Network optimization method, network optimization system, communication device and storage medium

Abstract

Embodiments of the present application relate to the technical field of communications, and provide a network optimization method, a network optimization system, a communication device and a storage medium, for use in improving the accuracy of network optimization. The network optimization method comprises: on the basis of network optimization requirements of a physical network, determining a first configuration parameter to be optimized; on the basis of a digital twin network corresponding to the physical network, optimizing the first configuration parameter, to obtain an optimal parameter value of the first configuration parameter; and issuing the optimal parameter value of the first configuration parameter to the physical network.

Description

Network optimization method, network optimization system, communication device and storage medium

[0001] This application claims priority to Chinese patent application No. 202310927377.0, filed on July 25, 2023, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of communication technology, and in particular to a network optimization method, a network optimization system, a communication device, and a storage medium. Background Art

[0003] With the development of communication technology, various network problems may arise. To ensure network performance, problem networks need to be optimized. Network optimization involves analyzing and collecting data from existing networks, performing hardware inspections, and identifying the causes affecting network quality. This process then involves modifying parameters or adjusting the network structure to ensure high-quality network operation. The network optimization process requires accurate analysis of existing network problems and identifying solutions to address these issues.

[0004] Currently, system simulation methods can be used to build models and then perform network optimization based on these models. However, due to factors such as the difficulty in collecting network data and the inability to frequently adjust wireless network parameters to ensure wireless communication network stability, the authenticity of the created models is low, which in turn affects the accuracy of network optimization.

[0005] Summary of the Invention

[0006] The present application provides a network optimization method, a network optimization system, a communication device and a storage medium for improving the accuracy of network optimization.

[0007] In order to achieve the above objectives, this application adopts the following technical solutions.

[0008] In a first aspect, a network optimization method is provided, the method comprising:

[0009] Determine a first configuration parameter to be optimized based on the network optimization requirements of the physical network;

[0010] Optimizing the first configuration parameter based on the digital twin network corresponding to the physical network to obtain an optimal parameter value of the first configuration parameter;

[0011] The optimal parameter value of the first configuration parameter is delivered to the physical network.

[0012] In a second aspect, a network optimization system is also provided, which includes a network optimization module, a digital twin module, and an artificial intelligence module:

[0013] An artificial intelligence module, configured to determine a first configuration parameter to be optimized based on network optimization requirements of the physical network;

[0014] The digital twin module is used to build a digital twin network corresponding to the physical network based on the construction parameters of the physical network;

[0015] An artificial intelligence module is configured to optimize the first configuration parameter based on the digital twin network corresponding to the physical network to obtain an optimal parameter value of the first configuration parameter;

[0016] The network optimization module is used to send the optimal parameter value of the first configuration parameter to the physical network.

[0017] In a third aspect, a communication device is also provided, which includes a memory and a processor; the memory and the processor are coupled; the memory is used to store instructions executable by the processor; when the processor executes the instructions, it performs the method of the first aspect.

[0018] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions. When the computer instructions are executed on a computer, the computer executes the method provided in the first aspect.

[0019] In a fifth aspect, a computer program product comprising computer instructions is provided, which, when executed on a computer, enables the computer to execute the method provided in the first aspect.

[0020] This application provides a network optimization method that can optimize the configuration parameters of a physical network in combination with a digital twin network to determine the configuration parameters that meet the current network optimization requirements. In this way, the digital twin network can be used to perform multiple high-level simulations of the physical network to be optimized during the optimization process, and the status of the digital twin network and the physical network can be obtained in real time to correct the digital twin network, thereby improving the efficiency and accuracy of network optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings are used to provide a further understanding of the technical solution of the present invention and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the technical solution of the present invention and do not constitute a limitation on the technical solution of the present invention.

[0022] FIG1 is a schematic diagram of a physical network provided in an embodiment of the present application;

[0023] FIG2 is a schematic diagram of the composition of a network optimization system provided in an embodiment of the present application;

[0024] FIG3 is a flow chart of a network optimization method provided in an embodiment of the present application;

[0025] FIG4 is a flow chart of another network optimization method provided in an embodiment of the present application;

[0026] FIG5 is a schematic diagram of another network optimization system provided in an embodiment of the present application;

[0027] FIG6 is a schematic diagram of an internal circulation process provided in an embodiment of the present application;

[0028] FIG7 is a schematic diagram of an external circulation process provided in an embodiment of the present application;

[0029] FIG8 is a schematic diagram of another external circulation process provided in an embodiment of the present application;

[0030] FIG9 is a flow chart of another network optimization method provided in an embodiment of the present application;

[0031] FIG10 is a schematic diagram of another internal circulation process provided in an embodiment of the present application;

[0032] FIG11 is a schematic diagram of another internal circulation process provided in an embodiment of the present application;

[0033] FIG12 is a schematic structural diagram of a communication device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0035] In the description of this application, unless otherwise specified, " / " means "or", for example, A / B can mean A or B. "And / or" in this article is merely a description of the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, "at least one" means one or more, and "a plurality" means two or more. Words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not limit them to be necessarily different.

[0036] It should be noted that, in this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described in this application as "exemplary" or "for example" should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0037] To facilitate understanding, some basic concepts of terms or technologies involved in the embodiments of the present invention are first briefly introduced and explained.

[0038] A digital twin (DT) is a simulation process that integrates multiple disciplines, multiple physical quantities, multiple scales, and multiple probabilities, leveraging data such as physical models, sensor updates, and operational history. Based on digital twin technology, a physical device can be mapped into a virtual space, creating a corresponding virtual model (or digital model), which can be called a "digital twin." A digital twin is a virtual representation of the real world, encompassing physical objects, processes, relationships, and behaviors. It can reflect the entire lifecycle of its corresponding physical device and simulate its behavior using real-time data transmitted from sensors on the physical device and historical operational data from the physical device.

[0039] Artificial intelligence (AI) is the theory, methods, techniques, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, to perceive the environment, acquire knowledge, and use that knowledge to achieve optimal results. In other words, AI is a branch of computer science that seeks to understand the essence of intelligence and develop new intelligent machines that can respond in a manner similar to human intelligence. AI also studies the design principles and implementation methods of various intelligent machines, enabling them to possess the capabilities of perception, reasoning, and decision-making. Research in the field of AI includes robotics, natural language processing, computer vision, decision-making and reasoning, human-computer interaction, recommendation and search, and basic AI theory.

[0040] The above is an introduction to the technical terms involved in the embodiments of the present disclosure, which will not be repeated below.

