Communication network resource intelligent scheduling method, device, equipment and medium

By using dynamic label parsing and deep reinforcement learning on communication network service data packets, resource allocation strategies and path scores are generated. This solves the problems of dynamic adaptability and energy consumption control that cannot be solved by existing resource scheduling methods, thereby improving the resource scheduling capabilities of communication networks and optimizing resource utilization efficiency.

CN121842848APending Publication Date: 2026-04-10TIANJIN UNIV
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Patent Information

Application Number
CN202610038268.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-01-13
Publication Date
2026-04-10

AI Technical Summary

Technical Problem

Existing communication network resource scheduling methods cannot dynamically adapt to changes in network status, resulting in obstruction of high-priority service transmission and waste of low-priority service resources. Furthermore, base station energy consumption cannot be dynamically adapted, leading to insufficient resource utilization and energy consumption control capabilities.

Method used

By parsing the IP header of service data packets to generate dynamic service tags, and combining real-time link status and energy consumption coefficients, a deep reinforcement learning network is used to generate resource allocation strategies and path score weight parameters, calculate path scores and generate port suppression instructions, and dynamically adjust base station power.

Benefits of technology

It improves the dynamic adaptability and accuracy of business priority determination, enhances resource utilization and energy consumption control capabilities, and ensures dedicated resource protection for high-priority businesses and dynamic adaptation of base station energy consumption.

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Abstract

The invention relates to an intelligent scheduling method and device for communication network resources, equipment and a medium. The method comprises the following steps: performing IP packet header analysis on a service data packet to obtain an initial service priority identifier, and performing adjustment in combination with a preset priority rule, an equipment alarm state and a network congestion rate to generate a dynamic service label; based on the dynamic service label, the real-time link state and the energy consumption coefficient, generating a resource allocation strategy and a path scoring weight parameter through a preset deep reinforcement learning network; and calculating a path score based on the resource allocation strategy and the path score weight parameter, generating a path mapping table, generating a port suppression instruction based on the dynamic service label and the resource allocation strategy, and extracting a base station power regulation instruction. According to the method, the dynamic adaptability, the resource utilization rate and the energy consumption management and control capability of communication network resource scheduling are improved by means of dynamically adjusting the service priority, comprehensively modeling a resource allocation strategy and the like, and the transmission guarantee level of high-priority services is enhanced.
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