2115 results about "Data retrieval" patented technology

The invention relates to an intelligent question-answering system optimization method and device based on a knowledge graph, and the method comprises the steps: obtaining original knowledge data of a target knowledge domain, and constructing a knowledge graph structure model; extracting term information of entity nodes in the knowledge graph structure model, and constructing an entity term set; receiving a natural language question input by a user, executing a semantic understanding operation based on the standardized expression set to obtain a structured question semantic representation, and matching the question semantic representation with the case training set to obtain context semantic features; constructing a cue word template, and executing a query instruction generation operation to obtain a target query statement of the graph database; submitting the target query statement to a graph database to execute data retrieval operation, and obtaining query result data corresponding to the question semantic representation; and performing personalized rendering processing on the query result data based on the user portrait information to generate final question and answer return content. The method has the effect of improving the query accuracy.

The invention provides an intelligent retrieval method and system for unstructured asset content based on a large model, and relates to the technical field of data retrieval, and the method comprises the steps: carrying out semantic expansion on retrieval keywords through a knowledge graph enhancement module, obtaining asset feature vectors through a multi-modal feature extraction model, and constructing a hierarchical asset feature index database; the comprehensive similarity is calculated through a weighted fusion algorithm, and retrieval results are optimized and reordered through semantic comprehension and correlation analysis capacity of the pre-trained large model. According to the method, the retrieval precision and efficiency of the unstructured asset content are improved, and intelligent association analysis and semantic understanding of cross-modal assets are realized.

The present invention discloses an artificial intelligence-based adaptive big data storage and retrieval optimization system and method designed to intelligently manage and optimize large-scale distributed data environments. The system integrates data acquisition, distributed storage, metadata processing, adaptive learning, and retrieval optimization units configured to work collaboratively for continuous self-optimization. The invention employs deep reinforcement learning and predictive neural network techniques to dynamically analyze system telemetry, workload behavior, and data access patterns in real time, enabling proactive adjustment of data placement, caching, replication, and compression parameters across distributed nodes. The metadata processing framework utilizes graph-based dependency modeling to maintain semantic and contextual relationships among datasets, facilitating intelligent and context-aware data retrieval. The retrieval optimization unit interprets user queries semantically and computes the optimal retrieval route using latency prediction models and dynamic routing techniques.

The invention discloses a mechanical arm control method, system and equipment for realizing a multi-modal universal operation task. The method comprises the following steps: acquiring multi-modal data of text, voice and vision; performing modal alignment on the multi-modal input data, encoding the multi-modal input data into a uniform input representation vector, and splicing the uniform input representation vector into a multi-modal instruction paragraph; retrieving a user information voice database based on voiceprint features of the voice instruction data to obtain task associated voice data; instruction information is input into the strategy large model to obtain an action plan; a post-processing task action planning input controller controls a mechanical arm, and a multi-mode input instruction is updated in real time to perceive the latest task state; and performing closed-loop control based on the initial target instruction information and the environment perception information until the task is completed. The task planning and error correction capacity of the mechanical arm is improved through closed-loop control and voice data retrieval based on the large strategy model, and the dynamic adaptive capacity, the autonomous error correction capacity and the multi-mode interaction capacity of the mechanical arm are improved.

The present invention proposes a vector database retrieval method and system, which relates to the technical field of big data processing and intelligent information retrieval. The method includes preprocessing high-dimensional multimodal data to obtain a dimension reduction feature vector; constructing a cluster center based on the dimension reduction feature vector; establishing an inverted file and an intra-cluster plane index based on the cluster center, compressing the inverted file and the intra-cluster plane index to obtain a compressed inverted file and an intra-cluster plane index; obtaining a user's query vector, using the inverted file to screen candidate clusters for the query vector, introducing a hierarchical navigable small-world graph in the intra-cluster plane index, performing an intra-cluster search on the cluster cluster, and obtaining a vector database retrieval result. The present invention effectively takes into account the efficiency and accuracy of high-dimensional data retrieval, and improves the retrieval efficiency and accuracy.

