4results about How to "Automated Extraction" patented technology

The application discloses a small sample knowledge base construction method and device for a vertical field, and the method comprises the following steps: acquiring data of the vertical field from multiple channels, and performing cleaning, light annotation and structural processing on the acquired data to obtain field sample data; fine-tuning a pre-trained large model based on the field sample data, extracting field knowledge including entities, relationships and attributes based on the fine-tuned model, and verifying and integrating the extracted field knowledge to form a standardized knowledge base; acquiring new knowledge data, incrementally training the model using a weighted incremental loss function, outputting an optimized field model, extracting new knowledge from the new data using the optimized field model, verifying the new knowledge and fusing the new knowledge into the existing knowledge base; periodically evaluating the quality of the knowledge base and the performance of the model according to a field knowledge update cycle, generating a comprehensive evaluation score, and optimizing the knowledge extraction rules, the incremental training strategy and the knowledge base Schema according to the evaluation result.

The application discloses a kind of plant factory environment under soybean high-flux plant height phenotype acquisition method and system, belong to depth image acquisition and identification technical field, method includes: obtaining the depth image and RGB image containing soybean plant and potted platform;Image registration is carried out, and based on registration parameter, depth image is synchronously spliced, and depth splicing chart is obtained;Depth splicing chart is converted into pseudo-color depth chart and actual gray chart in sequence, and depth gray chart is obtained;The reference gray value of ground area is extracted from depth gray chart, and the correction coefficient is calculated based on the actual physical height of potted platform distance ground and the gray value of calibration reference point;Based on the top gray value of plant and the minimum gray value of image, the actual plant height of soybean plant is obtained by gray difference proportion mapping.The application realizes the nondestructive, high-flux, high-precision automatic measurement of soybean seedling stage plant height under plant factory environment, and provides reliable technical support for soybean generation breeding and phenotype identification.

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.