Intelligent scheduling method of comprehensive guarantee service platform

By evaluating the scheduling behavior of the comprehensive guarantee service platform and building genetic algorithms and deep neural network algorithm models, optimizing complex task allocation and resource scheduling, the problem that intelligent scheduling methods in the existing technology are difficult to meet personalized and complex business needs, and achieving more efficient, flexible and intelligent scheduling effects.

CN119692699AInactive Publication Date: 2025-03-25POLY SHIP TECH(BEIJING) CO LTD
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Patent Information

Application Number
CN202411780985.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-05
Publication Date
2025-03-25
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

The intelligent scheduling method of the existing comprehensive guarantee service platform is difficult to meet the personalized and complex business needs of users, especially in terms of resource allocation, task scheduling and service guarantee, which has insufficient efficiency and flexibility.

Method used

By evaluating the scheduling behavior of the comprehensive guarantee service platform, a genetic algorithm model and a deep neural network algorithm model are constructed, complex task allocation and resource scheduling are optimized, and the efficiency and intelligence of the system are improved.

Benefits of technology

The global optimization of complex task allocation and resource scheduling issues has been achieved, the system's efficiency, flexibility and intelligence level have been improved, and the needs of different types of users can be better met.

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Abstract

The invention discloses an intelligent scheduling method for a comprehensive guarantee service platform, and particularly relates to the field of intelligent platform scheduling, which comprises the following steps: monitoring and collecting information data generated in a target platform scheduling behavior, including response time information, resource utilization information and anti-interference information; and obtaining historical data information during construction of the genetic algorithm model and the deep neural network algorithm model. According to the method, the scheduling behavior of the platform is evaluated, the necessity of algorithm optimization of the target platform is confirmed, a genetic algorithm model is constructed for the necessary intelligent scheduling platform, then an algorithm implementation evaluation model is constructed, and global optimization of complex task allocation and resource scheduling problems is realized; and constructing a deep neural network algorithm model according to data information acquired by the genetic algorithm model, and optimizing the intelligent scheduling method of the comprehensive guarantee service platform in a low-load period or in simple task configuration.
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