Information transmission methods, first communication node, second communication node and storage medium

Abstract

Disclosed in the present application are information transmission methods, a first communication node, a second communication node and a storage medium. An information transmission method comprises: acquiring orchestration information transmitted by an intelligent management entity, wherein the orchestration information indicates the creation of a network digital twin instance for simulating a physical network; on the basis of the orchestration information and network data of the physical network, creating and running the network digital twin instance; acquiring a network policy of the physical network corresponding to a network request; performing simulation verification on the network policy by means of the digital twin instance to obtain a simulation verification result; and transmitting the simulation verification result to the intelligent management entity.

Description

Information transmission method, first communication node, second communication node and storage medium Technical Field

[0001] This application relates to the field of communication technology, such as information transmission methods, first communication nodes, second communication nodes, and storage media. Background Technology

[0002] Digital twin is a technology related to system automation. A digital twin object is a virtual, digital entity that corresponds to a physical entity in the real world. Digital twin technology models and simulates physical entities in the real world to achieve real-time monitoring, prediction, and optimization. Network digital twin (NDT) technology applies digital twin techniques to build a real-time mirror of a physical network, reflecting information such as network configuration, topology, and status. It allows for visualization, network optimization simulation and evaluation, verification, prediction of network behavior and trends, analysis of root causes of network failures, and validation of solutions.

[0003] In addition to network digital twin technology providing intelligent analysis and decision-making capabilities for networks, other intelligent management entities (IAMs) can also provide intelligent analysis and decision-making capabilities for networks.

[0004] When NDT and IAM are deployed simultaneously in a network, there will be an overlap of intelligent management functions, and NDT and IAM will be unable to work together. Summary of the Invention

[0005] This application provides an information transmission method, a first communication node, a second communication node, and a storage medium.

[0006] In a first aspect, embodiments of this application provide an information transmission method, including:

[0007] Acquire orchestration information transmitted by the intelligent management entity, wherein the orchestration information indicates the creation of a network digital twin instance for simulating the physical network;

[0008] Based on the orchestration information and the network data of the physical network, create and run a network digital twin instance;

[0009] Obtain the network policy of the physical network corresponding to the network request;

[0010] The network strategy is simulated and verified using the digital twin instance, and the simulation verification results are obtained.

[0011] The simulation verification results are transmitted to the intelligent management entity.

[0012] Secondly, embodiments of this application provide an information transmission method, including:

[0013] Retrieve network requests for the physical network;

[0014] The decision-making process utilizes a network digital twin to respond to network requests;

[0015] Arrange the information model of the network digital twin corresponding to the network request to obtain arrangement information;

[0016] Transmit the orchestration information and request the creation of a network digital twin instance based on the orchestration information;

[0017] The network policy of the physical network corresponding to the network request is transmitted, and a simulation verification operation is requested to be performed.

[0018] Obtain simulation verification results, which include the physical network optimization effect obtained from the simulation verification based on the network strategy.

[0019] Thirdly, embodiments of this application provide a first communication node, including:

[0020] One or more processors;

[0021] Storage device for storing one or more programs;

[0022] When the one or more programs are executed by the one or more processors, the one or more processors implement the method provided in the first aspect of the embodiments of this application.

[0023] Fourthly, embodiments of this application provide a second communication node, including:

[0024] One or more processors;

[0025] Storage device for storing one or more programs;

[0026] When the one or more programs are executed by the one or more processors, the one or more processors implement the method provided in the second aspect of the embodiments of this application.

[0027] Fifthly, embodiments of this application provide a storage medium storing a computer program, which, when executed by a processor, implements any of the methods described in the embodiments of this application.

[0028] Further details regarding the above embodiments and other aspects of this application, as well as their implementations, are provided in the accompanying drawings, detailed description, and claims. Attached Figure Description

[0029] Figure 1 is a flowchart illustrating an information transmission method provided in an embodiment of this application;

[0030] Figure 2 is a schematic diagram of a digital twin network architecture provided in an embodiment of this application;

[0031] Figure 3 is a flowchart illustrating another information transmission method provided in an embodiment of this application;

[0032] Figure 4 is a schematic diagram of a process for achieving NDT orchestration and high fidelity through IAM according to an embodiment of this application;

[0033] Figure 5 is a schematic diagram of an IAM collaborative NDTM simulation process provided in an embodiment of this application;

[0034] Figure 6 is a schematic diagram of a network model training and network fault inference process provided in an embodiment of this application;

[0035] Figure 7 is a schematic diagram of an NDTI data acquisition and modeling process from IAM provided in an embodiment of this application;

[0036] Figure 8 is a schematic diagram of the structure of an information transmission device provided in an embodiment of this application;

[0037] Figure 9 is a schematic diagram of the structure of another information transmission device provided in an embodiment of this application;

[0038] Figure 10 is a schematic diagram of the structure of a first communication node provided in an embodiment of this application;

[0039] Figure 11 is a schematic diagram of the structure of a second communication node provided in an embodiment of this application. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in detail below with reference to the accompanying drawings. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be arbitrarily combined with each other.

[0041] The steps illustrated in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases the steps shown or described may be performed in a different order than that presented here.

[0042] In this application, the terms "first," "second," etc., are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0043] Digital twins (DTs) are an increasingly popular technology related to system automation. A digital twin object is a virtual copy of a real-world system, a "physical" system on which operations can be performed.

[0044] Network Digital Twin (NDT) technology can construct a real-time mirror of a physical network, reflecting its configuration, topology, and status. It enables visualization, network optimization simulation and evaluation, and verification of the physical network. It can predict network behavior and trends, analyze the root causes of network failures, and validate solutions. Furthermore, it can integrate Artificial Intelligence (AI) and Machine Learning (ML) algorithms to provide intelligent decision support for network management.

[0045] Besides NDT, which can provide intelligent analysis and decision-making capabilities for the network, there are other intelligent and automation management functions (IAM) that can also provide intelligent analysis and decision-making capabilities for the network. For example, the AI / ML management function defined by the 3rd Generation Partnership Project (3GPP) can support intelligent management and automation in 5G systems; the Network Data Analytics Function (NWDAF) is used for mobility management, Quality of Service (QoS), and intelligent management and optimization of other network elements in the 5G core network; the Management Data Analytics Function (MDAF) is used to analyze collected management plane and network data to realize self-organizing network (SON) functions, such as network topology parameter configuration and QoS assurance; and the Network Large Model (NLM) processes and analyzes large amounts of network data, including traffic patterns and network performance indicators, to identify network trends and behavior patterns, predict future network behaviors or events, provide network optimization decision support, help optimize network configuration, resource allocation, and QoS, and automate network management tasks, such as automated fault detection and recovery and intelligent routing selection.

[0046] NDT, AI / ML management functions, MDAF, NWDAF, Network Large Model and other systems or functional entities all provide intelligent management capabilities for the network. When NDT and other intelligent management functions are deployed in the network at the same time, it is necessary to study how NDT can effectively coordinate with other network intelligent management entities to avoid the problem of overlapping intelligent management functions and the inability of multiple intelligent management entities to coordinate when NDT is deployed with other functional entities at the same time, which would consume network management resources and reduce network performance.

[0047] MDAF is a network management function. MDAF is an enabler for the automation and cognition of network and service management and orchestration. Combined with artificial intelligence (AI) and machine learning (ML) technologies, it brings intelligence and automation to network service management and orchestration. Based on network management data processing and analysis capabilities, MDAF can realize the value mining of data in the network management field; it can combine AI / ML technologies to process and analyze network management data, thereby outputting analysis reports and corresponding network management operation suggestions to promote the intelligence and automation of network management and orchestration, achieving closed-loop management of the network management domain. The core role of MDAF is to use data analysis to help the management system set reasonable network topology parameters for network configuration to ensure service quality. The main functions of MDAF include:

[0048] (1) Data Acquisition and Analysis: MDAF first needs to collect data from different parts of the network, which may include network performance indicators, user behavior data, device status information, etc. The collected data will be analyzed by MDAF to identify patterns, trends and potential problems in the network.

[0049] (2) Decision Support and Network Optimization: MDAF transforms the analysis results into decision support information, providing network administrators or automated systems with suggestions on how to optimize network performance. Based on MDAF analysis and suggestions, the network can be automatically adjusted and optimized, or under the guidance of administrators, to improve efficiency, reduce congestion, and save energy consumption.

[0050] (3) Automation and intelligence: MDAF has the ability to continuously learn and optimize its analysis model and decision-making strategy based on changes in the network environment and new data. At the same time, MDAF can improve the automation level of network management, reduce manual intervention, and enable the network to self-optimize and self-repair.

[0051] Large network models typically refer to large-scale machine learning or deep learning models applied to telecommunications networks. These models require training on massive datasets to learn patterns and features from the data. By pre-training on large-scale general datasets and then fine-tuning on specific tasks, large network models can adapt to different application scenarios, providing telecommunications networks with multifaceted capabilities, primarily including the following:

[0052] (1) Intelligent operation and maintenance: The network big model can analyze and predict the operation and maintenance data of the telecommunications network, realize automated fault detection, diagnosis and repair suggestions, thereby improving operation and maintenance efficiency and reducing manual intervention.

[0053] (2) Situational awareness: By using big data analysis and machine learning technology, the network big model can monitor the network status in real time, predict network traffic and usage patterns, and provide decision support for network management and optimization.

[0054] (3) Customer service optimization: By understanding customer queries and needs, the web big model can provide more personalized and efficient customer service, including intelligent customer service and automated problem solving.

[0055] (4) Network planning and construction: Network big data models can assist in network planning and construction. By analyzing historical data and predicting future trends, they can help operators optimize network layout and resource allocation.

[0056] (5) Automated decision-making: Based on deep learning models, large network models can simulate complex decision-making processes and provide data-driven decision support for telecom operators.

[0057] (6) Network Optimization and Maintenance: In the event of network problems or emergencies, the network big data model can quickly provide solutions and emergency measures to ensure network stability and reliability. Simultaneously, in terms of wireless network optimization, the network big data model can analyze wireless signal coverage and quality, provide optimization suggestions, and improve user service quality.

