17results about How to "Improve retrieval accuracy" patented technology

The video character retrieval method, device, equipment and storage medium of the present application, wherein the method comprises: establishing an original video database, obtaining a target video from the original video database; extracting a character segment in the target video; extracting a character audio in the character segment; performing semantic analysis on the character audio to obtain an audio text; combining the audio text and a video text in the target video to obtain a combined text; and using a preset character identity recognition model to extract a character identity in the combined text, wherein the preset character identity recognition model is obtained by training a convolutional neural network. The combined text combining the audio text and the video text includes the character name and the character relationship of the target video, and the character identity in the combined text can be automatically extracted by the trained preset character identity recognition model, thereby achieving high retrieval accuracy and efficiency.

This application discloses a trademark retrieval database generation, retrieval method, and apparatus that enhances local features. The method involves constructing a multi-classification training set for trademark graphic elements and training it in an image multi-classification model to obtain a trademark element multi-classification model; constructing a target detection training set for important graphic elements based on trademark data and training it in an image target detection model to obtain a target detection model; inputting all trademark data into the trademark element multi-classification model for prediction and selecting trademark elements as field information; inputting all trademark data into the target detection model to detect important graphic element targets and extracting them into sub-images; inputting all sub-images and the original trademark data image into a trademark encoding model for encoding to obtain encoding vectors; and inputting the field information and encoding vectors into a vector database to obtain the trademark retrieval database. Using the trademark retrieval database provided in this application can significantly improve trademark retrieval results.

The application provides a retrieval method, device and equipment for software product prototype information, a medium and a product. The method comprises the following steps: obtaining a plurality of prototypes of a target software product and corresponding associated information of each prototype, wherein the associated information comprises prototype source files, software product information and plug-in resources used by the prototypes, and the software product information comprises function characteristic information and version information. For any prototype, the prototype source files, the software product information and the plug-in resources used by the prototype are associated and bound to generate an associated data set, and a prototype library containing a function characteristic dimension and a version dimension is constructed based on the associated data set of each prototype. Then, in response to a retrieval condition input by a user, the prototype library is retrieved according to the function characteristic dimension or the version dimension to obtain matched prototype information. The application improves the prototype retrieval efficiency and accuracy.

The application provides a medical question and answer method and system based on cooperative dual-source retrieval enhancement generation, which comprises the following steps: according to an input original medical question and candidate items, performing task self-adaptive query rewriting through a large language model to generate differential diagnosis keywords, entity enhanced queries and hypothetical medical abstracts; for the differential diagnosis keywords, performing an iterative network retrieval with a reflection mechanism to obtain network candidate evidence; for the entity enhanced queries and the hypothetical medical abstracts, performing a hybrid retrieval in a local medical knowledge base to obtain local candidate evidence; aggregating and deduplicating the network candidate evidence and the local candidate evidence, obtaining the aggregated heterogeneous evidence, performing deep semantic correlation scoring through a cross-encoder model, screening out a target evidence set, and splicing the original medical question as context input into a generative large language model to generate a final medical answer. The application improves the accuracy, timeliness and explainability of the medical question and answer system.

This invention relates to the field of intelligent fabric matching and recognition technology, and discloses a fabric matching method and system based on multimodal information matching. The method includes: semantically structuring and enhancing user input text; acquiring the original fabric image and performing mask extraction, texture enhancement, and normalization processing; calculating the matching degree based on a cross-modal semantic alignment mechanism, and outputting fabric recommendation results by combining multi-factor joint ranking. Compared to existing technologies that use simple text retrieval or single-channel feature matching, especially when user descriptions are non-standard, fabric image textures are complex, or structures are repetitive, failing to achieve accurate fabric location and consistent semantic matching between text and images, this application improves fabric retrieval accuracy in open descriptive contexts by introducing an industry ontology-driven semantic enhancement mechanism and a saliency-guided cross-modal matching architecture, achieving multi-granular modeling and fine-grained semantic alignment of fabric semantic features.

