212 results about "Semantic matchmaking" patented technology

The invention provides a knowledge and data fused medical content image-text generation system and method. Relates to the technical field of digital medical treatment and artificial intelligence. The multi-modal feature extraction module is used for extracting multi-modal features from the multi-modal data; the multi-modal feature fusion module is used for carrying out cross-modal dynamic fusion on the multi-modal features to generate cross-modal high-consistency fusion features; the model training optimization module is used for configuring a soft and hard target loss function and a parameter optimizer; the information analysis and reasoning module is used for carrying out semantic matching on the fusion features, the disease classification and the clinical path based on a dynamic medical knowledge network, and generating disease prediction probability distribution and a semantic reasoning path; and the image-text report generation module is used for generating an image-text report containing a diagnosis conclusion and an interpretation basis according to the prediction probability and the semantic reasoning path. According to the invention, intelligent and precise generation of medical contents can be realized.

The invention discloses a method for constructing a cross-modal knowledge graph, and relates to the technical field of knowledge graphs, and the method comprises the steps: obtaining multi-source heterogeneous data, and carrying out the collection, structural analysis, modal recognition and classification, cleaning and standardization processing, so as to form structured multi-modal data; extracting entities and relationships of the identified modals from the structured multi-modal data, and summarizing the entities and relationships to form a multi-modal knowledge element set; mapping different modal entities in the multi-modal knowledge element set to a unified semantic space, and generating a unified entity relationship set through semantic matching, alignment and fusion; and normalizing the data into knowledge triples, and storing and organizing the knowledge triples through a graph database to form a cross-modal knowledge graph. According to the method, the problems of difficulty in multi-modal heterogeneous information alignment and difficulty in entity relationship extraction can be relieved, semantic association is enhanced, and knowledge graph integrity and multi-scene adaptability are improved.

The invention relates to a government affair file information extraction and question answering method and device and a medium, and the method comprises the steps: carrying out the entity extraction of a government affair file through employing a BERT-CRF joint model, and obtaining a structured entity set; performing relation extraction on the structured entity set to generate a semantic relation set between the entities; constructing a knowledge graph according to the structured entity set and the semantic relationship set, storing entity nodes into a graph database, and storing an embedded vector of an entity text into a vector database; when a query request of a user is received, relation query of the graph database and semantic retrieval of the vector database are carried out, sub-graph structures and semantic matching vectors related to query are extracted, and a mixed retrieval result is obtained; and inputting the mixed retrieval result into a large language model, and generating a question and answer response text conforming to a preset format by applying a dynamic prompt template. According to the method, the document processing efficiency and accuracy are effectively improved, and a solid technical support is provided for intelligent management of government affair documents.

The invention discloses an elderly disease health question and answer method based on multiple agents and a knowledge graph, and belongs to the crossing field of artificial intelligence and medical information technologies. Structured representation of medical knowledge is realized by constructing a multi-modal medical knowledge graph and through multiple entities and association relationships thereof. In combination with a domain customized large language model and a multi-agent dynamic routing mechanism, a two-stage training strategy is adopted to optimize a base model, including all-parameter pre-training and parameter efficient fine tuning based on a low-rank decomposition technology, so that the professionality and reliability of the model are effectively improved. In a multi-agent collaborative architecture, the system dynamically selects an optimal processing path according to input characteristics, including knowledge graph query, professional answer generation or external knowledge retrieval, and maintains context continuity of multiple rounds of conversations through a dynamic abstract compression algorithm. According to the method, the multi-agent collaborative architecture is combined with the knowledge graph reasoning ability, and the advantages of relation query and semantic matching are integrated through the mixed retrieval strategy.

The embodiment of the invention provides an intelligent factory semantic decision generation method and system based on a knowledge graph, and the method comprises the steps: obtaining an operation data set of an intelligent factory, carrying out the semantic feature extraction of the operation data set, and generating the target semantic feature of an equipment operation parameter and the context correlation feature of a semantic description text; and based on a pre-constructed knowledge graph structure, performing dynamic semantic matching processing on the target semantic features and the context association features, generating a semantic decision instruction set corresponding to the equipment operation parameters, generating an equipment control strategy set according to instruction priorities and instruction execution conditions in the semantic decision instruction set, and sending the equipment control strategy set to a server. And feeding back the equipment control strategy set to a control system of the intelligent factory to trigger operation optimization operation, and updating a semantic node association relationship in the knowledge graph structure. According to the method, the problem of fragmentation of equipment operation state representation is effectively solved, and the interpretability of feature extraction is improved by utilizing a collaborative verification mechanism of numerical parameters and text description.