[0058] Based on the above issues, this application addresses how NDT and IAM can work together efficiently to provide network optimization strategies and solutions to resolve potential network problems when consumers have network optimization needs or when the network may experience potential faults or anomalies.

[0059] In one exemplary embodiment, Figure 1 is a flowchart illustrating an information transmission method provided in this application. This information transmission method is applicable to situations where NDT and IAM are used in conjunction to achieve physical network simulation and address network requirements. This embodiment describes the collaboration between NDT and IAM from the NDT perspective. The information transmission method can be executed by an information transmission device, which can be integrated into a first communication node. The first communication node can be a single terminal device or a combination of multiple terminal devices, as long as it can implement NDT. For example, Network Digital Twin Management (NDTM) and Network Digital Twin Instance (NDTI) can be integrated into a single terminal device, or NDTM and NDTI can be integrated into different terminal devices. NDTI can be considered an NDT instance used to simulate the physical network, performing physical network simulation on the NDTI. NDTM can be considered a management function within NDT, used to manage various NDTIs.

[0060] A Digital Twin Network (DTN) is a network system that digitally creates virtual twins of physical network entities and can interact and map with them in real time. Figure 2 is a schematic diagram of a digital twin network architecture provided in an embodiment of this application. Referring to Figure 2, the digital twin network can be designed as a "three-layer, three-domain, dual-closed-loop" architecture as shown in Figure 2: the three layers refer to the physical network layer, the twin network layer, and the network application layer that constitute the digital twin network system; the three domains refer to the data domain, model domain, and management domain of the twin network layer, corresponding to the three subsystems of data sharing warehouse, service mapping model, and network twin management, respectively; the "dual-closed-loop" refers to the "inner closed-loop" simulation and optimization based on the service mapping model within the twin network layer, and the "outer closed-loop" control, feedback, and optimization of network applications based on the three-layer architecture.

[0061] Building a DTN system requires the use of the following three types of interfaces:

[0062] (1) Twin Southbound Interface: This includes the data acquisition interface and the control delivery interface between the twin network layer and the physical network layer. The data acquisition interface is responsible for acquiring data from the twin network layer's data sharing repository, while the control delivery interface is responsible for sending control commands, verified by the service mapping model simulation, to the network elements of the physical network layer.

[0063] (2) Twin Northbound Interface: This includes the intent translation interface and capability invocation interface between the network application layer and the twin network layer. The network application layer can use the intent translation interface to pass application layer intents to the twin network layer, providing abstract requirement inputs for the functional model.

[0064] (3) Twin internal interfaces: including a set of interfaces such as the interface between the internal data warehouse and functional model of the twin network layer, the interface between the functional model and the digital twin management, and the interface between functional models.

[0065] As shown in Figure 1, the information transmission method provided in this application includes the following steps:

[0066] S110. Obtain the orchestration information transmitted by the intelligent management entity.

[0067] Intelligent Management Entities (IAM), also known as Intelligent Management Functions, provide intelligent analysis and decision-making capabilities to the network. Orchestration information can be considered as NDT orchestration information. Orchestration information may include twin network elements, twin connection topology, network resource configuration, and functional models such as session establishment models and user registration models for specific function implementation. It also includes operational environment models such as the number of UE accesses, network scale, service types, deployment areas, and Service Level Agreements (SLAs).

[0068] This operation can be performed by NDTM in NDT, which can obtain the orchestration information of IAM transmissions.

[0069] IAM and NDT collaborate to update and iteratively verify network policies and solutions, meeting network optimization requirements. IAM provides NDT orchestration capabilities and verifies the generated network policies through NDT instance simulation; the network digital twin NDT simulates and verifies network policies and provides simulation verification results to IAM for IAM to revise network policies.

[0070] Network policy can be considered as the strategy for addressing needs on the network. This includes network configuration parameters and software update plans.

[0071] In this operation, the orchestration information indicates the creation of a network digital twin instance for simulating the physical network.

[0072] S120. Based on the orchestration information and the network data of the physical network, create and run a network digital twin instance.

[0073] Network data can be considered as data associated with a network, and it can include network modeling data and network environment data. Network modeling data refers to the data required for modeling during network simulation. Network environment data refers to the data that characterizes the physical network environment during network simulation.

[0074] This operation determines the information model required to create a network digital twin instance based on orchestration information. The network digital twin instance is then built based on network modeling data and the information model, realizing modeling. Finally, network environment data is imported into the network digital twin instance to simulate the physical network.

[0075] Information models can be considered as an abstract, structured representation of relevant information in the real world. Information models include mapped network element models, topology models, functional models, etc.

[0076] Network element models can focus on the characteristics, attributes, and behaviors of various elements in a network, such as servers, switches, and routers. For example, a network element model might describe information such as the number of ports, processing capacity, and operating status of a router.

[0077] A topology model can show the connections and layout structure between various elements in a network. It depicts how network elements are interconnected to form a unified network architecture.

[0078] A functional model can describe the functions that network elements possess, as well as the interactions and collaborations between these functions.

[0079] Information models provide a comprehensive, clear, and accurate framework for understanding, analyzing, and managing network systems by modeling network elements, topology, and functions.

[0080] S130. Obtain the network policy of the physical network corresponding to the network request.

[0081] Network strategy can be considered as a set of rules, guidelines, schemes, and / or measures formulated in a network environment to achieve specific goals. Network strategy may include network solutions for resolving network problems.

[0082] This operation retrieves network policies from IAM. A network request can be considered a request associated with a physical network, such as a request for network optimization. A network policy is a strategy developed to respond to a network request. The physical network can be an actual network, such as a network composed of real physical devices and physical links.

[0083] S140. The network strategy is simulated and verified using the digital twin instance to obtain the simulation verification results.

[0084] NDTI simulates physical networks. After acquiring network policies, these policies can be executed on NDTI to verify their effectiveness. The simulation results can be considered the verification of the network policies. Analyzing these results can determine whether the executed network policies can resolve network requests, such as whether they can optimize the physical network.

[0085] S150. Transmit the simulation verification results to the intelligent management entity.

[0086] After determining the simulation verification results, this operation can transmit the simulation verification results to IAM for processing. This includes, for example, directly transmitting the results to the physical network, or verifying whether the simulation verification results meet expectations, such as whether they can optimize the physical network. If so, the results are transmitted to the physical network; if not, the network policy is updated, and the network policy is further verified until it meets expectations.

[0087] This application provides an information transmission method that orchestrates information through interaction with IAM (Integrated Information Management), then combines the orchestrated information with network data to simulate a physical network. The simulated physical network is then used to verify network policies corresponding to network requests, enabling responses to these requests. This achieves intelligent analysis and decision-making capabilities for the network through collaboration between IAM and NDT (Network Data Transmission).

[0088] Based on the above embodiments, modified embodiments of the above embodiments are proposed. It should be noted that, in order to keep the description brief, only the differences from the above embodiments are described in the modified embodiments.

[0089] In one embodiment, the network data includes network modeling data and network environment data, and the creation and operation of a network digital twin instance based on the orchestration information and the network data of the physical network includes:

[0090] Obtain a request to create a network digital twin instance based on the orchestration information;

[0091] Obtain the network modeling data of the physical network;

[0092] Create a network digital twin instance based on the network modeling data and the orchestration information;

[0093] The network environment data is imported into the network digital twin instance, and the network digital twin instance is used to simulate the operation of the physical network.

[0094] The request to create a network digital twin instance can be transmitted via IAM. In this embodiment, the request to create a network digital twin instance transmitted via IAM can be obtained. In response to this request, this embodiment can create an NDTI.

[0095] Network modeling data can be obtained from the IAM side or from the physical network; there is no limitation here.

[0096] In this embodiment, after receiving a request to create a network digital twin instance, an NDTI can be created based on orchestration information and network modeling data. The orchestration information indicates the information model required for the NDTI. The network modeling data can be the data needed to build the model.

[0097] After modeling NDTI, network environment data can be imported into NDTI to simulate the physical network.

[0098] In one embodiment, obtaining the network modeling data of the physical network includes one or more of the following:

[0099] A request to transmit the network modeling data to the intelligent management entity;

[0100] The network modeling data of the physical network is obtained from the intelligent management entity.

[0101] In this embodiment, obtaining network modeling data can be achieved by transmitting a request to IAM to obtain network modeling data, and then obtaining network modeling data from the IAM side.

[0102] When creating an NDT instance, NDTM can obtain network modeling data and network operating environment data from IAM, create an NDTI, and simulate the real network environment.

[0103] In one embodiment, importing the network environment data into the network digital twin instance includes:

[0104] The network environment data of the physical network is obtained from the intelligent management entity through network digital twin management;

[0105] The network environment data is obtained through network digital twin management;

[0106] The network environment data is imported into the network digital twin instance through network digital twin management.

[0107] In this embodiment, when importing network environment data, network environment data can be obtained from IAM via NDTM. For example, a request to transmit network environment data to IAM can be made via NDTM.

[0108] IAM can transmit network environment data to NDTM, NDTM acquires the network environment data, and then imports the network environment data into NDTI so that NDTI can simulate the physical network.

[0109] In this embodiment, network environment data can be imported into NDTI via NDTM.

[0110] In one embodiment, importing the network environment data into the network digital twin instance includes:

[0111] The network environment data of the physical network is obtained from the intelligent management entity through network digital twin management;

[0112] The network environment data imported by the intelligent management entity is obtained through a network digital twin instance.

[0113] In this embodiment, when importing network environment data, network environment data can be obtained from IAM via NDTM. For example, a request to transmit network environment data to IAM can be made via NDTM.

[0114] IAM can directly import network environment data into NDTI, so that NDTI can simulate physical networks.

