WO2022199089A1 - 网络管控方法及其系统、网络系统、存储介质 - Google Patents

网络管控方法及其系统、网络系统、存储介质 Download PDF

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Publication number
WO2022199089A1
WO2022199089A1 PCT/CN2021/132834 CN2021132834W WO2022199089A1 WO 2022199089 A1 WO2022199089 A1 WO 2022199089A1 CN 2021132834 W CN2021132834 W CN 2021132834W WO 2022199089 A1 WO2022199089 A1 WO 2022199089A1
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Prior art keywords
network
case
management
control
network management
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English (en)
French (fr)
Inventor
王大江
王其磊
王振宇
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ZTE Corp
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ZTE Corp
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Priority to JP2023558761A priority patent/JP7645396B2/ja
Priority to US18/552,332 priority patent/US12224901B2/en
Publication of WO2022199089A1 publication Critical patent/WO2022199089A1/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
    • H04L41/08—Configuration management of networks or network elements
    • H04L41/0803—Configuration setting
    • H04L41/0806—Configuration setting for initial configuration or provisioning, e.g. plug-and-play
    • 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
    • H04L41/142—Network analysis or design using statistical or mathematical methods
    • 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
    • H04L41/08—Configuration management of networks or network elements
    • H04L41/0803—Configuration setting
    • H04L41/0813—Configuration setting characterised by the conditions triggering a change of settings
    • H04L41/082—Configuration setting characterised by the conditions triggering a change of settings the condition being updates or upgrades of network functionality
    • 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
    • H04L41/08—Configuration management of networks or network elements
    • H04L41/0803—Configuration setting
    • H04L41/0823—Configuration setting characterised by the purposes of a change of settings, e.g. optimising configuration for enhancing reliability
    • 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
    • H04L41/122—Discovery or management of network topologies of virtualised topologies, e.g. software-defined networks [SDN] or network function virtualisation [NFV]
    • 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
    • H04L41/08—Configuration management of networks or network elements
    • H04L41/085—Retrieval of network configuration; Tracking network configuration history
    • H04L41/0853—Retrieval of network configuration; Tracking network configuration history by actively collecting configuration information or by backing up configuration information
    • 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
    • H04L41/16—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
    • 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
    • H04L41/40—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using virtualisation of network functions or resources, e.g. SDN or NFV entities

Definitions

  • the present application relates to, but is not limited to, the field of network communications, and in particular, relates to a network management and control method and system, network system, and storage medium.
  • the complex network system is abstracted from the digital model, and a multi-dimensional network digital simulation system that can integrate the actual network system, operation mechanism and management, control and maintenance methods is constructed, and with the help of artificial intelligence (Artificial Intelligence, AI) technology, completes the actual physical network operation status. real-time dynamic feedback, evaluation and optimization, simulation and prediction, thus building a digital analysis foundation for efficient and intelligent network management.
  • AI Artificial Intelligence
  • Digital Twin (DT) technology can create high-fidelity digital virtual models of physical objects, simulate the behavior of physical objects, describe the running state of physical objects, and realize the fusion of digital information and physical objects.
  • Applying DT technology to network communication can extract the required network data from the transmission network, and build a Digital Twin case (DT case) model in combination with AI technology and requirements to realize the analysis, optimization and simulation of the transmission network forecasting, etc.
  • DT case Digital Twin case
  • the embodiments of the present application provide a network management and control method, system, network system, and storage medium, which can improve the dynamic detection capability and digital analysis capability of the network system.
  • an embodiment of the present application provides a network management and control method, which is applied to a network management and control system.
  • the network management and control method includes:
  • an embodiment of the present application provides a network management and control method, which is applied to a data acquisition device, where the data acquisition device is communicatively connected to a network management and control system, and the network management and control method includes:
  • the network completes network management and control according to the network configuration information, wherein the network configuration information is generated by the network management and control system according to the target analysis result, and the target analysis result is generated by the network management and control system according to the collaborative relationship and all DT cases with different levels.
  • the described DT cases with different levels are generated according to the DT model data by the network management and control system, the level of the DT case is corresponding to the level of the entity object, and the described DT cases with different levels have functional differences. collaborative relationship.
  • an embodiment of the present application provides a network management and control system, including: a memory, a processor, and a computer program stored in the memory and running on the processor, where the processor implements the following when executing the computer program.
  • an embodiment of the present application provides a network system, the network system includes a network management and control system and a data acquisition device, the network management and control system is communicatively connected to the data acquisition device, and the network management and control system is used to execute According to the network management and control method according to the first aspect, the data acquisition device is configured to execute the network management and control method according to the second aspect.
  • FIG. 1 is a flowchart of a network management and control method applied to a network management and control system provided by an embodiment of the present application
  • Fig. 2 is a flow chart of acquiring DT model data provided by another embodiment of the present application.
  • Fig. 3 is the flow chart of generating DT case provided by another embodiment of the present application.
  • Fig. 4 is the flow chart of obtaining target analysis result provided by another embodiment of the present application.
  • FIG. 5 is a flowchart of processing based on an AI algorithm provided by another embodiment of the present application.
  • FIG. 6 is a flowchart of determining a management and control scenario provided by another embodiment of the present application.
  • Fig. 7 is the flow chart of realizing DT model data synchronization update provided by another embodiment of the present application.
  • FIG. 8 is a flowchart of acquiring and displaying network status information provided by another embodiment of the present application.
  • FIG. 9 is a flowchart of a network management and control method applied to a data acquisition device provided by another embodiment of the present application.
  • FIG. 11 is a flowchart of updating DT model data provided by another embodiment of the present application.
  • FIG. 12 is a structure of an OTN system applying a network management and control method provided by another embodiment of the present application
  • FIG. 13 is a flowchart of Example 1 provided by another embodiment of the present application.
  • FIG. 14 is a flowchart of Example 2 provided by another embodiment of the present application.
  • FIG. 15 is an apparatus diagram of a network management and control system provided by another embodiment of the present application.
  • the present application provides a network management and control method, a system, a network system, and a storage medium.
  • the network management and control method includes: acquiring DT model data corresponding to an entity object in a physical network; A digital twin instance DT case, wherein the level of the DT case corresponds to the level of the entity object, and the DT cases with different levels have a functional synergistic relationship; the target analysis results are obtained according to the synergistic relationship and all DT cases; according to The target analysis result generates network configuration information, and delivers the network configuration information to the physical network, so that the physical network can complete network management and control according to the network configuration information.
  • network management and control can be realized according to the DT case and the collaborative relationship, and the dynamic detection capability and digital analysis capability of the network system can be improved.
  • the network management and control methods provided in the embodiments of the present application can be applied to any network system, such as an optical network (Optical Transport Network, OTN), a packet transport network (Packet Transport Network, PTN), a packet optical transport network (Packet Optical Transport Network, POTN), for the sake of simplicity, the embodiments of the present application take the application to the OTN system as an example to explain the technical solutions. Those skilled in the art have the ability to apply the technical solutions of the embodiments of the present application to other network systems. This does not limit the protection scope of the present application.
  • OTN optical Transport Network
  • PTN Packet Transport Network
  • POTN packet optical transport network
  • the network management and control system can be a device provided with multiple functional modules.
  • functional modules such as databases, processors, and executors can be set in the network management and control system according to actual needs.
  • the specific device type It can be determined according to the specific network system.
  • the network management and control system can be the HDTON intelligent management and control system used to control HDTON; at the same time, in order to realize the generation of DT case, it can be In the HDTON intelligent management and control system, the HDTON case orchestrator is set as the controller, and the Software Defined Optical Network (SDON) system is set as the executor; at the same time, in order to target different management and control scenarios, the settings are stored with multiple presets A good AI algorithm engine library for AI algorithms.
  • SDON Software Defined Optical Network
  • the above-mentioned SDON system, HDTON case orchestrator and AI algorithm engine library can also be independent devices in actual network deployment, and can be set up in different physical devices or physical equipment, functionally combined into a network management and control system That's it. It should be noted that the above device is only an example for explaining the network management and control system, and those skilled in the art have the motivation to add or reduce corresponding functional modules in the network management and control system according to actual needs, which is not limited in this embodiment. .
  • FIG. 1 is a network management and control method provided by an embodiment of the present application, which is applied to a network management and control system.
  • the network management and control method includes but is not limited to the following steps:
  • Step S110 acquiring DT model data corresponding to the entity object in the physical network.
  • the physical network can be any network system.
  • the physical objects of multiple levels in the physical network have subordinate relationships, and there is a functional coordination relationship.
  • the ODU service of the entire network is sufficient. It is composed of multiple Optical Data Units (ODUs).
  • ODUs Optical Data Units
  • the prediction results of the traffic throughput of ODU nodes have a certain impact on the optimization and simulation of ODU services in the entire network.
  • the traffic throughput prediction of each ODU node needs to be integrated, and this embodiment does not limit the physical network to which the network management method is applied.
  • an entity object can be a specific physical device, or a system composed of multiple physical devices.
  • the entity object can be the entire OTN, an optical module in the OTN, or It is each ODU node in the OTN, and the entity object can be determined according to the specific requirements.
  • the specific content of the DT model data can be determined according to the actual needs of the entity object. For example, it is necessary to analyze the fault of the OTN network optical module.
  • the obtained DT model data can include the OTN network topology, the entire network in the OTN network topology.
  • the specific types of DT model data are not limited.
  • the DT model data can be obtained from the physical network by the network management and control system according to specific data requirements. Based on the digital twin technology, the corresponding parameters and attributes can be extracted from the physical network to construct a high-fidelity digital virtual model. , so as to form DT model data, so that the DT model data can reflect the real operation status of the physical network in real time, and improve the accuracy of the evaluation and prediction of the network system.
  • Step S120 generating digital twin instances DT cases with different levels according to the DT model data, wherein the levels of the DT cases correspond to the levels of the entity objects, and there is a functional synergy between the DT cases with different levels.
  • the level can represent the scale or influence scope of the entity objects in the physical network corresponding to the DT case.
  • the entity object with the largest level can be the entire OTN network, and the entity object with the smallest level can be the OTN network.
  • the corresponding relationship between the size of the specific entity object and the size of the hierarchy can be adjusted according to the actual needs, which is not limited.
  • the level of the generated DT case corresponds to the level of the entity object.
  • the entity object is an OTN network and its level is the network level
  • the level of the DT case corresponding to the OTN network generated according to the DT model data is also for the network level.
  • entities of different scales usually have subordinate relationships, that is, the entity objects corresponding to the DT case with a larger hierarchy are usually assembled or assembled by the entity objects corresponding to the DT case with a smaller hierarchy.
