拓扑模型，然后基于与第k解析结果对应的拓扑模型对与第k解析结果对应的单体模型进行编排，得到第k编排结果，从而实现了DTN针对具体的场景仿真需求信息的网元编排。
附图说明
图1为相关技术中DTN的架构示意图；
图2A为本申请实施例提供DTN的编排方法的第一流程示意图；
图2B为本申请实施例提供的孪生网络子层获取与第k解析结果对应的单体模型以及拓扑模型的流程示意图；
图2C为本申请实施例提供的对第k编排结果的仿真验证的流程示意图；
图3为本申请实施例提供的DTN编排方法的第二流程示意图；
图4为本申请实施例提供的DTN的第一结构示意图；
图5为本申请实施例提供的DTN的第二结构示意图；
图6为本申请实施例提供的DTN的第三结构示意图。
具体实施方式
下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述。
应当理解，此处所描述的具体实施例仅仅用以解释本申请，并不用于限定本申请。
DTN是一种具有物理网络实体以及虚拟孪生体、且能够实现物理网络实体以及虚拟孪生体之间实时相互映射的网络系统。DTN的核心价值在于网络实时闭环控制，低成本试错、从设计到组网的全生命周期管理以及网络可视化。在相关技术中，DTN的构建还停留在对网络设备以及拓扑可视化的研究和探索方面，而可视化的建模方法并不能实现DTN的网络虚拟映射、低成本试错以及内外闭环控制等功能，因此也无法实现对具体场景的网元的编排和仿真。
图1为相关技术中DTN的架构示意图。
如图1所示，DTN包括网络应用层101、孪生网络层102以及物理网络层103；其中，网络应用层101用于实现网络创新技术验证、网络可视化、意图验证、网络管理以及网络维护和优化等功能。
孪生网络层102为DTN的核心部分，其包括数据共享仓库1021、服务映射模型1022以及网络孪生体管理模块1023三个部分。其中，数据共享仓库1021用于实现数据管理、数据服务、数据存储以及数据采集等功能，数据共享仓库1021中的数据涉及用户业务、网络配置以及运行状态；数据共享仓库1021可以与服务映射模型1022之间进行数据交互，服务映射模型1022从数据共享仓库1021获取数据之后能够通过迭代优化以及仿真验证能够实现得到功能模型以及基础模型。服务映射模型1022用于规划、建设、维护、优化以及运行，在其生成基础模型即网元模型、以及功能模型即拓扑模型之后，可以在网络规 划、流量建模、安全建模、故障诊断、调度优化、质量保障等发面进行迭代优化以及仿真验证。网络孪生体管理模块1023能够实现模型管理、安全管理以及拓扑管理，网络孪生体管理模块1023也能够与服务映射模型1022之间进行数据交互。
物理网络层103包括各种物理网络实体、以及各种物理网络实体之间的网络连接结构。数据共享仓库1021能够从物理网络层103采集各种网络数据，服务映射模型用于向物理网络层103下发控制命令。网络应用层101与孪生网络层102之间实现能力调用以及意图翻译的数据交互。
然而，上述架构虽然完备，但也只是提供了一种网络架构，相关技术中并不存在针对具体场景的网络编排方案。
基于以上问题，本申请实施例提供了一种DTN的编排方法、DTN及介质。本申请实施例提供DTN的编排方法，能够实现根据具体的场景仿真需求确定对应的单体模型以及拓扑模型，并基于拓扑模型对单体模型进行编排，从而得到编排结果，进而实现了DTN针对具体的场景仿真需求的编排部署。
本申请实施例提供DTN的编排方法，可以通过DTN的处理器实现。
需要说明的是，上述处理器可以为特定用途集成电路(Application Specific Integrated Circuit，ASIC)、数字信号处理器(Digital Signal Processor，DSP)、数字信号处理装置(Digital Signal Processing Device，DSPD)、可编程逻辑装置(Programmable Logic Device，PLD)、现场可编程逻辑门阵列(Field Programmable Gate Array，FPGA)、中央处理器(Central Processing Unit，CPU)、控制器、微控制器、微处理器中的至少一种。
在本申请实施例中，DTN的孪生网络层包括功能模型子层、孪生网络子层以及基础模型子层。
在一种实施方式中，功能模型子层，可以包括图1所示的孪生网络层102中的部分模块；示例性的，功能模型子层，可以包括图1所示的服务映射模型1022中用于实现规划、建设、维护、优化、运营等功能的模块；示例性的，功能模型子层，可以实现DTN与网络应用层的对接，其能够接收网络应用层发送的场景需求信息，并对场景需求信息进行解析，从而确定第k场景仿真需求信息。
在一种实施方式中，孪生网络子层，可以包括图1所示的孪生网络层102中的部分模块，示例性的，孪生网络子层可以包括图1中的服务映射模型1022；示例性的，孪生网络子层可以包括图1中的服务映射模型1022以及网络孪生体管理模块1023；示例性的，孪生网络子层，可以包括服务映射模型1022中的部分子模块以及网络孪生体管理模块1023中的部分子模块；示例性的，可以对服务映射模型1022以及网络孪生体管理模块1023的所有功能对应的子模块进行分析，将服务映射模型1022以及网络孪生体管理模块1023中的部分子模块划分至孪生网络子层。
在一种实施方式中，基础模型子层，可以为DTN中包含有多种单体模型以及多种拓扑模型的模块；示例性的，基础模型子层，可以为图1所示的服务映射模型1022中包括网元模型以及拓扑模型的模块部分。
图2A为本申请实施例提供DTN的编排方法的第一流程示意图。如图1所示，该编排方法可以包括步骤201至步骤203：
步骤201、孪生网络子层接收到功能模型子层发送的第k场景仿真需求信息的情况下，对第k场景仿真需求信息进行解析，确定第k解析结果。
其中，k为大于或等于1的整数。
相应的，在未接收到第k场景仿真需求信息的情况下，孪生网络子层可以不执行解析操作。
在一种实施方式中，第k场景仿真需求信息，可以包括至少一个维度的仿真时间、仿真环境、与该场景对应的优化目标等至少一种信息。
