Dynamic resource scheduling method and device based on AI agent

By employing a dynamic resource scheduling method based on AI agents, utilizing a distributed computing system for environmental perception and feature quantification, and combining load prediction and strategy formulation, this approach solves the problem of low efficiency in resource demand identification and allocation in existing technologies, achieving accurate resource allocation and continuous optimization of scheduling.

CN121957924BActive Publication Date: 2026-06-23ZHEJIANG SHUXIN NETWORK CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
ZHEJIANG SHUXIN NETWORK CO LTD
Filing Date
2026-04-03
Publication Date
2026-06-23

AI Technical Summary

Technical Problem

Existing resource scheduling methods perform poorly in terms of environmental perception and task feature extraction, failing to effectively identify resource needs, lacking a sound resource matching mechanism and priority scheduling strategy, resulting in less than ideal resource allocation efficiency, and lacking in-depth monitoring of the execution process, making it difficult to achieve efficient model updates through reinforcement learning.

Method used

By employing a dynamic resource scheduling method based on AI agents, a distributed computing system is used for environmental perception processing to generate resource monitoring information and perform feature quantification. Combined with load prediction and strategy formulation, a reliable resource allocation strategy is established. Furthermore, feedback optimization is achieved through state monitoring and reinforcement learning, enabling accurate identification and continuous improvement of resource requirements.

Benefits of technology

It achieves accurate identification of resource needs, constructs a reliable resource allocation strategy, ensures continuous improvement of scheduling, solves the shortcomings of traditional technologies in demand identification, resource allocation and feedback optimization, and provides technical support for dynamic resource scheduling.

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Abstract

The embodiment of the application provides a dynamic resource scheduling method and device based on an AI agent, accurate demand identification is realized through feature quantization and state vector construction. A scheduling mechanism is constructed, combined with load prediction and strategy formulation, to establish a reliable resource allocation strategy. Feedback optimization is introduced, through state monitoring and reinforcement learning, to ensure continuous improvement of scheduling. This method effectively solves the shortcomings of traditional technology in demand identification, resource allocation and feedback optimization, and provides technical support for dynamic resource scheduling.
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Citation Information

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