Service adaptation method and device based on skill dependency graph and QoS perception
By introducing skill dependency graphs and QoS awareness, the robot's microservice composition is dynamically adjusted, solving the problem of low task execution efficiency under environmental changes in existing technologies and achieving efficient task adaptation and execution.
CN121349706BActive Publication Date: 2026-03-27HANGZHOU FANJIA TECH CO LTD
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Patent Information
- Application Number
- CN202511892185.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-16
- Publication Date
- 2026-03-27
- Estimated Expiration
- 2045-12-16
AI Technical Summary
Technical Problem
Existing robot microservice architectures cannot dynamically adjust service composition based on environmental changes at runtime and lack awareness of runtime quality of service (QoS), resulting in low task execution efficiency.
Method used
By introducing skill dependency graphs and QoS awareness methods, we can dynamically retrieve and optimize atomic microservice composition schemes, monitor QoS indicators and resource information in real time, and optimize service composition to adapt to environmental changes.
Benefits of technology
This improves the adaptability and task execution efficiency of the robot system, ensuring that tasks are completed efficiently in dynamic environments.
✦ Generated by Eureka AI based on patent content.
Abstract
The application discloses a service adaptation method and device based on skill dependency graph and QoS perception, and a robot method comprises the following steps: in response to a task execution request sent by a client, searching for a plurality of alternative atomic microservices according to task demand information carried in the task execution request and a preset skill dependency graph; the preset skill dependency graph is composed of a plurality of nodes and edges between the nodes; under the resource constraint of a preset multi-objective optimization model, an optimal service combination scheme is determined from the plurality of alternative atomic microservices; each atomic microservice in the optimal service combination scheme is containerized and deployed to obtain a microservice instance after deployment to execute a task; during the execution, when it is detected according to real-time perceived QoS indicators and resource information that there is service performance degradation or a resource bottleneck, the optimal service combination scheme is optimized and the step of containerized deployment is continued. Therefore, by adopting the embodiment of the application, the execution efficiency of the task can be improved.
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