在一种实施方式中，在k大于或等于2的情况下，第k场景仿真需求信息可以与第k-1场景仿真需求信息不同。
在一种实施方式中，第k场景仿真需求信息，可以包含针对网络布局优化、数据传输效率优化、网络容量优化、以及网络能耗优化中的至少一种场景优化的需求信息。
示例性的，在第k场景仿真需求信息针对的网络为已部署网络的情况下，第k场景仿真需求信息中任一需求的满足，可以通过对已部署网络中至少一个网元的设置方式、连接关系、状态控制、功耗管理、以及功能实现中的至少一个方面进行改善优化而实现。
示例性的，在第k场景仿真需求信息针对的网络为尚未部署网络的情况下，第k场景仿真需求信息中任一需求的满足，可以通过根据对尚未部署网络的整体功能实现，对尚未部署网络中所需要的网元的数量、类型、功能、状态、以及连接关系中的至少一个方面的设置和优化。
也就是说，在本申请实施例中，第k场景仿真需求信息中任一需求的满足，均可以通过对网元的设置优化以及改善实现。
在一种实施方式中，第k解析结果，可以包括为了满足第k场景需求信息所需要的网元的功能、种类、数量、网元之间的关联关系、网元之间关联关系的强弱、以及至少一种网元的复用状态中的至少一种信息。
在一种实施方式中，孪生网络子层对第k场景仿真需求信息进行解析，确定第k解析结果，可以通过以下任一方式实现：
孪生网络子层对第k场景仿真需求信息进行解析，对第k场景仿真需求信息中相同类型的需求信息进行更小粒度的划分，并将划分结果确定为第k解析结果。
孪生网络子层根据第k场景仿真需求信息确定解析方式，然后按照解析方式对第k场景仿真需求信息中的每一场景仿真需求按照优先级或重要性进行分类划分，并将分类划分的结果确定为第k解析结果。
步骤202、孪生网络子层从基础模型子层获取与第k解析结果对应的单体模型以及拓扑模型。
其中，单体模型，包括网元的多元表示模型；拓扑模型，包括至少两个网元之间的拓扑关系信息。
在一种实施方式中，网元可以包括图1所示的物理网络层中实际的物理网元，比如交换机、路由器等，还可以包括图1所示的物理网络层中的虚拟网元，比如容器节点。
在一种实施方式中，网元的多元表示模型，可以是对网元进行多维度描述得到的。示例性的，网元的多元表示模型，可以包括网元的名称、类型、功能、以及端口数量中的至少两种信息。
在一种实施方式中，拓扑模型，可以以拓扑结构的形式，表征其所包含的至少两个网元之间的拓扑结构、或者其实现数据处理功能时数据在各个网元之间的流转拓扑结构；示例性的，拓扑结构的表示形式，可以包括向量形式、图像形式、语义表述形式、以及拓扑图形式的可视化模型文件。
示例性的，拓扑关系信息，可以包括拓扑模型中至少两个网元之间是否连接以及如何连接的信息。
在一种实施方式中，由于网元与单体模型之间具备一一对应的关联关系，因此，拓扑模型所对应的网络结构包含的至少两个网元之间的拓扑关系信息，可以通过至少两个单体模型之间的拓扑结构的形式表现。比如，拓扑模型中的拓扑关系信息，可以通过至少两个单体模型之间的连接方式、单体模型的接口数量以及单体模型之间的信令交互方式，体现拓扑模型对应的网络结构所包含的至少两个网元之间的连接方式、网元的接口数量以及各个网元之间的信令交互方式。
步骤203、孪生网络子层基于与第k解析结果对应的拓扑模型，对与第k解析结果对 应的单体模型进行编排，得到第k编排结果。
在一种实施方式中，孪生网络子层基于与第k解析结果对应的拓扑模型，对与第k解析结果对应的单体模型进行编排，得到第k编排结果，可以是通过以下方式实现的：
孪生网络子层对与第k解析结果对应的单体模型即多元表示模型进行多元梳理，得到梳理结果，然后基于与第k解析结果对应的拓扑模型，对多元梳理结果中各个网元的功能、端口、以及连接关系等进行编排，从而得到第k编排结果。
由以上可以知道，本申请实施例提供的应用于DTN的编排方法，在孪生网络子层接收到功能模型子层发送的第k场景仿真需求信息并对其进行解析确定第k解析结果的情况下，能够从基础模型子层中获取与第k解析结果对应的单体模型以及拓扑模型，然后基于与第k解析结果对应的拓扑模型对与第k解析结果对应的单体模型进行编排，得到第k编排结果，从而实现了DTN针对具体的场景仿真需求信息的网元编排。
基于前述实施例，本申请实施例提供的DTN的编排方法中，DTN还包括网络应用层；
示例性的，本申请实施例提供的DTN的编排方法，还可以包括以下操作：
功能模型子层接收网络应用层发送的场景需求信息，对场景需求信息进行解析，确定第k场景仿真需求信息，并通过第一接口发送第k场景仿真需求信息至孪生网络子层。
其中，第一接口为孪生网络子层与DTN的功能模型子层之间的数据传输接口。
示例性的，网络应用层，可以为图1所示的网络应用层101，其所发送的场景需求信息可以包括网络创新技术验证、网络可视化、意图验证、网络管理、以及网络维护和优化中的至少一种；示例性的，网络应用层发送的场景需求信息，可以是针对实际的物理网络的，比如针对物联网部分区域的网络时延优化；示例性的，网络应用层发送的场景需求信息，可以是针对待部署网络的，比如针对即将在某区域部署的无线通信网络中至少一个小区的容量的仿真验证。
示例性的，场景需求信息的数量可以为多个，也就是说，网络应用层可以一次性发送多个场景需求信息至功能模型子层，功能模型子层可以对多个场景需求信息进行解析，得到第k场景仿真需求信息，此时，第k场景仿真需求信息可以包括对一个网络的至少两个维度、或者至少两种应用场景的优化需求信息。
示例性的，网络应用层发送的场景需求信息，可以是以配置文件的形式体现的，其中，配置文件中各种数据的组织形式，可以根据网络应用层与功能模型子层之间的传输协议确定。
在一种实施方式中，第一接口，可以为孪生网络子层与功能模型子层之间传输场景需求信息、反馈场景仿真结果、以及各种控制命令的接口。
由以上可知，功能模型子层在接收到网络应用层发送的场景需求信息之后，能够对场景需求信息进行解析确定第k场景仿真需求信息并发送至孪生网络子层，如此，功能模型子层与孪生网络子层之间相互独立又相互依赖，一方面能够实现对网络应用层的场景需求信息的高效解析，另一方面又能提高孪生网络子层针对具体应用场景的编排效率。
