Network data transmission method for route optimization based on deep reinforcement learning and related equipment

By employing a deep reinforcement learning-based routing optimization method that combines routing and caching strategies, this approach addresses the challenge of achieving multi-QoS optimization in complex networks using traditional methods. It improves network resource utilization and transmission efficiency, enabling highly efficient and reliable data transmission.

CN121887703APending Publication Date: 2026-04-17FIBRLINK NETWORKS
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Patent Information

Application Number
CN202511779272.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-11-28
Publication Date
2026-04-17

AI Technical Summary

Technical Problem

Traditional routing optimization and data transmission mechanisms struggle to achieve comprehensive optimization of multiple service quality indicators in complex, large-scale converged networks. They are unable to effectively cope with sudden changes in network status and fluctuations in service load, resulting in low transmission efficiency and uneven resource allocation.

Method used

A deep reinforcement learning-based approach is adopted to obtain network environment state information, jointly optimize routing and caching strategies, generate a joint strategy that includes link weights, key cache nodes and cache data stream types, and combine the association information of the data to be transmitted to determine a transmission scheme that minimizes transmission time under preset constraints.

Benefits of technology

It improves network resource utilization, reduces data transmission latency, and achieves efficient and reliable network data transmission. It can also achieve collaborative optimization of multiple QoS indicators in complex converged network environments.

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Abstract

The invention provides a network data transmission method for route optimization based on deep reinforcement learning and related equipment. The method comprises the following steps: acquiring environment state information of a network environment; joint optimization of a routing strategy and a caching strategy is carried out based on the environment state information, and joint strategy information about routing and caching is obtained; the joint strategy information comprises a link weight, a key cache node and a cache data flow type; acquiring transmission associated information of the to-be-transmitted data; based on the transmission association information, determining data transmission information with minimum transmission duration when a preset constraint is met; and executing a routing action based on the joint policy information, executing a data scheduling action based on the data transmission information, and transmitting the to-be-transmitted data.
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