4results about How to "Solve the result" patented technology

This invention discloses a method and system for injecting knowledge into large-scale models for vertical industries, belonging to the field of large-scale model knowledge injection technology. The method involves: collecting professional knowledge data from vertical industries, performing denoising, structured transformation, and terminology standardization; dividing the data into knowledge modules and associating them with vectorized indexes and metadata tags to form a vertical industry knowledge base; parsing the semantic feature vector of the input question using a dynamic routing classifier and routing it to the target knowledge module in the vertical industry knowledge base; employing a hybrid retrieval strategy to recall relevant document sets from the target knowledge module and deduplicate them to generate enhanced retrieval results; based on this, concatenating the enhanced retrieval results with the input question using industry-specific prompt templates; and inputting the LoRA-tuned large-scale model to generate accurate answers conforming to industry standards. This invention solves the problem of insufficient knowledge adaptability of large-scale models in vertical industries, improving the accuracy and applicability of large-scale models in vertical industries.

This invention provides a non-destructive characterization method for hollow anti-resonant optical fibers based on multimodal deep learning. The specific implementation path includes: (1) acquiring microscopic images (image modalities) of the end face of hollow anti-resonant optical fibers covering differentiated microstructures and their corresponding original transmission spectral data to construct a multimodal original sample set; (2) performing feature extraction and standardization processing on the multimodal data; (3) building a cascaded deep learning model integrating a visual feature capture subnetwork and a transmission spectral feature fusion regression subnetwork; (4) implementing joint training, using a convolutional neural network (CNN) to automatically extract the geometric feature parameters of the fiber core and cladding microstructures in the microscopic images, and fusing them with the transmission spectrum features, constructing a mapping relationship through a deep neural network (DNN); (5) inputting the microscopic image and transmission spectral data of the optical fiber under test into the model to achieve non-destructive prediction of loss characteristics. This invention features high characterization accuracy, fast speed, and zero material loss.

The application discloses a kind of real estate data matching method and system based on priority search chain, it is related to data matching and query system field, this real estate data matching method and system based on priority search chain, data end is marked to house information by different mark, user end collects user demand and carries out priority sorting, according to sorting in database search, in the aspect of house information processing, the corresponding mark identification of the house information collected, according to different mark, the classification and search of house information, in user, according to the actual demand of the user collected, and the priority sorting of user demand, search tool according to the order of user demand in house source database carries out search, from last item collected data to the back sorting, obtains the data list of most matching user demand, avoid the data disorder caused by multiple different needs, and useless even false advertisement influence user's audio-visual.

This application discloses a hierarchical adaptive intelligent association and alignment method for multimodal flight data, comprising: acquiring multi-source airborne flight data and preprocessing it; mapping data of different modes in the preprocessed multi-source airborne flight data to a common feature space to obtain cross-modal alignment features, wherein an aircraft physical dynamic model is introduced as a constraint during the mapping process; performing local precise association and global semantic association in parallel based on the cross-modal alignment features; wherein the local precise association is used to obtain pixel-level or point-level matching results, and the global semantic association is used to obtain globally consistent semantic association results; and fusing the results of the local precise association and the global semantic association to obtain association information of corresponding granularity. This invention effectively solves the "data silo" problem caused by differences in format, spatiotemporal reference, and modality of multi-source flight data, significantly improving association accuracy, robustness, and system adaptability.