基于前述实施例，本申请实施例提供的DTN的编排方法，孪生网络子层从基础模型子层获取与第k解析结果对应的单体模型以及拓扑模型，可以通过图2B实现，图2B为本申请实施例提供的孪生网络子层获取与第k解析结果对应的单体模型以及拓扑模型的流程示意图，如图2B所示，该流程可以包括步骤202-1至步骤202-3：
步骤202-1、孪生网络子层对第k解析结果进行处理，得到第k单体配置信息以及第k拓扑配置信息，并通过第二接口发送第k单体配置信息以及第k拓扑配置信息至基础模型子层。
其中，第二接口为基础模型子层与孪生网络子层之间的数据传输接口。
在一种实施方式中，第k单体配置信息，可以包括单体模型所需要实现的功能、单体模型的数量、以及单体模型的类型中的至少一种信息。
在一种实施方式中，第k拓扑配置信息，可以表示包括网络拓扑的类型、结构、以及网络拓扑的单体模型之间的连接关系中的至少一种信息。
在一种实施方式中，第二接口，可以实现孪生网络子层与基础模型子层之间包括单体模型、拓扑模型、第k单体配置信息以及第k拓扑配置信息在内的数据传输。
步骤202-2、基础模型子层通过第二接口接收到第k单体配置信息以及第k拓扑配置信息之后，基于第k单体配置信息从单体模型库中搜索与第k解析结果对应的单体模型，以及基于第k拓扑配置信息从拓扑模型库中搜索与第k解析结果对应的拓扑模型。
在一种实施方式中，单体模型库中，可以包含多个单体模型，示例性的，这些单体模型的类型、功能、端口数量、以及名称可以各不相同。
在一种实施方式中，拓扑模型库中，可以包含多种拓扑模型，这些拓扑模型的类型、功能、以及应用场景可以各不相同；示例性的，不同的拓扑模型中包含的网元或单体模型的数量以及功能可以均不相同。
在一种实施方式中，单体模型库对单体模型的管理、以及拓扑模型库对拓扑模型的管理，可以通过对单体模型添加单体索引信息、以及为拓扑模型添加拓扑索引信息的方式实现，如此，可以改善单体模型以及拓扑模型的搜索效率。示例性的，单体索引信息，可以包括单体模型的类型、功能、端口数量、以及应用场景中的至少一种信息；拓扑索引信息，可以包括拓扑模型的类型、功能、应用场景、以及网元数量中的至少一种信息。
在一种实施方式中，基础模型子层基于第k单体配置信息从单体配置库中搜索与第k解析结果对应的单体模型，可以是基于第k单体配置信息中的单体模型的类型、功能、端口数量、以及应用场景等至少一种信息，与单体索引信息中相同维度的信息进行匹配的方式实现的。
在一种实施方式中，基础模型子层基于第k拓扑配置信息从拓扑配置库中搜索与第k解析结果对应的拓扑模型，可以是基于第k拓扑配置信息中的拓扑模型的类型、功能、网元数量、以及应用场景等至少一种信息，与拓扑索引信息中相同维度的信息进行匹配的方式实现的。
步骤202-3、孪生网络子层通过第二接口获取与第k解析结果对应的单体模型以及拓扑模型。
由以上可知，本申请实施例提供DTN的编排方法中，孪生网络子层在确定第k单体配置信息以及第k拓扑配置信息之后，能够将这些信息发送至基础模型子层，在基础模型子层基于第k单体配置信息以及第k拓扑配置信息确定与第k解析结果对应的单体模型以及拓扑模型之后，还能将与第k解析结果对应的单体模型以及拓扑模型发送至孪生网络子层，从而实现了DTN中各个子层之间的功能解耦合，提高了各个子层之间的数据处理效率，从而能够提高孪生网络子层针对具体应用场景的编排效率。
基于前述实施例，本申请实施例提供的DTN的编排方法中，DTN的孪生网络子层还包括数据采集存储子层。
示例性的，在基础模型子层基于第k单体配置信息从单体模型库中搜索与第k解析结果对应的单体模型，以及基于与第k拓扑配置信息从拓扑模型库中搜索与第k解析结果对应的拓扑模型之前，还可以执行步骤B1至步骤B2：
步骤B1、基础模型子层从数据采集存储子层获取网络数据，对网络数据中的网元数据进行多维度建模，得到单体模型库。
示例性的，网络数据可以包括设备信息、故障告警、关键绩效指标(Key Performance Indicator，KPI)等结构化数据，以及网元之间的拓扑信息、链路运行状态等至少一种类型的数据。
在一种实施方式中，数据采集存储子层，可以为DTN中用于从物理网络层采集数据、整理数据的模块，示例性的，数据采集存储子层，可以为图1所示的服务映射模型102 中的数据共享仓库1021。
在一种实施方式中，对网元数据进行多维度建模表征，可以包括获取网元数据的多元信息表示，并基于网元的功能特征对多元信息表示进行关联建模而得到的信息表征。
在一种实施方式中，单体模型库，可以是基础模型子层接收到数据采集存储子层采集的网络数据和存储的网络数据的至少一项之后，对网络数据进行整合以及分析后得到的。
示例性的，基础模型子层可以通过第三接口从数据采集存储子层中获取网络数据；示例性的，第三接口，为基础模型子层与数据采集存储子层之间的数据传输接口。
在一种实施方式中，第三接口，可以实现数据采集存储子层与基础模型子层之间的网络数据以及网络数据采集指令等数据的传输。
在本申请实施例中，第一接口、第二接口与第三接口数据传输所采用的协议，可以根据第一接口、第二接口以及第三接口传输数据类型而改变。本申请实施例对此不作限定。
在一种实施方式中，单体模型库，可以是基础模型子层接收到数据采集存储子层采集的网络数据和存储的网络数据的至少一项之后，对网络数据进行解析分类，得到多个与网元功能相关的数据之后，再对网元功能相关的数据进行整合而得到的。
步骤B2、基础模型子层对网络数据中的网元关联关系进行分析，得到拓扑模型库。
在一种实施方式中，拓扑模型库可以包含与物理网络层中的实际网络部署对应的多种拓扑模型，还可以包括与历史编排结果对应的历史拓扑模型，即通过本申请实施例提供的编排方式得到的、智能化的自动生成的拓扑模型。
在一种实施方式中，网元关联关系，可以包括网元之间是否具备关联关系、关联关系强弱、关联关系为单向还是双向中的至少一种信息，本申请实施例对此不作限定。
