Digital twinning method for data center optical network

Abstract

The invention relates to a digital twinning method for a data center optical network. The method is applied to an optical network, a plurality of edge computing nodes and a center node are deployed in the optical network, corresponding digital twin models are deployed on the edge computing nodes, and the method comprises the following steps: obtaining edge computing node data, the edge computing node data comprising optical signal state information and equipment state information; inputting the edge computing node data into a corresponding digital twinborn model to obtain a node prediction result; and transmitting the edge computing node data and the node prediction result to a central node, performing optimization calculation based on the edge computing node data and the node prediction result to obtain an optimization result, and using the optimization result to indicate an operation of coordinating the edge computing node. Compared with traditional centralized processing, the distributed digital twinborn model has the advantages that due to the fact that the edge computing nodes are located at the key positions of the optical network, data can be directly acquired and processed, delay of data transmission is reduced, and the overall computing capacity is enhanced.

Description

Technical Field

[0001] The present invention belongs to the field of computer technology, and in particular relates to a digital twin method for data center optical networks. Background Art

[0002] With the development of computer technology, digital twin technology has emerged. As an important means of simulation and simulation, digital twin technology plays an increasingly important role in data center management, operation and optimization. Applying digital twin technology to data center optical networks can reflect the physical state and operating status of data centers in real time, providing decision makers with accurate and comprehensive information support. However, traditional data center architectures often rely on centralized processing modes, and their digital twin models are often deployed on central nodes, which faces problems such as data transmission delays and tight computing resources. Summary of the invention

[0003] Based on this, it is necessary to provide a digital twin method for data center optical networks to address the above-mentioned technical issues in order to improve the operating efficiency and responsiveness of data center optical networks.

[0004] In a first aspect, the present application provides a digital twin method for a data center optical network, which is applied to an optical network, wherein a plurality of edge computing nodes and a central node are deployed in the optical network, and a corresponding digital twin model is deployed on the edge computing node. The method comprises:

[0005] Obtain edge computing node data, which includes optical signal status information and device status information;

[0006] Input edge computing node data into the corresponding digital twin model to obtain node prediction results;

[0007] The edge computing node data and node prediction results are transmitted to the central node, and optimization calculations are performed based on the edge computing node data and node prediction results to obtain optimization results, which are used to indicate the operation of coordinating edge computing nodes.

[0008] In one of the embodiments, the digital twin model includes an optical device model and an optical network performance evaluation model;

[0009] The optical device model is used to simulate the working principle of the optical device and output the predicted device performance indicators of the optical device; the optical network performance evaluation model is used to simulate and analyze the performance parameters of the optical network and output the predicted optical network performance indicators.

[0010] In one embodiment, optimization calculation is performed based on edge computing node data and node prediction results to obtain optimization results, including:

[0011] The edge computing node data and the node prediction results are fused to obtain node fusion data;

[0012] The node fusion data is input into the deep learning-based optimization model to obtain the optimization results.

[0013] In one embodiment, edge computing node data and node prediction results are fused to obtain node fusion data, including:

[0014] Build a fusion model;

[0015] Determine the error function based on edge computing node data and node prediction results;

[0016] Calculate the optimal model parameters based on the error function;

[0017] Update the best model parameters to the fusion model to obtain the best fusion model;

[0018] The edge computing node data and node prediction results are input into the optimal fusion model to obtain the node fusion data.

[0019] In one embodiment, the method further comprises:

[0020] Generate scheduling instructions based on the optimization results, which include task allocation, resource allocation, and parameter adjustment requirements for edge computing nodes;

[0021] Adjust the working parameters of the edge computing nodes according to the scheduling instructions.

[0022] In one embodiment, the method further comprises:

[0023] The node prediction result is compared with the preset threshold to obtain the node evaluation result, which is used to characterize whether there is an abnormality in the edge computing node and indicate whether to execute local scheduling measures.

[0024] In one embodiment, the method further comprises:

[0025] The edge computing node data, node prediction results and optimization results are converted into visualization parameters; the visualization parameters are used to display the edge computing node data, node prediction results and optimization results in the display medium.