[0115] In one embodiment, the information transmission method further includes:

[0116] Obtain the updated network policy of the physical network corresponding to the network request;

[0117] The updated network policy is further simulated and verified using the network digital twin instance, and the simulation verification results corresponding to the updated network policy are obtained and transmitted.

[0118] After transmitting the simulation verification results to IAM, if the simulation verification results of the network policy do not meet expectations, IAM can update the network policy and transmit the updated network policy to the first communication node, i.e., the NDT side. The NDT side can continue to simulate and verify the updated network policy through the network digital twin instance, obtain and transmit the simulation verification results corresponding to the updated network policy, so that IAM can verify it again. That is, IAM analyzes the simulation verification results of network optimization fed back by NDTI. If it does not meet expectations, it analyzes the reasons and updates the original network policy or creates a new network policy based on the analysis results, and then re-simulates and verifies until the generated network policy meets the network requirements.

[0119] In one embodiment, the information transmission method further includes:

[0120] The simulation verification results are used to determine whether they meet the requirements of the network request through a network digital twin instance.

[0121] If the simulation verification result does not meet the requirements of the network request, the network policy is updated through the network digital twin instance, and the updated network policy is simulated and verified again through the network digital twin instance to obtain the simulation verification result corresponding to the updated network policy. Then, the process returns to continue to determine whether the simulation verification result meets the requirements of the network request.

[0122] If the simulation verification results meet the network request, the simulation verification results are transmitted through a network digital twin instance.

[0123] In this embodiment, NDTI can have AI capabilities. NDTI determines whether the simulation verification results meet the requirements of the network request. If the requirements are met, the simulation verification results can be transmitted to IAM for processing, such as re-verification or direct transmission to the physical network. If the requirements are not met, NDTI can update the network policy and then continue to simulate and verify the updated network policy to obtain the simulation verification results of the updated network policy. Then, it continues to determine whether the simulation verification results meet the requirements.

[0124] IAM allows NDTI to optimize and iteratively verify network strategies and solutions. During simulation verification, if NDTI finds that the network strategy does not meet expectations, NDTI can automatically update the network strategy and perform iterative verification until the updated network strategy and solution meet the requirements.

[0125] In one embodiment, the information transmission method further includes:

[0126] The network digital twin management obtains the twin simulation verification request and the target strategy verified by the physical network.

[0127] The target policy is verified using a network digital twin instance to obtain the policy verification result of the target policy;

[0128] The policy verification results are transmitted via a network digital twin instance.

[0129] A twin simulation verification request can be considered a request to verify the twin network of a physical network. A target policy verified through the physical network can be considered a policy executed and verified within the physical network. The policy verification result can be considered the result obtained after NDTI executes the target policy. IAM analyzes the fidelity between the NDT instance and the mapped physical network based on the policy verification result, corrects the NDT information model, and updates the NDT instance, enabling the NDT instance to map part or all of the live network with high fidelity.

[0130] To verify whether NDTI can accurately simulate the live network environment, IAM ran some network strategies on NDTI and the live network, compared the actual differences between NDTI and the live network, analyzed the reasons for the differences, updated the NDT orchestration and updated NDTI to reduce the differences from the live network environment, until NDTI achieved a high-fidelity simulation effect.

[0131] IAM compares the differences between NDTI and its mapped network environment when implementing the same network strategy, adjusts the NDT orchestration based on the difference analysis, and updates the NDT information model.

[0132] IAM initiates an NDT instance update request to NDTM, carrying the updated NDT information model and the NDTI identifier.

[0133] NDTM can update NDTI. Then, it continues to execute the validated target policy until the difference between NDTI and the physical network's operating environment is less than the set value.

[0134] In one embodiment, the information transmission method further includes:

[0135] Requests for target scenarios used for model training are obtained through network digital twin management;

[0136] The target scenario is simulated using a network digital twin instance;

[0137] The target data in the target scenario is transmitted to the intelligent management entity through a network digital twin instance.

[0138] The target scene can be considered the environment used by IAM for model training. IAM can acquire target data from the target scene to achieve model training. Target data can be data from the target scene. For example, target data can be data used for AI / ML training of the model, enabling IAM to more accurately simulate and predict network behavior.

[0139] When IAM trains network models, the NDT instance can simulate various model training scenarios required by IAM, i.e. target scenarios. IAM obtains data for AI / ML training from the NDT instance for model training.

[0140] In this embodiment, NDTM can obtain a request for the target scene used for training the simulation model from IAM, simulate the target scene through NDTI, and then transmit the target data to IAM for training.

[0141] In one embodiment, the information transmission method further includes:

[0142] Requests to simulate network anomaly scenarios in the physical network are obtained through network digital twin management;

[0143] The network anomaly scenario is simulated using a network digital twin example;

[0144] The network anomaly data in the aforementioned network anomaly scenario is transmitted to the intelligent management entity through a network digital twin instance;

[0145] The abnormal strategy transmitted by the intelligent management entity is obtained and verified through a network digital twin instance, and the abnormal verification result is obtained.

[0146] The anomaly verification result is transmitted to the intelligent management entity.

[0147] Network anomaly scenarios can be considered as simulated network anomalies in a physical network. These scenarios can include various network anomalies and faults, such as network configuration errors, network congestion, terminal access anomalies, service outage anomalies, network performance degradation, and wireless base station failures, such as antenna coverage issues and cell handover problems.

[0148] Network anomaly data can be considered as data generated under abnormal network conditions. This data is used by IAM (Internet Information Management) for network analysis to develop anomaly strategies. Anomaly strategies are the methods used to respond to and resolve network anomalies. Anomaly verification results are the results obtained after implementing the anomaly strategies. These results are used to verify whether the anomalies in the network anomaly scenario have been resolved.

[0149] In this embodiment, for potential network anomalies and faults, the consumer or IAM requests NDTI to simulate various network anomaly and fault scenarios that cannot be simulated in the current network, i.e., network anomaly scenarios.

[0150] IAM collects data from abnormal scenarios simulated by NDTI to analyze the causes of network anomalies and generates possible anomaly strategies and solutions. NDTI then verifies whether these solutions can resolve the network anomalies through simulation. NDTI performs simulation verification and feeds back the anomaly verification results to IAM. IAM analyzes the anomaly verification results and updates the anomaly strategies, which are then verified by NDTI again. If the anomaly strategies and solutions can resolve the network anomaly problem, then IAM can provide a solution.

[0151] In one exemplary embodiment, this application also provides an information transmission method. Figure 3 is a flowchart illustrating another information transmission method provided in an embodiment of this application. The information transmission method can be applied to situations where NDT and IAM are used to achieve physical network simulation and address network requirements. The information transmission method can be executed by an information transmission device and integrated on a second communication node, i.e., the IAM side. Details not covered in this embodiment can be found in the above embodiments and will not be elaborated upon here.

[0152] As shown in Figure 3, the information transmission method provided in this application includes the following steps:

[0153] S310, Obtain network requests for the physical network.

[0154] Network requests can be network optimization needs. For example, improving the performance of the Radio Access Network (RAN) may require optimizing the path of Protocol Data Unit (PDU) sessions, or it may require predicting potential network failures and optimizing the network to address any possible network problems.

[0155] S320, The decision adopts a network digital twin to respond to network requests.

[0156] After IAM receives a network request, it can decide whether to respond to the network request using NDT.

[0157] S330. Arrange the information model of the network digital twin corresponding to the network request to obtain the arrangement information.

[0158] IAM can be used to orchestrate information models for NDT, and the orchestration results can be used by NDT to perform physical network simulation.

[0159] S340. Transmit the orchestration information and request the creation of a network digital twin instance based on the orchestration information.

[0160] After determining the orchestration information, IAM can transmit the orchestration information to NDT and request NDT to create a network digital twin instance based on the orchestration information.

[0161] S350. Transmit the network policy of the physical network corresponding to the network request, and request the execution of simulation verification operation.

[0162] After IAM formulates a network policy for a network request, it can transmit the network policy to NDT. IAM then transmits a request to NDT to perform simulation verification operations so that NDT can perform simulation verification.

[0163] S360, Obtain simulation verification results.

[0164] The simulation verification results include the physical network optimization effects obtained from the simulation verification based on the network strategy. IAM can obtain the simulation verification results from NDT.

[0165] The information transmission method provided in this application, through interaction with NDT to orchestrate information, network strategies, and simulation verification results, enables the simulation of physical networks and the verification of network strategies, thereby enabling IAM and NDT to collaboratively provide intelligent analysis and decision-making capabilities for the network.

[0166] Based on the above embodiments, modified embodiments of the above embodiments are proposed. It should be noted that, in order to keep the description brief, only the differences from the above embodiments are described in the modified embodiments.

[0167] In one embodiment, orchestrating the information model of the network digital twin corresponding to the network request to obtain orchestration information includes:

[0168] The collaborative network digital twin management orchestrates the information model of the network digital twin corresponding to the network request.

[0169] In the process of determining the orchestration information for NDT, IAM can work with NDTM to determine the orchestration information, and the NDT side can have the ability to participate in the orchestration information.

[0170] In one embodiment, the information transmission method further includes:

[0171] Transmit twin simulation verification requests and target strategies verified by the physical network;

[0172] Obtain the policy verification result corresponding to the target policy;

[0173] Determine the difference information between the policy verification result and the reference verification result of the physical network verifying the target policy;

[0174] Based on the difference information, the updated orchestration information is determined and transmitted.

[0175] IAM analyzes the high fidelity of NDT instances and mapped physical networks, corrects the NDT information model, and updates NDT instances so that NDT instances can map part or all of the existing network with high fidelity.

[0176] The reference verification result can be considered as the result of the physical network verifying the target strategy. The difference information can be considered as information indicating the difference between the strategy verification result and the reference verification result.

[0177] If the difference information indicates that the difference does not meet the requirements, the orchestration information can be updated and then transmitted to the NDT side.

[0178] In one embodiment, the information transmission method further includes:

[0179] The simulation verification results are analyzed to obtain the evaluation results of the simulation verification results;

[0180] If the evaluation results indicate that the simulation verification results need to be optimized, update the network policy of the physical network and continue to request the execution of the simulation verification operation;

[0181] Obtain the simulation verification results of the updated network policy.

