444 results about "Semantic relevance" patented technology

The embodiment of the invention discloses a retrieval generation method and device based on a large language model and a knowledge graph. The problems that retrieval semantic relevance is weak, and recalled data lacks global knowledge can be solved. The method comprises the following steps: performing vector analysis on a plurality of preset domain documents, and constructing a vector database; constructing a knowledge graph according to the entity description information and the relationship description information extracted from the plurality of field documents, and storing the knowledge graph into a graph database; performing feature extraction on a target question input by a user to obtain a multi-dimensional keyword corresponding to the target question; retrieving the multi-dimensional keywords through a preset search engine, a vector database and a graph database to obtain a target retrieval result; and determining target input information according to the target retrieval result, and inputting the target input information into the target language model to obtain target output response information.

The invention relates to the technical field of voice processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a voice intention recognition method, device and equipment and a medium, and the method comprises the steps: obtaining a to-be-processed voice signal, carrying out the voice activity detection processing of the voice signal, dividing the voice signal into a plurality of voice segments, analyzing semantic contents of the plurality of voice segments, determining semantic correlation information of each voice segment, analyzing sound source attributes of the plurality of voice segments, determining sound field type information of each voice segment, screening out a target voice segment from the plurality of voice segments according to the semantic correlation information and the sound field type information, and executing intention recognition processing based on the target voice segment to generate an intention recognition result. According to the invention, through a dual analysis mechanism of semantic correlation information and sound field type information, effective screening of voice segments is realized before voice recognition, non-target voice or interference segments are effectively prevented from being sent to an intention recognition model, and the accuracy of a recognition result is improved.

The invention relates to the technical field of power plant management and control, and discloses an intelligent power plant management and control system based on the Internet of Things, and the system comprises the steps: when a plurality of labels related to the same equipment or parameter exist in different subsystems, according to a similarity index between the labels and an equipment association relationship; on the basis of the detected conflict label group, combining equipment historical operation and maintenance data and an upstream and downstream parameter flow relationship, constructing a label semantic evolution graph, and performing reasoning analysis on label equipment through a fusion rule engine and a graph neural network; through mapping knowledge domain fusion and semantic embedding comparison, matching and clustering among conflict labels are completed based on structural similarity and semantic relevancy, and a label alignment rule is constructed; according to a label coordination result, designing a mapping rule of a data field; and performing inter-system synchronous verification on a result after structure conversion and label standardization processing, and writing a standardized label into a unified semantic database. The method has the advantage of improving data semantic consistency.

The invention discloses a retrieval method and device based on a knowledge graph, computer equipment and a storage medium. The method comprises the steps that the knowledge graph is acquired, a node relation vector is output through a preset relation learning module, nodes related to user query content are retrieved from the knowledge graph, and an external knowledge representation result is generated; performing feature fusion on the user query content and the external knowledge representation result by using the correlation matrix to obtain a fused feature vector; performing image-text bidirectional semantic alignment on the fused feature vector based on an attention mechanism to generate a joint feature vector; and performing matching calculation based on the joint feature vector to obtain a matching score for representing the semantic correlation degree. According to the method, the user query content and the generated external knowledge representation result are fused through the correlation matrix, image-text semantics are aligned through an attention mechanism, the joint feature vector is generated, the matching score is calculated, and cross-modal retrieval is realized by using the matching score, so that the information retrieval accuracy of the community property management system is greatly improved.

The invention relates to the technical field of artificial intelligence security, and discloses a multi-modal large model confrontation security detection method and system, and the method comprises the steps: obtaining initial multi-modal data from original data sources, such as images, texts and audios, simulating the behavior of an attacker through reinforcement learning to generate a confrontation sample, and generating a cross-domain confrontation sample through transfer learning, extracting a multi-modal feature vector; the modal weight is dynamically adjusted through an attention mechanism based on the feature vector, the vulnerable modal weight is reduced, the credible modal weight is enhanced, inconsistency between abnormal modals is recognized in combination with disturbance analysis, and a weighted feature vector is output; abnormal input is detected by comparing semantic relevance of different modes, if semantic conflicts are found, an alarm is triggered, input is refused, and a corrected feature vector is output; and dynamically closing the untrusted mode and enhancing the trusted mode based on the semantic verification result, and outputting a final security detection result. According to the invention, the security of the multi-modal large model in a complex attack environment can be improved.

