A big data-based automobile part technology adaptation method and system
By normalizing and logically reducing multi-source heterogeneous technical data of automotive parts, a multi-dimensional technical knowledge graph is constructed. Combined with semantic mapping and topological deduction, the inaccuracy of existing parts matching schemes is solved, and efficient and reasonable parts matching results are achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Filing Date
- 2026-04-27
- Publication Date
- 2026-07-17
AI Technical Summary
In the existing technology, the multi-source heterogeneous technical data of automotive parts lacks standardized processing, making it impossible to form a unified standardized technical dataset. This makes it difficult to realize the effective correlation between parts entities, resulting in insufficient accuracy of the adaptation scheme and difficulty in ensuring the rationality and practicality of the adaptation results.
By acquiring multi-source heterogeneous technical data and vehicle context information, normalization and logical reduction are performed to construct a multi-dimensional technical knowledge graph. Combined with semantic mapping and topology deduction, path suitability scoring and optimal selection are conducted to generate the final recommended adaptation scheme.
It has achieved systematic data support for the technical adaptation of automotive parts, improved the accuracy and scientific nature of the adaptation scheme, and ensured the rationality and efficiency of the adaptation results.
Smart Images
Figure CN122413367A_ABST