由以上可知，在本申请实施例提供的DTN的编排方法中，基础模型子层能够预先从数据采集存储子层中获取网络数据，并对网络数据以及网络数据中的网元关联关系进行分析，得到单体模型库以及拓扑模型库，从而为孪生网络子层对第k场景仿真需求信息进行解析以及进一步的编排奠定基础，提高了DTN的编排效率。
在本申请实施例提供的DTN的编排方法中，拓扑模型库包括网元关联信息以及与网元关联信息对应的可视化模型；单体模型库包括网元的N元组信息以及与N元组信息对应的可视化模型。
其中，网元关联信息包括网元之间的向量关系信息；N为大于或等于2的整数。
在一种实施方式中，网元关联信息，可以包括网元之间是否具备关联关系、网元之间的关联关系的强弱、以及网元之间关联关系产生的条件中的至少一种信息。
在一种实施方式中，网元之间的向量关系信息，可以包括网元之间连接的方向性，比如单向连接还是双向连接。示例性的，网元之间的向量关系信息，可以通过可视化模型的形式体现，也可以通过语义表达的形式体现，本申请实施例对此不作限定。
在一种实施方式中，网元关联信息的可视化模型，可以包括图例、图标、图像、以及图例图标图像之间的连接关系的直观呈现模型，其可以更直观的、以二维或三维的形式呈现网元之间的关联信息。
示例性的，网元的N元组信息，可以包括网元的多元表示模型，其可以包括网元属性、网元类型、网元的数据处理规则、网元与其它网元之间的关系、网元的状态切换条件、以及网元数据处理相关的公理等中的至少两种。其中，网元属性，可以包括网元的特征、特点和参数；网元类型，可以包括网元的种类、名称以及标识等；网元的数据处理规则，可以包括网元的数据转发逻辑、数据传输协议等；网元与其它网元之间的关系，可以包括网元与其它网元之间的连接关系；网元的状态切换条件，可以包括网元状态切换前后的属性、及其与其它网元之间连接关系的变化；网元数据处理相关的公理，可以 包括网元的数据处理的先验知识的声明。
示例性的，网元的多元表示模型包括N个维度的信息的情况下，可以称为网元的N元组信息。示例性的，与N元组信息对应的可视化模型，可以是通过包含图像、图示、图例的可视化组合形式展示网元的N元组信息的模型。
由以上可知，本申请实施例中，单体模型库中包含了网元的N元组信息以及与N元组信息对应的可视化模型，拓扑模型库中包含了网元关联关系以及与网元关联关系对应的可视化模型，也就是说，单体模型库以及拓扑模型库中包含了物理网络层中网元以及网络结构的、多个维度的信息，从而使得基于单体模型库中的单体模型以及拓扑模型库中的拓扑模型进行编排时，能有效的提高编排的效率，缩短编排时间，从而实现了编排流程的优化。
基于前述实施例，本申请实施例提供的DTN的编排方法，还可以包括对第k编排结果的仿真验证操作，图2C为本申请实施例提供的对第k编排结果的仿真验证的流程示意图，如图2C所示，该流程可以包括步骤204至步骤205：
步骤204、孪生网络子层对第k编排结果进行仿真验证，得到第k验证结果。
在一种实施方式中，第k验证结果，可以包括第k编排结果以及对第k编排结果进行仿真验证得到的至少一个维度的、表征第k编排结果的数据处理能力的信息；示例性的，表征第k编排结果的数据处理能力的信息，可以包括第k编排结果的数据处理时延、数据处理稳定性、数据吞吐量、以及数据并发能力中的至少一种信息。
在一种实施方式中，孪生网络子层对第k编排结果进行仿真验证，得到第k验证结果，可以是通过以下任一方式实现的：
在第k场景仿真需求信息中未设置仿真验证条件的情况下，孪生网络子层获取默认仿真验证环境，并设置默认仿真验证条件，然后基于默认仿真验证条件，在默认仿真验证环境中对第k编排结果进行仿真验证，得到第k验证结果。
在第k场景仿真需求信息中设置仿真验证条件的情况下，孪生网络子层从第k场景仿真需求信息中获取仿真验证环境以及仿真验证条件，并基于仿真验证条件，在仿真验证环境中进行仿真验证，得到第k验证结果。
其中，仿真验证环境，包括执行仿真验证的操作系统以及采用的仿真软件等；仿真验证条件，可以包括仿真验证所依据的数据、仿真验证执行的时间、以及触发仿真验证的时机中的至少一种数据。
步骤205、在第k验证结果与目标验证结果不匹配的情况下，孪生网络子层发送第k验证结果至功能模型子层；在第k验证结果与目标验证结果匹配的情况下，孪生网络子层确定第k编排结果为最终编排结果。
在一种实施方式中，目标验证结果，可以是功能模型子层发送至孪生网络子层的，还可以是孪生网络子层从功能模型子层发送的第k场景仿真需求信息中解析得到的，本申请实施例对此不作限定。
在一种实施方式中，第k验证结果与目标验证结果匹配的情况下，孪生网络子层确定第k编排结果为最终编排结果之后，还可以通过第一接口将第k验证结果发送至功能模型子层，功能模型子层接收到第k验证结果之后，可以向网络应用层输出第k验证结果。
示例性的，若第k验证结果与目标验证结果不匹配，则孪生网络子层可以通过第一接口发送第k验证结果至功能模型子层，以供功能模型子层基于第k验证结果下发进一步的编排指示。
由以上可知，本申请实施例提供的编排方法，在孪生网络子层得到第k验证结果之后，能够根据第k验证结果与目标验证结果之间的匹配关系确定第k编排结果是否为最终编排结果，并且，在确定第k编排结果不为最终编排结果的情况下，还能够向功能模 型子层发送第k验证结果。也就是说，在本申请实施例中，无论第k编排结果是否为最终编排结果，DTN都能够对第k编排结果进行全方位的判断和处理，从而能够进一步提高DTN编排的效率，改善DTN编排的稳定性和全面性。并且，通过对孪生网络子层的编排操作的自动化控制，达到了解析操作以及编排操作的自组织、自驱动以及自运行的目的，提高了功能模型子层的解析操作、以及DTN的编排操作的智能化水平，还能够减少整个过程中的人工干预，提高解析以及编排的效率以及准确性。
基于前述实施例，本申请实施例提供的DTN的编排方法中，孪生网络层发送第k验证结果至功能模型子层之后，还可以执行以下操作：
功能模型子层基于第k验证结果，确定第k+1场景仿真需求信息，并发送第k+1场景仿真需求信息至孪生网络子层。