The invention relates to the technical field of intelligent retrieval, in particular to an enterprise RAG intelligent question answering system based on vector database reordering. The system specifically comprises: a document recall module, which retrieves a vector database to obtain candidate document blocks containing business metadata; the comprehensive scoring module is used for calculating a semantic correlation score by adopting a later-stage interaction architecture based on bidirectional token importance weighting, performing path semantic matching and context sensing rule evaluation according to a preset metadata ontology graph to obtain a service attribute score, and analyzing the evidence sub-graph to obtain a fact path score; fusing the semantic correlation score, the service attribute score and the fact path score to generate a comprehensive correlation score; and the sorting output module performs optimization resorting based on a preset punishment mechanism and the comprehensive correlation score to generate an optimized context set, and calls a generation model to output answers based on the optimized context set. According to the method, semantic accuracy, business compliance and fact reliability can be considered, and more trustworthy high-quality enterprise-level answers can be generated.

The invention provides a video plot generation and scene synthesis method and system based on natural language processing, and relates to the technical field of video generation, and the method comprises the steps: receiving a script text to construct a multilayer scene map, extracting keywords to calculate semantic relevancy, executing feature decomposition and reconstruction to obtain a scene synthesis vector, and generating a video scene based on distance measurement. And extracting spatio-temporal features by using the feature pyramid, executing adaptive feature fusion, segmenting the video by applying a self-attention mechanism, and after a transition effect is inserted, performing style migration to output a finished video.

The present disclosure leverages self-supervised learning to generate positive and negative question-context pairs, enabling the model to learn robust representations. This process involves data augmentation techniques to create variations of the original questions and contexts while preserving semantic relevance. A large corpus of unlabelled text data containing questions and their corresponding contexts, ensuring diversity and representativeness across various topics are used to train a self-supervised Large Language retrieval model.

The invention discloses a dialogue memory priority system based on multidimensional weighting, and the system comprises the following modules: a multi-factor scoring module which is used for calculating the importance score of dialogue memory, and multiple factors comprise a time attenuation factor, a semantic correlation factor, an emotion intensity factor and a user feedback factor; the hierarchical storage decision module is used for distributing memories to corresponding storage hierarchies according to importance scores; the dynamic weight adjusting module is used for automatically adjusting the weight of each factor according to the dialogue mode; and the memory retrieval priority ranking module is used for determining the ranking of the retrieval results based on the multi-dimensional scores. According to the invention, core information and secondary information can be distinguished conveniently, and waste of memory resources is avoided; key information of the dialogue context is captured more accurately, so that the memory retrieval efficiency and the system performance are optimized; and the method adapts to different dialogue scenes and user interaction modes.

The invention relates to the technical field of data processing, and discloses a multi-protocol fusion Internet of Things equipment intelligent gateway data conversion method and system. The method comprises the following steps: collecting a multi-protocol equipment data packet, and extracting protocol features to construct a vector library; protocol types are identified based on the vector library, data are analyzed, and a data object set containing semantic tags is constructed; semantic correlation is analyzed through an adaptive learning algorithm, and a dynamic protocol semantic mapping matrix is established; converting the data into a standard format according to the mapping matrix and recording a matching degree to form a target data pool; and extracting fusion data from the data pool, recoding according to a target protocol format, and outputting a data frame. The problem that an existing multi-protocol fusion data conversion method lacks protocol semantic understanding and self-adaptive learning ability is solved, and semantic consistency and conversion quality of data conversion among multi-protocol equipment are improved.