Systems and methods for translating natural language to SQL on a custom enterprise data warehouse powered by Generative AI. With an embodiment of the present invention, a natural language question may be converted to a meaningful and accurate database query, e.g., SQL query, relevant to tables existing in an enterprise data warehouse. An embodiment of the present invention is directed to a comprehensive approach of transforming a natural language query to a focused SQL query using domain specific data models across firmwide metadata systems and data systems. In response to a user query, an embodiment of the present invention performs metadata analysis, targeted data retrieval and then SQL generation. An embodiment of the present invention may apply data warehousing standards and guidelines followed in the enterprise and provide a plug-and-play type architecture and solution that is scalable to large warehousing and other systems.

The invention discloses a meteorological service center data visualization and interaction platform, and particularly relates to the field of meteorological data visualization, which comprises a multi-source heterogeneous data acquisition and preprocessing module, a space-time fusion and data enhancement module, a distributed storage and intelligent index module, a dynamic visualization module, an intelligent decision and early warning module and an interaction module. According to the invention, multi-source meteorological data are collected through an API and a protocol, and wavelet transform noise reduction processing is carried out by using an edge computing node; carrying out space-time alignment and fusion, and filling missing data with LSTM; dynamic visualization and interaction are realized by using WebGL (Web Graphics Library) and ARCore / ARKit; the WRF and the Transform are integrated to generate early warning information, and decision suggestions are pushed through a natural language interface; according to the method, the WRF numerical model and the Transform algorithm are combined, accurate meteorological early warning is provided for the user, meanwhile, the geographic space index is constructed based on the GeoHash algorithm, efficient geographic space query and data retrieval are achieved, a real-time feedback mechanism and a multi-dimensional visual view are provided, and the operation process and experience of the user are optimized.

The invention relates to the technical field of electric digital data processing, in particular to a traditional Chinese medicine data management system and method for data retrieval. The method comprises the following steps: acquiring morphology data of a traditional Chinese medicinal material sample; performing grade evaluation based on the traditional Chinese medicinal material morphology data to generate traditional Chinese medicinal material grade label data; acquiring a to-be-detected traditional Chinese medicinal material image; preprocessing the to-be-detected traditional Chinese medicinal material image, and performing entity recognition and relation extraction to obtain traditional Chinese medicinal material semantic annotation data; and fusing the traditional Chinese medicinal material grade label data and the traditional Chinese medicinal material semantic annotation data to obtain traditional Chinese medicinal material semantic annotation enhanced data. According to the method, the information of the traditional Chinese medicinal materials is expressed through structured processing and semantic enhancement, the knowledge graph containing dominant and implicit semantic relationships is constructed, and the retrieval depth, flexibility and intelligent application of knowledge of the traditional Chinese medicinal materials are improved.

The invention relates to an intelligent operation and maintenance method and apparatus based on a knowledge graph and a large model, and an electronic device. The method comprises the steps of collecting multi-source runtime data of a Kubernetes cluster; constructing a knowledge graph with time dimension based on the resource change event, recording termination time in response to graph relationship failure, recording starting time in response to a newly added relationship and not setting the termination time, and associating the entity with the performance index and the log data; responding to the diagnosis request, scheduling a specialized agent by a coordination agent through multi-agent cooperation to retrieve associated information from multi-source data, and iteratively integrating to generate a structured context; and inputting the generated structured context information into a large model reasoning service to output a fault root cause diagnosis and solution. The technical problems that information dispersion and relevance are weak, root cause positioning is difficult and time-consuming, comprehensive context sensing ability is lacked and expert experience is excessively relied on are solved.