在一种实施方式中，功能模型子层可以对第k验证结果进行分析，确定第k编排结果中不能满足目标验证结果的原因，并基于该原因以及场景需求信息，确定第k+1场景仿真需求信息，然后将第k+1场景仿真需求信息发送至孪生网络子层。
在一种实施方式中，孪生网络子层在接收到第k+1场景仿真需求信息的情况下，可以对第k+1场景仿真需求信息进行解析，得到第k+1解析结果，然后从基础模型子层中获取与第k+1解析结果对应的拓扑模型以及单体模型，并基于与第k+1解析结果对应的拓扑模型，对第k+1解析结果对应的单体模型进行编排，得到第k+1编排结果，从而实现DTN的迭代编排操作。
由以上可知，本申请实施例提供的DTN的编排方法，功能模型子层接收到第k验证结果之后，能够基于第k验证结果确定第k+1场景仿真需求信息，并发送第k+1场景仿真需求信息至孪生网络子层，从而通过功能模型子层以及孪生网络子层之间的相互协作完成编排操作的迭代优化，进而提高了编排操作的自动化以及智能化程度。
图3为本申请实施例提供的DTN编排方法的第二流程示意图。
在图3中，DTN30的孪生网络层301可以包括功能模型子层3011、孪生网络子层3012、基础模型子层3013以及数据采集存储子层3014。需要说明的是，图3中的各个步骤可以是通过DTN30的处理器实现的。如图3所示，该流程可以包括以下步骤：
步骤1、实时同步物理网络数据。
示例性的，实时同步物理网络数据，可以是数据采集存储子层3014主动发起的操作；示例性的，数据采集存储子层3014能够实时的获取物理网络层103中的网络数据。
步骤2、对网络数据执行处理、存储、服务等操作。
示例性的，数据采集存储子层3014在对网络数据进行处理、存储、服务等操作之后，还可以将网络数据划分为网元相关的数据以及与拓扑结构相关的数据。
步骤3、获取网元数据以及拓扑数据。
示例性的，基础模型子层3013可以向数据采集存储子层3014发送获取网元数据以及拓扑数据的指令。
步骤4、发送网元数据以及拓扑数据。
示例性的，数据采集存储子层3014在接收到获取网元数据以及拓扑数据的指令之后，可以发送网元数据以及拓扑数据至基础模型子层3013。
步骤5、构建单体模型库和拓扑模型库。
示例性的，基础模型子层3013可以对网元数据以及拓扑数据进行多个维度的分析，从而得到单体模型以及拓扑模型，基于多个单体模型以及多个拓扑模型，可以分别构建单体模型库以及拓扑模型库。
步骤6、向DTN发送场景需求信息。
示例性的，步骤6可以是网络应用层101执行的；示例性的，网络应用层101可以将场景需求信息至功能模型子层3011。
示例性的，场景需求信息可以为包括至少两种场景需求的配置文件。
步骤7、对场景仿真需求信息进行解析，得到场景仿真策略。
示例性的，步骤7可以是功能模型子层3011执行的；其中，这里的场景仿真策略，可以为前述实施例中的第k场景仿真需求信息。
步骤8、发送场景仿真策略。
示例性的，功能模型子层3011可以通过第一接口发送场景仿真策略即第k场景仿真需求信息至孪生网络子层3012。
步骤9、编排体解析场景仿真策略得到解析结果。
示例性的，这里的解析结果，可以为前述实施例中的第k解析结果。
示例性的，编排体，可以为孪生网络子层3012中用于执行编排操作的模块。
步骤10、孪生网络子层3012中的编排体根据解析结果，获取单体模型和拓扑模型。
示例性的，编排体可以通过第二接口向基础模型子层3013发送第k单体配置信息以及第k拓扑配置信息。这里的单体模型以及拓扑模型，可以是与第k解析结果对应的单体模型以及拓扑模型。
步骤11、基础模型子层3013发送单体模型和拓扑模型。
示例性的，基础模型子层3013可以根据第k单体配置信息以及第k拓扑配置信息分别在单体模型库以及拓扑模型库中，搜索与第k解析结果对应的单体模型以及拓扑模型，在搜索结束之后，可以通过第二接口发送与第k解析结果对应的单体模型以及拓扑模型至孪生网络子层3012。
步骤12、编排体根据拓扑模型将单体模型进行编排，并进行场景仿真。
示例性的，编排体可以根据与第k解析结果对应的拓扑模型，对与第k解析结果对应的单体模型进行编排，得到第k编排结果，然后再对第k编排结果进行场景仿真，从而得到第k仿真结果。
步骤13、发送仿真结果。
示例性的，这里的仿真结果可以为前述实施例中的与目标验证结果匹配的第k仿真结果。示例性的，功能模型子层3011在接收到第k仿真结果之后，可以向网络应用层101展示第k仿真结果中的第k编排结果。
步骤14、发送目标仿真结果。
示例性的，目标仿真结果，可以为前述实施例中的与目标验证结果匹配的第k验证结果。
示例性的，功能模型子层3011发送目标仿真结果至网络应用层101。
示例性的，网络应用层101在接收到目标仿真结果之后，可以对其中的第k编排结果及最终编排结果进行可视化的呈现。
由以上可知，本申请实施例提供的DTN30的孪生网络层301中的基础模型子层3013，能够获取数据采集存储子层3014中的网络数据，实时构建单体模型库以及拓扑模型库，功能模型子层3011能够接收网络应用层101的场景需求信息，并基于场景需求信息确定第k场景仿真需求信息，并将该信息发送至孪生网络子层3012，以供孪生网络子层3012对第k场景仿真需求信息进行解析得到第k解析结果，并基于第k解析结果从基础模型子层3013中获取单体模型以及拓扑模型，然后基于拓扑模型对单体模型进行编排、仿真验证，从而得到第k验证结果。
如此，DTN30中的各个子层的功能相互独立又相互关联，从而能够提高了解析、编排以及仿真的效率，从而提供了一种高效可靠的网络结构的编排解析方法。
并且，在解析编排之前，数据采集存储子层3014中已经存储有单体模型以及拓扑模型，从而使得DTN在编排的过程中具备了快速部署、动态调整、可复用的能力，进而很大程度上增强了其灵活性、扩展性、节能性，可实现以场景为驱动的仿真、验证等。
基于前述实施例，本申请实施例还提供了一种DTN30，图4为本申请实施例提供的DTN30的第一结构示意图。如图4所示，DTN30的孪生网络层301可以包括功能模型子层3011、孪生网络子层3012以及基础模型子层3013。
