Network operation and maintenance method and system based on digital twin technology

Abstract

The invention discloses a network operation and maintenance method and system based on a digital twinning technology, and the method comprises the steps: collecting and analyzing various data in a real network based on big data, cloud computing and artificial intelligence technologies, and constructing digital twinning network models in one-to-one correspondence with the real network; various fault scenes are simulated in the digital twin network model, and the probability and position of fault occurrence are predicted through a machine learning algorithm; when a fault occurs in the real network, using the digital twin network model to locate the fault type and position; based on the fault type and position, calling a preset repair strategy library or generating a new repair scheme, and implementing repair through an automatic tool; based on network load prediction, user demand analysis and fault prediction results, simulation and optimization of a resource allocation scheme are carried out by using a digital twin network model, and the method is implemented in a real network. According to the invention, rapid response, accurate positioning and intelligent repair of network faults and dynamic optimal configuration of network resources can be realized.

Description

Technical Field

[0001] The present invention relates to the fields of communication technology, information technology and artificial intelligence technology, and in particular to a network operation and maintenance method and system based on digital twin technology. Background Art

[0002] With the rapid development of information technology, the Internet has been deeply rooted in every corner of modern society and has become a key infrastructure driving economic development, social progress and cultural exchange. From smart homes to smart cities, from online education to telemedicine, the Internet is everywhere, greatly enriching people's lifestyles and work patterns. However, with the rapid expansion of network scale and the increasing complexity of structure, network management and maintenance are facing unprecedented challenges and opportunities. Traditional network maintenance methods, such as manual regular inspections and passive post-fault repair, have gradually revealed their limitations and are difficult to meet the current high standards for network efficiency, stability, security and intelligent operation.

[0003] The limitations of existing technologies are specifically manifested in the following aspects:

[0004] Inaccurate fault location: Traditional methods mainly rely on manual analysis of alarm information and log files of network devices, which is often time-consuming and error-prone. Since network failures may involve multiple devices or links, and alarm information is often ambiguous and redundant, it is difficult to quickly and accurately locate the source of the fault. This not only prolongs the fault recovery time, but may also cause wider network impact.

[0005] Slow response: From fault discovery, diagnosis to final resolution, traditional processes often involve the collaboration of multiple departments and personnel, with inefficient information transmission and lengthy decision-making processes. This lag not only affects user experience, but can also have a serious impact on business continuity, especially in key areas such as finance and healthcare, where every minute of downtime can result in huge losses.

[0006] Inflexible resource allocation: Traditional network resource management is usually based on preset static rules, which makes it difficult to dynamically adjust according to real-time network traffic, load changes or specific business needs. This leads to inefficient use of resources, which may cause idle resources on the one hand, and resource bottlenecks during peak hours on the other hand, affecting the overall performance and scalability of the network.

[0007] Lack of foresight: Traditional network management systems mostly focus on current status monitoring and post-processing, lacking effective prediction and prevention mechanisms for future network behavior. For potential network congestion, security threats, or hardware aging, they can only respond passively after the problem breaks out, and cannot take measures to intervene in advance, increasing the risk and cost of network operations.

[0008] In summary, in the face of the rapid development and complexity of the network environment, it is particularly important to explore and implement more intelligent, efficient and adaptive network management and maintenance strategies. This includes but is not limited to the use of advanced technologies such as artificial intelligence, big data analysis, and machine learning to achieve accurate and rapid fault location, dynamic optimization and allocation of resources, and predictive management of network status, thereby building a more robust, flexible and intelligent future network ecology. Summary of the invention

[0009] In order to solve the above problems, the present invention proposes a network operation and maintenance method and system based on digital twin technology, which can realize comprehensive, real-time and high-precision simulation of the real network, thereby achieving rapid response, accurate positioning, intelligent repair of network failures, and dynamic optimization configuration of network resources, thereby improving the overall performance of the network and user experience.