[0026] In the second aspect, the present application also provides a digital twin system for a data center optical network, which is applied to an optical network, wherein multiple edge computing nodes and a central node are deployed in the optical network, and corresponding digital twin models are deployed on the edge computing nodes. The system includes:

[0027] A node data acquisition module is used to acquire edge computing node data, where the edge computing node data includes optical signal status information and device status information;

[0028] The node prediction module is used to input the edge computing node data into the corresponding digital twin model to obtain the node prediction results;

[0029] The global optimization module is used to transmit edge computing node data and node prediction results to the central node, perform optimization calculations based on the edge computing node data and node prediction results, and obtain optimization results. The optimization results are used to indicate the operation of coordinating edge computing nodes.

[0030] In a third aspect, the present application also provides a computer device including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of a digital twin method for a data center optical network when executing the computer program.

[0031] In a fourth aspect, the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a digital twin method for a data center optical network.

[0032] The digital twin method, system, computer device and storage medium for the data center optical network adopt a distributed architecture to deploy the digital twin model to multiple edge computing nodes of the data center optical network, collect edge computing node data in real time for simulation analysis, and obtain node prediction results of each edge computing node, thereby reducing data transmission delay and improving the operation efficiency and resource utilization of the data center optical network. By optimizing and calculating the edge computing node data and the corresponding node prediction results at the central node, the optimization results are obtained, so that the data center optical network can realize the control and coordination of each edge computing node according to the real-time load and prediction requirements, thereby improving the overall decision support capability of the data center optical network. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0034] Figure 1 A schematic flow chart of a digital twin method for a data center optical network is provided in one embodiment of the present application;

[0035] Figure 2A structural schematic diagram of a digital twin method for a data center optical network is provided in one embodiment of the present application. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0037] In one embodiment, Figure 1 As shown, a digital twin method for a data center optical network is provided. This embodiment uses the method applied to a terminal as an example. It can be understood that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method is applied to an optical network, and multiple edge computing nodes and a central node are deployed in the optical network. The corresponding digital twin model is deployed on the edge computing node. The method includes the following steps:

[0038] S101: Acquire edge computing node data, where the edge computing node data includes optical signal status information and device status information.

[0039] Specifically, a data center optical network refers to an optical communication network used for high-speed data transmission within or between data centers. By deploying sensors or real-time data stream processing frameworks on multiple edge computing nodes in the data center optical network, edge computing node data can be obtained to achieve real-time monitoring of the data center optical network status. These edge computing node data may include optical signal status information and device status information, where the optical signal status information includes the transmission rate, bit error rate, and spectrum of the optical signal, while the device status information covers the device type, device parameters, and device power consumption. Data center optical networks are usually composed of optical amplifiers, optical routers, optical fibers, optical detectors, and optical switches to achieve data transmission.

[0040] S102: Input the edge computing node data into the corresponding digital twin model to obtain the node prediction result.

[0041] The digital twin model is a virtual representation of a physical object, process or system. It can interact with the dynamic behavior of the physical system through real-time data updates to simulate the actual operating status of the system. In the data center optical network, each edge computing node has a corresponding digital twin model. The edge computing node data obtained in real time is input into the corresponding digital twin model, and the edge computing node is simulated and analyzed, such as optical signal performance evaluation, equipment status prediction, fault prediction and resource optimization, to obtain the node prediction result. The node prediction result can be used as feedback to further adjust and optimize the digital twin model, and it helps to monitor the state changes of the data center optical network. It can also provide a basis for future decision-making, such as resource allocation, troubleshooting and network planning, etc., to improve the reliability of the data center optical network.

[0042] S103: The edge computing node data and the node prediction results are transmitted to the central node, and optimization calculations are performed based on the edge computing node data and the node prediction results to obtain optimization results, which are used to indicate the operation of coordinating the edge computing nodes.

[0043] The data of multiple edge computing nodes and the corresponding node prediction results of the data center optical network are transmitted to the central node for centralized optimization calculation. The optimization calculation can include resource allocation optimization, potential fault prediction, etc., so as to obtain the optimization result. First, the optimization goal is determined, such as minimizing the delay of the optical network, maximizing bandwidth utilization, reducing energy consumption, etc., to guide the direction of the optimization calculation. Secondly, the corresponding optimization algorithm is designed based on the edge computing node data and node prediction results. Schematically, genetic algorithms, particle swarm optimization, reinforcement learning and other methods can be used to dynamically adjust the working parameters and scheduling strategies of edge computing nodes. This centralized processing can further simulate and evaluate the overall state of the data center optical network, and can control and coordinate each edge computing node through the optimization results, such as changing the power of signal transmission, updating routing selection or adjusting equipment parameters, etc., to achieve efficient management and optimization of the data center optical network, and significantly improve the performance and stability of the optical network.

