Edge cloud cooperation-based inspection robot visual analysis task dynamic scheduling method and system

By using a dynamic scheduling method that integrates edge cloud collaboration, network and computing power information is collected in real time. Combined with task characteristics and reinforcement learning, the latency, energy consumption, and accuracy of visual analysis tasks for inspection robots are optimized. This solves the problems of insufficient static and multi-objective optimization in existing scheduling strategies, and enables efficient and real-time visual analysis.

CN122653787APending Publication Date: 2026-08-28BEIJING WUSHUI TECH CO LTD
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Patent Information

Application Number
CN202610946298.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-06-29
Publication Date
2026-08-28

AI Technical Summary

Technical Problem

Existing technologies cannot adapt to dynamically changing network and computing environments in the scheduling of visual analysis tasks for inspection robots. They lack dedicated scheduling models and cannot perform joint optimization among multiple objectives such as latency, energy consumption, and accuracy.

Method used

We adopt a dynamic scheduling method based on edge-cloud collaboration, collect network status and computing resource information in real time, and combine task characteristics. Through a multi-objective optimization scheduling model and reinforcement learning mechanism, we dynamically adjust the execution position of tasks at the edge or in the cloud to optimize latency, energy consumption and accuracy.

Benefits of technology

It achieves multi-objective optimization in dynamic environments, improving scheduling efficiency by more than 40%, reducing latency by 68%, and improving analysis accuracy by 25%, thus meeting the real-time requirements of inspection.

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Abstract

The application discloses a kind of based on edge cloud cooperation's inspection robot visual analysis task dynamic scheduling method and system, belong to inspection robot visual analysis and edge computing technical field.The method includes: real-time acquisition network state parameter and computing power resource information;Task feature vector is constructed to the visual analysis task and is extracted feature;Based on multi-objective optimization scheduling model, delay, energy consumption and accuracy index are determined optimal execution position by comprehensive evaluation;Task is distributed to corresponding node execution;Based on Q-learning reinforcement learning, scheduling weight is dynamically updated.The application can realize the optimal scheduling of visual analysis task under the dynamic change of network and computing power environment, compared with the scheduling efficiency of existing static allocation strategy is improved by more than 40%, and the comprehensive performance is significantly better than local execution, full cloud execution and static allocation scheme.
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