[0010] The technical solution adopted by the present invention is as follows:

[0011] A network operation and maintenance method based on digital twin technology, comprising:

[0012] Based on big data, cloud computing and artificial intelligence technologies, various types of data in the real network are collected and analyzed to build a digital twin network model that corresponds to the real network one by one;

[0013] Simulate various fault scenarios in the digital twin network model and predict the probability and location of faults through machine learning algorithms; when a fault occurs in the real network, use the digital twin network model to locate the fault type and location;

[0014] Based on the fault type and location, call the preset repair strategy library or generate a new repair plan, and implement the repair through automated tools;

[0015] Based on network load forecasting, user demand analysis, and fault prediction results, the digital twin network model is used to simulate and optimize resource allocation plans and implement them in the real network.

[0016] Furthermore, the construction of a digital twin network model corresponding one-to-one to the real network includes:

[0017] Network topology modeling: Based on the physical connection relationship of the real network, a network topology model is constructed, including the representation of nodes and edges;

[0018] Equipment parameter modeling: Map the operating parameters of network equipment to the digital twin network model so that the digital twin network model can reflect the real-time status of network equipment;

[0019] Business logic modeling: Based on the business logic in the real network, simulate the operation process of the business flow in the digital twin network model;

[0020] Real-time synchronization mechanism: Through API interface and real-time data streaming technology, real-time data synchronization between the digital twin network model and the real network is achieved.

[0021] Furthermore, the construction of a digital twin network model corresponding one-to-one to the real network also includes:

[0022] Model verification: Verify the accuracy of the digital twin network model by comparing the output of the digital twin network model with the operating data of the real network; use historical data to train and test the model so that the digital twin network model can accurately predict the operating status of the real network;

[0023] Model optimization: According to the verification results, adjust the parameters and structure of the digital twin network model to optimize the prediction accuracy and response speed of the digital twin network model; introduce an adaptive learning mechanism to enable the digital twin network model to automatically adjust according to changes in the network environment.

[0024] Furthermore, simulating various fault scenarios in the digital twin network model and predicting the probability and location of the fault through a machine learning algorithm include:

[0025] Simulate various possible failure scenarios in the digital twin network model and evaluate the impact on network performance;

[0026] Analyze historical fault data in the digital twin network model through machine learning algorithms to predict possible future faults;

[0027] Predict the operating status change trend of network equipment through time series analysis and identify potential failure risks in advance;

[0028] Generate a fault prediction report based on the simulation results, providing the probability of fault occurrence and possible impact range.

[0029] Furthermore, when a fault occurs in the real network, the digital twin network model is used to locate the fault type and location, including:

[0030] Data collection: collect the operation data of the real network in real time and synchronize it to the digital twin network model;

[0031] Anomaly detection: Detect abnormal behavior and abnormal data in the network through machine learning algorithms;

[0032] Path analysis: Use graph theory algorithms to analyze the propagation path of abnormal data and determine the source of the fault;

[0033] Fault confirmation: Confirm the location and type of the fault point through the simulation results of the digital twin network model.

[0034] Furthermore, based on the fault type and location, calling a preset repair strategy library or generating a new repair solution, and implementing the repair through an automated tool, includes:

[0035] Repair strategy library construction: pre-build a repair strategy library, which includes repair solutions for different types of faults, where the repair solutions are based on historical fault handling experience and expert knowledge;

[0036] Dynamic repair plan generation: When a new fault type occurs, a new repair plan is generated through a machine learning algorithm. The repair strategy is continuously optimized based on historical repair results using a reinforcement learning algorithm.

[0037] Implementation of repair solutions: Implement the repair solutions through automated tools, including remote control scripts and API interfaces;

[0038] Repair plan verification: simulate the implementation process of the repair plan in the digital twin network model and evaluate the impact on network performance; feed back the repair results to the digital twin network model to update the model parameters and repair strategy library.

[0039] Furthermore, based on the network load prediction, user demand analysis and fault prediction results, the digital twin network model is used to simulate and optimize the resource allocation scheme and implement it in the real network, including:

[0040] Resource demand prediction: Use machine learning algorithms to predict future demand for network resources; dynamically adjust resource demand prediction models based on user behavior data and network load data;

[0041] Resource allocation plan generation: Generate resource allocation plan based on resource demand forecast results; optimize resource allocation plan using optimization algorithm;

[0042] Implementation of resource allocation plan: simulate the implementation process of the resource allocation plan in the digital twin network model and evaluate the impact on network performance; adjust the resource allocation plan based on the simulation results and implement it in the real network; dynamically adjust the resource allocation plan according to changes in network load and user demand.