[0041] In order to improve the accuracy of network optimization, an embodiment of the present application provides a network optimization method, which includes: determining a first configuration parameter to be optimized based on the network optimization requirements of the physical network, optimizing the first configuration parameter based on a digital twin network corresponding to the physical network, obtaining the optimal parameter value of the first configuration parameter, and sending the optimal parameter value of the first configuration parameter to the physical network. In this way, the digital twin network can be used to perform multiple high-level simulations of the physical network to be optimized during the optimization process, and the status of the digital twin network and the physical network can be obtained in real time to correct the digital twin network, thereby improving the efficiency and accuracy of network optimization.

[0042] A physical network is a network formed by connecting various physical devices (such as hosts, routers, switches, etc.) and media (such as optical cables, electrical cables, and twisted-pair cables). A physical network can include mobile access networks and core networks. Alternatively, a physical network can be a fixed-line network.

[0043] As shown in Figure 1, a physical network may include an access network, an aggregation network, and a core network. A physical network may also include terminal devices, access switches, aggregation switches, and core switches. In some embodiments, a physical network may include network devices such as hubs, switches, bridges, routers, gateways, network interface cards, and wireless access points.

[0044] Among them, the access network refers to all equipment between the backbone network and the user terminal, and is composed of a series of transmission entities between the service node interface and the user-network interface.

[0045] The core network consists of multiple functional units, including control plane and data plane functional entities, which perform functions such as user access control, mobility management, session management, user security authentication, and billing. The access and mobility management function (AMF) is responsible for user access management, security authentication, and mobility management. The user plane function (UPF) manages user plane data transmission, traffic statistics, and secure eavesdropping.

[0046] In some embodiments, the terminal device can be a device with wireless transceiver capabilities, which can be deployed on land, including indoors or outdoors, handheld, wearable or vehicle-mounted; it can also be deployed on the water (such as ships, etc.); it can also be deployed in the air (for example, on airplanes, balloons and satellites, etc.). The terminal can be a mobile phone, a tablet computer, a computer with wireless transceiver capabilities, a virtual reality (VR) terminal, an augmented reality (AR) terminal, a wireless terminal in industrial control, a wireless terminal in autonomous driving, a wireless terminal in remote medical care, a wireless terminal in a smart grid, a wireless terminal in transportation safety, a wireless terminal in a smart city, a wireless terminal in a smart home, etc. The embodiments of the present application do not limit the application scenarios. The terminal may sometimes also be referred to as a user, user equipment (UE), access terminal, UE unit, UE station, mobile station, mobile station, remote station, remote terminal, mobile device, UE terminal, wireless communication equipment, UE agent or UE device, etc., but the embodiments of the present application are not limited to this.

[0047] As shown in Figure 2, an embodiment of the present application also provides a network optimization system that can be applied to the above-mentioned mobile communication network. The network optimization system 200 may include a network optimization module 210, an artificial intelligence module 220, and a digital twin module 230. In some embodiments, the network optimization system 200 may also include a physical network 240.

[0048] In an embodiment of the present application, the network optimization module 210 is used to execute the network optimization method provided in the present application to analyze network problems existing in the network and determine corresponding solutions to the network problems.

[0049] In some embodiments, the network optimization module 210 also has intelligent decision-making functions, performance evaluation functions, and parameter distribution functions.

[0050] The intelligent decision-making function refers to the network optimization module 210 being able to determine the sub-service type based on the network optimization requirements of the physical network, and determine the sub-service type corresponding to the network optimization requirement, so as to complete network optimization based on the sub-service type. Examples include traffic optimization services, interference optimization services, and resource utilization services.

[0051] In some embodiments, each sub-service type has its own corresponding artificial intelligence algorithm function, artificial intelligence algorithm structure, and parameter configuration. The artificial intelligence algorithm structure includes a neural network structure, such as a convolutional network structure, or a non-neural network algorithm, such as Bayesian optimization. Each sub-service type may also have corresponding algorithm parameter settings, such as the number of iterations and optimization direction.

[0052] In some embodiments, the network optimization module 210 can obtain a first digital twin result, which is a digital twin result obtained by the digital twin network based on the optimal parameter value of the first configuration parameter, and when the first digital twin result meets the network optimization requirements, the optimal parameter value of the first configuration parameter is sent to the physical network.

[0053] In addition, the network optimization module 210 may also obtain an operating result of the physical network based on the optimal parameter values ​​of the first configuration parameters; and determine an adjustment strategy for the digital twin network based on the performance relationship between the operating result and the first digital twin result. The first digital twin result is the digital twin result of the digital twin network based on the optimal parameter values ​​of the first configuration parameters. If the operating result is inferior to the first digital twin result, the adjustment strategy includes performing performance correction processing on the digital twin network.

[0054] In addition, the network optimization module 210 can also send the adjustment strategy to the digital twin module 230 so that the digital twin module 230 can adjust the digital twin network according to the adjustment strategy.

[0055] In some embodiments, the network optimization module 210 can also obtain a first digital twin result, which is a digital twin result obtained by the digital twin network based on the optimal parameter value of the first configuration parameter; when the first digital twin result meets the network optimization requirements, the optimal parameter value of the first configuration parameter is sent to the physical network.

[0056] In some embodiments, the network optimization module 210 can also receive the second digital twin result uploaded by the artificial intelligence module 220, and if the second digital twin result meets the network optimization requirements, send the optimal parameter value of the second configuration parameter to the physical network.

[0057] In some embodiments, the network optimization module 210 is also used to send the parameter value of a third configuration parameter to the physical network when the second digital twin result does not meet the network optimization requirements. The parameter value of the third configuration parameter is the parameter value of the configuration parameter corresponding to the optimal digital twin result between the first digital twin result and the second digital twin result.

[0058] The parameter delivery function refers to the network optimization module 210 being able to deliver digital twin network configuration parameters to the digital twin module 230, deliver artificial intelligence configuration parameters to the artificial intelligence module 220, and, upon determining a first configuration parameter, deliver the optimal parameter value of the first configuration parameter to the physical network. For example, the network optimization module 210 may first determine the sub-service type corresponding to the network optimization requirement, and based on this sub-service type, determine the artificial intelligence algorithm function, artificial intelligence algorithm structure, and parameter configuration that need to be delivered, and then deliver the determined parameters to the corresponding module.

[0059] The performance evaluation function means that the network optimization module 210 can be used to analyze and evaluate the network performance of the digital twin network and the physical network based on the collected data of the two networks.

[0060] In an embodiment of the present application, the artificial intelligence module 220 is used to determine the first configuration parameter to be optimized based on the network optimization requirements of the physical network, and optimize the first configuration parameter based on the digital twin network corresponding to the physical network to obtain the optimal parameter value of the first configuration parameter.

[0061] In some embodiments, the artificial intelligence module 220 also has artificial intelligence algorithm parameter configuration function, artificial intelligence optimization function, configuration distribution function, and result reporting function.