  • the entity object corresponding to a DT case with a larger level is a ROADM node
  • the entity object corresponding to a DT case with a smaller level is an optical module
  • the ROADM node is usually composed of optical modules. Therefore, the entity objects with affiliation also have a cooperative relationship in function.
  • the DT case can be generated after obtaining the DT model data to ensure that there is a corresponding DT case in the process of collaborative analysis, which will not be repeated in the future.
  • step S130 the target analysis result is obtained according to the collaborative relationship and all DT cases.
  • each DT case can first perform a predictive analysis, and then implement the nested analysis according to the collaborative relationship to obtain the target analysis result, or it can be a larger level.
  • the DT case will conduct comprehensive analysis after receiving the analysis results reported by the DT case with the smaller level, and use the analysis result obtained by the DT case with the largest level as the target analysis result, that is, to achieve collaborative analysis through the trigger mechanism, and the specific method will be based on actual needs. If selected, the collaborative analysis between DT cases can be realized.
  • the DT cases corresponding to the entity objects with affiliation usually have a collaborative relationship.
  • the optical module failure in the existing network is often the OTN network failure alarm and service caused by the optical module failure.
  • the root cause of the interruption is fault, and the synergy and spillover relationship between the part and the whole in the actual network will also be reflected in the digital twin OTN (Digital Twin OTN, DTON). Therefore, network-level fault analysis DT case and optical module-level fault prediction There is a synergistic relationship between DT cases.
  • Step S140 Generate network configuration information according to the target analysis result, and deliver the network configuration information to the physical network, so that the physical network can complete network management and control according to the network configuration information.
  • a physical network is usually a collection of physical devices, and needs to be managed and controlled through a common intelligent management and control system, such as a common SDON system to control the physical network.
  • a common intelligent management and control system such as a common SDON system to control the physical network. This embodiment does not limit the specific intelligent management and control system.
  • the network configuration information can be any type of information, which can enable the physical network to realize network management and control by adjusting operating parameters.
  • the target analysis result is the analysis of ODU service cutover and rerouting optimization of the entire network.
  • the SDON system can generate corresponding network configuration information according to processing requirements such as rerouting optimization of related services and node capacity expansion on the current OTN network.
  • step S110 in the embodiment shown in FIG. 1 further includes but is not limited to the following steps:
  • Step S210 determining a management and control scenario, and determining data requirements according to the management and control scenario
  • Step S220 Acquire DT model data corresponding to the entity object in the physical network according to the data requirement.
  • management and control scenarios may be scenarios that need to reflect network management and control requirements, such as network service scenarios reflecting user business requirements or network analysis scenarios reflecting physical network detection and analysis requirements, which are not limited in this embodiment.
  • the entity objects that need to collect data can be determined through the management and control scenario, thereby determining the specific data requirements and improving the efficiency of data processing.
  • the management and control scenario is determined to be an OTN network traffic analysis scenario, it needs to be obtained from the physical network.
  • the DT model data of the OTN network traffic may include the OTN network topology, the network-wide ODU service distribution on the network topology, and the traffic throughput of each node. After the data requirements are determined, the level of the DT case that needs to be built in the future is further determined.
  • a network-level DT case needs to be constructed to correspond to the ODU service of the whole network, and a node-level DT case needs to be constructed.
  • the DT case corresponds to each ODU node, and the above-mentioned network level and node level are one of the examples of the level, which does not limit the specific selection of the level.
  • the form of Hierarchical Digital Twin (HDT) model data can be used.
  • the data level is determined in advance. For example, in the above OTN network traffic analysis scenario, the OTN network topology and the network-wide ODU service distribution on the network topology are determined as network-level data, and the traffic throughput of each node is node-level data. data, so that the ownership of DT case containers of different data can be quickly determined from the HDT model data.
  • step S120 in the embodiment shown in FIG. 1 further includes but is not limited to the following steps:
  • Step S310 according to the level of DT model data and entity objects, generate DT case in target DT case containers with different container levels, wherein, the target DT case container is from the preset DT case container according to the level of the control scene and the entity object. It is determined in the DT case container that the DT case container is used to generate and manage the DT case.
  • the levels corresponding to the DT cases generated by the DT case containers of different container levels are different from each other.
  • the HDTON case orchestrator can be set up in the intelligent management and control system, and at least two DT case containers are preset in the HDTON case orchestrator.
  • the orchestrator determines the data requirements, and after obtaining the DT model data, determines at least two target DT case containers from the preset DT case containers, and generates DT cases from the target DT case containers.
  • the specific container level can be determined according to actual needs. For example, according to the network level, device level and device level of the hierarchy, the advanced DT case container, the intermediate DT case container and the low-level DT case container are set respectively. Corresponding to one level, it is sufficient that DT cases generated by DT case containers of different levels correspond to different levels, which is not limited in this embodiment.
  • the data requirements can also be determined by the determined target DT case container.
  • the DT case container level when it is determined according to the management and control scenario, it needs to be determined from the network level and the device level. For analysis, you need to generate the corresponding DT case through the high-level DT case container and the low-level DT case container. At this time, you can generate data requirements according to the data required by each target DT case container. After obtaining the DT model data, each The target DT case container obtains the data of the corresponding level from the DT model data, and generates the DT case according to the obtained DT model data, which can ensure the availability and accuracy of the obtained data.
  • the DT case container can be used to generate DT cases, and can also be used to manage DT cases, such as analyzing and processing DT cases through AI algorithms, or obtaining intermediate analysis reported by DT case containers with the next container level
  • DT cases such as analyzing and processing DT cases through AI algorithms, or obtaining intermediate analysis reported by DT case containers with the next container level
  • the results are collaboratively analyzed, and the functions that can be implemented for the DT case can also be increased or decreased according to actual needs, which is not limited in this embodiment.
  • the target DT case containers that need to be used are not necessarily different. For example, some scenarios need to use all the preset DT case containers, while other scenarios only need to use Several of the container-level DT case containers are determined according to the actual needs of the management and control scenarios, and this embodiment does not limit the specific number too much.
  • step S130 in the embodiment shown in FIG. 1 further includes but is not limited to the following steps:
  • Step S410 according to the synergistic relationship and the container level corresponding to the target DT case container, perform AI algorithm-based processing on the DT case generated by the target DT case container with the next-level container level to obtain an intermediate analysis result, and combine the intermediate analysis result.
  • Step S420 the collaborative analysis result obtained by the target DT case container with the largest container level is determined as the target analysis result.
  • the container level can be Corresponding to the level; and, the analysis result obtained by the DT case with the smaller level is usually used for the collaborative processing to obtain the analysis result of the DT case with the larger level.
  • the analysis result is reported.
  • the target DT case container with the upper container level receives the intermediate analysis result
  • the DT case generated by the DT case container is processed based on the AI algorithm based on the analysis result, so that the obtained collaborative analysis result is obtained.
  • step S420 having an upper-level container level and having a lower-level container level described in step S420 are determined according to the collaborative relationship, not according to the container level. For example, for some control scenarios, only the lower level is involved.
  • the DT case container and the high-level DT case container correspond to step S420, the target DT case container with the upper-level container level is the high-level DT case container, and the target DT case container with the upper-level container level is the low-level DT case container case container; for another example, for some control scenarios involving low-level DT case containers, medium-level DT case containers, and high-level DT case containers, the target DT case container for AI algorithm-based processing of DT cases for the first time is low-level DT case container, that is, the target DT case container with the lower container level in the first operation, then the corresponding target DT case container with the upper container level is the middle level DT case container.
  • the middle-level DT case container is the target DT case container with the lower-level container level
  • the high-level DT case container is the target DT case container with the upper-level container level.
  • the collaborative analysis results include not only the results obtained by processing the corresponding DT case based on the AI algorithm, but also all the intermediate analysis results of the next level, so that the collaborative analysis results can have more abundant data.
  • the target analysis result can be the collaborative result obtained by the most advanced target DT case container processing the DT case based on the AI algorithm, therefore, after the target analysis result is obtained, the highest level DT case container can The results of the target analysis inform the SDON system so that it can generate network configuration information to ensure that the generated network configuration information can synergistically consider DT cases at all levels.
  • a DT case container may have multiple DT case containers with the next container level.
  • a DT case container may receive multiple intermediate analysis results at the same time.
  • the DT case container with the upper-level container level can be in the receiving state within a certain period of time by setting the time threshold. After the period of time, the receiving of the intermediate analysis results is stopped and the DT case Based on AI algorithm processing, and cooperate to receive all intermediate processing results within this time period, other methods can also be used.
  • the results are coordinated, which is not limited in this embodiment, and a specific judgment method may be selected according to actual needs.
  • step S420 in the embodiment shown in FIG. 4 further includes but is not limited to the following steps:
  • Step S510 determine the target AI algorithm from a preset AI algorithm library
  • Step S520 analyze and process the DT case according to the target AI algorithm.
  • the AI algorithm library can be in the form of a database, which can match the corresponding target AI algorithm through the management and control scenarios. It is understandable that the number of AI algorithms in the AI algorithm library can be arbitrary, and can be increased or decreased according to actual needs.
  • the AI algorithm library can include any type of AI algorithm, such as reinforcement learning (Reinforcement Learning, RL) algorithm, convolutional neural network (Convolution Neural Network, CNN), deep neural network (Deep Neural Network, DNN) , Graph Convolution Network (GCN) or Recurrent Neural Network (RNN), those skilled in the art are motivated to increase or decrease the types of AI algorithms according to actual needs.
  • reinforcement learning Reinforcement Learning
  • RL reinforcement learning
  • Convolution Neural Network CNN
  • DNN deep neural network
  • GCN Graph Convolution Network
  • RNN Recurrent Neural Network
  • the required operations are different. Therefore, setting multiple AI algorithms in the AI algorithm library and determining the specific target AI algorithm through the control scenario can ensure that the best AI algorithm is used.
  • the algorithm completes the corresponding DT case processing.
  • the corresponding relationship between the management and control scenarios and the target AI algorithm can be preset. For example, for the end-to-end OTN premium private line service requirements based on intent-based delay optimization, the OTN topology scale is determined according to the management and control scenarios, and the matching analysis results are obtained.
  • the algorithm with the best computing effect is the Reinforcement Learning (RL) algorithm.
  • the determination of the target AI algorithm can be determined once when each DT case container processes the DT case based on the AI algorithm, so as to ensure that each DT case container can use the most suitable AI algorithm for the DT case.
  • a target AI algorithm can also be determined under the same collaborative relationship, and each DT case container under the collaborative relationship uses the same target AI algorithm to process the DT case based on the AI algorithm. The method can be selected according to actual needs.
