66 results about "Semantic knowledge base" patented technology

The invention relates to a multi-class archive knowledge question and answer agent based on a large language model, and belongs to the technical field of artificial intelligence and archive information management. The intelligent agent comprises a data preparation module, a semantic knowledge base construction module, a perception and analysis module, a decision and matching module, an execution and retrieval module, a cognition and reasoning module and an interaction and generation module. According to the method, the complex query intention of the user can be accurately understood, automatic association and semantic reasoning across archive categories are achieved, and the semantic splitting problem of a traditional retrieval mode in cross-category and multi-level query is effectively solved.

The invention provides an attack link restoration method and system based on a large language model and a traditional AI model, and effectively solves the problem that attack link restoration is incomplete and inaccurate when a current attack link restoration scheme faces complex factors such as network address translation. The method comprises the following steps: acquiring an attack semantic knowledge base containing an attack context knowledge matrix, wherein the knowledge base is generated by processing an attack framework, a security log and a traceability report by a generative large model; obtaining an asset access relation graph generated by analyzing the metadata of the target network infrastructure by the large model and reasoning in combination with an attack semantic knowledge base; regularly acquiring security alarm logs in a first time window and scoring, and acquiring context logs if the security alarm logs exceed a threshold value; based on a time sequence diagram attention network model, determining the confidence degree that edges in an asset access relation graph corresponding to logs containing access pairs in the alarm logs and the context logs belong to attack links; and performing sub-graph extraction in the atlas to obtain candidate sub-graphs, and then determining a target attack link.

The invention provides a channel state information feedback enhancement method and system based on bidirectional channel correlation, and the method is executed at a base station end, and comprises the steps: receiving a downlink channel state information semantic vector which is coded, compressed and quantized by a user end through a deep joint source channel; carrying out inverse quantization on the semantic vector to obtain an intermediate semantic vector; based on the intermediate semantic vector, retrieving a related historical uplink semantic vector from an uplink channel semantic knowledge base; performing fusion enhancement on the intermediate semantic vector and the retrieval result through an attention mechanism to generate an enhanced semantic vector; inputting the enhanced semantic vector into a decoder, and outputting a reconstructed downlink channel state information matrix; wherein the encoder, the enhancement module and the decoder perform end-to-end joint training optimization by minimizing a reconstruction error. According to the method, the semantics is enhanced at the receiving end by utilizing the correlation of the uplink and downlink channels, the feedback precision and the system robustness are remarkably improved, and the method is suitable for a frequency division duplex system.

The invention discloses an intelligent data question and answer method and system based on index semantic enhancement, and relates to the technical field of intelligent data question and answer. The method comprises the following steps: acquiring a data analysis demand in a natural language form; the method comprises the following steps: performing information extraction on a data analysis demand by utilizing an index semantic enhanced large language model, generating a query statement according to the extracted information, recalling and rearranging most relevant indexes and elements in an index semantic knowledge base by utilizing a knowledge retrieval enhancement layer according to the data analysis demand and a historical session on the basis of mixed retrieval, and generating a query statement according to the query statement. Generating a prompt template according to a retrieval result of the index semantic injection layer by utilizing the index semantic injection layer, and performing semantic understanding reasoning according to the prompt template to obtain corresponding information; and checking compliance according to the query statement, and returning a query result. The method aims at achieving efficient mapping, dynamic modeling, intelligent SQL generation and optimization, authority safety management and control and multi-round intelligent interaction of natural languages and enterprise-level index assets.

The invention provides an entity pair guided scientific and technical literature document level relation extraction method and system, and the method comprises the steps: carrying out the entity recognition of an input scientific and technical literature document, and obtaining an entity set in the scientific and technical literature document; based on an entity pair pre-screening mechanism of multiple sampling and similarity verification, screening out a candidate entity pair set from all possible entity pairs of the entity set; then generating enhanced relation description fusing corresponding entity type information and relation semantics between the entity pairs; based on a pre-constructed relation semantic knowledge base, a double-layer filtering mechanism is adopted, and a corresponding fine screening candidate relation set is retrieved for each enhanced relation description; and guiding the large language model to perform triple fact judgment by using detailed semantic description of the candidate relationship to obtain an output result. According to the method, high-precision relation extraction is ensured, and meanwhile, the calculation overhead of long text processing is remarkably reduced, so that scientific and technical literature document-level relation extraction is more accurate and efficient.

