An AI Agent Adaptive Collaboration and Resource Scheduling Method for Distributed Operations and Maintenance

Abstract

This invention provides an AI Agent adaptive collaboration and resource scheduling method for distributed operations and maintenance. It generates deviation information by dynamically comparing real-time collected operational status data with service quality requirements, triggering precise diagnosis of performance bottlenecks. Structured hot context summaries enable second-level knowledge synchronization for new agents, and sandbox simulations ensure collaboration consistency, avoiding inefficiencies during team integration. Simultaneously, task execution deviation serves as the core feedback signal driving closed-loop adjustments to the collaboration structure and resource allocation. This solves three major technical problems in existing distributed operations and maintenance: rigid AI Agent collaboration structures, inefficiencies due to knowledge asynchrony among new members, and a disconnect between resource scheduling and business objectives. It achieves adaptive collaboration in the operations and maintenance system, significantly improving fault handling efficiency, resource utilization, and system reliability in complex distributed environments.

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