  • step S210 in the embodiment shown in FIG. 2 further includes but is not limited to the following steps:
  • Step S610 acquiring the current operating state of the physical network, and determining a management and control scenario according to the operating state and preset management information;
  • step S620 the management and control requirements are obtained, and the management and control scenarios are determined according to the management and control requirements.
  • the management and control scenarios may include network analysis scenarios and network service scenarios.
  • the network analysis scenario is determined by the network analysis requirements.
  • the network analysis requirements may be the user's requirements for fault prediction, operation status analysis or query of the current physical network, which may be generated by the user sending the requirement information to the network management and control system through the client;
  • the network management and control system may also be generated by itself according to the management objectives of physical network self-inspection, self-optimization, and self-healing, which is not limited in this embodiment.
  • the network service scenario can be generated based on the user's service requirements.
  • an OTN intelligent network management and control application Application, APP
  • the service requirements can be Bandwidth On Demand (BOD), Multi-Layer Optimization (MLO), Service-Level Agreement (SLA), Optical Virtual Private Network (Optical) Virtual Private Network, OVPN), Intent-Based Optical Network (IBON), etc.
  • BOD Bandwidth On Demand
  • MLO Multi-Layer Optimization
  • SLA Service-Level Agreement
  • Optical Virtual Private Network Optical Virtual Private Network
  • OVPN Optical Virtual Private Network
  • IBON Intent-Based Optical Network
  • the type of specific service requirements can be determined according to the actual network resources, so I won't go into details here.
  • step S140 in the embodiment shown in FIG. 1 is executed, the following steps are further included but not limited to:
  • Step S710 when the new DT model data reported by the physical network after completing the network management and control according to the network configuration information is obtained, the network configuration information is regenerated according to the new DT model data.
  • the related parameters of the physical network entity objects will change, that is, the related parameters in the DT model data will also change.
  • the DT model data can be updated according to the data of the new physical network, so that the next DT case generation can be based on the latest DT model data.
  • the entire network management and control can also form a closed loop.
  • the DT case is continuously updated and the target analysis results are determined, which effectively improves the dynamic detection efficiency. ability.
  • step S140 in the embodiment shown in FIG. 1 is performed, the following steps are included but not limited to:
  • Step S810 acquiring and displaying the network status information of the physical network after the network management and control is completed.
  • the acquired network status information can be displayed through the OTN intelligent network management and control APP described in the embodiment in FIG.
  • the running status of the physical network is sufficient.
  • the network status information may be related to the physical network.
  • the network status information may include real-time OTN network topology, OTN network service running status, network performance status, and no network resource usage status.
  • the specific display content can be selected according to actual needs.
  • the network status information may be acquired and displayed in real time, or may be displayed on the OTN intelligent network management and control APP according to the user's requirements, which is not limited in this embodiment.
  • FIG. 9 is a network management and control method provided by an embodiment of the present application, which is applied to a data acquisition device, and the data acquisition device is communicatively connected to a network management and control system.
  • the network management and control method includes but is not limited to the following steps:
  • Step S910 generating DT model data corresponding to the entity objects in the physical network.
  • SDON can realize the management and control of the physical network, such as the dynamic management and control of end-to-end OTN services, network topology management, and OTN service fault protection in the OTN system.
  • the DT model data is obtained from the physical network. Therefore, the data obtaining device in the embodiment of the present application may be a device for obtaining data from the physical network and generating DT model data.
  • the specific entity of the device may be arbitrary. This is not limited, as long as the corresponding functions can be implemented.
  • the data acquisition device can acquire the DT model data from the physical OTN network according to the requirements of the HDTON case orchestrator, and report the acquired DT model data to the HDTON case orchestrator, so that each DT case container can be based on the DT Model data generates DT case.
  • Step S920 sending the DT model data to the network management and control system, so that the network management and control system generates network configuration information according to the DT model data, and sends the network configuration information to the physical network, so that the physical network completes network management and control according to the network configuration information
  • the network configuration information is generated by the network management and control system according to the target analysis results
  • the target analysis results are obtained by the network management and control system according to the collaborative relationship and all DT cases with different levels
  • the DT cases with different levels are generated by the network management and control system according to the DT model data.
  • the level of DT cases corresponds to the level of entity objects
  • DT cases with different levels have functional synergies.
  • the method and principle for generating DT case and network configuration information by the network management system can refer to the description of the embodiment shown in FIG. 1 .
  • the specific interpretation of the hierarchy and the synergistic relationship can also refer to the implementation shown in FIG. 1 .
  • the description of the example is not repeated here for the sake of brevity.
  • step S910 in the embodiment shown in FIG. 9 further includes but is not limited to the following steps:
  • Step S1010 acquiring data requirements issued by the network management and control system, and the data requirements are determined by the network management and control system according to the determined management and control scenarios;
  • Step S1020 Generate DT model data corresponding to the entity objects in the physical network according to the data requirements.
  • the data requirements can be sent to the data acquisition device by the HDTON case organizer of the network management and control system.
  • the required parameters and attributes are obtained from the physical model, and the DT model data is extracted from the obtained parameters and attributes to ensure that the DT model data is consistent with the parameters of the actual physical network entity object.
  • step S1020 in the embodiment shown in FIG. 10 is performed, the following steps are included but not limited to:
  • Step S1110 in the life cycle of the DT model data, when it is detected that the physical parameters and/or physical attributes of the entity object change, update the DT model data according to the changed entity object;
  • Step S1120 Synchronize the updated DT model data to the network management and control system, so that the network management and control system obtains network configuration information according to the updated DT model data.
  • the life cycle of DT model data can start from the determination of the management and control scenarios until the completion of network management and control of the physical network, or it can be a set running time.
  • the specific form is selected according to actual needs, which can ensure that the life cycle Keep the physical model corresponding to the DT model data consistent.
  • maintaining the real-time synchronization of DT model data during the life cycle of DT model data can ensure that the acquired DT model data can reflect the real-time parameters of the physical objects of the current physical network, so that the obtained target analysis results can reflect The actual network situation ensures the dynamic detection capability of the network.
  • the OTN system shown in FIG. 12 includes the OTN transmission plane and the HDTON intelligent management and control system.
  • an OTN intelligent network management and control APP is also provided to communicate with the HDTON intelligent management and control system.
  • the HDTON intelligent management and control system is equipped with SDON system, HDTON case orchestrator and AI algorithm engine library, high-level DT case container, medium-level DT case container and low-level DT case container are preset in HDTON case orchestrator, AI algorithm
  • the engine library includes application scenario analysis adapters and several pre-trained AI algorithms;
  • the OTN transmission plane includes the physical network and the DT model, the physical network includes several physical devices, and the DT model contains digital models corresponding to the physical devices.
  • the above-mentioned components may be physical devices or functional modules with corresponding functions.
  • This embodiment does not limit the specific implementation manner, and the selection of the above-mentioned devices is an example selected for the convenience of description. It does not limit the technical solution of the present application.
  • Example 1 OTN network traffic analysis application scenario, referring to Figure 13, in this scenario, the network management and control method includes but is not limited to the following steps:
  • the HDTON case orchestrator obtains the OTN network traffic DT model data from the OTN transmission plane according to the OTN network traffic analysis requirements, wherein the OTN network traffic DT model data includes the OTN network topology, the network-wide ODU service distribution on the OTN network topology, Traffic throughput of each ODU node;
  • Step S1320 after obtaining the DT model data of the OTN network traffic, the HDTON case orchestrator DT converts the ODU services on the OTN topology of the entire network in the high-level DT case container to generate a network-level ODU service DT case; in the middle-level DT case The traffic throughput of each ODU node in the container is DTized, and the traffic throughput DT case of each ODU node is created separately;
  • Step S1330 the medium-level DT case container analyzes the application scenario according to the OTN network traffic, obtains the AI algorithm from the AI algorithm engine library through the scenario analysis adapter, and constructs and trains the ODU node traffic throughput prediction model for each ODU node traffic throughput DT case.
  • the ODU node traffic throughput DT case can obtain the traffic throughput prediction result of the ODU node within the specified prediction period according to its own ODU node traffic throughput prediction model, and the traffic throughput increase is close to the ODU node's switching capacity.
  • DT case and get the expansion analysis results;
  • Step S1340 the medium-level DT case container reports the traffic throughput prediction result of each ODU node traffic throughput DT case to the corresponding network-level ODU service DT case in the high-level DT case container, and the network-level ODU service DT case is based on the network-level ODU service DT case.
  • the DT case of the switching capacity of each ODU node predicts the traffic throughput of the ODU node and the capacity expansion analysis result, and performs cutover rerouting optimization simulation for the ODU service carried by the ODU node that needs to be expanded.
  • the optimization simulation includes the following steps: the high-level DT case container analyzes the application scenario according to the OTN network traffic, obtains the AI algorithm from the AI algorithm engine library through the scenario analysis adapter, and obtains the ODU to be optimized for cutover and rerouting in the DT case of the network-level ODU service. For services, perform the optimization calculation of a single ODU service according to the optimization strategy of each ODU service, or perform concurrent optimization calculation of multiple ODU services, and obtain the optimized simulation effect of network-wide ODU service distribution, which is recorded in the DT case of the network-level ODU service;
  • the high-level DT case container uses the HDTON case orchestrator to optimize the analysis results of the cutover and rerouting of the network-wide ODU service performed by the network-level ODU service DT case in step S1340, and all recorded ODUs that need to be expanded and analyzed.
  • the node information is notified to the SDON system;
  • Step S1360 the SDON system performs related service rerouting optimization and node expansion processing on the current OTN physical network of the OTN transport plane according to the cutover rerouting optimization analysis result obtained in step S1350 and all the recorded ODU node information that needs to be expanded;
  • Step S1370 after the management and control of the OTN physical network is completed, update the OTN network traffic DT model data, and perform step S1310 again.
  • the traffic throughput may include the add/drop traffic and the pass-through traffic of the ODU node, which can be selected according to actual requirements.
  • the traffic throughput prediction result can be the traffic value in any period, such as the average and peak traffic throughput of the next 15 days, or the average and peak traffic throughput of the next month. This example is not limited.
  • the cutover rerouting optimization analysis result obtained in step S1340 may be the best time window for rerouting optimization. In this time window, service re-optimization deployment is performed, and the operation of the OTN physical network of the OTN transmission plane is implemented. minimal impact.
