AI Network Planning from Natural Language Intent
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Solution Overview
Problem
Existing network planning and construction methods require specialized network languages that are difficult for users to understand, limiting the ability to collect and adapt to new or changing intentions, and are restricted to fixed pre-configured schemes, lacking user-friendliness and scalability.
Innovation Solution
Utilizing a human-computer interaction mode to collect natural language intentions, processed by an AI network large model to generate a target network construction scheme, including network topology and configuration information, and configuring network devices accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual network planning and optimization is performed, then certain level of network quality can be achieved, but the process is time-consuming and costly
Solution Approach 1:
The patent replaces manual mechanical network planning operations with an automated system that uses machine learning models and algorithms to perform network quality assessment, site selection, and optimization tasks, thereby reducing time consumption while maintaining or improving accuracy
Solution Approach 2:
The system enables automated self-assessment of network quality by collecting and analyzing data from multiple sources (traffic data, quality data, environmental data) without human intervention, allowing the network planning process to serve itself through automated decision-making algorithms
2Measurement precision
If traditional network quality assessment methods are used, then basic network performance can be evaluated, but the assessment is not comprehensive and accurate enough
Solution Approach 1:
The assessment system is designed to perform multiple functions including collecting traffic data, quality data, and environmental data; assessing network quality; selecting sites; and generating planning suggestions, thereby achieving comprehensive evaluation without proportionally increasing system complexity
Solution Approach 2:
The system segments the network quality assessment into distinct modules: data collection module, quality assessment module using machine learning models, site selection module, and suggestion generation module, allowing each component to be optimized independently while maintaining overall system manageability
3Manufacturing precision
If more parameters and dimensions are considered in network planning, then planning accuracy improves, but the complexity of the planning process increases
Solution Approach 1:
The system incorporates multiple parameters including traffic data, quality data, and environmental data, and uses machine learning models to automatically weight and evaluate these parameters, transforming the complex multi-parameter planning process into an automated computational task that improves accuracy without requiring manual management of complexity
Solution Approach 2:
The patent introduces machine learning models as intermediaries that process and integrate multiple planning parameters, automatically generating optimization suggestions that reduce the burden on planners while considering comprehensive factors for accurate network planning
Data Source
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AI summary
The examples of the present disclosure provide a method and apparatus for network planning and construction, a device and a medium, which relates to the field of artificial intelligence technology. The method comprises: collecting a first natural language indicating an intention of the network planning and construction based on a human-computer interaction mode; inputting the first natural language into an AI network large model to obtain a target network construction scheme, which includes network topology information and network configuration information; and configuring, after detecting that a network device corresponding to the network topology information is powered on, the network device according to the network configuration information. The disclosure of the technical solution provided in the examples of the present disclosure can increase the understanding ability of user business level intentions, improve the scalability of intention collection, and enhance the adaptation ability to scenarios and networking.