A function model updating method, computer device and product

CN122240149APending Publication Date: 2026-06-19ZHEJIANG GEELY HLDG GRP CO LTD +1

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
ZHEJIANG GEELY HLDG GRP CO LTD
Filing Date
2026-03-12
Publication Date
2026-06-19

AI Technical Summary

Technical Problem

The existing methods for distributing and updating functional models lack precision and flexibility, resulting in high resource consumption, low update efficiency, difficulty in direct adaptation and operation on the device side, and a lack of on-demand update capabilities.

Method used

The target device receives the model update list from the server, compares the model identifier and version number, sends a download request, deploys the functional model using the complete update data package, and monitors the status parameters on the device to realize on-demand updates and disaster recovery mechanisms.

Benefits of technology

It enables on-demand updates, improves update flexibility and resource utilization efficiency, ensures the complete deployment and efficient operation of the model, and guarantees the availability of intelligent inference functions and services on the device side.

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Abstract

This invention relates to the field of artificial intelligence and discloses a method, computer device, and product for updating a functional model. The method includes: receiving a model update list from a server, wherein the model update list includes at least one model download information; responding to a trigger operation for the model download information, sending a download request to the server, so that the server responds with a corresponding update data packet based on the download request; and deploying a corresponding target device-side functional model on a target device using the update data packet, so that the target device runs the target device-side functional model. This invention solves the problems of crude model distribution and updating methods, low update efficiency, and lack of accurate push and complete deployment support in the prior art.
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