An AI-based equity process transaction matching implementation method and system
By using AI-driven semantic alignment and urgency weight vector generation technology, the problem of inconsistent order data in equity product transactions has been solved, enabling executable and stable matching sequence generation and parameter adaptive iteration, thereby improving transaction efficiency and risk control stability.
Patent Information
- Application Number
- CN202610511799.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Filing Date
- 2026-04-17
- Publication Date
- 2026-07-17
AI Technical Summary
In equity product trading scenarios, existing technologies struggle to generate executable and stable iterative matching sequences under conditions of concurrent orders from multiple parties, converging time windows, and significant differences in risk constraints. Furthermore, inconsistent data caliber across different order sources leads to insufficient transaction efficiency and risk control stability.
By using AI-based methods to perform semantic alignment driven by product data management, an urgency weight vector is generated. An urgency classification model is used to cluster or sort order data. Scoring is performed by combining value interval overlap and risk boundary difference to generate matching sequences. Execution feedback drives weight updates to achieve adaptive parameter iteration.
It achieves a unified standard and compliant verifiable data for cross-source order data, improves the price compatibility and risk consistency of matching sequences, and enhances transaction efficiency and risk control stability.
Smart Images
Figure CN122415188A_ABST