The invention relates to the technical field of computer vision and geographic information, and particularly discloses a cross-view geographic positioning acceleration method based on semantic description, which comprises the following steps of: performing instruction fine tuning on an input query image and a database image by adopting a visual language model to generate natural language description; coding the natural language description into a low-dimensional semantic vector by utilizing a word embedding model; constructing a semantic keyword association graph based on a graph embedding technology, and fusing a general semantic knowledge base and a geographic domain ontology to construct a surface feature special semantic tree; screening candidate subsets according to a preset ground feature priority and a saliency detection result of the query image; extracting global features and local key point descriptors of the images in the candidate subsets by adopting a lightweight model; constructing a visual similarity calculation model based on the state space model, and modeling a feature sequence dependency relationship; and fusing the semantic matching score and the visual similarity, outputting a final matching result by adopting a weighted sorting strategy, and cooperatively accelerating according to the matching result in combination with a lightweight model.

This disclosure relates to the field of data governance technology, and provides a method, system, apparatus, medium, and device for constructing data indicator services. The method includes: acquiring data source information and extracting indicator metadata and underlying basic data from it; inputting the indicator metadata into an indicator governance framework and the underlying basic data into an indicator calculation framework; performing semantic transformation on the indicator metadata in the indicator governance framework to obtain an indicator semantic knowledge base and generate indicator definition information; establishing a bidirectional communication channel between indicator governance and indicator calculation, and sending the indicator definition information to the indicator calculation framework through this channel; receiving indicator query requests; performing indicator calculations in the indicator calculation framework based on the request and the indicator definition information, combined with the underlying basic data, to generate indicator calculation results; outputting the calculation results to a user terminal and feeding back the calculation results to the indicator governance framework through the bidirectional channel. This embodiment improves the consistency and response efficiency of indicator services.

A semantic feature extraction method and apparatus, a device, and a communication system. The method comprises: on the basis of a local available resource, determining to perform semantic feature extraction locally or in a cloud (S202); if it is determined to perform semantic feature extraction locally, extracting a semantic feature on the basis of a local semantic knowledge base (S204); and if it is determined to perform semantic feature extraction in the cloud, sending instruction information to a server, the instruction information being used for instructing the server to extract the semantic feature on the basis of a public semantic knowledge base (S206).

The invention discloses electronic whiteboard equipment with AI image generation and interactive drawing functions, and relates to the technical field of electronic interactive equipment. The equipment comprises an input module, a content identification module, an AI generation module, a display control module, a processor and a storage module, and a communication module can be additionally arranged to realize data synchronization and knowledge base updating. After the input module collects user sketch data, the content recognition module recognizes sketch semantics through a built-in sketch semantic knowledge base and a dynamic matching algorithm, the AI generation module calls a pre-training model based on a recognition result to generate a standardized high-quality image, and finally the display control module writes the image back to a whiteboard. And a complete interactive closed loop of hand-drawing input, semantic recognition, AI optimization and write-back display is formed. The problems that a traditional electronic whiteboard is weak in drawing intellectualization, low in creation efficiency and the like are solved, sketches can be automatically beautified, professional symbols can be accurately recognized, and the multi-scene requirements of education, commerce, engineering and the like are met.

The invention discloses an agricultural machinery label identification method based on an image, and relates to the technical field of label identification, and the method comprises the steps: obtaining an original image of an agricultural machinery label, carrying out the image enhancement preprocessing of the original image, inputting the enhanced image into a text detection network based on adaptive Bezier curve regression, and carrying out the recognition of the agricultural machinery label. And obtaining a preliminary text recognition result, performing verification, error correction and formatting conversion, generating a key-value pair data packet, and uploading the key-value pair data packet to a remote management platform. According to the method, illumination unevenness and background interference are inhibited through image enhancement preprocessing, the low-contrast character edge definition is improved, a character detection network of adaptive Bezier curve regression is adopted, a deformed text area is accurately fitted, robust detection of the agricultural machinery sign under the complex background is achieved, and the method is suitable for popularization and application. According to the method, the Bessel feature alignment and lightweight attention decoding mechanism are combined, the processing efficiency is improved while the recognition precision is ensured, then verification and error correction are performed by using a structured rule and a semantic knowledge base, the accuracy of text recognition is improved, and data are output.

The present disclosure provides a semantic communication method and apparatus, and a storage medium. In the present disclosure, a first communication device acquires first information from a second communication device, wherein the first information comprises parameter information of a first semantic knowledge base, so that matching of the parameter information of the first semantic knowledge base can be achieved between the first communication device and the second communication device, thereby improving semantic communication performance.