在一种实施方式中，功能模型子层3011，配置为在接收网络应用层发送的场景需求信息的情况下，对场景需求信息进行解析，确定第k场景仿真需求信息，通过第一接口发送第k场景仿真需求信息至孪生网络子层3012；其中，第一接口，为孪生网络子层与功能模型子层之间的数据传输接口。
在一种实施方式中，DTN30还包括网络应用层；孪生网络子层3012，配置为对第k解析结果进行处理，得到第k单体配置信息以及第k拓扑配置信息，并通过第二接口发送第k单体配置信息以及第k拓扑配置信息至基础模型子层3013；其中，第二接口为基础模型子层与孪生网络子层之间的数据传输接口；
基础模型子层3013，配置为通过第二接口接收到第k单体配置信息以及第k拓扑配置信息之后，基于第k单体配置信息从单体模型库中搜索与第k解析结果对应的单体模型，以及基于第k拓扑配置信息从拓扑模型库中搜索与第k解析结果对应的拓扑模型；
孪生网络子层3012，还配置为通过第二接口从基础模型子层3013获取与第k解析结果对应的单体模型以及拓扑模型。
在一些实施方式中，孪生网络层301还包括数据采集存储子层3014；基础模型子层3013，配置为从DTN的数据采集存储子层3014获取网络数据，对网络数据中的网元数据进行多维度建模表征，得到单体模型库；
基础模型子层3013，还配置为对网络数据中的网元关联关系进行分析，得到拓扑模型库。
在一种实施方式中，拓扑模型库包括网元关联信息以及与网元关联信息对应的可视化模型；其中，网元关联信息包括网元之间的向量关系信息；
单体模型库包括网元的N元组信息以及与N元组信息对应的可视化模型；其中，N为大于或等于2的整数。
在一种实施方式中，孪生网络子层3012，配置为对第k编排结果进行仿真验证，得到第k验证结果；在第k验证结果与目标验证结果不匹配的情况下，孪生网络子层发送第k验证结果至功能模型子层3011；在第k验证结果与目标验证结果匹配的情况下，孪生网络子层3012确定第k编排结果为最终编排结果。
在一种实施方式中，功能模型子层3011，配置为基于第k验证结果，确定第k+1场景仿真需求信息，并发送第k+1场景仿真需求信息至孪生网络子层3012。
基于前述实施例，本申请实施例还提供了另一种DTN30，图5为本申请实施例提供的DTN30的第二结构示意图。如图5所示，DTN30的孪生网络层可以包括功能模型子层3011、孪生网络子层3012、基础模型子层3013以及数据采集存储子层3014；DTN30还可以包括网络应用层101。其中，功能模型子层3011能够接收网络应用层101发送的场景需求信息，并对场景需求信息进行解析，得到第k场景仿真需求信息，第k场景仿真需求信息可以包括流量拟真、资源分配、资源均衡调度以及新设备部署等；然后通过功能模型子层3011与孪生网络子层3012之间的第一接口将第k场景仿真需求信息发送至孪生网络子层3012中。
孪生网络子层3012包含智能编排体30121，配置为对第k场景仿真需求信息进行解析，得到第k解析结果，并通过孪生网络子层3012与基础模型子层3013之间的第二接口获取与第k解析结果对应的单体模型以及拓扑模型，然后基于与第k解析结果对应的拓扑模型，对于第k解析结果对应的单体模型进行编排，得到第k编排结果，示例性的，第k编排结果可以包括多个虚拟机(Virtual Machine，VM)与多个交换机(Switch，SW)之间的数据连接(Data Communication，DC)。
在第k验证结果与目标验证结果不匹配的情况下，功能模型子层3011可以基于第k验证结果确定第k+1场景仿真需求信息，并发送至孪生网络子层3012，从而启动编排操作的迭代优化过程；在第k验证结果与目标验证结果匹配的情况下，确定第k编排结果为最终编排结果。
基础模型子层3013中可以包含单体模型库30131以及拓扑模型库30132；示例性的，单体模型库30131可以为前述实施例中的第一模型库；拓扑模型库30132可以为前述实施例中的第二模型库。单体模型库30131中可以包括孪生交换机、孪生服务器、以及孪生路由器等多种单体模型；拓扑模型库可以包括真实拓扑模型以及智能生成拓扑模型，其中，真实拓扑模型，可以与物理网络层103中的网络拓扑对应，智能生成拓扑模型，可以是最终的目标编排结果。
数据采集存储子层3014，可以实现采集测量、运行控制、数据处理以及数据服务功能。数据采集存储子层3014可以将其从物理网络层采集的包括VM、SW、以及DC相关的网络数据发送至基础模型子层3013，以供其更新单体模型库30131以及拓扑模型库30132。
基于前述实施例，本申请实施例还提供了DTN30的第二结构示意图，图6为本申请实施例提供的DTN30的第三结构示意图，如图6所示，DTN30可以包括处理器601以及存储器602，其中，存储器602中存储有计算机程序，处理器601执行该计算机程序时能实现如前任一实施例所述的DTN的编排方法。
上述处理器601可以为ASIC、DSP、DSPD、PLD、FPGA、CPU、控制器、微控制器、微处理器中的至少一种。
上述存储器602，可以是易失性存储器(volatile memory)，例如随机存取存储器(Random Access Memory，RAM)；或者非易失性存储器(non-volatile memory)，例如只读存储器(Read-Only Memory，ROM)，flash memory，硬盘驱动器(Hard Disk Drive，HDD)或固态硬盘(Solid State Disk，SSD)；或者上述种类的存储器的组合，并向处理器601提供指令和数据。基于前述实施例，本申请实施例还提供了一种计算机可读存储介质，该计算机可读存储介质中存储有计算机程序，该计算机程序被电子设备的处理器执行时，能够实现如前任一实施例所述的编排方法或如前任一实施例所述的解析方法。
在示例性实施例中，还提供了一种包含指令的计算机程序产品，当其在计算机上运行时，使得计算机执行如前任一实施例所述的编排方法。
上文对各个实施例的描述倾向于强调各个实施例之间的不同之处，其相同或相似之处可以互相参考，为了简洁，本文不再赘述。