[0062] The AI ​​algorithm parameter configuration function refers to the AI ​​module 220 determining the AI ​​algorithm parameters for this network optimization based on the received AI algorithm parameters issued by the network configuration module 210, and configuring the AI ​​functions or services corresponding to the AI ​​algorithm parameters. The AI ​​algorithm parameters include AI algorithm functions and AI algorithm structures, including neural network structures such as convolutional network structures, or non-neural network algorithms such as Bayesian optimization. Each sub-service type may also have corresponding algorithm parameter settings, such as the number of iterations and optimization direction.

[0063] In some embodiments, the artificial intelligence module 220 may further perform a predetermined number of iterative updates on the parameter value of the first configuration parameter to obtain multiple parameter values ​​of the first configuration parameter. The artificial intelligence algorithm parameters may further include an artificial intelligence training set, and the multiple parameter values ​​obtained by the iterative updates may also constitute the artificial intelligence training set.

[0064] The AI ​​optimization function refers to the function of AI module 220 to optimize the first configuration parameter. AI module 220 can input the multiple parameter values ​​obtained through the iterative update into the digital twin network to obtain the digital twin results corresponding to the parameter values ​​of the first configuration parameter after each iterative update, and then determine the optimal parameter value of the first configuration parameter from the digital twin results corresponding to the multiple parameters.

[0065] In some embodiments, the artificial intelligence module 220 can iteratively update the parameter value of the first configuration parameter a preset number of times; input the parameter value of the first configuration parameter after each iterative update into the digital twin network to obtain the digital twin result corresponding to the parameter value of the first configuration parameter after each iterative update; use the optimal digital twin result among the digital twin results corresponding to the parameter value of the first configuration parameter after each iterative update as the first digital twin result; and use the parameter value of the first configuration parameter corresponding to the first digital twin result as the optimal parameter value of the first configuration parameter.

[0066] In some embodiments, the artificial intelligence module 220 can also optimize the second configuration parameters based on the digital twin network corresponding to the physical network to obtain the optimal parameter value of the second configuration parameter and the second digital twin result. The second digital twin result is the digital twin result obtained by the digital twin network based on the optimal parameter value of the second configuration parameter.

[0067] The configuration sending function means that the artificial intelligence module 220 can send multiple parameter values ​​obtained by iteratively updating the first configuration parameters to the digital twin module.

[0068] The result reporting function refers to the artificial intelligence module 220 determining the optimal parameter value of the first configuration parameter in the digital twin results corresponding to multiple parameters, and reporting the optimal parameter value to the network optimization module 210.

[0069] In an embodiment of the present application, the digital twin module 230 is used to construct a digital twin network corresponding to the physical network based on the construction parameters of the physical network. In some embodiments, the digital twin module 230 also has a mirror mapping function, a simulation function, and a result reporting function.

[0070] The mirroring function refers to the ability of the digital twin module 230 to determine the construction parameters of the digital twin network based on the configuration parameters of the physical network and execute the digital twin mirroring process to construct a digital twin network corresponding to the physical network. In some embodiments, if a change in the physical state of the physical network is detected, the construction parameters of the digital twin network can be updated to obtain updated construction parameters. The digital twin network can then be reconstructed based on the updated construction parameters using the mirroring function of the digital twin module 230.

[0071] The simulation function means that the digital twin module 230 receives the parameter value obtained by iteratively updating the first configuration parameter sent by the artificial intelligence module 220 to the digital twin module, and adjusts and updates the network configuration information of the digital twin network based on the parameter value to configure a digital twin network that matches the parameter value.

[0072] The result reporting function means that the digital twin module 230 can report the digital twin results obtained after each iterative update to the artificial intelligence module 220.

[0073] In some embodiments, the network optimization system 200 may further include a physical network 240. The physical network 240 is configured to receive an optimal parameter value of a first configuration parameter and adjust the physical network based on the optimal parameter value of the first configuration parameter.

[0074] In some embodiments, the physical network 240 may have business service functions and status reporting functions.

[0075] The business service function refers to the physical network 240 being able to adjust the physical network based on the user's business needs, that is, adjusting the physical network based on the optimal parameter value of the first configuration parameter determined by the network optimization requirement based on the user's business needs.

[0076] The status reporting function enables the physical network 240 to periodically or event-basedly report the actual physical network status to the network optimization module 210. Event-based reporting of the actual physical network status to the network optimization module 210 is triggered by an event occurring in the physical network, such as a change in the physical network's physical status or a physical network status detection event.

[0077] The network optimization method provided by this application is described below with reference to the accompanying drawings.

[0078] As shown in FIG3 , an embodiment of the present application provides a network optimization method, which includes S101 to S103 .

[0079] S101. Determine a first configuration parameter to be optimized based on a network optimization requirement of a physical network.

[0080] In some embodiments, the network optimization requirement includes any one of the following: environment configuration requirement, resource utilization requirement, retransmission number requirement, capacity requirement, and service experience requirement.

[0081] Exemplarily, the environmental configuration requirements may be requirements related to the geographic information configuration of the physical network, requirements related to the system information configuration, requirements related to the frequency information configuration, or requirements related to the access configuration. The resource utilization requirements may be requirements related to the average utilization of the uplink / downlink physical resource blocks (PRBs) of the physical network, such as improving the average PRB utilization in the uplink. The number of retransmission requirements may be requirements related to the uplink / downlink packet loss rate of the physical network, requirements related to the uplink / downlink retransmission rate, such as reducing the downlink packet loss rate. The capacity requirements may be requirements related to the average throughput per PRB in the uplink / downlink of the physical network, such as improving the average throughput per PRB in the uplink. The service experience requirements may be requirements for traffic optimization, etc.

[0082] In some embodiments, network optimization requirements for the physical network may be determined based on different types of received network problem feedback or network requirement feedback information. Different types of network problem feedback may include network problem feedback or network requirement feedback information from users, operators, network planning or network optimization engineers, etc.

[0083] For example, taking the network optimization system 200 shown in FIG2 as an example, users of the network optimization system 200 may include users, operators, network planners, or network optimization engineers. When the network optimization system 200 receives network demand feedback information input by a user asking "How to speed up video downloads?" or network demand feedback information input by an operator asking "How to obtain maximum capacity?" or network demand feedback information input by a network planner or network optimization engineer asking "How to configure AMC algorithm or power control algorithm parameters to obtain maximum uplink and downlink throughput?", it may determine that the network optimization requirement of the physical network is to obtain the maximum data volume without changing the physical state of the physical network.

[0084] In some embodiments, the first configuration parameter includes at least one of the following: geographic information configuration parameter, system information configuration parameter, frequency information configuration parameter, access configuration parameter, physical layer parameter, regulation algorithm parameter, power control algorithm parameter, adaptive modulation and coding (AMC) parameter.