  • Example 2 OTN network optical module fault prediction application scenario, in this scenario, the network management and control methods include but are not limited to the following steps:
  • Step S1410 the HDTON case arranger obtains the DT model data of the OTN network optical module fault analysis from the OTN transmission plane according to the OTN network optical module fault analysis requirements, wherein the OTN network optical module fault analysis DT model data includes the OTN network topology and the OTN network topology.
  • Step S1420 after obtaining the DT model data for the failure analysis of the OTN network optical module, the HDTON case orchestrator DT converts the OCH optical layer services on the OTN topology of the entire network in the high-level DT case container, and generates a network-level OCH optical layer service DT. case; DTize the optical module data on each ROADM node in the low-level DT case container, and create and generate the fault prediction DT case for each optical module on each ROADM node respectively;
  • Step S1430 the low-level DT case container obtains the AI algorithm from the AI algorithm engine library through the scene analysis adapter according to the application scenario of the OTN network optical module failure prediction, and constructs the training result for the failure prediction DT case of each optical module on each ROADM node.
  • the failure prediction model of the module in which each optical module failure prediction DT case can predict the time window of the module failure according to its own optical module failure prediction model, and obtain the replacement of the predicted failure optical module. Analysis results;
  • Step S1440 the low-level DT case container reports the replacement analysis result of the optical module predicted to fail to the corresponding network-level OCH optical layer service DT case in the high-level DT case container, and the network-level OCH optical layer service DT case is based on each optical module. According to the conclusion of fault prediction and analysis, the optimization simulation of cutover and rerouting is performed on the OCH optical layer services carried by the optical modules on the ROADM nodes that need to be replaced.
  • the optimization simulation of cutover and rerouting includes the following steps: high-level DT case
  • the AI algorithm is obtained from the AI algorithm engine library through the scenario analysis adapter, and the OCH optical module to be cut and re-routed optimized due to optical module fault prediction in the network-level OCH optical layer service DT case is predicted.
  • Layer services according to the optimization strategy of each OCH optical layer service, perform the optimization calculation of a single OCH optical layer service, or optimize the calculation of multiple OCH optical layer services concurrently, and obtain the optimized simulation effect of the network-wide OCH optical layer service distribution, which is recorded in In the DT case of the network-level OCH optical layer service;
  • step S1450 the network-wide OCH optical layer service performed by the network-level OCH optical layer service DT case in step S1440 is cut and re-routed by the high-level DT case container through the HDTON case orchestrator, and the results of the optimization analysis and all the records that need to be done The information of the optical module of the ROADM node replaced by the fault prediction is notified to the SDON system;
  • step S1460 the SDON system performs an analysis on the current OTN physical network of the OTN transmission plane according to the optimization analysis result of the OCH optical layer service cutover and rerouting of the entire network obtained in step S1450 and the recorded information of all ROADM nodes optical modules that need to be predicted and replaced. Rerouting optimization of related OCH optical layer services and prediction and replacement of optical module faults;
  • Step S1470 after the management and control of the OTN physical network is completed, update the OTN network optical module fault analysis DT model data, and perform step S1410 again.
  • optical module data on each ROADM node may be modeling data for fault prediction, such as optical module input and output power, laser bias current, optical module temperature, etc., which are not limited here.
  • the traffic throughput prediction result can be the best time window for rerouting optimization.
  • the service re-optimization deployment in this time window has the least impact on the operation of the OTN physical network of the OTN transport plane.
  • the network management and control system 1500 includes: a memory 1510 , a processor 1520 , and a computer stored in the memory 1510 and running on the processor 1520 program.
  • the processor 1520 and the memory 1510 may be connected by a bus or otherwise.
  • the non-transitory software programs and instructions required to implement the network management and control methods of the above embodiments are stored in the memory 1510, and when executed by the processor 1520, the network management and control methods applied to the network management and control system 1500 in the above-mentioned embodiments are executed, for example , execute the above-described method steps S110 to S140 in FIG. 1 , method steps S210 to S220 in FIG. 2 , method steps S310 in FIG. 3 , method steps S410 to S420 in FIG. Method steps S510 to S520 , method steps S610 to S620 in FIG. 6 , method step S710 in FIG. 7 , method step S810 in FIG. 8 .
  • the embodiment of the present application includes: obtaining DT model data corresponding to the entity object in the physical network; generating a digital twin instance DT case with different levels according to the DT model data, wherein the level of the DT case corresponds to the level of the entity object , there is a functional synergistic relationship between the DT cases with different levels; obtain target analysis results according to the collaborative relationship and all the DT cases; generate network configuration information according to the target analysis results, and configure the network The information is delivered to the physical network, so that the physical network completes network management and control according to the network configuration information.