[0044] This method deploys multiple edge computing nodes and a central node in the data center optical network. After acquiring the edge computing node data, the corresponding digital twin model is deployed on each edge node for simulation analysis. Compared with the traditional centralized processing that sends all the raw data to the center for analysis and decision-making, it significantly reduces the amount of data that needs to be transmitted to the central node and improves the data transmission efficiency. By receiving the edge computing node data and node prediction results, the central node can perform centralized optimization calculations, thereby more effectively coordinating the operations of each edge computing node. This optimization not only improves resource utilization and reduces energy consumption, but also improves the overall performance of the network.

[0045] In an exemplary embodiment, the digital twin model may include an optical device model and an optical network performance evaluation model;

[0046] The optical device model is used to simulate the working principle of the optical device and output the predicted device performance indicators of the optical device; the optical network performance evaluation model is used to simulate and analyze the performance parameters of the optical network and output the predicted optical network performance indicators.

[0047] Specifically, the optical device model can capture the input-output relationship of the optical device and its impact on signal processing by simulating the working mechanism of various optical devices (such as optical amplifiers, optical switches, optical routers, etc.) in the data center optical network based on the physical model, and obtain the predicted device performance indicators by inputting the acquired edge computing node data, such as the maximum data transmission rate of the optical device under different working conditions, the power consumption of the optical device under different load conditions, and the bit error rate during signal transmission.

[0048] The optical network performance evaluation model can adopt a hierarchical structure to construct a physical layer, a network layer, and a service layer to simulate the performance parameters of the data center optical network. The physical layer is used to simulate the propagation of optical signals in optical fibers, including signal attenuation, dispersion, nonlinear effects, etc.; the network layer is used to model the optical network topology, analyze the connection relationship between nodes, and calculate the bandwidth and delay of the path; the service layer is used to simulate the traffic pattern and service quality requirements of user requests, and evaluate the performance of the network under specific service requirements. Exemplarily, numerical simulation of optical signal propagation can be performed based on the finite difference method or the finite element method. The acquired edge computing node data is input into the optical network performance evaluation model to obtain predicted optical network performance indicators, such as throughput, bandwidth utilization, transmission delay, etc.

[0049] Through the optical device model and the optical network performance evaluation model, multiple edge computing nodes in the data center optical network can more effectively understand and optimize the performance of the optical network. This not only improves the adaptability and resource utilization of edge computing nodes, but also significantly enhances the reliability and service quality of the network, thereby improving the operational efficiency of the entire optical network.

[0050] In an exemplary embodiment, optimization calculation is performed based on edge computing node data and node prediction results to obtain optimization results, including:

[0051] The edge computing node data and the node prediction results are fused to obtain node fusion data;

[0052] The node fusion data is input into the deep learning-based optimization model to obtain the optimization results.

[0053] By fusing the edge computing node data and the node prediction results at the central node to obtain node fusion data, we can have a more comprehensive understanding of the operating status of the data center optical network, reduce the errors that may exist in a single data source, eliminate redundant data, and improve the accuracy of performance analysis and decision-making for the entire optical network. Deep learning is a machine learning technology that is suitable for dealing with complex pattern recognition and prediction problems. Inputting node fusion data into the optimization model based on deep learning can maintain efficient prediction capabilities in the face of nonlinear and complex network behaviors such as burst traffic and equipment failures, and then obtain optimization results, coordinate and schedule each edge computing node, improve the scientificity and accuracy of decision-making, and ensure that the optical network can operate efficiently and stably.

[0054] In an exemplary embodiment, edge computing node data and node prediction results are fused to obtain node fusion data, including:

[0055] Build a fusion model;

[0056] Determine the error function based on edge computing node data and node prediction results;

[0057] Calculate the optimal model parameters based on the error function;

[0058] Update the best model parameters to the fusion model to obtain the best fusion model;

[0059] The edge computing node data and node prediction results are input into the optimal fusion model to obtain the node fusion data.