[0085] In some embodiments, a target parameter is determined based on a network optimization requirement, and then a first configuration parameter is determined from a set of configuration parameters related to the target parameter. For example, if the network optimization requirement for a physical network is to obtain the maximum data volume without changing the physical state of the physical network, the target parameter can be determined as a parameter determined based on the uplink and downlink throughput. Configuration parameters related to the uplink and downlink throughput may include power control algorithm parameters and AMC parameters. That is, the first configuration parameter may include the power control algorithm parameters and / or AMC parameters.

[0086] In some embodiments, the first configuration parameter can also be determined based on the degree of influence of each configuration parameter on the network optimization requirement. Specifically, the first configuration parameter can be determined based on the degree of influence of each configuration parameter on the target parameter. Thus, based on the network optimization requirement of the physical network, one or more configuration parameters that can influence the target parameter associated with the network optimization requirement can be determined as the first configuration parameter.

[0087] In some embodiments, multiple network optimization requirements may be received. Furthermore, the multiple network optimization requirements may be sorted based on the optimization priorities of the respective network optimization requirements. Network optimization is first performed based on the first network optimization requirement with the highest optimization priority. After the network optimization process is completed, network optimization is then performed based on the second network optimization requirement with the second highest optimization priority, and so on, until network optimization is completed for the network optimization requirement with the lowest optimization priority.

[0088] In some embodiments, the construction parameters of the digital twin network can be determined based on the construction parameters of the physical network, and then a digital twin network corresponding to the physical network can be constructed based on the determined construction parameters of the digital twin network. Furthermore, the first configuration parameters can be optimized based on the digital twin network corresponding to the physical network.

[0089] In some embodiments, the construction parameters of the above-mentioned digital twin network include one or more of network status configuration parameters, relevant algorithm parameters, and environmental parameters.

[0090] Among them, the network status configuration parameters include one or more of the network KPI (Key Performance Indicator), cell load parameters, cell interference parameters, and user statistical characteristic parameters; the related algorithm parameters include one or more of the physical layer parameters, scheduling algorithm parameters, power control algorithm parameters, and adaptive coding modulation parameters; and the environmental parameters include one or more of the geographical parameters, system parameters, frequency parameters, and access configuration parameters.

[0091] In some embodiments, the digital twin network may be constructed before performing network optimization requirements based on the physical network and determining the first configuration parameter to be optimized. Alternatively, the digital twin network may be constructed before performing step S102 below and optimizing the first configuration parameter.

[0092] In some embodiments, the physical state of the physical network can be periodically detected. In response to detecting a change in the physical state of the physical network, the construction parameters of the digital twin network are updated to obtain updated construction parameters, and the digital twin network is reconstructed based on the updated construction parameters.

[0093] In some embodiments, in response to detecting a change in the physical state of the physical network, a first configuration parameter to be optimized may be determined based on a network optimization requirement of the physical network.

[0094] It should be noted that in the method provided in the embodiment of the present application, after the physical state of the physical network changes, the construction parameters of the digital twin network can be updated, thereby improving the authenticity of the digital twin network construction and avoiding the problem of simulation data errors caused by unstable physical network state.

[0095] S102. Optimize the first configuration parameter based on the digital twin network corresponding to the physical network to obtain the optimal parameter value of the first configuration parameter.

[0096] In some embodiments, the optimal parameter value refers to the parameter value of the first configuration parameter that best meets the network optimization requirements determined based on the digital twin network corresponding to the physical network.

[0097] In some embodiments, as shown in FIG. 4 , step S102 may be specifically implemented as the following steps S1021 - S1024 .

[0098] S1021: Iterate and update the parameter value of the first configuration parameter a preset number of times.

[0099] In some embodiments, the parameter value of the first configuration parameter after the i-th iteration update is determined based on the parameter value of the first configuration parameter after the i-1-th iteration update and the digital twin result corresponding to the parameter value after the i-1-th iteration update, where i is a positive integer.

[0100] In some embodiments, the parameter value of the first configuration parameter may be iteratively updated a preset number of times based on an artificial intelligence algorithm.

[0101] Based on the network optimization requirements, artificial intelligence algorithm parameters can be determined, and then, based on the artificial intelligence algorithm corresponding to the artificial intelligence algorithm parameters, the parameter value of the first configuration parameter can be iteratively updated a preset number of times. The artificial intelligence algorithm parameters include artificial intelligence algorithm functions, artificial intelligence algorithm structures, etc. In some embodiments, based on the network optimization requirements, a sub-service type corresponding to the network optimization requirements can be determined, and then, based on the sub-service type, artificial intelligence algorithm parameters such as artificial intelligence algorithm functions and artificial intelligence algorithm structures can be determined.

[0102] For example, when the network optimization requirement of the physical network is to obtain the maximum amount of data without changing the physical state of the physical network, the sub-service type can be determined to be a traffic optimization sub-service type, and its corresponding artificial intelligence algorithm function can be the following formula (1), and the artificial intelligence algorithm structure can be a hyperparameter optimization algorithm (Tree-structured Parzen Estimator, TPE) model.

[0103] To meet this network optimization requirement, it is necessary to determine the digital twin network configuration parameters with the maximum throughput. The target parameters can be determined based on the uplink and downlink throughput. For example, the target parameter can be determined as the sum of the downlink throughput with a weight of 1 / 2 and the uplink throughput with a weight of 1. The artificial intelligence algorithm function can be:

[0104] Object is the target parameter determined based on the network optimization requirements, ul_throughput is the uplink throughput, and dl_throughput is the downlink throughput.

[0105] Furthermore, the configuration parameter with the highest degree of influence on the target parameter in the digital twin network can be selected as the first configuration parameter. For example, the downlink bit error rate parameter and downlink AMC adjustment step size related to the AMC algorithm can be selected.

[0106] Thus, the initial parameter value and value range of the first configuration parameter can be determined first. For example, the initial parameter value is a downlink bit error rate of 0.0, a downlink AMC adjustment step of 0.75, and the value ranges of the downlink bit error rate and the downlink AMC adjustment step can be as shown in Table 1.

[0107] Table 1

[0108] Furthermore, the TPE model can fit a random model that conforms to the historical data based on the historical learning parameters to iteratively update the parameter value of the first configuration parameter a preset number of times. For example, based on the network optimization requirement of a physical network that obtains the maximum amount of data, the preset number of times can be 50, with the learning direction set to "maximum." Thus, the parameter value of the first configuration parameter can be iteratively updated 50 times, obtaining the parameter values ​​of the first configuration parameter after 50 iterations.

[0109] S1022. Input the parameter value of the first configuration parameter after each iterative update into the digital twin network to obtain the digital twin result corresponding to the parameter value of the first configuration parameter after each iterative update.

