基于语义空间映射的企业战略目标结构化解码方法及系统
By generating semantic anchors and context snapshots based on semantic space mapping, and combining them with a lightweight language model for local scanning, the problem of high computational overhead and semantic incoherence in the structured decoding of enterprise strategic goals is solved. This achieves efficient and interpretable key indicator extraction and vertical consistency, making it suitable for high-frequency strategic planning environments.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- GUANGDONG POWER GRID CO LTD
- Filing Date
- 2026-06-17
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies suffer from high computational overhead, low response efficiency, and semantic incoherence in the structured decoding of enterprise strategic objectives. They are unable to meet the real-time requirements of high-frequency update scenarios, and lack lightweight processing capabilities and interpretability, making it impossible to locate the changing areas of key indicators without re-analyzing the entire text.
The semantic space mapping-based approach generates semantic anchors with unique identifiers, constructs compact structured tuples and context snapshots, locates changed regions, and uses a lightweight language model for local scanning to generate semantically equivalent substitutions and semantic compensation conclusions derived from indicators. It also establishes a cross-version backtracking context snapshot chain to ensure vertical consistency and interpretability.
It significantly reduces processing latency, improves the response speed of indicator extraction and resource utilization efficiency, achieves semantic coherence and comparability of key indicators across versions, supports clear audit traceability and modular expansion, and is suitable for high-frequency revision environments of enterprise strategic planning.
Smart Images
Figure CN122414201A_ABST