The invention discloses a code fingerprint-based open source component identification and vulnerability detection method, which comprises the following steps of: receiving a local or remote code, extracting a difference file, generating an AST and constructing a code attribute graph; sHA-256 Hash fingerprints and GNN semantic fingerprints are calculated for the function level sub-graphs to form mixed fingerprints, accurate matching is conducted through a Bloom filter, semantic matching is completed through nearest neighbor, and a component version is determined through a distribution difference algorithm. The method comprises the steps that firstly, a component identifier is mapped into a PURL or an SWID, an OSV / NVD library is inquired to obtain a CVE, comprehensive risks are calculated in combination with CVSS, EPSS and dependency depth, and an SBOM and a vulnerability report conforming to CycloneDX or SPDX are output. The method is high in speed and high in accuracy, and the open source risk can be automatically treated in continuous integration.

Techniques for multimodal document retrieval are disclosed herein. Multimodal documents that include both textual and graphical components are retrieved from a knowledge base by a multimodal retrieval augmented generation (RAG) agent in response to a query. The documents and / or components or chunks thereof are retrievable by the RAG agent from the knowledge base using the semantic summaries and / or vector search of embeddings in the knowledge base that are generated from text extracted from processing non-textual components of the data. The RAG agent classifies the query type to determine whether to use a semantic match for text or image summaries, full text semantic search, vector cosine similarity search, and / or other multimodal vector search. The RAG agent performs types of searches selected based on the modality used to generate the response to the query.

The invention relates to the technical field of intelligent recommendation, and discloses an AI intelligent matching method based on a knowledge graph, and the method comprises the steps: constructing a quaternary knowledge graph containing a time dimension; identifying legal entities and semantic relationships by adopting an entity identification and relationship extraction technology; multi-level semantic features are extracted through a two-layer progressive semantic matching algorithm of a grammar layer, a semantic layer and a reasoning layer; constructing lawyer ability portraits based on a heterogeneous graph neural network and a time sequence perception graph convolution technology; progressive matching calculation is adopted, the optimal matching weight is learned through a multi-layer attention mechanism, and dynamically optimized intelligent matching is achieved. The technical problems of cold start, insufficient semantic understanding ability and poor timeliness processing ability in the existing legal consultation matching system can be solved, and the matching precision and the user satisfaction are improved.

The invention relates to a knowledge graph enhanced multi-modal file retrieval method, and belongs to the field of artificial intelligence and multi-modal information retrieval. The invention aims to solve the problems of multi-modal information semantic segmentation, weak semantic reasoning ability and low semantic matching precision in the existing multi-modal archive resource retrieval process. Through four stages of archive multi-modal data preprocessing and feature extraction, knowledge graph construction and enhancement, semantic retrieval request analysis and intention modeling, and multi-modal semantic matching and sorting, a semantic relationship is enhanced by utilizing a knowledge graph, so that the semantic relevancy and context consistency of a retrieval result are remarkably improved; a user is allowed to input and inquire in various forms such as texts, images and voices, and semantic extension retrieval is supported. According to the method, more efficient and accurate archive resource retrieval can be realized, and the user retrieval experience and archive knowledge utilization are improved.

The invention provides an elevator multi-mode crowd feature perception and intelligent advertisement putting method and system, and relates to the technical field of intelligent advertisement putting, and the method comprises the steps: collecting multi-mode perception data in an elevator through an edge computing terminal; performing feature decoupling on the data, performing cross-modal semantic alignment, establishing a directed association relationship between modals, and constructing a scene feature map; calculating the topology importance degree of map nodes, screening feature nodes, and extracting context information for semantic coding; mapping the scene semantic code and the advertisement audience semantic code to a two-dimensional coordinate system to construct a semantic matching graph, and extracting an optimal matching path to form a candidate set; predicting a scene evolution trend based on the scene characteristic spectrum evolution trajectory, and calculating an advertisement adaptive score to generate a playing sequence; putting and collecting user interaction data feedback according to the sequence to update the graph structure. According to the invention, accurate crowd feature recognition and advertisement dynamic matching are realized, and the advertisement putting efficiency and the user experience are improved.

