An industrial large model unstructured data governance and adaptive fine-tuning method
By acquiring multi-source unstructured data from industrial sites, performing multimodal parsing and semantic standardization, and combining industrial knowledge graphs for hardware-aware quantitative optimization, the challenges of large industrial models in unstructured data processing and fine-tuning are solved. This enables efficient and automated model building and adaptive iteration, making it suitable for various intelligent industrial applications.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-04-22
- Publication Date
- 2026-07-17
AI Technical Summary
In the industrial sector, the preprocessing of unstructured data makes it difficult to effectively extract semantically consistent structured information. Furthermore, the general large model faces a contradiction between computing power adaptability and professional reliability during fine-tuning, resulting in unstable inference in root cause analysis and process optimization tasks. This makes it difficult to achieve efficient and high-precision model optimization under limited resources.
By acquiring multi-source unstructured data from industrial sites, data type identification and source labeling are performed. Semantic standardization is carried out using multimodal parsing and industrial entity recognition to construct structured sample data. Hardware perception quantization and memory optimization are combined with industrial knowledge graphs, and the attention layer of a pre-set large model is injected to perform multi-objective joint training for language modeling and knowledge consistency verification.
It achieves full automation from unstructured data to model fine-tuning, adapts to dynamic changes in industry, shortens the model development cycle, is applicable to a variety of intelligent industrial scenarios, has good versatility and transferability, and avoids high retraining costs.
Smart Images
Figure CN122412879A_ABST