The invention relates to a semantic feature extraction method and device, equipment and a communication system. The method comprises the steps of determining to perform semantic feature extraction locally or in a cloud according to local available resources; if it is determined that semantic feature extraction is performed locally, semantic features are extracted based on a local semantic knowledge base; if it is determined that semantic feature extraction is performed at the cloud, sending indication information to a server; the indication information is used for indicating the server to extract semantic features based on the public semantic knowledge base. According to the method, the semantic feature extraction mode is determined according to the local available resources, and the sending end can be assisted to complete semantic feature extraction in an end-cloud cooperation mode, so that the semantic communication system can transmit information more effectively, and the quality and efficiency of the whole semantic communication process are improved.

This invention relates to the field of bridge structural safety inspection technology, and in particular to a bridge damage assessment method, system, and medium based on a visual segmentation model. The method includes: acquiring prior structural information and post-damage images of the bridge to be inspected; matching the image to be inspected generated based on the post-damage images with the prior structural information and a preset damage semantic knowledge base to generate multimodal prompts; inputting the fused multimodal prompts and the image to be inspected into a visual segmentation model with input parameters frozen, and adaptively iterating to obtain pixel-level segmentation masks for each damage candidate region; superimposing each pixel-level segmentation mask onto the image to be inspected, and inputting it along with the damage analysis prompt template into a visual language model to generate a structured damage assessment report. This invention utilizes the general segmentation capability of the visual segmentation model to achieve rapid and accurate pixel-level segmentation and recognition of sudden bridge damage, and automatically generates a structured damage assessment report.

The invention provides an intelligent SQL question and answer mode access method. The method comprises the steps that S1, a basic environment and configuration are initialized; s2, inputting an SQL question and answer pair and constructing an original semantic knowledge base; s3, semantic analysis and feature preprocessing; s4, constructing a class ER structure storage model; s5, multi-dimensional model training is carried out; and S6, performing intelligent access execution and closed-loop updating. According to the method, full automation from natural language to SQL access is realized through an integrated process of basic configuration construction, question and answer pair input, semantic preprocessing, structured storage, multi-dimensional training, intelligent access and closed-loop updating.

The application provides an information synchronization method and device, equipment and a non-transient readable storage medium. The information synchronization method comprises the following steps: sending a synchronization request for a semantic knowledge base to a first device; the first device is a base station or an edge side device or a second terminal; the second terminal comprises a peer terminal in communication with the first terminal or a neighboring terminal of the first terminal; receiving a synchronization response sent by the first device; and synchronizing the semantic knowledge base according to the synchronization response. The scheme can support intelligent and flexible semantic knowledge base synchronization, thereby improving the efficiency and accuracy of coding and decoding in the semantic transmission process, and can also avoid the high communication overhead and the influence on system communication efficiency caused by frequent signaling interaction to a certain extent, and solves the problem that the semantic knowledge base synchronization cannot be supported in the prior art.

This invention discloses a method and apparatus for detecting harmful Chinese memes that combines relative intent reasoning and knowledge enhancement. It generates an expanded set of meme semantic keywords by training memes, and for each keyword, it searches a set of background knowledge documents in a Chinese harmful semantic knowledge base. A re-ranking model constructs prompt words for each candidate document in the background knowledge document set and its corresponding query. Based on the meme content of the trained memes, it optimizes and updates a multimodal detection model to obtain a classification multimodal detection model. The meme to be detected is input into the classification multimodal detection model to obtain the corresponding label and reason, where the label is either harmful or harmless. This invention integrates an external cultural knowledge base and possesses multi-faceted dialectical reasoning capabilities for the detection and interpretation of harmful Chinese memes, thereby improving the accuracy, robustness, and interpretability of the detection.

The invention discloses a home-based care health assessment dynamic service orchestration method and system based on a large language model, and the method comprises the steps: (1) receiving a natural language health assessment request inputted by a user, and generating a structured task description tetrad through a large language model; (2) the task description tetrad is combined with a Prompt template to be input into a large language model, and an abstract evaluation plan is generated; (3) finding out a candidate service list meeting conditions in the service semantic knowledge base according to the service type required by each step in the abstract evaluation plan, and dynamically binding each step to an optimal main agent service provider and an optimal standby agent service provider; (4) deeply participating in an agent cooperation mechanism through a large language model; and (5) converting the bound task assembly line into a standard BPMN workflow, and driving an intelligent agent to orderly execute services through a workflow engine. According to the invention, the big language model is deeply integrated into the whole process of agent collaboration, and real dynamic intelligent service orchestration is realized.

This specification provides a method, computing system, and storage medium for generating a user interface. In the method, the computing system receives an interface generation request from a client. This request includes target text described by an operator in natural language. An intelligent agent group then performs an interface generation task based on the target text and a semantic knowledge base to obtain at least one user interface. The semantic knowledge base includes predefined information for multiple interface elements, each comprising multiple interface components and multiple visual attributes. The predefined information for each interface element characterizes its style and usage description.