  • network management and control can be implemented according to DT cases and collaborative relationships with different levels, and the dynamic detection capability and digital analysis capability of the network system can be improved.
  • an embodiment of the present application also provides a computer-readable storage medium, where the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are executed by a processor or controller, for example, by the above-mentioned Executed by a processor in the embodiment, the above-mentioned processor can execute the network management and control method applied to the network management and control system in the above-mentioned embodiment, for example, the above-described method steps S110 to S140 in FIG. method step S210 to step S220 in FIG. 3 , method step S410 to step S420 in FIG. 4 , method step S510 to step S520 in FIG. 5 , method step S610 to step S620 in FIG.
  • Those of ordinary skill in the art can understand that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and appropriate combinations thereof.
  • Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit .
  • a processor such as a central processing unit, digital signal processor or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit .
  • Such software may be distributed on computer-readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media).
  • computer storage media includes both volatile and nonvolatile implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules or other data flexible, removable and non-removable media.
  • Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cartridges, magnetic tape, magnetic disk storage or other magnetic storage devices, or may Any other medium used to store desired information and which can be accessed by a computer.
  • communication media typically embodies computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and can include any information delivery media, as is well known to those of ordinary skill in the art .

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Abstract

一种网络管控方法及其系统、网络系统、存储介质，该网络管控方法包括：获取与物理网络中的实体对象对应的DT模型数据(S110)；根据DT模型数据生成具有不同层级的DT case，其中，DT case的层级和实体对象的层级相对应，所述具有不同层级的DT case之间具有功能上的协同关系(S120)；根据协同关系和所有DT case得到目标分析结果(S130)；根据目标分析结果生成网络配置信息，将网络配置信息下发至物理网络，以使物理网络根据网络配置信息完成网络管控(S140)。

Description

网络管控方法及其系统、网络系统、存储介质
相关申请的交叉引用
本申请基于申请号为202110326389.9、申请日为2021年03月26日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
本申请涉及但不限于网络通信领域，尤其涉及一种网络管控方法及其系统、网络系统、存储介质。
背景技术
随着通信技术的发展，传输网络正面临资源多样化、覆盖范围广、部署环境复杂等局面。同时，随着数字转型时代的来临，传输服务也朝着业务敏捷化、网络功能虚拟化的方向快速发展。在传输网络的硬件设备和数字技术越来越复杂和多样化的背景之下，为了实现网络自优、自愈和自治的高效智能化管理，需要依靠系统、准确和实时的数字化分析技术，对复杂的网络系统进行数字化模型抽象，构建出能够综合实际的网络系统、运行机制和管控维护方法的多维网络数字化仿真系统，并借助人工智能(Artificial Intelligence，AI)技术，完成对实际物理网络运行状况的实时动态反馈、评估优化、模拟和预测，从而构建出网络高效智能化管理的数字化分析基础。
数字孪生(Digital Twin，DT)技术能够创建物理对象的高保真数字虚拟模型，模拟物理对象的行为，描绘了物理对象的运行状态，能够实现数字信息和物理对象的融合。将DT技术应用到网络通信中，能够从传输网络中提取所需要的网络数据，结合AI技术和需求构建出数字孪生实例(Digital Twin case，DT case)模型，实现传输网络的分析、优化和仿真预测等功能。
在实际应用过程中，需要针对不同的分析对象，构建出相应层级的DT case，根据DT case的分析结果对传输网络进行智能管控。然而在传输网络中，不同层级的DT case所对应的对象之间可能是相关联的，例如某个功能模块的故障是引起传输网络告警或中断的根因故障。因此，如何考虑不同层级的DT case之间的协同关系，是提高网络系统的动态检测能力和数字化分析能力的关键。
发明内容
以下是对本文详细描述的主题的概述。本概述并非是为了限制权利要求的保护范围。
本申请实施例提供了一种网络管控方法及其系统、网络系统、存储介质，能够提高网络系统的动态检测能力和数字化分析能力。
第一方面，本申请实施例提供了一种网络管控方法，应用于网络管控系统，所述网络管控方法包括：
获取与物理网络中的实体对象对应的DT模型数据；
根据所述DT模型数据生成具有不同层级的数字孪生实例DT case，其中，DT case的层级和实体对象的层级相对应，所述具有不同层级的DT case之间具有功能上的协同关系；
根据所述协同关系和所有所述DT case得到目标分析结果；
根据所述目标分析结果生成网络配置信息，将所述网络配置信息下发至所述物理网络，以使所述物理网络根据所述网络配置信息完成网络管控。
第二方面，本申请实施例提供了一种网络管控方法，应用于数据获取装置，所述数据获取装置与网络管控系统通信连接，所述网络管控方法包括：
生成与物理网络中的实体对象对应的DT模型数据；
将所述DT模型数据发送至所述网络管控系统，以使所述网络管控系统根据所述DT模型数据生成网络配置信息，并将所述网络配置信息下发至物理网络，从而使所述物理网络根据所述网络配置信息完成网络管控，其中，网络配置信息由所述网络管控系统根据目标分析结果生成，所述目标分析结果由所述网络管控系统根据协同关系和具有不同层级的所有DT case得到，所述具有不同层级的DT case由所述网络管控系统根据所述DT模型数据生成，DT case的层级和实体对象的层级相对应，所述具有不同层级的DT case之间具有功能上的协同关系。第三方面，本申请实施例提供了一种网络管控系统，包括：存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现如第一方面所述的网络管控方法。
第三方面，本申请实施例提供了一种网络系统，所述网络系统包括网络管控系统和数据获取装置，所述网络管控系统与所述数据获取装置通信连接，所述网络管控系统用于执行如第一方面所述的网络管控方法，所述数据获取装置用于执行如第二方面所述的网络管控方法。
本申请的其它特征和优点将在随后的说明书中阐述，并且，部分地从说明书中变得显而易见，或者通过实施本申请而了解。本申请的目的和其他优点可通过在说明书、权利要求书以及附图中所特别指出的结构来实现和获得。
附图说明
附图用来提供对本申请技术方案的进一步理解，并且构成说明书的一部分，与本申请的实施例一起用于解释本申请的技术方案，并不构成对本申请技术方案的限制。
图1是本申请一个实施例提供的应用于网络管控系统的网络管控方法的流程图；
图2是本申请另一个实施例提供的获取DT模型数据的流程图；
图3是本申请另一个实施例提供的生成DT case的流程图；
图4是本申请另一个实施例提供的得到目标分析结果的流程图；
图5是本申请另一个实施例提供的基于AI算法处理的流程图；
图6是本申请另一个实施例提供的确定管控场景的流程图；
图7是本申请另一个实施例提供的实现DT模型数据同步更新的流程图；
图8是本申请另一个实施例提供的获取和显示网络状态信息的流程图；
图9是本申请另一个实施例提供的应用于数据获取装置的网络管控方法的流程图；
图10是本申请另一个实施例提供的生成DT模型数据的流程图；
图11是本申请另一个实施例提供的更新DT模型数据的流程图；
图12是本申请另一个实施例提供的应用网络管控方法的OTN系统的结构
图13是本申请另一个实施例提供的示例一的流程图；
图14是本申请另一个实施例提供的示例二的流程图；以及
图15是本申请另一个实施例提供的网络管控系统的装置图。
具体实施方式
为了使本申请的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本申请进行进一步详细说明。应当理解，此处所描述的具体实施例仅用以解释本申请，并不用于限定本申请。
需要说明的是，虽然在装置示意图中进行了功能模块划分，在流程图中示出了逻辑顺序，但是在某些情况下，可以以不同于装置中的模块划分，或流程图中的顺序执行所示出或描述的步骤。说明书、权利要求书或上述附图中的术语“第一”、“第二”等是用于区别类似的对象，而不必用于描述特定的顺序或先后次序。
本申请提供了一种网络管控方法及其系统、网络系统、存储介质，该网络管控方法包括：获取与物理网络中的实体对象对应的DT模型数据；根据所述DT模型数据生成具有不同层级的数字孪生实例DT case，其中，DT case的层级和实体对象的层级相对应，所述具有不同层级的DT case之间具有功能上的协同关系；根据协同关系和所有DT case得到目标分析结果；根据目标分析结果生成网络配置信息，将网络配置信息下发至物理网络，以使物理网络根据网络配置信息完成网络管控。根据本申请实施例提供的方案，能够根据DT case和协同关系实现网络管控，提高网络系统的动态检测能力和数字化分析能力。
需要说明的是，本申请实施例提供的网络管控方法可以应用到任意网络系统中，例如光网络(Optical Transport Network，OTN)、分组传送网(Packet Transport Network，PTN)、分组光传送网(Packet Optical Transport Network，POTN)，为了叙述简便，本申请实施例以应用到OTN系统作为例进行技术方案的解释说明，本领域技术人员有能力将本申请实施例的技术方案应用到其他网络系统中，这并不会对本申请的保护范围造成限定。
需要说明的是，网络管控系统可以是设置有多个功能模块的装置，例如为了实现管控功能，网络管控系统中可以根据实际需求设定数据库、处理器和执行器等功能模块，具体的装置类型根据具体的网络系统确定即可，例如对于等级数字孪生光网络(Hierarchical Digital Twin OTN，HDTON)，网络管控系统可以是用于管控HDTON的HDTON智能管控系统；同时，为了实现DT case的生成，可以在HDTON智能管控系统中设置HDTON case编排器作为控制器，设置软件定义光网络(Software Defined Optical Network，SDON)系统作为执行器；同时，为了针对不同的管控场景，设置存储有多个预先设定好的AI算法的AI算法引擎库。当然，上述SDON系统、HDTON case编排器和AI算法引擎库在实际的网络部署中也可以是独立运行的装置，可以设置在不同的物理装置或者物理设备中，从功能上能够组合成网络管控系统即可。需要说明的是，上述装置仅为对网络管控系统进行解释说明的示例，本领域技术人员有动机根据实际需求在网络管控系统中增加或者减少相应的功能模块，本实施例对此不多作限定。
下面结合附图，对本申请实施例作进一步阐述。
如图1所示，图1是本申请一个实施例提供的一种网络管控方法，应用于网络管控系统，该网络管控方法包括但不限于有以下步骤：
步骤S110，获取与物理网络中的实体对象对应的DT模型数据。