本申请所提供的各方法实施例中所揭露的方法，在不冲突的情况下可以任意组合，得到新的方法实施例。
本申请所提供的各产品实施例中所揭露的特征，在不冲突的情况下可以任意组合，得到新的产品实施例。
本申请所提供的各方法或设备实施例中所揭露的特征，在不冲突的情况下可以任意组合，得到新的方法实施例或设备实施例。
需要说明的是，上述计算机可读存储介质可以是ROM、可编程只读存储器(Programmable Read-Only Memory，PROM)、可擦除可编程只读存储器(Erasable Programmable Read-Only Memory，EPROM)、电可擦除可编程只读存储器(Electrically Erasable Programmable Read-Only Memory，EEPROM)、磁性随机存取存储器(Ferromagnetic Random Access Memory，FRAM)、快闪存储器(Flash Memory)、磁表面存储器、光盘、或只读光盘(Compact Disc Read-Only Memory，CD-ROM)等存储器；也可以是包括上述存储器之一或任意组合的各种电子设备，如移动电话、计算机、平板设备、个人数字助理等。
需要说明的是，在本文中，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者装置不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者装置所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括该要素的过程、方法、物品或者装置中还存在另外的相同要素。
上述本申请实施例序号仅仅为了描述，不代表实施例的优劣。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到上述实施例方法可借助软件加必需的通用硬件节点的方式来实现，当然也可以通过硬件，但很多情况下前者是更佳的实施方式。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质(如ROM/RAM、磁碟、光盘)中，包括若干指令用以使得一台终端设备(可以是手机，计算机，服务器，空调器，或者网络设备等)执行本申请各个实施例所描述的方法。
本申请是参照根据本申请实施例的方法、设备(系统)、和计算机程序产品的流程图和方框图的至少之一来描述的。应理解可由计算机程序指令实现流程图中的每一流程和方框图中的每一方框的至少之一、流程图中的流程的结合、方框图中的方框的结合、或流程图中的流程与方框图中的方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现流程图的流程指定的功能和方框图的方框指定的功能的至少一项的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现流程图的流程指定的功能和方框图的方框指定的功能的至少一项。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现流程图的流程指定的功能和方框图的方框指定的功能的至少一项。
以上仅为本申请的优选实施例，并非因此限制本申请的专利范围，凡是利用本申请说明书及附图内容所作的等效结构或等效流程变换，或直接或间接运用在其他相关的技术领域，均同理包括在本申请的专利保护范围内。
工业实用性
本申请实施例提供了一种DTN的编排方法、DTN、介质和程序。所述DTN的孪生网络层包括功能模型子层、孪生网络子层以及基础模型子层；所述孪生网络子层在接收到所述功能模型子层发送的第k场景仿真需求信息的情况下，对所述第k场景仿真需求信息进行解析，确定第k解析结果；其中，k为大于或等于1的整数；所述孪生网络子层从所述基础模型子层获取与所述第k解析结果对应的单体模型以及拓扑模型；其中，所述单体模型，包括网元的多元表示模型；所述拓扑模型，包括至少两个所述网元之间的拓扑关系信息；所述孪生网络子层基于与所述第k解析结果对应的拓扑模型，对与所述第k解析结果对应的单体模型进行编排，得到第k编排结果。本申请实施例提供的DTN的编排方法，在DTN的孪生网络子层接收到功能模型子层发送的第k场景仿真需求信息并对其进行解析确定第k解析结果的情况下，能够从DTN的基础模型子层获取与第k解析结果对应的单体模型以及拓扑模型，然后基于与第k解析结果对应的拓扑模型对与第k解析结果对应的单体模型进行编排，得到第k编排结果，从而实现了DTN针对具体的场景仿真需求信息的网元编排。

Claims (17)

  1. 一种数字孪生网络DTN的编排方法，所述DTN的孪生网络层包括功能模型子层、孪生网络子层以及基础模型子层；所述方法包括：
    所述孪生网络子层在接收到所述功能模型子层发送的第k场景仿真需求信息的情况下，对所述第k场景仿真需求信息进行解析，确定第k解析结果；其中，k为大于或等于1的整数；
    所述孪生网络子层从所述基础模型子层获取与所述第k解析结果对应的单体模型以及拓扑模型；其中，所述单体模型包括网元的多元表示模型；所述拓扑模型包括至少两个所述网元之间的拓扑关系信息；
    所述孪生网络子层基于与所述第k解析结果对应的拓扑模型，对与所述第k解析结果对应的单体模型进行编排，得到第k编排结果。
  2. 根据权利要求1所述的方法，其中，所述DTN还包括网络应用层；所述方法还包括：
    所述功能模型子层接收所述网络应用层发送的场景需求信息，对所述场景需求信息进行解析，确定所述第k场景仿真需求信息，并通过第一接口发送所述第k场景仿真需求信息至所述孪生网络子层；所述第一接口为所述孪生网络子层与所述功能模型子层之间的数据传输接口。
  3. 根据权利要求1所述的方法，其中，所述孪生网络子层从所述基础模型子层获取与所述第k解析结果对应的单体模型以及拓扑模型，包括：
