Digital Twin Network Control With Emulation-Verified Configuration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current cognitive optical network technologies lack collaboration between Digital Twin (DT) virtual models and physical networks, limiting the effectiveness of real-time fault detection, performance monitoring, and optimization.
Innovation Solution
A network management and control method that utilizes a pre-trained perception model to analyze parameter changes from a DT virtual model, followed by a cognitive model for configuration adjustments, with emulation verification to ensure physical network adjustments are effective.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Digital Twin virtual model is used to simulate physical network, then network performance monitoring and fault detection capability is improved, but the virtual model currently serves only as data basis without facilitating collaboration with cognitive optical network technology
Solution Approach 1:
The patent merges the Digital Twin virtual model with cognitive optical network technology by integrating the perception model, cognitive model, and emulation verification mechanism into a unified system. The virtual model is no longer isolated but actively collaborates with cognitive network functions to achieve automated management and control, resolving the contradiction between monitoring capability and collaboration versatility.
Solution Approach 2:
The patent implements a feedback loop where the virtual model provides parameter change values to the perception model, which generates state predictions fed to the cognitive model, and whose outputs are verified through emulation before physical network adjustment. This closed-loop feedback mechanism enables the virtual model to actively influence and collaborate with cognitive network operations.
2Productivity
If configuration adjustments are made to physical network based on virtual model predictions, then network optimization is improved, but unnecessary adjustments may occur reducing system stability
Solution Approach 1:
The patent performs preliminary action by conducting emulation verification before applying configuration adjustments to the physical network. The cognitive model generates configuration adjustment information that is first tested in the virtual environment to predict outcomes, and only after successful verification is the adjustment applied physically, preventing unnecessary or harmful changes.
Solution Approach 2:
The emulation verification mechanism serves as an intermediary between the cognitive model's configuration recommendations and the physical network adjustments. This intermediary layer validates and filters the configuration changes, ensuring that only necessary and safe adjustments are applied, thus maintaining network stability while achieving optimization.
Data Source
AI summary
A network management and control method and system, and a storage medium are disclosed. The method may include: obtaining a parameter change value of a target object, wherein the parameter change value is from a Digital Twin (DT) virtual model, the DT virtual model is constructed based on a physical model, the physical model comprises entity objects of a physical network, and the parameter change value represents a change in transmission performance of the target object; inputting the parameter change value into a pre-trained perception model, to obtain a state prediction result output by the perception model; inputting the state prediction result into a pre-trained cognitive model, to obtain configuration adjustment information output by the cognitive model; and in response to the configuration adjustment information passing emulation verification, adjusting the physical model according to the configuration adjustment information.