Edge AI collaboration method and system based on cloud-based large model enhancement

By introducing a collaborative method based on cloud-based large-model enhancement in edge AI systems, the shortcomings of existing edge AI systems in user interface design, integration, automation and privacy protection are solved, and an edge AI collaboration system that is more efficient, flexible and scalable is achieved.

CN119396497BActive Publication Date: 2025-03-28FUDAN UNIVERSITY
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Patent Information

Application Number
CN202510005390.X
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-01-03
Publication Date
2025-03-28
Estimated Expiration
2045-01-03

AI Technical Summary

Technical Problem

Existing edge AI systems have shortcomings in user interface design, integration of sensors and AI models, automated organization and privacy protection, resulting in poor user experience, high demand for computing resources, low collaborative processing flexibility and scalability.

Method used

The edge AI collaboration method based on cloud-based large-scale models is adopted to analyze user requests through natural language processing technology, evaluate edge device processing capabilities, dynamic unload tasks, and automatically generate joint learning codes using cloud-based large-scale models to conduct distributed model training, and synchronize model parameters through cross-device consistency maintenance algorithms.

Benefits of technology

It reduces the demand for computing resources, improves the flexibility and scalability of the system, optimizes resource utilization, reduces task execution delay, ensures the consistency of model parameters, improves overall performance and generalization capabilities, and is suitable for multiple intelligent system application scenarios.

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Abstract

The present invention relates to an edge AI collaboration method and system based on cloud-based large model enhancement. The method includes: parsing natural language requests through natural language processing technology and converting them into various subtasks; evaluating the processing capabilities of each edge device, and coordinating and unloading the subtasks to the corresponding edge device; training edge AI models on each edge device in a distributed manner; using a cross-device consistency maintenance algorithm to synchronize mechanisms and consistency constraints on each preliminary edge AI model, aligning the model parameters of the preliminary edge AI model between each edge device, and completing edge AI collaboration. By using natural language processing technology to deeply parse the user's natural language request, accurately converting it into subtasks executable by edge devices, and dynamically allocating it in real time, it is possible to optimize resource utilization and reduce task execution delays; by performing synchronization mechanisms and consistency constraints on the model, the overall performance and generalization capabilities of the model can be improved.
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