AI Network Activation for Natural-Language Application Launch
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current network activation methods for application launch in data center networks require professional network language input, limiting user-friendliness and scalability, and are unable to adapt to new intentions or scenarios without pre-configured schemes.
Innovation Solution
A network activation method utilizing artificial intelligence (AI) to process natural language inputs for user intentions, simulating and evaluating network activation schemes, and activating networks based on AI-generated plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pre-configured network activation schemes are used, then network activation can be performed with existing schemes, but the system cannot adapt to new intentions or scenarios
Solution Approach 1:
The system uses an AI large model to automatically generate network activation schemes based on user intentions without requiring pre-configured schemes. The AI model processes natural language inputs, simulates network activation scenarios, and generates appropriate activation schemes autonomously, enabling the system to adapt to new scenarios without manual reconfiguration.
Solution Approach 2:
The system changes the fundamental parameter of scheme generation from static pre-configured schemes to dynamic AI-generated schemes. By using AI large models to generate activation schemes based on simulated scenarios, the system can adapt to varying network conditions and user intentions while maintaining manageable complexity through automated generation.
2Ease of operation
If professional network language is required for input, then precise network activation can be achieved, but user-friendliness is reduced
Solution Approach 1:
The system introduces an AI large model as an intermediary between the user and the network activation system. The AI model processes natural language inputs from users, translates them into precise network activation intentions, and generates appropriate activation schemes. This intermediary layer enables users to interact using everyday language while maintaining accurate and precise network activation.
3Adaptability or versatility
If only unchangeable intention can be collected, then stable network activation is achieved, but the system cannot understand new intentions or changes
Solution Approach 1:
The system implements a feedback mechanism where the AI large model processes user inputs, simulates network activation scenarios, and generates activation schemes. The simulation process provides feedback on the feasibility and appropriateness of generated schemes, allowing the system to understand and adapt to new intentions while maintaining reliable activation through simulated validation.
Data Source
Figure 1~3
Figure 4~5
Figure 6~8
AI summary
Examples of the present disclosure provide a network activation method and apparatus for application launch, a device, and a medium, which relate to the technology field of artificial intelligence. The method includes: based on a human-computer interaction mode, collecting a first natural language for indicating an application launch service intention; inputting the first natural language into an AI network large model to obtain a target network activation scheme; performing simulation and evaluation on the target network activation scheme; activating the network based on the target network activation scheme after a result of simulation and evaluation meets a preset condition. The technical schemes provided by the examples of the present disclosure can be applied to improve the ability to understand a user's service-level intention, improve the scalability of intention collecting, and improve the adaptability to scenarios and networking.