Systems and methods are provided to improve data retrieval from a cache memory by using semantic matching to retrieve data from the cache memory. The system includes a two-tiered cache system, with a first tier implementing “key-value” pairs, and a second tier that includes a table that is configured as an artificial intelligence (AI) search indexed source. When a new input does not have a matching “key” at the first tier, the system performs a semantic search at the second tier of the cache to determine if relevant data is stored in the cache. The current systems and methods increase the likelihood of obtaining data for queries from the cache memory, reduce the response time to the queries, improve search consistency, reduce computing resource utilization, improve system performance, and reduce costs.

The invention discloses a bidding and tendering knowledge graph construction method and system based on OCR and NLP. The method comprises the following steps: collecting a multi-source bidding document, and converting an unstructured document into text data by utilizing OCR (Optical Character Recognition); key entities, relations and attributes are extracted through the NLP technology; constructing a knowledge graph with entities as nodes and relationships as edges, and generating entity node vectors; analyzing the bidding and tendering file, extracting text demands such as qualification requirements and scoring standards, and converting the text demands into demand vectors; performing semantic matching on the demand vector and the entity node vector, and calculating the similarity; and extracting high-similarity entities and attribute relationships thereof from the knowledge graph based on a matching result, and automatically filling and generating bidding document core chapters according to a preset template. The system can realize structured storage and intelligent reuse of bidding and tendering knowledge, and improves the bidding document compiling efficiency and quality.

The invention relates to the technical field of video conferences, and discloses a real-time document collaborative editing and synchronizing method in a video conference, which is used for solving the problem of insufficient concurrent editing conflict solving mechanism in the traditional method. The method comprises the following steps: firstly, receiving document editing operations submitted by a plurality of participants through a terminal, and establishing an operation sequence; comparing the timestamps with the position information, identifying concurrent operation groups as conflict sets, and arranging the conflict sets according to a time sequence; types and contents are extracted from the conflict set, semantic matching is carried out, matching operation is converted into a compatible sequence, and subsequent position offset is adjusted; distributing the compatible sequence to a synchronous buffer area, updating a server document state, and generating an incremental update package; a packet is pushed to the online terminal, application changes are buffered locally, and sequence playback is provided for a new participant; the monitoring terminal confirms the signal, re-deduces the unconfirmed change, and circularly checks the Hash consistency; according to the method, conflict resolution is optimized through semantic matching and offset adjustment, and efficient synchronization is achieved.

The invention relates to the technical field of packaging image processing, and provides a customizable cultural and creative packaging design system based on AIGC, and the system comprises the following modules: a data input module which is used for receiving cultural and creative themes, cultural element preferences and packaging specification parameters inputted by a user; the culture element database is used for storing multi-dimensional culture symbol data including patterns, color pedigree, historical allusions and semantic association rules; and the AIGC generation engine is used for extracting features from the culture element database according to the input parameters based on a generative adversarial network (GAN) and a Transform model, and generating an initial design draft. 10 versions of high-quality design schemes can be generated within 5 minutes through an AIGC engine, the speed is increased by 8 times compared with a traditional process, meanwhile, the modification feedback period is shortened to 2 hours through AR real-time preview, the user satisfaction degree reaches 94%, the pattern semantic matching accuracy is improved to 89%, the historical allusion misuse rate is reduced to 3% or below, and the design efficiency is improved. And the material waste rate of the automatically generated printing file is reduced to 12%, and the delivery cycle is shortened by 40%.