The invention relates to the technical field of customer service quality inspection, in particular to an online customer service intelligent quality inspection system and method based on artificial intelligence. And automatically extracting a user demand keyword, an emotion expression keyword and a potential violation term keyword, and generating a structured keyword sequence. Performing emotion analysis on each dialogue round through a Transform model, and accurately outputting a customer emotion classification and an intensity score; and semantic correlation of the context is carried out through a neural network model. And automatically identifying the content of each round of dialogue and counting illegal verbal skills. Furthermore, the customer emotion value, the context coherence score and the occurrence frequency of violation verbal skills are input into a quality inspection scoring model, a comprehensive quality inspection score is automatically calculated, and whether the service is qualified or not is judged according to the comprehensive quality inspection score, so that the dynamic evaluation of the service quality is realized, the quality inspection efficiency is improved, and the customer experience is truly reflected.

The invention provides a cross-domain multi-dimensional query feature index selection method, which comprises the following steps of: judging a query field and a query purpose of a user through query subject classification and query intention identification according to historical query behavior data of the user, and obtaining a field tag and an intention tag of the query; a navigation path of knowledge links is optimized, semantic correlation between nodes and small world index algorithm characteristics are comprehensively considered, an optimal knowledge navigation path is recommended for a user, and an optimization strategy is dynamically adjusted according to query historical behaviors and feedback of the user; a knowledge navigation result is presented in a visual interaction mode, a multi-dimensional attribute screening and sorting function is provided, exploration type browsing and deep mining of a user are supported, and personalized knowledge recommendation service is provided according to a query scene and preference of the user.

The invention provides a sensitive data security compliance processing system and method. The sensitive data security compliance processing method comprises the steps of obtaining sensitive data to be accessed; setting a plurality of sensitivity levels through semantic relevance; determining the information sensitivity of each piece of text information in each sensitivity level, and extracting a privacy risk value when a user accesses the text information in different sensitivity levels through all the information sensitivity; acquiring a historical access record, generating a behavior deviation mode of a user through the historical access record, and determining behavior credibility when the user accesses text information in different sensitivity levels according to the behavior deviation mode; and performing dynamic risk assessment according to all the behavior credibility and all the privacy risk values to obtain a plurality of risk situation values, and adjusting a security processing strategy when the user accesses the sensitive data in real time through all the risk situation values. By adopting the scheme of the invention, the sensitive data can be dynamically processed based on the behavior analysis of the user.

The invention provides a retrieval method and device based on document segmentation and a document retrieval system. The method comprises the following steps: acquiring a to-be-segmented document; based on an NLP algorithm, calculating the semantic similarity between the partial texts of the to-be-segmented document to obtain a first semantic relevancy; according to the first semantic relevancy of all the partial texts, the document to be segmented is segmented, a plurality of semantic text blocks are obtained, and each semantic text block comprises at least one partial text; under the condition that a query request is received, calculating semantic similarity between a query text corresponding to the query request and each semantic text block based on an NLP algorithm to obtain a plurality of second semantic relevancy, and determining the semantic text block with the highest second semantic relevancy of the query text corresponding to the query request as a target semantic text block, and displaying the target semantic text block in a display interface. According to the scheme, the problem that in the prior art, the accuracy rate is low during text retrieval is solved.

The disclosure relates to systems and methods of automatically generating unique content including natural language text based on a corpus of previously generated response documents and discrete requirements defined in a requirements specification. The system may use generative stitching that includes multi-layer processes that execute to influence the generation of unique content including natural language text through an artificial intelligence (AI) language transformer model trained to output the content based on previously written material that is semantically relevant to the discrete requirements and is weighted against labeled attributes. The labeled attributes may determine the influence asserted against the language transformer, thereby generating unique on-target content that may be combined to create a computer-generated response document.

The invention discloses a large model-based government affair official document quotation retrieval method, device and equipment and a medium, and relates to the technical field of artificial intelligence, the method comprises the following steps: obtaining a government affair official document, determining a document slice corresponding to the government affair official document, and creating a document knowledge base and a vector knowledge base based on the document slice; identifying the terminologies in the government affair document by using a target pre-training natural language processing model to obtain a corresponding identification result; based on the document knowledge base and the vector knowledge base, performing keyword matching on the recognition result by using a target information retrieval algorithm to obtain a plurality of corresponding candidate quotation documents; and performing semantic correlation scoring on each candidate citation document and the recognition result through a Rianker model, reordering each candidate citation document according to a corresponding scoring result, and outputting a target citation document according to a corresponding reordering result. Therefore, the quotation retrieval efficiency can be improved.

