Real-time optimization method and system for robot control board based on tsn and heterogeneous architecture

By collecting real-time status data in the robot control system and using a global resource decision model for collaborative optimization, the computational tasks and network traffic are dynamically adjusted, solving the problem of heterogeneous resource fragmentation, achieving efficient resource utilization and real-time performance assurance, and improving the system's dynamic environmental adaptability.

CN122372499APending Publication Date: 2026-07-10
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Filing Date
2026-05-11
Publication Date
2026-07-10

AI Technical Summary

Technical Problem

In existing technologies, the heterogeneous computing resources of robot control systems are disconnected from the scheduling of time-sensitive networks, making it difficult to achieve globally optimal resource utilization and real-time performance assurance in dynamic environments, resulting in insufficient performance and reliability.

Method used

By constructing a real-time optimization method for robot control boards based on TSN and heterogeneous architecture, the system status is collected in real time, a collaborative optimization instruction set is generated using a global resource decision model, and the computational task mapping and network traffic scheduling are dynamically adjusted to form a closed-loop optimization process of perception-decision-execution.

Benefits of technology

It significantly improves the end-to-end real-time determinism and resource utilization efficiency of the robot control system under dynamic loads, and enhances its adaptability to complex environments.

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Abstract

This invention discloses a real-time optimization method and system for a robot control board based on TSN and a heterogeneous architecture. First, the task status, resource utilization, and communication status of the robot's heterogeneous computing units are collected to generate a system state matrix. A global resource decision model is then input to generate a collaborative optimization instruction set, including resource mapping adjustment instructions for computing tasks and traffic scheduling configuration instructions for the TSN network. Based on this instruction set, the heterogeneous computing scheduler and TSN configurator are invoked respectively to dynamically adjust the mapping relationship of computing tasks and the traffic scheduling strategy of the network, thereby generating adjusted task mapping relationships and network scheduling strategies. The adjusted strategies are then applied to run the robot control board, and state acquisition is restarted, forming a closed-loop optimization. This application achieves joint optimization of computing and network resources through global collaborative decision-making and scheduling, effectively improving the real-time determinism, resource utilization efficiency, and dynamic environment adaptability of the robot control system.
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