一种基于知识图谱的产能协同优化方法及系统
By using a knowledge graph-based capacity collaboration optimization method, a capacity collaboration decision-making model was constructed and a distributed homomorphic neural computing framework was designed. This solved the problems of data privacy protection and computational efficiency in the steel industry cluster, and achieved data security and efficient collaborative decision-making.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- ZHEJIANG YEZHOU DIGITAL TECHNOLOGY IND CO LTD
- Filing Date
- 2025-09-04
- Publication Date
- 2026-07-17
AI Technical Summary
In the steel industry cluster, there is insufficient protection of data privacy among enterprises. Traditional computing architectures are inefficient in handling large-scale collaborative decision-making. The integration of decision-making models and knowledge graphs is insufficient, resulting in low willingness of enterprises to collaborate, poor decision quality, and slow response speed.
The knowledge graph-based capacity collaboration optimization method constructs a capacity collaboration decision-making model, designs a distributed homomorphic neural computing framework, realizes neural network computation of encrypted data, verifies the computation results using zero-knowledge proof, and combines a result decryption mechanism with separation of rights and responsibilities to ensure data security and computational efficiency.
It enables collaborative decision-making between enterprises without leaking sensitive data, reduces the energy consumption and latency of complex calculations, and improves decision quality and response speed.
Smart Images
Figure CN121168947B_ABST