[0182] The evaluation results can be considered as information evaluating the simulation verification results. IAM analyzes the evaluation results fed back by NDTI. If the results do not meet expectations, the reasons are analyzed, and the original network strategy is updated or a new network strategy is created based on the analysis results. The simulation verification is then repeated until the generated network optimization strategy meets the consumer's network requirements.

[0183] In one embodiment, the information transmission method further includes:

[0184] If the evaluation result indicates that the simulation certification result meets the requirements of the network request, the network policy corresponding to the evaluation result is transmitted to the physical network.

[0185] In one embodiment, the information transmission method further includes:

[0186] Upon receiving confirmation information about the network policy, the network policy is transmitted to the physical network, and the simulation verification results are transmitted to the target device.

[0187] Confirmation information can be obtained through backend or manual verification. Simulation verification results can be considered as notifications to users and third-party applications regarding the expected network optimization effects. The target device can be considered the device for which the simulation verification results are required, such as the user's device.

[0188] In one embodiment, the information transmission method further includes:

[0189] Obtain target data from the target scene;

[0190] The network model is trained based on the target data.

[0191] IAM transmits a request for the target scenario used for model training to the intelligent management entity. It then acquires the target data for training the network model.

[0192] In one embodiment, the information transmission method further includes:

[0193] Acquire network anomaly data in network anomaly scenarios;

[0194] Based on the network anomaly data, determine the anomaly strategy for the network anomaly scenario;

[0195] Transmit the aforementioned exception policy.

[0196] IAM transmits requests simulating network anomaly scenarios in the physical network. It then acquires network anomaly data, executes anomaly policies, and transmits these policies to NDT.

[0197] The following is an exemplary description of this application. The information transmission method provided in this application can be considered as a method for the collaboration of digital twin and intelligent management functions. This application proposes how to effectively collaborate between NDT function and other intelligent management functions of the network, such as MDAF and network big model, in the network operation and maintenance management system, so as to achieve efficient network management and operation and maintenance capabilities through mutual cooperation.

[0198] When network problems such as performance degradation occur, consumers request network optimization from the Intelligent Management Function (IAM), also known as the Intelligent Management Entity. IAM decisions and NDT collaborate to resolve these network issues. IAM analyzes the physical network that needs to be simulated using NDT. IAM acts as the orchestrator for NDT, obtaining orchestration information such as the mapped NDT information model, including the mapped network element model, topology model, and functional model. IAM then requests NDTM to create an NDT instance that maps the physical network based on the NDT information model. To ensure that the NDT instance can accurately simulate the mapped physical network, IAM compares and analyzes the differences between the two instances running the same network strategy or network scheme, continuously refining the NDT instance to ensure high-fidelity simulation of the mapped network.

[0199] To optimize existing network issues, IAM collected and analyzed network data from the live network, developing various possible optimization strategies and solutions. IAM requested NDTI to simulate the live network environment and validate the optimization strategies and solutions within that environment. Based on the simulation results, IAM continuously revised and simulated the optimization strategies and solutions until they met the network optimization requirements. Through multiple interactions with NDTI, IAM ultimately obtained the network strategies and solutions.

[0200] If NDTI has AI capabilities, IAM can allow NDTI to independently deduce network strategies and solutions. When NDTI assesses that the initial network strategies and solutions from IAM do not meet network optimization requirements, NDTI can independently revise the network strategies and solutions and perform iterative simulation verification until the revised network strategies and solutions meet expectations. NDTI sends the revised network strategies and solutions and simulation verification results to IAM (i.e., determining whether the simulation verification results meet the requirements of the network request through a network digital twin instance; if the simulation verification results do not meet the requirements of the network request, updating the network strategy through the network digital twin instance, continuing to simulate and verify the updated network strategy through the digital twin instance to obtain the simulation verification results corresponding to the updated network strategy, and returning to continue judging whether the simulation verification results meet the requirements of the network request; if the simulation verification results meet the network request, transmitting the simulation verification results through the network digital twin instance), for IAM to analyze and decide whether the revised network strategies and solutions need to be updated again, and then submitted to NDTI for verification.

[0201] IAM can also be combined with NDTM, thus giving NDT intelligent capabilities, providing network optimization analysis and generating network strategies and solutions, and the ability to iteratively optimize network strategies and solutions based on simulation verification results.

[0202] To optimize networks in response to potential future network anomalies and failures, IAM needs to collect and analyze network data in the network environment and develop various possible network optimization strategies and solutions. Consumers or IAMs may request NDTI to realistically simulate various potential future network anomalies. In this environment, IAM collects and analyzes network data, develops network strategies and solutions, and performs simulation verification in the network anomaly environment run by NDTI until the network optimization strategies and solutions meet expectations.

[0203] Furthermore, NDTI and IAM can provide each other with network data. NDTI can simulate various network operating environments, while IAM can collect massive amounts of AI / ML data from NDTI for model training. When NDTM needs to model for NDTI, it also needs to obtain network data from IAM. IAM can provide historical network data and has the ability to generate live network data, providing this network data to NDTM for NDT modeling and creating NDT instances.

[0204] In one embodiment, the Intelligent Management Entity (IAM) is responsible for NDT orchestration and achieving high fidelity simulation of the physical network. This embodiment describes a scenario where a consumer, such as an operator or third-party service provider, requests network optimization. The Intelligent Management Function (IAM) analyzes the network optimization request and decides to use NDT to implement network optimization simulation verification (i.e., decides to use a network digital twin to respond to the network request). The IAM acts as an NDT orchestrator or collaborates with the Network Digital Twin Management Module (NDTM) to orchestrate NDT instances (i.e., collaborates with the Network Digital Twin Management Module to orchestrate the information model of the network digital twin corresponding to the network request). Based on the network optimization request, it orchestrates the required NDT information model, which includes, but is not limited to, basic models such as twin network elements, twin connection topology, and network resource configuration, as well as functional models such as session establishment models and user registration models for specific function implementation. It also includes an operating environment model, such as the number of UE accesses, network scale, service type, deployment area, and SLA. The IAM requests the NDTM to create an NDT instance based on the orchestrated NDT information model description (i.e., requests the creation of a network digital twin instance based on the orchestration information). NDTM uses data from the existing network to model relevant models and create NDTI (such as creating and running a network digital twin instance based on the orchestration information and network data of the physical network).

[0205] To verify whether NDTI can accurately simulate the live network environment, IAM runs some network policies on both NDTI and the live network, compares the actual differences between NDTI and the live network, analyzes the causes of these differences, updates the NDT orchestration and NDTI to reduce the differences from the live network environment, until NDTI achieves a high-fidelity simulation effect (i.e., obtaining the twin simulation verification request and the target policy verified by the physical network through network digital twin management; verifying the target policy through network digital twin instances to obtain the policy verification result of the target policy; and transmitting the policy verification result through network digital twin instances).

[0206] Figure 4 is a schematic diagram of a process for achieving NDT orchestration and high fidelity through IAM according to an embodiment of this application. Referring to Figure 4, the specific process includes the following steps:

[0207] Step 101: Determine whether the network needs optimization based on its operating status.

[0208] Consumer devices determine whether network optimization is needed based on network operating status. Consumer devices can be considered the terminal devices corresponding to consumers, such as operators or third-party service providers. They obtain data from physical entities, such as the existing network operating environment, including network performance data and network operating status information, to determine if network problems exist, such as network congestion, and thus whether network optimization is necessary.

[0209] Step 102: Network simulation verification request.

[0210] In this step, the consumer device sends a network simulation verification request to the IAM. Consumers, such as operators and third-party service providers, submit network optimization requests, and the IAM obtains these requests for the physical network. These requests may include improving RAN performance, optimizing PDU session paths, or predicting potential network failures and optimizing for any emerging issues.

[0211] Consumers send their network optimization requests to IAM.

[0212] Step 103: Analyze requirements and NDT orchestration.

[0213] After receiving a network optimization request, the Intelligent Management Entity (IAM) decides to use Digital Twin (NDT) technology to simulate and verify the network optimization. This involves obtaining network requests for the physical network and responding to those requests using a network digital twin.

[0214] After analyzing the requirements, the information model of the network digital twin corresponding to the network request is arranged to obtain the arrangement information.

[0215] Step 104: NDT instance creation request (NDT information model).

[0216] The Intelligent Management (IAM) function analyzes network optimization requirements and acts as an NDT orchestrator or collaborates with NDTM to orchestrate NDT instances. It extracts relevant information for orchestrating the required NDT information models, including but not limited to basic models such as twin network elements, twin connection topology, and network resource configuration, as well as functional models such as session establishment models and user registration models, for specific function implementation. It also includes runtime environment models such as the number of UE accesses, network scale, service type, deployment area, and SLA.

[0217] IAM requests NDTM to create an NDT instance based on the orchestrated NDT information model description (if the orchestration information is transmitted, a request is made to create a network digital twin instance based on the orchestration information).

[0218] IAM can also be combined with NDTM to provide NDT orchestration capabilities.

[0219] Step 105: Create an NDT instance.

[0220] After receiving the NDT instance creation request, NDTM uses relevant data from the existing network to model the relevant model and create the NDTI based on the NDT information model. That is, based on the orchestration information and the network data of the physical network, it creates and runs a network digital twin instance.

[0221] Step 106: Request twin emulation operation (verified network policy).

[0222] NDTM sends a message to IAM indicating that NDTI creation was successful, and can carry relevant information about NDTI, such as the NDTI identifier, related twin network elements and resource configurations included in the NDTI.

[0223] To ensure that the NDTI can accurately map some or all of the physical network environment (such as RAN, core network (CN), base station, and some core network elements), IAM needs to compare the differences between the NDTI and its mapped physical network environment when implementing the same network policy. Based on the difference analysis, the orchestration of the NDT is adjusted so that the final NDTI can accurately map the physical network.