值得注意的是，物理网络可以是任意网络系统，物理网路中的多个层次的实体对象之间具有从属关系，并且在功能上存在协同关系即可，例如对于OTN系统，全网的ODU业务由多个光数据单元(Optical Data Unit，ODU)组成，ODU节点的流量吞吐量的预测结果对全网ODU业务的优化仿真存在一定的影响，因此，在进行对全网ODU业务的优化仿真时需要综合 每个ODU节点的流量吞吐量预测，本实施例并不对应用网络管理方法的物理网络多作限定。
可以理解的是，实体对象可以是一个具体的物理设备，也可以是由多个物理设备所组成系统，例如对于OTN系统，实体对象可以是整个OTN，也可以是OTN中的光模块，也可以是OTN中的每一个ODU节点，根据具体的需求确定实体对象即可。
需要说明的是，DT模型数据的具体内容根据实际需要的实体对象确定即可，例如需要对OTN网络光模块进行故障分析，获取的DT模型数据可以包括OTN网络拓扑、该OTN网络拓扑中全网光通道(Optical Channel，OCH)光层业务分布、该OTN网络拓扑中每个可重构光分插复用器(Reconfigurable Optical Add-Drop Multiplexer，ROADM)节点上的光模块数据，本实施例并不对DT模型数据的具体类型多作限定。
值得注意的是，DT模型数据可以是由网络管控系统根据具体的数据需求从物理网络中获取，基于数字孪生技术，能够从物理网络中提取对应的参数和属性，构建出高保真的数字虚拟模型，从而形成DT模型数据，使得DT模型数据能够实时反映物理网络中的真实运行状况，提高对网络系统的评估预测的准确性。
步骤S120，根据DT模型数据生成具有不同层级的数字孪生实例DT case，其中，DT case的层级和实体对象的层级相对应，具有不同层级的DT case之间具有功能上的协同关系。
需要说明的是，层级可以表征DT case所对应的物理网络中的实体对象的规模或者影响范围，例如对于OTN系统，层级最大的实体对象可以是整个OTN网络，层级最小的实体对象可以是OTN网络中的光模块，具体的实体对象的规模与层级的大小的对应关系可以根据实际需求调整，对此不多作限定。
需要说明的是，生成的DT case的层级与实体对象的层级相对应，例如实体对象为OTN网络，其层级为网络级，则根据DT模型数据生成的与OTN网络相对应的DT case的层级也为网络级。
值得注意的是，由于在物理网络中，不同规模的实体对象之间通常具有从属关系，即层级较大的DT case所对应的实体对象通常由层级较小的DT case所对应的实体对象装配或组合成，例如，层级较大的DT case对应的实体对象为ROADM节点，层级较小的DT case对应的实体对象为光模块，而ROADM节点通常是由光模块组成。因此，具有从属关系的实体对象在功能上也存在协同关系，例如对ROADM节点所对应的DT case进行预测分析时，需要综合归属于该ROADM节点的光模块的故障预测的结果，通过协同关系，能够使得层级较大的DT case进行预测分析时综合层级较小的DT case的分析结果，使得最终得到的目标分析结果准确性更高。
值得注意的是，DT case可以在获取到DT模型数据之后生成，以确保在进行协同分析的过程中存在相对应的DT case，后续不再赘述。
步骤S130，根据协同关系和所有DT case得到目标分析结果。
需要说明的是，由于DT case生成之后可以维持在运行状态，因此，可以是每个DT case先执行一次预测分析，再根据协同关系实现嵌套分析以得到目标分析结果，也可以是层级较大的DT case在接收到层级较小的DT case上报的分析结果之后再进行综合分析，将层级最大的DT case得到的分析结果作为目标分析结果，即通过触发机制实现协同分析，具体方式根据实际需求选取，能够实现DT case之间的协同分析即可。
值得注意的是，在实际应用中，具有从属关系的实体对象所对应的DT case之间通常具 有协同关系，例如现网中光模块故障往往是由该光模块故障引起的OTN网络故障告警和业务中断的根因故障，这种实际现网中局部与整体之间的协同与波及关系也会反映到数字孪生OTN(Digital Twin OTN，DTON)中，因此网络级故障分析DT case和光模块级故障预测DT case之间存在协同关系。
步骤S140，根据目标分析结果生成网络配置信息，将网络配置信息下发至物理网络，以使物理网络根据网络配置信息完成网络管控。
需要说明的是，物理网络通常是物理设备的集合，需要通过常见的智能管控系统完成管控，例如通过常见的SDON系统对物理网络进行控制，本实施例并不具体的智能管控系统多作限定。
可以理解的是，网络配置信息可以是任意类型的信息，能够使得物理网络通过调整运行参数从而实现网络管控即可，例如对于OTN系统，目标分析结果为全网ODU业务割接重路由优化的分析结果，则SDON系统可以根据对当前的OTN网络进行相关业务的重路由优化和节点扩容等处理需求，生成相对应的网络配置信息。
另外，参照图2，在一实施例中，图1所示实施例中的步骤S110还包括但不限于有以下步骤：
步骤S210，确定管控场景，根据管控场景确定数据需求；
步骤S220，根据数据需求，获取与物理网络中的实体对象对应的DT模型数据。
需要说明的是，管控场景可以是需要反映网络管控需求的场景，例如反映用户业务需求的网路服务场景或者反映物理网络检测分析需求的网络分析场景，本实施例对此不多作限定。
值得注意的是，通过管控场景能够确定需要采集数据的实体对象，从而确定具体的数据需求，提高数据处理的效率，例如，当确定管控场景为OTN网络流量分析场景，则需要从物理网络中获取OTN网络流量的DT模型数据，可以包括OTN网络拓扑、该网络拓扑上的全网ODU业务分布和每个节点的流量吞吐量等。当确定数据需求后，也就进一步确定了后续所需要构建的DT case的层级，例如在上述OTN网络流量分析场景中，需要构建网络级的DT case以对应全网的ODU业务，构建节点级的DT case以对应每个ODU节点，上述的网络级和节点级为层级的其中一种示例，并不会对层级的具体选择造成限定。
可以理解的是，为了进一步体现实体对象所对应的层级，在构建DT模型数据的时候，可以采用等级数字孪生(Hierarchical Digital Twin，HDT)模型数据的形式，在该HDT模型数据中，按照层级的大小预先确定好数据的等级，例如在上述OTN网络流量分析场景中，确定OTN网络拓扑和该网络拓扑上的全网ODU业务分布为网络级的数据，每个节点的流量吞吐量为节点级的数据，从而能够快速从HDT模型数据中确定不同数据的DT case容器的归属。
另外，参照图3，在一实施例中，图1所示实施例中的步骤S120还包括但不限于有以下步骤：
步骤S310，根据DT模型数据和实体对象的层级，在具有不同容器等级的目标DT case容器中生成DT case，其中，目标DT case容器根据管控场景和实体对象的层级从预先设定的DT case容器中确定，DT case容器用于生成和管理DT case，不同容器等级的DT case容器所生成的DT case所对应的层级互不相同。
需要说明的是，为了便于DT case容器的管理，可以在智能管控系统中设置HDTON case编排器，在HDTON case编排器中预先设定好至少两个DT case容器，在确定管控场景后，HDTON  case编排器确定数据需求，并在获取到DT模型数据后，从预先设定的DT case容器中确定出至少两个目标DT case容器，并目标DT case容器中生成DT case。
需要说明的是，具体的容器等级可以根据实际需求确定，例如根据层级的网络级、设备级和器件级，分别设置高级DT case容器、中级DT case容器和低级DT case容器，每个容器等级分别对应一种层级，使得不同等级的DT case容器生成的DT case对应不同的层级即可，本实施例对此不多作限定。
需要说明的是，结合图2所示实施例的描述，数据需求也可以通过确定出的目标DT case容器所确定，例如参考上述DT case容器等级，当根据管控场景确定需要从网络级和器件级的进行分析，则需要通过高级DT case容器和低级DT case容器生成对应的DT case，此时可以根据每个目标DT case容器所需要的数据生成数据需求，在获取到DT模型数据后，每个目标DT case容器从DT模型数据中获取相对应等级的数据，并根据获取的DT模型数据生成DT case，能够确保获取到的数据的可用性和准确性。
需要说明的是，DT case容器可以用于生成DT case，也可以用于管理DT case，例如通过AI算法对DT case进行分析处理，或者获取具有下一级容器等级的DT case容器上报的中间分析结果进行协同分析，也可以根据实际需求增加或减少针对DT case所能实现的功能，本实施例对此不多作限定。
值得注意的是，在不同的管控场景下，所需要使用的目标DT case容器并不一定不相同，例如某些场景需要用到全部预先设定的DT case容器，而另一些场景只需要用到其中的几个容器等级的DT case容器，这都是根据管控场景的实际需求确定的，本实施例并不对具体数量作过多的限定。
另外，参照图4，在一实施例中，图1所示实施例中的步骤S130还包括但不限于有以下步骤：
步骤S410，根据协同关系和目标DT case容器所对应的容器等级，对由具有下一级容器等级的目标DT case容器生成的DT case进行基于AI算法的处理得到中间分析结果，并将中间分析结果上报至具有上一级容器等级的目标DT case容器，根据中间分析结果对由具有上一级容器等级的目标DT case容器生成的DT case进行基于AI算法的处理，得到协同分析结果；
步骤S420，将容器等级最大的目标DT case容器所得到的协同分析结果确定为目标分析结果。
可以理解的是，由于不同层级的DT case所对应的实体对象之间的具有功能上的协同关系，而不同容器等级的DT case容器所生成的DT case的层级互不相同，因此容器等级可以是与层级相对应的；并且，协同处理通常是由层级较小的DT case所得到的分析结果用于协同层级较大的DT case以得到分析结果，因此，可以根据容器等级从小到大依次进行中间分析结果的上报，具有上一级容器等级的目标DT case容器接收到中间分析结果后，综合该分析结果对该DT case容器所生成的DT case进行基于AI算法的处理，使得得到的协同分析结果既包括该目标DT case容器所对应的层级的分析结果，也包括具有协同关系的较小层级的DT case所对应的分析结果，最终使得目标分析结果能够综合所有相关层级的DT case之间的协同关系，提高网络系统的动态检测能力和数字化分析能力。
需要说明的是，步骤S420中所描述的具有上一级容器等级和具有下一级容器等级是根据 协同关系所确定的，而并非根据容器等级所确定，例如对于一些管控场景，仅涉及低等级DT case容器和高等级DT case容器，则对应至步骤S420中，具有上一级容器等级的目标DT case容器为高等级DT case容器，具有上一级容器等级的目标DT case容器为低等级DT case容器；又如，对于一些管控场景，同时涉及低等级DT case容器、中等级DT case容器和高等级DT case容器，则在首次对DT case进行基于AI算法处理的目标DT case容器为低等级DT case容器，即首次操作中的具有下一级容器等级的目标DT case容器，则对应的具有上一级容器等级的目标DT case容器为中等级DT case容器，同理，在通过中等级DT case容器对DT case进行基于AI算法处理时，该中等级DT case容器为具有下一级容器等级的目标DT case容器，高等级DT case容器为具有上一级容器等级的目标DT case容器，直至最高等级的目标DT case容器对DT case完成基于AI算法处理后，将该最高等级的目标DT case容器所得到的协同分析结果确定为目标分析结果，以确保协同分析结果能够逐层协同，提高数据的可参考性。
可以理解的是，协同分析结果既包括所对应的DT case进行基于AI算法处理所得到的结果，也可以同时包括全部的下一级的中间分析结果，使得协同分析结果能够具有更加丰富的数据。
需要说明的是，由于目标分析结果可以是最高级的目标DT case容器对DT case进行基于AI算法处理所得到的协同结果，因此，可以在得到目标分析结果之后，由最高级的DT case容器将目标分析结果告知SDON系统，以使其生成网络配置信息，以确保生成的网络配置信息能够协同考虑所有层级的DT case。
值得注意的是，在一些实施例中，一个DT case容器可能存在多个具有下一级容器等级的DT case容器，此时，可能会出现一个DT case容器同时接收到多个中间分析结果的情况，为了避免发生冲突，可以通过设置时间阈值的方法，在一定的时间段内具有上一级容器等级的DT case容器处于接收状态，在该时间段之后停止中间分析结果的接收，对DT case进行基于AI算法处理，并协同在该时间段内接收到全部中间处理结果，也可以采用其他方式，例如对DT case进行基于AI算法处理时每接收到一个中间分析结果，则重新对全部的中间分析结果进行协同，本实施例对此不多作限定，根据实际需求选取具体的判断方式即可。
另外，参照图5，在一实施例中，图4所示实施例中的步骤S420还包括但不限于有以下步骤：
步骤S510，根据管控场景，从预先设定的AI算法库中确定目标AI算法；
步骤S520，根据目标AI算法对DT case进行分析处理。
需要说明的是，AI算法库可以是数据库的形式，能够通过管控场景匹配出对应的目标AI算法即可。可以理解的是，AI算法库中的AI算法数量可以是任意，根据实际需求增加或者减少即可。
可以理解的是，AI算法库中可以包括任意类型的AI算法，例如强化学习(Reinforcement Learning，RL)算法、卷积神经网络(Convolution Neural Network，CNN)、深度神经网络(Deep Neural Network，DNN)、图卷积网络(Graph Convolution Network，GCN)或循环神经网络(Recurrent Neural Network，RNN)，本领域技术人员有动机根据实际需求增加或者减少AI算法的种类。另外，上述AI算法为预先设定好的，本申请实施例并不涉及具体的算法训练过程，在此不多作赘述。
值得注意的是，对于不同的管控场景，所需要进行的操作并不相同，因此，在AI算法库中设置多个AI算法，通过管控场景确定具体的目标AI算法，能够确保以最佳的AI算法完成对应的DT case处理。另外，管控场景与目标AI算法的对应关系可以是预先设定，例如，针对用户需要的基于意图时延优化的端到端OTN精品专线服务需求，根据管控场景确定OTN拓扑规模后，匹配分析出计算效果最佳的算法为强化学习(Reinforcement Learning，RL)算法.