[0043] A network operation and maintenance system based on digital twin technology, comprising:

[0044] The digital twin network model building module is configured to collect and analyze various types of data in the real network based on big data, cloud computing and artificial intelligence technologies, and build a digital twin network model that corresponds to the real network one by one;

[0045] The fault prediction and location module is configured to simulate various fault scenarios in the digital twin network model and predict the probability and location of faults through machine learning algorithms. When a fault occurs in the real network, the digital twin network model is used to locate the fault type and location.

[0046] The intelligent repair strategy generation and implementation module is configured to call a preset repair strategy library or generate a new repair plan based on the fault type and location, and implement the repair through an automated tool;

[0047] The resource optimization configuration module is configured to simulate and optimize the resource allocation plan based on the network load forecast, user demand analysis and fault prediction results using the digital twin network model, and implement it in the real network.

[0048] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor implements a network operation and maintenance method based on digital twin technology when executing the computer program.

[0049] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements a network operation and maintenance method based on digital twin technology.

[0050] The beneficial effects of the present invention are:

[0051] 1. Improve fault response speed

[0052] Build a digital twin network model: Use digital twin technology to build a virtual network model that is synchronized with the physical network in real time to achieve real-time monitoring and simulation of the network status.

[0053] Quickly locate the fault point: When a network failure occurs, the digital twin network model is used to simulate and locate the fault, quickly determine the fault point, and shorten the fault handling time.

[0054] Automated fault repair: Combined with automated operation and maintenance tools, common faults can be automatically repaired, further improving fault response speed.

[0055] 2. Enhance fault location accuracy

[0056] Utilize machine learning algorithms: Use machine learning algorithms to analyze historical fault data, establish a fault prediction model, and improve the accuracy of fault prediction.

[0057] Simulate fault scenarios: Use digital twin network models to simulate various fault scenarios, analyze fault propagation paths and impact ranges, and improve the accuracy of fault location.

[0058] Combine multi-source data: Combine multi-source data such as network alarms, logs, and traffic to conduct comprehensive analysis to improve the accuracy of fault location.

[0059] 3. Optimize resource allocation

[0060] Real-time monitoring of network status: Use network monitoring tools to monitor network traffic, performance, device status and other information in real time to provide a basis for dynamic resource allocation.

[0061] Policy-based dynamic adjustment: Dynamically adjust network resource allocation, such as bandwidth, routing, and load balancing, based on pre-defined policies, combined with network status and business needs.

[0062] 4. Improve network stability

[0063] Predict potential failures: Use machine learning algorithms and digital twin network models to predict potential network failures, such as device failures and link congestion.

[0064] Preventive maintenance: Based on the prediction results, preventive maintenance can be carried out in advance, such as replacing aging equipment and adjusting network configuration to avoid failures.

[0065] 5. Reduce operation and maintenance costs:

[0066] Automated operation and maintenance tools: Use automated operation and maintenance tools, such as automated configuration management and automated fault handling, to reduce manual intervention and lower operation and maintenance costs.

[0067] Intelligent operation and maintenance platform: Use the intelligent operation and maintenance platform to realize the centralization, visualization and intelligence of network operation and maintenance, improve operation and maintenance efficiency, and reduce operation and maintenance costs.

[0068] Reduce manual dependence: Through automated and intelligent operation and maintenance methods, reduce dependence on manual labor and reduce labor costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 It is a flow chart of a network operation and maintenance method based on digital twin technology in Example 1 of the present invention. DETAILED DESCRIPTION

[0070] In order to have a clearer understanding of the technical features, purposes and effects of the present invention, the specific implementation methods of the present invention are now described. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention, that is, the embodiments described are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention.

[0071] Example 1

[0072] like Figure 1As shown, this embodiment provides a network operation and maintenance method based on digital twin technology, including:

[0073] Based on big data, cloud computing and artificial intelligence technologies, various types of data in the real network (such as device status, traffic data, user behavior, etc.) are collected and analyzed to build a digital twin network model that corresponds to the real network one by one (such as network topology, device parameters, business logic, etc.);

[0074] Simulate various fault scenarios in the digital twin network model and predict the probability and location of faults through machine learning algorithms. When a fault occurs in the real network, use the digital twin network model to locate the fault type and location to reduce troubleshooting time.