[0224] IAM requests NDTM to perform a simulation verification operation, executes the network policy (i.e., the target policy) verified by the physical network on the created NDTI, and carries the network policy and NDTI identifier; NDTM obtains the twin simulation verification request and the target policy verified by the physical network.

[0225] Step 107: Perform twin simulation operation.

[0226] NDTM requests NDTI to perform a simulation operation to verify the network policy (i.e., the target policy). NDTI then verifies the network policy.

[0227] Step 108: Feedback on operation results.

[0228] NDTI feeds back the verification results (i.e., policy verification results) to IAM directly or through NDTM.

[0229] Step 109: Evaluate the simulation accuracy of NDT and update the NDT orchestration.

[0230] IAM compares the differences between NDTI and its mapped network environment when implementing the same network strategy, adjusts the NDT orchestration based on the difference analysis, and updates the NDT information model.

[0231] Step 110: NDT instance update request (updated NDT information model).

[0232] IAM initiates an NDT instance update request to NDTM, carrying the updated NDT information model and the NDTI identifier.

[0233] Step 111: Update the NDT instance.

[0234] NDTM updates the created NDTI based on the updated NDT information model, such as updating the functional model and resource model.

[0235] Step 112: Repeat steps 106-111 until IAM determines that the simulation accuracy of the NDT instance meets expectations.

[0236] IAM continues to verify the simulation fidelity of NDTI, repeating steps 106 to 111 until the high fidelity of NDTI meets expectations and the NDTI simulation network environment is basically consistent with its mapped physical network environment.

[0237] Step 113: Request network simulation verification operation.

[0238] IAM generates potential network optimization strategies or schemes based on network requirements, and requests NDTM to verify through simulation using the NDTI instance whether the network optimization strategy or scheme can meet the network requirements. This includes transmitting the network policy of the physical network corresponding to the network request and requesting the execution of the simulation verification operation.

[0239] Step 114: Feedback on network simulation verification results.

[0240] NDTM feeds back the simulation verification results of network optimization strategies to IAM, i.e., obtains the simulation verification results, IAM selects network optimization strategies, and feeds back the network optimization strategies or network solutions that meet the network requirements to the consumer.

[0241] In one embodiment, the intelligent management entity (IAM) collaborates with NDT (Network Targeting Technology) to simulate and verify network strategies. Figure 5 is a schematic diagram of an IAM-collaborative NDTM simulation process provided in an embodiment of this application. Referring to Figure 5, when consumers, such as operators or third-party service providers, propose network optimization requirements based on the actual network operation conditions, such as network performance degradation, the intelligent management function (IAM) analyzes the network optimization requirements, generates possible network optimization strategies and solutions, and uses NDTI (Network Targeting Technology) to simulate and verify whether these network optimization strategies can improve network performance and meet user needs. NDTI simulates and verifies all possible network strategies and solutions and sends the final simulation verification results to IAM. IAM analyzes the network optimization results fed back from the simulation verification. If the results do not meet expectations, it analyzes the reasons, finds the problem, updates the original network strategy or creates a new network strategy, and re-simulates and verifies until the generated network optimization strategies and solutions meet the consumer's network needs.

[0242] If NDTM / NDTI has embedded AI capabilities and possesses a certain level of intelligence and reasoning ability, NDTM / NDTI can provide analysis of simulation verification data and autonomously optimize network strategies or schemes. It can then provide simulation verification operations for the optimized network strategies or schemes and provide the original network strategy simulation verification results, the optimized network strategy, and the optimized network strategy simulation verification results to IAM, which will then decide how to update the network strategy.

[0243] In this example, IAM can also be combined with NDTM, so that NDT has intelligent capabilities, providing network optimization analysis and generating network strategies and solutions, and the ability to iteratively optimize network strategies and solutions based on simulation verification results.

[0244] Step 201: Network optimization request.

[0245] Consumers, such as operators and third-party service providers, obtain data from the existing network operating environment, such as network performance data and network operating status information, to determine whether there are problems with the network, such as network congestion or network performance degradation.

[0246] Consumers submit network optimization requests. These might include improving RAN performance, optimizing PDU session paths, or predicting potential network failures and optimizing for any resulting issues. The consumer then sends these network optimization requests to the IAM (Internet Information Center).

[0247] Step 202: Collect network data and analyze network problems.

[0248] After receiving a network optimization request, the Intelligent Management Entity (IAM) collects network data from the physical network, such as network operation data, analyzes the current network status, such as network performance and service characteristics, identifies network problems from the analysis, and analyzes potential causes.

[0249] Step 203: Analyze the requirements and formulate network optimization strategies or solutions.

[0250] The Intelligent Management Entity (IAM) analyzes network optimization needs and generates possible network optimization strategies and / or solutions for existing or potential network problems.

[0251] Step 204: Network simulation verification request.

[0252] The Intelligent Management Function (IAM) requests the NDTM to perform simulation verification operations. The IAM requests the NDTM to simulate and verify possible network optimization strategies and solutions on the created NDTI, carrying the network strategy and NDTI identifier.

[0253] Step 205: Request to execute simulation verification operation

[0254] The NDTM requests the NDTI to perform a simulation verification operation. For example, it transmits the network policy of the physical network corresponding to the network request and requests the execution of the simulation verification operation.

[0255] Step 206: Perform simulation verification operation, optionally perform intelligent iterative operation.

[0256] NDTI performs simulation verification of the network optimization strategies and schemes generated by IAM and generates verification results. For example, it obtains the network policy of the physical network corresponding to the network request; it then performs simulation verification of the network policy using the digital twin instance to obtain simulation verification results.

[0257] If NDTI possesses AI capabilities, including a certain level of intelligence and reasoning ability, it can determine whether the results meet network optimization requirements, analyze the result data, and further autonomously optimize network strategies or solutions. It then provides simulation verification operations for the optimized network strategies or solutions until the network optimization results meet the requirements. For example, it can determine whether the simulation verification results meet the network request requirements through a network digital twin instance; if the simulation verification results do not meet the network request requirements, it updates the network strategy through the network digital twin instance, continues to simulate and verify the updated network strategy through the digital twin instance, obtains the simulation verification results corresponding to the updated network strategy, and returns to continue determining whether the simulation verification results meet the network request requirements; if the simulation verification results meet the network request, it transmits the simulation verification results through the network digital twin instance.

[0258] Step 207: Feedback on simulation results.

[0259] NDTI then verifies the results of the first network policy simulation, and can further provide the optimized network policy and the simulation results to IAM, which will then decide how to update the network policy.

[0260] Step 208: Evaluate and update network policies.

[0261] After receiving the network strategy simulation verification results from NDTM, IAM analyzes the relevant data, evaluates the effectiveness of network optimization, and further optimizes the initial network strategy and scheme based on the analysis and evaluation. If NDTI has already provided optimized network strategies and evaluation results, IAM also needs to analyze whether they meet network requirements, whether there is potential for further optimization, and determine whether the network strategy needs to be updated based on the evaluation results.

[0262] Step 209: Repeat steps 204-207 until IAM determines that the network policy meets the network optimization requirements.

[0263] IAM continues to initiate simulation verification operations to NDTM, carrying the updated network strategy and solution. NDTI continues the simulation verification operation of this network optimization strategy. Repeat steps 204 to 208 until the updated network strategy and solution meet the network optimization requirements.

[0264] Step 210: Manual evaluation, and advance notification of the network optimization effect to the UE and application function (AF).

[0265] Step 211: Network policy distribution.

[0266] IAM distributes the network optimization strategies and schemes verified by NDT to the physical network, which then executes these strategies and schemes.

[0267] Step 212: Feedback on the actual network operation results.

[0268] IAM collects physical network data and analyzes whether the network performance improvement or network optimization after the network policy is implemented meets expectations.

[0269] Step 213: Feedback on the network optimization execution results.

[0270] If the network optimization strategy and solution do not meet user needs in the physical network, IAM can repeat steps 202-211 for further optimization until the network strategy meets the network optimization requirements. IAM then provides feedback to the consumer on the network optimization strategy and results.

[0271] In one embodiment, this application provides a scheme for network model training and intelligent network fault prediction. Figure 6 is a schematic diagram of a network model training and network fault prediction process provided in an embodiment of this application. Referring to Figure 6, consumers, such as operators and third-party service providers, generate massive amounts of network data through digital twin instances to train large and small network models using AI / ML. The network digital twin instances can simulate specific scenarios for model training, thereby generating a large amount of simulated data. This data can be used to train and optimize the large model, making it more accurate in simulating and predicting network behavior. The small network model can be considered a small-scale machine learning or deep learning model. The large network model can refer to a neural network model with a large number of parameters and a complex architecture. The small network model is relatively small in scale, has fewer parameters, and lower computational complexity. The division of scale is not limited here and can be defined according to the actual situation.

[0272] For intelligent network fault prediction solutions, when consumers, such as operators and third-party service providers, deploy and operate existing networks, they need to predict and predict various faults and anomalies that may occur during network operation, and find optimization strategies and solutions for various possible abnormal situations and network faults to prevent possible network anomalies. Various network anomalies and faults include network configuration anomalies, network congestion, terminal access anomalies, service outage anomalies, network performance degradation, and wireless base station faults such as antenna coverage and cell handover.

[0273] Network Digital Twin (NDT) can simulate various network anomalies and faults that may occur during network deployment and operation. The Intelligent Management Analytics (IAM) function analyzes network anomaly data from the Network Digital Twin Inference Technology (NDTI) simulation and proposes network optimization strategies or solutions for the observed anomalies and faults. IAM then distributes these optimization strategies and solutions to NDTI for execution, verifying their effectiveness in resolving the network anomalies and faults. NDTI sends the verification results of the network strategy and solution back to IAM. IAM analyzes the simulation results; if they do not meet expectations, it updates the initial network strategy and solution and iterates the verification process in NDTI until the updated strategy and solution can resolve the network anomaly or fault.