需要说明的是，对于目标AI算法的确定，可以是在每个DT case容器对DT case进行基于AI算法的处理时确定一次，以确保每个DT case容器能够利用最合适的AI算法进行DT case的处理；当然，也可以是在同一个协同关系下确定出一个目标AI算法，在该协同关系下的每一次DT case容器对DT case进行基于AI算法的处理均采用相同的目标AI算法，具体方式根据实际需求选取即可。
另外，参照图6，在一实施例中，图2所示实施例中的步骤S210还包括但不限于有以下步骤：
步骤S610，获取当前的物理网络的运行状态，根据运行状态和预先设定的管理信息确定管控场景；
或者，
步骤S620，获取管控需求，根据管控需求确定管控场景。
需要说明的是，管控场景可以包括网络分析场景和网络服务场景。其中，网络分析场景由网络分析需求确定，网络分析需求可以是用户对当前的物理网络进行故障预测、运行状态分析或查询的需求，可以由用户通过客户端向网络管控系统发送需求信息所产生；也可以是网络管控系统根据对物理网络自检、自优、自愈的管理目标自行产生，本实施例对此不多作限定。
可以理解的是，网络服务场景可以基于是用户的服务需求产生，例如设置与网络管控系统通信连接的OTN智能网络管控应用程序(Application，APP)，通过该OTN智能网络管控APP获取用户对OTN网络的服务需求，服务需求可以是带宽按需分配(Bandwidth On Demand，BOD)、多层优化(Multi-Layer Optimization，MLO)、服务等级协议(Service-Level Agreement，SLA)、光虚拟专用网络(Optical Virtual Private Network，OVPN),意图光网络(Intent-Based Optical Network，IBON)等，具体的服务需求的类型可以根据实际的网络资源确定，在此不多作赘述。
另外，参照图7，在一实施例中，在执行图1所示实施例中的步骤S140之前，还包括但不限于有以下步骤：
步骤S710，当获取到物理网络在根据网络配置信息完成网络管控后上报的新的DT模型数据，根据新的DT模型数据重新生成网络配置信息。
值得注意的是，通过网络配置信息完成网络管控后，物理网络的实体对象的相关参数会发生变化，即DT模型数据中的相关参数也会发生变化，为了确保DT模型数据与物理网络的实体对象的参数相对应，可以在完成物理网络的网络管控之后，根据新的物理网络的数据对DT模型数据进行更新，从而使得下一次的DT case生成能够基于最新的DT模型数据。此外，通过更新DT模型数据以触发进一步的DT case生成，也能够使得整个网络管控形成闭环，在DT模型数据的生命周期内不断更新DT case并进行目标分析结果的确定，有效提高了动态检 测的能力。
另外，参照图8，在一实施例中，在执行图1所示实施例中的步骤S140之后，还包括但不限于有以下步骤：
步骤S810，获取并显示完成网络管控之后的物理网络的网络状态信息。
需要说明的是，获取到的网络状态信息可以通过图6中实施例所述的OTN智能网络管控APP显示，也可以通过其他与网络管控系统通信连接的设备显示，能够使得用户直观地看到当前物理网络的运行状态即可。需要说明的是，网络状态信息可以是与物理网络相关内容，例如，对于OTN系统，网络状态信息可以包括实时的OTN网络拓扑、OTN网络服务运行状态、网络性能状态、无网络资源使用状态等，具体的显示内容根据实际需求选取即可。
可以理解的是，网络状态信息可以是实时获取显示，也可以是根据用户的需求在OTN智能网络管控APP显示，本实施例对此不多作限定。
如图9所示，图9是本申请一个实施例提供的一种网络管控方法，应用于数据获取装置，数据获取装置与网络管控系统通信连接，该网络管控方法包括但不限于有以下步骤：
步骤S910，生成与物理网络中的实体对象对应的DT模型数据。
需要说明的是，对于网络管控系统，SDON能够实现物理网络的管控，例如在OTN系统中实现端到端OTN业务的动态管控、网络拓扑管理、OTN业务故障保护等功能，但是SDON并不能直接从物理网络中获取DT模型数据，因此，本申请实施例中的数据获取装置，可以是用于从物理网络中获取数据并生成DT模型数据的装置，装置的具体实体可以是任意，本实施例对此不多作限定，能够实现相应功能即可。
需要说明的是，数据获取装置可以根据HDTON case编排器的需求，从物理OTN网络中获取DT模型数据，并将获取到的DT模型数据上报至HDTON case编排器，以使各个DT case容器根据DT模型数据生成DT case。
步骤S920，将DT模型数据发送至网络管控系统，以使网络管控系统根据DT模型数据生成网络配置信息，并将网络配置信息下发至物理网络，从而使物理网络根据网络配置信息完成网络管控，其中，网络配置信息由网络管控系统根据目标分析结果生成，目标分析结果由网络管控系统根据协同关系和具有不同层级的所有DT case得到，具有不同层级的DT case由网络管控系统根据DT模型数据生成，DT case的层级和实体对象的层级相对应，具有不同层级的DT case之间具有功能上的协同关系。
需要说明的是，网络管理系统生成DT case和网络配置信息的方法和原理可以参考图1中所示实施例的描述，同理，层级和协同关系的具体释义也可以参考图1中所示实施例的描述，为了叙述简便在此不多作赘述。
另外，参照图10，在一实施例中，图9所示实施例中的步骤S910还包括但不限于有以下步骤：
步骤S1010，获取网络管控系统下发的数据需求，数据需求由网络管控系统根据确定的管控场景所确定；
步骤S1020，根据数据需求生成与物理网络中的实体对象对应的DT模型数据。
可以理解的是，数据需求的具体生成方法和原理可以参考图2所示实施例的描述，为了叙述简便在此不多作赘述。
需要说明的是，数据需求可以由网络管控系统的HDTON case编排器下发至数据获取装置， 数据获取装置根据具体的数据需求，利用数字孪生技术，确定物理网络中实体对象的物理模型，并从该物理模型中获取所需要的参数和属性，并将获取到的参数和属性提取出DT模型数据，以确保DT模型数据与实际的物理网络的实体对象的参数一致。
另外，参照图11，在一实施例中，在执行图10所示实施例中的步骤S1020之后，还包括但不限于有以下步骤：
步骤S1110，在DT模型数据的生命周期中，当检测到实体对象的物理参数和/或物理属性发生变化，根据变化后的实体对象更新DT模型数据；
步骤S1120，将更新后的DT模型数据同步至网络管控系统，以使网络管控系统根据更新后的DT模型数据得到网络配置信息。
需要说明的是，DT模型数据的生命周期可以从确定管控场景开始，直到物理网络完成网络管控结束，也可以是设定好的一段运行时间，具体形式根据实际需求选取，能够确保在生命周期内保持DT模型数据所对应的物理模型一致即可。
可以理解的是，在DT模型数据的生命周期内保持DT模型数据的实时同步，能够确保获取到的DT模型数据能够反映当前物理网络的实体对象的实时参数，从而使得得到的目标分析结果能够反映实际的网络情况，确保网络的动态检测能力。
可以理解的是，物理参数和/或物理属性发生变化，可以是数值上发生变化，也可以是一个采集周期内采集到的数据发生变化，也可以是新增加或者减少一个属性等，具体的变化情况本实施例不多作赘述。
以下结合图12所示的OTN系统的结构，通过两个具体管控场景对本申请实施例的技术方案进行进一步举例说明。
需要说明的是，图12所示的OTN系统包括OTN传送平面、HDTON智能管控系统，另外，还设置有OTN智能网络管控APP与HDTON智能管控系统通信连接。其中，HDTON智能管控系统中设置有SDON系统、HDTON case编排器和AI算法引擎库，HDTON case编排器中预先设置好高等级DT case容器、中等级DT case容器和低等级DT case容器，AI算法引擎库包括应用场景分析适配器和若干个预先训练好的AI算法；OTN传送平面包括物理网络和DT模型，物理网络包括若干个物理设备，DT模型中构建有与物理设备相对应的数字模型。需要说明的是，上述部件可以是物理装置，也可以是具有相应功能的功能模块，本实施例对具体的实现方式不多作限定，并且，上述装置的选取是为了叙述方便所选取的示例，并不会对本申请的技术方案造成限制。
示例一：OTN网络流量分析应用场景,参照图13，在该场景下，网络管控方法包括但不限于有以下步骤：
步骤S1310，HDTON case编排器根据OTN网络流量分析需求，从OTN传送平面获得OTN网络流量DT模型数据，其中,OTN网络流量DT模型数据包括OTN网络拓扑、OTN网络拓扑上的全网ODU业务分布、每个ODU节点的流量吞吐量；
步骤S1320，HDTON case编排器在获取到OTN网络流量DT模型数据后，在高等级DT case容器内对全网OTN拓扑上的ODU业务DT化，生成网络级ODU业务DT case；在中等级DT case容器内对每个ODU节点的流量吞吐量DT化，分别创建生成每个ODU节点的流量吞吐量DT case；
步骤S1330，中等级DT case容器根据OTN网络流量分析应用场景，通过场景分析适配器从AI算法引擎库获取AI算法，为每个ODU节点流量吞吐量DT case构建训练得出ODU节 点流量吞吐量预测模型，其中，ODU节点流量吞吐量DT case可根据自身的ODU节点流量吞吐量的预测模型，得出ODU节点在指定预测周期内的流量吞吐量预测结果，对流量吞吐量上涨接近该ODU节点交换容量的DT case，并得出扩容分析结果；
步骤S1340，中等级DT case容器将每个ODU节点流量吞吐量DT case的流量吞吐量预测结果上报给高等级DT case容器中对应的网络级ODU业务DT case，由该网络级ODU业务DT case根据各ODU节点交换容量的DT case对ODU节点流量吞吐量的预测结果及扩容分析结果，对由需要做扩容处理的ODU节点所承载的ODU业务做割接重路由优化仿真，其中，割接重路由优化仿真包括以下步骤：由高等级DT case容器根据OTN网络流量分析应用场景，通过场景分析适配器从AI算法引擎库获取AI算法，对该网络级ODU业务DT case中待割接重路由优化的ODU业务，按照各ODU业务的优化策略进行单条ODU业务优化计算，或是多条ODU业务并发优化计算，并获得优化后的全网ODU业务分布仿真效果，记录在该网络级ODU业务DT case中；
步骤S1350，由高等级DT case容器经HDTON case编排器将步骤S1340中的网络级ODU业务DT case所做的全网ODU业务的割接重路由优化分析结果、所记录的所有需要做扩容分析ODU节点信息通知给SDON系统；
步骤S1360，SDON系统根据步骤S1350得到的割接重路由优化分析结果和所记录的所有需要做扩容分析ODU节点信息，对OTN传送平面当前的OTN物理网络进行相关业务重路由优化和节点扩容处理；
步骤S1370，在OTN物理网络的管控完成后，更新OTN网络流量DT模型数据，重新执行步骤S1310。
需要说明的是，流量吞吐量可以包括ODU节点的上下路流量和穿通流量，根据实际需求选取即可。
可以理解的是，流量吞吐量预测结果可以是任意期限内的流量值，例如接下来15天的流量吞吐量平均值和峰值，又如，下一个月的流量吞吐量平均值和峰值，本实施例对此不多作限定。
需要说明的是，步骤S1340中得到的割接重路由优化分析结果可以是做重路由优化的最佳时间窗口，在该时间窗口做业务重优化部署，对OTN传送平面现网OTN物理网络的运营影响最小。
示例二：OTN网络光模块故障预测应用场景，在该场景下，网络管控方法包括但不限于有以下步骤：
步骤S1410，HDTON case编排器根据OTN网络光模块故障分析需求，从OTN传送平面获得OTN网络光模块故障分析DT模型数据，其中，OTN网络光模块故障分析DT模型数据包括OTN网络拓扑、OTN网络拓扑上的全网OCH光层业务分布、OTN网络拓扑中的每个ROADM节点上的光模块数据；
步骤S1420，HDTON case编排器在获取到OTN网络光模块故障分析DT模型数据后，在高等级DT case容器内对全网OTN拓扑上的OCH光层业务DT化，生成网络级OCH光层业务DT case；在低等级DT case容器内对每个ROADM节点上的光模块数据DT化，分别创建生成每个ROADM节点上每个光模块的故障预测DT case；
