2results about How to "Facilitate cognition" patented technology

The invention relates to the technical field of vehicle calibration electric appliance systems, and particularly discloses a method for importing calibration quantity and observed quantity information through A2L file fusion, which is characterized in that model logic processing, calibration quantity and observed quantity are created and realized through a model building tool Simulink, and the calibration quantity and observed quantity information is perfected. Information in the two A2L files is fused through ASAP2, and scaling quantity and observed quantity address information in the ELF file is generated through a software integration tool and updated to the fused A2L file; compared with a traditional mode of directly compiling the A2L file, the method is faster and more accurate, the requirement for professionals is not high, and the development time is saved. Compared with a mode of extracting the calibration quantity and the observation quantity through an ELF file, the method has the advantages that the remark information of the calibration quantity and the observation quantity is more complete, professionals have better cognition on the calibration quantity and the observation quantity, and the calibration process can be better achieved.

This invention discloses an AI-based method for identifying and personally recommending children's reading interests, specifically relating to the fields of data processing and intelligent recommendation technology. The method includes: collecting multimodal data from children's reading and performing spatiotemporal alignment preprocessing; using a deep learning model to perform multimodal fusion identification of emotional pleasure, focus index, and interest focus; constructing a knowledge graph of reading materials and a graph of children's developmental stages; performing similarity retrieval based on interest focus; calculating the "zone of proximal development deviation value" to filter a preliminary recommendation list; using interactive trial reading verification; and generating a final personalized recommendation list. This invention accurately captures children's latent interests through multimodal emotion computing, combines dual knowledge graphs to match cognitive development stages, and forms a closed loop through interactive verification. It solves the technical problem that traditional recommendation methods are unable to adapt to the dynamic reading needs of young children, significantly improving the accuracy and personalization of recommendations.