[0274] Consumers download the network policies and solutions verified by IAM simulation to the physical network for execution, in order to avoid possible network anomalies and failures.

[0275] Step 301: Request a simulation of the model training scenario / potential anomaly scenario in the physical network.

[0276] Step 301 is divided into step 301a and step 301b according to network model training and intelligent inference of network faults.

[0277] Step 301a: For the requested simulation model training scenario (i.e., the target scenario), train the large or small network model to accurately simulate or predict network behavior. Consumers, such as operators, need to extract massive amounts of data from the network for training and simulation. However, collecting massive amounts of data from the live network severely impacts network performance and reduces business processing capabilities. Furthermore, data from some network anomaly scenarios cannot be collected in real-time from the live network.

[0278] Consumers can use the mirror network NDT of the physical network to simulate various scenarios requiring model training and abnormal scenarios, and obtain AI / ML training data from the NDT simulation environment. Consumers send requests to simulate model training scenarios to NDTM, requesting that the scenario be simulated on the corresponding NDTI.

[0279] Step 301b: In response to potential abnormal scenarios in the physical network, when deploying and operating the live network, consumers, such as operators, need to anticipate and simulate various faults and anomalies that may occur during network operation, and find optimization strategies and solutions for various possible abnormal conditions and network faults to prevent possible abnormal network states, such as network congestion, abnormal terminal access, and abnormal mobility handover.

[0280] After a consumer requests the creation of a twin network instance of part or all of the physical network, they request NDTM to simulate possible network anomalies and network failures, i.e., to simulate network anomaly scenarios of the physical network. The consumer can include the type of network anomaly to be simulated and the NDTI identifier. The network anomaly scenarios are simulated using NDTI, and IAM analyzes and resolves the network anomalies.

[0281] Step 302: Request a simulated model training scenario / physical network anomaly scenario.

[0282] Step 302 is divided into step 302a and step 302b according to network model training and intelligent inference of network faults.

[0283] Step 302a: The NDTM requests the model training scenario required by the consumer from the created NDTI instance simulation. The scenario type can be carried in the message.

[0284] Step 302b: The NDTM requests the created NDTI instance to simulate the network anomaly scenario required by the consumer, which is specified by the network anomaly type.

[0285] Step 303: Simulate abnormal network scenarios / model training scenarios.

[0286] Steps 303a / b: NDTI simulates network model training scenarios and network anomaly scenarios, such as network congestion and abnormal service processing. This pre-reproduces potential network anomalies that may occur during live network operation.

[0287] Step 304a: Request network model training.

[0288] Consumers request the Intelligent Management (IAM) function for training by collecting NDTI data, carrying the model training scenario information of that NDTI.

[0289] Step 304b: Request to resolve network error.

[0290] Consumers request the Intelligent Management Assistant (IAM) function to resolve network anomalies or network failures predicted by NDTI.

[0291] Step 305a: Collect AI / ML training data.

[0292] The Intelligent Management Entity (IAM) can interact with the NDTM to request the necessary network data from the corresponding NDTI for AI / ML training.

[0293] Step 305b: Network operation data.

[0294] After receiving a request to resolve a network anomaly, the Intelligent Management Entity (IAM) collects network operation data from the twin network that NDTI is currently simulating, analyzes the twin network status (such as network performance and service characteristics), discovers network anomalies and faults from the analysis, and analyzes potential causes.

[0295] Step 306a: Network model training.

[0296] IAM feeds back the results of the network model training to the consumer, who can repeat steps 301a to 305a until the consumer believes that the results of the network model training have met expectations.

[0297] Step 306b: Analyze network faults and formulate network optimization strategies or solutions.

[0298] IAM generates possible network optimization strategies and solutions for existing or potential network problems.

[0299] Step 307a: Feedback on network model training results, at which point model training ends.

[0300] The Intelligent Management Function (IAM) requests simulation verification of possible network optimization strategies and solutions from the NDTI.

[0301] Step 307b: Request simulation verification of network policies and solutions.

[0302] Step 308: Network policy simulation evaluation.

[0303] NDTI performs simulation evaluations on the network optimization strategies and schemes generated by IAM and generates simulation evaluation results.

[0304] If NDTI possesses AI capabilities and a certain level of intelligence and reasoning ability, IAM can allow NDTI to perform network strategy and solution optimization and simulation iteration operations. NDTI can determine whether the results have resolved network anomalies and faults. If not, it can use AI capabilities to extrapolate and further improve network strategies or solutions. Then, it can provide simulation verification operations for the optimized network strategies or solutions, and iteratively verify until the network anomalies or faults are resolved.

[0305] Step 309: Feedback on network optimization results.

[0306] NDTI then verifies the results of the first network policy simulation, and can further provide the optimized network policy and the simulation results to IAM, which will then decide how to update the network policy.

[0307] Step 310: Optimize network strategies and solutions.

[0308] After receiving the network policy simulation verification results from NDTM, IAM analyzes the relevant data, evaluates the effectiveness of the network policy implementation, and further optimizes the initial network policy and scheme based on the analysis and evaluation. If NDTI has already provided optimized network policies and evaluation results, IAM also needs to analyze whether they meet its requirements, whether there is any possibility of further optimization, and determine whether the network policy needs to be updated based on the evaluation results.

[0309] Step 311: Iterative simulation verification until the simulated network fault is resolved.

[0310] IAM continues to initiate simulation verification operations to NDTI, carrying the updated network strategy and solution. NDTI continues the simulation verification operation of this network optimization strategy. Steps 307 to 311 are repeated until the updated network strategy and solution resolve the network anomaly or network failure.

[0311] Step 312: Feedback on network anomaly resolution.

[0312] IAM provides consumers with feedback on the resolution of network anomalies or network failures, along with network strategies and solutions.

[0313] Step 313: Adopt verified network strategies and solutions to avoid potential network anomalies.

[0314] IAM distributes the network optimization strategies and solutions verified by NDT to the physical network, which then executes these strategies and solutions to avoid potential network anomalies and failures.

[0315] In one embodiment, NDTI obtains data from IAM for modeling. IAM, including network intelligence features such as large network models, has already been trained using massive amounts of data from the physical network. These large network models can be used to provide intelligent network decision-making, network fault prediction, and network policy generation. Similarly, when NDTM creates an NDTI instance, it also needs to acquire network data to create the basic network model and functional models. Frequent use of network data consumes physical network resources and reduces network performance. Therefore, NDTM can obtain physical network-related data from IAM for modeling and simulation. IAM can generate the network modeling data and network simulation data required by NDTI.

[0316] Figure 7 is a schematic diagram of an NDTI data acquisition and modeling process from IAM according to an embodiment of this application. Referring to Figure 7, the data acquisition includes the following steps:

[0317] Step 401: Collect network data for intelligent analysis and reasoning.

[0318] Intelligent network functions (IAM), such as large network models, are trained using massive amounts of network data acquired from physical networks. These large network models can be used to provide intelligent network decision-making, network fault prediction, and network policy generation.

[0319] Step 402: NDT modeling request.

[0320] Consumers, such as operators and third-party service providers, can initiate NDT instance creation requests, or NDT modeling requests, to NDTM according to their business needs. NDT can be used for network configuration and optimization simulation operations, as well as for network fault root cause analysis and network fault prediction.

[0321] Step 403: Collect data.

[0322] Upon receiving an NDT instance creation request, to avoid consuming physical network resources and reducing network performance due to data collection from the physical network, NDTM may request data related to the physical network from IAM (such as obtaining network modeling data of the physical network from the intelligent management entity) for modeling basic models and functional models.

[0323] Step 404: Modeling data generation.

[0324] IAM, such as large network models, has massive amounts of historical network data. After training, it has the ability to intelligently manage networks, providing capabilities such as network decision-making, network strategy optimization, and network fault handling. It also has the ability to generate network data, charts, and other data.

[0325] Based on NDTM's data requirements, IAM provides historical data of the physical network and, using AI generation capabilities, generates current network data of the physical network as network modeling data.

[0326] Step 405: Upload modeling data.

[0327] IAM sends historical data related to the physical network and the generated current network data to NDTM.

[0328] Step 406: NDT modeling.

[0329] NDTM successfully creates various network models based on the collected network data, thereby creating NDTI, which is to create a network digital twin instance based on the network modeling data and the orchestration information.

[0330] Step 407: Network emulation request.

[0331] Consumers request the NDTM to perform simulation operations, such as verifying network optimization strategies.

[0332] Step 408: Request network environment data.

[0333] NDTM needs to acquire network operation data (also known as network environment data) and configure NDTI to ensure that NDTI is consistent with the current network operation environment.

[0334] To avoid impacting the physical network, NDTM requests IAM to provide live network environment data. Specifically, it obtains the network environment data of the physical network from the intelligent management entity through network digital twin management.

[0335] Step 409: Network environment data generation.

[0336] IAM can provide historical data on the physical network operating environment and generate live network operating environment data.

[0337] Step 410: Import network environment data and simulate live network operation.

[0338] IAM can import physical network operating environment data into NDTI through NDTM (i.e., obtain the network environment data through network digital twin management; import the network environment data into the network digital twin instance through network digital twin management), making the NDTI operating environment a high-fidelity live network operating environment. Alternatively, it can obtain the NDTI identifier or address from NDTM and directly import data into NDTI (i.e., obtain the network environment data imported by the intelligent management entity through the network digital twin instance).

[0339] Step 411: NDT simulation.

[0340] Step 412: Network simulation results.

[0341] NDTI executes the network simulation operation requested by the consumer and notifies NDTM of the network simulation results. NDTM then reports the results to IAM, and IAM reports the network simulation results to the consumer.

[0342] In one exemplary embodiment, this application provides an information transmission device. The information transmission device of this application embodiment can be integrated into a first communication node. FIG8 is a schematic structural diagram of an information transmission device provided in an embodiment of this application. As shown in FIG8, an information transmission device provided in an embodiment of this application includes:

[0343] The orchestration information acquisition module 810 is configured to acquire orchestration information transmitted by the intelligent management entity. The orchestration information indicates the creation of a network digital twin instance for simulating the physical network.