This invention relates to the field of NLP technology and discloses a plug-in function testing method for a pre-trained power system model based on NLP. The method includes: receiving a technical supervision query question and performing vector encoding and retrieval to obtain an initial retrieval result set; obtaining reference text with standard citation annotations through weighted sorting and concatenating it with multi-level prompt word templates to obtain domain-structured prompt words; performing a bundle search using a large technical supervision domain model fine-tuned with LoRA low-rank adaptation to generate candidate answer texts; and conducting quality assessments including citation integrity detection, logical consistency detection, and entity illusion detection to obtain a qualified technical supervision standard answer. This method solves the technical problems of low standard retrieval accuracy, poor answer accuracy, lack of citation tracing, and insufficient domain customization capabilities in existing technologies, providing a complete technical solution for intelligent applications in the field of power technical supervision.

The application discloses a kind of academic question and answer-oriented multi-model paper retrieval method, it is related to natural language processing and information retrieval technical field, including: first, construct unified corpus and training dataset, utilize the model in first model set and second target model respectively encode generation document vector set;Then, based on the initial retrieval result of second model, difficult negative sample is filtered, and the contrast learning sample pair is constructed to fine-tune and re-encode corpus;With the model group of the second target model after fine-tuning and the model in first model set, each model in model group is executed similarity retrieval in parallel respectively, and the corresponding original similarity matrix is obtained, based on the original similarity matrix, the document is filtered, and the first target document list of target query is generated.The application can effectively improve the accuracy and robustness of academic literature retrieval.

This invention discloses a retrieval enhancement generation method and system based on contradiction detection feedback, aiming to solve the problems of single data source, insufficient retrieval accuracy, and lack of factual verification in existing technologies. The method mainly includes three core steps: First, document block storage, using OCR and VLLM technologies to extract features from text, tables, and images and uniformly vectorize and store them; second, multi-source query retrieval, using an iterative retrieval controller to introduce a self-reflective closed loop, dynamically evaluating and optimizing the quality of the retrieved content; and finally, contradiction detection generation, comparing the generated content with the source documents for factual accuracy, and feeding back any conflicts to the model for forced correction. This invention effectively expands the sources of multimodal knowledge, suppresses model "illusions," and significantly improves the accuracy and fidelity of the generated answers.

The application discloses a dangerous chemical substance intelligent question and answer method and system based on CAS structured retrieval, which comprises the following steps: obtaining question data; performing semantic analysis on the question data to generate a structured SQL query instruction; constructing a hierarchical database, performing CAS hierarchical retrieval by using the structured SQL query instruction, and recalling authoritative information of dangerous chemical substances; constructing a constraint prompt word, jointly coding the user question, the authoritative information and the scene by using a large language model, and generating a constrained dangerous chemical substance management and emergency suggestion; and through a semantic analysis mechanism of "keyword extraction, question classification and SQL classification construction", combining a hierarchical retrieval strategy taking CAS number as an anchor point and a large language model assisted normalization CAS query mechanism, stable mapping of diversified names of dangerous chemical substances to unique CAS numbers is realized, retrieval difficulties and generation inaccuracies caused by diversified names of dangerous chemical substances and fuzzy questions are effectively relieved, and the accuracy, stability and traceability of information retrieval and scenario-based suggestion generation of dangerous chemical substances are improved.

The application relates to a text retrieval method and device, computer equipment, a storage medium and a computer program product, relates to the technical field of computers, and can also be used in the field of financial technology or other related fields. The method comprises the following steps: acquiring a text to be retrieved; performing semantic feature extraction processing on the text to be retrieved to obtain initial semantic features of the text to be retrieved; performing reconstruction processing on the initial semantic features to obtain target semantic features of the text to be retrieved; the completeness of the semantics represented by the target semantic features is greater than the completeness of the semantics represented by the initial semantic features; and based on the target semantic features, target text matching the text to be retrieved is determined from a plurality of candidate texts. The method can improve the retrieval accuracy of text retrieval.