[0060] Specifically, the fusion model can be a linear model, a nonlinear model or other complex machine learning model. Preferably, when the relationship between the data is more complex, compared with the linear model, nonlinear models such as polynomial regression and support vector machines can be used to capture more subtle and complex patterns. At the same time, these models can also avoid the high computational requirements that may be brought about by certain machine learning models to a certain extent. The error function is used to measure the difference between the output of the fusion model and the true value, and is usually evaluated using mean square error (MSE) and absolute error. The mean square error is more sensitive to the penalty of small deviations and is more suitable for scenarios that require high precision and high sensitivity. Preferably, the mean square error can be selected as the error function, and its calculation formula is as follows:

[0061]

[0062] Where N is the number of edge computing nodes; M is the number of prediction values ​​provided by each edge computing node; y ij is the true value of the jth predicted value at the i-th node; is the fusion model prediction value of the j-th prediction value on the i-th node.

[0063] Based on the edge computing node data and node prediction results, a data set is generated to train the fusion model. The data set is divided into a training set and a validation set. The error function can be minimized by an optimization algorithm such as Stochastic Gradient Descent (SGD) to update the model parameters. For example, the parameter update calculation process for each step using SGD is as follows:

[0064]

[0065] Among them, θ is the model parameter; α is the learning rate, a hyperparameter that controls the step size of parameter update; is the partial derivative of MSE with respect to parameter θ, i.e., the gradient.

[0066] When the model training is completed, the best model parameters are obtained and updated to the fusion model to obtain the best fusion model, and the performance of the training set and the validation set are compared to ensure that the model has good prediction capabilities. The edge computing node data and node prediction results are input into the best fusion model to generate node fusion data, which may include the fusion of the optical signal status information collected on each edge computing node and its predicted value, the real-time status information of the equipment on each edge computing node and its predicted status information, and the predicted values ​​of the optical device performance and network performance.

[0067] In an exemplary embodiment, the method further comprises:

[0068] Generate scheduling instructions based on the optimization results, which include task allocation, resource allocation, and parameter adjustment requirements for edge computing nodes;

[0069] Adjust the working parameters of the edge computing nodes according to the scheduling instructions.

[0070] After the central node obtains the optimization result through optimization calculation, the system can generate corresponding scheduling instructions according to the optimization result. Scheduling instructions include task allocation, resource allocation and parameter adjustment requirements of edge computing nodes. Specifically, the task allocation determines which edge computing nodes undertake specific computing or processing tasks to improve the response speed of the optical network; the resource allocation dynamically adjusts the resource allocation of each edge computing node based on the optimization result, such as bandwidth, storage and computing power, to ensure the effective use of resources; the parameter adjustment requirements include specific adjustment suggestions for the working parameters of each device, such as modifying the operating frequency of the device, changing the transmission mode of the optical signal, optimizing the algorithm parameters, etc., to improve the performance of the device and the operating efficiency of the optical network. Finally, the edge computing node adjusts the corresponding working parameters by receiving the scheduling instruction. For example, in task allocation, the edge computing node identifies the specific tasks to be performed, including data processing, computing tasks, network communication tasks, etc., according to the task allocation information in the scheduling instruction. It can adjust its task execution queue through the priority mechanism according to the importance and urgency of the task to ensure that high-priority tasks are given priority.

[0071] In an exemplary embodiment, the method further comprises:

[0072] The node prediction result is compared with the preset threshold to obtain the node evaluation result, which is used to characterize whether there is an abnormality in the edge computing node and indicate whether to execute local scheduling measures.

[0073] The edge computing node first obtains and collects its own status data, including optical signal status information and device status information, and inputs it into the corresponding digital twin model to generate node prediction results. These results reflect the performance expectations and status of the edge computing node in the future. The preset thresholds are various performance indicators formulated by professionals based on historical data, industry standards or operational experience to determine whether the node is in normal working condition. At the edge computing node, the node prediction results are compared with the preset thresholds to obtain the node evaluation results.

[0074] Indicatively, if the node prediction result is within the preset threshold, it means that the node is working properly. If the node prediction result exceeds the preset threshold (for example, the signal strength is lower than the acceptable level or the device load is too high), it means that the node has a potential abnormality. Based on the node evaluation results, the system can take corresponding local scheduling measures. For example, if the node has a potential abnormality, it will be optimized, which may include adjusting the signal strength, reallocating bandwidth, or rerouting the network. This step enables local decision-making of edge computing nodes, enhances the flexibility of the system, and allows edge computing nodes to make independent adjustments when encountering local failures or changes without waiting for global instructions. This autonomous capability improves the robustness and self-healing capabilities of the optical network, and can provide higher adaptability and stability in complex and dynamic optical network environments.