[0110] In some embodiments, the digital twin result is used to indicate the network performance of the digital twin network related to the network optimization requirements. Exemplarily, the digital twin result can be represented by the parameter value of the above-mentioned target parameter.

[0111] For example, the configuration parameters of the digital twin network can be updated and adjusted based on the parameter value configuration after the iterative update of the first configuration parameter. After the update of the digital twin network is completed, the parameter value of the target parameter obtained after this iteration is determined.

[0112] Taking the network optimization requirements of the physical network for obtaining the maximum data volume as an example, the system runs for 200 seconds, each base station contains 3 cells, the number of users is 10, and the straight-line distance between each user and the base station is 9 meters. Based on the initial value of the first parameter, the downlink bit error rate is 0.0, the downlink AMC adjustment step is 0.75, and the downlink throughput is 123.15 Mbps and the uplink throughput is 38.71 Mbps. Based on the above formula (1), the parameter value of the target parameter is 100.28 Mbps. For example, the parameter values ​​of the target parameters obtained by multiple iterative updates are shown in Table 2.

[0113] Table 2

[0114] In some embodiments, when the physical state of the physical network changes, the digital twin network can be rebuilt using the parameter values ​​of the first configuration parameters after each iterative update. The parameter values ​​of the target parameters obtained through multiple iterative updates are shown in Table 3.

[0115] Table 3

[0116] S1023. Take the optimal digital twin result among the digital twin results corresponding to the parameter values ​​of the first configuration parameter after each iterative update as the first digital twin result.

[0117] For example, taking the aforementioned network optimization requirement of obtaining the maximum data volume for a physical network, if the parameter value after the seventh iteration is the optimal digital twin result among the digital twin results corresponding to the iteratively updated parameter values, the digital twin result corresponding to the parameter value after the seventh iteration can be used as the first digital twin result. The first configuration parameter is a downlink bit error rate parameter of 0.25, an AMC adjustment step of 0.5625, and a maximum throughput result of 100.28 Mbps.

[0118] Alternatively, based on the embodiment shown in Table 3 above, when the parameter value after the 45th iteration update is the optimal digital twin result among the digital twin results corresponding to the iteratively updated parameter values, the digital twin result corresponding to the parameter value after the 45th iteration update can be the first digital twin result. Among them, the first configuration parameter is a downlink bit error rate parameter of 0.1, an AMC adjustment step of 0.1875, and a maximum throughput result of 23.07 Mbps.

[0119] In some embodiments, in the network optimization system shown in Figure 2, after determining the first digital twin result, the artificial intelligence module 220 can report the first digital twin result and the parameter value of the first configuration parameter corresponding to the digital twin result to the network optimization module 210.

[0120] S1024. Use the parameter value of the first configuration parameter corresponding to the first digital twin result as the optimal parameter value of the first configuration parameter.

[0121] S103: Send the optimal parameter value of the first configuration parameter to the physical network.

[0122] In some embodiments, a first digital twin result is obtained, where the first digital twin result is a digital twin result obtained by the digital twin network based on an optimal parameter value of a first configuration parameter. If the first digital twin result meets the network optimization requirements, the optimal parameter value of the first configuration parameter is sent to the physical network.

[0123] It should be noted that the method provided in this application can also judge the first digital twin network based on network optimization requirements, and send configuration parameter values ​​when the requirements are met, which can further improve the accuracy of network optimization.

[0124] In one example, in combination with the network optimization system shown in FIG2 , the process of determining the optimal parameter value of the first configuration parameter can be an inner loop process in the network optimization process. As shown in FIG5 or FIG6 , the inner loop process includes the network optimization module 210 sending the configuration parameters of the digital twin network to the digital twin module 230, and sending the artificial intelligence algorithm parameters to the artificial intelligence module 220 based on the network optimization requirements. The artificial intelligence module 220 optimizes the first configuration parameter based on the artificial intelligence algorithm parameters, sends the parameter value of the first configuration parameter updated n times by iteration to the digital twin module 230, receives the digital twin result corresponding to the parameter value after each iteration update, and reports the best first digital twin result to the network optimization module 210. Furthermore, when the first digital twin result meets the network optimization requirements, the optimal parameter value of the first configuration parameter is sent to the physical network, that is, the outer loop process is started.

[0125] In some embodiments, when the physical state of the physical network does not change, the optimal parameter value of the first configuration parameter is sent to the physical network.

[0126] In some embodiments, the optimal parameter value of the first configuration parameter may also be transmitted to the user.

[0127] In another example, in combination with the network optimization system shown in Figure 2, on the basis of the inner loop, the data transmission process combining the network optimization module 210 and the physical network 240 can be the outer loop process of the network optimization system. As shown in Figure 5 or Figure 7, the outer loop process includes the network optimization module 210 receiving the network optimization requirements input by the user, and transmitting the network optimization results to the user, that is, transmitting the optimal parameter value of the first configuration parameter. The network optimization module 210 can also send the optimal parameter value of the first configuration parameter to the physical network, and the physical network 240 reports the physical state of the physical network to the network optimization module 210, and the physical state of the physical network remains unchanged. In some embodiments, the physical network 240 can also report the operating results based on the optimal parameter value of the first configuration parameter to the network optimization module 210.

[0128] In some embodiments, when the physical state of the physical network changes, the digital twin network is reconstructed. At this time, the configuration parameters of the physical network can be re-optimized based on the reconstructed digital twin network to obtain the optimal parameter value of the first configuration parameter.

[0129] In another example, in combination with the network optimization system shown in FIG2 , as shown in FIG8 , when the physical state of the physical network changes, multiple inner loop processes can be started based on the embodiment shown in FIG7 .

[0130] This application provides a network optimization method that can optimize the configuration parameters of a physical network in combination with a digital twin network to determine the configuration parameters that meet the current network optimization requirements. In this way, the digital twin network can be used to perform multiple high-level simulations of the physical network to be optimized during the optimization process, and the status of the digital twin network and the physical network can be obtained in real time to correct the digital twin network, thereby improving the efficiency and accuracy of network optimization.

[0131] In some embodiments, as shown in FIG9 , the network optimization method provided in the embodiment of the present application further includes the following steps S201 to S203 .

[0132] S201. When the first digital twin result does not meet the network optimization requirements, determine the second configuration parameters to be optimized based on the network optimization requirements.

[0133] In some embodiments, the first configuration parameter and the second configuration parameter differ in at least one of the following: parameter type, parameter value range, and number of parameters.

[0134] In one example, taking the aforementioned network optimization requirement of obtaining the maximum data volume for a physical network as an example, since the optimization requirement is to determine the maximum data volume, to determine whether the data volume can be further increased, the parameter selection range can be expanded to determine the second configuration parameters to be optimized. For example, the target receive power and parameters related to partial path loss compensation can be added to the first configuration parameters to obtain the second configuration parameters. Specifically, the second configuration parameters include the downlink bit error rate, downlink AMC adjustment step size, target receive power, and parameters related to partial path loss compensation.