[0344] Create module 820, configured to create and run a network digital twin instance based on the orchestration information and the network data of the physical network;

[0345] The network policy acquisition module 830 is configured to acquire the network policy of the physical network corresponding to the network request;

[0346] The simulation module 840 is configured to perform simulation verification of the network strategy using the digital twin instance, and obtain simulation verification results;

[0347] The transmission module 850 is configured to transmit the simulation verification results to the intelligent management entity.

[0348] The information transmission device provided in this embodiment is used to implement the information transmission method shown in Figure 1. The implementation principle and technical effect of the information transmission device provided in this embodiment are similar to those of the information transmission method shown in Figure 1, and will not be repeated here.

[0349] Based on the above embodiments, modified embodiments of the above embodiments are proposed. It should be noted that, in order to keep the description brief, only the differences from the above embodiments are described in the modified embodiments.

[0350] In one embodiment, the network data includes network modeling data and network environment data, and the creation module 820 includes:

[0351] The request acquisition unit is configured to acquire a request to create a network digital twin instance based on the orchestration information;

[0352] The network modeling data acquisition unit is configured to acquire the network modeling data of the physical network;

[0353] A creation unit is configured to create a network digital twin instance based on the network modeling data and the orchestration information;

[0354] The import unit is configured to import the network environment data into the network digital twin instance, and simulate the operation of the physical network through the network digital twin instance.

[0355] In one embodiment, the network modeling data acquisition unit is configured to include one or more of the following:

[0356] A request to transmit the network modeling data to the intelligent management entity;

[0357] The network modeling data of the physical network is obtained from the intelligent management entity.

[0358] In one embodiment, the import unit is specifically configured as follows:

[0359] The network environment data of the physical network is obtained from the intelligent management entity through network digital twin management;

[0360] The network environment data is obtained through network digital twin management;

[0361] The network environment data is imported into the network digital twin instance through network digital twin management.

[0362] In one embodiment, the import unit is specifically configured as follows:

[0363] The network environment data of the physical network is obtained from the intelligent management entity through network digital twin management;

[0364] The network environment data imported by the intelligent management entity is obtained through a network digital twin instance.

[0365] In one embodiment, the information transmission device further includes a verification module, configured as follows:

[0366] Obtain the updated network policy of the physical network corresponding to the network request;

[0367] The updated network policy is further simulated and verified using the network digital twin instance, and the simulation verification results corresponding to the updated network policy are obtained and transmitted.

[0368] In one embodiment, the information transmission device further includes an update module, configured to:

[0369] The simulation verification results are used to determine whether they meet the requirements of the network request through a network digital twin instance.

[0370] If the simulation verification result does not meet the requirements of the network request, the network policy is updated through the network digital twin instance, and the updated network policy is simulated and verified again through the network digital twin instance to obtain the simulation verification result corresponding to the updated network policy. Then, the process returns to continue to determine whether the simulation verification result meets the requirements of the network request.

[0371] If the simulation verification results meet the network request, the simulation verification results are transmitted through a network digital twin instance.

[0372] In one embodiment, the information transmission device further includes a target policy acquisition module, configured as follows:

[0373] The network digital twin management obtains the twin simulation verification request and the target strategy verified by the physical network.

[0374] The target policy is verified using a network digital twin instance to obtain the policy verification result of the target policy;

[0375] The policy verification results are transmitted via a network digital twin instance.

[0376] In one embodiment, the information transmission device further includes a request acquisition module, configured as follows:

[0377] Requests for target scenarios used for model training are obtained through network digital twin management;

[0378] The target scenario is simulated using a network digital twin instance;

[0379] The target data in the target scenario is transmitted to the intelligent management entity through a network digital twin instance.

[0380] In one embodiment, the information transmission device further includes: a network anomaly scenario acquisition module, configured as follows:

[0381] Requests to simulate network anomaly scenarios in the physical network are obtained through network digital twin management;

[0382] The network anomaly scenario is simulated using a network digital twin example;

[0383] The network anomaly data in the aforementioned network anomaly scenario is transmitted to the intelligent management entity through a network digital twin instance;

[0384] The abnormal strategy transmitted by the intelligent management entity is obtained and verified through a network digital twin instance, and the abnormal verification result is obtained.

[0385] The anomaly verification result is transmitted to the intelligent management entity.

[0386] In one exemplary embodiment, this application provides an information transmission device integrated into a second communication node. Figure 9 is a schematic diagram of the structure of another information transmission device provided in an embodiment of this application. The information transmission device provided in this embodiment includes:

[0387] The network request acquisition module 910 is configured to acquire network requests for the physical network.

[0388] Decision module 920 is configured to use a network digital twin to respond to network requests for decision-making.

[0389] The orchestration module 930 is configured to orchestrate the information model of the network digital twin corresponding to the network request, and obtain orchestration information.

[0390] The orchestration information transmission module 940 is configured to transmit the orchestration information and request the creation of a network digital twin instance based on the orchestration information;

[0391] The network policy transmission module 950 is configured to transmit the network policy of the physical network corresponding to the network request, and request the execution of the simulation verification operation.

[0392] The simulation verification result acquisition module 960 is configured to acquire simulation verification results, which include the physical network optimization effect obtained based on the network strategy simulation verification.

[0393] The information transmission device provided in this embodiment is used to implement the information transmission method shown in Figure 3. The implementation principle and technical effect of the information transmission device provided in this embodiment are similar to those of the information transmission method shown in Figure 3, and will not be repeated here.

[0394] Based on the above embodiments, modified embodiments of the above embodiments are proposed. It should be noted that, in order to keep the description brief, only the differences from the above embodiments are described in the modified embodiments.

[0395] In one embodiment, the orchestration module 930 is specifically configured as follows:

[0396] The collaborative network digital twin management orchestrates the information model of the network digital twin corresponding to the network request.

[0397] In one embodiment, the information transmission device further includes a target strategy transmission module, configured as follows:

[0398] Transmit twin simulation verification requests and target strategies verified by the physical network;

[0399] Obtain the policy verification result corresponding to the target policy;

[0400] Determine the difference information between the policy verification result and the reference verification result of the physical network verifying the target policy;

[0401] Based on the difference information, the updated orchestration information is determined and transmitted.

[0402] In one embodiment, the information transmission device further includes an analysis module, configured as follows:

[0403] The simulation verification results are analyzed to obtain the evaluation results of the simulation verification results;

[0404] If the evaluation results indicate that the simulation verification results need to be optimized, update the network policy of the physical network and continue to request the execution of the simulation verification operation;

[0405] Obtain the simulation verification results of the updated network policy.

[0406] In one embodiment, the information transmission device further includes an evaluation result transmission module, configured as follows:

[0407] If the evaluation result indicates that the simulation certification result meets the requirements of the network request, the network policy corresponding to the evaluation result is transmitted to the physical network.

[0408] In one embodiment, the information transmission device further includes a network policy transmission module, configured as follows:

[0409] Upon receiving confirmation information about the network policy, the network policy is transmitted to the physical network, and the simulation verification results are transmitted to the target device.

[0410] In one embodiment, the information transmission device further includes a training module, configured as follows:

[0411] Obtain target data from the target scene;

[0412] The network model is trained based on the target data.

[0413] In one embodiment, the information transmission device further includes an anomaly policy determination module, configured to:

[0414] Acquire network anomaly data in network anomaly scenarios;

[0415] Based on the network anomaly data, determine the anomaly strategy for the network anomaly scenario;

[0416] Transmit the aforementioned exception policy.

[0417] In one exemplary embodiment, this application also provides a first communication node. FIG10 is a schematic diagram of the structure of a first communication node provided in this application embodiment. As shown in FIG10, the first communication node provided in this application includes one or more processors 1001 and a storage device 1002. The processors 1001 in the first communication node may be one or more, and FIG10 takes one processor 1001 as an example. The storage device 1002 is used to store one or more programs. The one or more programs are executed by the one or more processors 1001, so that the one or more processors 1001 implement the information transmission method as described in the embodiment of this application.

[0418] The first communication node also includes: a communication device 1003, an input device 1004, and an output device 1005.

[0419] The processor 1001, storage device 1002, communication device 1003, input device 1004, and output device 105 in the first communication node can be connected by a bus or other means. Figure 10 shows an example of connection via a bus.

[0420] The input device 1004 can be used to receive input digital or character information, and to generate key signal inputs related to user settings and function control of the first communication node. The output device 1005 may include a display screen or other display device.

[0421] The communication device 1003 may include a receiver and a transmitter. The communication device 1003 is configured to perform information transmission and reception communication under the control of the processor 1001.

[0422] Storage device 1002, as a computer-readable storage medium, can be configured to store software programs, computer-executable programs, and modules, such as program instructions / modules corresponding to the information transmission method described in the embodiments of this application (e.g., the information arrangement acquisition module 810, creation module 820, network policy acquisition module 830, simulation module 840, and transmission module 850 in the information transmission device). Storage device 1002 may include a program storage area and a data storage area, wherein the program storage area may store the operating system and at least one application program required for a function; the data storage area may store data created based on the use of the first communication node, etc. In addition, storage device 1002 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some instances, storage device 1002 may further include memory remotely located relative to processor 1001, and these remote memories can be connected to the first communication node via a network. Examples of the above-mentioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0423] In one exemplary embodiment, this application also provides a second communication node. FIG11 is a schematic diagram of the structure of a second communication node provided in this application embodiment. As shown in FIG11, the second communication node provided in this application includes one or more processors 111 and a storage device 112. The processors 111 in the second communication node may be one or more, and FIG11 takes one processor 111 as an example. The storage device 112 is used to store one or more programs. The one or more programs are executed by the one or more processors 111, so that the one or more processors 111 implement the information transmission method as described in the embodiment of this application.

[0424] The second communication node also includes: a communication device 113, an input device 114, and an output device 115.

