AI model combinatorial optimization-based AI business process automatic generation method

By constructing a multi-layered AI capability decoupling and reconstruction module, and combining agent model-driven evolutionary computation optimization and data-driven process mining, we have achieved on-demand elastic combination of AI capabilities and business process automation. This solves the problems of adaptability and execution efficiency of AI capabilities in dynamic and complex environments, and improves the application effect of AI technology in actual business.

CN120912159APending Publication Date: 2025-11-07SOUTH CHINA UNIV OF TECH
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Patent Information

Application Number
CN202510856815.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-25
Publication Date
2025-11-07

AI Technical Summary

Technical Problem

In dynamic and complex scenarios, the reusability of AI capabilities of digital employees is insufficient, the service quality of AI components in differentiated environments is not high, and the adaptability to dynamic environments is poor, resulting in a low success rate of AI technology execution and limiting its widespread application in actual business.

Method used

By constructing a multi-layered AI capability decoupling and reconstruction module, AI capabilities can be flexibly combined and optimized on demand. AI business processes can be automated in a dynamic and open environment. Combined with agent model-driven evolutionary computation optimization and data-driven process mining, a containerized deployment solution for AI services can be generated and optimized.

Benefits of technology

It improves the adaptability and execution efficiency of AI capabilities in complex scenarios, realizes the efficient supply and flexible use of AI capabilities, and adapts to dynamic environments with multiple scenarios and multiple needs.

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Abstract

The invention discloses an AI business process automatic generation method based on AI model combinatorial optimization, and the method comprises the following steps: constructing a multi-level AI capability decoupling and reconstruction module, and carrying out the bottom-up hierarchical modeling and top-down modular decoupling, dynamic mapping of an AI atomic power layer, an AI modular production capacity layer, an AI general capability layer and an AI application business layer is realized; an elastic AI capability combinatorial optimization module is constructed, AI capability combinatorial optimization oriented to three dimensions of data, features and models is carried out based on service quality requirements, and multiplexing, combination and arrangement of AI capabilities are realized through evolutionary computation optimization driven by an agent model; and an AI business process automatic generation module is constructed, an AI service containerization deployment scheme is generated and optimized based on data-driven process mining and a hyper-heuristic algorithm, and AI business process automatic generation is realized. According to the invention, the adaptability and execution efficiency of the AI technology in a complex scene can be improved.
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