[0135] S202. Based on the digital twin network corresponding to the physical network, optimize the second configuration parameters to obtain the optimal parameter value of the second configuration parameter and the second digital twin result.

[0136] Among them, the second digital twin result is the digital twin result obtained by the digital twin network based on the optimal parameter value of the second configuration parameter.

[0137] For example, taking the above-mentioned network optimization requirement of obtaining the maximum data volume of the physical network as an example, the second configuration parameters are the downlink bit error rate, the downlink AMC adjustment step size, the target receiving power, and the parameters related to partial path loss compensation.

[0138] The value ranges of the downlink bit error rate, downlink AMC adjustment step size, target receive power, and partial path loss compensation related parameters can be shown in Table 4.

[0139] Table 4

[0140] In one example, the initial value of the second configuration parameter may be a downlink bit error rate of 0, a downlink AMC adjustment step of 0.75, a target receive power of -70 dBm, and a partial path loss compensation related parameter of 0.

[0141] Therefore, based on the description of step S1021 above, the artificial intelligence algorithm structure is a TPE model. Based on the historical learning parameters, a random model that conforms to the historical data can be fitted, and the parameter value of the second configuration parameter can be iteratively updated a preset number of times. For example, the preset number of times can be 50, and the learning direction is "maximum." Thus, the parameter value of the second configuration parameter can be iteratively updated 50 times, respectively, to obtain the parameter value of the first configuration parameter after 50 iterative updates.

[0142] Furthermore, the parameter value of the second configuration parameter after each iterative update can be input into the digital twin network to obtain the digital twin result corresponding to the parameter value of the second configuration parameter after each iterative update. The optimal digital twin result among the digital twin results corresponding to the parameter value of the second configuration parameter after each iterative update is used as the second digital twin result.

[0143] For example, the parameter values ​​of the target parameters obtained by multiple iterative updates are shown in Table 5, where the downlink throughput determined based on the initial value of the second configuration parameter is 123.12 Mbps, and the uplink throughput is 1.72 Mbps. Based on formula (1), the parameter value of the target parameter can be determined to be 63.29 Mbps.

[0144] Table 5

[0145] Furthermore, if the parameter value after the 18th iteration is the optimal digital twin result among the digital twin results corresponding to the second configuration parameter value after the iterative update, the digital twin result corresponding to the parameter value after the 18th iteration can be determined as the second digital twin result. The second configuration parameters are a downlink bit error rate parameter of 0.1, an AMC adjustment step of 0.5625, a target received power of -60dBm, and a partial path loss compensation parameter of 5, resulting in a maximum throughput result of 100.28Mbps.

[0146] That is, the optimal parameter values ​​of the second configuration parameters are: the downlink bit error rate parameter is 0.1, the AMC adjustment step is 0.5625, the target receive power is -60dBm, and the partial path loss compensation related parameter is 5.

[0147] In one example, based on the embodiment shown in Table 3 above, the second configuration parameter can be a downlink bit error rate, a downlink AMC adjustment step size, and a physical uplink control channel power control parameter. Furthermore, the parameter values ​​of the target parameters obtained through multiple iterative updates are shown in Table 6.

[0148] Table 6

[0149] Furthermore, when the parameter value after the 0th iteration update is the optimal digital twin result among the digital twin results corresponding to the second configuration parameter value after the iterative update, the digital twin result corresponding to the parameter value after the 0th iteration update can be the second digital twin result. Among them, the second configuration parameters are a downlink bit error rate parameter of 0.1, a downlink AMC adjustment step of 0.5625, a target received power of -60dBm, and a partial path loss compensation related parameter of 5. The maximum throughput result that can be obtained is 21.57Mbps.

[0150] That is, the optimal parameter values ​​of the second configuration parameters are: the downlink bit error rate parameter is 0.1, the AMC adjustment step is 0.5625, the target receive power is -60dBm, and the partial path loss compensation related parameter is 5.

[0151] In addition, a more detailed description of the technical features in step S202 can refer to the relevant description in the above step S102, which will not be repeated here.

[0152] S203. When the second digital twin result meets the network optimization requirements, the optimal parameter value of the second configuration parameter is sent to the physical network.

[0153] In some embodiments, when the second digital twin result does not meet the network optimization requirements, the parameter value of the third configuration parameter is sent to the physical network, and the parameter value of the third configuration parameter is the parameter value of the configuration parameter corresponding to the optimal digital twin result between the first digital twin result and the second digital twin result.

[0154] Exemplarily, based on the embodiment shown in Table 6 above, the maximum throughput result obtainable in the second digital twin result of the digital twin result corresponding to the parameter value updated after the 0th iteration is 21.57 Mbps, and the maximum throughput result obtainable in the first digital twin result is 21.90 Mbps. Thus, the parameter value corresponding to the first digital twin result, the downlink bit error rate parameter 0.1, and the downlink AMC adjustment step 0.1875 can be determined as the parameter value of the third configuration parameter, and the parameter value of the third configuration parameter is sent to the physical network.

[0155] In some embodiments, when the second digital twin result does not meet the network optimization requirements, the configuration parameters to be optimized can be updated and determined again based on the network optimization requirements to execute the above optimization process again until the digital twin result meets the network optimization requirements.

[0156] In some embodiments, based on FIG6 , as shown in FIG10 , if the first digital twin result does not meet the network optimization requirements, the inner loop process can be executed again. Furthermore, if the second digital twin result does not meet the network optimization requirements, the inner loop process can be executed a third time… until the digital twin result meets the network optimization requirements, and the outer loop process is initiated.

[0157] Based on the above embodiment, when the digital twin result does not meet the requirements, the "inner loop" process can be re-executed multiple times, so as to further improve the accuracy of network optimization.

[0158] In some embodiments, as shown in FIG11 , the network optimization method provided in the embodiment of the present application may further include the following steps S301 and S302 .

[0159] S301: Obtain an operation result of a physical network based on an optimal parameter value of a first configuration parameter.

[0160] In some embodiments, the operation result is used to indicate the network performance of the physical network related to the network optimization requirements. Exemplarily, the operation result can be represented by the parameter value of the above-mentioned target parameter.

[0161] For example, taking the above-mentioned network optimization requirement of obtaining the maximum data volume of the physical network as an example, the target parameter can be determined based on the uplink and downlink throughput.

[0162] In some embodiments, if the first digital twin result does not meet the network optimization requirements, the physical network's operating results based on the optimal parameter values ​​of the second configuration parameter can also be obtained. Alternatively, the physical network's operating results based on the optimal parameter values ​​of the third configuration parameter can be obtained. Furthermore, based on the relationship between the operating results and the corresponding digital twin results, an adjustment strategy for the digital twin network is determined.