The invention relates to the technical field of wireless high-definition audio and video transmission, and discloses a plug-and-play wireless high-definition audio and video transmission method and system.The method comprises the steps that foreground space texture features and audio and voice segments are obtained, and an initial feature set containing space and semantic features is obtained through semantic correlation analysis; a unified representation vector is output through multi-modal feature fusion, and a dynamic importance score is obtained by determining time sequence consistency, calculating an importance weight and performing normalization; and when the score exceeds a threshold value, segmenting the video frame in real time to determine an attention focus area and optimize a boundary, thereby generating an attention weight matrix, preferentially allocating bandwidth and forming a partition differentiation compression result. And in combination with a network bandwidth state, protection is enhanced for a key stream, the priority and the bit rate are dynamically adjusted, and high-quality audios and videos are output through decoding and recombination. According to the invention, the bandwidth allocation and compression strategy can be dynamically optimized, the transmission quality of a key area is guaranteed when the bandwidth fluctuates, and the audio and video transmission efficiency and experience are improved.

The invention provides a fluororubber field knowledge graph construction method based on a large language model, and the method comprises the steps: obtaining process related knowledge data of the fluororubber field, and carrying out the classification, and obtaining the classification knowledge data of the fluororubber field; based on a preset ChatGLM model, performing knowledge extraction according to the fluororubber field classification knowledge data to extract key entities and relationships; carrying out deep analysis on the key entity and the relationship by utilizing semantic correlation and context association analysis, and enhancing the connectivity of the key entity and knowledge in the relationship through semantic similarity and association strength to obtain fused knowledge data; explaining and expanding the fused knowledge data by using a ChatGLM model to obtain expanded triple data; and performing knowledge fusion on the triple data, and performing graph storage by using a Neo4j database to obtain a knowledge graph. According to the method, the knowledge graph construction efficiency is remarkably improved through automatic knowledge extraction and processing of the large language model, and the time cost of manual labeling and data processing is reduced.

The invention relates to the technical field of causal reasoning, and discloses a traffic event analysis method, device and equipment based on multi-hop causal path exploration, and the method comprises the steps: constructing a knowledge graph based on multi-source heterogeneous data in the traffic field; calculating the semantic correlation between each node in the knowledge graph and the question semantic vector, and taking the node with the highest semantic correlation as an initial reasoning node; and performing reasoning by starting from the initial reasoning node and combining a heuristic scoring function and a Monte Carlo tree search algorithm to obtain a multi-hop causal path. According to the method, a complex input problem can be effectively analyzed, rapid matching with the most relevant nodes of the problem is realized by utilizing the knowledge graph, a complex causal path is effectively identified and explored by combining chain reasoning and path optimization technologies, and the method is more efficient by combining a heuristic scoring function and a Monte Carlo tree search algorithm. And carrying out multi-dimensional event analysis and decision support on the basis of dynamic optimization. And the efficiency and accuracy of causal path exploration are remarkably improved.

The invention provides an intelligent manufacturing field big language model construction method fused with field knowledge distillation, and relates to the field of intelligent manufacturing, which collects intelligent manufacturing field knowledge and performs knowledge clearness and structured coding to obtain an intelligent manufacturing field knowledge graph, and synchronously selects a pre-trained big language model as a teacher model. And the intelligent manufacturing domain knowledge graph is combined to obtain an intelligent manufacturing domain teacher model. Then, intelligent manufacturing field task data is collected, and knowledge distillation training is performed on the student model in combination with the intelligent manufacturing field teacher model to obtain an intelligent manufacturing field student model, so that structured field knowledge is effectively integrated, and the structural characteristics and semantic relevance of the field knowledge are concerned; therefore, the semantic similarity of the soft tag and the supervision signal of the hard tag are considered, and generalization and domain specificity are balanced.