The invention discloses a literature semantic search method and system based on elastic search, and relates to data retrieval. The literature semantic search method comprises the steps that vectorization processing is conducted on a noun phrase list by means of a text2vec-based-multilingual model trained based on a CoSENT method; according to the semantic vector, performing approximate nearest neighbor search in a second retrieval module to obtain first candidate data; inputting the query text data into a first retrieval module, and performing keyword matching through a BM25 algorithm to obtain second candidate data; fusing the first candidate data and the second candidate data to obtain third candidate data; a Sequence Matcher algorithm is adopted to calculate character string similarity between expansion words in the third candidate data, a similarity threshold value is set based on the length of the longest common subsequence, duplicate removal is carried out, and fourth candidate data is obtained; and performing weight distribution based on positions and similarity scores on the fourth candidate data, and enhancing the distinction degree of the extension words by expanding a score interval to obtain extension word recommendation list data. According to the method, the accuracy of document retrieval is remarkably improved.

Aspects of the present disclosure provide techniques for enhanced electronic data retrieval. Embodiments include receiving a natural language query and identifying one or more electronic data sources indicated in the natural language query using a named entity recognition (NER) machine learning model trained through a supervised learning process based on training natural language strings associated with labels indicating entity names. Embodiments include determining one or more additional electronic data sources related to the one or more electronic data sources using a knowledge graph that maps relationships among electronic data sources. Embodiments include retrieving data related to the natural language query by transmitting requests to the one or more electronic data sources and the one or more additional electronic data sources and providing a response to the natural language query based on the data related to the natural language query.

The embodiment of the invention provides a multi-modal knowledge graph construction method in cross-media retrieval. The method comprises the steps that extracted multi-modal features are mapped to a multi-modal feature space through a linear transformation layer; in the multi-modal feature space, the intra-modal attention weight of each modal feature is calculated according to a self-attention mechanism, the cross-modal attention weight of different modal features is calculated according to a cross attention mechanism, the two weights are fused to obtain a final fusion weight, each modal feature is weighted and input into a graph attention network, and the multi-modal feature is obtained. Obtaining a multi-modal fusion graph structure; performing semantic analysis on each modal feature, matching with a preset multi-modal semantic knowledge base, determining potential semantic association, performing semantic alignment on the multi-modal fusion graph structure according to a preset graph matching algorithm and the potential semantic association to obtain a multi-modal knowledge graph, performing cross-modal data retrieval according to the multi-modal knowledge graph, and performing cross-modal data retrieval according to the multi-modal knowledge graph. The multi-modal data cross-media retrieval method and device can improve the efficiency and accuracy of multi-modal data cross-media retrieval.

The invention discloses a course data intelligent multi-dimensional retrieval system based on deep learning, and relates to the technical field of course retrieval. The course data retrieval platform is in communication connection with a knowledge graph construction module, a user intention analysis module, a user portrait construction module, a personalized recommendation engine module and a retrieval display module, and all the modules are in electric signal connection; the knowledge graph construction module is used for collecting course data from multiple sources and constructing a course domain knowledge graph. According to the natural language processing technology based on deep learning, deep semantics queried by a user can be deeply analyzed, fuzzy or implicit requirements can be understood, limitation of traditional keyword matching is broken through, intent features and entity relationships can be extracted by combining a knowledge graph, specific entities and relationships queried by the user can be accurately mapped, and user experience is improved. Therefore, a more accurate retrieval result is provided, and personalized requirements of users are met.

The invention belongs to the field of data retrieval, and provides a multi-modal scientific data retrieval method and system based on abstract-assisted cognitive enhancement. According to the technical scheme, a multi-modal scientific data semantic representation model based on a large model is constructed, professional domain knowledge and a structure guiding mechanism are combined, and a multi-modal scientific data semantic representation model is constructed; generating a structured abstract containing the key knowledge; and then, on the basis of knowledge such as keywords and description information provided by the structured abstract, a multi-modal scientific data retrieval model guided by directional thinking chain multipath is constructed, multi-dimensional correlation analysis of scientific data is realized through combination of cognitive prompt knowledge enhancement and multi-step dynamic reasoning, and the data resource retrieval performance in the field of scientific data is improved.

