A ship cloud computing load balancing method based on a hybrid intelligent algorithm

CN122317079APending Publication Date: 2026-06-30GUILIN UNIVERSITY OF TECHNOLOGY
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
GUILIN UNIVERSITY OF TECHNOLOGY
Filing Date
2026-03-30
Publication Date
2026-06-30

AI Technical Summary

Technical Problem

In existing technologies, ship cloud computing systems suffer from high single-point failure risk, large data transmission and processing delays, and unbalanced computing resource loads. Furthermore, traditional load balancing algorithms fail to effectively adapt to the intermittent nature of ship networks and the differences in the real-time nature of tasks.

Method used

A load balancing method based on hybrid intelligent algorithms is adopted, which achieves efficient collaboration and dynamic load balancing of edge and cloud resources through task classification modeling, edge-cloud resource status monitoring, multi-objective optimization decision-making, and dynamic load adjustment.

Benefits of technology

It reduces the load standard deviation of the ship edge-cloud node, optimizes task processing efficiency, reduces the average latency of real-time tasks, shortens the total processing time of non-real-time tasks, reduces system energy consumption, and improves the robustness and adaptability of scheduling strategies.

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Abstract

This invention discloses a ship cloud computing load balancing method based on a hybrid intelligent algorithm, relating to the field of ship intelligent computing and cloud computing integration technology. The method includes the following steps: task classification modeling, edge-cloud resource status monitoring, multi-objective optimization decision-making, and dynamic load adjustment. This invention reduces the load standard deviation of ship edge-cloud nodes through a multi-objective optimization algorithm and a dynamic adjustment mechanism. It optimizes task processing efficiency, reducing the average latency of real-time tasks and shortening the total processing time of non-real-time tasks. It reduces system energy consumption by optimizing the energy consumption coefficient, thereby reducing the total energy consumption of edge nodes and cloud servers. It adapts to ship scenarios, improving the robustness and adaptability of scheduling strategies to address the heterogeneity of ship tasks and network dynamics.
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