[0075] In an exemplary embodiment, the method further comprises:

[0076] The edge computing node data, node prediction results and optimization results are converted into visualization parameters; the visualization parameters are used to display the edge computing node data, node prediction results and optimization results in the display medium.

[0077] Specifically, the process of converting edge computing node data, node prediction results, and optimization results into visual parameters involves converting complex data information into easy-to-understand graphics or charts, such as trend charts, pie charts, or heat maps, so that relevant personnel can quickly identify system status, performance indicators, and potential problems. The display of visual parameters can help operators quickly obtain the real-time status of each edge computing node in the optical network, understand the relationship and dependency between nodes, and better support decision-making and operation coordination. In addition, through visual display, users can intuitively see the results of node predictions and optimization calculations, and adjust network configurations or perform maintenance in a timely manner. This not only improves the transparency of the system, but also enhances the efficiency of management and operation and maintenance, so that responses and adjustments can be made faster and more effectively when faced with complex situations.

[0078] Based on the same inventive concept, the present application also provides a digital twin system 200 for a data center optical network, such as Figure 2 As shown, it is applied to an optical network, in which multiple edge computing nodes and a central node are deployed, and corresponding digital twin models are deployed on the edge computing nodes. The system 200 includes:

[0079] A node data acquisition module 201 is used to acquire edge computing node data, where the edge computing node data includes optical signal status information and device status information;

[0080] The node prediction module 202 is used to input the edge computing node data into the corresponding digital twin model to obtain the node prediction result;

[0081] The global optimization module 203 is used to transmit the edge computing node data and the node prediction results to the central node, perform optimization calculations based on the edge computing node data and the node prediction results, and obtain optimization results, which are used to indicate the operation of coordinating the edge computing nodes.

[0082] Specifically, the system can deploy edge computing nodes at key locations of the optical network of the data center, which are closer to the data source than the central node. Therefore, the optical signal status information and device status information can be acquired and processed in real time at the edge computing node through the node data acquisition module 201 and the node prediction module 202. The local data is predicted and analyzed by using the digital twin model deployed on the edge computing node to obtain the node prediction result. This step not only helps the edge computing node to self-adjust and optimize, but also provides valuable input for global optimization. Finally, the global optimization module 203 performs optimization calculations to obtain optimization results, which provide guidance on how to coordinate and adjust the operation of edge computing nodes, ensure that network resources are reasonably allocated, optimize overall performance, and achieve global decision-making. In summary, the system realizes efficient management and optimization of the optical network of the data center through the cooperation between multiple modules based on edge computing and digital twin technology.

[0083] In one embodiment, the digital twin model in the node prediction module 202 includes an optical device model and an optical network performance evaluation model;

[0084] The optical device model is used to simulate the working principle of the optical device and output the predicted device performance indicators of the optical device; the optical network performance evaluation model is used to simulate and analyze the performance parameters of the optical network and output the predicted optical network performance indicators.

[0085] In one embodiment, the global optimization module 203 is further configured to:

[0086] The edge computing node data and the node prediction results are fused to obtain node fusion data;

[0087] The node fusion data is input into the deep learning-based optimization model to obtain the optimization results.

[0088] Exemplarily, the global optimization module 203 is also used for:

[0089] Build a fusion model;

[0090] Determine the error function based on edge computing node data and node prediction results;

[0091] Calculate the optimal model parameters based on the error function;

[0092] Update the best model parameters to the fusion model to obtain the best fusion model;

[0093] The edge computing node data and node prediction results are input into the optimal fusion model to obtain the node fusion data.

[0094] In one embodiment, the system further includes a global scheduling module 204 for:

[0095] Generate scheduling instructions based on the optimization results, which include task allocation, resource allocation, and parameter adjustment requirements for edge computing nodes;

[0096] Adjust the working parameters of the edge computing nodes according to the scheduling instructions.

[0097] In one embodiment, the system further includes a local decision module 205, configured to:

[0098] The node prediction result is compared with the preset threshold to obtain the node evaluation result, which is used to characterize whether there is an abnormality in the edge computing node and indicate whether to execute local scheduling measures.