The invention provides a knowledge question and answer rapid processing method and system based on artificial intelligence, and relates to the field of artificial intelligence. A multi-modal knowledge graph is constructed, collected multi-source teaching data is fused through a mixed retrieval strategy, and the mixed retrieval strategy comprises semantic retrieval, vector retrieval and metadata retrieval; multi-level question and answer processing is executed based on an RAG enhancement framework, a multi-modal input intention is analyzed, cross-library joint retrieval is performed, and an optimization answer is generated in combination with a teaching scene; distilling the global model to a lightweight TinyBERT architecture, dynamically optimizing question and answer quality through a cognitive reinforcement learning framework, positioning a key document from a comprehensive retrieval list, evaluating an optimized answer, and reconstructing an answer with a key document verification score; according to the invention, the professional skill level of teachers and students in the fields of artificial intelligence and large model application can be improved, and the personalized requirements of teachers and students in teaching, scientific research and innovation courses can be met.

The invention relates to the technical field of artificial intelligence large language models, and discloses a data attribution analysis task processing method, system and device based on a large language model and a storage medium. The data attribution analysis task processing method is applied to data attribution equipment and specifically comprises the following steps that S101, user input is received through a multi-mode input interface, and the user input comprises natural language problems, structured data files or API data streams; by means of the natural language understanding ability of the large language model, the system can directly analyze service problems put forward by a user in a daily term, professional data query languages are not needed, non-technical personnel can conveniently use the system, the data retrieval time is shortened to be within 3 minutes from 30 minutes on average through the automatic SQL query generation technology, the efficiency is improved by 10 times, and the method is suitable for large-scale popularization and application. The system can intelligently identify the database fields corresponding to the business indexes and generate optimized query statements.

An emergency response assistant system may generate emergency response insights and provide the insights to an emergency responder application for an emergency communications center (ECC), an operations center, or a first responder. The emergency response assistant system may include a data retrieval system and an augmentation and generation system. The data retrieval system may be configured to transform emergency response procedures into vectors that are stored in a vector database. The augmentation and generation system may be configured to perform a vector search of the vector database with an AI model using context from call (e.g., a 911 call) characteristics and external data sources and may be configured to display the results on a user interface of an emergency management application to provide emergency response procedure suggestions to a user of an ECC computing system, an operations center computing system, or a first responder computing device.

A method includes obtaining chunks of information and generating training samples for an embedding model using the chunks of information. Generating the training samples includes using at least one large language model to generate training samples in which different ones of the chunks of information are and are not relevant to different potential queries. The method also includes training the embedding model using the training samples. In some cases, the embedding model may represent a retriever model. For example, an input query may be obtained at the retriever model, and the retriever model may be configured to identify a specified number of chunks of information relevant to the input query. One or more of the chunks of information may be provided from the retriever model to a generative model, and the generative model may be used to create a response to the input query.

The invention discloses a multi-modal data retrieval method and device, a storage medium and computer equipment. The method comprises the following steps: collecting multi-modal original data; on the basis of description information of metadata of original data, all metadata belonging to the same associated items and logic relations among all the metadata are obtained, a metadata chain is constructed, a distributed graph database is constructed on the basis of the metadata chain, the metadata chain is expressed in the distributed graph database in the form of a graph, the graph comprises nodes and edges, the nodes represent the metadata, and the edges represent the metadata. The edge represents a logical relationship between the metadata; and when a data retrieval instruction is received, traversing each node in the metadata chain along the logical relationship of the metadata chain in the distributed graph database, obtaining a target node matched with a data retrieval requirement corresponding to the data retrieval instruction, and returning original data corresponding to metadata represented by the target node. Multi-modal data dynamic association retrieval can be realized, and cross-modal information mining efficiency and accuracy are improved.

The invention relates to the technical field of data retrieval, in particular to an information technology retrieval consultation system and method. The method comprises the following steps: acquiring user input data; extracting modal key information of the user input data to obtain multi-modal key semantic information data; performing data preprocessing on the multi-modal key semantic information data to generate standard user input data; performing historical retrieval information retrieval on the standard user input data, and when a historical retrieval record is retrieved, performing accurate portrait construction on the standard user input data to generate a user accurate retrieval portrait; and when no historical record is retrieved, performing retrieval reasoning on the standard user input data to generate a user fuzzy retrieval portrait. By combining multi-modal information extraction, accurate and fuzzy portrait construction, cross-context inheritance, dynamic knowledge fusion and sentiment analysis optimization, the problems of low retrieval precision, incomplete information coverage and poor user experience in the prior art are solved.