[0075] Based on the fault type and location, the preset repair strategy library is called or a new repair plan is generated, and the repair is implemented through automated tools. The effectiveness of the repair plan is verified in the digital twin network model to ensure that there is no secondary impact on the real network.

[0076] Based on network load forecasting, user demand analysis, and fault prediction results, the digital twin network model is used to simulate and optimize resource allocation plans, and implemented in the real network to ensure effective use of resources.

[0077] Preferably, various types of data in the real network are collected and analyzed, including:

[0078] Data collection: Through sensors, logging systems, traffic monitoring tools, etc. in the network, real-time collection of network equipment operating status data (such as CPU utilization, memory usage, bandwidth occupancy, etc.), network traffic data (such as packet transmission rate, packet loss rate, etc.) and user behavior data (such as access frequency, request type, etc.). Data sources include but are not limited to network devices such as routers, switches, servers, firewalls, and user terminal devices.

[0079] Data preprocessing: Data cleaning: Remove duplicate, invalid or erroneous data, such as abnormal device status data or network traffic data. Data denoising: Remove noise from the data through filtering algorithms (such as Kalman filtering, wavelet transform, etc.) to ensure data accuracy. Data normalization: Standardize data from different sources to have the same dimension and range to facilitate subsequent modeling and analysis.

[0080] Preferably, a digital twin network model corresponding one-to-one to the real network is constructed, including:

[0081] Network topology modeling: Based on the physical connection relationship of the real network, a network topology model is constructed, including the representation of nodes (such as routers and switches) and edges (such as links);

[0082] Device parameter modeling: Map the operating parameters of network devices (such as CPU, memory, bandwidth, etc.) into the digital twin network model to ensure that the model can reflect the real-time status of the device;

[0083] Business logic modeling: Based on the business logic in the real network (such as data transmission path, load balancing strategy, etc.), simulate the operation process of the business flow in the digital twin network model;

[0084] Real-time synchronization mechanism: Through API interfaces and real-time data streaming technologies (such as Kafka, MQTT, etc.), real-time data synchronization between the digital twin network model and the real network is achieved to ensure the accuracy and timeliness of the model.

[0085] It should be noted that digital twin network models can be modeled using tools such as MATLAB and Python, combined with machine learning algorithms (such as neural networks, deep learning, etc.). During the model construction process, graph theory and network topology analysis techniques are used to ensure that the model can accurately reflect the topology of the real network.

[0086] More preferably, building a digital twin network model that corresponds one-to-one with the real network also includes:

[0087] Model verification: Verify the accuracy of the digital twin network model by comparing the output of the digital twin network model with the operating data of the real network; use historical data to train and test the model so that the digital twin network model can accurately predict the operating status of the real network;

[0088] Model optimization: According to the verification results, adjust the parameters and structure of the digital twin network model to optimize the prediction accuracy and response speed of the digital twin network model; introduce an adaptive learning mechanism to enable the digital twin network model to automatically adjust according to changes in the network environment.

[0089] Preferably, various fault scenarios are simulated in the digital twin network model, and the probability and location of fault occurrence are predicted through machine learning algorithms, including:

[0090] Fault prediction algorithm: Use machine learning algorithms (such as support vector machines, random forests, deep learning, etc.) to analyze historical fault data in the digital twin network model to predict possible future faults; use time series analysis (such as ARIMA, LSTM, etc.) to predict the changing trend of the operating status of network equipment and identify potential fault risks in advance.

[0091] Fault scenario simulation: Simulate various possible fault scenarios (such as equipment downtime, link interruption, traffic overload, etc.) in the digital twin network model to evaluate their impact on network performance. Generate a fault prediction report based on the simulation results, providing the probability of fault occurrence and possible impact range.

[0092] It should be noted that when a fault occurs in the real network, the digital twin network model obtains fault data through a real-time synchronization mechanism and uses fault location algorithms (such as path analysis based on graph theory, anomaly detection based on machine learning, etc.) to quickly locate the fault point. By comparing the operating status of the digital twin network model and the real network, the specific location where the fault occurs (such as a certain device or link) is identified.

