Intelligent data processing method based on large model atomization packaging and batch processing arrangement
By generating executable constraint sets and pre-validating them on an enterprise-level application platform, and combining them with large model operator units for batch processing orchestration, the problem of weak constraint reuse capabilities across sessions and tasks is solved, constrain interpretability and maintainability are achieved, and output quality and efficiency are improved.
Patent Information
- Application Number
- CN202511979526.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-25
- Publication Date
- 2026-04-28
AI Technical Summary
In existing technologies, when users process data objects such as documents, knowledge base content, and external tool interfaces on enterprise application platforms, the ability to constrain cross-session or cross-task reuse is weak, and there is a lack of structured expression and verifiable mechanisms, which leads to output deviation and increased maintenance costs. Furthermore, in batch processing orchestration scenarios, excessive constraints, incorrect constraints, or constraint conflicts are prone to occur, affecting output quality and task execution efficiency.
By receiving user constraint configuration input on the application platform, an executable constraint set is generated, and the constraint set is verified through a pre-verification mechanism. The target constraint set is determined based on the task intent and the characteristics of the input data object. Batch processing and arrangement are performed in combination with large model operator units to achieve constraint self-adaptation and controlled output.
It improves the reusability of constraints, reduces the cost of repeated setting across sessions and tasks, enhances the interpretability and maintainability of constraints, reduces the impact of incorrect constraint setting and constraint conflicts on output results, and improves batch processing efficiency.