The invention provides a data quality rule automatic matching method, system and device for public security service data, and belongs to the technical field of computer information processing. The method comprises the following steps: structurally analyzing a public security data element standard document; constructing a basic rule and an association rule according to an analysis result; constructing a quality rule knowledge graph, creating four types of entity nodes including data element nodes, rule nodes, standard nodes and synonymous name nodes, and establishing three types of relation edges including a dependency relation, a synonymous name relation and a reference relation; extracting multi-modal features of the to-be-retrieved field, wherein the multi-modal features comprise semantic features and structured features; a rule is generated through a hierarchical matching engine, and matching processes of an accurate matching layer, a semantic matching layer and a statistical matching layer are executed in sequence; and executing online optimization. According to the method, the bottleneck problems of insufficient rule coverage, semantic understanding deficiency, poor dynamic adaptability and the like of a traditional method are solved, and the method has remarkable technical progress and practical value.

The invention discloses an AI-based digital project performance evaluation data processing method and system, and relates to the technical field of artificial intelligence and digital project management, and the method comprises a multi-source data collection module which is used for obtaining data; the entity alignment module is used for establishing an association relationship among cross-system data entities; the dynamic index generation module is used for dynamically adjusting the evaluation index weight by utilizing a reinforcement learning framework; the efficiency prediction module is used for modeling a task dependency relationship according to the time sequence diagram convolutional network and outputting a delay risk probability; and the visual interface is used for displaying the data. According to the method, cross-system entity alignment, dynamic index weight adjustment, task delay risk prediction and root cause analysis are realized through AI technologies such as BERT semantic matching and a graph neural network, visual data display and interaction functions are provided by virtue of a visual interface, the problems of data dispersion, index static state, risk lag and the like in traditional project evaluation are solved, and the project evaluation efficiency is improved. And the decision support capability of project management is improved.

The invention discloses a large model calling system and method based on an MCP tool chain, and the method comprises the steps: receiving user input, and carrying out the preliminary matching of tools: querying whether the user input contains an entity in an entity database or not through a fuzzy matching technology, and carrying out the preliminary screening of a plurality of candidate tools based on a matching result, so as to obtain limited to-be-selected tools; user input is converted into semantic vectors, semantic matching is conducted in a vector database, and semantic similarities of all tools to be selected are ranked through a similarity calculation method; extracting key information in user input; abstracting the user input into a specified format; and matching the specified format with the historical workflow template, and if the historical workflow template is matched, replacing the key information to a corresponding position of the historical workflow template by adopting a structure-preserving parameter replacement algorithm to realize workflow reuse. According to the method and the device, a complete technical link from accurate tool screening to intelligent workflow multiplexing can be realized.

The index query method based on the LLM comprises the following steps: obtaining metadata of an index to be queried, and constructing a training sample set for performing fine tuning training on a basic LLM based on the metadata; the training sample comprises a structured query description information sample constructed based on metadata; the user question sample is matched with the query semantics of the query description information sample; performing fine tuning training on the basic LLM based on the training sample set to obtain a service LLM; the service LLM is used for converting a user question input by a user into the structured query description information matched with the query semantics of the user question; converting a user question input by a user and used for initiating query into the structured query description information matched with the query semantics of the user question on the basis of the service LLM, and querying for a target index stored in a database on the basis of the query description information; wherein the target index is an index specified by the user in the user question.

The invention relates to the technical field of industrial communication protocol analysis, in particular to a protocol semantic matching analysis method based on reinforcement learning, and the method comprises the steps: constructing an industrial interaction environment containing industrial control equipment, a communication module and a protocol simulation assembly, defining a state space containing a flow attribute and a communication state, field processing, semantic annotation and dependency configuration related action space are carried out; calculating a reward value through a multi-level reward function of dynamic weight adjustment; based on the preprocessed multi-source protocol data set, adopting a Transform and graph neural network dual model to pre-train and generate a template library, and embedding an intelligent agent state processor; the intelligent agent updates the strategy after state processing, action distribution output by the strategy network, verification and execution of the action executor and interaction with the environment; and storing the interaction experience for iteration, and finally outputting protocol structure analysis, field classification and abnormity prompt information. According to the method, low-manual-dependence automatic analysis, unknown protocol self-adaptive adaptation and high-precision semantic matching can be realized.

A flux system includes a memory and a processor in communication with the memory and a sensing device, the memory storing a plurality of capabilities and a plurality of semantic fluxes associated with the plurality of capabilities. The computing system is configured to infer a semantic based on received inputs and to infer an activity interest semantic based on an input, and to assign at least one augmentation servicing agent to service an activity interest based on semantic matching.