[0163] S302. Determine an adjustment strategy for the digital twin network based on the relationship between the operation result and the first digital twin result.

[0164] Among them, the first digital twin result is the digital twin result obtained by the digital twin network based on the optimal parameter value of the first configuration parameter.

[0165] In some embodiments, if the operating result is inferior to the first digital twin result, the adjustment strategy includes performing performance correction on the digital twin network. Furthermore, the network optimization process described above can be re-executed based on the digital twin network that has undergone performance correction to re-optimize the configuration parameters of the physical network to obtain the optimal parameter values ​​of the first configuration parameters.

[0166] Alternatively, if the running result is better than the first digital twin result, the network optimization process can be terminated. In addition, the optimal parameter value of the first configuration parameter can be fed back to the user.

[0167] In some embodiments, the network optimization result may also be determined based on a relationship between the operation result and the operation result of the physical network in the previous network optimization cycle. The operation result of the physical network in the previous network optimization cycle is the operation result of the physical network obtained from the last network optimization process performed based on the network optimization requirements.

[0168] In one example, the operation results in a maximum throughput of 95.72 Mbps. The previous network optimization process resulted in a maximum throughput of 75 Mbps for the physical network. Therefore, the network optimization process can be terminated. Furthermore, optimal values ​​for the configuration parameters can be provided to the user. For example, these optimal values ​​may include a downlink bit error rate parameter of 0.1, a downlink AMC adjustment step of 0.5625, a target receive power of -60 dBm, and a partial path loss compensation parameter of 5.

[0169] In another example, the operation results in a maximum throughput of 21.90 Mbps. The physical network obtained in the previous network optimization process showed a maximum throughput of 20.03 Mbps. Therefore, the network optimization process can be terminated. Furthermore, the optimal parameter values ​​for the configuration parameters can be fed back to the user. For example, the optimal parameter values ​​may include a downlink bit error rate parameter of 0.1 and a downlink AMC adjustment step size of 0.1875.

[0170] If the various modules of the above-mentioned network optimization system 200 are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the various embodiments of the present application. The storage medium for storing computer software products includes various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0171] When the functions of the various modules of the network optimization system 200 are implemented in hardware, the present embodiment further provides a communication device 300 for implementing the network optimization method provided herein. As shown in Figure 12 , the communication device 300 includes a communication interface 303, a processor 302, and a bus 304. Optionally, the communication device may also include a memory 301.

[0172] Processor 302 may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the embodiments of this application. Processor 302 may be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field-programmable gate array, or other programmable logic device, a transistor logic device, a hardware component, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the embodiments of this application. Processor 302 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0173] The communication interface 303 is used to connect to other devices via a communication network, such as Ethernet, wireless access network, wireless local area network (WLAN), etc.

[0174] The memory 301 may be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0175] As a possible implementation, the memory 301 may exist independently of the processor 302. The memory 301 may be connected to the processor 302 via a bus 304 and is used to store instructions or program codes executable by the processor 302, such as computer program instructions. When the processor 302 calls and executes the instructions or program codes stored in the memory 301, the communication method provided in the embodiment of the present application can be implemented.

[0176] In another possible implementation, the memory 301 may also be integrated with the processor 302 .

[0177] Bus 304 can be an Extended Industry Standard Architecture (EISA) bus, etc. Bus 304 can be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, FIG12 shows only one thick line, but this does not mean that there is only one bus or only one type of bus.

[0178] Some embodiments of the present application provide a computer-readable storage medium (e.g., a non-transitory computer-readable storage medium) storing computer program instructions. When the computer program instructions are executed on a computer (e.g., the aforementioned communication device, the communication device's processor, or the aforementioned environmental reflection functional module), the computer executes the environmental reflection transmission method described in any of the aforementioned embodiments. It should be understood that the present application does not limit the specific form of the computer.

[0179] In some examples, the computer-readable storage media described above may include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, or magnetic tapes), optical disks (e.g., compact disks (CDs), digital versatile disks (DVDs), etc.), smart cards, and flash memory devices (e.g., erasable programmable read-only memories (EPROMs), cards, sticks, or key drives, etc.). The various computer-readable storage media described herein may represent one or more devices and / or other machine-readable storage media for storing information. The term "machine-readable storage medium" may include, but is not limited to, wireless channels and various other media capable of storing, containing, and / or carrying instructions and / or data.

[0180] An embodiment of the present application provides a computer program product comprising instructions, which, when executed on a computer, enables the computer to execute the method described in any one of the above embodiments.

[0181] The above is only a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or replacements within the technical scope disclosed in the present application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A network optimization method, wherein: The method comprises: Based on the network optimization requirements of the physical network, determine the first configuration parameter to be optimized; Based on the digital twin network corresponding to the physical network, optimizing the first configuration parameter to obtain an optimal parameter value of the first configuration parameter; Sending the optimal parameter value of the first configuration parameter to the physical network.

2. The method according to claim 1, wherein: The sending the optimal parameter value of the first configuration parameter to the physical network includes: Obtaining a first digital twin result, where the first digital twin result is a digital twin result obtained by the digital twin network based on an optimal parameter value of the first configuration parameter; When the first digital twin result meets the network optimization requirement, the optimal parameter value of the first configuration parameter is sent to the physical network.

3. The method according to claim 1, wherein: The network optimization requirement includes any one of the following: environment configuration requirement, resource utilization requirement, retransmission number requirement, capacity requirement, and service experience requirement.

4. The method according to claim 1, wherein: The first configuration parameter includes at least one of the following: geographic information configuration parameter, system information configuration parameter, frequency information configuration parameter, access configuration parameter, physical layer parameter, regulation algorithm parameter, power control algorithm parameter, and adaptive coding modulation parameter.

5. The method according to claim 1, wherein: The optimizing the first configuration parameter based on the digital twin network corresponding to the physical network to obtain the optimal parameter value of the first configuration parameter includes: Iteratively updating the parameter value of the first configuration parameter a preset number of times; Inputting the parameter value of the first configuration parameter after each iterative update into the digital twin network to obtain the digital twin result corresponding to the parameter value of the first configuration parameter after each iterative update; The optimal digital twin result among the digital twin results corresponding to the parameter values ​​of the first configuration parameters after each iterative update is used as the first digital twin result; The parameter value of the first configuration parameter corresponding to the first digital twin result is used as the optimal parameter value of the first configuration parameter.

6. The method according to claim 5, wherein: The parameter value of the first configuration parameter after the i-th iteration update is determined based on the parameter value of the first configuration parameter after the i-1-th iteration update and the digital twin result corresponding to the parameter value after the i-1-th iteration update, where i is a positive integer.

