2results about How to "Guaranteed Conversion Accuracy" patented technology

The invention discloses a method and platform for carrying out drawing conversion generation through drawing analysis, and relates to the related field of image processing, and the method comprises the steps: placing a to-be-converted drawing on a preset platform for scanning, and obtaining a digital image of the to-be-converted drawing; separating through an image segmentation network; performing character recognition on the separated character image list and the separated character distribution coordinate list to obtain character information and size marks; performing pattern recognition on the separated pattern image list and the separated pattern distribution coordinate list to obtain pattern features; performing vector image configuration to generate an editable digital drawing; receiving drawing conversion constraint information, executing editing optimization, generating a target conversion drawing, and sending the target conversion drawing to the user side; and when the user feedback information comprises a conversion allowing instruction, printing control is executed on the target conversion drawing. The technical problems that existing drawing digital conversion is low in efficiency and insufficient in precision are solved, and the technical effects of improving the conversion efficiency and guaranteeing the conversion precision are achieved.

This application provides a meta-training method for a natural language query model, a natural language query method, a device, a storage medium, and a product. The meta-training method includes: classifying task types based on at least one of query type and query statement paradigm; collecting sample data from multiple task types to construct a first sample set and a second sample set for each task type; training a first model using a model-independent meta-learning algorithm based on the first sample set of multiple task types to obtain a second model; and fine-tuning the second model based on the second sample set of a single task type to obtain a third model for the corresponding task type. The sample data includes natural query statements annotated with structured query statements, and the first model is a pre-trained general natural language query model. This application combines transfer learning and meta-learning techniques and applies them to the field of natural language querying, effectively reducing model training costs while ensuring the accuracy of SQL statement conversion.