A variety of smart inference systems and robotic control devices are disclosed. A device networking system interconnects mobile devices, robotic devices, and others. A robotic controller secures and manipulates a controlled device user interface. A smart device system interprets data based on a plurality of models operating on a plurality of runtimes based on a plurality7 of capabilities. A smart payment processor system assigns payments accounts automatically as a function of a semantic matching between one or more inferred semantic identities. A robotic emulation device captures and analyzes a video signal from a target device and transmits manipulation signals emulating a peripheral input device. A system of carts are physically coupled in a charging configuration. A conveyor is configured to couple with another conveyor and having sensors to detect weight, size, location or other aspects of conveyed items, and implementing semantic analysis for management and augmentation.

The invention discloses an industrial control equipment vulnerability identification method and system based on a double-BERT model, and the method comprises the steps: an asset analysis stage: analyzing the flow to generate structured asset data; in the semantic matching stage, a multilingual Sension-BERT model is used for extracting vectors and matching a vulnerability library; in the version identification stage, the BERT is finely adjusted to judge the influence condition of the version; and in the vulnerability confirmation stage, an identification result is manually rechecked. The system comprises an asset processing subsystem and a vulnerability intelligent identification subsystem, and the asset processing subsystem comprises a flow analysis module, an equipment fingerprint identification module, an asset information management module, a scanning task scheduling module and a vulnerability display and screening module. And the vulnerability intelligent identification subsystem comprises a semantic vector matching module, a version matching judgment module and an identification result output module. According to the method, the problems of high asset isomerism, weak semantic matching capability and low version comparison accuracy in the current industrial network can be solved, and the vulnerability identification quality and efficiency are improved.

The invention provides a database anti-ransomware method and device based on user SQL (Structured Query Language) feature behavior analysis, and relates to the technical field of database security. The method comprises the following steps: constructing an SQL data set by utilizing a multi-source data aggregation algorithm, a real-time and historical combination algorithm and a data cleaning and checking algorithm; extracting SQL statement structure features according to BERT and a semantic matching method; according to the features, training a user SQL behavior model based on a Transform; in the real-time monitoring stage, a monitoring engine captures a new SQL operation in real time, rapidly extracts features and inputs the features into the trained behavior model for evaluation. Once the deviation between the operation and a normal behavior model exceeds a preset threshold value and suspicious behavior modes such as data encryption, large-scale data deletion or abnormal high-authority operation similar to ransomware are presented, an alarm mechanism is triggered immediately, and a preset protection strategy is executed automatically, so that data loss and business influence are reduced to the greatest extent.

Semantic matching between a source screen or source data and a target screen using semantic artificial intelligence (AI) for robotic process automation (RPA) workflows is disclosed. The source data or source screen and the target screen are selected on a matching interface, semantic matching is performed between the source data / screen and the target screen using an artificial intelligence / machine learning (AI / ML) model, and matching graphical elements and unmatched graphical elements are highlighted, allowing the developer to see which graphical elements match and which do not. The matching interface may also provide a confidence score of the individual matches, provide an overall mapping score, and allow the developer to hide / unhide the matched / unmatched graphical elements. Activities of an RPA workflow may be automatically created based on the semantic mapping that can be executed to perform the automation.

The invention relates to an intelligent identification and import method for a building industry communication project contract scanning copy. The method comprises the steps of obtaining a target template and a to-be-processed contract scanning copy, preprocessing the contract scanning copy, performing structured analysis on the target template, extracting a field standard value, marking a field type, and generating a field type mapping table; inputting the preprocessed contract scanning copy into a multi-modal feature analysis model to obtain a contract information extraction set; performing semantic matching on the contract information extraction set and a field type mapping table to generate a field matching result, and inputting successfully matched contract information into a target template; and if the necessary fields are missing, retrieving associated historical information from the dynamic knowledge base through an entity identification and link technology, obtaining contract information of missing field mapping, and inputting the contract information of missing field mapping into the target template. According to the invention, the recognition accuracy is improved, and the labor cost is reduced.