[0093] Preferably, when a fault occurs in the real network, the digital twin network model is used to locate the fault type and location, including:

[0094] Data collection: collect the operation data of the real network in real time and synchronize it to the digital twin network model;

[0095] Anomaly detection: Detect abnormal behaviors (such as sudden increase in traffic, decreased device performance, etc.) and abnormal data in the network through machine learning algorithms;

[0096] Path analysis: Use graph theory algorithms to analyze the propagation path of abnormal data and determine the source of the fault;

[0097] Fault confirmation: Confirm the location and type of the fault point through the simulation results of the digital twin network model.

[0098] Preferably, based on the fault type and location, a preset repair strategy library is called or a new repair solution is generated, and the repair is implemented through an automated tool, including:

[0099] Repair strategy library construction: Pre-build a repair strategy library, which includes repair solutions for different types of faults (such as restarting devices, switching backup links, adjusting load balancing strategies, etc.), where the repair solutions are based on historical fault handling experience and expert knowledge;

[0100] Dynamic repair plan generation: When a new fault type occurs, a new repair plan is generated through a machine learning algorithm. The repair strategy is continuously optimized based on historical repair results using a reinforcement learning algorithm.

[0101] Implementation of repair plan: (1) Implement the repair plan through automated tools, including remote control scripts and API interfaces. For example, when a device is detected to be down, a restart command is automatically sent; when a link is detected to be broken, a backup link is automatically switched; (2) For complex or unrepairable faults, repair suggestions will be generated and the operation and maintenance personnel will be notified for manual intervention. Through augmented reality (AR) technology, the operation and maintenance personnel can remotely view the real-time status of the faulty device and perform operations;

[0102] Verification of repair plan: Simulate the implementation process of the repair plan in the digital twin network model, evaluate the impact on network performance, and use simulation results to ensure that the repair plan will not cause secondary impact on the real network; feed back the repair results to the digital twin network model, update the model parameters and repair strategy library, and through the feedback mechanism, continuously optimize the repair strategy and improve the repair efficiency.

[0103] Preferably, based on network load prediction, user demand analysis and fault prediction results, the digital twin network model is used to simulate and optimize the resource allocation scheme and implement it in the real network, including:

[0104] Resource demand forecasting: Use machine learning algorithms (such as regression analysis, time series forecasting, etc.) to predict future demand for network resources (such as bandwidth, computing resources, etc.);

[0105] Resource allocation plan generation: Generate resource allocation plans (such as bandwidth allocation, load balancing strategy, etc.) based on resource demand forecast results; use optimization algorithms (such as linear programming, genetic algorithms, etc.) to optimize resource allocation plans to ensure efficient use of resources;

[0106] Implementation of resource allocation plan: simulate the implementation process of the resource allocation plan in the digital twin network model and evaluate the impact on network performance; adjust the resource allocation plan based on the simulation results and implement it in the real network; dynamically adjust the resource allocation plan according to changes in network load and user demand, and ensure the flexibility and efficiency of resource allocation through real-time monitoring and feedback mechanisms.

[0107] It should be noted that the network operation and maintenance method based on digital twin technology in this embodiment has the following core innovations:

[0108] 1. Full-dimensional digital twin modeling: Integrate multi-dimensional data to build a digital twin network model that is highly consistent with the real network and realize a comprehensive simulation of the network environment.

[0109] 2. Intelligent fault prediction and location: Use machine learning algorithms to predict faults in the digital twin network model and quickly locate fault points in the real network.

[0110] 3. Dynamic resource optimization configuration: Dynamically adjust resource allocation according to network status and prediction results to improve resource utilization and network performance.

[0111] 4. Closed-loop operation and maintenance process automation: Automated operation and maintenance processes, from fault discovery to repair, without the need for human intervention.

[0112] In summary, the network operation and maintenance method based on digital twin technology in this embodiment has the following technical effects:

[0113] 1. Improve operation and maintenance efficiency: Through automated and intelligent operation and maintenance, the fault handling time can be significantly shortened and the operation and maintenance efficiency can be improved.

[0114] 2. Enhance fault location accuracy: Use digital twin network models and machine learning algorithms to improve the accuracy and speed of fault location.