The invention provides a semantic knowledge base adaptation method based on an intermediate layer, and relates to the technical field of intelligent communication, and the method comprises the following steps: a base station determines unmatched users and constructs an intermediate layer, and enters a step S2 to train the intermediate layer at a base station end; for each unmatched user, the base station uses the transmitting end knowledge base to encode the training data and transmits the training data through the constructed channel to obtain a first receiving signal, and after the first receiving signal is processed by the intermediate layer, the receiving end knowledge base of the unmatched user stored in the base station is used to decode the first receiving signal to obtain recovery data; taking the cross entropy of the training data and the recovery data as a loss function, and updating the parameters of the middle layer; and after training is completed, the base station sends the parameters of the trained intermediate layers to the corresponding unmatched users, and the unmatched users construct and start the intermediate layers according to the received parameters. The semantic communication performance under the condition that the knowledge base is incomplete can be effectively improved.

The application relates to the technical field of natural language processing, in particular to a large model semantic representation generation method based on scene knowledge fusion, which comprises the following steps: inputting professional texts of a specified technical category into a general semantic representation framework, extracting representation of middle-layer output of semantic coding as scene knowledge representation quantity, and constructing a scene semantic knowledge base supporting semantic retrieval; setting a scene knowledge fusion unit in the middle layer of semantic coding, keeping inherent architecture and parameters of the original framework unchanged, and associating the fusion unit with the knowledge base; inputting a text to be processed into the framework, retrieving semantic related scene knowledge representation quantity from the knowledge base through the fusion unit based on basic semantic representation extracted from the middle layer of semantic coding, fusing the basic semantic representation and the scene knowledge representation quantity, and generating optimized semantic representation; and the application realizes dynamic injection and deep fusion of field knowledge without changing original parameters of the general framework.

The application discloses a problem-driven enterprise operation knowledge co-creation platform and method, and belongs to the technical field of enterprise data intelligence and organization knowledge management. The platform comprises a data source semantic retrieval service module, an AI analysis framework generator, an AI-assisted programmable task generator, a double-track auditing engine, a plug-in report life cycle management system, a dynamic access control module based on data source permission inheritance, and a report and knowledge asset tagging organization and sharing service module. The method of the application converts enterprise technical assets into a business-understandable semantic knowledge base through the data source semantic retrieval service module, automatically disassembles the problems input by employees using AI, generates executable code and report fragments, and after being audited by business and technology double tracks, the executable code and report fragments are used as reusable plug-ins to be deposited as organization knowledge assets. The application reduces the technical threshold for non-technical personnel to use enterprise data assets, and realizes end-to-end automatic generation from natural language problems to executable analysis reports.

This invention discloses a method and system for intelligent drug monitoring, belonging to the field of drug regulatory technology. It includes acquiring multi-source monitoring data, preprocessing the multi-source monitoring data and extracting multi-dimensional features to obtain multi-dimensional drug feature data; constructing a spatiotemporal graph neural network model to deeply fuse the multi-dimensional drug feature data to obtain a multi-dimensional fused feature representation; constructing a variant semantic knowledge base, matching variant keywords with prohibited drug prototype keywords through a variant semantic reasoning model, and combining the multi-dimensional fused feature representation to perform semantic mapping between the essential attributes of the drug and the prohibited category; integrating key intermediate results from the entire monitoring process, generating a structured evidence chain, and providing visual output. This invention, based on spatiotemporal graph neural networks and variant semantic reasoning, enables real-time monitoring of drugs in complex market environments.

This invention belongs to the field of mechanical health condition monitoring and fault diagnosis, and specifically discloses a method and system for diagnosing complex faults in wind turbine gearboxes based on zero-shot learning. The method includes the following steps: constructing a semantic knowledge base using the fault type of a single fault as a semantic attribute; constructing attribute vectors representing all fault types based on the semantic knowledge base; acquiring single fault data of the wind turbine gearbox, extracting the corresponding fault feature vectors, and mapping the fault feature vectors to the attribute vectors to form a single fault dataset; training a Bayesian model based on XGB using the single fault dataset to obtain a fault diagnosis model; acquiring complex fault data of the wind turbine gearbox and extracting the corresponding fault feature vectors; inputting the fault feature vectors and the complex fault attribute vector set into the fault diagnosis model to obtain the complex fault type. This invention enables the diagnosis of complex faults in wind turbine gearboxes even with only single fault samples.