    所述孪生网络子层对所述第k解析结果进行处理，得到第k单体配置信息以及第k拓扑配置信息，并通过第二接口发送所述第k单体配置信息以及所述第k拓扑配置信息至所述基础模型子层；所述第二接口为所述基础模型子层与所述孪生网络子层之间的数据传输接口；
    所述基础模型子层通过所述第二接口接收到所述第k单体配置信息以及所述第k拓扑配置信息之后，基于所述第k单体配置信息从单体模型库中搜索与所述第k解析结果对应的单体模型，以及基于所述第k拓扑配置信息从拓扑模型库中搜索与所述第k解析结果对应的拓扑模型；
    所述孪生网络子层通过所述第二接口获取与所述第k解析结果对应的单体模型以及拓扑模型。
  4. 根据权利要求3所述的方法，其中，所述孪生网络层还包括数据采集存储子层；所述基于所述第k单体配置信息从单体模型库中搜索与所述第k解析结果对应的单体模型，以及基于所述第k拓扑配置信息从拓扑模型库中搜索与所述第k解析结果对应的拓扑模型之前，还包括：
    所述基础模型子层从所述数据采集存储子层获取网络数据，对所述网络数据中的网元数据进行多维度建模表征，得到所述单体模型库；
    所述基础模型子层对所述网络数据中的网元关联关系进行分析，得到所述拓扑模型库。
  5. 根据权利要求3至4任一所述的方法，其中，所述拓扑模型库包括网元关联信息以及与所述网元关联信息对应的可视化模型；所述网元关联信息包括所述网元之间的向量关系信息；
    所述单体模型库包括所述网元的N元组信息以及与所述N元组信息对应的可视化模型；所述N为大于或等于2的整数。
  6. 根据权利要求1所述的方法，其中，所述方法还包括：
    所述孪生网络子层对所述第k编排结果进行仿真验证，得到第k验证结果；
    在所述第k验证结果与目标验证结果不匹配的情况下，所述孪生网络子层发送所述第k验证结果至所述功能模型子层；
    在所述第k验证结果与所述目标验证结果匹配的情况下，所述孪生网络子层确定所述第k编排结果为最终编排结果。
  7. 根据权利要求6所述的方法，其中，所述孪生网络子层发送所述第k验证结果至所述功能模型子层之后，还包括：
    所述功能模型子层基于所述第k验证结果，确定第k+1场景仿真需求信息，并发送所述第k+1场景仿真需求信息至所述孪生网络子层。
  8. 一种数字孪生网络DTN，所述DTN的孪生网络层包括功能模型子层、孪生网络子层以及基础模型子层；其中：
    所述功能模型子层，配置为确定第k场景仿真需求信息，并发送所述第k场景仿真需求信息至所述孪生网络子层；
    所述孪生网络子层，配置为在接收到所述第k场景仿真需求信息的情况下，对所述第k场景仿真需求信息进行解析，确定第k解析结果；其中，k为大于或等于1的整数；
    所述孪生网络子层，还配置为从所述基础模型子层获取与所述第k解析结果对应的单体模型以及拓扑模型，并基于与所述第k解析结果对应的拓扑模型，对与所述第k解析结果对应的单体模型进行编排，得到第k编排结果；其中，所述单体模型，包括网元的多元表示模型；所述拓扑模型，包括至少两个所述网元之间的拓扑关系信息。
  9. 根据权利要求8所述的DTN，其中，所述DTN还包括网络应用层；所述功能模型子层配置为在接收网络应用层发送的场景需求信息的情况下，对场景需求信息进行解析，确定第k场景仿真需求信息，通过第一接口发送第k场景仿真需求信息至孪生网络子层；所述第一接口，为孪生网络子层与功能模型子层之间的数据传输接口。
  10. 根据权利要求8所述的DTN，其中，所述孪生网络子层，配置为对第k解析结果进行处理，得到第k单体配置信息以及第k拓扑配置信息，并通过第二接口发送所述第k单体配置信息以及所述第k拓扑配置信息至所述基础模型子层；所述第二接口为所述基础模型子层与所述孪生网络子层之间的数据传输接口；
    所述基础模型子层，配置为通过所述第二接口接收到所述第k单体配置信息以及所述第k拓扑配置信息之后，基于所述第k单体配置信息从单体模型库中搜索与所述第k解析结果对应的单体模型，以及基于所述第k拓扑配置信息从拓扑模型库中搜索与所述第k解析结果对应的拓扑模型；
    所述孪生网络子层，还配置为通过所述第二接口获取与所述第k解析结果对应的单体模型以及拓扑模型。
  11. 根据权利要求10所述的DTN，其中，所述孪生网络层还包括数据采集存储子层；所述基础模型子层，配置为从所述数据采集存储子层获取网络数据，对所述网络数据中的网元数据进行多维度建模表征，得到所述单体模型库；所述基础模型子层，还配置为对所述网络数据中的网元关联关系进行分析，得到所述拓扑模型库。
  12. 根据权利要求10或11所述的DTN，其中，所述拓扑模型库包括网元关联信息以及与所述网元关联信息对应的可视化模型；所述网元关联信息包括所述网元之间的向量关系信息；所述单体模型库包括所述网元的N元组信息以及与所述N元组信息对应的可视化模型；所述N为大于或等于2的整数。
  13. 根据权利要求8所述的DTN，其中，所述孪生网络子层，配置为对所述第k编排结果进行仿真验证，得到第k验证结果；在所述第k验证结果与目标验证结果不匹配的情况下，发送所述第k验证结果至所述功能模型子层；在所述第k验证结果与所述目 标验证结果匹配的情况下，确定所述第k编排结果为最终编排结果。
  14. 根据权利要求13所述的DTN，其中，所述功能模型子层，配置为基于所述第k验证结果，确定第k+1场景仿真需求信息，并发送所述第k+1场景仿真需求信息至所述孪生网络子层。
  15. 一种数字孪生网络DTN，包括处理器和存储器；其中，所述存储器中存储有计算机程序，所述计算机程序被所述处理器执行时，能实现如权利要求1至7任一所述的编排方法。
  16. 一种计算机可读存储介质，其中存储有计算机程序，所述计算机程序被电子设备的处理器执行时，能够实现如权利要求1至7任一所述的编排方法。
  17. 一种计算机程序，包括计算机可读代码，当所述计算机可读代码在电子设备中运行时，所述电子设备中的处理器执行用于实现权利要求1至7任一所述的编排方法。
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