[0115] 3. Optimize resource allocation: Dynamically adjust resource allocation and improve resource utilization based on real-time network status and prediction results.

[0116] 4. Improve network stability: Reduce network failures and performance fluctuations through preventive maintenance and intelligent optimization.

[0117] Example 2

[0118] This embodiment provides a network operation and maintenance system based on digital twin technology, including:

[0119] The digital twin network model building module is configured to collect and analyze various types of data in the real network based on big data, cloud computing and artificial intelligence technologies, and build a digital twin network model that corresponds to the real network one by one;

[0120] The fault prediction and location module is configured to simulate various fault scenarios in the digital twin network model and predict the probability and location of faults through machine learning algorithms. When a fault occurs in the real network, the digital twin network model is used to locate the fault type and location.

[0121] The intelligent repair strategy generation and implementation module is configured to call a preset repair strategy library or generate a new repair plan based on the fault type and location, and implement the repair through an automated tool;

[0122] The resource optimization configuration module is configured to simulate and optimize the resource allocation plan based on the network load forecast, user demand analysis and fault prediction results using the digital twin network model, and implement it in the real network.

[0123] It should be noted that the network operation and maintenance system based on digital twin technology in this embodiment has the following key technologies and algorithms:

[0124] Digital twin modeling technology: including key technologies such as data fusion, model construction, and real-time synchronization.

[0125] Machine learning algorithms: used for fault prediction, network performance analysis, resource demand prediction, etc.

[0126] Automated operation and maintenance technology: including API interface design, remote control script writing, operation and maintenance process automation, etc.

[0127] In summary, the network operation and maintenance system based on digital twin technology in this embodiment has the following advantages:

[0128] Comprehensiveness: Full-dimensional digital twin modeling fully simulates the real network environment.

[0129] Intelligence: Use machine learning algorithms to achieve intelligent fault prediction, performance analysis, and resource optimization.

[0130] Automation: The closed-loop operation and maintenance process is automated to reduce manual intervention and improve operation and maintenance efficiency.

[0131] Scalability: The system architecture is flexible and scalable, making it easy to integrate new technologies and algorithms.

[0132] Example 3

[0133] This embodiment is based on embodiment 1:

[0134] This embodiment provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the network operation and maintenance method based on digital twin technology of embodiment 1 is implemented. The computer program may be in source code form, object code form, executable file, or some intermediate form.

[0135] Example 4

[0136] This embodiment is based on embodiment 1:

[0137] This embodiment provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the network operation and maintenance method based on digital twin technology of Embodiment 1. The computer program may be in source code form, object code form, executable file, or some intermediate form, etc. The storage medium includes: any entity or device capable of carrying computer program code, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content contained in the storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the storage medium does not include electric carrier signals and telecommunication signals.

[0138] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity of description, they are expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

Claims

1. A network operation and maintenance method based on digital twin technology, characterized in that: include: Based on big data, cloud computing and artificial intelligence technologies, various types of data in the real network are collected and analyzed to build a digital twin network model that corresponds to the real network one by one; Simulate various fault scenarios in the digital twin network model and predict the probability and location of faults through machine learning algorithms; when a fault occurs in the real network, use the digital twin network model to locate the fault type and location; Based on the fault type and location, call the preset repair strategy library or generate a new repair plan, and implement the repair through automated tools; Based on network load forecasting, user demand analysis, and fault prediction results, the digital twin network model is used to simulate and optimize resource allocation plans and implement them in the real network.

2. According to claim 1, a network operation and maintenance method based on digital twin technology is characterized in that: The construction of a digital twin network model corresponding to the real network one by one includes: Network topology modeling: Based on the physical connection relationship of the real network, a network topology model is constructed, including the representation of nodes and edges; Equipment parameter modeling: Map the operating parameters of network equipment to the digital twin network model so that the digital twin network model can reflect the real-time status of network equipment; Business logic modeling: Based on the business logic in the real network, simulate the operation process of the business flow in the digital twin network model; Real-time synchronization mechanism: Through API interface and real-time data streaming technology, real-time data synchronization between the digital twin network model and the real network is achieved.