The present application relates to the technical field of motor operation monitoring, and discloses a factory motor energy-saving operation monitoring system based on big data analysis, including: a data acquisition module, which is used to collect multi-dimensional data of motor operation in real time; a data processing module, which pre-processes the collected data and marks abnormal data; an analysis and diagnosis module, which performs trend analysis and fault prediction on the motor operation status based on big data algorithms; a storage module, which is used to store raw data and analysis results, and supports historical data retrieval; a communication module, which is connected to a cloud server or a local terminal by wireless or wired means to achieve data interaction. Winding faults are judged by electrical parameters, and early hidden dangers are identified by audio characteristics. The characteristics of multi-dimensional data fusion enable the system to warn of faults in advance and make targeted diagnostic suggestions, reducing downtime and economic losses caused by faults.

An apparatus for data fault detection and repair is disclosed. The apparatus comprises at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive a user profile relating to a user, wherein the user profile comprises at least provider data of a user. The memory instructs the processor to generate practitioner data as a function of the user profile. The memory additionally instructs the processor to retrieve remittance data as a function of the practitioner data. The memory then instructs the processor to identify a data fault in at least one of the practitioner data and the remittance data. The memory instructs the processor to initiate a data correction action based on the identified data fault.

The invention discloses a structured data retrieval system and method based on semantic matching and hierarchical indexing, and the related retrieval system comprises a first construction module which is used for extracting slice data in a preset vector library and meta-information corresponding to the slice data, and constructing a text node object containing an id; the second construction module is used for traversing a text node object to obtain meta-information subjected to hierarchical structure processing, and constructing a nested index tree; the directory decomposition module is used for receiving an input text, performing decomposition based on a hierarchical structure and generating a corresponding query vector; the retrieval module is used for performing semantic retrieval and hierarchical retrieval on the text in sequence to obtain a retrieval result; the grouping and sorting module is used for grouping the retrieval results according to the hit hierarchy, sorting the retrieval results in each group according to a descending order, and combining all groups to obtain a final retrieval result list; and the data backtracking module is used for acquiring an original text field from the vector database according to the id corresponding to the retrieval result.

The invention relates to the technical field of artificial intelligence and thermal power production, in particular to a thermal power plant fault analysis report generation method based on retrieval enhancement and related equipment, and the method adopts a distributed sensor to obtain time sequence operation data and equipment logs of thermal power plant equipment to construct multi-modal data. Multi-modal data are processed through a time sequence fault diagnosis model (including a retrieval enhancement generation module and a fault diagnosis module), the retrieval enhancement generation module retrieves in a thermal power plant equipment knowledge base and generates retrieval prompt data, and the fault diagnosis module performs diagnosis according to the retrieval prompt data. And finally, generating an equipment analysis report containing fault reasons, processing measures and prevention suggestions by adopting a predefined template according to a diagnosis result.

The invention relates to the technical field of database optimization, provides a massive astronomical data query processing method, and aims to solve the efficiency bottleneck of astronomical data in storage, management and query processes. By constructing a distributed spatial database, efficient storage and management of large-scale astronomical data are realized; an index structure of astronomical data is optimized by utilizing an LB-ANN index, so that query load balance and data retrieval efficiency are improved; besides, efficient fusion of structured query and unstructured query is realized through SQL-ANN mixed query in combination with a JIT compiling technology, the query execution sequence is optimized, and the query efficiency is remarkably improved; the overall method breaks through the bottlenecks of traditional astronomical data query in calculation complexity, storage efficiency and index optimization, and an accurate and efficient solution is provided for efficient intelligent processing of large-scale astronomical data.