The application belongs to the field of semantic computing and knowledge base construction, and particularly relates to a semantic knowledge base construction system, method and device, aiming to solve the problem that the existing semantic knowledge base construction system based on a data lake cannot describe rules with more semantic connotations, and the existing rule engine does not allow users to set independently, resulting in that the constructed semantic knowledge base cannot be applied across platforms. The system comprises a data acquisition module, a first rule generation module, a second rule generation module, a cost calculation module, a rule optimization module, a rule subnetwork generation and pre-allocation module, a rule subnetwork division module, a semantic feature data extraction module and a semantic knowledge base construction module. The application extracts rules with more connotations, models the semantic rules of users through a structured rule description language, and further realizes the cross-platform application of the semantic knowledge base.

Provided in the present disclosure are semantic communication methods, communication devices, a communication system and a storage medium. In the present disclosure, a first communication device segments first data on the basis of a semantic knowledge base to obtain a plurality of first semantic segments, performs encoding on the basis of the plurality of first semantic segments to obtain second data, and then sends the second data to a second communication device, such that the second communication device can implement data decoding and semantic recovery on the basis of the second data, thereby implementing data transmission based on semantic communication, and improving communication efficiency. In addition, the processing of the first data to be transmitted is implemented on the basis of the semantic knowledge base, thereby ensuring the accuracy of semantic data processing at sending ends, and improving the performance of semantic communication.

The application provides a kind of entity pair guided scientific and technical literature document level relationship extraction method and system, its method includes: the entity identification of input scientific and technical literature document is obtained in scientific and technical literature document entity set;Based on the entity pair pre-screening mechanism of multiple sampling and similarity verification, from all possible entity pairs of entity set, candidate entity pair set is screened out;Then generate enhanced relationship description that integrates corresponding entity type information and relationship semantics between entity pairs;Based on the pre-constructed relationship semantic knowledge base, a double-layer filtering mechanism is used to retrieve the corresponding fine-screening candidate relationship set for each enhanced relationship description;Then use the detailed semantic description of candidate relationship to guide the large language model to make triple fact judgment, and get the output result.The application significantly reduces the computing overhead of long text processing while ensuring high-precision relationship extraction, making scientific and technical literature document level relationship extraction more accurate and efficient.

The present application belongs to the field of natural language processing, and particularly relates to a word unit consistency framework recommendation method for framework semantic knowledge base construction. The method combines the application scenarios faced by framework recommendation in the knowledge base construction process, designs and provides a framework recommendation method based on word unit consistency for framework creation, word unit expansion and framework semantic data set construction. From the perspective of framework recommendation, the method fuses a Chinese framework recommendation method with syntactic structure information, provides related auxiliary analysis tools for improving the coverage of frameworks and word units in the knowledge base, and provides a more robust representation technology for the same word unit in different contexts. In addition, the method takes into account research and practicality, and from the engineering perspective, can be integrated into the existing framework semantic labeling platform to improve the usability of the platform and accelerate the construction of the knowledge base.

The invention relates to the technical field of information system design service, e-commerce service and the like, and provides a cross-platform data intercommunication method and system for Jingdong logistics based on middleware, and the middleware adopts a layered architecture and comprises a data access layer, a protocol adaptation layer, a data exchange bus layer, a business rule engine layer and a security monitoring layer. The method comprises the following steps: receiving and standardizing heterogeneous system data through a data access and protocol adaptation layer; asynchronous transmission based on a message queue is carried out through the data exchange bus layer; dynamic routing based on business priorities and system states and field mapping based on a semantic knowledge base are realized through a business rule engine layer; and whole-process identity authentication and full-link tracking are implemented through the security monitoring layer. In addition, a data consistency strategy combining an event-driven mechanism, incremental synchronization and full-quantity verification and a multi-stage retry and transaction compensation mechanism are adopted, thorough decoupling between systems can be achieved, and the expansibility and reliability of the systems are improved.

The invention relates to the technical field of artificial intelligence, and provides a question number query method and device, equipment and a medium. The method comprises the following steps: in response to a received question number query statement of a user, matching the question number query statement according to a longest character string matching algorithm to obtain a determined word list; performing word segmentation on the question number query statement in which all the determined words in the determined word list are deleted to obtain a to-be-determined word list; performing preset knowledge base semantic retrieval on each to-be-determined word to update the determined word list and the to-be-determined word list; and ending until the to-be-determined word list is empty or reaches a preset number of iterations. Through matching of the longest character string in the semantic knowledge base and subsequent iterative optimization of the word segmentation result, the situation of word segmentation errors is effectively reduced, the word segmentation precision under the conditions of complex texts, text omission and context asking is improved, and subsequent semantic analysis and entity recognition based on word segmentation are more reliable.