This application provides a music retrieval method, a music retrieval device, an electronic device, and a storage medium, belonging to the field of artificial intelligence technology. The method includes: acquiring target descriptive text and candidate music, wherein the target descriptive text includes the target object's description of the music; performing word recognition on the target descriptive text to obtain genre description words; performing spectral transformation on the candidate music to obtain candidate music spectrum sequences; based on the candidate music spectrum sequences, obtaining candidate music genre representation vectors corresponding to the candidate music; performing genre identification on the candidate music based on the candidate music genre representation vectors to obtain genre tag data for the candidate music; filtering the candidate music based on the genre description words and genre tag data to obtain target music; and feeding the target music back to the target object. This application embodiment can improve the accuracy of music retrieval.

The application provides an intelligent power data retrieval system, which comprises a data acquisition module, a data processing module, a retrieval interaction module, an intelligent matching module, a retrieval output module and a retrieval security module; the data acquisition module is used for acquiring real-time and historical multi-source power data and storing the multi-source power data in a power database; the data processing module is used for performing a preprocessing operation on the acquired multi-source power data and extracting power data features; the retrieval interaction module is used for receiving retrieval request information of a user and analyzing the retrieval request information; the intelligent matching module is used for performing similarity matching between the analyzed retrieval request and the power data features; the retrieval output module is used for feeding back the matching result of the intelligent matching module to the user in a preset format; and the retrieval security module is used for guaranteeing the safety of the multi-source power data in the acquisition, transmission, storage and retrieval processes. The application can realize efficient retrieval of power data and improve retrieval accuracy and response speed.

The application provides a financial variable retrieval method based on semantic-statistical dual-space fusion and polysemy abbreviation closed-loop disambiguation verification. The application can realize high-precision retrieval of financial variables by constructing a semantic vector retrieval space and a statistical metadata retrieval space. At the same time, the financial abbreviation polysemy disambiguation module is used to accurately analyze the multiple meanings of abbreviations combined with context information, solving the problem of abbreviation ambiguity and variable retrieval fragmentation in traditional methods. Through a bidirectional closed-loop consistency verification mechanism, the system uses the retrieval results to verify the meaning of the abbreviation in reverse, dynamically updates, and ensures the statistical consistency of variable matching. Specifically, the application includes receiving user queries and preprocessing, disambiguating abbreviations and generating retrieval intent, parallel retrieval in dual space, fusion scoring and sorting of results, reverse verification and updating of abbreviation meaning, and finally outputting accurate variable candidates and their statistical attributes. The application can effectively reduce financial variable mismatching and abbreviation misinterpretation, improve the reliability, interpretability and reproducibility of retrieval results, and has a wide application prospect, especially suitable for retrieval fields assisted by big data and large language models.

This invention discloses a method and system for intelligent generation and processing of meeting content based on a multimodal large model, comprising: collecting raw meeting data and performing standardized preprocessing; inputting the multimodal large model, extracting multimodal features and semantically aligning them; performing cross-modal hash encoding to generate binary codes and establish an index library; hash-retrieval of relevant segments to construct a directed graph of meeting content; optimizing the path using Monte Carlo tree search based on the directed graph structure; generating structured meeting content and outputting minutes, summaries, and action items. This invention achieves efficient extraction, accurate retrieval, and intelligent structured generation of meeting content by integrating a multimodal large model, cross-modal hash encoding, and Monte Carlo tree search.

A low-quality remote sensing image depth hash retrieval method, system, device and medium based on vector quantization, the method comprising: first, obtaining high-quality and low-quality remote sensing images as input, extracting the features of high-quality and low-quality remote sensing images through a deep convolutional neural network respectively, and inputting them into a vector quantization module to quantize them into independent discrete spaces to generate quantized features, then generating hash codes for image retrieval through a deep hash network, and finally obtaining feature representations and constraining the feature representations by applying a loss function, which includes Pairwise Loss, reconstruction loss and cross-entropy loss, to ensure semantic information retention, feature distance constraint and collaborative learning of the encoder, codebook and decoder; the system, device and medium are used to implement the method; the present application improves the retrieval accuracy of low-quality remote sensing images, reduces the storage space occupation and computing overhead, improves the robustness and generalization ability of the model, and ensures high retrieval performance.