The invention discloses an ultrasonic mammary gland image segmentation method based on fine-grained guidance, relates to the technical field of image segmentation, and attempts to use a fine-grained guidance attention decoding diffusion model. The defects of weak association of foreground and background semantics, unbalance of global and local features, poor fine-grained feature capture capability and the like in a traditional segmentation method are overcome. The core design is as follows: a clear de-noised image is obtained by using a de-noising diffusion probability model; based on a context cross-attention decoding diffusion network guided by fine granularity and a contained self-adaptive detail guide attention module, image prior information is learned and fused, and semantic correlation between a foreground and a background is enhanced; a context decoding cross attention layer is added, global information of input features and complex correlation between channels are effectively captured, and the image segmentation precision and efficiency are integrally improved. A contrast experiment result on a related data set shows that compared with a similar popular method, the method disclosed by the invention obtains a better image segmentation effect.

The invention provides a private deployment-oriented multi-modal AI knowledge base construction system. The private deployment-oriented multi-modal AI knowledge base construction system comprises a knowledge storage module, an intelligent document loading module, a document partitioning engine module, a data enhancement engine module, a multi-language semantic vector alignment module and a private deployment module, the knowledge storage module comprises a knowledge authority management sub-module and a knowledge source management sub-module, the knowledge authority management sub-module is used for managing and storing knowledge from different sources, and the knowledge source management sub-module is used for managing electronic documents and multimedia documents; according to the method, intelligent identification, partitioning, vectorization and source file storage can be carried out on different types of electronic files, knowledge graph construction is carried out for specific fields, semantic relevance between texts and topics and key entities is fully considered, the method has wider applicability, higher robustness and controllability, the data leakage risk is effectively reduced, and the method is suitable for popularization and application. The method is suitable for enterprise sensitive data protection and personal user elastic computing power requirements.

The invention relates to the field of data processing, and discloses an intelligent data processing system based on an AI large model, which comprises the following steps: when detecting that a plurality of candidate entities exist in a text, judging whether the candidate entities need to be subjected to semantic disambiguation processing or not according to context semantic relevancy and entity historical reference frequency; constructing a cross-domain context representation vector, introducing a pre-training language model to encode the context, generating deep semantic representation of candidate entities, and judging whether a clustering result has ambiguity or not; whether the candidate entities have conflicts or not is judged by fusing language model embedding and structuring knowledge graph information; constructing an entity relationship graph based on a graph neural network, performing causality, temporal and attribute dependence reasoning on the relationship between the existing candidate entities, and judging whether the relationship between the candidate entities should be combined or split; and carrying out label replacement on the candidate entities in the target text in combination with the context and the reasoning result. The method has the advantage of improving the accuracy of data processing.

Computer-implemented systems and methods implement semantic search in high-dimensional vector spaces, specifically tailored for use with large language models (LLMs). In particular, clustering is combined with Euclidean distance measurements to facilitate real-time vector searches. By implementing clustering, the invention reduces the computational complexity and costs associated with Euclidean distance calculations, which are typically more resource-intensive than other methods such as cosine similarity. This reduction is achieved by limiting the scope of distance calculations to within clusters, thereby avoiding the inefficiencies and diminished accuracy otherwise encountered by existing systems when using Euclidean distance in high-dimensional spaces. As a result, the invention retains the benefits of Euclidean distance, such as its superior granularity and precision in measuring semantic relevance, without succumbing to the usual drawbacks of high computational demands and poor scalability.

The invention relates to the field of power question answering, in particular to a power knowledge question-answer matching method and system based on an improved retrieval enhancement generation technology, and the method comprises the steps: employing a training set to carry out the decomposition of a pre-constructed Embedding model, and carrying out the training of the Embedding model, and obtaining an Embedding fine tuning model; the method comprises the following steps: vectorizing a power field file by adopting an Embedding fine tuning model to obtain a power knowledge base, and constructing a regular expression rule base; the user consultation content is converted into a query vector, the query vector is matched with the regular expression rule base to obtain an accurate matching result, and meanwhile the query vector is matched with the power knowledge base to obtain a fuzzy matching result; semantic correlation sorting is carried out on the accurate matching result and the fuzzy matching result, and then resorting is carried out by adopting a resorter in combination with a service rule; and inputting the reordering result into a large language model to obtain a result corresponding to the user consultation content. The method can effectively solve the problem of inaccuracy during retrieval of a specific ID, and improves the retrieval precision of a retrieval enhancement generation system.