7. The method according to claim 5, wherein: The iterative updating of the parameter value of the first configuration parameter for a preset number of times includes: Based on an artificial intelligence algorithm, the parameter value of the first configuration parameter is iteratively updated a preset number of times.

8. The method according to claim 1, wherein: The determining of the first configuration parameter to be optimized based on the network optimization requirement of the physical network includes: Based on the network optimization requirements, determining target parameters; The first configuration parameter is determined from a set of configuration parameters associated with the target parameter.

9. The method according to claim 1, wherein: The determining of the first configuration parameter to be optimized based on the network optimization requirement of the physical network includes: In response to detecting that a physical state of the physical network has changed, a first configuration parameter to be optimized is determined based on a network optimization requirement of the physical network.

10. The method according to claim 2, wherein: The method further comprises: When the first digital twin result does not meet the network optimization requirement, determining a second configuration parameter to be optimized based on the network optimization requirement; Based on the digital twin network corresponding to the physical network, optimizing the second configuration parameter to obtain the optimal parameter value of the second configuration parameter and a second digital twin result, where the second digital twin result is the digital twin result obtained by the digital twin network based on the optimal parameter value of the second configuration parameter; When the second digital twin result meets the network optimization requirement, the optimal parameter value of the second configuration parameter is sent to the physical network.

11. The method according to claim 10, wherein: The method further comprises: When the second digital twin result fails to meet the network optimization requirements, a parameter value of a third configuration parameter is sent to the physical network, and the parameter value of the third configuration parameter is the parameter value of the configuration parameter corresponding to the optimal digital twin result between the first digital twin result and the second digital twin result.

12. The method according to claim 10, wherein: The first configuration parameter and the second configuration parameter differ in at least one of the following: parameter type, parameter value range, and parameter number.

13. The method according to claim 1, wherein: The method further comprises: Obtaining an operation result of the physical network based on an optimal parameter value of the first configuration parameter; Based on the relationship between the running result and the first digital twin result, an adjustment strategy for the digital twin network is determined; wherein the first digital twin result is a digital twin result obtained by the digital twin network based on the optimal parameter value of the first configuration parameter.

14. The method according to claim 13, wherein: When the operating result is inferior to the first digital twin result, the adjustment strategy includes performing performance correction processing on the digital twin network.

15. The method according to claim 1, wherein: The method further comprises: Determining construction parameters of the digital twin network according to construction parameters of the physical network; Construct the digital twin network according to the construction parameters of the digital twin network.

16. The method according to claim 15, wherein: The construction parameters of the digital twin network include one or more of network state configuration parameters, relevant algorithm parameters, and environmental parameters; Among them, the network status configuration parameters include one or more of the network key performance indicators KPI, cell load parameters, cell interference parameters, and user statistical characteristic parameters; the related algorithm parameters include one or more of the physical layer parameters, scheduling algorithm parameters, power control algorithm parameters, and adaptive coding modulation parameters; the environmental parameters include one or more of the geographical parameters, system parameters, frequency parameters, and access configuration parameters.

17. The method according to claim 15, wherein: The method further comprises: In response to detecting that a physical state of the physical network has changed, updating construction parameters of the digital twin network to obtain updated construction parameters; Based on the updated construction parameters, the digital twin network is reconstructed.

18. A network optimization system, wherein: The network optimization system includes a network optimization module and an artificial intelligence module: The artificial intelligence module is used to determine a first configuration parameter to be optimized based on a network optimization requirement of the physical network; The artificial intelligence module is further used to optimize the first configuration parameter based on the digital twin network corresponding to the physical network to obtain the optimal parameter value of the first configuration parameter; The network optimization module is used to send the optimal parameter value of the first configuration parameter to the physical network.

19. The network optimization system according to claim 18, wherein: The network optimization system further includes a digital twin module; the digital twin module is used to: Determining construction parameters of the digital twin network according to construction parameters of the physical network; Construct the digital twin network according to the construction parameters of the digital twin network.

20. The network optimization system according to claim 18, wherein: The artificial intelligence module is used to: Iteratively updating the parameter value of the first configuration parameter a preset number of times; Inputting the parameter value of the first configuration parameter after each iterative update into the digital twin network to obtain the digital twin result corresponding to the parameter value of the first configuration parameter after each iterative update; The optimal digital twin result among the digital twin results corresponding to the parameter values ​​of the first configuration parameters after each iterative update is used as the first digital twin result; The parameter value of the first configuration parameter corresponding to the first digital twin result is used as the optimal parameter value of the first configuration parameter.

21. The network optimization system according to claim 18, wherein: The network optimization module is specifically used for: Obtaining a first digital twin result, where the first digital twin result is a digital twin result obtained by the digital twin network based on an optimal parameter value of the first configuration parameter; When the first digital twin result meets the network optimization requirement, the optimal parameter value of the first configuration parameter is sent to the physical network.

22. The network optimization system according to claim 21, wherein: The network optimization module is further configured to determine a second configuration parameter to be optimized based on the network optimization requirement when the first digital twin result does not meet the network optimization requirement; The artificial intelligence module is further used to optimize the second configuration parameters based on the digital twin network corresponding to the physical network to obtain the optimal parameter value of the second configuration parameter and the second digital twin result, where the second digital twin result is the digital twin result obtained by the digital twin network based on the optimal parameter value of the second configuration parameter; The network optimization module is also used to send the optimal parameter value of the second configuration parameter to the physical network when the second digital twin result meets the network optimization requirements.

23. The network optimization system according to claim 22, wherein: The network optimization module is also used to send the parameter value of a third configuration parameter to the physical network when the second digital twin result does not meet the network optimization requirements, and the parameter value of the third configuration parameter is the parameter value of the configuration parameter corresponding to the optimal digital twin result between the first digital twin result and the second digital twin result.

24. The network optimization system according to claim 19, wherein: The network optimization module is further used to: obtain an operation result of the physical network based on the optimal parameter value of the first configuration parameter; and determine an adjustment strategy for the digital twin network based on a superiority relationship between the operation result and the first digital twin result; Sending the adjustment strategy to the digital twin module; wherein the first digital twin result is a digital twin result obtained by the digital twin network based on the optimal parameter value of the first configuration parameter; The digital twin module is also used to adjust the digital twin network according to the adjustment strategy.

25. A communication device, wherein: include: Memory and processor; The memory is coupled to the processor; The memory is used to store instructions executable by the processor; When the processor executes the instructions, the method according to any one of claims 1 to 17 is performed.

26. A computer-readable storage medium, wherein: The computer-readable storage medium stores computer instructions, and when the computer instructions are executed on a communication device, the communication device is caused to execute the method according to any one of claims 1 to 17.

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