3. According to a network operation and maintenance method based on digital twin technology according to claim 2, it is characterized in that: The construction of a digital twin network model corresponding to the real network one by one also includes: Model verification: Verify the accuracy of the digital twin network model by comparing the output of the digital twin network model with the operating data of the real network; use historical data to train and test the model so that the digital twin network model can accurately predict the operating status of the real network; Model optimization: According to the verification results, adjust the parameters and structure of the digital twin network model to optimize the prediction accuracy and response speed of the digital twin network model; introduce an adaptive learning mechanism to enable the digital twin network model to automatically adjust according to changes in the network environment.

4. According to a network operation and maintenance method based on digital twin technology according to claim 1, it is characterized in that: The method of simulating various fault scenarios in the digital twin network model and predicting the probability and location of faults through machine learning algorithms includes: Simulate various possible failure scenarios in the digital twin network model and evaluate the impact on network performance; Analyze historical fault data in the digital twin network model through machine learning algorithms to predict possible future faults; Predict the operating status change trend of network equipment through time series analysis and identify potential failure risks in advance; Generate a fault prediction report based on the simulation results, providing the probability of fault occurrence and possible impact range.

5. According to a network operation and maintenance method based on digital twin technology according to claim 1, it is characterized in that: When a fault occurs in the real network, the digital twin network model is used to locate the fault type and location, including: Data collection: collect the operation data of the real network in real time and synchronize it to the digital twin network model; Anomaly detection: Detect abnormal behavior and abnormal data in the network through machine learning algorithms; Path analysis: Use graph theory algorithms to analyze the propagation path of abnormal data and determine the source of the fault; Fault confirmation: Confirm the location and type of the fault point through the simulation results of the digital twin network model.

6. According to a network operation and maintenance method based on digital twin technology according to claim 1, it is characterized in that: Based on the fault type and location, the preset repair strategy library is called or a new repair plan is generated, and the repair is implemented through an automated tool, including: Repair strategy library construction: pre-build a repair strategy library, which includes repair solutions for different types of faults, where the repair solutions are based on historical fault handling experience and expert knowledge; Dynamic repair plan generation: When a new fault type occurs, a new repair plan is generated through a machine learning algorithm. The repair strategy is continuously optimized based on historical repair results using a reinforcement learning algorithm. Implementation of repair solutions: Implement the repair solutions through automated tools, including remote control scripts and API interfaces; Repair plan verification: simulate the implementation process of the repair plan in the digital twin network model and evaluate the impact on network performance; feed back the repair results to the digital twin network model to update the model parameters and repair strategy library.

7. The network operation and maintenance method based on digital twin technology according to claim 1 is characterized in that: The resource allocation scheme is simulated and optimized using a digital twin network model based on network load prediction, user demand analysis, and fault prediction results, and is implemented in a real network, including: Resource demand prediction: Use machine learning algorithms to predict future demand for network resources; dynamically adjust resource demand prediction models based on user behavior data and network load data; Resource allocation plan generation: Generate resource allocation plan based on resource demand forecast results; optimize resource allocation plan using optimization algorithm; Implementation of resource allocation plan: simulate the implementation process of the resource allocation plan in the digital twin network model and evaluate the impact on network performance; adjust the resource allocation plan based on the simulation results and implement it in the real network; dynamically adjust the resource allocation plan according to changes in network load and user demand.

8. A network operation and maintenance system based on digital twin technology, characterized in that: include: The digital twin network model building module is configured to collect and analyze various types of data in the real network based on big data, cloud computing and artificial intelligence technologies, and build a digital twin network model that corresponds to the real network one by one; The fault prediction and location module is configured to simulate various fault scenarios in the digital twin network model and predict the probability and location of faults through machine learning algorithms. When a fault occurs in the real network, the digital twin network model is used to locate the fault type and location. The intelligent repair strategy generation and implementation module is configured to call a preset repair strategy library or generate a new repair plan based on the fault type and location, and implement the repair through an automated tool; The resource optimization configuration module is configured to simulate and optimize the resource allocation plan based on the network load forecast, user demand analysis and fault prediction results using the digital twin network model, and implement it in the real network.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the network operation and maintenance method based on digital twin technology described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the network operation and maintenance method based on digital twin technology described in any one of claims 1 to 7 is implemented.

Images