步骤S1430,低等级DT case容器根据OTN网络光模块故障预测应用场景，通过场景分析 适配器从AI算法引擎库获取AI算法，为每个ROADM节点上每个光模块的故障预测DT case构建训练得出光模块的故障预测模型，其中，每个光模块故障预测DT case可根据自身的光模块故障的预测模型，预测得出本模块故障发生的时间窗口，并得出对预测出故障的光模块的更换分析结果；
步骤S1440,低等级DT case容器将预测出故障的光模块的更换分析结果上报给高等级DT case容器中对应的网络级OCH光层业务DT case，网络级OCH光层业务DT case根据各光模块故障预测分析结论，对需要做故障更换的ROADM节点上的光模块所承载的OCH光层业务做割接重路由优化仿真，其中，割接重路由优化仿真具体包括以下步骤：高等级DT case容器根据OTN网络光模块故障预测应用场景，通过场景分析适配器从AI算法引擎库获取AI算法，对该网络级OCH光层业务DT case中因光模块故障预测引起的待割接重路由优化的OCH光层业务，按照各OCH光层业务的优化策略进行单条OCH光层业务优化计算，或是多条OCH光层业务并发优化计算，并获得优化后的全网OCH光层业务分布仿真效果，记录在该网络级OCH光层业务DT case中；
步骤S1450,由高等级DT case容器经HDTON case编排器将步骤S1440中的网络级OCH光层业务DT case所做的全网OCH光层业务割接重路由优化分析结果、所记录的所有需要做故障预测更换的ROADM节点光模块信息通知给SDON系统；
步骤S1460,SDON系统根据步骤S1450得到的全网OCH光层业务割接重路由优化分析结果和所记录的所有需要做故障预测更换的ROADM节点光模块信息，对OTN传送平面当前的OTN物理网络进行相关OCH光层业务重路由优化和光模块故障预测更换处理；
步骤S1470,在OTN物理网络的管控完成后，更新OTN网络光模块故障分析DT模型数据，重新执行步骤S1410。
需要说明的是，每个ROADM节点上的光模块数据可以是用于故障预测的建模数据，如光模块输入输出功率、激光器偏置电流、光模块温度等，在此不多作限定。
可以理解的是，流量吞吐量预测结果可以是做重路由优化的最佳时间窗口，在该时间窗口做业务重优化部署，对OTN传送平面现网OTN物理网络的运营影响最小。
另外，参照图15，本申请的一个实施例还提供了一种网络管控系统，该网络管控系统1500包括：存储器1510、处理器1520及存储在存储器1510上并可在处理器1520上运行的计算机程序。
处理器1520和存储器1510可以通过总线或者其他方式连接。
实现上述实施例的网络管控方法所需的非暂态软件程序以及指令存储在存储器1510中，当被处理器1520执行时，执行上述实施例中的应用于网络管控系统1500的网络管控方法，例如，执行以上描述的图1中的方法步骤S110至步骤S140、图2中的方法步骤S210至步骤S220、图3中的方法步骤S310、图4中的方法步骤S410至步骤S420、图5中的方法步骤S510至步骤S520、图6中的方法步骤S610至步骤S620、图7中的方法步骤S710、图8中的方法步骤S810。
本申请实施例包括：获取与物理网络中的实体对象对应的DT模型数据；根据所述DT模型数据生成具有不同层级的数字孪生实例DT case，其中，DT case的层级和实体对象的层级相对应，所述具有不同层级的DT case之间具有功能上的协同关系；根据所述协同关系和所有所述DT case得到目标分析结果；根据所述目标分析结果生成网络配置信息，将所述网络 配置信息下发至所述物理网络，以使所述物理网络根据所述网络配置信息完成网络管控。根据本申请实施例提供的方案，能够根据具有不同层级的DT case和协同关系实现网络管控，提高网络系统的动态检测能力和数字化分析能力。
以上所描述的装置实施例仅仅是示意性的，其中作为分离部件说明的单元可以是或者也可以不是物理上分开的，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部模块来实现本实施例方案的目的。
此外，本申请的一个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存储有计算机可执行指令，该计算机可执行指令被一个处理器或控制器执行，例如，被上述实施例中的一个处理器执行，可使得上述处理器执行上述实施例中的应用于网络管控系统的网络管控方法，例如，执行以上描述的图1中的方法步骤S110至步骤S140、图2中的方法步骤S210至步骤S220、图3中的方法步骤S310、图4中的方法步骤S410至步骤S420、图5中的方法步骤S510至步骤S520、图6中的方法步骤S610至步骤S620、图7中的方法步骤S710、图8中的方法步骤S810；或者，被上述实施例中的一个处理器执行，可使得上述处理器执行上述实施例中的应用于数据获取装置的网络管控方法，例如，执行以上描述的图9中的方法步骤S910至步骤S920、图10中的方法步骤S1010至步骤S1020、图11中的方法步骤S1110至步骤S1120。本领域普通技术人员可以理解，上文中所公开方法中的全部或某些步骤、系统可以被实施为软件、固件、硬件及其适当的组合。某些物理组件或所有物理组件可以被实施为由处理器，如中央处理器、数字信号处理器或微处理器执行的软件，或者被实施为硬件，或者被实施为集成电路，如专用集成电路。这样的软件可以分布在计算机可读介质上，计算机可读介质可以包括计算机存储介质(或非暂时性介质)和通信介质(或暂时性介质)。如本领域普通技术人员公知的，术语计算机存储介质包括在用于存储信息(诸如计算机可读指令、数据结构、程序模块或其他数据)的任何方法或技术中实施的易失性和非易失性、可移除和不可移除介质。计算机存储介质包括但不限于RAM、ROM、EEPROM、闪存或其他存储器技术、CD-ROM、数字多功能盘(DVD)或其他光盘存储、磁盒、磁带、磁盘存储或其他磁存储装置、或者可以用于存储期望的信息并且可以被计算机访问的任何其他的介质。此外，本领域普通技术人员公知的是，通信介质通常包含计算机可读指令、数据结构、程序模块或者诸如载波或其他传输机制之类的调制数据信号中的其他数据，并且可包括任何信息递送介质。
以上是对本申请的较佳实施进行了具体说明，但本申请并不局限于上述实施方式，熟悉本领域的技术人员在不违背本申请精神的前提下还可作出种种的等同变形或替换，这些等同的变形或替换均包含在本申请权利要求所限定的范围内。

Claims (14)

  1. 一种网络管控方法，应用于网络管控系统，所述网络管控方法包括：
    获取与物理网络中的实体对象对应的数字孪生DT模型数据；
    根据所述DT模型数据生成具有不同层级的数字孪生实例DT case，其中，DT case的层级和实体对象的层级相对应，所述具有不同层级的DT case之间具有功能上的协同关系；
    根据所述协同关系和所有所述DT case得到目标分析结果；以及
    根据所述目标分析结果生成网络配置信息，将所述网络配置信息下发至所述物理网络，以使所述物理网络根据所述网络配置信息完成网络管控。
  2. 根据权利要求1所述的方法，其中，所述获取与物理网络中的实体对象对应的DT模型数据，包括：
    确定管控场景，根据所述管控场景确定数据需求；以及
    根据所述数据需求，获取与物理网络中的实体对象对应的DT模型数据。
  3. 根据权利要求2所述的方法，其中，所述根据所述DT模型数据生成具有不同层级的DT case，包括：
    根据所述DT模型数据和实体对象的层级，在具有不同容器等级的目标DT case容器中生成DT case，其中，所述目标DT case容器根据所述管控场景和所述实体对象的层级从预先设定的DT case容器中确定，所述DT case容器用于生成和管理DT case，不同容器等级的DT case容器所生成的DT case所对应的层级互不相同。
  4. 根据权利要求3所述的方法，其中，所述根据所述协同关系和所有所述DT case得到目标分析结果，包括：
    根据所述协同关系和所述目标DT case容器所对应的容器等级，对由具有下一级容器等级的目标DT case容器生成的DT case进行基于人工智能AI算法的处理得到中间分析结果，并将所述中间分析结果上报至具有上一级容器等级的目标DT case容器，根据所述中间分析结果对由所述具有上一级容器等级的目标DT case容器生成的DT case进行所述基于AI算法的处理，得到协同分析结果；以及
    将容器等级最大的目标DT case容器所得到的协同分析结果确定为目标分析结果。
  5. 根据权利要求4所述的方法，其中，所述基于AI算法的处理，包括：
    根据所述管控场景，从预先设定的AI算法库中确定目标AI算法；以及
    根据所述目标AI算法对所述DT case进行分析处理。
  6. 根据权利要求2所述的方法，其中，所述确定管控场景，包括：
    获取当前的所述物理网络的运行状态，根据所述运行状态和预先设定的管理信息确定所述管控场景；
    或者，
    获取管控需求，根据所述管控需求确定所述管控场景。
  7. 根据权利要求1所述的方法，其中，在所述将所述网络配置信息下发至所述物理网络，以使所述物理网络根据所述网络配置信息完成网络管控之前，所述方法还包括：
    当获取到所述物理网络在根据所述网络配置信息完成网络管控后上报的新的DT模型数据，根据所述新的DT模型数据重新生成所述网络配置信息。
  8. 根据权利要求1所述的方法，其中,在所述将所述网络配置信息下发至所述物理网络，以使所述物理网络根据所述网络配置信息完成网络管控之后，还包括：
    获取并显示完成网络管控之后的所述物理网络的网络状态信息。
  9. 一种网络管控方法，应用于数据获取装置，所述数据获取装置与网络管控系统通信连接，所述网络管控方法包括：
    生成与物理网络中的实体对象对应的DT模型数据；以及
    将所述DT模型数据发送至所述网络管控系统，以使所述网络管控系统根据所述DT模型数据生成网络配置信息，并将所述网络配置信息下发至物理网络，从而使所述物理网络根据所述网络配置信息完成网络管控，其中，网络配置信息由所述网络管控系统根据目标分析结果生成，所述目标分析结果由所述网络管控系统根据协同关系和具有不同层级的所有DT case得到，所述具有不同层级的DT case由所述网络管控系统根据所述DT模型数据生成，DT case的层级和实体对象的层级相对应，所述具有不同层级的DT case之间具有功能上的协同关系。
  10. 根据权利要求9所述的方法，其中，所述生成与物理网络中的实体对象对应的DT模型数据，包括：
    获取所述网络管控系统下发的数据需求，所述数据需求由所述网络管控系统根据确定的管控场景所确定；以及
    根据所述数据需求生成与物理网络中的实体对象对应的DT模型数据。
  11. 根据权利要求10所述的方法，其中，在所述生成与物理网络中的实体对象对应的DT模型数据之后，所述方法还包括：
    在所述DT模型数据的生命周期中，当检测到实体对象的物理参数和/或物理属性发生变化，根据变化后的实体对象更新所述DT模型数据；以及
    将更新后的所述DT模型数据同步至所述网络管控系统，以使所述网络管控系统根据更新后的所述DT模型数据得到所述网络配置信息。
  12. 一种网络管控系统，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，其中，所述处理器执行所述计算机程序时实现如权利要求1至8中任意一项所述的网络管控方法。
  13. 一种网络系统，包括网络管控系统和数据获取装置，其中，所述网络管控系统与所述数据获取装置通信连接，所述网络管控系统用于执行如权利要求1至8任意一项所述的网络管控方法，所述数据获取装置用于执行如权利要求9至11任意一项所述的网络管控方法。
  14. 一种计算机可读存储介质，存储有计算机可执行指令，其中，所述计算机可执行指令用于执行如权利要求1至8中任意一项所述的网络管控方法，或者，执行如权利要求9至11中任意一项所述的网络管控方法。
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