[0099] In one embodiment, the system further includes a visualization module 206 for:

[0100] The edge computing node data, node prediction results and optimization results are converted into visualization parameters; the visualization parameters are used to display the edge computing node data, node prediction results and optimization results in the display medium.

[0101] Based on the same inventive concept, the embodiment of the present application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of a digital twin method for a data center optical network when executing the computer program. Specifically, the steps may include data acquisition, model analysis, and optimization calculation, so as to utilize distributed digital twin technology to realize monitoring and management of the data center optical network, improve the performance and reliability of the optical network, and realize flexible and efficient resource management.

[0102] Based on the same inventive concept, the present application also provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the digital twin method for a data center optical network are implemented. The computer storage medium may include a random access memory, a read-only memory, a flash memory or other solid-state storage technology, a digital versatile disc or other optical storage, a cassette, a tape, a disk storage or other magnetic storage device.

[0103] The above-mentioned embodiments only express several implementation methods of the embodiments of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the patent of the embodiments of the present application. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the embodiments of the present application, and these all belong to the protection scope of the embodiments of the present application.

Claims

1. A digital twin method for data center optical networks, characterized in that: Applied to an optical network, wherein a plurality of edge computing nodes and a central node are deployed in the optical network, and a corresponding digital twin model is deployed on the edge computing node, the method comprising: Acquire the edge computing node data, wherein the edge computing node data includes optical signal status information and device status information; Input the edge computing node data into the corresponding digital twin model to obtain a node prediction result; The edge computing node data and the node prediction result are transmitted to the central node, and optimization calculation is performed based on the edge computing node data and the node prediction result to obtain an optimization result, wherein the optimization result is used to indicate and coordinate the operation of the edge computing node.

2. The digital twin method according to claim 1, characterized in that: The digital twin model includes an optical device model and an optical network performance evaluation model; The optical device model is used to simulate the working principle of the optical device and output the predicted device performance index of the optical device; The optical network performance evaluation model is used to simulate and analyze the performance parameters of the optical network and output predicted optical network performance indicators.

3. The digital twin method according to claim 1, characterized in that: The performing optimization calculation based on the edge computing node data and the node prediction result to obtain the optimization result includes: Fusing the edge computing node data and the node prediction result to obtain node fusion data; The node fusion data is input into an optimization model based on deep learning to obtain an optimization result.

4. The digital twin method according to claim 3, characterized in that: The step of fusing the edge computing node data and the node prediction result to obtain node fusion data includes: Build a fusion model; Determine an error function according to the edge computing node data and the node prediction result; Calculating based on the error function to obtain optimal model parameters; Updating the optimal model parameters to the fusion model to obtain an optimal fusion model; The edge computing node data and the node prediction result are input into the optimal fusion model to obtain the node fusion data.

5. The digital twin method according to claim 1, characterized in that: Also includes: Generate a scheduling instruction based on the optimization result, wherein the scheduling instruction includes task allocation, resource allocation, and parameter adjustment requirements of the edge computing node; According to the scheduling instruction, the operating parameters of the edge computing node are adjusted.

6. The digital twin method according to claim 1, characterized in that: The method further comprises: The node prediction result is compared with a preset threshold to obtain a node evaluation result, and the node evaluation result is used to characterize whether there is an abnormality in the edge computing node and indicate whether to execute local scheduling measures.

7. The digital twin method according to claim 1, characterized in that: Also includes: Converting the edge computing node data, the node prediction results and the optimization results into visualization parameters; The visualization parameters are used to display the edge computing node data, the node prediction results and the optimization results in a display medium.

8. Digital twin system for data center optical network, characterized by: Applied to an optical network, a plurality of edge computing nodes and a central node are deployed in the optical network, and a corresponding digital twin model is deployed on the edge computing node. The system includes: A node data acquisition module, used to acquire the edge computing node data, wherein the edge computing node data includes optical signal status information and device status information; A node prediction module, used to input the edge computing node data into the corresponding digital twin model to obtain a node prediction result; A global optimization module is used to transmit the edge computing node data and the node prediction results to the central node, perform optimization calculations based on the edge computing node data and the node prediction results, and obtain optimization results, wherein the optimization results are used to indicate and coordinate the operations of the edge computing nodes.

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 steps of the digital twin method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the digital twin method according to any one of claims 1 to 7 are implemented.

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