The invention provides an intelligent recommendation method and system for scientific and technological achievement transfer, and the system comprises a data processing module which is used for collecting and preprocessing achievements, enterprise demands, and third-party data; the knowledge graph module is used for constructing a knowledge graph through entity recognition and relation extraction and supporting incremental updating; the intelligent recommendation module is used for calculating the semantic association degree, the semantic matching degree and the collaborative filtering degree, generating a comprehensive recommendation score through weighted fusion and carrying out sorting recommendation on the scientific and technological achievements; and the feedback and optimization module is used for collecting user feedback scores to dynamically optimize weight distribution. According to the method, the knowledge graph and multi-index fusion are utilized, recommendation accuracy and adaptability are improved, and scientific and technological achievement transfer is promoted.

The invention discloses an automatic artistic advertisement design method and system based on AI, and relates to the technical field of advertisement design, and the method comprises the steps: firstly, extracting visual features and text features from historical advertisement materials, constructing a creative gene vector, and building a creative feature map based on the visual features and the text features; secondly, a creative popularity parameter is calculated by utilizing user interaction data, and weighted optimization is carried out on the advertisement creativity by using real-time feedback information; secondly, semantic matching is carried out on the target product features and the creative gene vectors, and a transportability score is calculated through corrected semantic relevancy and historical validity data; and finally, the most suitable creative gene is selected, content fusion and rendering are carried out, and final advertisement content is generated. Based on dynamic optimization of creativity popularity and user feedback, the real-time performance and accuracy of the advertisement effect are improved, the cold start problem of new product advertisement creativity is effectively solved, and cross-platform and cross-domain creativity migration and adaptation can be achieved.

The invention discloses an automatic debugging system and method for deep learning compiler numerical errors, and the system comprises a model analysis module, a semantic matching module, a tracking module and a verification module, the analysis module receives and analyzes a defect model with compilation errors, carries out the extraction of the input of the defect model, sub-functions, and operators of each sub-function, and carries out the verification of the compilation errors. Constructing a symbolic calculation graph and an index table before and after optimization of the defect model; the semantic matching module performs hash processing on each node in the symbolic calculation graph, matches equivalent nodes before and after optimization by comparing the approximation degree of hash values between the nodes, and generates a matching relationship between the nodes and the equivalent nodes; the tracking module compares the data streams of the models before and after optimization according to the matching relationship, generates an error cumulant diagram by tracking the generation and propagation process of errors, and locates the modes causing the errors and compiler optimization transformation causing mode rewriting; and the inspection module inspects the correctness of the positioning result and finally outputs root information after inspection. According to the method, each computing node of the neural network model is quantified into a hash value capable of measuring the distance, compared with an existing positioning technology, the method can better adapt to the scene of a complex deep learning model, and compared with an incremental debugging technology, the error positioning speed and accuracy are remarkably improved.

The invention relates to the technical field of computers, discloses a production command case-oriented interactive deduction method and system, and aims to solve the problems of static solidification of deduction cases, low degree of freedom of interaction, disjunction with real-time production data and low case construction efficiency in the prior art. The method comprises the following steps: constructing a digital twinborn model synchronized with a physical environment in real time; receiving an event injection or parameter adjustment instruction input by a user; driving a multi-agent simulation engine to simulate production process evolution based on the instruction, and generating process simulation data; analyzing the simulation data and quantifying the influence of the decision on the production indexes to form a performance evaluation report; searching a semantic matched historical case knowledge graph, and extracting a disposal plan; according to the scheme, scientific deduction and intelligent assistance of production command decisions are realized, emergency response efficiency is improved, precipitation and reuse of command experience are promoted, multi-user collaborative deduction is supported, and team decision collaborative ability is enhanced.

Techniques for multimodal document retrieval are disclosed herein. Multimodal documents that include both textual and graphical components are retrieved from a knowledge base by a multimodal retrieval augmented generation (RAG) agent in response to a query. The documents and / or components or chunks thereof are retrievable by the RAG agent from the knowledge base using the semantic summaries and / or vector search of embeddings in the knowledge base that are generated from text extracted from processing non-textual components of the data. The RAG agent classifies the query type to determine whether to use a semantic match for text or image summaries, full text semantic search, vector cosine similarity search, and / or other multimodal vector search. The RAG agent performs types of searches selected based on the modality used to generate the response to the query.