[0425] The processor 111, storage device 112, communication device 113, input device 114, and output device 115 in the second communication node can be connected by a bus or other means. Figure 11 shows an example of connection via a bus.

[0426] Input device 114 can be used to receive input digital or character information, and to generate key signal inputs related to user settings and function control of the second communication node. Output device 115 may include display devices such as a display screen.

[0427] The communication device 113 may include a receiver and a transmitter. The communication device 113 is configured to perform information transmission and reception communication under the control of the processor 111.

[0428] Storage device 112, as a computer-readable storage medium, can be configured to store software programs, computer-executable programs, and modules, such as program instructions / modules corresponding to the information transmission method described in the embodiments of this application (e.g., the information arrangement acquisition module 811, creation module 820, network policy acquisition module 830, simulation module 840, and transmission module 850 in the information transmission device). Storage device 112 may include a program storage area and a data storage area, wherein the program storage area may store the operating system and at least one application program required for a function; the data storage area may store data created based on the use of the second communication node, etc. In addition, storage device 112 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some instances, storage device 112 may further include memory remotely located relative to processor 111, and these remote memories can be connected to the second communication node via a network. Examples of the above-mentioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0429] In one exemplary embodiment, this application also provides a storage medium storing a computer program that, when executed by a processor, implements any of the methods described in this application. The storage medium stores a computer program that, when executed by a processor, implements any of the information transmission methods described in the embodiments of this application. Examples include an information transmission method applied to a first communication node and an information transmission method applied to a second communication node. The information transmission method applied to the first communication node includes: acquiring orchestration information for intelligent management entity transmission, wherein the orchestration information indicates the creation of a network digital twin instance for simulating the physical network.

[0430] Based on the orchestration information and the network data of the physical network, create and run a network digital twin instance;

[0431] Obtain the network policy of the physical network corresponding to the network request;

[0432] The network strategy is simulated and verified using the network digital twin instance, and the simulation verification results are obtained.

[0433] The simulation verification results are transmitted to the intelligent management entity.

[0434] The information transmission method applied to the second communication node includes: obtaining network requests for the physical network;

[0435] The decision-making process utilizes a network digital twin to respond to network requests;

[0436] Arrange the information model of the network digital twin corresponding to the network request to obtain arrangement information;

[0437] Transmit the orchestration information and request the creation of a network digital twin instance based on the orchestration information;

[0438] The network policy of the physical network corresponding to the network request is transmitted, and a simulation verification operation is requested to be performed.

[0439] Obtain simulation verification results, which include the physical network optimization effect obtained from the simulation verification based on the network strategy.

[0440] The computer storage medium in this application embodiment can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. For example, a computer-readable storage medium can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. The computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0441] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit programs for use by or in connection with an instruction execution system, apparatus, or device.

[0442] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, radio frequency (RF), etc., or any suitable combination thereof.

[0443] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0444] The above description is merely an exemplary embodiment of this application and is not intended to limit the scope of protection of this application.

[0445] Those skilled in the art will understand that the term terminal equipment covers any suitable type of wireless user equipment, such as mobile phones, portable data processing devices, portable web browsers, or vehicle-mounted mobile stations.

[0446] Generally, the various embodiments of this application can be implemented in hardware or dedicated circuitry, software, logic, or any combination thereof. For example, some aspects can be implemented in hardware, while others can be implemented in firmware or software that can be executed by a controller, microprocessor, or other computing device, although this application is not limited thereto.

[0447] Embodiments of this application can be implemented by executing computer program instructions through the data processor of a mobile device, for example, in a processor entity, or through hardware, or through a combination of software and hardware. The computer program instructions can be assembly instructions, Instruction Set Architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages.

[0448] Any block diagram of logical flow in the accompanying drawings of this application may represent program steps, or may represent interconnected logic circuits, modules, and functions, or may represent a combination of program steps and logic circuits, modules, and functions. The computer program may be stored on memory. Memory may be of any type suitable to the local technical environment and may be implemented using any suitable data storage technology, such as, but not limited to, read-only memory (ROM), random access memory (RAM), optical storage devices and systems (Digital Video Disc (DVD) or Compact Disk (CD)), etc. Computer-readable media may include non-transitory storage media. The data processor may be of any type suitable to the local technical environment, such as, but not limited to, general-purpose computers, special-purpose computers, microprocessors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), and processors based on multi-core processor architectures.

[0449] A detailed description of exemplary embodiments of this application has been provided above through exemplary and non-limiting examples. However, various modifications and adjustments to the above embodiments will be apparent to those skilled in the art when considered in conjunction with the accompanying drawings and claims, without departing from the scope of this application. Therefore, the proper scope of this application will be determined by the claims.

Claims

1. An information transmission method, comprising: obtaining orchestration information transmitted by an intelligent management entity, the orchestration information indicating to create a network digital twin instance for simulating a physical network; creating and running the network digital twin instance based on the orchestration information and network data of the physical network; obtaining network policies of the physical network corresponding to a network request; simulating and verifying the network policies through the digital twin instance to obtain simulation verification results; transmitting the simulation verification results to the intelligent management entity.

2. The method of claim 1, wherein, The network data includes network modeling data and network environment data, and the creating and running the network digital twin instance based on the orchestration information and network data of the physical network comprises: obtaining a request to create a network digital twin instance according to the orchestration information; obtaining the network modeling data of the physical network; creating a network digital twin instance according to the network modeling data and the orchestration information; importing the network environment data into the network digital twin instance to simulate the operation of the physical network through the network digital twin instance.

3. The method of claim 2, wherein, The obtaining the network modeling data of the physical network comprises: transmitting a request for the network modeling data to the intelligent management entity; obtaining the network modeling data of the physical network from the intelligent management entity.

4. The method of claim 2, wherein, The importing the network environment data into the network digital twin instance comprises: obtaining the network environment data of the physical network from the intelligent management entity through network digital twin management; obtaining the network environment data through network digital twin management; importing the network environment data into the network digital twin instance through network digital twin management.

5. The method of claim 2, wherein, The importing the network environment data into the network digital twin instance comprises: obtaining the network environment data of the physical network from the intelligent management entity through network digital twin management; obtaining the network environment data imported by the intelligent management entity through the network digital twin instance.

6. The method of claim 1, further comprising: obtaining updated network policies of the physical network corresponding to the network request; continuing to simulate and verify the updated network policies through the network digital twin instance to obtain and transmit simulation verification results corresponding to the updated network policies.

7. The method of claim 1, further comprising: determining whether the simulation verification results meet the requirements of the network request through the network digital twin instance; in the case that the simulation verification results do not meet the requirements of the network request, updating the network policies through the network digital twin instance, continuing to simulate and verify the updated network policies through the network digital twin instance to obtain simulation verification results corresponding to the updated network policies, and returning to determine whether the simulation verification results meet the requirements of the network request; in the case that the simulation verification results meet the requirements of the network request, transmitting the simulation verification results through the network digital twin instance.

8. The method of claim 1, further comprising: obtaining a twin simulation verification request and a target policy verified by the physical network through network digital twin management; verifying the target policy through a network digital twin instance to obtain a policy verification result of the target policy; transmitting the policy verification result through the network digital twin instance.

9. The method of claim 1, further comprising: obtaining a request for a target scene for model training through network digital twin management; simulating the target scene through a network digital twin instance; transmitting target data in the target scene to the intelligent management entity through the network digital twin instance.

10. The method of claim 1, further comprising: obtaining a request for simulating a network abnormal scene of the physical network through network digital twin management; simulating the network abnormal scene through a network digital twin instance; transmitting network abnormal data in the network abnormal scene to the intelligent management entity through the network digital twin instance; obtaining and verifying an abnormal policy transmitted by the intelligent management entity through a network digital twin instance to obtain an abnormal verification result; transmitting the abnormal verification result to the intelligent management entity.

11. An information transmission method, comprising: obtaining a network request for a physical network; deciding to respond to the network request using a network digital twin; orchestrating an information model of a network digital twin corresponding to the network request to obtain orchestrated information; transmitting the orchestrated information to request creation of a network digital twin instance according to the orchestrated information; transmitting a network policy of the physical network corresponding to the network request and requesting execution of a simulation verification operation; obtaining a simulation verification result, the simulation verification result including a physical network optimization effect obtained based on simulation verification of the network policy.

12. The method of claim 11, wherein, The orchestrating an information model of a network digital twin corresponding to the network request to obtain orchestrated information comprises: orchestrating an information model of a network digital twin corresponding to the network request in cooperation with network digital twin management.

13. The method of claim 11, further comprising: transmitting a twin simulation verification request and a target policy verified by the physical network; obtaining a policy verification result corresponding to the target policy; determining difference information of the policy verification result and a reference verification result of the physical network verifying the target policy; based on the difference information, determining and transmitting updated orchestrated information.

14. The method of claim 11, further comprising: analyzing the simulation verification result to obtain an evaluation result of the simulation verification result; in a case where the evaluation result indicates that the simulation verification result is to be optimized, updating a network policy of the physical network and continuing to request execution of a simulation verification operation; obtaining a simulation verification result of the updated network policy.

15. The method of claim 14, further comprising: in a case where the evaluation result indicates that the simulation verification result meets a requirement of the network request, transmitting the network policy corresponding to the evaluation result to the physical network.

16. The method of claim 11, further comprising: In a case where the confirmation information of the network policy is acquired, the network policy is transmitted to the physical network, and a simulation verification result is transmitted to a target device. 17.The method of claim 11, further comprising: acquiring target data in a target scenario; training a network model based on the target data. 18.The method of claim 11, further comprising: acquiring network anomaly data of a network anomaly scenario; determining an anomaly policy of the network anomaly scenario based on the network anomaly data; transmitting the anomaly policy. 19.A first communication node, comprising: one or more processors; a storage device storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of claims 1-10. 20.A second communication node, comprising: one or more processors; a storage device storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of claims 11-18. 21.A storage medium storing a computer program, the computer program being executed by a processor to implement the method of any one of claims 1-18.

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