An AI agent-based math-physics-chemistry-biology major data labeling system and method
By using an AI-based intelligent agent-based data annotation system for mathematics, physics, chemistry, and biology, and leveraging multidisciplinary knowledge graphs and reinforcement learning algorithms, the system addresses the issues of low efficiency, low accuracy, and poor cross-disciplinary adaptability in data annotation for these disciplines, achieving efficient and accurate data annotation and self-optimization capabilities.
Patent Information
- Application Number
- CN202610553106.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-04-24
- Publication Date
- 2026-07-17
AI Technical Summary
Existing data annotation technologies are inefficient and costly when processing data from mathematics, physics, chemistry, and biology disciplines, and lack interdisciplinary adaptability, resulting in low annotation accuracy and difficulty in quality control.
We employ an AI-based data annotation system for mathematics, physics, chemistry, and biology, utilizing multidisciplinary integrated knowledge graphs and reinforcement learning algorithms to achieve intelligent pre-annotation and automatic verification of data, generate targeted annotation strategies, and ensure annotation quality through a dual-dimensional quality control mechanism.
It significantly improves annotation efficiency, reduces manual workload, increases annotation accuracy, reduces cross-disciplinary adaptation costs, and has self-optimization capabilities to adapt to the needs of different application scenarios.