Methods and systems provide content searching and retrieval using generative artificial intelligence (AI) Models. The system is configured to receive a user search for content, media or item listings. The system receives a natural language-based input associated with a client device of a user. The system generates a search criterion for the received natural language-based input. The system, via the generative AI-bases search and retrieval system, generates a relevancy-ranked output listing of content items. The relevancy-ranked output listing content items responsive to the generated search criterion content items having an associated content identifier and a content description. The system causes portions of the relevancy-ranked output listing to be rendered at the client device of the user.

The invention belongs to the technical field of power equipment operation inspection auxiliary decision making, and particularly relates to a power operation inspection knowledge service method and device, equipment and a medium. The method comprises the following steps: analyzing semantics of query input of a user by utilizing a large semantic model, and determining a corresponding target sub-knowledge base in combination with an intention classification sample library and an intention set; performing sentence segmentation on the document of the target sub-knowledge base, generating document fragments containing continuous sentences through context supplementation, and enabling semantics of the document fragments to be overlapped; calculating a keyword correlation score of the query word and the document fragment; generating a text vector through a semantic coding model, and calculating a semantic correlation score of the query input and the document fragments; fusing the keyword correlation score and the semantic correlation score, and outputting each related document fragment according to a comprehensive score sequence; and inputting query input of a user and related document fragments into the large semantic model, and generating a structured answer in combination with the logic in the operation and inspection field of the power equipment. And the electric power operation and maintenance knowledge service with high accuracy and strong generalization is realized.

The invention relates to the technical field of information processing, and discloses a big data-based information matching management system, which comprises an acquisition module, an attribute analysis module, a multi-dimensional modeling module and a dynamic matching module. The acquisition module acquires a user behavior data set and extracts active information demand features; the attribute analysis module analyzes the real-time updating frequency, the semantic association degree, the historical matching success rate and the data topological structure characteristics of the target information resources; a multi-dimensional modeling module performs heterogeneous modeling on each feature to generate timeliness, semantic association, matching probability feature vectors and topological structure vectors; and the dynamic matching module generates an adaptation degree score and determines an optimal matching object through heterogeneous space projection and feature coupling operation. The system improves the accuracy, real-time performance and efficiency of information matching through multi-dimensional feature processing, topological structure analysis and a dynamic matching algorithm, and is suitable for an accurate information matching scene in a big data environment.

The invention provides a visual language navigation method and system based on a dynamic grid map, and belongs to the technical field of navigation. Comprising the following steps: extracting visual language features of an RGB panoramic image by adopting a CLIP model; combining the visual language features and the depth features of the depth image with the absolute coordinates of the grid units to construct a grid map; updating the grid map according to the semantic correlation between the grid unit and the navigation instruction; obtaining a cross-modal interaction feature corresponding to the grid map based on a cascade attention mechanism, and analyzing the cross-modal interaction feature by using a Mama module to output a predicted waypoint; taking DD-PPO as a local strategy, taking the predicted waypoint as input to carry out action analysis, and generating probability distribution; and selecting and executing a navigation action according to the probability distribution. According to the invention, on the basis of avoiding long-distance dependence, the dynamic continuous environment change can be quickly responded, so that accurate visual language navigation for the intelligent agent is realized.

The invention discloses a retrieval enhancement generation method for multi-source heterogeneous data fusion, and relates to the technical field of cross-modal retrieval generation, and the method comprises the following steps: collecting structured data and unstructured data, respectively carrying out standardization processing and semantic cleaning, and outputting a multi-source heterogeneous data set; constructing double-view knowledge block representation by using a multi-source heterogeneous data set, and respectively generating a retrieval view and a generation view; carrying out weight adjustment and fusion on the retrieval view and the generation view according to the current query content, and outputting a fusion semantic vector; performing approximate vector matching retrieval on the knowledge fragments by using the fused semantic vector, performing semantic reordering on a retrieval result according to semantic correlation, and outputting an ordered knowledge fragment set; according to the method, the context adaptability and semantic expression capability of the generated input are enhanced, so that the accuracy of knowledge retrieval and the quality and pertinence of text generation are remarkably improved.