The invention discloses a data retrieval enhancement generation method in the medical insurance field based on a large model, and aims to improve the accuracy and efficiency of medical insurance data retrieval. Comprising the following steps: cleaning, standardizing and marking medical insurance data; training a large model by using the preprocessed data, analyzing the natural language query of the user, and generating a corresponding problem vector; according to the generated question vector, obtaining candidate texts highly related to the question from a knowledge base by adopting a dynamic hybrid retrieval method, and performing fine rearrangement and reverse rearrangement on the candidate texts; the rearranged text is sent into the large model, and a final output result is generated in combination with generation rule and output rule constraint conditions; optimizing a retrieval result by combining an interactive attention mechanism based on # imgabs0 # with a self-adaptive weight allocation method; according to the method, the query intention of the user can be reflected more accurately, the correlation and accuracy of the retrieval result are improved, and various data retrieval requirements in the medical insurance field are met.

Domain-specific system and method for enhancing firmographic search through query understanding and expansion presents an innovative approach to revolutionizing firmographic search processes. This system employs advanced domain-specific query understanding and expansion techniques to bridge the gap between natural language queries and structured firmographic data, significantly improving precision and relevance in search results. This method utilizes a multi-model approach to dissect natural language queries into named entities, subsequently mapping them to specific structured attributes within a database. This refined query understanding enhances the alignment of unstructured queries with the structured format required for accurate data retrieval. The system also introduces custom confidence scoring to assess the reliability of model outputs, further improving the accuracy of structured query formulation. The presented system stands as a pioneering advancement in enhancing the firmographic search experience, catering to diverse user needs in the ever-evolving domain of business data retrieval.

The invention discloses a file retrieval method, device and equipment based on multi-modal AI and a storage medium, relates to the technical field of data retrieval, and aims to solve the problem that traditional file retrieval is low in efficiency and precision. The method comprises the following steps: performing content analysis on a multi-modal file including a picture file, a video file and a document file of text and / or visual information, and extracting a text semantic feature, a picture visual feature and a video time sequence feature; mapping the text semantic feature, the picture visual feature and the video time sequence feature to a cross-modal semantic space used for representing a high-dimensional vector associated with different modal features, and generating a cross-modal association vector; according to the modal type of the retrieval information, a corresponding retrieval module in a mixed retrieval engine is called to conduct retrieval in a cross-modal semantic space, an initial retrieval result is obtained, and the mixed retrieval engine comprises a text retrieval module, a visual retrieval module and a cross-modal fusion module; and performing dynamic weight distribution sorting on the initial retrieval result, and outputting a target matching result.

The invention relates to the technical field of shipping ports, and discloses a port intelligent customer service method and device, computer equipment and a storage medium, and the method comprises the steps: receiving multi-modal question data sent by a target user through a large language model agent, and converting the multi-modal question data into to-be-analyzed question data in a preset data format; performing semantic analysis processing on the to-be-analyzed question data to obtain question field data of the target user; performing intention understanding according to the question field data and the question data to be analyzed by using the large language model agent to obtain intention prediction data of the target user; retrieving a pre-constructed port multi-modal knowledge base according to the intention prediction data, generating answer data of at least one modal by using a retrieval result, and returning the answer data to the target user; through the above mode, the user data service capability of the port intelligent customer and the communication efficiency of the port intelligent customer service are improved.

A Retrieval-Augmented Generation (“RAG”) framework may include an RAG vector data store with information about vector embeddings. An RAG pre-processing platform may access unstructured data and perform vector embedding to generate a vector embedding for unstructured data to be stored in the RAG vector data store. The RAG pre-processing platform may also access structured data from the knowledge base and create a summary and metadata about the structured data to be stored in the RAG vector data store. An RAG retriever platform may receive a user prompt from a user, perform vector embedding, and retrieve context-relevant information for unstructured and structured data by searching for similar embeddings in the RAG vector data store. An RAG reader platform may combine the context-relevant information with the user prompt and an RAG prompt to create a LLM prompt. A context-aware response is then output to the user.