Network AI Model Training Control for Real-Time Operator Management
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Solution Overview
Problem
The complexity of modern networks due to vertical industries and diversified services increases network operation and maintenance costs, making it difficult to efficiently manage and control network AI model training, which relies on manual intervention and lacks real-time online management.
Innovation Solution
A method and apparatus for training a management and control model that allows operators to configure and manage network AI model training functions remotely, using configuration information to activate, deactivate, or trigger training, and specify data sources and types, enabling flexible and efficient management of network model training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual control interface is used for network AI model training, then device vendor operation and maintenance personnel can locally control training, but operator cannot control training online in real time and manual efficiency is low
Solution Approach 1:
A management unit is introduced as an intermediary between the operator and the device vendor's local control interface. The management unit receives configuration information from the operator, translates it into executable training parameters, and transmits them to the network element for model training. This intermediary enables real-time online control by the operator while maintaining the existing local control architecture.
Solution Approach 2:
The control system is segmented into distinct functional modules: the operator interface for configuration input, the management unit for processing and translation, and the network element for execution. This segmentation allows the operator to control training remotely through standardized configuration information while the management unit handles the complexity of translation and transmission.
2Extent of automation
If network AI model training is performed locally, then model training can be executed at network element, but operator needs to notify device vendor operation and maintenance personnel and cannot control training online
Solution Approach 1:
The system establishes a feedback loop where the management unit monitors the training status of network elements and reports it to the operator. Configuration information flows from the operator to the management unit, which then transmits training parameters to network elements. The system tracks and reports training completion, enabling automated control with real-time visibility.
Solution Approach 2:
The management unit serves multiple functions: receiving configuration information from the operator, translating it into appropriate training parameters, transmitting commands to network elements, and monitoring training status. This multi-functional approach consolidates control capabilities into a single management unit, reducing overall system complexity while enabling automated operator control.
3Productivity
If manual interface is used for model training control, then local control is possible, but manpower is consumed and management efficiency is reduced
Solution Approach 1:
The system enables self-service operation where the operator can independently configure and control model training through standardized configuration information without requiring device vendor personnel intervention. The management unit automatically processes the configuration, translates it to appropriate parameters, and executes training on network elements, eliminating the need for manual notification and reducing manpower consumption.
Data Source
AI summary
Embodiments of this application provide a method, an apparatus, and a system for training a management and control model. The method includes: receiving configuration information from a first network management unit, where the configuration information is used to configure a model training function, and the configuration information includes at least one of the following information: state information, for activating or deactivating the model training function; trigger information, for triggering model training; and data information, indicating data for model training. The method further includes performing model training based on the configuration information, to obtain network model information. According to embodiments of this application, a second network management unit configures the model training function based on the received configuration information sent by the first network management unit, to perform model training based on the configuration information, to obtain the network model information.