2results about How to "Automated Extraction" patented technology

The invention discloses a business personnel seat number identification method and device, electronic equipment and a program product, relates to the field of artificial intelligence, and is applied to the field of financial science and technology, and the method comprises the steps: carrying out the preprocessing of collection information of a transaction liquidation request when the transaction liquidation request is detected, and obtaining a preprocessed text; extracting a text semantic feature, a character space feature and a business logic feature from the preprocessed text, and performing feature fusion to obtain a fused feature vector; and inputting the fusion feature vector into a target recognition model, and outputting a business personnel seat number recognized from the collection information, the target recognition model being obtained by training a preset recognition model by using historical collection information, and the preset recognition model at least comprising: a bidirectional long short-term memory network, a converter encoder, and a conditional random field model. Through the method and the device, the problem that the seat number of the business personnel in the collection information is difficult to identify based on a traditional keyword matching method in related technologies is solved.

This application provides a method and apparatus for generating a knowledge graph for cross-layer data processing activities. The method includes: defining entities and relationships corresponding to the business logic layer, data operation layer, and technical implementation layer, as well as cross-layer relationships between entities in adjacent layers; generating an entity-relationship model based on entities, relationships, and cross-layer relationships; extracting entity data from the data processing activity documents corresponding to the business logic layer, data operation layer, and technical implementation layer to obtain entity-relationship data; and generating a knowledge graph based on the entity-relationship model and entity-relationship data. This application achieves full-link association analysis through three-layer cross-layer entity modeling, forming a structured, traceable, high-quality knowledge graph. This provides accurate reference and data support for risk analysis of data processing behavior, helping to quickly identify abnormal operations and potential risks.